From 649f387c3d1bafe5839d517c11a592527bed477e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
<66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 28 Oct 2024 20:53:29 +0000
Subject: [PATCH 1/2] [pre-commit.ci] pre-commit autoupdate
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
updates:
- [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v5.0.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v5.0.0)
- [github.com/asottile/pyupgrade: v3.3.0 → v3.19.0](https://github.com/asottile/pyupgrade/compare/v3.3.0...v3.19.0)
- [github.com/PyCQA/autoflake: v2.0.0 → v2.3.1](https://github.com/PyCQA/autoflake/compare/v2.0.0...v2.3.1)
- [github.com/pycqa/isort: 5.10.1 → 5.13.2](https://github.com/pycqa/isort/compare/5.10.1...5.13.2)
- [github.com/psf/black: 22.10.0 → 24.10.0](https://github.com/psf/black/compare/22.10.0...24.10.0)
- [github.com/PyCQA/flake8: 5.0.4 → 7.1.1](https://github.com/PyCQA/flake8/compare/5.0.4...7.1.1)
---
.pre-commit-config.yaml | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4e01869..248f840 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.4.0
+ rev: v5.0.0
hooks:
- id: check-ast
- id: check-toml
@@ -21,7 +21,7 @@ repos:
- id: check-merge-conflict
- repo: https://github.com/asottile/pyupgrade
- rev: v3.3.0
+ rev: v3.19.0
hooks:
- id: pyupgrade
args:
@@ -29,7 +29,7 @@ repos:
- --keep-runtime-typing
- repo: https://github.com/PyCQA/autoflake
- rev: v2.0.0
+ rev: v2.3.1
hooks:
- id: autoflake
args:
@@ -43,7 +43,7 @@ repos:
- __init__.py
- repo: https://github.com/pycqa/isort
- rev: 5.10.1
+ rev: 5.13.2
hooks:
- id: isort
args:
@@ -56,7 +56,7 @@ repos:
- --gitignore
- repo: https://github.com/psf/black
- rev: 22.10.0
+ rev: 24.10.0
hooks:
- id: black
args:
@@ -65,7 +65,7 @@ repos:
- --color
- repo: https://github.com/PyCQA/flake8
- rev: 5.0.4
+ rev: 7.1.1
hooks:
- id: flake8
args:
From d1acb26db2adb110f1b15e7c7ae743974ff8cfc3 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
<66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 28 Oct 2024 20:56:03 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---
mmsegmentation/.dev/benchmark_inference.py | 82 +-
mmsegmentation/.dev/check_urls.py | 65 +-
.../gather_benchmark_evaluation_results.py | 58 +-
.../.dev/gather_benchmark_train_results.py | 45 +-
mmsegmentation/.dev/gather_models.py | 130 +-
.../generate_benchmark_evaluation_script.py | 105 +-
.../.dev/generate_benchmark_train_script.py | 77 +-
.../.dev/log_collector/example_config.py | 18 +-
.../.dev/log_collector/log_collector.py | 94 +-
mmsegmentation/.dev/log_collector/utils.py | 7 +-
mmsegmentation/.dev/md2yml.py | 297 +-
mmsegmentation/.dev/upload_modelzoo.py | 34 +-
mmsegmentation/_git/packed-refs | 2 +-
.../configs/_base_/datasets/ade20k.py | 66 +-
.../configs/_base_/datasets/ade20k_640x640.py | 66 +-
.../configs/_base_/datasets/chase_db1.py | 69 +-
.../configs/_base_/datasets/cityscapes.py | 66 +-
.../_base_/datasets/cityscapes_1024x1024.py | 45 +-
.../_base_/datasets/cityscapes_768x768.py | 45 +-
.../_base_/datasets/cityscapes_769x769.py | 45 +-
.../_base_/datasets/cityscapes_832x832.py | 45 +-
.../configs/_base_/datasets/coco-stuff10k.py | 66 +-
.../configs/_base_/datasets/coco-stuff164k.py | 66 +-
.../configs/_base_/datasets/drive.py | 69 +-
mmsegmentation/configs/_base_/datasets/hrf.py | 69 +-
.../configs/_base_/datasets/isaid.py | 66 +-
.../configs/_base_/datasets/loveda.py | 66 +-
.../configs/_base_/datasets/occlude_face.py | 89 +-
.../configs/_base_/datasets/pascal_context.py | 72 +-
.../_base_/datasets/pascal_context_59.py | 72 +-
.../configs/_base_/datasets/pascal_voc12.py | 72 +-
.../_base_/datasets/pascal_voc12_aug.py | 11 +-
.../configs/_base_/datasets/potsdam.py | 66 +-
.../configs/_base_/datasets/stare.py | 69 +-
.../configs/_base_/datasets/vaihingen.py | 66 +-
.../configs/_base_/default_runtime.py | 11 +-
.../configs/_base_/models/ann_r50-d8.py | 30 +-
.../configs/_base_/models/apcnet_r50-d8.py | 30 +-
.../_base_/models/bisenetv1_r18-d32.py | 37 +-
.../configs/_base_/models/bisenetv2.py | 42 +-
.../configs/_base_/models/ccnet_r50-d8.py | 28 +-
mmsegmentation/configs/_base_/models/cgnet.py | 43 +-
.../configs/_base_/models/danet_r50-d8.py | 28 +-
.../configs/_base_/models/deeplabv3_r50-d8.py | 28 +-
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.../configs/_base_/models/dmnet_r50-d8.py | 30 +-
.../configs/_base_/models/dnl_r50-d8.py | 30 +-
.../configs/_base_/models/dpt_vit-b16.py | 24 +-
.../configs/_base_/models/emanet_r50-d8.py | 28 +-
.../configs/_base_/models/encnet_r50-d8.py | 31 +-
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.../configs/_base_/models/fcn_hr18.py | 43 +-
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.../configs/_base_/models/fcn_unet_s5-d16.py | 28 +-
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.../configs/_base_/models/gcnet_r50-d8.py | 34 +-
.../configs/_base_/models/icnet_r50-d8.py | 39 +-
.../configs/_base_/models/isanet_r50-d8.py | 28 +-
.../configs/_base_/models/lraspp_m-v3-d8.py | 23 +-
.../configs/_base_/models/nonlocal_r50-d8.py | 30 +-
.../configs/_base_/models/ocrnet_hr18.py | 51 +-
.../configs/_base_/models/ocrnet_r50-d8.py | 28 +-
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.../mmseg/models/backbones/fast_scnn.py | 208 +-
.../mmseg/models/backbones/hrnet.py | 357 +-
.../mmseg/models/backbones/icnet.py | 83 +-
mmsegmentation/mmseg/models/backbones/mae.py | 114 +-
mmsegmentation/mmseg/models/backbones/mit.py | 239 +-
.../mmseg/models/backbones/mobilenet_v2.py | 96 +-
.../mmseg/models/backbones/mobilenet_v3.py | 160 +-
.../mmseg/models/backbones/resnest.py | 115 +-
.../mmseg/models/backbones/resnet.py | 265 +-
.../mmseg/models/backbones/resnext.py | 47 +-
mmsegmentation/mmseg/models/backbones/stdc.py | 243 +-
mmsegmentation/mmseg/models/backbones/swin.py | 400 +-
.../mmseg/models/backbones/timm_backbone.py | 20 +-
.../mmseg/models/backbones/twins.py | 427 +-
mmsegmentation/mmseg/models/backbones/unet.py | 287 +-
mmsegmentation/mmseg/models/backbones/vit.py | 260 +-
mmsegmentation/mmseg/models/builder.py | 22 +-
.../mmseg/models/decode_heads/__init__.py | 39 +-
.../mmseg/models/decode_heads/ann_head.py | 96 +-
.../mmseg/models/decode_heads/apc_head.py | 53 +-
.../mmseg/models/decode_heads/aspp_head.py | 26 +-
.../decode_heads/cascade_decode_head.py | 6 +-
.../mmseg/models/decode_heads/cc_head.py | 7 +-
.../mmseg/models/decode_heads/da_head.py | 45 +-
.../mmseg/models/decode_heads/decode_head.py | 113 +-
.../mmseg/models/decode_heads/dm_head.py | 38 +-
.../mmseg/models/decode_heads/dnl_head.py | 44 +-
.../mmseg/models/decode_heads/dpt_head.py | 200 +-
.../mmseg/models/decode_heads/ema_head.py | 55 +-
.../mmseg/models/decode_heads/enc_head.py | 73 +-
.../mmseg/models/decode_heads/fcn_head.py | 18 +-
.../mmseg/models/decode_heads/fpn_head.py | 22 +-
.../mmseg/models/decode_heads/gc_head.py | 13 +-
.../mmseg/models/decode_heads/isa_head.py | 29 +-
.../mmseg/models/decode_heads/knet_head.py | 196 +-
.../mmseg/models/decode_heads/lraspp_head.py | 43 +-
.../mmseg/models/decode_heads/nl_head.py | 11 +-
.../mmseg/models/decode_heads/ocr_head.py | 24 +-
.../mmseg/models/decode_heads/point_head.py | 141 +-
.../mmseg/models/decode_heads/psa_head.py | 106 +-
.../mmseg/models/decode_heads/psp_head.py | 33 +-
.../models/decode_heads/segformer_head.py | 15 +-
.../decode_heads/segmenter_mask_head.py | 54 +-
.../models/decode_heads/sep_aspp_head.py | 30 +-
.../mmseg/models/decode_heads/sep_fcn_head.py | 11 +-
.../models/decode_heads/setr_mla_head.py | 16 +-
.../mmseg/models/decode_heads/setr_up_head.py | 39 +-
.../mmseg/models/decode_heads/stdc_head.py | 66 +-
.../mmseg/models/decode_heads/uper_head.py | 34 +-
.../mmseg/models/losses/__init__.py | 25 +-
.../mmseg/models/losses/accuracy.py | 12 +-
.../mmseg/models/losses/cross_entropy_loss.py | 159 +-
.../mmseg/models/losses/dice_loss.py | 54 +-
.../mmseg/models/losses/focal_loss.py | 179 +-
.../mmseg/models/losses/lovasz_loss.py | 140 +-
.../mmseg/models/losses/tversky_loss.py | 57 +-
mmsegmentation/mmseg/models/losses/utils.py | 15 +-
mmsegmentation/mmseg/models/necks/__init__.py | 4 +-
.../mmseg/models/necks/featurepyramid.py | 36 +-
mmsegmentation/mmseg/models/necks/fpn.py | 70 +-
mmsegmentation/mmseg/models/necks/ic_neck.py | 71 +-
mmsegmentation/mmseg/models/necks/jpu.py | 57 +-
mmsegmentation/mmseg/models/necks/mla_neck.py | 51 +-
.../mmseg/models/necks/multilevel_neck.py | 32 +-
.../mmseg/models/segmentors/__init__.py | 2 +-
.../mmseg/models/segmentors/base.py | 99 +-
.../segmentors/cascade_encoder_decoder.py | 51 +-
.../models/segmentors/encoder_decoder.py | 126 +-
mmsegmentation/mmseg/models/utils/__init__.py | 18 +-
mmsegmentation/mmseg/models/utils/embed.py | 131 +-
.../mmseg/models/utils/inverted_residual.py | 123 +-
.../mmseg/models/utils/res_layer.py | 65 +-
mmsegmentation/mmseg/models/utils/se_layer.py | 21 +-
.../models/utils/self_attention_block.py | 59 +-
.../mmseg/models/utils/shape_convert.py | 4 +-
.../mmseg/models/utils/up_conv_block.py | 47 +-
mmsegmentation/mmseg/ops/__init__.py | 2 +-
mmsegmentation/mmseg/ops/encoding.py | 35 +-
mmsegmentation/mmseg/ops/wrappers.py | 41 +-
mmsegmentation/mmseg/utils/__init__.py | 9 +-
mmsegmentation/mmseg/utils/collect_env.py | 6 +-
mmsegmentation/mmseg/utils/logger.py | 2 +-
mmsegmentation/mmseg/utils/misc.py | 14 +-
mmsegmentation/mmseg/utils/set_env.py | 38 +-
.../mmseg/utils/util_distribution.py | 43 +-
mmsegmentation/mmseg/version.py | 10 +-
mmsegmentation/setup.py | 135 +-
.../tests/test_apis/test_single_gpu.py | 21 +-
mmsegmentation/tests/test_config.py | 71 +-
.../test_layer_decay_optimizer_constructor.py | 198 +-
.../tests/test_core/test_optimizer.py | 21 +-
.../tests/test_data/test_dataset.py | 767 ++--
.../tests/test_data/test_dataset_builder.py | 100 +-
.../tests/test_data/test_loading.py | 147 +-
.../tests/test_data/test_transform.py | 584 ++-
mmsegmentation/tests/test_data/test_tta.py | 173 +-
mmsegmentation/tests/test_digit_version.py | 32 +-
mmsegmentation/tests/test_eval_hook.py | 123 +-
mmsegmentation/tests/test_inference.py | 9 +-
mmsegmentation/tests/test_metrics.py | 241 +-
.../test_models/test_backbones/__init__.py | 2 +-
.../test_models/test_backbones/test_beit.py | 25 +-
.../test_backbones/test_bisenetv1.py | 36 +-
.../test_backbones/test_bisenetv2.py | 3 +-
.../test_models/test_backbones/test_blocks.py | 39 +-
.../test_models/test_backbones/test_cgnet.py | 6 +-
.../test_models/test_backbones/test_erfnet.py | 7 +-
.../test_backbones/test_fast_scnn.py | 10 +-
.../test_models/test_backbones/test_hrnet.py | 43 +-
.../test_models/test_backbones/test_icnet.py | 17 +-
.../test_models/test_backbones/test_mae.py | 29 +-
.../test_models/test_backbones/test_mit.py | 28 +-
.../test_backbones/test_mobilenet_v3.py | 4 +-
.../test_backbones/test_resnest.py | 8 +-
.../test_models/test_backbones/test_resnet.py | 157 +-
.../test_backbones/test_resnext.py | 15 +-
.../test_models/test_backbones/test_stdc.py | 74 +-
.../test_models/test_backbones/test_swin.py | 3 +-
.../test_backbones/test_timm_backbone.py | 45 +-
.../test_models/test_backbones/test_twins.py | 45 +-
.../test_models/test_backbones/test_unet.py | 172 +-
.../test_models/test_backbones/test_vit.py | 27 +-
.../tests/test_models/test_backbones/utils.py | 12 +-
.../tests/test_models/test_forward.py | 115 +-
.../test_models/test_heads/test_ann_head.py | 3 +-
.../test_models/test_heads/test_apc_head.py | 20 +-
.../test_models/test_heads/test_aspp_head.py | 15 +-
.../test_models/test_heads/test_cc_head.py | 4 +-
.../test_heads/test_decode_head.py | 88 +-
.../test_models/test_heads/test_dm_head.py | 20 +-
.../test_models/test_heads/test_dnl_head.py | 10 +-
.../test_models/test_heads/test_dpt_head.py | 19 +-
.../test_models/test_heads/test_ema_head.py | 5 +-
.../test_models/test_heads/test_enc_head.py | 10 +-
.../test_models/test_heads/test_fcn_head.py | 27 +-
.../test_models/test_heads/test_gc_head.py | 2 +-
.../test_models/test_heads/test_isa_head.py | 7 +-
.../test_models/test_heads/test_knet_head.py | 79 +-
.../test_heads/test_lraspp_head.py | 34 +-
.../test_models/test_heads/test_nl_head.py | 2 +-
.../test_models/test_heads/test_ocr_head.py | 3 +-
.../test_models/test_heads/test_point_head.py | 32 +-
.../test_models/test_heads/test_psa_head.py | 40 +-
.../test_models/test_heads/test_psp_head.py | 9 +-
.../test_heads/test_segformer_head.py | 12 +-
.../test_heads/test_segmenter_mask_head.py | 3 +-
.../test_heads/test_setr_mla_head.py | 13 +-
.../test_heads/test_setr_up_head.py | 8 +-
.../test_models/test_heads/test_stdc_head.py | 15 +-
.../test_models/test_heads/test_uper_head.py | 11 +-
.../test_models/test_losses/test_ce_loss.py | 205 +-
.../test_models/test_losses/test_dice_loss.py | 50 +-
.../test_losses/test_focal_loss.py | 62 +-
.../test_losses/test_lovasz_loss.py | 83 +-
.../test_losses/test_tversky_loss.py | 40 +-
.../test_models/test_losses/test_utils.py | 30 +-
.../test_necks/test_feature2pyramid.py | 9 +-
.../tests/test_models/test_necks/test_fpn.py | 6 +-
.../test_models/test_necks/test_ic_neck.py | 17 +-
.../tests/test_models/test_necks/test_jpu.py | 4 +-
.../test_cascade_encoder_decoder.py | 44 +-
.../test_segmentors/test_encoder_decoder.py | 36 +-
.../test_models/test_segmentors/utils.py | 65 +-
.../test_models/test_utils/test_embed.py | 102 +-
.../test_utils/test_shape_convert.py | 3 +-
mmsegmentation/tests/test_sampler.py | 16 +-
mmsegmentation/tests/test_utils/test_misc.py | 20 +-
.../tests/test_utils/test_set_env.py | 87 +-
.../test_utils/test_util_distribution.py | 33 +-
mmsegmentation/tools/analyze_logs.py | 64 +-
mmsegmentation/tools/benchmark.py | 69 +-
mmsegmentation/tools/browse_dataset.py | 128 +-
mmsegmentation/tools/confusion_matrix.py | 102 +-
.../tools/convert_datasets/chase_db1.py | 80 +-
.../tools/convert_datasets/cityscapes.py | 34 +-
.../tools/convert_datasets/coco_stuff10k.py | 118 +-
.../tools/convert_datasets/coco_stuff164k.py | 88 +-
.../tools/convert_datasets/drive.py | 105 +-
mmsegmentation/tools/convert_datasets/hrf.py | 109 +-
.../tools/convert_datasets/isaid.py | 188 +-
.../tools/convert_datasets/loveda.py | 64 +-
.../tools/convert_datasets/pascal_context.py | 125 +-
.../tools/convert_datasets/potsdam.py | 187 +-
.../tools/convert_datasets/stare.py | 158 +-
.../tools/convert_datasets/vaihingen.py | 179 +-
.../tools/convert_datasets/voc_aug.py | 67 +-
mmsegmentation/tools/deploy_test.py | 253 +-
mmsegmentation/tools/get_flops.py | 52 +-
mmsegmentation/tools/inference.py | 8 +-
.../tools/model_converters/beit2mmseg.py | 41 +-
.../tools/model_converters/mit2mmseg.py | 77 +-
.../tools/model_converters/stdc2mmseg.py | 72 +-
.../tools/model_converters/swin2mmseg.py | 58 +-
.../tools/model_converters/twins2mmseg.py | 68 +-
.../tools/model_converters/vit2mmseg.py | 57 +-
.../tools/model_converters/vitjax2mmseg.py | 114 +-
mmsegmentation/tools/model_ensemble.py | 52 +-
mmsegmentation/tools/onnx2tensorrt.py | 257 +-
mmsegmentation/tools/print_config.py | 58 +-
mmsegmentation/tools/publish_model.py | 21 +-
mmsegmentation/tools/pytorch2onnx.py | 284 +-
mmsegmentation/tools/pytorch2torchscript.py | 99 +-
mmsegmentation/tools/test.py | 293 +-
mmsegmentation/tools/test_jwseo.py | 293 +-
.../tools/torchserve/mmseg2torchserve.py | 83 +-
.../tools/torchserve/mmseg_handler.py | 20 +-
.../tools/torchserve/test_torchserve.py | 37 +-
mmsegmentation/tools/train.py | 196 +-
mmsegmentation/tools/train_jwseo.py | 5 +-
src/baseline_fcn_resnet50.ipynb | 3466 ++++++++---------
src/inference.py | 3 +-
src/train.py | 3 +-
src/utils.py | 1 +
src/utils/convert_to_mmseg.py | 5 +-
src/utils/convert_to_mmseg_checking.ipynb | 776 ++--
src/visualizer.py | 1 +
1016 files changed, 22143 insertions(+), 17533 deletions(-)
diff --git a/mmsegmentation/.dev/benchmark_inference.py b/mmsegmentation/.dev/benchmark_inference.py
index 3ab681b..1a6e5dd 100644
--- a/mmsegmentation/.dev/benchmark_inference.py
+++ b/mmsegmentation/.dev/benchmark_inference.py
@@ -13,29 +13,29 @@
from mmseg.utils import get_root_logger
# ignore warnings when segmentors inference
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
def download_checkpoint(checkpoint_name, model_name, config_name, collect_dir):
"""Download checkpoint and check if hash code is true."""
- url = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{checkpoint_name}' # noqa
+ url = f"https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{checkpoint_name}" # noqa
r = requests.get(url)
- assert r.status_code != 403, f'{url} Access denied.'
+ assert r.status_code != 403, f"{url} Access denied."
- with open(osp.join(collect_dir, checkpoint_name), 'wb') as code:
+ with open(osp.join(collect_dir, checkpoint_name), "wb") as code:
code.write(r.content)
- true_hash_code = osp.splitext(checkpoint_name)[0].split('-')[1]
+ true_hash_code = osp.splitext(checkpoint_name)[0].split("-")[1]
# check hash code
- with open(osp.join(collect_dir, checkpoint_name), 'rb') as fp:
+ with open(osp.join(collect_dir, checkpoint_name), "rb") as fp:
sha256_cal = hashlib.sha256()
sha256_cal.update(fp.read())
cur_hash_code = sha256_cal.hexdigest()[:8]
- assert true_hash_code == cur_hash_code, f'{url} download failed, '
- 'incomplete downloaded file or url invalid.'
+ assert true_hash_code == cur_hash_code, f"{url} download failed, "
+ "incomplete downloaded file or url invalid."
if cur_hash_code != true_hash_code:
os.remove(osp.join(collect_dir, checkpoint_name))
@@ -43,32 +43,31 @@ def download_checkpoint(checkpoint_name, model_name, config_name, collect_dir):
def parse_args():
parser = ArgumentParser()
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("checkpoint_root", help="Checkpoint file root path")
+ parser.add_argument("-i", "--img", default="demo/demo.png", help="Image file")
+ parser.add_argument("-a", "--aug", action="store_true", help="aug test")
+ parser.add_argument("-m", "--model-name", help="model name to inference")
+ parser.add_argument("-s", "--show", action="store_true", help="show results")
parser.add_argument(
- '-i', '--img', default='demo/demo.png', help='Image file')
- parser.add_argument('-a', '--aug', action='store_true', help='aug test')
- parser.add_argument('-m', '--model-name', help='model name to inference')
- parser.add_argument(
- '-s', '--show', action='store_true', help='show results')
- parser.add_argument(
- '-d', '--device', default='cuda:0', help='Device used for inference')
+ "-d", "--device", default="cuda:0", help="Device used for inference"
+ )
return parser.parse_args()
def inference_model(config_name, checkpoint, args, logger=None):
cfg = Config.fromfile(config_name)
if args.aug:
- if 'flip' in cfg.data.test.pipeline[
- 1] and 'img_scale' in cfg.data.test.pipeline[1]:
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
- ]
+ if (
+ "flip" in cfg.data.test.pipeline[1]
+ and "img_scale" in cfg.data.test.pipeline[1]
+ ):
+ cfg.data.test.pipeline[1].img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
cfg.data.test.pipeline[1].flip = True
elif logger is None:
- print(f'{config_name}: unable to start aug test', flush=True)
+ print(f"{config_name}: unable to start aug test", flush=True)
else:
- logger.error(f'{config_name}: unable to start aug test')
+ logger.error(f"{config_name}: unable to start aug test")
model = init_segmentor(cfg, checkpoint, device=args.device)
# test a single image
@@ -94,23 +93,25 @@ def main(args):
if not isinstance(model_infos, list):
model_infos = [model_infos]
for model_info in model_infos:
- config_name = model_info['config'].strip()
- print(f'processing: {config_name}', flush=True)
- checkpoint = osp.join(args.checkpoint_root,
- model_info['checkpoint'].strip())
+ config_name = model_info["config"].strip()
+ print(f"processing: {config_name}", flush=True)
+ checkpoint = osp.join(
+ args.checkpoint_root, model_info["checkpoint"].strip()
+ )
try:
# build the model from a config file and a checkpoint file
inference_model(config_name, checkpoint, args)
except Exception:
- print(f'{config_name} test failed!')
+ print(f"{config_name} test failed!")
continue
return
else:
- raise RuntimeError('model name input error.')
+ raise RuntimeError("model name input error.")
# test all model
logger = get_root_logger(
- log_file='benchmark_inference_image.log', log_level=logging.ERROR)
+ log_file="benchmark_inference_image.log", log_level=logging.ERROR
+ )
for model_name in config:
model_infos = config[model_name]
@@ -118,20 +119,23 @@ def main(args):
if not isinstance(model_infos, list):
model_infos = [model_infos]
for model_info in model_infos:
- print('processing: ', model_info['config'], flush=True)
- config_path = model_info['config'].strip()
+ print("processing: ", model_info["config"], flush=True)
+ config_path = model_info["config"].strip()
config_name = osp.splitext(osp.basename(config_path))[0]
- checkpoint_name = model_info['checkpoint'].strip()
+ checkpoint_name = model_info["checkpoint"].strip()
checkpoint = osp.join(args.checkpoint_root, checkpoint_name)
# ensure checkpoint exists
try:
if not osp.exists(checkpoint):
- download_checkpoint(checkpoint_name, model_name,
- config_name.rstrip('.py'),
- args.checkpoint_root)
+ download_checkpoint(
+ checkpoint_name,
+ model_name,
+ config_name.rstrip(".py"),
+ args.checkpoint_root,
+ )
except Exception:
- logger.error(f'{checkpoint_name} download error')
+ logger.error(f"{checkpoint_name} download error")
continue
# test model inference with checkpoint
@@ -142,6 +146,6 @@ def main(args):
logger.error(f'{config_path} " : {repr(e)}')
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
main(args)
diff --git a/mmsegmentation/.dev/check_urls.py b/mmsegmentation/.dev/check_urls.py
index c98d0a1..a8f12cb 100644
--- a/mmsegmentation/.dev/check_urls.py
+++ b/mmsegmentation/.dev/check_urls.py
@@ -25,12 +25,10 @@ def check_url(url):
def parse_args():
- parser = ArgumentParser('url valid check.')
+ parser = ArgumentParser("url valid check.")
parser.add_argument(
- '-m',
- '--model-name',
- type=str,
- help='Select the model needed to check')
+ "-m", "--model-name", type=str, help="Select the model needed to check"
+ )
return parser.parse_args()
@@ -42,56 +40,63 @@ def main():
# yml path generate.
# If model_name is not set, script will check all of the models.
if model_name is not None:
- yml_list = [(model_name, f'configs/{model_name}/{model_name}.yml')]
+ yml_list = [(model_name, f"configs/{model_name}/{model_name}.yml")]
else:
# check all
- yml_list = [(x, f'configs/{x}/{x}.yml') for x in os.listdir('configs/')
- if x != '_base_']
+ yml_list = [
+ (x, f"configs/{x}/{x}.yml") for x in os.listdir("configs/") if x != "_base_"
+ ]
- logger = get_root_logger(log_file='url_check.log', log_level=logging.ERROR)
+ logger = get_root_logger(log_file="url_check.log", log_level=logging.ERROR)
for model_name, yml_path in yml_list:
# Default yaml loader unsafe.
- model_infos = yml.load(
- open(yml_path, 'r'), Loader=yml.CLoader)['Models']
+ model_infos = yml.load(open(yml_path), Loader=yml.CLoader)["Models"]
for model_info in model_infos:
- config_name = model_info['Name']
- checkpoint_url = model_info['Weights']
+ config_name = model_info["Name"]
+ checkpoint_url = model_info["Weights"]
# checkpoint url check
status_code, flag = check_url(checkpoint_url)
if flag:
- logger.info(f'checkpoint | {config_name} | {checkpoint_url} | '
- f'{status_code} valid')
+ logger.info(
+ f"checkpoint | {config_name} | {checkpoint_url} | "
+ f"{status_code} valid"
+ )
else:
logger.error(
- f'checkpoint | {config_name} | {checkpoint_url} | '
- f'{status_code} | error')
+ f"checkpoint | {config_name} | {checkpoint_url} | "
+ f"{status_code} | error"
+ )
# log_json check
- checkpoint_name = checkpoint_url.split('/')[-1]
- model_time = '-'.join(checkpoint_name.split('-')[:-1]).replace(
- f'{config_name}_', '')
+ checkpoint_name = checkpoint_url.split("/")[-1]
+ model_time = "-".join(checkpoint_name.split("-")[:-1]).replace(
+ f"{config_name}_", ""
+ )
# two style of log_json name
# use '_' to link model_time (will be deprecated)
- log_json_url_1 = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}_{model_time}.log.json' # noqa
+ log_json_url_1 = f"https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}_{model_time}.log.json" # noqa
status_code_1, flag_1 = check_url(log_json_url_1)
# use '-' to link model_time
- log_json_url_2 = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}-{model_time}.log.json' # noqa
+ log_json_url_2 = f"https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}-{model_time}.log.json" # noqa
status_code_2, flag_2 = check_url(log_json_url_2)
if flag_1 or flag_2:
if flag_1:
logger.info(
- f'log.json | {config_name} | {log_json_url_1} | '
- f'{status_code_1} | valid')
+ f"log.json | {config_name} | {log_json_url_1} | "
+ f"{status_code_1} | valid"
+ )
else:
logger.info(
- f'log.json | {config_name} | {log_json_url_2} | '
- f'{status_code_2} | valid')
+ f"log.json | {config_name} | {log_json_url_2} | "
+ f"{status_code_2} | valid"
+ )
else:
logger.error(
- f'log.json | {config_name} | {log_json_url_1} & '
- f'{log_json_url_2} | {status_code_1} & {status_code_2} | '
- 'error')
+ f"log.json | {config_name} | {log_json_url_1} & "
+ f"{log_json_url_2} | {status_code_1} & {status_code_2} | "
+ "error"
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/.dev/gather_benchmark_evaluation_results.py b/mmsegmentation/.dev/gather_benchmark_evaluation_results.py
index a8bfb4c..cb4a403 100644
--- a/mmsegmentation/.dev/gather_benchmark_evaluation_results.py
+++ b/mmsegmentation/.dev/gather_benchmark_evaluation_results.py
@@ -9,24 +9,24 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Gather benchmarked model evaluation results')
- parser.add_argument('config', help='test config file path')
+ description="Gather benchmarked model evaluation results"
+ )
+ parser.add_argument("config", help="test config file path")
parser.add_argument(
- 'root',
- type=str,
- help='root path of benchmarked models to be gathered')
+ "root", type=str, help="root path of benchmarked models to be gathered"
+ )
parser.add_argument(
- '--out',
+ "--out",
type=str,
- default='benchmark_evaluation_info.json',
- help='output path of gathered metrics and compared '
- 'results to be stored')
+ default="benchmark_evaluation_info.json",
+ help="output path of gathered metrics and compared " "results to be stored",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
root_path = args.root
@@ -40,52 +40,54 @@ def parse_args():
if not isinstance(model_infos, list):
model_infos = [model_infos]
for model_info in model_infos:
- previous_metrics = model_info['metric']
- config = model_info['config'].strip()
+ previous_metrics = model_info["metric"]
+ config = model_info["config"].strip()
fname, _ = osp.splitext(osp.basename(config))
# Load benchmark evaluation json
metric_json_dir = osp.join(root_path, fname)
if not osp.exists(metric_json_dir):
- print(f'{metric_json_dir} not existed.')
+ print(f"{metric_json_dir} not existed.")
continue
- json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
+ json_list = glob.glob(osp.join(metric_json_dir, "*.json"))
if len(json_list) == 0:
- print(f'There is no eval json in {metric_json_dir}.')
+ print(f"There is no eval json in {metric_json_dir}.")
continue
log_json_path = list(sorted(json_list))[-1]
metric = mmcv.load(log_json_path)
- if config not in metric.get('config', {}):
- print(f'{config} not included in {log_json_path}')
+ if config not in metric.get("config", {}):
+ print(f"{config} not included in {log_json_path}")
continue
# Compare between new benchmark results and previous metrics
differential_results = {}
new_metrics = {}
for record_metric_key in previous_metrics:
- if record_metric_key not in metric['metric']:
- raise KeyError('record_metric_key not exist, please '
- 'check your config')
+ if record_metric_key not in metric["metric"]:
+ raise KeyError(
+ "record_metric_key not exist, please " "check your config"
+ )
old_metric = previous_metrics[record_metric_key]
- new_metric = round(metric['metric'][record_metric_key] * 100,
- 2)
+ new_metric = round(metric["metric"][record_metric_key] * 100, 2)
differential = new_metric - old_metric
- flag = '+' if differential > 0 else '-'
- differential_results[
- record_metric_key] = f'{flag}{abs(differential):.2f}'
+ flag = "+" if differential > 0 else "-"
+ differential_results[record_metric_key] = (
+ f"{flag}{abs(differential):.2f}"
+ )
new_metrics[record_metric_key] = new_metric
result_dict[config] = dict(
differential=differential_results,
previous=previous_metrics,
- new=new_metrics)
+ new=new_metrics,
+ )
if metrics_out:
mmcv.dump(result_dict, metrics_out, indent=4)
- print('===================================')
+ print("===================================")
for config_name, metrics in result_dict.items():
print(config_name, metrics)
- print('===================================')
+ print("===================================")
diff --git a/mmsegmentation/.dev/gather_benchmark_train_results.py b/mmsegmentation/.dev/gather_benchmark_train_results.py
index e729ca2..e47cf90 100644
--- a/mmsegmentation/.dev/gather_benchmark_train_results.py
+++ b/mmsegmentation/.dev/gather_benchmark_train_results.py
@@ -9,23 +9,24 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Gather benchmarked models train results')
- parser.add_argument('config', help='test config file path')
+ description="Gather benchmarked models train results"
+ )
+ parser.add_argument("config", help="test config file path")
parser.add_argument(
- 'root',
- type=str,
- help='root path of benchmarked models to be gathered')
+ "root", type=str, help="root path of benchmarked models to be gathered"
+ )
parser.add_argument(
- '--out',
+ "--out",
type=str,
- default='benchmark_train_info.json',
- help='output path of gathered metrics to be stored')
+ default="benchmark_train_info.json",
+ help="output path of gathered metrics to be stored",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
root_path = args.root
@@ -39,14 +40,14 @@ def parse_args():
if not isinstance(model_infos, list):
model_infos = [model_infos]
for model_info in model_infos:
- config = model_info['config']
+ config = model_info["config"]
# benchmark train dir
model_name = osp.split(osp.dirname(config))[1]
config_name = osp.splitext(osp.basename(config))[0]
exp_dir = osp.join(root_path, model_name, config_name)
if not osp.exists(exp_dir):
- print(f'{config} hasn\'t {exp_dir}')
+ print(f"{config} hasn't {exp_dir}")
continue
# parse config
@@ -57,34 +58,32 @@ def parse_args():
exp_metrics = [exp_metric]
# determine whether total_iters ckpt exists
- ckpt_path = f'iter_{total_iters}.pth'
+ ckpt_path = f"iter_{total_iters}.pth"
if not osp.exists(osp.join(exp_dir, ckpt_path)):
- print(f'{config} hasn\'t {ckpt_path}')
+ print(f"{config} hasn't {ckpt_path}")
continue
# only the last log json counts
- log_json_path = list(
- sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
+ log_json_path = list(sorted(glob.glob(osp.join(exp_dir, "*.log.json"))))[-1]
# extract metric value
model_performance = get_final_results(log_json_path, total_iters)
if model_performance is None:
- print(f'log file error: {log_json_path}')
+ print(f"log file error: {log_json_path}")
continue
differential_results = {}
old_results = {}
new_results = {}
for metric_key in model_performance:
- if metric_key in ['mIoU']:
+ if metric_key in ["mIoU"]:
metric = round(model_performance[metric_key] * 100, 2)
- old_metric = model_info['metric'][metric_key]
+ old_metric = model_info["metric"][metric_key]
old_results[metric_key] = old_metric
new_results[metric_key] = metric
differential = metric - old_metric
- flag = '+' if differential > 0 else '-'
- differential_results[
- metric_key] = f'{flag}{abs(differential):.2f}'
+ flag = "+" if differential > 0 else "-"
+ differential_results[metric_key] = f"{flag}{abs(differential):.2f}"
result_dict[config] = dict(
differential_results=differential_results,
old_results=old_results,
@@ -94,7 +93,7 @@ def parse_args():
# 4 save or print results
if metrics_out:
mmcv.dump(result_dict, metrics_out, indent=4)
- print('===================================')
+ print("===================================")
for config_name, metrics in result_dict.items():
print(config_name, metrics)
- print('===================================')
+ print("===================================")
diff --git a/mmsegmentation/.dev/gather_models.py b/mmsegmentation/.dev/gather_models.py
index 6158623..c1c18a2 100644
--- a/mmsegmentation/.dev/gather_models.py
+++ b/mmsegmentation/.dev/gather_models.py
@@ -11,29 +11,29 @@
import torch
# build schedule look-up table to automatically find the final model
-RESULTS_LUT = ['mIoU', 'mAcc', 'aAcc']
+RESULTS_LUT = ["mIoU", "mAcc", "aAcc"]
def calculate_file_sha256(file_path):
"""calculate file sha256 hash code."""
- with open(file_path, 'rb') as fp:
+ with open(file_path, "rb") as fp:
sha256_cal = hashlib.sha256()
sha256_cal.update(fp.read())
return sha256_cal.hexdigest()
def process_checkpoint(in_file, out_file):
- checkpoint = torch.load(in_file, map_location='cpu')
+ checkpoint = torch.load(in_file, map_location="cpu")
# remove optimizer for smaller file size
- if 'optimizer' in checkpoint:
- del checkpoint['optimizer']
+ if "optimizer" in checkpoint:
+ del checkpoint["optimizer"]
# if it is necessary to remove some sensitive data in checkpoint['meta'],
# add the code here.
torch.save(checkpoint, out_file)
# The hash code calculation and rename command differ on different system
# platform.
sha = calculate_file_sha256(out_file)
- final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
+ final_file = out_file.rstrip(".pth") + f"-{sha[:8]}.pth"
os.rename(out_file, final_file)
# Remove prefix and suffix
@@ -44,50 +44,53 @@ def process_checkpoint(in_file, out_file):
def get_final_iter(config):
- iter_num = config.split('_')[-2]
- assert iter_num.endswith('k')
+ iter_num = config.split("_")[-2]
+ assert iter_num.endswith("k")
return int(iter_num[:-1]) * 1000
def get_final_results(log_json_path, iter_num):
result_dict = {}
last_iter = 0
- with open(log_json_path, 'r') as f:
+ with open(log_json_path) as f:
for line in f:
log_line = json.loads(line)
- if 'mode' not in log_line.keys():
+ if "mode" not in log_line.keys():
continue
# When evaluation, the 'iter' of new log json is the evaluation
# steps on single gpu.
- flag1 = 'aAcc' in log_line or log_line['mode'] == 'val'
+ flag1 = "aAcc" in log_line or log_line["mode"] == "val"
flag2 = last_iter in [iter_num - 50, iter_num]
if flag1 and flag2:
- result_dict.update({
- key: log_line[key]
- for key in RESULTS_LUT if key in log_line
- })
+ result_dict.update(
+ {key: log_line[key] for key in RESULTS_LUT if key in log_line}
+ )
return result_dict
- last_iter = log_line['iter']
+ last_iter = log_line["iter"]
def parse_args():
- parser = argparse.ArgumentParser(description='Gather benchmarked models')
+ parser = argparse.ArgumentParser(description="Gather benchmarked models")
parser.add_argument(
- '-f', '--config-name', type=str, help='Process the selected config.')
+ "-f", "--config-name", type=str, help="Process the selected config."
+ )
parser.add_argument(
- '-w',
- '--work-dir',
- default='work_dirs/',
+ "-w",
+ "--work-dir",
+ default="work_dirs/",
type=str,
- help='Ckpt storage root folder of benchmarked models to be gathered.')
+ help="Ckpt storage root folder of benchmarked models to be gathered.",
+ )
parser.add_argument(
- '-c',
- '--collect-dir',
- default='work_dirs/gather',
+ "-c",
+ "--collect-dir",
+ default="work_dirs/gather",
type=str,
- help='Ckpt collect root folder of gathered models.')
+ help="Ckpt collect root folder of gathered models.",
+ )
parser.add_argument(
- '--all', action='store_true', help='whether include .py and .log')
+ "--all", action="store_true", help="whether include .py and .log"
+ )
args = parser.parse_args()
return args
@@ -101,17 +104,16 @@ def main():
mmcv.mkdir_or_exist(collect_dir)
# find all models in the root directory to be gathered
- raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
+ raw_configs = list(mmcv.scandir("./configs", ".py", recursive=True))
# filter configs that is not trained in the experiments dir
used_configs = []
for raw_config in raw_configs:
config_name = osp.splitext(osp.basename(raw_config))[0]
if osp.exists(osp.join(work_dir, config_name)):
- if (selected_config_name is None
- or selected_config_name == config_name):
+ if selected_config_name is None or selected_config_name == config_name:
used_configs.append(raw_config)
- print(f'Find {len(used_configs)} models to be gathered')
+ print(f"Find {len(used_configs)} models to be gathered")
# find final_ckpt and log file for trained each config
# and parse the best performance
@@ -121,16 +123,16 @@ def main():
exp_dir = osp.join(work_dir, config_name)
# check whether the exps is finished
final_iter = get_final_iter(used_config)
- final_model = f'iter_{final_iter}.pth'
+ final_model = f"iter_{final_iter}.pth"
model_path = osp.join(exp_dir, final_model)
# skip if the model is still training
if not osp.exists(model_path):
- print(f'{used_config} train not finished yet')
+ print(f"{used_config} train not finished yet")
continue
# get logs
- log_json_paths = glob.glob(osp.join(exp_dir, '*.log.json'))
+ log_json_paths = glob.glob(osp.join(exp_dir, "*.log.json"))
log_json_path = log_json_paths[0]
model_performance = None
for _log_json_path in log_json_paths:
@@ -140,71 +142,77 @@ def main():
break
if model_performance is None:
- print(f'{used_config} model_performance is None')
+ print(f"{used_config} model_performance is None")
continue
- model_time = osp.split(log_json_path)[-1].split('.')[0]
+ model_time = osp.split(log_json_path)[-1].split(".")[0]
model_infos.append(
dict(
config_name=config_name,
results=model_performance,
iters=final_iter,
model_time=model_time,
- log_json_path=osp.split(log_json_path)[-1]))
+ log_json_path=osp.split(log_json_path)[-1],
+ )
+ )
# publish model for each checkpoint
publish_model_infos = []
for model in model_infos:
- config_name = model['config_name']
+ config_name = model["config_name"]
model_publish_dir = osp.join(collect_dir, config_name)
- publish_model_path = osp.join(model_publish_dir,
- f'{config_name}_' + model['model_time'])
+ publish_model_path = osp.join(
+ model_publish_dir, f"{config_name}_" + model["model_time"]
+ )
- trained_model_path = osp.join(work_dir, config_name,
- f'iter_{model["iters"]}.pth')
+ trained_model_path = osp.join(
+ work_dir, config_name, f'iter_{model["iters"]}.pth'
+ )
if osp.exists(model_publish_dir):
for file in os.listdir(model_publish_dir):
- if file.endswith('.pth'):
- print(f'model {file} found')
- model['model_path'] = osp.abspath(
- osp.join(model_publish_dir, file))
+ if file.endswith(".pth"):
+ print(f"model {file} found")
+ model["model_path"] = osp.abspath(osp.join(model_publish_dir, file))
break
- if 'model_path' not in model:
- print(f'dir {model_publish_dir} exists, no model found')
+ if "model_path" not in model:
+ print(f"dir {model_publish_dir} exists, no model found")
else:
mmcv.mkdir_or_exist(model_publish_dir)
# convert model
- final_model_path = process_checkpoint(trained_model_path,
- publish_model_path)
- model['model_path'] = final_model_path
+ final_model_path = process_checkpoint(
+ trained_model_path, publish_model_path
+ )
+ model["model_path"] = final_model_path
new_json_path = f'{config_name}_{model["log_json_path"]}'
# copy log
shutil.copy(
- osp.join(work_dir, config_name, model['log_json_path']),
- osp.join(model_publish_dir, new_json_path))
+ osp.join(work_dir, config_name, model["log_json_path"]),
+ osp.join(model_publish_dir, new_json_path),
+ )
if args.all:
- new_txt_path = new_json_path.rstrip('.json')
+ new_txt_path = new_json_path.rstrip(".json")
shutil.copy(
- osp.join(work_dir, config_name,
- model['log_json_path'].rstrip('.json')),
- osp.join(model_publish_dir, new_txt_path))
+ osp.join(work_dir, config_name, model["log_json_path"].rstrip(".json")),
+ osp.join(model_publish_dir, new_txt_path),
+ )
if args.all:
# copy config to guarantee reproducibility
- raw_config = osp.join('./configs', f'{config_name}.py')
+ raw_config = osp.join("./configs", f"{config_name}.py")
mmcv.Config.fromfile(raw_config).dump(
- osp.join(model_publish_dir, osp.basename(raw_config)))
+ osp.join(model_publish_dir, osp.basename(raw_config))
+ )
publish_model_infos.append(model)
models = dict(models=publish_model_infos)
- mmcv.dump(models, osp.join(collect_dir, 'model_infos.json'), indent=4)
+ mmcv.dump(models, osp.join(collect_dir, "model_infos.json"), indent=4)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/.dev/generate_benchmark_evaluation_script.py b/mmsegmentation/.dev/generate_benchmark_evaluation_script.py
index fd49f2b..d859710 100644
--- a/mmsegmentation/.dev/generate_benchmark_evaluation_script.py
+++ b/mmsegmentation/.dev/generate_benchmark_evaluation_script.py
@@ -7,64 +7,69 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert benchmark test model list to script')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('--port', type=int, default=28171, help='dist port')
+ description="Convert benchmark test model list to script"
+ )
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("--port", type=int, default=28171, help="dist port")
parser.add_argument(
- '--work-dir',
- default='work_dirs/benchmark_evaluation',
- help='the dir to save metric')
+ "--work-dir",
+ default="work_dirs/benchmark_evaluation",
+ help="the dir to save metric",
+ )
parser.add_argument(
- '--out',
+ "--out",
type=str,
- default='.dev/benchmark_evaluation.sh',
- help='path to save model benchmark script')
+ default=".dev/benchmark_evaluation.sh",
+ help="path to save model benchmark script",
+ )
return parser.parse_args()
def process_model_info(model_info, work_dir):
- config = model_info['config'].strip()
+ config = model_info["config"].strip()
fname, _ = osp.splitext(osp.basename(config))
job_name = fname
- checkpoint = model_info['checkpoint'].strip()
+ checkpoint = model_info["checkpoint"].strip()
work_dir = osp.join(work_dir, fname)
- evals = model_info['eval'] if isinstance(model_info['eval'],
- list) else [model_info['eval']]
+ evals = (
+ model_info["eval"]
+ if isinstance(model_info["eval"], list)
+ else [model_info["eval"]]
+ )
- eval = ' '.join(evals)
+ eval = " ".join(evals)
return dict(
config=config,
job_name=job_name,
checkpoint=checkpoint,
work_dir=work_dir,
- eval=eval)
+ eval=eval,
+ )
-def create_test_bash_info(commands, model_test_dict, port, script_name,
- partition):
- config = model_test_dict['config']
- job_name = model_test_dict['job_name']
- checkpoint = model_test_dict['checkpoint']
- work_dir = model_test_dict['work_dir']
- eval = model_test_dict['eval']
+def create_test_bash_info(commands, model_test_dict, port, script_name, partition):
+ config = model_test_dict["config"]
+ job_name = model_test_dict["job_name"]
+ checkpoint = model_test_dict["checkpoint"]
+ work_dir = model_test_dict["work_dir"]
+ eval = model_test_dict["eval"]
- echo_info = f'\necho \'{config}\' &'
+ echo_info = f"\necho '{config}' &"
commands.append(echo_info)
- commands.append('\n')
+ commands.append("\n")
- command_info = f'GPUS=4 GPUS_PER_NODE=4 ' \
- f'CPUS_PER_TASK=2 {script_name} '
+ command_info = f"GPUS=4 GPUS_PER_NODE=4 " f"CPUS_PER_TASK=2 {script_name} "
- command_info += f'{partition} '
- command_info += f'{job_name} '
- command_info += f'{config} '
- command_info += f'$CHECKPOINT_DIR/{checkpoint} '
+ command_info += f"{partition} "
+ command_info += f"{job_name} "
+ command_info += f"{config} "
+ command_info += f"$CHECKPOINT_DIR/{checkpoint} "
- command_info += f'--eval {eval} '
- command_info += f'--work-dir {work_dir} '
- command_info += f'--cfg-options dist_params.port={port} '
- command_info += '&'
+ command_info += f"--eval {eval} "
+ command_info += f"--work-dir {work_dir} "
+ command_info += f"--cfg-options dist_params.port={port} "
+ command_info += "&"
commands.append(command_info)
@@ -72,20 +77,21 @@ def create_test_bash_info(commands, model_test_dict, port, script_name,
def main():
args = parse_args()
if args.out:
- out_suffix = args.out.split('.')[-1]
- assert args.out.endswith('.sh'), \
- f'Expected out file path suffix is .sh, but get .{out_suffix}'
+ out_suffix = args.out.split(".")[-1]
+ assert args.out.endswith(
+ ".sh"
+ ), f"Expected out file path suffix is .sh, but get .{out_suffix}"
commands = []
- partition_name = 'PARTITION=$1'
+ partition_name = "PARTITION=$1"
commands.append(partition_name)
- commands.append('\n')
+ commands.append("\n")
- checkpoint_root = 'CHECKPOINT_DIR=$2'
+ checkpoint_root = "CHECKPOINT_DIR=$2"
commands.append(checkpoint_root)
- commands.append('\n')
+ commands.append("\n")
- script_name = osp.join('tools', 'slurm_test.sh')
+ script_name = osp.join("tools", "slurm_test.sh")
port = args.port
work_dir = args.work_dir
@@ -96,17 +102,18 @@ def main():
if not isinstance(model_infos, list):
model_infos = [model_infos]
for model_info in model_infos:
- print('processing: ', model_info['config'])
+ print("processing: ", model_info["config"])
model_test_dict = process_model_info(model_info, work_dir)
- create_test_bash_info(commands, model_test_dict, port, script_name,
- '$PARTITION')
+ create_test_bash_info(
+ commands, model_test_dict, port, script_name, "$PARTITION"
+ )
port += 1
- command_str = ''.join(commands)
+ command_str = "".join(commands)
if args.out:
- with open(args.out, 'w') as f:
- f.write(command_str + '\n')
+ with open(args.out, "w") as f:
+ f.write(command_str + "\n")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/.dev/generate_benchmark_train_script.py b/mmsegmentation/.dev/generate_benchmark_train_script.py
index 32d0a71..2276686 100644
--- a/mmsegmentation/.dev/generate_benchmark_train_script.py
+++ b/mmsegmentation/.dev/generate_benchmark_train_script.py
@@ -4,23 +4,26 @@
# Default using 4 gpu when training
config_8gpu_list = [
- 'configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py', # noqa
- 'configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py',
- 'configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py',
+ "configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py", # noqa
+ "configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py",
+ "configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py",
]
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert benchmark model json to script')
+ description="Convert benchmark model json to script"
+ )
parser.add_argument(
- 'txt_path', type=str, help='txt path output by benchmark_filter')
- parser.add_argument('--port', type=int, default=24727, help='dist port')
+ "txt_path", type=str, help="txt path output by benchmark_filter"
+ )
+ parser.add_argument("--port", type=int, default=24727, help="dist port")
parser.add_argument(
- '--out',
+ "--out",
type=str,
- default='.dev/benchmark_train.sh',
- help='path to save model benchmark script')
+ default=".dev/benchmark_train.sh",
+ help="path to save model benchmark script",
+ )
args = parser.parse_args()
return args
@@ -30,59 +33,59 @@ def create_train_bash_info(commands, config, script_name, partition, port):
cfg = config.strip()
# print cfg name
- echo_info = f'echo \'{cfg}\' &'
+ echo_info = f"echo '{cfg}' &"
commands.append(echo_info)
- commands.append('\n')
+ commands.append("\n")
_, model_name = osp.split(osp.dirname(cfg))
config_name, _ = osp.splitext(osp.basename(cfg))
# default setting
if cfg in config_8gpu_list:
- command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \
- f'CPUS_PER_TASK=2 {script_name} '
+ command_info = f"GPUS=8 GPUS_PER_NODE=8 " f"CPUS_PER_TASK=2 {script_name} "
else:
- command_info = f'GPUS=4 GPUS_PER_NODE=4 ' \
- f'CPUS_PER_TASK=2 {script_name} '
- command_info += f'{partition} '
- command_info += f'{config_name} '
- command_info += f'{cfg} '
- command_info += f'--cfg-options ' \
- f'checkpoint_config.max_keep_ckpts=1 ' \
- f'dist_params.port={port} '
- command_info += f'--work-dir work_dirs/{model_name}/{config_name} '
+ command_info = f"GPUS=4 GPUS_PER_NODE=4 " f"CPUS_PER_TASK=2 {script_name} "
+ command_info += f"{partition} "
+ command_info += f"{config_name} "
+ command_info += f"{cfg} "
+ command_info += (
+ f"--cfg-options "
+ f"checkpoint_config.max_keep_ckpts=1 "
+ f"dist_params.port={port} "
+ )
+ command_info += f"--work-dir work_dirs/{model_name}/{config_name} "
# Let the script shut up
- command_info += '>/dev/null &'
+ command_info += ">/dev/null &"
commands.append(command_info)
- commands.append('\n')
+ commands.append("\n")
def main():
args = parse_args()
if args.out:
- out_suffix = args.out.split('.')[-1]
- assert args.out.endswith('.sh'), \
- f'Expected out file path suffix is .sh, but get .{out_suffix}'
+ out_suffix = args.out.split(".")[-1]
+ assert args.out.endswith(
+ ".sh"
+ ), f"Expected out file path suffix is .sh, but get .{out_suffix}"
- root_name = './tools'
- script_name = osp.join(root_name, 'slurm_train.sh')
+ root_name = "./tools"
+ script_name = osp.join(root_name, "slurm_train.sh")
port = args.port
- partition_name = 'PARTITION=$1'
+ partition_name = "PARTITION=$1"
- commands = [partition_name, '\n', '\n']
+ commands = [partition_name, "\n", "\n"]
- with open(args.txt_path, 'r') as f:
+ with open(args.txt_path) as f:
model_cfgs = f.readlines()
for cfg in model_cfgs:
- create_train_bash_info(commands, cfg, script_name, '$PARTITION',
- port)
+ create_train_bash_info(commands, cfg, script_name, "$PARTITION", port)
port += 1
- command_str = ''.join(commands)
+ command_str = "".join(commands)
if args.out:
- with open(args.out, 'w') as f:
+ with open(args.out, "w") as f:
f.write(command_str)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/.dev/log_collector/example_config.py b/mmsegmentation/.dev/log_collector/example_config.py
index bc2b4d6..1aea861 100644
--- a/mmsegmentation/.dev/log_collector/example_config.py
+++ b/mmsegmentation/.dev/log_collector/example_config.py
@@ -1,18 +1,18 @@
-work_dir = '../../work_dirs'
-metric = 'mIoU'
+work_dir = "../../work_dirs"
+metric = "mIoU"
# specify the log files we would like to collect in `log_items`
log_items = [
- 'segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup',
- 'segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr',
- 'segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr',
- 'segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr'
+ "segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup",
+ "segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr",
+ "segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr",
+ "segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr",
]
# or specify ignore_keywords, then the folders whose name contain
# `'segformer'` won't be collected
# ignore_keywords = ['segformer']
# should not include metric
-other_info_keys = ['mAcc']
-markdown_file = 'markdowns/lr_in_trans.json.md'
-json_file = 'jsons/trans_in_cnn.json'
+other_info_keys = ["mAcc"]
+markdown_file = "markdowns/lr_in_trans.json.md"
+json_file = "jsons/trans_in_cnn.json"
diff --git a/mmsegmentation/.dev/log_collector/log_collector.py b/mmsegmentation/.dev/log_collector/log_collector.py
index cc7b413..81e80ed 100644
--- a/mmsegmentation/.dev/log_collector/log_collector.py
+++ b/mmsegmentation/.dev/log_collector/log_collector.py
@@ -25,8 +25,8 @@
def parse_args():
- parser = argparse.ArgumentParser(description='extract info from log.json')
- parser.add_argument('config_dir')
+ parser = argparse.ArgumentParser(description="extract info from log.json")
+ parser.add_argument("config_dir")
return parser.parse_args()
@@ -37,23 +37,23 @@ def has_keyword(name: str, keywords: list):
def main():
args = parse_args()
cfg = load_config(args.config_dir)
- work_dir = cfg['work_dir']
- metric = cfg['metric']
- log_items = cfg.get('log_items', [])
- ignore_keywords = cfg.get('ignore_keywords', [])
- other_info_keys = cfg.get('other_info_keys', [])
- markdown_file = cfg.get('markdown_file', None)
- json_file = cfg.get('json_file', None)
-
- if json_file and osp.split(json_file)[0] != '':
+ work_dir = cfg["work_dir"]
+ metric = cfg["metric"]
+ log_items = cfg.get("log_items", [])
+ ignore_keywords = cfg.get("ignore_keywords", [])
+ other_info_keys = cfg.get("other_info_keys", [])
+ markdown_file = cfg.get("markdown_file", None)
+ json_file = cfg.get("json_file", None)
+
+ if json_file and osp.split(json_file)[0] != "":
os.makedirs(osp.split(json_file)[0], exist_ok=True)
- if markdown_file and osp.split(markdown_file)[0] != '':
+ if markdown_file and osp.split(markdown_file)[0] != "":
os.makedirs(osp.split(markdown_file)[0], exist_ok=True)
- assert not (log_items and ignore_keywords), \
- 'log_items and ignore_keywords cannot be specified at the same time'
- assert metric not in other_info_keys, \
- 'other_info_keys should not contain metric'
+ assert not (
+ log_items and ignore_keywords
+ ), "log_items and ignore_keywords cannot be specified at the same time"
+ assert metric not in other_info_keys, "other_info_keys should not contain metric"
if ignore_keywords and isinstance(ignore_keywords, str):
ignore_keywords = [ignore_keywords]
@@ -64,7 +64,8 @@ def main():
if not log_items:
log_items = [
- item for item in sorted(os.listdir(work_dir))
+ item
+ for item in sorted(os.listdir(work_dir))
if not has_keyword(item, ignore_keywords)
]
@@ -72,50 +73,54 @@ def main():
for config_dir in log_items:
preceding_path = os.path.join(work_dir, config_dir)
log_list = [
- item for item in os.listdir(preceding_path)
- if item.endswith('.log.json')
+ item for item in os.listdir(preceding_path) if item.endswith(".log.json")
]
log_list = sorted(
log_list,
key=lambda time_str: datetime.datetime.strptime(
- time_str, '%Y%m%d_%H%M%S.log.json'))
+ time_str, "%Y%m%d_%H%M%S.log.json"
+ ),
+ )
val_list = []
last_iter = 0
for log_name in log_list:
- with open(os.path.join(preceding_path, log_name), 'r') as f:
+ with open(os.path.join(preceding_path, log_name)) as f:
# ignore the info line
f.readline()
all_lines = f.readlines()
- val_list.extend([
- json.loads(line) for line in all_lines
- if json.loads(line)['mode'] == 'val'
- ])
+ val_list.extend(
+ [
+ json.loads(line)
+ for line in all_lines
+ if json.loads(line)["mode"] == "val"
+ ]
+ )
for index in range(len(all_lines) - 1, -1, -1):
line_dict = json.loads(all_lines[index])
- if line_dict['mode'] == 'train':
- last_iter = max(last_iter, line_dict['iter'])
+ if line_dict["mode"] == "train":
+ last_iter = max(last_iter, line_dict["iter"])
break
- new_log_dict = dict(
- method=config_dir, metric_used=metric, last_iter=last_iter)
+ new_log_dict = dict(method=config_dir, metric_used=metric, last_iter=last_iter)
for index, log in enumerate(val_list, 1):
new_ordered_dict = OrderedDict()
- new_ordered_dict['eval_index'] = index
+ new_ordered_dict["eval_index"] = index
new_ordered_dict[metric] = log[metric]
for key in other_info_keys:
if key in log:
new_ordered_dict[key] = log[key]
val_list[index - 1] = new_ordered_dict
- assert len(val_list) >= 1, \
- f"work dir {config_dir} doesn't contain any evaluation."
- new_log_dict['last eval'] = val_list[-1]
- new_log_dict['best eval'] = max(val_list, key=lambda x: x[metric])
+ assert (
+ len(val_list) >= 1
+ ), f"work dir {config_dir} doesn't contain any evaluation."
+ new_log_dict["last eval"] = val_list[-1]
+ new_log_dict["best eval"] = max(val_list, key=lambda x: x[metric])
experiment_info_list.append(new_log_dict)
- print(f'{config_dir} is processed')
+ print(f"{config_dir} is processed")
if json_file:
- with open(json_file, 'w') as f:
+ with open(json_file, "w") as f:
json.dump(experiment_info_list, f, indent=4)
if markdown_file:
@@ -125,15 +130,18 @@ def main():
f"|{index}|{log['method']}|{log['best eval'][metric]}"
f"|{log['best eval']['eval_index']}|"
f"{log['last eval'][metric]}|"
- f"{log['last eval']['eval_index']}|{log['last_iter']}|\n")
- with open(markdown_file, 'w') as f:
- f.write(f'|exp_num|method|{metric} best|best index|'
- f'{metric} last|last index|last iter num|\n')
- f.write('|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n')
+ f"{log['last eval']['eval_index']}|{log['last_iter']}|\n"
+ )
+ with open(markdown_file, "w") as f:
+ f.write(
+ f"|exp_num|method|{metric} best|best index|"
+ f"{metric} last|last index|last iter num|\n"
+ )
+ f.write("|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n")
f.writelines(lines_to_write)
- print('processed successfully')
+ print("processed successfully")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/.dev/log_collector/utils.py b/mmsegmentation/.dev/log_collector/utils.py
index 848516a..356eee6 100644
--- a/mmsegmentation/.dev/log_collector/utils.py
+++ b/mmsegmentation/.dev/log_collector/utils.py
@@ -6,15 +6,12 @@
def load_config(cfg_dir: str) -> dict:
- assert cfg_dir.endswith('.py')
+ assert cfg_dir.endswith(".py")
root_path, file_name = osp.split(cfg_dir)
temp_module = osp.splitext(file_name)[0]
sys.path.insert(0, root_path)
mod = import_module(temp_module)
sys.path.pop(0)
- cfg_dict = {
- k: v
- for k, v in mod.__dict__.items() if not k.startswith('__')
- }
+ cfg_dict = {k: v for k, v in mod.__dict__.items() if not k.startswith("__")}
del sys.modules[temp_module]
return cfg_dict
diff --git a/mmsegmentation/.dev/md2yml.py b/mmsegmentation/.dev/md2yml.py
index 1d68498..ef50c86 100755
--- a/mmsegmentation/.dev/md2yml.py
+++ b/mmsegmentation/.dev/md2yml.py
@@ -15,14 +15,43 @@
from lxml import etree
from mmcv.fileio import dump
-MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
+MMSEG_ROOT = osp.dirname(osp.dirname(osp.dirname(__file__)))
COLLECTIONS = [
- 'ANN', 'APCNet', 'BiSeNetV1', 'BiSeNetV2', 'CCNet', 'CGNet', 'DANet',
- 'DeepLabV3', 'DeepLabV3+', 'DMNet', 'DNLNet', 'DPT', 'EMANet', 'EncNet',
- 'ERFNet', 'FastFCN', 'FastSCNN', 'FCN', 'GCNet', 'ICNet', 'ISANet', 'KNet',
- 'NonLocalNet', 'OCRNet', 'PointRend', 'PSANet', 'PSPNet', 'Segformer',
- 'Segmenter', 'FPN', 'SETR', 'STDC', 'UNet', 'UPerNet'
+ "ANN",
+ "APCNet",
+ "BiSeNetV1",
+ "BiSeNetV2",
+ "CCNet",
+ "CGNet",
+ "DANet",
+ "DeepLabV3",
+ "DeepLabV3+",
+ "DMNet",
+ "DNLNet",
+ "DPT",
+ "EMANet",
+ "EncNet",
+ "ERFNet",
+ "FastFCN",
+ "FastSCNN",
+ "FCN",
+ "GCNet",
+ "ICNet",
+ "ISANet",
+ "KNet",
+ "NonLocalNet",
+ "OCRNet",
+ "PointRend",
+ "PSANet",
+ "PSPNet",
+ "Segformer",
+ "Segmenter",
+ "FPN",
+ "SETR",
+ "STDC",
+ "UNet",
+ "UPerNet",
]
COLLECTIONS_TEMP = []
@@ -39,10 +68,10 @@ def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
Bool: If the target YAML file is different from the original.
"""
- str_dump = dump(obj, None, file_format='yaml', sort_keys=sort_keys)
+ str_dump = dump(obj, None, file_format="yaml", sort_keys=sort_keys)
if osp.isfile(filename):
file_exists = True
- with open(filename, 'r', encoding='utf-8') as f:
+ with open(filename, encoding="utf-8") as f:
str_orig = f.read()
else:
file_exists = False
@@ -52,7 +81,7 @@ def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
is_different = False
else:
is_different = True
- with open(filename, 'w', encoding='utf-8') as f:
+ with open(filename, "w", encoding="utf-8") as f:
f.write(str_dump)
return is_different
@@ -71,17 +100,12 @@ def parse_md(md_file):
collection = dict(
Name=collection_name,
- Metadata={'Training Data': []},
- Paper={
- 'URL': '',
- 'Title': ''
- },
+ Metadata={"Training Data": []},
+ Paper={"URL": "", "Title": ""},
README=md_file,
- Code={
- 'URL': '',
- 'Version': ''
- })
- collection.update({'Converted From': {'Weights': '', 'Code': ''}})
+ Code={"URL": "", "Version": ""},
+ )
+ collection.update({"Converted From": {"Weights": "", "Code": ""}})
models = []
datasets = []
paper_url = None
@@ -97,110 +121,118 @@ def parse_md(md_file):
# should be set with head or neck of this config file.
is_backbone = None
- with open(md_file, 'r', encoding='UTF-8') as md:
+ with open(md_file, encoding="UTF-8") as md:
lines = md.readlines()
i = 0
- current_dataset = ''
+ current_dataset = ""
while i < len(lines):
line = lines[i].strip()
# In latest README.md the title and url are in the third line.
if i == 2:
- paper_url = lines[i].split('](')[1].split(')')[0]
- paper_title = lines[i].split('](')[0].split('[')[1]
+ paper_url = lines[i].split("](")[1].split(")")[0]
+ paper_title = lines[i].split("](")[0].split("[")[1]
if len(line) == 0:
i += 1
continue
- elif line[:3] == ' batch_size: 8
samples_per_gpu=1,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
+ test=dict(pipeline=test_pipeline),
+)
diff --git a/mmsegmentation/configs/segmenter/segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py b/mmsegmentation/configs/segmenter/segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py
index 4e6a0b1..bcaf068 100644
--- a/mmsegmentation/configs/segmenter/segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py
+++ b/mmsegmentation/configs/segmenter/segmenter_vit-l_mask_8x1_640x640_160k_ade20k.py
@@ -1,61 +1,66 @@
_base_ = [
- '../_base_/models/segmenter_vit-b16_mask.py',
- '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/segmenter_vit-b16_mask.py",
+ "../_base_/datasets/ade20k_640x640.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth" # noqa
model = dict(
pretrained=checkpoint,
backbone=dict(
- type='VisionTransformer',
+ type="VisionTransformer",
img_size=(640, 640),
embed_dims=1024,
num_layers=24,
- num_heads=16),
+ num_heads=16,
+ ),
decode_head=dict(
- type='SegmenterMaskTransformerHead',
+ type="SegmenterMaskTransformerHead",
in_channels=1024,
channels=1024,
num_heads=16,
- embed_dims=1024),
- test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(608, 608)))
+ embed_dims=1024,
+ ),
+ test_cfg=dict(mode="slide", crop_size=(640, 640), stride=(608, 608)),
+)
optimizer = dict(lr=0.001, weight_decay=0.0)
-img_norm_cfg = dict(
- mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+img_norm_cfg = dict(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
crop_size = (640, 640)
train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', reduce_zero_label=True),
- dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+ dict(type="LoadImageFromFile"),
+ dict(type="LoadAnnotations", reduce_zero_label=True),
+ dict(type="Resize", img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
+ dict(type="RandomCrop", crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type="RandomFlip", prob=0.5),
+ dict(type="PhotoMetricDistortion"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="Pad", size=crop_size, pad_val=0, seg_pad_val=255),
+ dict(type="DefaultFormatBundle"),
+ dict(type="Collect", keys=["img", "gt_semantic_seg"]),
]
test_pipeline = [
- dict(type='LoadImageFromFile'),
+ dict(type="LoadImageFromFile"),
dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(2560, 640),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="ImageToTensor", keys=["img"]),
+ dict(type="Collect", keys=["img"]),
+ ],
+ ),
]
data = dict(
# num_gpus: 8 -> batch_size: 8
samples_per_gpu=1,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
+ test=dict(pipeline=test_pipeline),
+)
diff --git a/mmsegmentation/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py b/mmsegmentation/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py
index adc8c1b..95e01a1 100644
--- a/mmsegmentation/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py
@@ -1,14 +1,15 @@
-_base_ = './segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py'
+_base_ = "./segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py"
model = dict(
decode_head=dict(
_delete_=True,
- type='FCNHead',
+ type="FCNHead",
in_channels=384,
channels=384,
num_convs=0,
dropout_ratio=0.0,
concat_input=False,
num_classes=150,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
+ loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+ )
+)
diff --git a/mmsegmentation/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py b/mmsegmentation/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py
index 7e0eeb1..46d868a 100644
--- a/mmsegmentation/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py
@@ -1,12 +1,13 @@
_base_ = [
- '../_base_/models/segmenter_vit-b16_mask.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/segmenter_vit-b16_mask.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_small_p16_384_20220308-410f6037.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_small_p16_384_20220308-410f6037.pth" # noqa
-backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
+backbone_norm_cfg = dict(type="LN", eps=1e-6, requires_grad=True)
model = dict(
pretrained=checkpoint,
backbone=dict(
@@ -15,7 +16,7 @@
num_heads=6,
),
decode_head=dict(
- type='SegmenterMaskTransformerHead',
+ type="SegmenterMaskTransformerHead",
in_channels=384,
channels=384,
num_classes=150,
@@ -23,44 +24,46 @@
num_heads=6,
embed_dims=384,
dropout_ratio=0.0,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
+ loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+ ),
+)
optimizer = dict(lr=0.001, weight_decay=0.0)
-img_norm_cfg = dict(
- mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+img_norm_cfg = dict(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', reduce_zero_label=True),
- dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+ dict(type="LoadImageFromFile"),
+ dict(type="LoadAnnotations", reduce_zero_label=True),
+ dict(type="Resize", img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+ dict(type="RandomCrop", crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type="RandomFlip", prob=0.5),
+ dict(type="PhotoMetricDistortion"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="Pad", size=crop_size, pad_val=0, seg_pad_val=255),
+ dict(type="DefaultFormatBundle"),
+ dict(type="Collect", keys=["img", "gt_semantic_seg"]),
]
test_pipeline = [
- dict(type='LoadImageFromFile'),
+ dict(type="LoadImageFromFile"),
dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(2048, 512),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="ImageToTensor", keys=["img"]),
+ dict(type="Collect", keys=["img"]),
+ ],
+ ),
]
data = dict(
# num_gpus: 8 -> batch_size: 8
samples_per_gpu=1,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
+ test=dict(pipeline=test_pipeline),
+)
diff --git a/mmsegmentation/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py b/mmsegmentation/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py
index ec0107d..4c70698 100644
--- a/mmsegmentation/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py
@@ -1,56 +1,60 @@
_base_ = [
- '../_base_/models/segmenter_vit-b16_mask.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/segmenter_vit-b16_mask.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth" # noqa
model = dict(
pretrained=checkpoint,
backbone=dict(embed_dims=192, num_heads=3),
decode_head=dict(
- type='SegmenterMaskTransformerHead',
+ type="SegmenterMaskTransformerHead",
in_channels=192,
channels=192,
num_heads=3,
- embed_dims=192))
+ embed_dims=192,
+ ),
+)
optimizer = dict(lr=0.001, weight_decay=0.0)
-img_norm_cfg = dict(
- mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+img_norm_cfg = dict(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', reduce_zero_label=True),
- dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+ dict(type="LoadImageFromFile"),
+ dict(type="LoadAnnotations", reduce_zero_label=True),
+ dict(type="Resize", img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+ dict(type="RandomCrop", crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type="RandomFlip", prob=0.5),
+ dict(type="PhotoMetricDistortion"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="Pad", size=crop_size, pad_val=0, seg_pad_val=255),
+ dict(type="DefaultFormatBundle"),
+ dict(type="Collect", keys=["img", "gt_semantic_seg"]),
]
test_pipeline = [
- dict(type='LoadImageFromFile'),
+ dict(type="LoadImageFromFile"),
dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(2048, 512),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="ImageToTensor", keys=["img"]),
+ dict(type="Collect", keys=["img"]),
+ ],
+ ),
]
data = dict(
# num_gpus: 8 -> batch_size: 8
samples_per_gpu=1,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
+ test=dict(pipeline=test_pipeline),
+)
diff --git a/mmsegmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py b/mmsegmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
index 7f8710d..1d0c7e0 100644
--- a/mmsegmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './fpn_r50_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./fpn_r50_512x1024_80k_cityscapes.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py b/mmsegmentation/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
index 2654096..3597dc1 100644
--- a/mmsegmentation/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
@@ -1,2 +1,2 @@
-_base_ = './fpn_r50_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./fpn_r50_512x512_160k_ade20k.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py b/mmsegmentation/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
index 4bf3edd..de5af01 100644
--- a/mmsegmentation/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
@@ -1,4 +1,6 @@
_base_ = [
- '../_base_/models/fpn_r50.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/fpn_r50.py",
+ "../_base_/datasets/cityscapes.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
diff --git a/mmsegmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py b/mmsegmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
index 5cdfc8c..65119d5 100644
--- a/mmsegmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
@@ -1,5 +1,7 @@
_base_ = [
- '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/fpn_r50.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
model = dict(decode_head=dict(num_classes=150))
diff --git a/mmsegmentation/configs/setr/setr_mla_512x512_160k_b16_ade20k.py b/mmsegmentation/configs/setr/setr_mla_512x512_160k_b16_ade20k.py
index c8418c6..9c6ae88 100644
--- a/mmsegmentation/configs/setr/setr_mla_512x512_160k_b16_ade20k.py
+++ b/mmsegmentation/configs/setr/setr_mla_512x512_160k_b16_ade20k.py
@@ -1,4 +1,4 @@
-_base_ = ['./setr_mla_512x512_160k_b8_ade20k.py']
+_base_ = ["./setr_mla_512x512_160k_b8_ade20k.py"]
# num_gpus: 8 -> batch_size: 16
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/setr/setr_mla_512x512_160k_b8_ade20k.py b/mmsegmentation/configs/setr/setr_mla_512x512_160k_b8_ade20k.py
index e1a07ce..e35c57d 100644
--- a/mmsegmentation/configs/setr/setr_mla_512x512_160k_b8_ade20k.py
+++ b/mmsegmentation/configs/setr/setr_mla_512x512_160k_b8_ade20k.py
@@ -1,85 +1,96 @@
_base_ = [
- '../_base_/models/setr_mla.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/setr_mla.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
+norm_cfg = dict(type="SyncBN", requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
- drop_rate=0.,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
+ drop_rate=0.0,
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
decode_head=dict(num_classes=150),
auxiliary_head=[
dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=256,
channels=256,
in_index=0,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=0,
kernel_size=1,
concat_input=False,
num_classes=150,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=256,
channels=256,
in_index=1,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=0,
kernel_size=1,
concat_input=False,
num_classes=150,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=256,
channels=256,
in_index=2,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=0,
kernel_size=1,
concat_input=False,
num_classes=150,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=256,
channels=256,
in_index=3,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=0,
kernel_size=1,
concat_input=False,
num_classes=150,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
],
- test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)),
+ test_cfg=dict(mode="slide", crop_size=(512, 512), stride=(341, 341)),
)
optimizer = dict(
lr=0.001,
weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)}),
+)
# num_gpus: 8 -> batch_size: 8
data = dict(samples_per_gpu=1)
diff --git a/mmsegmentation/configs/setr/setr_naive_512x512_160k_b16_ade20k.py b/mmsegmentation/configs/setr/setr_naive_512x512_160k_b16_ade20k.py
index 8ad8c9f..dadf93a 100644
--- a/mmsegmentation/configs/setr/setr_naive_512x512_160k_b16_ade20k.py
+++ b/mmsegmentation/configs/setr/setr_naive_512x512_160k_b16_ade20k.py
@@ -1,67 +1,76 @@
_base_ = [
- '../_base_/models/setr_naive.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/setr_naive.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
+norm_cfg = dict(type="SyncBN", requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
- drop_rate=0.,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
+ drop_rate=0.0,
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
decode_head=dict(num_classes=150),
auxiliary_head=[
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=0,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=1,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=1,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=1,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=2,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=1,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4))
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
],
- test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)),
+ test_cfg=dict(mode="slide", crop_size=(512, 512), stride=(341, 341)),
)
optimizer = dict(
lr=0.01,
weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)}),
+)
# num_gpus: 8 -> batch_size: 16
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/setr/setr_pup_512x512_160k_b16_ade20k.py b/mmsegmentation/configs/setr/setr_pup_512x512_160k_b16_ade20k.py
index 83997a2..f70679c 100644
--- a/mmsegmentation/configs/setr/setr_pup_512x512_160k_b16_ade20k.py
+++ b/mmsegmentation/configs/setr/setr_pup_512x512_160k_b16_ade20k.py
@@ -1,67 +1,76 @@
_base_ = [
- '../_base_/models/setr_pup.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/setr_pup.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
+norm_cfg = dict(type="SyncBN", requires_grad=True)
model = dict(
pretrained=None,
backbone=dict(
img_size=(512, 512),
- drop_rate=0.,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
+ drop_rate=0.0,
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
decode_head=dict(num_classes=150),
auxiliary_head=[
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=0,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=1,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=2,
num_classes=150,
dropout_ratio=0,
norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
+ act_cfg=dict(type="ReLU"),
num_convs=2,
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
],
- test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)),
+ test_cfg=dict(mode="slide", crop_size=(512, 512), stride=(341, 341)),
)
optimizer = dict(
lr=0.001,
weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)}),
+)
# num_gpus: 8 -> batch_size: 16
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py b/mmsegmentation/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py
index 4237cd5..0565f20 100644
--- a/mmsegmentation/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py
+++ b/mmsegmentation/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py
@@ -1,17 +1,21 @@
_base_ = [
- '../_base_/models/setr_mla.py', '../_base_/datasets/cityscapes_768x768.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/setr_mla.py",
+ "../_base_/datasets/cityscapes_768x768.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
model = dict(
pretrained=None,
backbone=dict(
drop_rate=0,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
- test_cfg=dict(mode='slide', crop_size=(768, 768), stride=(512, 512)))
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
+ test_cfg=dict(mode="slide", crop_size=(768, 768), stride=(512, 512)),
+)
optimizer = dict(
lr=0.002,
weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)}),
+)
data = dict(samples_per_gpu=1)
diff --git a/mmsegmentation/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py b/mmsegmentation/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py
index 0c6621e..2c53ec9 100644
--- a/mmsegmentation/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py
+++ b/mmsegmentation/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py
@@ -1,18 +1,20 @@
_base_ = [
- '../_base_/models/setr_naive.py',
- '../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/setr_naive.py",
+ "../_base_/datasets/cityscapes_768x768.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
model = dict(
pretrained=None,
backbone=dict(
- drop_rate=0.,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
- test_cfg=dict(mode='slide', crop_size=(768, 768), stride=(512, 512)))
+ drop_rate=0.0,
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
+ test_cfg=dict(mode="slide", crop_size=(768, 768), stride=(512, 512)),
+)
optimizer = dict(
- weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ weight_decay=0.0, paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)})
+)
data = dict(samples_per_gpu=1)
diff --git a/mmsegmentation/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py b/mmsegmentation/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py
index e108988..ac15a6e 100644
--- a/mmsegmentation/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py
+++ b/mmsegmentation/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py
@@ -1,19 +1,21 @@
_base_ = [
- '../_base_/models/setr_pup.py', '../_base_/datasets/cityscapes_768x768.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/setr_pup.py",
+ "../_base_/datasets/cityscapes_768x768.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
+norm_cfg = dict(type="SyncBN", requires_grad=True)
crop_size = (768, 768)
model = dict(
pretrained=None,
backbone=dict(
- drop_rate=0.,
- init_cfg=dict(
- type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
+ drop_rate=0.0,
+ init_cfg=dict(type="Pretrained", checkpoint="pretrain/vit_large_p16.pth"),
+ ),
auxiliary_head=[
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=0,
@@ -25,9 +27,11 @@
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=1,
@@ -39,9 +43,11 @@
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
dict(
- type='SETRUPHead',
+ type="SETRUPHead",
in_channels=1024,
channels=256,
in_index=2,
@@ -53,12 +59,15 @@
kernel_size=3,
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4))
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4
+ ),
+ ),
],
- test_cfg=dict(mode='slide', crop_size=crop_size, stride=(512, 512)))
+ test_cfg=dict(mode="slide", crop_size=crop_size, stride=(512, 512)),
+)
optimizer = dict(
- weight_decay=0.0,
- paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
+ weight_decay=0.0, paramwise_cfg=dict(custom_keys={"head": dict(lr_mult=10.0)})
+)
data = dict(samples_per_gpu=1)
diff --git a/mmsegmentation/configs/stdc/stdc1_512x1024_80k_cityscapes.py b/mmsegmentation/configs/stdc/stdc1_512x1024_80k_cityscapes.py
index 849e771..babec13 100644
--- a/mmsegmentation/configs/stdc/stdc1_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/stdc/stdc1_512x1024_80k_cityscapes.py
@@ -1,8 +1,10 @@
_base_ = [
- '../_base_/models/stdc.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/stdc.py",
+ "../_base_/datasets/cityscapes.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
-lr_config = dict(warmup='linear', warmup_iters=1000)
+lr_config = dict(warmup="linear", warmup_iters=1000)
data = dict(
samples_per_gpu=12,
workers_per_gpu=4,
diff --git a/mmsegmentation/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py b/mmsegmentation/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py
index f295bf4..78d792b 100644
--- a/mmsegmentation/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py
@@ -1,6 +1,7 @@
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth' # noqa
-_base_ = './stdc1_512x1024_80k_cityscapes.py'
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth" # noqa
+_base_ = "./stdc1_512x1024_80k_cityscapes.py"
model = dict(
backbone=dict(
- backbone_cfg=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
+ backbone_cfg=dict(init_cfg=dict(type="Pretrained", checkpoint=checkpoint))
+ )
+)
diff --git a/mmsegmentation/configs/stdc/stdc2_512x1024_80k_cityscapes.py b/mmsegmentation/configs/stdc/stdc2_512x1024_80k_cityscapes.py
index f7afb50..cc190d2 100644
--- a/mmsegmentation/configs/stdc/stdc2_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/stdc/stdc2_512x1024_80k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './stdc1_512x1024_80k_cityscapes.py'
-model = dict(backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2')))
+_base_ = "./stdc1_512x1024_80k_cityscapes.py"
+model = dict(backbone=dict(backbone_cfg=dict(stdc_type="STDCNet2")))
diff --git a/mmsegmentation/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py b/mmsegmentation/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py
index 4148ac4..d6e20f3 100644
--- a/mmsegmentation/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py
@@ -1,6 +1,7 @@
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth' # noqa
-_base_ = './stdc2_512x1024_80k_cityscapes.py'
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth" # noqa
+_base_ = "./stdc2_512x1024_80k_cityscapes.py"
model = dict(
backbone=dict(
- backbone_cfg=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
+ backbone_cfg=dict(init_cfg=dict(type="Pretrained", checkpoint=checkpoint))
+ )
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
index 027bd6f..dcfc509 100644
--- a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
@@ -1,15 +1,16 @@
_base_ = [
- 'upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
- 'pretrain_224x224_1K.py'
+ "upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_" "pretrain_224x224_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
- window_size=12),
+ window_size=12,
+ ),
decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150),
- auxiliary_head=dict(in_channels=512, num_classes=150))
+ auxiliary_head=dict(in_channels=512, num_classes=150),
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
index e662d4f..7629672 100644
--- a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
@@ -1,8 +1,7 @@
_base_ = [
- './upernet_swin_base_patch4_window12_512x512_160k_ade20k_'
- 'pretrain_384x384_1K.py'
+ "./upernet_swin_base_patch4_window12_512x512_160k_ade20k_" "pretrain_384x384_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth" # noqa
model = dict(
- backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)))
+ backbone=dict(init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file))
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
index 6e05677..8b557d6 100644
--- a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
@@ -1,13 +1,14 @@
_base_ = [
- './upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
- 'pretrain_224x224_1K.py'
+ "./upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_" "pretrain_224x224_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
embed_dims=128,
depths=[2, 2, 18, 2],
- num_heads=[4, 8, 16, 32]),
+ num_heads=[4, 8, 16, 32],
+ ),
decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150),
- auxiliary_head=dict(in_channels=512, num_classes=150))
+ auxiliary_head=dict(in_channels=512, num_classes=150),
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
index 7a9c506..118845f 100644
--- a/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
@@ -1,8 +1,7 @@
_base_ = [
- './upernet_swin_base_patch4_window7_512x512_160k_ade20k_'
- 'pretrain_224x224_1K.py'
+ "./upernet_swin_base_patch4_window7_512x512_160k_ade20k_" "pretrain_224x224_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth" # noqa
model = dict(
- backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)))
+ backbone=dict(init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file))
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py b/mmsegmentation/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py
index a43e5be..92b748e 100644
--- a/mmsegmentation/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py
+++ b/mmsegmentation/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py
@@ -1,10 +1,11 @@
_base_ = [
- 'upernet_swin_large_patch4_window7_512x512_'
- 'pretrain_224x224_22K_160k_ade20k.py'
+ "upernet_swin_large_patch4_window7_512x512_" "pretrain_224x224_22K_160k_ade20k.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window12_384_22k_20220412-6580f57d.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
pretrain_img_size=384,
- window_size=12))
+ window_size=12,
+ )
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py b/mmsegmentation/configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py
index 8a78f32..3eb190c 100644
--- a/mmsegmentation/configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py
+++ b/mmsegmentation/configs/swin/upernet_swin_large_patch4_window7_512x512_pretrain_224x224_22K_160k_ade20k.py
@@ -1,15 +1,16 @@
_base_ = [
- 'upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
- 'pretrain_224x224_1K.py'
+ "upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_" "pretrain_224x224_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220412-aeecf2aa.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220412-aeecf2aa.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
pretrain_img_size=224,
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
- window_size=7),
+ window_size=7,
+ ),
decode_head=dict(in_channels=[192, 384, 768, 1536], num_classes=150),
- auxiliary_head=dict(in_channels=768, num_classes=150))
+ auxiliary_head=dict(in_channels=768, num_classes=150),
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/mmsegmentation/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
index 1958e0e..5eccd4c 100644
--- a/mmsegmentation/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
@@ -1,11 +1,12 @@
_base_ = [
- './upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_'
- 'pretrain_224x224_1K.py'
+ "./upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_" "pretrain_224x224_1K.py"
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
- depths=[2, 2, 18, 2]),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
+ depths=[2, 2, 18, 2],
+ ),
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=150),
- auxiliary_head=dict(in_channels=384, num_classes=150))
+ auxiliary_head=dict(in_channels=384, num_classes=150),
+)
diff --git a/mmsegmentation/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/mmsegmentation/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
index 6d8c413..b8d5386 100644
--- a/mmsegmentation/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+++ b/mmsegmentation/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
@@ -1,45 +1,52 @@
_base_ = [
- '../_base_/models/upernet_swin.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/upernet_swin.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
+checkpoint_file = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint_file),
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
use_abs_pos_embed=False,
drop_path_rate=0.3,
- patch_norm=True),
+ patch_norm=True,
+ ),
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=150),
- auxiliary_head=dict(in_channels=384, num_classes=150))
+ auxiliary_head=dict(in_channels=384, num_classes=150),
+)
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
- 'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ "absolute_pos_embed": dict(decay_mult=0.0),
+ "relative_position_bias_table": dict(decay_mult=0.0),
+ "norm": dict(decay_mult=0.0),
+ }
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index b79fefd..d517d04 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,8 +1,9 @@
-_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
+_base_ = ["./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
- depths=[3, 4, 18, 3]), )
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint), depths=[3, 4, 18, 3]
+ ),
+)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py
index 8c299d3..f233291 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py
@@ -1,11 +1,13 @@
-_base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py']
+_base_ = ["./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
depths=[3, 4, 18, 3],
- drop_path_rate=0.3))
+ drop_path_rate=0.3,
+ )
+)
data = dict(samples_per_gpu=2, workers_per_gpu=2)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index abb652e..28d2019 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,8 +1,9 @@
-_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
+_base_ = ["./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
- depths=[3, 8, 27, 3]))
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint), depths=[3, 8, 27, 3]
+ )
+)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py
index f6f7d27..f1e06e3 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py
@@ -1,11 +1,13 @@
-_base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py']
+_base_ = ["./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
depths=[3, 8, 27, 3],
- drop_path_rate=0.3))
+ drop_path_rate=0.3,
+ )
+)
data = dict(samples_per_gpu=2, workers_per_gpu=2)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index 3d7be96..c62d10c 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/twins_pcpvt-s_fpn.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/twins_pcpvt-s_fpn.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
+optimizer = dict(_delete_=True, type="AdamW", lr=0.0001, weight_decay=0.0001)
diff --git a/mmsegmentation/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py
index c888b92..9a9dc89 100644
--- a/mmsegmentation/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py
@@ -1,26 +1,28 @@
_base_ = [
- '../_base_/models/twins_pcpvt-s_upernet.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/twins_pcpvt-s_upernet.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
- paramwise_cfg=dict(custom_keys={
- 'pos_block': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ paramwise_cfg=dict(
+ custom_keys={"pos_block": dict(decay_mult=0.0), "norm": dict(decay_mult=0.0)}
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
diff --git a/mmsegmentation/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index 00d8957..46e876c 100644
--- a/mmsegmentation/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,12 +1,13 @@
-_base_ = ['./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
+_base_ = ["./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[96, 192, 384, 768],
num_heads=[3, 6, 12, 24],
- depths=[2, 2, 18, 2]),
+ depths=[2, 2, 18, 2],
+ ),
neck=dict(in_channels=[96, 192, 384, 768]),
)
diff --git a/mmsegmentation/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py
index a969fed..7b791c5 100644
--- a/mmsegmentation/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py
@@ -1,12 +1,14 @@
-_base_ = ['./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py']
+_base_ = ["./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[96, 192, 384, 768],
num_heads=[3, 6, 12, 24],
- depths=[2, 2, 18, 2]),
+ depths=[2, 2, 18, 2],
+ ),
decode_head=dict(in_channels=[96, 192, 384, 768]),
- auxiliary_head=dict(in_channels=384))
+ auxiliary_head=dict(in_channels=384),
+)
diff --git a/mmsegmentation/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index c68bfd4..e68f2b4 100644
--- a/mmsegmentation/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,13 +1,14 @@
-_base_ = ['./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py']
+_base_ = ["./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[128, 256, 512, 1024],
num_heads=[4, 8, 16, 32],
depths=[2, 2, 18, 2],
- drop_path_rate=0.3),
+ drop_path_rate=0.3,
+ ),
neck=dict(in_channels=[128, 256, 512, 1024]),
)
diff --git a/mmsegmentation/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py
index f98c070..76d5cf8 100644
--- a/mmsegmentation/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py
@@ -1,13 +1,15 @@
-_base_ = ['./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py']
+_base_ = ["./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py"]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth" # noqa
model = dict(
backbone=dict(
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[128, 256, 512, 1024],
num_heads=[4, 8, 16, 32],
depths=[2, 2, 18, 2],
- drop_path_rate=0.3),
+ drop_path_rate=0.3,
+ ),
decode_head=dict(in_channels=[128, 256, 512, 1024]),
- auxiliary_head=dict(in_channels=512))
+ auxiliary_head=dict(in_channels=512),
+)
diff --git a/mmsegmentation/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
index dbb944c..e3f0add 100644
--- a/mmsegmentation/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py
@@ -1,22 +1,25 @@
_base_ = [
- '../_base_/models/twins_pcpvt-s_fpn.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/twins_pcpvt-s_fpn.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_small_20220308-7e1c3695.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_small_20220308-7e1c3695.pth" # noqa
model = dict(
backbone=dict(
- type='SVT',
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ type="SVT",
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[64, 128, 256, 512],
num_heads=[2, 4, 8, 16],
mlp_ratios=[4, 4, 4, 4],
depths=[2, 2, 10, 4],
windiow_sizes=[7, 7, 7, 7],
- norm_after_stage=True),
+ norm_after_stage=True,
+ ),
neck=dict(in_channels=[64, 128, 256, 512], out_channels=256, num_outs=4),
decode_head=dict(num_classes=150),
)
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
+optimizer = dict(_delete_=True, type="AdamW", lr=0.0001, weight_decay=0.0001)
diff --git a/mmsegmentation/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py b/mmsegmentation/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py
index 44bf60b..9643b77 100644
--- a/mmsegmentation/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py
@@ -1,43 +1,47 @@
_base_ = [
- '../_base_/models/twins_pcpvt-s_upernet.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/twins_pcpvt-s_upernet.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_small_20220308-7e1c3695.pth' # noqa
+checkpoint = "https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_small_20220308-7e1c3695.pth" # noqa
model = dict(
backbone=dict(
- type='SVT',
- init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
+ type="SVT",
+ init_cfg=dict(type="Pretrained", checkpoint=checkpoint),
embed_dims=[64, 128, 256, 512],
num_heads=[2, 4, 8, 16],
mlp_ratios=[4, 4, 4, 4],
depths=[2, 2, 10, 4],
windiow_sizes=[7, 7, 7, 7],
- norm_after_stage=True),
+ norm_after_stage=True,
+ ),
decode_head=dict(in_channels=[64, 128, 256, 512]),
- auxiliary_head=dict(in_channels=256))
+ auxiliary_head=dict(in_channels=256),
+)
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
- paramwise_cfg=dict(custom_keys={
- 'pos_block': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ paramwise_cfg=dict(
+ custom_keys={"pos_block": dict(decay_mult=0.0), "norm": dict(decay_mult=0.0)}
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
data = dict(samples_per_gpu=2, workers_per_gpu=2)
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
index c706cf3..b94014f 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
@@ -1,7 +1,8 @@
_base_ = [
- '../_base_/models/deeplabv3_unet_s5-d16.py',
- '../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/deeplabv3_unet_s5-d16.py",
+ "../_base_/datasets/chase_db1.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
index 0ef02dc..1c38b46 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/stare.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/deeplabv3_unet_s5-d16.py",
+ "../_base_/datasets/stare.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
index 118428b..1231a50 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/hrf.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/deeplabv3_unet_s5-d16.py",
+ "../_base_/datasets/hrf.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
index 1f8862a..0086570 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/drive.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/deeplabv3_unet_s5-d16.py",
+ "../_base_/datasets/drive.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
index 1c48cbc..a664d10 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
@@ -1,6 +1,9 @@
-_base_ = './deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py'
+_base_ = "./deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
index 1022ede..47c8abd 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
@@ -1,6 +1,9 @@
-_base_ = './deeplabv3_unet_s5-d16_128x128_40k_stare.py'
+_base_ = "./deeplabv3_unet_s5-d16_128x128_40k_stare.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
index fc17da7..85eacd6 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
@@ -1,6 +1,9 @@
-_base_ = './deeplabv3_unet_s5-d16_256x256_40k_hrf.py'
+_base_ = "./deeplabv3_unet_s5-d16_256x256_40k_hrf.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
index 3f1f12e..6ba0e03 100644
--- a/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
@@ -1,6 +1,9 @@
-_base_ = './deeplabv3_unet_s5-d16_64x64_40k_drive.py'
+_base_ = "./deeplabv3_unet_s5-d16_64x64_40k_drive.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
index 2bc52d9..48ad438 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/chase_db1.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/fcn_unet_s5-d16.py",
+ "../_base_/datasets/chase_db1.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
index 5d836c6..b0ccfab 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/stare.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/fcn_unet_s5-d16.py",
+ "../_base_/datasets/stare.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
index be8eec7..2f0328e 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hrf.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/fcn_unet_s5-d16.py",
+ "../_base_/datasets/hrf.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
index a2f7dbe..0538508 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/fcn_unet_s5-d16.py",
+ "../_base_/datasets/cityscapes.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
model = dict(
@@ -8,7 +10,8 @@
auxiliary_head=dict(num_classes=19),
# model training and testing settings
train_cfg=dict(),
- test_cfg=dict(mode='whole'))
+ test_cfg=dict(mode="whole"),
+)
data = dict(
samples_per_gpu=4,
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
index 80483ad..c0fd076 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/drive.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/fcn_unet_s5-d16.py",
+ "../_base_/datasets/drive.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
index 5264866..d7765b4 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
@@ -1,6 +1,9 @@
-_base_ = './fcn_unet_s5-d16_128x128_40k_chase_db1.py'
+_base_ = "./fcn_unet_s5-d16_128x128_40k_chase_db1.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
index cf5fa1f..9531c73 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
@@ -1,6 +1,9 @@
-_base_ = './fcn_unet_s5-d16_128x128_40k_stare.py'
+_base_ = "./fcn_unet_s5-d16_128x128_40k_stare.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
index a154d7e..54cbe1f 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
@@ -1,6 +1,9 @@
-_base_ = './fcn_unet_s5-d16_256x256_40k_hrf.py'
+_base_ = "./fcn_unet_s5-d16_256x256_40k_hrf.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
index 1b8f860..4403a53 100644
--- a/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
@@ -1,6 +1,9 @@
-_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py'
+_base_ = "./fcn_unet_s5-d16_64x64_40k_drive.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
index b085a17..4aad1e2 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
@@ -1,7 +1,8 @@
_base_ = [
- '../_base_/models/pspnet_unet_s5-d16.py',
- '../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/pspnet_unet_s5-d16.py",
+ "../_base_/datasets/chase_db1.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
index 9d729ce..c8c596a 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/stare.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/pspnet_unet_s5-d16.py",
+ "../_base_/datasets/stare.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
index f57c916..af8fb79 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/hrf.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/pspnet_unet_s5-d16.py",
+ "../_base_/datasets/hrf.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
index 7b5421a..4f1d683 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
@@ -1,6 +1,8 @@
_base_ = [
- '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/drive.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/pspnet_unet_s5-d16.py",
+ "../_base_/datasets/drive.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
-evaluation = dict(metric='mDice')
+evaluation = dict(metric="mDice")
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
index a63dc11..f4d2694 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
@@ -1,6 +1,9 @@
-_base_ = './pspnet_unet_s5-d16_128x128_40k_chase_db1.py'
+_base_ = "./pspnet_unet_s5-d16_128x128_40k_chase_db1.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
index 1a3b665..7358571 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
@@ -1,6 +1,9 @@
-_base_ = './pspnet_unet_s5-d16_128x128_40k_stare.py'
+_base_ = "./pspnet_unet_s5-d16_128x128_40k_stare.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
index e19d6cf..2cd7a05 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
@@ -1,6 +1,9 @@
-_base_ = './pspnet_unet_s5-d16_256x256_40k_hrf.py'
+_base_ = "./pspnet_unet_s5-d16_256x256_40k_hrf.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
index 7934923..0dcabb6 100644
--- a/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
+++ b/mmsegmentation/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
@@ -1,6 +1,9 @@
-_base_ = './pspnet_unet_s5-d16_64x64_40k_drive.py'
+_base_ = "./pspnet_unet_s5-d16_64x64_40k_drive.py"
model = dict(
- decode_head=dict(loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
- ]))
+ decode_head=dict(
+ loss_decode=[
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=3.0),
+ ]
+ )
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
index b90b597..7f84f0a 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x1024_40k_cityscapes.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
index 420ca2e..7c6959b 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x1024_80k_cityscapes.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x512_160k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r101_512x512_160k_ade20k.py
index 146f13e..d9750b6 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x512_160k_ade20k.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x512_160k_ade20k.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x512_20k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r101_512x512_20k_voc12aug.py
index 56345d1..409b141 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x512_20k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x512_20k_voc12aug.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x512_20k_voc12aug.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x512_40k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r101_512x512_40k_voc12aug.py
index 0669b74..0b98927 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x512_40k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x512_40k_voc12aug.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x512_40k_voc12aug.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_512x512_80k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r101_512x512_80k_ade20k.py
index abfb9c5..21af2dd 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_512x512_80k_ade20k.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_512x512_80k_ade20k.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py
index e5f3a3f..74e0ffc 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_769x769_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_769x769_40k_cityscapes.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r101_769x769_80k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r101_769x769_80k_cityscapes.py
index a709165..dc6df2b 100644
--- a/mmsegmentation/configs/upernet/upernet_r101_769x769_80k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r101_769x769_80k_cityscapes.py
@@ -1,2 +1,2 @@
-_base_ = './upernet_r50_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
+_base_ = "./upernet_r50_769x769_80k_cityscapes.py"
+model = dict(pretrained="open-mmlab://resnet101_v1c", backbone=dict(depth=101))
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py
index f5aec1f..50aec22 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x1024_40k_cityscapes.py
@@ -1,6 +1,7 @@
-_base_ = './upernet_r50_512x1024_40k_cityscapes.py'
+_base_ = "./upernet_r50_512x1024_40k_cityscapes.py"
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512]),
- auxiliary_head=dict(in_channels=256))
+ auxiliary_head=dict(in_channels=256),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py
index 444f362..49cd177 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x1024_80k_cityscapes.py
@@ -1,6 +1,7 @@
-_base_ = './upernet_r50_512x1024_80k_cityscapes.py'
+_base_ = "./upernet_r50_512x1024_80k_cityscapes.py"
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512]),
- auxiliary_head=dict(in_channels=256))
+ auxiliary_head=dict(in_channels=256),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x512_160k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r18_512x512_160k_ade20k.py
index 9ac6c35..9edb405 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x512_160k_ade20k.py
@@ -1,9 +1,12 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
- auxiliary_head=dict(in_channels=256, num_classes=150))
+ auxiliary_head=dict(in_channels=256, num_classes=150),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x512_20k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r18_512x512_20k_voc12aug.py
index 5cae4f5..2b8192e 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x512_20k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x512_20k_voc12aug.py
@@ -1,10 +1,12 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_20k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/pascal_voc12_aug.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_20k.py",
]
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21),
- auxiliary_head=dict(in_channels=256, num_classes=21))
+ auxiliary_head=dict(in_channels=256, num_classes=21),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x512_40k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r18_512x512_40k_voc12aug.py
index 652ded7..1223a80 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x512_40k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x512_40k_voc12aug.py
@@ -1,10 +1,12 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/pascal_voc12_aug.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=21),
- auxiliary_head=dict(in_channels=256, num_classes=21))
+ auxiliary_head=dict(in_channels=256, num_classes=21),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r18_512x512_80k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r18_512x512_80k_ade20k.py
index 1a7956d..e0a8eea 100644
--- a/mmsegmentation/configs/upernet/upernet_r18_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r18_512x512_80k_ade20k.py
@@ -1,9 +1,12 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
model = dict(
- pretrained='open-mmlab://resnet18_v1c',
+ pretrained="open-mmlab://resnet18_v1c",
backbone=dict(depth=18),
decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
- auxiliary_head=dict(in_channels=256, num_classes=150))
+ auxiliary_head=dict(in_channels=256, num_classes=150),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
index d621e89..e28a9bc 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
@@ -1,4 +1,6 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/cityscapes.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
index 95fffcc..e442f24 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
@@ -1,4 +1,6 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/cityscapes.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x512_160k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r50_512x512_160k_ade20k.py
index f5dd9aa..e9b0062 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x512_160k_ade20k.py
@@ -1,6 +1,7 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
+model = dict(decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py
index 95f5c09..1cd491f 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x512_20k_voc12aug.py
@@ -1,7 +1,7 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_20k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/pascal_voc12_aug.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_20k.py",
]
-model = dict(
- decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
+model = dict(decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x512_40k_voc12aug.py b/mmsegmentation/configs/upernet/upernet_r50_512x512_40k_voc12aug.py
index 9621fd1..b644546 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x512_40k_voc12aug.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x512_40k_voc12aug.py
@@ -1,7 +1,7 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/pascal_voc12_aug.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
-model = dict(
- decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
+model = dict(decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/mmsegmentation/configs/upernet/upernet_r50_512x512_80k_ade20k.py b/mmsegmentation/configs/upernet/upernet_r50_512x512_80k_ade20k.py
index f561e30..565e6de 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_512x512_80k_ade20k.py
@@ -1,6 +1,7 @@
_base_ = [
- '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
+model = dict(decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/mmsegmentation/configs/upernet/upernet_r50_769x769_40k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r50_769x769_40k_cityscapes.py
index 89b18aa..b4a26e0 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_769x769_40k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_769x769_40k_cityscapes.py
@@ -1,9 +1,11 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/cityscapes_769x769.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_40k.py",
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
+ test_cfg=dict(mode="slide", crop_size=(769, 769), stride=(513, 513)),
+)
diff --git a/mmsegmentation/configs/upernet/upernet_r50_769x769_80k_cityscapes.py b/mmsegmentation/configs/upernet/upernet_r50_769x769_80k_cityscapes.py
index 29af98f..7864ad6 100644
--- a/mmsegmentation/configs/upernet/upernet_r50_769x769_80k_cityscapes.py
+++ b/mmsegmentation/configs/upernet/upernet_r50_769x769_80k_cityscapes.py
@@ -1,9 +1,11 @@
_base_ = [
- '../_base_/models/upernet_r50.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/upernet_r50.py",
+ "../_base_/datasets/cityscapes_769x769.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
model = dict(
decode_head=dict(align_corners=True),
auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
+ test_cfg=dict(mode="slide", crop_size=(769, 769), stride=(513, 513)),
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
index 68f4bd4..4f35cf7 100644
--- a/mmsegmentation/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
@@ -1,6 +1,7 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
+ pretrained="pretrain/deit_base_patch16_224-b5f2ef4d.pth",
backbone=dict(drop_path_rate=0.1),
- neck=None)
+ neck=None,
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
index 7204826..30c040e 100644
--- a/mmsegmentation/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
@@ -1,6 +1,7 @@
-_base_ = './upernet_vit-b16_mln_512x512_80k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_80k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
+ pretrained="pretrain/deit_base_patch16_224-b5f2ef4d.pth",
backbone=dict(drop_path_rate=0.1),
- neck=None)
+ neck=None,
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
index 32909ff..5bd3f9c 100644
--- a/mmsegmentation/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
@@ -1,5 +1,6 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
- backbone=dict(drop_path_rate=0.1, final_norm=True))
+ pretrained="pretrain/deit_base_patch16_224-b5f2ef4d.pth",
+ backbone=dict(drop_path_rate=0.1, final_norm=True),
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
index 4abefe8..9b09f8a 100644
--- a/mmsegmentation/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
@@ -1,6 +1,6 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
+ pretrained="pretrain/deit_base_patch16_224-b5f2ef4d.pth",
backbone=dict(drop_path_rate=0.1),
)
diff --git a/mmsegmentation/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
index 290ff19..2a05804 100644
--- a/mmsegmentation/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
@@ -1,8 +1,9 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
+ pretrained="pretrain/deit_small_patch16_224-cd65a155.pth",
backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1),
decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
neck=None,
- auxiliary_head=dict(num_classes=150, in_channels=384))
+ auxiliary_head=dict(num_classes=150, in_channels=384),
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
index 605d264..885bd3d 100644
--- a/mmsegmentation/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
@@ -1,8 +1,9 @@
-_base_ = './upernet_vit-b16_mln_512x512_80k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_80k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
+ pretrained="pretrain/deit_small_patch16_224-cd65a155.pth",
backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1),
decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
neck=None,
- auxiliary_head=dict(num_classes=150, in_channels=384))
+ auxiliary_head=dict(num_classes=150, in_channels=384),
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
index ef743a2..756ad5b 100644
--- a/mmsegmentation/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
@@ -1,9 +1,9 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
- backbone=dict(
- num_heads=6, embed_dims=384, drop_path_rate=0.1, final_norm=True),
+ pretrained="pretrain/deit_small_patch16_224-cd65a155.pth",
+ backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1, final_norm=True),
decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
neck=dict(in_channels=[384, 384, 384, 384], out_channels=384),
- auxiliary_head=dict(num_classes=150, in_channels=384))
+ auxiliary_head=dict(num_classes=150, in_channels=384),
+)
diff --git a/mmsegmentation/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
index 069cab7..1c5850f 100644
--- a/mmsegmentation/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
@@ -1,8 +1,9 @@
-_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py'
+_base_ = "./upernet_vit-b16_mln_512x512_160k_ade20k.py"
model = dict(
- pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
+ pretrained="pretrain/deit_small_patch16_224-cd65a155.pth",
backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1),
decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
neck=dict(in_channels=[384, 384, 384, 384], out_channels=384),
- auxiliary_head=dict(num_classes=150, in_channels=384))
+ auxiliary_head=dict(num_classes=150, in_channels=384),
+)
diff --git a/mmsegmentation/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
index 51eeda0..d23b83c 100644
--- a/mmsegmentation/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
@@ -1,39 +1,44 @@
_base_ = [
- '../_base_/models/upernet_vit-b16_ln_mln.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/upernet_vit-b16_ln_mln.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
model = dict(
- pretrained='pretrain/vit_base_patch16_224.pth',
+ pretrained="pretrain/vit_base_patch16_224.pth",
backbone=dict(drop_path_rate=0.1, final_norm=True),
decode_head=dict(num_classes=150),
- auxiliary_head=dict(num_classes=150))
+ auxiliary_head=dict(num_classes=150),
+)
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
- 'pos_embed': dict(decay_mult=0.),
- 'cls_token': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ "pos_embed": dict(decay_mult=0.0),
+ "cls_token": dict(decay_mult=0.0),
+ "norm": dict(decay_mult=0.0),
+ }
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py b/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
index 5b148d7..8853e33 100644
--- a/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
@@ -1,38 +1,43 @@
_base_ = [
- '../_base_/models/upernet_vit-b16_ln_mln.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_160k.py'
+ "../_base_/models/upernet_vit-b16_ln_mln.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_160k.py",
]
model = dict(
- pretrained='pretrain/vit_base_patch16_224.pth',
+ pretrained="pretrain/vit_base_patch16_224.pth",
decode_head=dict(num_classes=150),
- auxiliary_head=dict(num_classes=150))
+ auxiliary_head=dict(num_classes=150),
+)
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
- 'pos_embed': dict(decay_mult=0.),
- 'cls_token': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ "pos_embed": dict(decay_mult=0.0),
+ "cls_token": dict(decay_mult=0.0),
+ "norm": dict(decay_mult=0.0),
+ }
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py b/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
index f893500..c0d9fc5 100644
--- a/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
+++ b/mmsegmentation/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
@@ -1,38 +1,43 @@
_base_ = [
- '../_base_/models/upernet_vit-b16_ln_mln.py',
- '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_80k.py'
+ "../_base_/models/upernet_vit-b16_ln_mln.py",
+ "../_base_/datasets/ade20k.py",
+ "../_base_/default_runtime.py",
+ "../_base_/schedules/schedule_80k.py",
]
model = dict(
- pretrained='pretrain/vit_base_patch16_224.pth',
+ pretrained="pretrain/vit_base_patch16_224.pth",
decode_head=dict(num_classes=150),
- auxiliary_head=dict(num_classes=150))
+ auxiliary_head=dict(num_classes=150),
+)
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optimizer = dict(
_delete_=True,
- type='AdamW',
+ type="AdamW",
lr=0.00006,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys={
- 'pos_embed': dict(decay_mult=0.),
- 'cls_token': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)
- }))
+ "pos_embed": dict(decay_mult=0.0),
+ "cls_token": dict(decay_mult=0.0),
+ "norm": dict(decay_mult=0.0),
+ }
+ ),
+)
lr_config = dict(
_delete_=True,
- policy='poly',
- warmup='linear',
+ policy="poly",
+ warmup="linear",
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0,
min_lr=0.0,
- by_epoch=False)
+ by_epoch=False,
+)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
diff --git a/mmsegmentation/demo/MMSegmentation_Tutorial.ipynb b/mmsegmentation/demo/MMSegmentation_Tutorial.ipynb
index 6429351..bffe031 100644
--- a/mmsegmentation/demo/MMSegmentation_Tutorial.ipynb
+++ b/mmsegmentation/demo/MMSegmentation_Tutorial.ipynb
@@ -1,657 +1,657 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "id": "view-in-github"
- },
- "source": [
- "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FVmnaxFJvsb8"
- },
- "source": [
- "# MMSegmentation Tutorial\n",
- "Welcome to MMSegmentation! \n",
- "\n",
- "In this tutorial, we demo\n",
- "* How to do inference with MMSeg trained weight\n",
- "* How to train on your own dataset and visualize the results. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QS8YHrEhbpas"
- },
- "source": [
- "## Install MMSegmentation\n",
- "This step may take several minutes. \n",
- "\n",
- "We use PyTorch 1.10 and CUDA 11.1 for this tutorial. You may install other versions by change the version number in pip install command. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "UWyLrLYaNEaL",
- "outputId": "32a47fe3-f10d-47a1-f6b9-b7c235abdab1"
- },
- "outputs": [],
- "source": [
- "# Check nvcc version\n",
- "!nvcc -V\n",
- "# Check GCC version\n",
- "!gcc --version"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ki3WUBjKbutg",
- "outputId": "14bd14b0-4d8c-4fa9-e3f9-da35c0efc0d5"
- },
- "outputs": [],
- "source": [
- "# Install PyTorch\n",
- "!pip install torch==1.12.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu113\n",
- "# Install MMCV\n",
- "!pip install openmim\n",
- "!mim install mmcv-full==1.6.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "nR-hHRvbNJJZ",
- "outputId": "10c3b131-d4db-458c-fc10-b94b1c6ed546"
- },
- "outputs": [],
- "source": [
- "!rm -rf mmsegmentation\n",
- "!git clone https://github.com/open-mmlab/mmsegmentation.git \n",
- "%cd mmsegmentation\n",
- "!pip install -e ."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "mAE_h7XhPT7d",
- "outputId": "83bf0f8e-fc69-40b1-f9fe-0025724a217c"
- },
- "outputs": [],
- "source": [
- "# Check Pytorch installation\n",
- "import torch, torchvision\n",
- "print(torch.__version__, torch.cuda.is_available())\n",
- "\n",
- "# Check MMSegmentation installation\n",
- "import mmseg\n",
- "print(mmseg.__version__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eUcuC3dUv32I"
- },
- "source": [
- "## Run Inference with MMSeg trained weight"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "2hd41IGaiNet",
- "outputId": "b7b2aafc-edf2-43e4-ea43-0b5dd0aa4b4a"
- },
- "outputs": [],
- "source": [
- "!mkdir checkpoints\n",
- "!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P checkpoints"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "H8Fxg8i-wHJE"
- },
- "outputs": [],
- "source": [
- "from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot\n",
- "from mmseg.core.evaluation import get_palette"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "umk8sJ0Xuace"
- },
- "outputs": [],
- "source": [
- "config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n",
- "checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "nWlQFuTgudxu",
- "outputId": "5e45f4f6-5bcf-4d04-bb9c-0428ee84a576"
- },
- "outputs": [],
- "source": [
- "# build the model from a config file and a checkpoint file\n",
- "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "izFv6pSRujk9"
- },
- "outputs": [],
- "source": [
- "# test a single image\n",
- "img = 'demo/demo.png'\n",
- "result = inference_segmentor(model, img)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 504
- },
- "id": "bDcs9udgunQK",
- "outputId": "7c55f713-4085-47fd-fa06-720a321d0795"
- },
- "outputs": [],
- "source": [
- "# show the results\n",
- "show_result_pyplot(model, img, result, get_palette('cityscapes'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Ta51clKX4cwM"
- },
- "source": [
- "## Train a semantic segmentation model on a new dataset\n",
- "\n",
- "To train on a customized dataset, the following steps are necessary. \n",
- "1. Add a new dataset class. \n",
- "2. Create a config file accordingly. \n",
- "3. Perform training and evaluation. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AcZg6x_K5Zs3"
- },
- "source": [
- "### Add a new dataset\n",
- "\n",
- "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure. \n",
- "\n",
- "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#customize-datasets-by-reorganizing-data) for details about dataset reorganization. \n",
- "\n",
- "We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
- "In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "TFIt7MHq5Wls",
- "outputId": "74a126e4-c8a4-4d2f-a910-b58b71843a23"
- },
- "outputs": [],
- "source": [
- "# download and unzip\n",
- "!wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O stanford_background.tar.gz\n",
- "!tar xf stanford_background.tar.gz"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 377
- },
- "id": "78LIci7F9WWI",
- "outputId": "c432ddac-5a50-47b1-daac-5a26b07afea2"
- },
- "outputs": [],
- "source": [
- "# Let's take a look at the dataset\n",
- "import mmcv\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
- "plt.figure(figsize=(8, 6))\n",
- "plt.imshow(mmcv.bgr2rgb(img))\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "L5mNQuc2GsVE"
- },
- "source": [
- "We need to convert the annotation into semantic map format as an image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WnGZfribFHCx"
- },
- "outputs": [],
- "source": [
- "import os.path as osp\n",
- "import numpy as np\n",
- "from PIL import Image\n",
- "# convert dataset annotation to semantic segmentation map\n",
- "data_root = 'iccv09Data'\n",
- "img_dir = 'images'\n",
- "ann_dir = 'labels'\n",
- "# define class and plaette for better visualization\n",
- "classes = ('sky', 'tree', 'road', 'grass', 'water', 'bldg', 'mntn', 'fg obj')\n",
- "palette = [[128, 128, 128], [129, 127, 38], [120, 69, 125], [53, 125, 34], \n",
- " [0, 11, 123], [118, 20, 12], [122, 81, 25], [241, 134, 51]]\n",
- "for file in mmcv.scandir(osp.join(data_root, ann_dir), suffix='.regions.txt'):\n",
- " seg_map = np.loadtxt(osp.join(data_root, ann_dir, file)).astype(np.uint8)\n",
- " seg_img = Image.fromarray(seg_map).convert('P')\n",
- " seg_img.putpalette(np.array(palette, dtype=np.uint8))\n",
- " seg_img.save(osp.join(data_root, ann_dir, file.replace('.regions.txt', \n",
- " '.png')))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 377
- },
- "id": "5MCSS9ABfSks",
- "outputId": "92b9bafc-589e-48fc-c9e9-476f125d6522"
- },
- "outputs": [],
- "source": [
- "# Let's take a look at the segmentation map we got\n",
- "import matplotlib.patches as mpatches\n",
- "img = Image.open('iccv09Data/labels/6000124.png')\n",
- "plt.figure(figsize=(8, 6))\n",
- "im = plt.imshow(np.array(img.convert('RGB')))\n",
- "\n",
- "# create a patch (proxy artist) for every color \n",
- "patches = [mpatches.Patch(color=np.array(palette[i])/255., \n",
- " label=classes[i]) for i in range(8)]\n",
- "# put those patched as legend-handles into the legend\n",
- "plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., \n",
- " fontsize='large')\n",
- "\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WbeLYCp2k5hl"
- },
- "outputs": [],
- "source": [
- "# split train/val set randomly\n",
- "split_dir = 'splits'\n",
- "mmcv.mkdir_or_exist(osp.join(data_root, split_dir))\n",
- "filename_list = [osp.splitext(filename)[0] for filename in mmcv.scandir(\n",
- " osp.join(data_root, ann_dir), suffix='.png')]\n",
- "with open(osp.join(data_root, split_dir, 'train.txt'), 'w') as f:\n",
- " # select first 4/5 as train set\n",
- " train_length = int(len(filename_list)*4/5)\n",
- " f.writelines(line + '\\n' for line in filename_list[:train_length])\n",
- "with open(osp.join(data_root, split_dir, 'val.txt'), 'w') as f:\n",
- " # select last 1/5 as train set\n",
- " f.writelines(line + '\\n' for line in filename_list[train_length:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HchvmGYB_rrO"
- },
- "source": [
- "After downloading the data, we need to implement `load_annotations` function in the new dataset class `StanfordBackgroundDataset`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "LbsWOw62_o-X"
- },
- "outputs": [],
- "source": [
- "from mmseg.datasets.builder import DATASETS\n",
- "from mmseg.datasets.custom import CustomDataset\n",
- "\n",
- "@DATASETS.register_module()\n",
- "class StanfordBackgroundDataset(CustomDataset):\n",
- " CLASSES = classes\n",
- " PALETTE = palette\n",
- " def __init__(self, split, **kwargs):\n",
- " super().__init__(img_suffix='.jpg', seg_map_suffix='.png', \n",
- " split=split, **kwargs)\n",
- " assert osp.exists(self.img_dir) and self.split is not None\n",
- "\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "yUVtmn3Iq3WA"
- },
- "source": [
- "### Create a config file\n",
- "In the next step, we need to modify the config for the training. To accelerate the process, we finetune the model from trained weights."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Wwnj9tRzqX_A"
- },
- "outputs": [],
- "source": [
- "from mmcv import Config\n",
- "cfg = Config.fromfile('configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1y2oV5w97jQo"
- },
- "source": [
- "Since the given config is used to train PSPNet on the cityscapes dataset, we need to modify it accordingly for our new dataset. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "eyKnYC1Z7iCV",
- "outputId": "6195217b-187f-4675-994b-ba90d8bb3078"
- },
- "outputs": [],
- "source": [
- "from mmseg.apis import set_random_seed\n",
- "from mmseg.utils import get_device\n",
- "\n",
- "# Since we use only one GPU, BN is used instead of SyncBN\n",
- "cfg.norm_cfg = dict(type='BN', requires_grad=True)\n",
- "cfg.model.backbone.norm_cfg = cfg.norm_cfg\n",
- "cfg.model.decode_head.norm_cfg = cfg.norm_cfg\n",
- "cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg\n",
- "# modify num classes of the model in decode/auxiliary head\n",
- "cfg.model.decode_head.num_classes = 8\n",
- "cfg.model.auxiliary_head.num_classes = 8\n",
- "\n",
- "# Modify dataset type and path\n",
- "cfg.dataset_type = 'StanfordBackgroundDataset'\n",
- "cfg.data_root = data_root\n",
- "\n",
- "cfg.data.samples_per_gpu = 8\n",
- "cfg.data.workers_per_gpu=8\n",
- "\n",
- "cfg.img_norm_cfg = dict(\n",
- " mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
- "cfg.crop_size = (256, 256)\n",
- "cfg.train_pipeline = [\n",
- " dict(type='LoadImageFromFile'),\n",
- " dict(type='LoadAnnotations'),\n",
- " dict(type='Resize', img_scale=(320, 240), ratio_range=(0.5, 2.0)),\n",
- " dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),\n",
- " dict(type='RandomFlip', flip_ratio=0.5),\n",
- " dict(type='PhotoMetricDistortion'),\n",
- " dict(type='Normalize', **cfg.img_norm_cfg),\n",
- " dict(type='Pad', size=cfg.crop_size, pad_val=0, seg_pad_val=255),\n",
- " dict(type='DefaultFormatBundle'),\n",
- " dict(type='Collect', keys=['img', 'gt_semantic_seg']),\n",
- "]\n",
- "\n",
- "cfg.test_pipeline = [\n",
- " dict(type='LoadImageFromFile'),\n",
- " dict(\n",
- " type='MultiScaleFlipAug',\n",
- " img_scale=(320, 240),\n",
- " # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],\n",
- " flip=False,\n",
- " transforms=[\n",
- " dict(type='Resize', keep_ratio=True),\n",
- " dict(type='RandomFlip'),\n",
- " dict(type='Normalize', **cfg.img_norm_cfg),\n",
- " dict(type='ImageToTensor', keys=['img']),\n",
- " dict(type='Collect', keys=['img']),\n",
- " ])\n",
- "]\n",
- "\n",
- "\n",
- "cfg.data.train.type = cfg.dataset_type\n",
- "cfg.data.train.data_root = cfg.data_root\n",
- "cfg.data.train.img_dir = img_dir\n",
- "cfg.data.train.ann_dir = ann_dir\n",
- "cfg.data.train.pipeline = cfg.train_pipeline\n",
- "cfg.data.train.split = 'splits/train.txt'\n",
- "\n",
- "cfg.data.val.type = cfg.dataset_type\n",
- "cfg.data.val.data_root = cfg.data_root\n",
- "cfg.data.val.img_dir = img_dir\n",
- "cfg.data.val.ann_dir = ann_dir\n",
- "cfg.data.val.pipeline = cfg.test_pipeline\n",
- "cfg.data.val.split = 'splits/val.txt'\n",
- "\n",
- "cfg.data.test.type = cfg.dataset_type\n",
- "cfg.data.test.data_root = cfg.data_root\n",
- "cfg.data.test.img_dir = img_dir\n",
- "cfg.data.test.ann_dir = ann_dir\n",
- "cfg.data.test.pipeline = cfg.test_pipeline\n",
- "cfg.data.test.split = 'splits/val.txt'\n",
- "\n",
- "# We can still use the pre-trained Mask RCNN model though we do not need to\n",
- "# use the mask branch\n",
- "cfg.load_from = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'\n",
- "\n",
- "# Set up working dir to save files and logs.\n",
- "cfg.work_dir = './work_dirs/tutorial'\n",
- "\n",
- "cfg.runner.max_iters = 200\n",
- "cfg.log_config.interval = 10\n",
- "cfg.evaluation.interval = 200\n",
- "cfg.checkpoint_config.interval = 200\n",
- "\n",
- "# Set seed to facitate reproducing the result\n",
- "cfg.seed = 0\n",
- "set_random_seed(0, deterministic=False)\n",
- "cfg.gpu_ids = range(1)\n",
- "cfg.device = get_device()\n",
- "\n",
- "# Let's have a look at the final config used for training\n",
- "print(f'Config:\\n{cfg.pretty_text}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QWuH14LYF2gQ"
- },
- "source": [
- "### Train and Evaluation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "jYKoSfdMF12B",
- "outputId": "422219ca-d7a5-4890-f09f-88c959942e64"
- },
- "outputs": [],
- "source": [
- "from mmseg.datasets import build_dataset\n",
- "from mmseg.models import build_segmentor\n",
- "from mmseg.apis import train_segmentor\n",
- "\n",
- "\n",
- "# Build the dataset\n",
- "datasets = [build_dataset(cfg.data.train)]\n",
- "\n",
- "# Build the detector\n",
- "model = build_segmentor(cfg.model)\n",
- "# Add an attribute for visualization convenience\n",
- "model.CLASSES = datasets[0].CLASSES\n",
- "\n",
- "# Create work_dir\n",
- "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
- "train_segmentor(model, datasets, cfg, distributed=False, validate=True, \n",
- " meta=dict())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DEkWOP-NMbc_"
- },
- "source": [
- "Inference with trained model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 645
- },
- "id": "ekG__UfaH_OU",
- "outputId": "1437419c-869a-4902-df86-d4f6f8b2597a"
- },
- "outputs": [],
- "source": [
- "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
- "\n",
- "model.cfg = cfg\n",
- "result = inference_segmentor(model, img)\n",
- "plt.figure(figsize=(8, 6))\n",
- "show_result_pyplot(model, img, result, palette)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "MMSegmentation Tutorial.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3.10.4 ('colab')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
- },
- "pycharm": {
- "stem_cell": {
- "cell_type": "raw",
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FVmnaxFJvsb8"
+ },
+ "source": [
+ "# MMSegmentation Tutorial\n",
+ "Welcome to MMSegmentation! \n",
+ "\n",
+ "In this tutorial, we demo\n",
+ "* How to do inference with MMSeg trained weight\n",
+ "* How to train on your own dataset and visualize the results. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QS8YHrEhbpas"
+ },
+ "source": [
+ "## Install MMSegmentation\n",
+ "This step may take several minutes. \n",
+ "\n",
+ "We use PyTorch 1.10 and CUDA 11.1 for this tutorial. You may install other versions by change the version number in pip install command. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UWyLrLYaNEaL",
+ "outputId": "32a47fe3-f10d-47a1-f6b9-b7c235abdab1"
+ },
+ "outputs": [],
+ "source": [
+ "# Check nvcc version\n",
+ "!nvcc -V\n",
+ "# Check GCC version\n",
+ "!gcc --version"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ki3WUBjKbutg",
+ "outputId": "14bd14b0-4d8c-4fa9-e3f9-da35c0efc0d5"
+ },
+ "outputs": [],
+ "source": [
+ "# Install PyTorch\n",
+ "!pip install torch==1.12.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu113\n",
+ "# Install MMCV\n",
+ "!pip install openmim\n",
+ "!mim install mmcv-full==1.6.0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nR-hHRvbNJJZ",
+ "outputId": "10c3b131-d4db-458c-fc10-b94b1c6ed546"
+ },
+ "outputs": [],
+ "source": [
+ "!rm -rf mmsegmentation\n",
+ "!git clone https://github.com/open-mmlab/mmsegmentation.git \n",
+ "%cd mmsegmentation\n",
+ "!pip install -e ."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mAE_h7XhPT7d",
+ "outputId": "83bf0f8e-fc69-40b1-f9fe-0025724a217c"
+ },
+ "outputs": [],
+ "source": [
+ "# Check Pytorch installation\n",
+ "import torch, torchvision\n",
+ "print(torch.__version__, torch.cuda.is_available())\n",
+ "\n",
+ "# Check MMSegmentation installation\n",
+ "import mmseg\n",
+ "print(mmseg.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eUcuC3dUv32I"
+ },
+ "source": [
+ "## Run Inference with MMSeg trained weight"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2hd41IGaiNet",
+ "outputId": "b7b2aafc-edf2-43e4-ea43-0b5dd0aa4b4a"
+ },
+ "outputs": [],
+ "source": [
+ "!mkdir checkpoints\n",
+ "!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P checkpoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "H8Fxg8i-wHJE"
+ },
+ "outputs": [],
+ "source": [
+ "from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot\n",
+ "from mmseg.core.evaluation import get_palette"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "umk8sJ0Xuace"
+ },
+ "outputs": [],
+ "source": [
+ "config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n",
+ "checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nWlQFuTgudxu",
+ "outputId": "5e45f4f6-5bcf-4d04-bb9c-0428ee84a576"
+ },
+ "outputs": [],
+ "source": [
+ "# build the model from a config file and a checkpoint file\n",
+ "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "izFv6pSRujk9"
+ },
+ "outputs": [],
+ "source": [
+ "# test a single image\n",
+ "img = 'demo/demo.png'\n",
+ "result = inference_segmentor(model, img)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 504
+ },
+ "id": "bDcs9udgunQK",
+ "outputId": "7c55f713-4085-47fd-fa06-720a321d0795"
+ },
+ "outputs": [],
+ "source": [
+ "# show the results\n",
+ "show_result_pyplot(model, img, result, get_palette('cityscapes'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ta51clKX4cwM"
+ },
+ "source": [
+ "## Train a semantic segmentation model on a new dataset\n",
+ "\n",
+ "To train on a customized dataset, the following steps are necessary. \n",
+ "1. Add a new dataset class. \n",
+ "2. Create a config file accordingly. \n",
+ "3. Perform training and evaluation. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AcZg6x_K5Zs3"
+ },
+ "source": [
+ "### Add a new dataset\n",
+ "\n",
+ "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure. \n",
+ "\n",
+ "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#customize-datasets-by-reorganizing-data) for details about dataset reorganization. \n",
+ "\n",
+ "We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
+ "In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TFIt7MHq5Wls",
+ "outputId": "74a126e4-c8a4-4d2f-a910-b58b71843a23"
+ },
+ "outputs": [],
+ "source": [
+ "# download and unzip\n",
+ "!wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O stanford_background.tar.gz\n",
+ "!tar xf stanford_background.tar.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 377
+ },
+ "id": "78LIci7F9WWI",
+ "outputId": "c432ddac-5a50-47b1-daac-5a26b07afea2"
+ },
+ "outputs": [],
+ "source": [
+ "# Let's take a look at the dataset\n",
+ "import mmcv\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "plt.imshow(mmcv.bgr2rgb(img))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L5mNQuc2GsVE"
+ },
+ "source": [
+ "We need to convert the annotation into semantic map format as an image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WnGZfribFHCx"
+ },
+ "outputs": [],
+ "source": [
+ "import os.path as osp\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "# convert dataset annotation to semantic segmentation map\n",
+ "data_root = 'iccv09Data'\n",
+ "img_dir = 'images'\n",
+ "ann_dir = 'labels'\n",
+ "# define class and plaette for better visualization\n",
+ "classes = ('sky', 'tree', 'road', 'grass', 'water', 'bldg', 'mntn', 'fg obj')\n",
+ "palette = [[128, 128, 128], [129, 127, 38], [120, 69, 125], [53, 125, 34], \n",
+ " [0, 11, 123], [118, 20, 12], [122, 81, 25], [241, 134, 51]]\n",
+ "for file in mmcv.scandir(osp.join(data_root, ann_dir), suffix='.regions.txt'):\n",
+ " seg_map = np.loadtxt(osp.join(data_root, ann_dir, file)).astype(np.uint8)\n",
+ " seg_img = Image.fromarray(seg_map).convert('P')\n",
+ " seg_img.putpalette(np.array(palette, dtype=np.uint8))\n",
+ " seg_img.save(osp.join(data_root, ann_dir, file.replace('.regions.txt', \n",
+ " '.png')))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 377
+ },
+ "id": "5MCSS9ABfSks",
+ "outputId": "92b9bafc-589e-48fc-c9e9-476f125d6522"
+ },
+ "outputs": [],
+ "source": [
+ "# Let's take a look at the segmentation map we got\n",
+ "import matplotlib.patches as mpatches\n",
+ "img = Image.open('iccv09Data/labels/6000124.png')\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "im = plt.imshow(np.array(img.convert('RGB')))\n",
+ "\n",
+ "# create a patch (proxy artist) for every color \n",
+ "patches = [mpatches.Patch(color=np.array(palette[i])/255., \n",
+ " label=classes[i]) for i in range(8)]\n",
+ "# put those patched as legend-handles into the legend\n",
+ "plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., \n",
+ " fontsize='large')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WbeLYCp2k5hl"
+ },
+ "outputs": [],
+ "source": [
+ "# split train/val set randomly\n",
+ "split_dir = 'splits'\n",
+ "mmcv.mkdir_or_exist(osp.join(data_root, split_dir))\n",
+ "filename_list = [osp.splitext(filename)[0] for filename in mmcv.scandir(\n",
+ " osp.join(data_root, ann_dir), suffix='.png')]\n",
+ "with open(osp.join(data_root, split_dir, 'train.txt'), 'w') as f:\n",
+ " # select first 4/5 as train set\n",
+ " train_length = int(len(filename_list)*4/5)\n",
+ " f.writelines(line + '\\n' for line in filename_list[:train_length])\n",
+ "with open(osp.join(data_root, split_dir, 'val.txt'), 'w') as f:\n",
+ " # select last 1/5 as train set\n",
+ " f.writelines(line + '\\n' for line in filename_list[train_length:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HchvmGYB_rrO"
+ },
+ "source": [
+ "After downloading the data, we need to implement `load_annotations` function in the new dataset class `StanfordBackgroundDataset`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "LbsWOw62_o-X"
+ },
+ "outputs": [],
+ "source": [
+ "from mmseg.datasets.builder import DATASETS\n",
+ "from mmseg.datasets.custom import CustomDataset\n",
+ "\n",
+ "@DATASETS.register_module()\n",
+ "class StanfordBackgroundDataset(CustomDataset):\n",
+ " CLASSES = classes\n",
+ " PALETTE = palette\n",
+ " def __init__(self, split, **kwargs):\n",
+ " super().__init__(img_suffix='.jpg', seg_map_suffix='.png', \n",
+ " split=split, **kwargs)\n",
+ " assert osp.exists(self.img_dir) and self.split is not None\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yUVtmn3Iq3WA"
+ },
+ "source": [
+ "### Create a config file\n",
+ "In the next step, we need to modify the config for the training. To accelerate the process, we finetune the model from trained weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Wwnj9tRzqX_A"
+ },
+ "outputs": [],
+ "source": [
+ "from mmcv import Config\n",
+ "cfg = Config.fromfile('configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1y2oV5w97jQo"
+ },
+ "source": [
+ "Since the given config is used to train PSPNet on the cityscapes dataset, we need to modify it accordingly for our new dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eyKnYC1Z7iCV",
+ "outputId": "6195217b-187f-4675-994b-ba90d8bb3078"
+ },
+ "outputs": [],
+ "source": [
+ "from mmseg.apis import set_random_seed\n",
+ "from mmseg.utils import get_device\n",
+ "\n",
+ "# Since we use only one GPU, BN is used instead of SyncBN\n",
+ "cfg.norm_cfg = dict(type='BN', requires_grad=True)\n",
+ "cfg.model.backbone.norm_cfg = cfg.norm_cfg\n",
+ "cfg.model.decode_head.norm_cfg = cfg.norm_cfg\n",
+ "cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg\n",
+ "# modify num classes of the model in decode/auxiliary head\n",
+ "cfg.model.decode_head.num_classes = 8\n",
+ "cfg.model.auxiliary_head.num_classes = 8\n",
+ "\n",
+ "# Modify dataset type and path\n",
+ "cfg.dataset_type = 'StanfordBackgroundDataset'\n",
+ "cfg.data_root = data_root\n",
+ "\n",
+ "cfg.data.samples_per_gpu = 8\n",
+ "cfg.data.workers_per_gpu=8\n",
+ "\n",
+ "cfg.img_norm_cfg = dict(\n",
+ " mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
+ "cfg.crop_size = (256, 256)\n",
+ "cfg.train_pipeline = [\n",
+ " dict(type='LoadImageFromFile'),\n",
+ " dict(type='LoadAnnotations'),\n",
+ " dict(type='Resize', img_scale=(320, 240), ratio_range=(0.5, 2.0)),\n",
+ " dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),\n",
+ " dict(type='RandomFlip', flip_ratio=0.5),\n",
+ " dict(type='PhotoMetricDistortion'),\n",
+ " dict(type='Normalize', **cfg.img_norm_cfg),\n",
+ " dict(type='Pad', size=cfg.crop_size, pad_val=0, seg_pad_val=255),\n",
+ " dict(type='DefaultFormatBundle'),\n",
+ " dict(type='Collect', keys=['img', 'gt_semantic_seg']),\n",
+ "]\n",
+ "\n",
+ "cfg.test_pipeline = [\n",
+ " dict(type='LoadImageFromFile'),\n",
+ " dict(\n",
+ " type='MultiScaleFlipAug',\n",
+ " img_scale=(320, 240),\n",
+ " # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],\n",
+ " flip=False,\n",
+ " transforms=[\n",
+ " dict(type='Resize', keep_ratio=True),\n",
+ " dict(type='RandomFlip'),\n",
+ " dict(type='Normalize', **cfg.img_norm_cfg),\n",
+ " dict(type='ImageToTensor', keys=['img']),\n",
+ " dict(type='Collect', keys=['img']),\n",
+ " ])\n",
+ "]\n",
+ "\n",
+ "\n",
+ "cfg.data.train.type = cfg.dataset_type\n",
+ "cfg.data.train.data_root = cfg.data_root\n",
+ "cfg.data.train.img_dir = img_dir\n",
+ "cfg.data.train.ann_dir = ann_dir\n",
+ "cfg.data.train.pipeline = cfg.train_pipeline\n",
+ "cfg.data.train.split = 'splits/train.txt'\n",
+ "\n",
+ "cfg.data.val.type = cfg.dataset_type\n",
+ "cfg.data.val.data_root = cfg.data_root\n",
+ "cfg.data.val.img_dir = img_dir\n",
+ "cfg.data.val.ann_dir = ann_dir\n",
+ "cfg.data.val.pipeline = cfg.test_pipeline\n",
+ "cfg.data.val.split = 'splits/val.txt'\n",
+ "\n",
+ "cfg.data.test.type = cfg.dataset_type\n",
+ "cfg.data.test.data_root = cfg.data_root\n",
+ "cfg.data.test.img_dir = img_dir\n",
+ "cfg.data.test.ann_dir = ann_dir\n",
+ "cfg.data.test.pipeline = cfg.test_pipeline\n",
+ "cfg.data.test.split = 'splits/val.txt'\n",
+ "\n",
+ "# We can still use the pre-trained Mask RCNN model though we do not need to\n",
+ "# use the mask branch\n",
+ "cfg.load_from = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'\n",
+ "\n",
+ "# Set up working dir to save files and logs.\n",
+ "cfg.work_dir = './work_dirs/tutorial'\n",
+ "\n",
+ "cfg.runner.max_iters = 200\n",
+ "cfg.log_config.interval = 10\n",
+ "cfg.evaluation.interval = 200\n",
+ "cfg.checkpoint_config.interval = 200\n",
+ "\n",
+ "# Set seed to facitate reproducing the result\n",
+ "cfg.seed = 0\n",
+ "set_random_seed(0, deterministic=False)\n",
+ "cfg.gpu_ids = range(1)\n",
+ "cfg.device = get_device()\n",
+ "\n",
+ "# Let's have a look at the final config used for training\n",
+ "print(f'Config:\\n{cfg.pretty_text}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QWuH14LYF2gQ"
+ },
+ "source": [
+ "### Train and Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jYKoSfdMF12B",
+ "outputId": "422219ca-d7a5-4890-f09f-88c959942e64"
+ },
+ "outputs": [],
+ "source": [
+ "from mmseg.datasets import build_dataset\n",
+ "from mmseg.models import build_segmentor\n",
+ "from mmseg.apis import train_segmentor\n",
+ "\n",
+ "\n",
+ "# Build the dataset\n",
+ "datasets = [build_dataset(cfg.data.train)]\n",
+ "\n",
+ "# Build the detector\n",
+ "model = build_segmentor(cfg.model)\n",
+ "# Add an attribute for visualization convenience\n",
+ "model.CLASSES = datasets[0].CLASSES\n",
+ "\n",
+ "# Create work_dir\n",
+ "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
+ "train_segmentor(model, datasets, cfg, distributed=False, validate=True, \n",
+ " meta=dict())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DEkWOP-NMbc_"
+ },
+ "source": [
+ "Inference with trained model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 645
+ },
+ "id": "ekG__UfaH_OU",
+ "outputId": "1437419c-869a-4902-df86-d4f6f8b2597a"
+ },
+ "outputs": [],
+ "source": [
+ "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
+ "\n",
+ "model.cfg = cfg\n",
+ "result = inference_segmentor(model, img)\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "show_result_pyplot(model, img, result, palette)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
"metadata": {
- "collapsed": false
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "include_colab_link": true,
+ "name": "MMSegmentation Tutorial.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3.10.4 ('colab')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ },
+ "pycharm": {
+ "stem_cell": {
+ "cell_type": "raw",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": []
+ }
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "407d2a53ddc3f8f7c4edd35e4d9b95b1c1ccdf5b3711df67dd21487022baf36e"
+ }
+ }
},
- "source": []
- }
- },
- "vscode": {
- "interpreter": {
- "hash": "407d2a53ddc3f8f7c4edd35e4d9b95b1c1ccdf5b3711df67dd21487022baf36e"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/mmsegmentation/demo/image_demo.py b/mmsegmentation/demo/image_demo.py
index 87d6d6c..2902f57 100644
--- a/mmsegmentation/demo/image_demo.py
+++ b/mmsegmentation/demo/image_demo.py
@@ -7,21 +7,22 @@
def main():
parser = ArgumentParser()
- parser.add_argument('img', help='Image file')
- parser.add_argument('config', help='Config file')
- parser.add_argument('checkpoint', help='Checkpoint file')
- parser.add_argument('--out-file', default=None, help='Path to output file')
+ parser.add_argument("img", help="Image file")
+ parser.add_argument("config", help="Config file")
+ parser.add_argument("checkpoint", help="Checkpoint file")
+ parser.add_argument("--out-file", default=None, help="Path to output file")
+ parser.add_argument("--device", default="cuda:0", help="Device used for inference")
parser.add_argument(
- '--device', default='cuda:0', help='Device used for inference')
+ "--palette",
+ default="cityscapes",
+ help="Color palette used for segmentation map",
+ )
parser.add_argument(
- '--palette',
- default='cityscapes',
- help='Color palette used for segmentation map')
- parser.add_argument(
- '--opacity',
+ "--opacity",
type=float,
default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
+ help="Opacity of painted segmentation map. In (0, 1] range.",
+ )
args = parser.parse_args()
# build the model from a config file and a checkpoint file
@@ -35,8 +36,9 @@ def main():
result,
get_palette(args.palette),
opacity=args.opacity,
- out_file=args.out_file)
+ out_file=args.out_file,
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/demo/inference_demo.ipynb b/mmsegmentation/demo/inference_demo.ipynb
index 66a447b..81cb548 100644
--- a/mmsegmentation/demo/inference_demo.ipynb
+++ b/mmsegmentation/demo/inference_demo.ipynb
@@ -1,110 +1,110 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!mkdir ../checkpoints\n",
- "!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P ../checkpoints"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "from mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot\n",
- "from mmseg.core.evaluation import get_palette"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "config_file = '../configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n",
- "checkpoint_file = '../checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# build the model from a config file and a checkpoint file\n",
- "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# test a single image\n",
- "img = 'demo.png'\n",
- "result = inference_segmentor(model, img)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# show the results\n",
- "show_result_pyplot(model, img, result, get_palette('cityscapes'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.0"
- },
- "pycharm": {
- "stem_cell": {
- "cell_type": "raw",
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!mkdir ../checkpoints\n",
+ "!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P ../checkpoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot\n",
+ "from mmseg.core.evaluation import get_palette"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "config_file = '../configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n",
+ "checkpoint_file = '../checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build the model from a config file and a checkpoint file\n",
+ "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test a single image\n",
+ "img = 'demo.png'\n",
+ "result = inference_segmentor(model, img)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# show the results\n",
+ "show_result_pyplot(model, img, result, get_palette('cityscapes'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
"metadata": {
- "collapsed": false
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.0"
+ },
+ "pycharm": {
+ "stem_cell": {
+ "cell_type": "raw",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": []
+ }
+ }
},
- "source": []
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/mmsegmentation/demo/video_demo.py b/mmsegmentation/demo/video_demo.py
index eb4fd69..27a4019 100644
--- a/mmsegmentation/demo/video_demo.py
+++ b/mmsegmentation/demo/video_demo.py
@@ -9,54 +9,52 @@
def main():
parser = ArgumentParser()
- parser.add_argument('video', help='Video file or webcam id')
- parser.add_argument('config', help='Config file')
- parser.add_argument('checkpoint', help='Checkpoint file')
+ parser.add_argument("video", help="Video file or webcam id")
+ parser.add_argument("config", help="Config file")
+ parser.add_argument("checkpoint", help="Checkpoint file")
+ parser.add_argument("--device", default="cuda:0", help="Device used for inference")
parser.add_argument(
- '--device', default='cuda:0', help='Device used for inference')
+ "--palette",
+ default="cityscapes",
+ help="Color palette used for segmentation map",
+ )
parser.add_argument(
- '--palette',
- default='cityscapes',
- help='Color palette used for segmentation map')
+ "--show", action="store_true", help="Whether to show draw result"
+ )
parser.add_argument(
- '--show', action='store_true', help='Whether to show draw result')
+ "--show-wait-time", default=1, type=int, help="Wait time after imshow"
+ )
parser.add_argument(
- '--show-wait-time', default=1, type=int, help='Wait time after imshow')
+ "--output-file", default=None, type=str, help="Output video file path"
+ )
parser.add_argument(
- '--output-file', default=None, type=str, help='Output video file path')
+ "--output-fourcc", default="MJPG", type=str, help="Fourcc of the output video"
+ )
parser.add_argument(
- '--output-fourcc',
- default='MJPG',
- type=str,
- help='Fourcc of the output video')
+ "--output-fps", default=-1, type=int, help="FPS of the output video"
+ )
parser.add_argument(
- '--output-fps', default=-1, type=int, help='FPS of the output video')
+ "--output-height", default=-1, type=int, help="Frame height of the output video"
+ )
parser.add_argument(
- '--output-height',
- default=-1,
- type=int,
- help='Frame height of the output video')
+ "--output-width", default=-1, type=int, help="Frame width of the output video"
+ )
parser.add_argument(
- '--output-width',
- default=-1,
- type=int,
- help='Frame width of the output video')
- parser.add_argument(
- '--opacity',
+ "--opacity",
type=float,
default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
+ help="Opacity of painted segmentation map. In (0, 1] range.",
+ )
args = parser.parse_args()
- assert args.show or args.output_file, \
- 'At least one output should be enabled.'
+ assert args.show or args.output_file, "At least one output should be enabled."
# build the model from a config file and a checkpoint file
model = init_segmentor(args.config, args.checkpoint, device=args.device)
# build input video
cap = cv2.VideoCapture(args.video)
- assert (cap.isOpened())
+ assert cap.isOpened()
input_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
input_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
input_fps = cap.get(cv2.CAP_PROP_FPS)
@@ -68,12 +66,13 @@ def main():
if args.output_file is not None:
fourcc = cv2.VideoWriter_fourcc(*args.output_fourcc)
output_fps = args.output_fps if args.output_fps > 0 else input_fps
- output_height = args.output_height if args.output_height > 0 else int(
- input_height)
- output_width = args.output_width if args.output_width > 0 else int(
- input_width)
- writer = cv2.VideoWriter(args.output_file, fourcc, output_fps,
- (output_width, output_height), True)
+ output_height = (
+ args.output_height if args.output_height > 0 else int(input_height)
+ )
+ output_width = args.output_width if args.output_width > 0 else int(input_width)
+ writer = cv2.VideoWriter(
+ args.output_file, fourcc, output_fps, (output_width, output_height), True
+ )
# start looping
try:
@@ -91,16 +90,18 @@ def main():
result,
palette=get_palette(args.palette),
show=False,
- opacity=args.opacity)
+ opacity=args.opacity,
+ )
if args.show:
- cv2.imshow('video_demo', draw_img)
+ cv2.imshow("video_demo", draw_img)
cv2.waitKey(args.show_wait_time)
if writer:
- if draw_img.shape[0] != output_height or draw_img.shape[
- 1] != output_width:
- draw_img = cv2.resize(draw_img,
- (output_width, output_height))
+ if (
+ draw_img.shape[0] != output_height
+ or draw_img.shape[1] != output_width
+ ):
+ draw_img = cv2.resize(draw_img, (output_width, output_height))
writer.write(draw_img)
finally:
if writer:
@@ -108,5 +109,5 @@ def main():
cap.release()
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/docs/en/conf.py b/mmsegmentation/docs/en/conf.py
index cd2113d..5803603 100644
--- a/mmsegmentation/docs/en/conf.py
+++ b/mmsegmentation/docs/en/conf.py
@@ -17,20 +17,20 @@
import pytorch_sphinx_theme
-sys.path.insert(0, os.path.abspath('../../'))
+sys.path.insert(0, os.path.abspath("../../"))
# -- Project information -----------------------------------------------------
-project = 'MMSegmentation'
-copyright = '2020-2021, OpenMMLab'
-author = 'MMSegmentation Authors'
-version_file = '../../mmseg/version.py'
+project = "MMSegmentation"
+copyright = "2020-2021, OpenMMLab"
+author = "MMSegmentation Authors"
+version_file = "../../mmseg/version.py"
def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
+ with open(version_file) as f:
+ exec(compile(f.read(), version_file, "exec"))
+ return locals()["__version__"]
# The full version, including alpha/beta/rc tags
@@ -42,36 +42,38 @@ def get_version():
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
- 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode',
- 'sphinx_markdown_tables', 'sphinx_copybutton', 'myst_parser'
+ "sphinx.ext.autodoc",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.viewcode",
+ "sphinx_markdown_tables",
+ "sphinx_copybutton",
+ "myst_parser",
]
-autodoc_mock_imports = [
- 'matplotlib', 'pycocotools', 'mmseg.version', 'mmcv.ops'
-]
+autodoc_mock_imports = ["matplotlib", "pycocotools", "mmseg.version", "mmcv.ops"]
# Ignore >>> when copying code
-copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_text = r">>> |\.\.\. "
copybutton_prompt_is_regexp = True
# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
source_suffix = {
- '.rst': 'restructuredtext',
- '.md': 'markdown',
+ ".rst": "restructuredtext",
+ ".md": "markdown",
}
# The master toctree document.
-master_doc = 'index'
+master_doc = "index"
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
@@ -79,56 +81,48 @@ def get_version():
# a list of builtin themes.
#
# html_theme = 'sphinx_rtd_theme'
-html_theme = 'pytorch_sphinx_theme'
+html_theme = "pytorch_sphinx_theme"
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
html_theme_options = {
- 'logo_url':
- 'https://mmsegmentation.readthedocs.io/en/latest/',
- 'menu': [
- {
- 'name':
- 'Tutorial',
- 'url':
- 'https://github.com/open-mmlab/mmsegmentation/blob/master/'
- 'demo/MMSegmentation_Tutorial.ipynb'
- },
+ "logo_url": "https://mmsegmentation.readthedocs.io/en/latest/",
+ "menu": [
{
- 'name': 'GitHub',
- 'url': 'https://github.com/open-mmlab/mmsegmentation'
+ "name": "Tutorial",
+ "url": "https://github.com/open-mmlab/mmsegmentation/blob/master/"
+ "demo/MMSegmentation_Tutorial.ipynb",
},
+ {"name": "GitHub", "url": "https://github.com/open-mmlab/mmsegmentation"},
{
- 'name':
- 'Upstream',
- 'children': [
+ "name": "Upstream",
+ "children": [
{
- 'name': 'MMCV',
- 'url': 'https://github.com/open-mmlab/mmcv',
- 'description': 'Foundational library for computer vision'
+ "name": "MMCV",
+ "url": "https://github.com/open-mmlab/mmcv",
+ "description": "Foundational library for computer vision",
},
- ]
+ ],
},
],
# Specify the language of shared menu
- 'menu_lang':
- 'en'
+ "menu_lang": "en",
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-html_css_files = ['css/readthedocs.css']
+html_static_path = ["_static"]
+html_css_files = ["css/readthedocs.css"]
# Enable ::: for my_st
-myst_enable_extensions = ['colon_fence']
+myst_enable_extensions = ["colon_fence"]
myst_heading_anchors = 3
-language = 'en'
+language = "en"
def builder_inited_handler(app):
- subprocess.run(['./stat.py'])
+ subprocess.run(["./stat.py"])
def setup(app):
- app.connect('builder-inited', builder_inited_handler)
+ app.connect("builder-inited", builder_inited_handler)
diff --git a/mmsegmentation/docs/en/stat.py b/mmsegmentation/docs/en/stat.py
index 1398a70..f9a3273 100755
--- a/mmsegmentation/docs/en/stat.py
+++ b/mmsegmentation/docs/en/stat.py
@@ -7,34 +7,34 @@
import numpy as np
-url_prefix = 'https://github.com/open-mmlab/mmsegmentation/blob/master/'
+url_prefix = "https://github.com/open-mmlab/mmsegmentation/blob/master/"
-files = sorted(glob.glob('../../configs/*/README.md'))
+files = sorted(glob.glob("../../configs/*/README.md"))
stats = []
titles = []
num_ckpts = 0
for f in files:
- url = osp.dirname(f.replace('../../', url_prefix))
+ url = osp.dirname(f.replace("../../", url_prefix))
- with open(f, 'r') as content_file:
+ with open(f) as content_file:
content = content_file.read()
- title = content.split('\n')[0].replace('#', '').strip()
- ckpts = set(x.lower().strip()
- for x in re.findall(r'https?://download.*\.pth', content)
- if 'mmsegmentation' in x)
+ title = content.split("\n")[0].replace("#", "").strip()
+ ckpts = {
+ x.lower().strip()
+ for x in re.findall(r"https?://download.*\.pth", content)
+ if "mmsegmentation" in x
+ }
if len(ckpts) == 0:
continue
- _papertype = [
- x for x in re.findall(r'', content)
- ]
+ _papertype = [x for x in re.findall(r"", content)]
assert len(_papertype) > 0
papertype = _papertype[0]
- paper = set([(papertype, title)])
+ paper = {(papertype, title)}
titles.append(title)
num_ckpts += len(ckpts)
@@ -44,12 +44,10 @@
stats.append((paper, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
-msglist = '\n'.join(x for _, _, x in stats)
+msglist = "\n".join(x for _, _, x in stats)
-papertypes, papercounts = np.unique([t for t, _ in allpapers],
- return_counts=True)
-countstr = '\n'.join(
- [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+papertypes, papercounts = np.unique([t for t, _ in allpapers], return_counts=True)
+countstr = "\n".join([f" - {t}: {c}" for t, c in zip(papertypes, papercounts)])
modelzoo = f"""
# Model Zoo Statistics
@@ -61,5 +59,5 @@
{msglist}
"""
-with open('modelzoo_statistics.md', 'w') as f:
+with open("modelzoo_statistics.md", "w") as f:
f.write(modelzoo)
diff --git a/mmsegmentation/docs/zh_cn/conf.py b/mmsegmentation/docs/zh_cn/conf.py
index 4dec48d..4dfacca 100644
--- a/mmsegmentation/docs/zh_cn/conf.py
+++ b/mmsegmentation/docs/zh_cn/conf.py
@@ -17,20 +17,20 @@
import pytorch_sphinx_theme
-sys.path.insert(0, os.path.abspath('../../'))
+sys.path.insert(0, os.path.abspath("../../"))
# -- Project information -----------------------------------------------------
-project = 'MMSegmentation'
-copyright = '2020-2021, OpenMMLab'
-author = 'MMSegmentation Authors'
-version_file = '../../mmseg/version.py'
+project = "MMSegmentation"
+copyright = "2020-2021, OpenMMLab"
+author = "MMSegmentation Authors"
+version_file = "../../mmseg/version.py"
def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
+ with open(version_file) as f:
+ exec(compile(f.read(), version_file, "exec"))
+ return locals()["__version__"]
# The full version, including alpha/beta/rc tags
@@ -42,36 +42,38 @@ def get_version():
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
- 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode',
- 'sphinx_markdown_tables', 'sphinx_copybutton', 'myst_parser'
+ "sphinx.ext.autodoc",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.viewcode",
+ "sphinx_markdown_tables",
+ "sphinx_copybutton",
+ "myst_parser",
]
-autodoc_mock_imports = [
- 'matplotlib', 'pycocotools', 'mmseg.version', 'mmcv.ops'
-]
+autodoc_mock_imports = ["matplotlib", "pycocotools", "mmseg.version", "mmcv.ops"]
# Ignore >>> when copying code
-copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_text = r">>> |\.\.\. "
copybutton_prompt_is_regexp = True
# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
source_suffix = {
- '.rst': 'restructuredtext',
- '.md': 'markdown',
+ ".rst": "restructuredtext",
+ ".md": "markdown",
}
# The master toctree document.
-master_doc = 'index'
+master_doc = "index"
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
@@ -79,56 +81,48 @@ def get_version():
# a list of builtin themes.
#
# html_theme = 'sphinx_rtd_theme'
-html_theme = 'pytorch_sphinx_theme'
+html_theme = "pytorch_sphinx_theme"
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
html_theme_options = {
- 'logo_url':
- 'https://mmsegmentation.readthedocs.io/zh-CN/latest/',
- 'menu': [
- {
- 'name':
- '教程',
- 'url':
- 'https://github.com/open-mmlab/mmsegmentation/blob/master/'
- 'demo/MMSegmentation_Tutorial.ipynb'
- },
+ "logo_url": "https://mmsegmentation.readthedocs.io/zh-CN/latest/",
+ "menu": [
{
- 'name': 'GitHub',
- 'url': 'https://github.com/open-mmlab/mmsegmentation'
+ "name": "教程",
+ "url": "https://github.com/open-mmlab/mmsegmentation/blob/master/"
+ "demo/MMSegmentation_Tutorial.ipynb",
},
+ {"name": "GitHub", "url": "https://github.com/open-mmlab/mmsegmentation"},
{
- 'name':
- '上游库',
- 'children': [
+ "name": "上游库",
+ "children": [
{
- 'name': 'MMCV',
- 'url': 'https://github.com/open-mmlab/mmcv',
- 'description': '基础视觉库'
+ "name": "MMCV",
+ "url": "https://github.com/open-mmlab/mmcv",
+ "description": "基础视觉库",
},
- ]
+ ],
},
],
# Specify the language of shared menu
- 'menu_lang':
- 'cn',
+ "menu_lang": "cn",
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-html_css_files = ['css/readthedocs.css']
+html_static_path = ["_static"]
+html_css_files = ["css/readthedocs.css"]
# Enable ::: for my_st
-myst_enable_extensions = ['colon_fence']
+myst_enable_extensions = ["colon_fence"]
myst_heading_anchors = 3
-language = 'zh-CN'
+language = "zh-CN"
def builder_inited_handler(app):
- subprocess.run(['./stat.py'])
+ subprocess.run(["./stat.py"])
def setup(app):
- app.connect('builder-inited', builder_inited_handler)
+ app.connect("builder-inited", builder_inited_handler)
diff --git a/mmsegmentation/docs/zh_cn/stat.py b/mmsegmentation/docs/zh_cn/stat.py
index b3a1d73..5beb577 100755
--- a/mmsegmentation/docs/zh_cn/stat.py
+++ b/mmsegmentation/docs/zh_cn/stat.py
@@ -7,34 +7,34 @@
import numpy as np
-url_prefix = 'https://github.com/open-mmlab/mmsegmentation/blob/master/'
+url_prefix = "https://github.com/open-mmlab/mmsegmentation/blob/master/"
-files = sorted(glob.glob('../../configs/*/README.md'))
+files = sorted(glob.glob("../../configs/*/README.md"))
stats = []
titles = []
num_ckpts = 0
for f in files:
- url = osp.dirname(f.replace('../../', url_prefix))
+ url = osp.dirname(f.replace("../../", url_prefix))
- with open(f, 'r') as content_file:
+ with open(f) as content_file:
content = content_file.read()
- title = content.split('\n')[0].replace('#', '').strip()
- ckpts = set(x.lower().strip()
- for x in re.findall(r'https?://download.*\.pth', content)
- if 'mmsegmentation' in x)
+ title = content.split("\n")[0].replace("#", "").strip()
+ ckpts = {
+ x.lower().strip()
+ for x in re.findall(r"https?://download.*\.pth", content)
+ if "mmsegmentation" in x
+ }
if len(ckpts) == 0:
continue
- _papertype = [
- x for x in re.findall(r'', content)
- ]
+ _papertype = [x for x in re.findall(r"", content)]
assert len(_papertype) > 0
papertype = _papertype[0]
- paper = set([(papertype, title)])
+ paper = {(papertype, title)}
titles.append(title)
num_ckpts += len(ckpts)
@@ -44,12 +44,10 @@
stats.append((paper, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
-msglist = '\n'.join(x for _, _, x in stats)
+msglist = "\n".join(x for _, _, x in stats)
-papertypes, papercounts = np.unique([t for t, _ in allpapers],
- return_counts=True)
-countstr = '\n'.join(
- [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+papertypes, papercounts = np.unique([t for t, _ in allpapers], return_counts=True)
+countstr = "\n".join([f" - {t}: {c}" for t, c in zip(papertypes, papercounts)])
modelzoo = f"""
# 模型库统计数据
@@ -61,5 +59,5 @@
{msglist}
"""
-with open('modelzoo_statistics.md', 'w') as f:
+with open("modelzoo_statistics.md", "w") as f:
f.write(modelzoo)
diff --git a/mmsegmentation/mmseg/__init__.py b/mmsegmentation/mmseg/__init__.py
index c28bf4e..66bda18 100644
--- a/mmsegmentation/mmseg/__init__.py
+++ b/mmsegmentation/mmseg/__init__.py
@@ -6,8 +6,8 @@
from .version import __version__, version_info
-MMCV_MIN = '1.3.13'
-MMCV_MAX = '1.8.0'
+MMCV_MIN = "1.3.13"
+MMCV_MAX = "1.8.0"
def digit_version(version_str: str, length: int = 4):
@@ -24,19 +24,21 @@ def digit_version(version_str: str, length: int = 4):
tuple[int]: The version info in digits (integers).
"""
version = parse(version_str)
- assert version.release, f'failed to parse version {version_str}'
+ assert version.release, f"failed to parse version {version_str}"
release = list(version.release)
release = release[:length]
if len(release) < length:
release = release + [0] * (length - len(release))
if version.is_prerelease:
- mapping = {'a': -3, 'b': -2, 'rc': -1}
+ mapping = {"a": -3, "b": -2, "rc": -1}
val = -4
# version.pre can be None
if version.pre:
if version.pre[0] not in mapping:
- warnings.warn(f'unknown prerelease version {version.pre[0]}, '
- 'version checking may go wrong')
+ warnings.warn(
+ f"unknown prerelease version {version.pre[0]}, "
+ "version checking may go wrong"
+ )
else:
val = mapping[version.pre[0]]
release.extend([val, version.pre[-1]])
@@ -55,8 +57,9 @@ def digit_version(version_str: str, length: int = 4):
mmcv_version = digit_version(mmcv.__version__)
-assert (mmcv_min_version <= mmcv_version < mmcv_max_version), \
- f'MMCV=={mmcv.__version__} is used but incompatible. ' \
- f'Please install mmcv>={mmcv_min_version}, <{mmcv_max_version}.'
+assert mmcv_min_version <= mmcv_version < mmcv_max_version, (
+ f"MMCV=={mmcv.__version__} is used but incompatible. "
+ f"Please install mmcv>={mmcv_min_version}, <{mmcv_max_version}."
+)
-__all__ = ['__version__', 'version_info', 'digit_version']
+__all__ = ["__version__", "version_info", "digit_version"]
diff --git a/mmsegmentation/mmseg/apis/__init__.py b/mmsegmentation/mmseg/apis/__init__.py
index c688180..28f78c4 100644
--- a/mmsegmentation/mmseg/apis/__init__.py
+++ b/mmsegmentation/mmseg/apis/__init__.py
@@ -1,11 +1,16 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .inference import inference_segmentor, init_segmentor, show_result_pyplot
from .test import multi_gpu_test, single_gpu_test
-from .train import (get_root_logger, init_random_seed, set_random_seed,
- train_segmentor)
+from .train import get_root_logger, init_random_seed, set_random_seed, train_segmentor
__all__ = [
- 'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
- 'inference_segmentor', 'multi_gpu_test', 'single_gpu_test',
- 'show_result_pyplot', 'init_random_seed'
+ "get_root_logger",
+ "set_random_seed",
+ "train_segmentor",
+ "init_segmentor",
+ "inference_segmentor",
+ "multi_gpu_test",
+ "single_gpu_test",
+ "show_result_pyplot",
+ "init_random_seed",
]
diff --git a/mmsegmentation/mmseg/apis/inference.py b/mmsegmentation/mmseg/apis/inference.py
index 5bbe666..24ecae9 100644
--- a/mmsegmentation/mmseg/apis/inference.py
+++ b/mmsegmentation/mmseg/apis/inference.py
@@ -9,7 +9,7 @@
from mmseg.models import build_segmentor
-def init_segmentor(config, checkpoint=None, device='cuda:0'):
+def init_segmentor(config, checkpoint=None, device="cuda:0"):
"""Initialize a segmentor from config file.
Args:
@@ -25,15 +25,17 @@ def init_segmentor(config, checkpoint=None, device='cuda:0'):
if isinstance(config, str):
config = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
- raise TypeError('config must be a filename or Config object, '
- 'but got {}'.format(type(config)))
+ raise TypeError(
+ "config must be a filename or Config object, "
+ "but got {}".format(type(config))
+ )
config.model.pretrained = None
config.model.train_cfg = None
- model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
+ model = build_segmentor(config.model, test_cfg=config.get("test_cfg"))
if checkpoint is not None:
- checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
- model.CLASSES = checkpoint['meta']['CLASSES']
- model.PALETTE = checkpoint['meta']['PALETTE']
+ checkpoint = load_checkpoint(model, checkpoint, map_location="cpu")
+ model.CLASSES = checkpoint["meta"]["CLASSES"]
+ model.PALETTE = checkpoint["meta"]["PALETTE"]
model.cfg = config # save the config in the model for convenience
model.to(device)
model.eval()
@@ -54,16 +56,16 @@ def __call__(self, results):
dict: ``results`` will be returned containing loaded image.
"""
- if isinstance(results['img'], str):
- results['filename'] = results['img']
- results['ori_filename'] = results['img']
+ if isinstance(results["img"], str):
+ results["filename"] = results["img"]
+ results["ori_filename"] = results["img"]
else:
- results['filename'] = None
- results['ori_filename'] = None
- img = mmcv.imread(results['img'])
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["filename"] = None
+ results["ori_filename"] = None
+ img = mmcv.imread(results["img"])
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
return results
@@ -95,7 +97,7 @@ def inference_segmentor(model, imgs):
# scatter to specified GPU
data = scatter(data, [device])[0]
else:
- data['img_metas'] = [i.data[0] for i in data['img_metas']]
+ data["img_metas"] = [i.data[0] for i in data["img_metas"]]
# forward the model
with torch.no_grad():
@@ -103,15 +105,17 @@ def inference_segmentor(model, imgs):
return result
-def show_result_pyplot(model,
- img,
- result,
- palette=None,
- fig_size=(15, 10),
- opacity=0.5,
- title='',
- block=True,
- out_file=None):
+def show_result_pyplot(
+ model,
+ img,
+ result,
+ palette=None,
+ fig_size=(15, 10),
+ opacity=0.5,
+ title="",
+ block=True,
+ out_file=None,
+):
"""Visualize the segmentation results on the image.
Args:
@@ -132,10 +136,9 @@ def show_result_pyplot(model,
out_file (str or None): The path to write the image.
Default: None.
"""
- if hasattr(model, 'module'):
+ if hasattr(model, "module"):
model = model.module
- img = model.show_result(
- img, result, palette=palette, show=False, opacity=opacity)
+ img = model.show_result(img, result, palette=palette, show=False, opacity=opacity)
plt.figure(figsize=fig_size)
plt.imshow(mmcv.bgr2rgb(img))
plt.title(title)
diff --git a/mmsegmentation/mmseg/apis/test.py b/mmsegmentation/mmseg/apis/test.py
index cc4fcc9..8c06126 100644
--- a/mmsegmentation/mmseg/apis/test.py
+++ b/mmsegmentation/mmseg/apis/test.py
@@ -26,20 +26,23 @@ def np2tmp(array, temp_file_name=None, tmpdir=None):
if temp_file_name is None:
temp_file_name = tempfile.NamedTemporaryFile(
- suffix='.npy', delete=False, dir=tmpdir).name
+ suffix=".npy", delete=False, dir=tmpdir
+ ).name
np.save(temp_file_name, array)
return temp_file_name
-def single_gpu_test(model,
- data_loader,
- show=False,
- out_dir=None,
- efficient_test=False,
- opacity=0.5,
- pre_eval=False,
- format_only=False,
- format_args={}):
+def single_gpu_test(
+ model,
+ data_loader,
+ show=False,
+ out_dir=None,
+ efficient_test=False,
+ opacity=0.5,
+ pre_eval=False,
+ format_only=False,
+ format_args={},
+):
"""Test with single GPU by progressive mode.
Args:
@@ -66,14 +69,16 @@ def single_gpu_test(model,
"""
if efficient_test:
warnings.warn(
- 'DeprecationWarning: ``efficient_test`` will be deprecated, the '
- 'evaluation is CPU memory friendly with pre_eval=True')
- mmcv.mkdir_or_exist('.efficient_test')
+ "DeprecationWarning: ``efficient_test`` will be deprecated, the "
+ "evaluation is CPU memory friendly with pre_eval=True"
+ )
+ mmcv.mkdir_or_exist(".efficient_test")
# when none of them is set true, return segmentation results as
# a list of np.array.
- assert [efficient_test, pre_eval, format_only].count(True) <= 1, \
- '``efficient_test``, ``pre_eval`` and ``format_only`` are mutually ' \
- 'exclusive, only one of them could be true .'
+ assert [efficient_test, pre_eval, format_only].count(True) <= 1, (
+ "``efficient_test``, ``pre_eval`` and ``format_only`` are mutually "
+ "exclusive, only one of them could be true ."
+ )
model.eval()
results = []
@@ -91,20 +96,20 @@ def single_gpu_test(model,
result = model(return_loss=False, **data)
if show or out_dir:
- img_tensor = data['img'][0]
- img_metas = data['img_metas'][0].data[0]
- imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
+ img_tensor = data["img"][0]
+ img_metas = data["img_metas"][0].data[0]
+ imgs = tensor2imgs(img_tensor, **img_metas[0]["img_norm_cfg"])
assert len(imgs) == len(img_metas)
for img, img_meta in zip(imgs, img_metas):
- h, w, _ = img_meta['img_shape']
+ h, w, _ = img_meta["img_shape"]
img_show = img[:h, :w, :]
- ori_h, ori_w = img_meta['ori_shape'][:-1]
+ ori_h, ori_w = img_meta["ori_shape"][:-1]
img_show = mmcv.imresize(img_show, (ori_w, ori_h))
if out_dir:
- out_file = osp.join(out_dir, img_meta['ori_filename'])
+ out_file = osp.join(out_dir, img_meta["ori_filename"])
else:
out_file = None
@@ -114,14 +119,16 @@ def single_gpu_test(model,
palette=dataset.PALETTE,
show=show,
out_file=out_file,
- opacity=opacity)
+ opacity=opacity,
+ )
if efficient_test:
- result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
+ result = [np2tmp(_, tmpdir=".efficient_test") for _ in result]
if format_only:
result = dataset.format_results(
- result, indices=batch_indices, **format_args)
+ result, indices=batch_indices, **format_args
+ )
if pre_eval:
# TODO: adapt samples_per_gpu > 1.
# only samples_per_gpu=1 valid now
@@ -137,14 +144,16 @@ def single_gpu_test(model,
return results
-def multi_gpu_test(model,
- data_loader,
- tmpdir=None,
- gpu_collect=False,
- efficient_test=False,
- pre_eval=False,
- format_only=False,
- format_args={}):
+def multi_gpu_test(
+ model,
+ data_loader,
+ tmpdir=None,
+ gpu_collect=False,
+ efficient_test=False,
+ pre_eval=False,
+ format_only=False,
+ format_args={},
+):
"""Test model with multiple gpus by progressive mode.
This method tests model with multiple gpus and collects the results
@@ -177,14 +186,16 @@ def multi_gpu_test(model,
"""
if efficient_test:
warnings.warn(
- 'DeprecationWarning: ``efficient_test`` will be deprecated, the '
- 'evaluation is CPU memory friendly with pre_eval=True')
- mmcv.mkdir_or_exist('.efficient_test')
+ "DeprecationWarning: ``efficient_test`` will be deprecated, the "
+ "evaluation is CPU memory friendly with pre_eval=True"
+ )
+ mmcv.mkdir_or_exist(".efficient_test")
# when none of them is set true, return segmentation results as
# a list of np.array.
- assert [efficient_test, pre_eval, format_only].count(True) <= 1, \
- '``efficient_test``, ``pre_eval`` and ``format_only`` are mutually ' \
- 'exclusive, only one of them could be true .'
+ assert [efficient_test, pre_eval, format_only].count(True) <= 1, (
+ "``efficient_test``, ``pre_eval`` and ``format_only`` are mutually "
+ "exclusive, only one of them could be true ."
+ )
model.eval()
results = []
@@ -208,11 +219,12 @@ def multi_gpu_test(model,
result = model(return_loss=False, rescale=True, **data)
if efficient_test:
- result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
+ result = [np2tmp(_, tmpdir=".efficient_test") for _ in result]
if format_only:
result = dataset.format_results(
- result, indices=batch_indices, **format_args)
+ result, indices=batch_indices, **format_args
+ )
if pre_eval:
# TODO: adapt samples_per_gpu > 1.
# only samples_per_gpu=1 valid now
diff --git a/mmsegmentation/mmseg/apis/train.py b/mmsegmentation/mmseg/apis/train.py
index be8e422..90dcfe3 100644
--- a/mmsegmentation/mmseg/apis/train.py
+++ b/mmsegmentation/mmseg/apis/train.py
@@ -7,18 +7,22 @@
import numpy as np
import torch
import torch.distributed as dist
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
- build_runner, get_dist_info)
+from mmcv.runner import (
+ HOOKS,
+ DistSamplerSeedHook,
+ EpochBasedRunner,
+ build_runner,
+ get_dist_info,
+)
from mmcv.utils import build_from_cfg
from mmseg import digit_version
from mmseg.core import DistEvalHook, EvalHook, build_optimizer
from mmseg.datasets import build_dataloader, build_dataset
-from mmseg.utils import (build_ddp, build_dp, find_latest_checkpoint,
- get_root_logger)
+from mmseg.utils import build_ddp, build_dp, find_latest_checkpoint, get_root_logger
-def init_random_seed(seed=None, device='cuda'):
+def init_random_seed(seed=None, device="cuda"):
"""Initialize random seed.
If the seed is not set, the seed will be automatically randomized,
@@ -68,13 +72,9 @@ def set_random_seed(seed, deterministic=False):
torch.backends.cudnn.benchmark = False
-def train_segmentor(model,
- dataset,
- cfg,
- distributed=False,
- validate=False,
- timestamp=None,
- meta=None):
+def train_segmentor(
+ model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None
+):
"""Launch segmentor training."""
logger = get_root_logger(cfg.log_level)
@@ -86,45 +86,58 @@ def train_segmentor(model,
num_gpus=len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
- drop_last=True)
+ drop_last=True,
+ )
# The overall dataloader settings
- loader_cfg.update({
- k: v
- for k, v in cfg.data.items() if k not in [
- 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
- 'test_dataloader'
- ]
- })
+ loader_cfg.update(
+ {
+ k: v
+ for k, v in cfg.data.items()
+ if k
+ not in [
+ "train",
+ "val",
+ "test",
+ "train_dataloader",
+ "val_dataloader",
+ "test_dataloader",
+ ]
+ }
+ )
# The specific dataloader settings
- train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
+ train_loader_cfg = {**loader_cfg, **cfg.data.get("train_dataloader", {})}
data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on devices
if distributed:
- find_unused_parameters = cfg.get('find_unused_parameters', False)
+ find_unused_parameters = cfg.get("find_unused_parameters", False)
# Sets the `find_unused_parameters` parameter in
# DDP wrapper
model = build_ddp(
model,
cfg.device,
- device_ids=[int(os.environ['LOCAL_RANK'])],
+ device_ids=[int(os.environ["LOCAL_RANK"])],
broadcast_buffers=False,
- find_unused_parameters=find_unused_parameters)
+ find_unused_parameters=find_unused_parameters,
+ )
else:
if not torch.cuda.is_available():
- assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
- 'Please use MMCV >= 1.4.4 for CPU training!'
+ assert digit_version(mmcv.__version__) >= digit_version(
+ "1.4.4"
+ ), "Please use MMCV >= 1.4.4 for CPU training!"
model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
- if cfg.get('runner') is None:
- cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
+ if cfg.get("runner") is None:
+ cfg.runner = {"type": "IterBasedRunner", "max_iters": cfg.total_iters}
warnings.warn(
- 'config is now expected to have a `runner` section, '
- 'please set `runner` in your config.', UserWarning)
+ "config is now expected to have a `runner` section, "
+ "please set `runner` in your config.",
+ UserWarning,
+ )
runner = build_runner(
cfg.runner,
@@ -134,12 +147,18 @@ def train_segmentor(model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
- meta=meta))
+ meta=meta,
+ ),
+ )
# register hooks
- runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
- cfg.checkpoint_config, cfg.log_config,
- cfg.get('momentum_config', None))
+ runner.register_training_hooks(
+ cfg.lr_config,
+ cfg.optimizer_config,
+ cfg.checkpoint_config,
+ cfg.log_config,
+ cfg.get("momentum_config", None),
+ )
if distributed:
# when distributed training by epoch, using`DistSamplerSeedHook` to set
# the different seed to distributed sampler for each epoch, it will
@@ -156,34 +175,35 @@ def train_segmentor(model,
# The specific dataloader settings
val_loader_cfg = {
**loader_cfg,
- 'samples_per_gpu': 1,
- 'shuffle': False, # Not shuffle by default
- **cfg.data.get('val_dataloader', {}),
+ "samples_per_gpu": 1,
+ "shuffle": False, # Not shuffle by default
+ **cfg.data.get("val_dataloader", {}),
}
val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
- eval_cfg = cfg.get('evaluation', {})
- eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
+ eval_cfg = cfg.get("evaluation", {})
+ eval_cfg["by_epoch"] = cfg.runner["type"] != "IterBasedRunner"
eval_hook = DistEvalHook if distributed else EvalHook
# In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
# priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
- runner.register_hook(
- eval_hook(val_dataloader, **eval_cfg), priority='LOW')
+ runner.register_hook(eval_hook(val_dataloader, **eval_cfg), priority="LOW")
# user-defined hooks
- if cfg.get('custom_hooks', None):
+ if cfg.get("custom_hooks", None):
custom_hooks = cfg.custom_hooks
- assert isinstance(custom_hooks, list), \
- f'custom_hooks expect list type, but got {type(custom_hooks)}'
+ assert isinstance(
+ custom_hooks, list
+ ), f"custom_hooks expect list type, but got {type(custom_hooks)}"
for hook_cfg in cfg.custom_hooks:
- assert isinstance(hook_cfg, dict), \
- 'Each item in custom_hooks expects dict type, but got ' \
- f'{type(hook_cfg)}'
+ assert isinstance(hook_cfg, dict), (
+ "Each item in custom_hooks expects dict type, but got "
+ f"{type(hook_cfg)}"
+ )
hook_cfg = hook_cfg.copy()
- priority = hook_cfg.pop('priority', 'NORMAL')
+ priority = hook_cfg.pop("priority", "NORMAL")
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
- if cfg.resume_from is None and cfg.get('auto_resume'):
+ if cfg.resume_from is None and cfg.get("auto_resume"):
resume_from = find_latest_checkpoint(cfg.work_dir)
if resume_from is not None:
cfg.resume_from = resume_from
diff --git a/mmsegmentation/mmseg/core/__init__.py b/mmsegmentation/mmseg/core/__init__.py
index 82f2422..80ba449 100644
--- a/mmsegmentation/mmseg/core/__init__.py
+++ b/mmsegmentation/mmseg/core/__init__.py
@@ -1,12 +1,9 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from .builder import (OPTIMIZER_BUILDERS, build_optimizer,
- build_optimizer_constructor)
+from .builder import OPTIMIZER_BUILDERS, build_optimizer, build_optimizer_constructor
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .optimizers import * # noqa: F401, F403
from .seg import * # noqa: F401, F403
from .utils import * # noqa: F401, F403
-__all__ = [
- 'OPTIMIZER_BUILDERS', 'build_optimizer', 'build_optimizer_constructor'
-]
+__all__ = ["OPTIMIZER_BUILDERS", "build_optimizer", "build_optimizer_constructor"]
diff --git a/mmsegmentation/mmseg/core/builder.py b/mmsegmentation/mmseg/core/builder.py
index 406dd9b..59732a7 100644
--- a/mmsegmentation/mmseg/core/builder.py
+++ b/mmsegmentation/mmseg/core/builder.py
@@ -4,30 +4,32 @@
from mmcv.runner.optimizer import OPTIMIZER_BUILDERS as MMCV_OPTIMIZER_BUILDERS
from mmcv.utils import Registry, build_from_cfg
-OPTIMIZER_BUILDERS = Registry(
- 'optimizer builder', parent=MMCV_OPTIMIZER_BUILDERS)
+OPTIMIZER_BUILDERS = Registry("optimizer builder", parent=MMCV_OPTIMIZER_BUILDERS)
def build_optimizer_constructor(cfg):
- constructor_type = cfg.get('type')
+ constructor_type = cfg.get("type")
if constructor_type in OPTIMIZER_BUILDERS:
return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
elif constructor_type in MMCV_OPTIMIZER_BUILDERS:
return build_from_cfg(cfg, MMCV_OPTIMIZER_BUILDERS)
else:
- raise KeyError(f'{constructor_type} is not registered '
- 'in the optimizer builder registry.')
+ raise KeyError(
+ f"{constructor_type} is not registered "
+ "in the optimizer builder registry."
+ )
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
- constructor_type = optimizer_cfg.pop('constructor',
- 'DefaultOptimizerConstructor')
- paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
+ constructor_type = optimizer_cfg.pop("constructor", "DefaultOptimizerConstructor")
+ paramwise_cfg = optimizer_cfg.pop("paramwise_cfg", None)
optim_constructor = build_optimizer_constructor(
dict(
type=constructor_type,
optimizer_cfg=optimizer_cfg,
- paramwise_cfg=paramwise_cfg))
+ paramwise_cfg=paramwise_cfg,
+ )
+ )
optimizer = optim_constructor(model)
return optimizer
diff --git a/mmsegmentation/mmseg/core/evaluation/__init__.py b/mmsegmentation/mmseg/core/evaluation/__init__.py
index 3d16d17..9e7a1c2 100644
--- a/mmsegmentation/mmseg/core/evaluation/__init__.py
+++ b/mmsegmentation/mmseg/core/evaluation/__init__.py
@@ -1,11 +1,24 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .class_names import get_classes, get_palette
from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import (eval_metrics, intersect_and_union, mean_dice,
- mean_fscore, mean_iou, pre_eval_to_metrics)
+from .metrics import (
+ eval_metrics,
+ intersect_and_union,
+ mean_dice,
+ mean_fscore,
+ mean_iou,
+ pre_eval_to_metrics,
+)
__all__ = [
- 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
- 'eval_metrics', 'get_classes', 'get_palette', 'pre_eval_to_metrics',
- 'intersect_and_union'
+ "EvalHook",
+ "DistEvalHook",
+ "mean_dice",
+ "mean_iou",
+ "mean_fscore",
+ "eval_metrics",
+ "get_classes",
+ "get_palette",
+ "pre_eval_to_metrics",
+ "intersect_and_union",
]
diff --git a/mmsegmentation/mmseg/core/evaluation/class_names.py b/mmsegmentation/mmseg/core/evaluation/class_names.py
index e3bff62..c29d266 100644
--- a/mmsegmentation/mmseg/core/evaluation/class_names.py
+++ b/mmsegmentation/mmseg/core/evaluation/class_names.py
@@ -5,259 +5,894 @@
def cityscapes_classes():
"""Cityscapes class names for external use."""
return [
- 'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
- 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
- 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
- 'bicycle'
+ "road",
+ "sidewalk",
+ "building",
+ "wall",
+ "fence",
+ "pole",
+ "traffic light",
+ "traffic sign",
+ "vegetation",
+ "terrain",
+ "sky",
+ "person",
+ "rider",
+ "car",
+ "truck",
+ "bus",
+ "train",
+ "motorcycle",
+ "bicycle",
]
def ade_classes():
"""ADE20K class names for external use."""
return [
- 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
- 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
- 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
- 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
- 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
- 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
- 'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
- 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
- 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
- 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
- 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
- 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
- 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
- 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
- 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
- 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
- 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
- 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
- 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
- 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
- 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
- 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
- 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
- 'clock', 'flag'
+ "wall",
+ "building",
+ "sky",
+ "floor",
+ "tree",
+ "ceiling",
+ "road",
+ "bed ",
+ "windowpane",
+ "grass",
+ "cabinet",
+ "sidewalk",
+ "person",
+ "earth",
+ "door",
+ "table",
+ "mountain",
+ "plant",
+ "curtain",
+ "chair",
+ "car",
+ "water",
+ "painting",
+ "sofa",
+ "shelf",
+ "house",
+ "sea",
+ "mirror",
+ "rug",
+ "field",
+ "armchair",
+ "seat",
+ "fence",
+ "desk",
+ "rock",
+ "wardrobe",
+ "lamp",
+ "bathtub",
+ "railing",
+ "cushion",
+ "base",
+ "box",
+ "column",
+ "signboard",
+ "chest of drawers",
+ "counter",
+ "sand",
+ "sink",
+ "skyscraper",
+ "fireplace",
+ "refrigerator",
+ "grandstand",
+ "path",
+ "stairs",
+ "runway",
+ "case",
+ "pool table",
+ "pillow",
+ "screen door",
+ "stairway",
+ "river",
+ "bridge",
+ "bookcase",
+ "blind",
+ "coffee table",
+ "toilet",
+ "flower",
+ "book",
+ "hill",
+ "bench",
+ "countertop",
+ "stove",
+ "palm",
+ "kitchen island",
+ "computer",
+ "swivel chair",
+ "boat",
+ "bar",
+ "arcade machine",
+ "hovel",
+ "bus",
+ "towel",
+ "light",
+ "truck",
+ "tower",
+ "chandelier",
+ "awning",
+ "streetlight",
+ "booth",
+ "television receiver",
+ "airplane",
+ "dirt track",
+ "apparel",
+ "pole",
+ "land",
+ "bannister",
+ "escalator",
+ "ottoman",
+ "bottle",
+ "buffet",
+ "poster",
+ "stage",
+ "van",
+ "ship",
+ "fountain",
+ "conveyer belt",
+ "canopy",
+ "washer",
+ "plaything",
+ "swimming pool",
+ "stool",
+ "barrel",
+ "basket",
+ "waterfall",
+ "tent",
+ "bag",
+ "minibike",
+ "cradle",
+ "oven",
+ "ball",
+ "food",
+ "step",
+ "tank",
+ "trade name",
+ "microwave",
+ "pot",
+ "animal",
+ "bicycle",
+ "lake",
+ "dishwasher",
+ "screen",
+ "blanket",
+ "sculpture",
+ "hood",
+ "sconce",
+ "vase",
+ "traffic light",
+ "tray",
+ "ashcan",
+ "fan",
+ "pier",
+ "crt screen",
+ "plate",
+ "monitor",
+ "bulletin board",
+ "shower",
+ "radiator",
+ "glass",
+ "clock",
+ "flag",
]
def voc_classes():
"""Pascal VOC class names for external use."""
return [
- 'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
- 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
- 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
- 'tvmonitor'
+ "background",
+ "aeroplane",
+ "bicycle",
+ "bird",
+ "boat",
+ "bottle",
+ "bus",
+ "car",
+ "cat",
+ "chair",
+ "cow",
+ "diningtable",
+ "dog",
+ "horse",
+ "motorbike",
+ "person",
+ "pottedplant",
+ "sheep",
+ "sofa",
+ "train",
+ "tvmonitor",
]
def cocostuff_classes():
"""CocoStuff class names for external use."""
return [
- 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
- 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
- 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
- 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
- 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
- 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
- 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
- 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
- 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
- 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
- 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
- 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
- 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
- 'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',
- 'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',
- 'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',
- 'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',
- 'floor-other', 'floor-stone', 'floor-tile', 'floor-wood', 'flower',
- 'fog', 'food-other', 'fruit', 'furniture-other', 'grass', 'gravel',
- 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat', 'metal',
- 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net', 'paper',
- 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
- 'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
- 'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper',
- 'snow', 'solid-other', 'stairs', 'stone', 'straw', 'structural-other',
- 'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable',
- 'wall-brick', 'wall-concrete', 'wall-other', 'wall-panel',
- 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
- 'window-blind', 'window-other', 'wood'
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "backpack",
+ "umbrella",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "dining table",
+ "toilet",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush",
+ "banner",
+ "blanket",
+ "branch",
+ "bridge",
+ "building-other",
+ "bush",
+ "cabinet",
+ "cage",
+ "cardboard",
+ "carpet",
+ "ceiling-other",
+ "ceiling-tile",
+ "cloth",
+ "clothes",
+ "clouds",
+ "counter",
+ "cupboard",
+ "curtain",
+ "desk-stuff",
+ "dirt",
+ "door-stuff",
+ "fence",
+ "floor-marble",
+ "floor-other",
+ "floor-stone",
+ "floor-tile",
+ "floor-wood",
+ "flower",
+ "fog",
+ "food-other",
+ "fruit",
+ "furniture-other",
+ "grass",
+ "gravel",
+ "ground-other",
+ "hill",
+ "house",
+ "leaves",
+ "light",
+ "mat",
+ "metal",
+ "mirror-stuff",
+ "moss",
+ "mountain",
+ "mud",
+ "napkin",
+ "net",
+ "paper",
+ "pavement",
+ "pillow",
+ "plant-other",
+ "plastic",
+ "platform",
+ "playingfield",
+ "railing",
+ "railroad",
+ "river",
+ "road",
+ "rock",
+ "roof",
+ "rug",
+ "salad",
+ "sand",
+ "sea",
+ "shelf",
+ "sky-other",
+ "skyscraper",
+ "snow",
+ "solid-other",
+ "stairs",
+ "stone",
+ "straw",
+ "structural-other",
+ "table",
+ "tent",
+ "textile-other",
+ "towel",
+ "tree",
+ "vegetable",
+ "wall-brick",
+ "wall-concrete",
+ "wall-other",
+ "wall-panel",
+ "wall-stone",
+ "wall-tile",
+ "wall-wood",
+ "water-other",
+ "waterdrops",
+ "window-blind",
+ "window-other",
+ "wood",
]
def loveda_classes():
"""LoveDA class names for external use."""
return [
- 'background', 'building', 'road', 'water', 'barren', 'forest',
- 'agricultural'
+ "background",
+ "building",
+ "road",
+ "water",
+ "barren",
+ "forest",
+ "agricultural",
]
def potsdam_classes():
"""Potsdam class names for external use."""
return [
- 'impervious_surface', 'building', 'low_vegetation', 'tree', 'car',
- 'clutter'
+ "impervious_surface",
+ "building",
+ "low_vegetation",
+ "tree",
+ "car",
+ "clutter",
]
def vaihingen_classes():
"""Vaihingen class names for external use."""
return [
- 'impervious_surface', 'building', 'low_vegetation', 'tree', 'car',
- 'clutter'
+ "impervious_surface",
+ "building",
+ "low_vegetation",
+ "tree",
+ "car",
+ "clutter",
]
def isaid_classes():
"""iSAID class names for external use."""
return [
- 'background', 'ship', 'store_tank', 'baseball_diamond', 'tennis_court',
- 'basketball_court', 'Ground_Track_Field', 'Bridge', 'Large_Vehicle',
- 'Small_Vehicle', 'Helicopter', 'Swimming_pool', 'Roundabout',
- 'Soccer_ball_field', 'plane', 'Harbor'
+ "background",
+ "ship",
+ "store_tank",
+ "baseball_diamond",
+ "tennis_court",
+ "basketball_court",
+ "Ground_Track_Field",
+ "Bridge",
+ "Large_Vehicle",
+ "Small_Vehicle",
+ "Helicopter",
+ "Swimming_pool",
+ "Roundabout",
+ "Soccer_ball_field",
+ "plane",
+ "Harbor",
]
def stare_classes():
"""stare class names for external use."""
- return ['background', 'vessel']
+ return ["background", "vessel"]
def cityscapes_palette():
"""Cityscapes palette for external use."""
- return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
- [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
- [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
- [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100],
- [0, 0, 230], [119, 11, 32]]
+ return [
+ [128, 64, 128],
+ [244, 35, 232],
+ [70, 70, 70],
+ [102, 102, 156],
+ [190, 153, 153],
+ [153, 153, 153],
+ [250, 170, 30],
+ [220, 220, 0],
+ [107, 142, 35],
+ [152, 251, 152],
+ [70, 130, 180],
+ [220, 20, 60],
+ [255, 0, 0],
+ [0, 0, 142],
+ [0, 0, 70],
+ [0, 60, 100],
+ [0, 80, 100],
+ [0, 0, 230],
+ [119, 11, 32],
+ ]
def ade_palette():
"""ADE20K palette for external use."""
- return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
- [102, 255, 0], [92, 0, 255]]
+ return [
+ [120, 120, 120],
+ [180, 120, 120],
+ [6, 230, 230],
+ [80, 50, 50],
+ [4, 200, 3],
+ [120, 120, 80],
+ [140, 140, 140],
+ [204, 5, 255],
+ [230, 230, 230],
+ [4, 250, 7],
+ [224, 5, 255],
+ [235, 255, 7],
+ [150, 5, 61],
+ [120, 120, 70],
+ [8, 255, 51],
+ [255, 6, 82],
+ [143, 255, 140],
+ [204, 255, 4],
+ [255, 51, 7],
+ [204, 70, 3],
+ [0, 102, 200],
+ [61, 230, 250],
+ [255, 6, 51],
+ [11, 102, 255],
+ [255, 7, 71],
+ [255, 9, 224],
+ [9, 7, 230],
+ [220, 220, 220],
+ [255, 9, 92],
+ [112, 9, 255],
+ [8, 255, 214],
+ [7, 255, 224],
+ [255, 184, 6],
+ [10, 255, 71],
+ [255, 41, 10],
+ [7, 255, 255],
+ [224, 255, 8],
+ [102, 8, 255],
+ [255, 61, 6],
+ [255, 194, 7],
+ [255, 122, 8],
+ [0, 255, 20],
+ [255, 8, 41],
+ [255, 5, 153],
+ [6, 51, 255],
+ [235, 12, 255],
+ [160, 150, 20],
+ [0, 163, 255],
+ [140, 140, 140],
+ [250, 10, 15],
+ [20, 255, 0],
+ [31, 255, 0],
+ [255, 31, 0],
+ [255, 224, 0],
+ [153, 255, 0],
+ [0, 0, 255],
+ [255, 71, 0],
+ [0, 235, 255],
+ [0, 173, 255],
+ [31, 0, 255],
+ [11, 200, 200],
+ [255, 82, 0],
+ [0, 255, 245],
+ [0, 61, 255],
+ [0, 255, 112],
+ [0, 255, 133],
+ [255, 0, 0],
+ [255, 163, 0],
+ [255, 102, 0],
+ [194, 255, 0],
+ [0, 143, 255],
+ [51, 255, 0],
+ [0, 82, 255],
+ [0, 255, 41],
+ [0, 255, 173],
+ [10, 0, 255],
+ [173, 255, 0],
+ [0, 255, 153],
+ [255, 92, 0],
+ [255, 0, 255],
+ [255, 0, 245],
+ [255, 0, 102],
+ [255, 173, 0],
+ [255, 0, 20],
+ [255, 184, 184],
+ [0, 31, 255],
+ [0, 255, 61],
+ [0, 71, 255],
+ [255, 0, 204],
+ [0, 255, 194],
+ [0, 255, 82],
+ [0, 10, 255],
+ [0, 112, 255],
+ [51, 0, 255],
+ [0, 194, 255],
+ [0, 122, 255],
+ [0, 255, 163],
+ [255, 153, 0],
+ [0, 255, 10],
+ [255, 112, 0],
+ [143, 255, 0],
+ [82, 0, 255],
+ [163, 255, 0],
+ [255, 235, 0],
+ [8, 184, 170],
+ [133, 0, 255],
+ [0, 255, 92],
+ [184, 0, 255],
+ [255, 0, 31],
+ [0, 184, 255],
+ [0, 214, 255],
+ [255, 0, 112],
+ [92, 255, 0],
+ [0, 224, 255],
+ [112, 224, 255],
+ [70, 184, 160],
+ [163, 0, 255],
+ [153, 0, 255],
+ [71, 255, 0],
+ [255, 0, 163],
+ [255, 204, 0],
+ [255, 0, 143],
+ [0, 255, 235],
+ [133, 255, 0],
+ [255, 0, 235],
+ [245, 0, 255],
+ [255, 0, 122],
+ [255, 245, 0],
+ [10, 190, 212],
+ [214, 255, 0],
+ [0, 204, 255],
+ [20, 0, 255],
+ [255, 255, 0],
+ [0, 153, 255],
+ [0, 41, 255],
+ [0, 255, 204],
+ [41, 0, 255],
+ [41, 255, 0],
+ [173, 0, 255],
+ [0, 245, 255],
+ [71, 0, 255],
+ [122, 0, 255],
+ [0, 255, 184],
+ [0, 92, 255],
+ [184, 255, 0],
+ [0, 133, 255],
+ [255, 214, 0],
+ [25, 194, 194],
+ [102, 255, 0],
+ [92, 0, 255],
+ ]
def voc_palette():
"""Pascal VOC palette for external use."""
- return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
- [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
- [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
- [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
- [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
+ return [
+ [0, 0, 0],
+ [128, 0, 0],
+ [0, 128, 0],
+ [128, 128, 0],
+ [0, 0, 128],
+ [128, 0, 128],
+ [0, 128, 128],
+ [128, 128, 128],
+ [64, 0, 0],
+ [192, 0, 0],
+ [64, 128, 0],
+ [192, 128, 0],
+ [64, 0, 128],
+ [192, 0, 128],
+ [64, 128, 128],
+ [192, 128, 128],
+ [0, 64, 0],
+ [128, 64, 0],
+ [0, 192, 0],
+ [128, 192, 0],
+ [0, 64, 128],
+ ]
def cocostuff_palette():
"""CocoStuff palette for external use."""
- return [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],
- [0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],
- [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],
- [0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],
- [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
- [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],
- [64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160], [0, 32, 0],
- [0, 128, 128], [64, 128, 160], [128, 160, 0], [0, 128, 0],
- [192, 128, 32], [128, 96, 128], [0, 0, 128], [64, 0, 32],
- [0, 224, 128], [128, 0, 0], [192, 0, 160], [0, 96, 128],
- [128, 128, 128], [64, 0, 160], [128, 224, 128], [128, 128, 64],
- [192, 0, 32], [128, 96, 0], [128, 0, 192], [0, 128, 32],
- [64, 224, 0], [0, 0, 64], [128, 128, 160], [64, 96, 0],
- [0, 128, 192], [0, 128, 160], [192, 224, 0], [0, 128, 64],
- [128, 128, 32], [192, 32, 128], [0, 64, 192], [0, 0, 32],
- [64, 160, 128], [128, 64, 64], [128, 0, 160], [64, 32, 128],
- [128, 192, 192], [0, 0, 160], [192, 160, 128], [128, 192, 0],
- [128, 0, 96], [192, 32, 0], [128, 64, 128], [64, 128, 96],
- [64, 160, 0], [0, 64, 0], [192, 128, 224], [64, 32, 0],
- [0, 192, 128], [64, 128, 224], [192, 160, 0], [0, 192, 0],
- [192, 128, 96], [192, 96, 128], [0, 64, 128], [64, 0, 96],
- [64, 224, 128], [128, 64, 0], [192, 0, 224], [64, 96, 128],
- [128, 192, 128], [64, 0, 224], [192, 224, 128], [128, 192, 64],
- [192, 0, 96], [192, 96, 0], [128, 64, 192], [0, 128, 96],
- [0, 224, 0], [64, 64, 64], [128, 128, 224], [0, 96, 0],
- [64, 192, 192], [0, 128, 224], [128, 224, 0], [64, 192, 64],
- [128, 128, 96], [128, 32, 128], [64, 0, 192], [0, 64, 96],
- [0, 160, 128], [192, 0, 64], [128, 64, 224], [0, 32, 128],
- [192, 128, 192], [0, 64, 224], [128, 160, 128], [192, 128, 0],
- [128, 64, 32], [128, 32, 64], [192, 0, 128], [64, 192, 32],
- [0, 160, 64], [64, 0, 0], [192, 192, 160], [0, 32, 64],
- [64, 128, 128], [64, 192, 160], [128, 160, 64], [64, 128, 0],
- [192, 192, 32], [128, 96, 192], [64, 0, 128], [64, 64, 32],
- [0, 224, 192], [192, 0, 0], [192, 64, 160], [0, 96, 192],
- [192, 128, 128], [64, 64, 160], [128, 224, 192], [192, 128, 64],
- [192, 64, 32], [128, 96, 64], [192, 0, 192], [0, 192, 32],
- [64, 224, 64], [64, 0, 64], [128, 192, 160], [64, 96, 64],
- [64, 128, 192], [0, 192, 160], [192, 224, 64], [64, 128, 64],
- [128, 192, 32], [192, 32, 192], [64, 64, 192], [0, 64, 32],
- [64, 160, 192], [192, 64, 64], [128, 64, 160], [64, 32, 192],
- [192, 192, 192], [0, 64, 160], [192, 160, 192], [192, 192, 0],
- [128, 64, 96], [192, 32, 64], [192, 64, 128], [64, 192, 96],
- [64, 160, 64], [64, 64, 0]]
+ return [
+ [0, 192, 64],
+ [0, 192, 64],
+ [0, 64, 96],
+ [128, 192, 192],
+ [0, 64, 64],
+ [0, 192, 224],
+ [0, 192, 192],
+ [128, 192, 64],
+ [0, 192, 96],
+ [128, 192, 64],
+ [128, 32, 192],
+ [0, 0, 224],
+ [0, 0, 64],
+ [0, 160, 192],
+ [128, 0, 96],
+ [128, 0, 192],
+ [0, 32, 192],
+ [128, 128, 224],
+ [0, 0, 192],
+ [128, 160, 192],
+ [128, 128, 0],
+ [128, 0, 32],
+ [128, 32, 0],
+ [128, 0, 128],
+ [64, 128, 32],
+ [0, 160, 0],
+ [0, 0, 0],
+ [192, 128, 160],
+ [0, 32, 0],
+ [0, 128, 128],
+ [64, 128, 160],
+ [128, 160, 0],
+ [0, 128, 0],
+ [192, 128, 32],
+ [128, 96, 128],
+ [0, 0, 128],
+ [64, 0, 32],
+ [0, 224, 128],
+ [128, 0, 0],
+ [192, 0, 160],
+ [0, 96, 128],
+ [128, 128, 128],
+ [64, 0, 160],
+ [128, 224, 128],
+ [128, 128, 64],
+ [192, 0, 32],
+ [128, 96, 0],
+ [128, 0, 192],
+ [0, 128, 32],
+ [64, 224, 0],
+ [0, 0, 64],
+ [128, 128, 160],
+ [64, 96, 0],
+ [0, 128, 192],
+ [0, 128, 160],
+ [192, 224, 0],
+ [0, 128, 64],
+ [128, 128, 32],
+ [192, 32, 128],
+ [0, 64, 192],
+ [0, 0, 32],
+ [64, 160, 128],
+ [128, 64, 64],
+ [128, 0, 160],
+ [64, 32, 128],
+ [128, 192, 192],
+ [0, 0, 160],
+ [192, 160, 128],
+ [128, 192, 0],
+ [128, 0, 96],
+ [192, 32, 0],
+ [128, 64, 128],
+ [64, 128, 96],
+ [64, 160, 0],
+ [0, 64, 0],
+ [192, 128, 224],
+ [64, 32, 0],
+ [0, 192, 128],
+ [64, 128, 224],
+ [192, 160, 0],
+ [0, 192, 0],
+ [192, 128, 96],
+ [192, 96, 128],
+ [0, 64, 128],
+ [64, 0, 96],
+ [64, 224, 128],
+ [128, 64, 0],
+ [192, 0, 224],
+ [64, 96, 128],
+ [128, 192, 128],
+ [64, 0, 224],
+ [192, 224, 128],
+ [128, 192, 64],
+ [192, 0, 96],
+ [192, 96, 0],
+ [128, 64, 192],
+ [0, 128, 96],
+ [0, 224, 0],
+ [64, 64, 64],
+ [128, 128, 224],
+ [0, 96, 0],
+ [64, 192, 192],
+ [0, 128, 224],
+ [128, 224, 0],
+ [64, 192, 64],
+ [128, 128, 96],
+ [128, 32, 128],
+ [64, 0, 192],
+ [0, 64, 96],
+ [0, 160, 128],
+ [192, 0, 64],
+ [128, 64, 224],
+ [0, 32, 128],
+ [192, 128, 192],
+ [0, 64, 224],
+ [128, 160, 128],
+ [192, 128, 0],
+ [128, 64, 32],
+ [128, 32, 64],
+ [192, 0, 128],
+ [64, 192, 32],
+ [0, 160, 64],
+ [64, 0, 0],
+ [192, 192, 160],
+ [0, 32, 64],
+ [64, 128, 128],
+ [64, 192, 160],
+ [128, 160, 64],
+ [64, 128, 0],
+ [192, 192, 32],
+ [128, 96, 192],
+ [64, 0, 128],
+ [64, 64, 32],
+ [0, 224, 192],
+ [192, 0, 0],
+ [192, 64, 160],
+ [0, 96, 192],
+ [192, 128, 128],
+ [64, 64, 160],
+ [128, 224, 192],
+ [192, 128, 64],
+ [192, 64, 32],
+ [128, 96, 64],
+ [192, 0, 192],
+ [0, 192, 32],
+ [64, 224, 64],
+ [64, 0, 64],
+ [128, 192, 160],
+ [64, 96, 64],
+ [64, 128, 192],
+ [0, 192, 160],
+ [192, 224, 64],
+ [64, 128, 64],
+ [128, 192, 32],
+ [192, 32, 192],
+ [64, 64, 192],
+ [0, 64, 32],
+ [64, 160, 192],
+ [192, 64, 64],
+ [128, 64, 160],
+ [64, 32, 192],
+ [192, 192, 192],
+ [0, 64, 160],
+ [192, 160, 192],
+ [192, 192, 0],
+ [128, 64, 96],
+ [192, 32, 64],
+ [192, 64, 128],
+ [64, 192, 96],
+ [64, 160, 64],
+ [64, 64, 0],
+ ]
def loveda_palette():
"""LoveDA palette for external use."""
- return [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
- [159, 129, 183], [0, 255, 0], [255, 195, 128]]
+ return [
+ [255, 255, 255],
+ [255, 0, 0],
+ [255, 255, 0],
+ [0, 0, 255],
+ [159, 129, 183],
+ [0, 255, 0],
+ [255, 195, 128],
+ ]
def potsdam_palette():
"""Potsdam palette for external use."""
- return [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
- [255, 255, 0], [255, 0, 0]]
+ return [
+ [255, 255, 255],
+ [0, 0, 255],
+ [0, 255, 255],
+ [0, 255, 0],
+ [255, 255, 0],
+ [255, 0, 0],
+ ]
def vaihingen_palette():
"""Vaihingen palette for external use."""
- return [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
- [255, 255, 0], [255, 0, 0]]
+ return [
+ [255, 255, 255],
+ [0, 0, 255],
+ [0, 255, 255],
+ [0, 255, 0],
+ [255, 255, 0],
+ [255, 0, 0],
+ ]
def isaid_palette():
"""iSAID palette for external use."""
- return [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
- [0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127,
- 127], [0, 0, 127],
- [0, 0, 191], [0, 0, 255], [0, 191, 127], [0, 127, 191],
- [0, 127, 255], [0, 100, 155]]
+ return [
+ [0, 0, 0],
+ [0, 0, 63],
+ [0, 63, 63],
+ [0, 63, 0],
+ [0, 63, 127],
+ [0, 63, 191],
+ [0, 63, 255],
+ [0, 127, 63],
+ [0, 127, 127],
+ [0, 0, 127],
+ [0, 0, 191],
+ [0, 0, 255],
+ [0, 191, 127],
+ [0, 127, 191],
+ [0, 127, 255],
+ [0, 100, 155],
+ ]
def stare_palette():
@@ -266,19 +901,25 @@ def stare_palette():
dataset_aliases = {
- 'cityscapes': ['cityscapes'],
- 'ade': ['ade', 'ade20k'],
- 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'],
- 'loveda': ['loveda'],
- 'potsdam': ['potsdam'],
- 'vaihingen': ['vaihingen'],
- 'cocostuff': [
- 'cocostuff', 'cocostuff10k', 'cocostuff164k', 'coco-stuff',
- 'coco-stuff10k', 'coco-stuff164k', 'coco_stuff', 'coco_stuff10k',
- 'coco_stuff164k'
+ "cityscapes": ["cityscapes"],
+ "ade": ["ade", "ade20k"],
+ "voc": ["voc", "pascal_voc", "voc12", "voc12aug"],
+ "loveda": ["loveda"],
+ "potsdam": ["potsdam"],
+ "vaihingen": ["vaihingen"],
+ "cocostuff": [
+ "cocostuff",
+ "cocostuff10k",
+ "cocostuff164k",
+ "coco-stuff",
+ "coco-stuff10k",
+ "coco-stuff164k",
+ "coco_stuff",
+ "coco_stuff10k",
+ "coco_stuff164k",
],
- 'isaid': ['isaid', 'iSAID'],
- 'stare': ['stare', 'STARE']
+ "isaid": ["isaid", "iSAID"],
+ "stare": ["stare", "STARE"],
}
@@ -291,11 +932,11 @@ def get_classes(dataset):
if mmcv.is_str(dataset):
if dataset in alias2name:
- labels = eval(alias2name[dataset] + '_classes()')
+ labels = eval(alias2name[dataset] + "_classes()")
else:
- raise ValueError(f'Unrecognized dataset: {dataset}')
+ raise ValueError(f"Unrecognized dataset: {dataset}")
else:
- raise TypeError(f'dataset must a str, but got {type(dataset)}')
+ raise TypeError(f"dataset must a str, but got {type(dataset)}")
return labels
@@ -308,9 +949,9 @@ def get_palette(dataset):
if mmcv.is_str(dataset):
if dataset in alias2name:
- labels = eval(alias2name[dataset] + '_palette()')
+ labels = eval(alias2name[dataset] + "_palette()")
else:
- raise ValueError(f'Unrecognized dataset: {dataset}')
+ raise ValueError(f"Unrecognized dataset: {dataset}")
else:
- raise TypeError(f'dataset must a str, but got {type(dataset)}')
+ raise TypeError(f"dataset must a str, but got {type(dataset)}")
return labels
diff --git a/mmsegmentation/mmseg/core/evaluation/eval_hooks.py b/mmsegmentation/mmseg/core/evaluation/eval_hooks.py
index 8f2be57..585304e 100644
--- a/mmsegmentation/mmseg/core/evaluation/eval_hooks.py
+++ b/mmsegmentation/mmseg/core/evaluation/eval_hooks.py
@@ -23,24 +23,22 @@ class EvalHook(_EvalHook):
list: The prediction results.
"""
- greater_keys = ['mIoU', 'mAcc', 'aAcc']
-
- def __init__(self,
- *args,
- by_epoch=False,
- efficient_test=False,
- pre_eval=False,
- **kwargs):
+ greater_keys = ["mIoU", "mAcc", "aAcc"]
+
+ def __init__(
+ self, *args, by_epoch=False, efficient_test=False, pre_eval=False, **kwargs
+ ):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.pre_eval = pre_eval
self.latest_results = None
if efficient_test:
warnings.warn(
- 'DeprecationWarning: ``efficient_test`` for evaluation hook '
- 'is deprecated, the evaluation hook is CPU memory friendly '
- 'with ``pre_eval=True`` as argument for ``single_gpu_test()`` '
- 'function')
+ "DeprecationWarning: ``efficient_test`` for evaluation hook "
+ "is deprecated, the evaluation hook is CPU memory friendly "
+ "with ``pre_eval=True`` as argument for ``single_gpu_test()`` "
+ "function"
+ )
def _do_evaluate(self, runner):
"""perform evaluation and save ckpt."""
@@ -48,11 +46,13 @@ def _do_evaluate(self, runner):
return
from mmseg.apis import single_gpu_test
+
results = single_gpu_test(
- runner.model, self.dataloader, show=False, pre_eval=self.pre_eval)
+ runner.model, self.dataloader, show=False, pre_eval=self.pre_eval
+ )
self.latest_results = results
runner.log_buffer.clear()
- runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+ runner.log_buffer.output["eval_iter_num"] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
self._save_ckpt(runner, key_score)
@@ -73,23 +73,21 @@ class DistEvalHook(_DistEvalHook):
list: The prediction results.
"""
- greater_keys = ['mIoU', 'mAcc', 'aAcc']
+ greater_keys = ["mIoU", "mAcc", "aAcc"]
- def __init__(self,
- *args,
- by_epoch=False,
- efficient_test=False,
- pre_eval=False,
- **kwargs):
+ def __init__(
+ self, *args, by_epoch=False, efficient_test=False, pre_eval=False, **kwargs
+ ):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.pre_eval = pre_eval
self.latest_results = None
if efficient_test:
warnings.warn(
- 'DeprecationWarning: ``efficient_test`` for evaluation hook '
- 'is deprecated, the evaluation hook is CPU memory friendly '
- 'with ``pre_eval=True`` as argument for ``multi_gpu_test()`` '
- 'function')
+ "DeprecationWarning: ``efficient_test`` for evaluation hook "
+ "is deprecated, the evaluation hook is CPU memory friendly "
+ "with ``pre_eval=True`` as argument for ``multi_gpu_test()`` "
+ "function"
+ )
def _do_evaluate(self, runner):
"""perform evaluation and save ckpt."""
@@ -101,8 +99,7 @@ def _do_evaluate(self, runner):
if self.broadcast_bn_buffer:
model = runner.model
for name, module in model.named_modules():
- if isinstance(module,
- _BatchNorm) and module.track_running_stats:
+ if isinstance(module, _BatchNorm) and module.track_running_stats:
dist.broadcast(module.running_var, 0)
dist.broadcast(module.running_mean, 0)
@@ -111,21 +108,23 @@ def _do_evaluate(self, runner):
tmpdir = self.tmpdir
if tmpdir is None:
- tmpdir = osp.join(runner.work_dir, '.eval_hook')
+ tmpdir = osp.join(runner.work_dir, ".eval_hook")
from mmseg.apis import multi_gpu_test
+
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect,
- pre_eval=self.pre_eval)
+ pre_eval=self.pre_eval,
+ )
self.latest_results = results
runner.log_buffer.clear()
if runner.rank == 0:
- print('\n')
- runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+ print("\n")
+ runner.log_buffer.output["eval_iter_num"] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
diff --git a/mmsegmentation/mmseg/core/evaluation/metrics.py b/mmsegmentation/mmseg/core/evaluation/metrics.py
index 31be596..9bb1443 100644
--- a/mmsegmentation/mmseg/core/evaluation/metrics.py
+++ b/mmsegmentation/mmseg/core/evaluation/metrics.py
@@ -18,17 +18,18 @@ def f_score(precision, recall, beta=1):
Returns:
[torch.tensor]: The f-score value.
"""
- score = (1 + beta**2) * (precision * recall) / (
- (beta**2 * precision) + recall)
+ score = (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)
return score
-def intersect_and_union(pred_label,
- label,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
+def intersect_and_union(
+ pred_label,
+ label,
+ num_classes,
+ ignore_index,
+ label_map=dict(),
+ reduce_zero_label=False,
+):
"""Calculate intersection and Union.
Args:
@@ -55,11 +56,10 @@ def intersect_and_union(pred_label,
if isinstance(pred_label, str):
pred_label = torch.from_numpy(np.load(pred_label))
else:
- pred_label = torch.from_numpy((pred_label))
+ pred_label = torch.from_numpy(pred_label)
if isinstance(label, str):
- label = torch.from_numpy(
- mmcv.imread(label, flag='unchanged', backend='pillow'))
+ label = torch.from_numpy(mmcv.imread(label, flag="unchanged", backend="pillow"))
else:
label = torch.from_numpy(label)
@@ -72,27 +72,32 @@ def intersect_and_union(pred_label,
label = label - 1
label[label == 254] = 255
- mask = (label != ignore_index)
+ mask = label != ignore_index
pred_label = pred_label[mask]
label = label[mask]
intersect = pred_label[pred_label == label]
area_intersect = torch.histc(
- intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
+ intersect.float(), bins=(num_classes), min=0, max=num_classes - 1
+ )
area_pred_label = torch.histc(
- pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
+ pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1
+ )
area_label = torch.histc(
- label.float(), bins=(num_classes), min=0, max=num_classes - 1)
+ label.float(), bins=(num_classes), min=0, max=num_classes - 1
+ )
area_union = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
-def total_intersect_and_union(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
+def total_intersect_and_union(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ label_map=dict(),
+ reduce_zero_label=False,
+):
"""Calculate Total Intersection and Union.
Args:
@@ -113,30 +118,35 @@ def total_intersect_and_union(results,
ndarray: The prediction histogram on all classes.
ndarray: The ground truth histogram on all classes.
"""
- total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
+ total_area_intersect = torch.zeros((num_classes,), dtype=torch.float64)
+ total_area_union = torch.zeros((num_classes,), dtype=torch.float64)
+ total_area_pred_label = torch.zeros((num_classes,), dtype=torch.float64)
+ total_area_label = torch.zeros((num_classes,), dtype=torch.float64)
for result, gt_seg_map in zip(results, gt_seg_maps):
- area_intersect, area_union, area_pred_label, area_label = \
- intersect_and_union(
- result, gt_seg_map, num_classes, ignore_index,
- label_map, reduce_zero_label)
+ area_intersect, area_union, area_pred_label, area_label = intersect_and_union(
+ result, gt_seg_map, num_classes, ignore_index, label_map, reduce_zero_label
+ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
- return total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label
-
-
-def mean_iou(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
+ return (
+ total_area_intersect,
+ total_area_union,
+ total_area_pred_label,
+ total_area_label,
+ )
+
+
+def mean_iou(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ nan_to_num=None,
+ label_map=dict(),
+ reduce_zero_label=False,
+):
"""Calculate Mean Intersection and Union (mIoU)
Args:
@@ -162,20 +172,23 @@ def mean_iou(results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
- metrics=['mIoU'],
+ metrics=["mIoU"],
nan_to_num=nan_to_num,
label_map=label_map,
- reduce_zero_label=reduce_zero_label)
+ reduce_zero_label=reduce_zero_label,
+ )
return iou_result
-def mean_dice(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
+def mean_dice(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ nan_to_num=None,
+ label_map=dict(),
+ reduce_zero_label=False,
+):
"""Calculate Mean Dice (mDice)
Args:
@@ -202,21 +215,24 @@ def mean_dice(results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
- metrics=['mDice'],
+ metrics=["mDice"],
nan_to_num=nan_to_num,
label_map=label_map,
- reduce_zero_label=reduce_zero_label)
+ reduce_zero_label=reduce_zero_label,
+ )
return dice_result
-def mean_fscore(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
+def mean_fscore(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ nan_to_num=None,
+ label_map=dict(),
+ reduce_zero_label=False,
+ beta=1,
+):
"""Calculate Mean F-Score (mFscore)
Args:
@@ -246,23 +262,26 @@ def mean_fscore(results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
- metrics=['mFscore'],
+ metrics=["mFscore"],
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label,
- beta=beta)
+ beta=beta,
+ )
return fscore_result
-def eval_metrics(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- metrics=['mIoU'],
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
+def eval_metrics(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ metrics=["mIoU"],
+ nan_to_num=None,
+ label_map=dict(),
+ reduce_zero_label=False,
+ beta=1,
+):
"""Calculate evaluation metrics
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
@@ -282,22 +301,30 @@ def eval_metrics(results,
ndarray: Per category evaluation metrics, shape (num_classes, ).
"""
- total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label = total_intersect_and_union(
- results, gt_seg_maps, num_classes, ignore_index, label_map,
- reduce_zero_label)
- ret_metrics = total_area_to_metrics(total_area_intersect, total_area_union,
- total_area_pred_label,
- total_area_label, metrics, nan_to_num,
- beta)
+ total_area_intersect, total_area_union, total_area_pred_label, total_area_label = (
+ total_intersect_and_union(
+ results,
+ gt_seg_maps,
+ num_classes,
+ ignore_index,
+ label_map,
+ reduce_zero_label,
+ )
+ )
+ ret_metrics = total_area_to_metrics(
+ total_area_intersect,
+ total_area_union,
+ total_area_pred_label,
+ total_area_label,
+ metrics,
+ nan_to_num,
+ beta,
+ )
return ret_metrics
-def pre_eval_to_metrics(pre_eval_results,
- metrics=['mIoU'],
- nan_to_num=None,
- beta=1):
+def pre_eval_to_metrics(pre_eval_results, metrics=["mIoU"], nan_to_num=None, beta=1):
"""Convert pre-eval results to metrics.
Args:
@@ -323,21 +350,28 @@ def pre_eval_to_metrics(pre_eval_results,
total_area_pred_label = sum(pre_eval_results[2])
total_area_label = sum(pre_eval_results[3])
- ret_metrics = total_area_to_metrics(total_area_intersect, total_area_union,
- total_area_pred_label,
- total_area_label, metrics, nan_to_num,
- beta)
+ ret_metrics = total_area_to_metrics(
+ total_area_intersect,
+ total_area_union,
+ total_area_pred_label,
+ total_area_label,
+ metrics,
+ nan_to_num,
+ beta,
+ )
return ret_metrics
-def total_area_to_metrics(total_area_intersect,
- total_area_union,
- total_area_pred_label,
- total_area_label,
- metrics=['mIoU'],
- nan_to_num=None,
- beta=1):
+def total_area_to_metrics(
+ total_area_intersect,
+ total_area_union,
+ total_area_pred_label,
+ total_area_label,
+ metrics=["mIoU"],
+ nan_to_num=None,
+ beta=1,
+):
"""Calculate evaluation metrics
Args:
total_area_intersect (ndarray): The intersection of prediction and
@@ -357,40 +391,39 @@ def total_area_to_metrics(total_area_intersect,
"""
if isinstance(metrics, str):
metrics = [metrics]
- allowed_metrics = ['mIoU', 'mDice', 'mFscore']
+ allowed_metrics = ["mIoU", "mDice", "mFscore"]
if not set(metrics).issubset(set(allowed_metrics)):
- raise KeyError('metrics {} is not supported'.format(metrics))
+ raise KeyError(f"metrics {metrics} is not supported")
all_acc = total_area_intersect.sum() / total_area_label.sum()
- ret_metrics = OrderedDict({'aAcc': all_acc})
+ ret_metrics = OrderedDict({"aAcc": all_acc})
for metric in metrics:
- if metric == 'mIoU':
+ if metric == "mIoU":
iou = total_area_intersect / total_area_union
acc = total_area_intersect / total_area_label
- ret_metrics['IoU'] = iou
- ret_metrics['Acc'] = acc
- elif metric == 'mDice':
- dice = 2 * total_area_intersect / (
- total_area_pred_label + total_area_label)
+ ret_metrics["IoU"] = iou
+ ret_metrics["Acc"] = acc
+ elif metric == "mDice":
+ dice = 2 * total_area_intersect / (total_area_pred_label + total_area_label)
acc = total_area_intersect / total_area_label
- ret_metrics['Dice'] = dice
- ret_metrics['Acc'] = acc
- elif metric == 'mFscore':
+ ret_metrics["Dice"] = dice
+ ret_metrics["Acc"] = acc
+ elif metric == "mFscore":
precision = total_area_intersect / total_area_pred_label
recall = total_area_intersect / total_area_label
f_value = torch.tensor(
- [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
- ret_metrics['Fscore'] = f_value
- ret_metrics['Precision'] = precision
- ret_metrics['Recall'] = recall
-
- ret_metrics = {
- metric: value.numpy()
- for metric, value in ret_metrics.items()
- }
+ [f_score(x[0], x[1], beta) for x in zip(precision, recall)]
+ )
+ ret_metrics["Fscore"] = f_value
+ ret_metrics["Precision"] = precision
+ ret_metrics["Recall"] = recall
+
+ ret_metrics = {metric: value.numpy() for metric, value in ret_metrics.items()}
if nan_to_num is not None:
- ret_metrics = OrderedDict({
- metric: np.nan_to_num(metric_value, nan=nan_to_num)
- for metric, metric_value in ret_metrics.items()
- })
+ ret_metrics = OrderedDict(
+ {
+ metric: np.nan_to_num(metric_value, nan=nan_to_num)
+ for metric, metric_value in ret_metrics.items()
+ }
+ )
return ret_metrics
diff --git a/mmsegmentation/mmseg/core/hook/__init__.py b/mmsegmentation/mmseg/core/hook/__init__.py
index 02fe93d..b954166 100644
--- a/mmsegmentation/mmseg/core/hook/__init__.py
+++ b/mmsegmentation/mmseg/core/hook/__init__.py
@@ -1,4 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .wandblogger_hook import MMSegWandbHook
-__all__ = ['MMSegWandbHook']
+__all__ = ["MMSegWandbHook"]
diff --git a/mmsegmentation/mmseg/core/hook/wandblogger_hook.py b/mmsegmentation/mmseg/core/hook/wandblogger_hook.py
index b35c526..d9df217 100644
--- a/mmsegmentation/mmseg/core/hook/wandblogger_hook.py
+++ b/mmsegmentation/mmseg/core/hook/wandblogger_hook.py
@@ -83,27 +83,28 @@ class MMSegWandbHook(WandbLoggerHook):
Default: 100
"""
- def __init__(self,
- init_kwargs=None,
- interval=50,
- log_checkpoint=False,
- log_checkpoint_metadata=False,
- num_eval_images=100,
- **kwargs):
- super(MMSegWandbHook, self).__init__(init_kwargs, interval, **kwargs)
+ def __init__(
+ self,
+ init_kwargs=None,
+ interval=50,
+ log_checkpoint=False,
+ log_checkpoint_metadata=False,
+ num_eval_images=100,
+ **kwargs,
+ ):
+ super().__init__(init_kwargs, interval, **kwargs)
self.log_checkpoint = log_checkpoint
- self.log_checkpoint_metadata = (
- log_checkpoint and log_checkpoint_metadata)
+ self.log_checkpoint_metadata = log_checkpoint and log_checkpoint_metadata
self.num_eval_images = num_eval_images
- self.log_evaluation = (num_eval_images > 0)
+ self.log_evaluation = num_eval_images > 0
self.ckpt_hook: CheckpointHook = None
self.eval_hook: EvalHook = None
self.test_fn = None
@master_only
def before_run(self, runner):
- super(MMSegWandbHook, self).before_run(runner)
+ super().before_run(runner)
# Check if EvalHook and CheckpointHook are available.
for hook in runner.hooks:
@@ -111,10 +112,12 @@ def before_run(self, runner):
self.ckpt_hook = hook
if isinstance(hook, EvalHook):
from mmseg.apis import single_gpu_test
+
self.eval_hook = hook
self.test_fn = single_gpu_test
if isinstance(hook, DistEvalHook):
from mmseg.apis import multi_gpu_test
+
self.eval_hook = hook
self.test_fn = multi_gpu_test
@@ -124,8 +127,9 @@ def before_run(self, runner):
self.log_checkpoint = False
self.log_checkpoint_metadata = False
runner.logger.warning(
- 'To log checkpoint in MMSegWandbHook, `CheckpointHook` is'
- 'required, please check hooks in the runner.')
+ "To log checkpoint in MMSegWandbHook, `CheckpointHook` is"
+ "required, please check hooks in the runner."
+ )
else:
self.ckpt_interval = self.ckpt_hook.interval
@@ -135,10 +139,11 @@ def before_run(self, runner):
self.log_evaluation = False
self.log_checkpoint_metadata = False
runner.logger.warning(
- 'To log evaluation or checkpoint metadata in '
- 'MMSegWandbHook, `EvalHook` or `DistEvalHook` in mmseg '
- 'is required, please check whether the validation '
- 'is enabled.')
+ "To log evaluation or checkpoint metadata in "
+ "MMSegWandbHook, `EvalHook` or `DistEvalHook` in mmseg "
+ "is required, please check whether the validation "
+ "is enabled."
+ )
else:
self.eval_interval = self.eval_hook.interval
self.val_dataset = self.eval_hook.dataloader.dataset
@@ -146,18 +151,20 @@ def before_run(self, runner):
if self.num_eval_images > len(self.val_dataset):
self.num_eval_images = len(self.val_dataset)
runner.logger.warning(
- f'The num_eval_images ({self.num_eval_images}) is '
- 'greater than the total number of validation samples '
- f'({len(self.val_dataset)}). The complete validation '
- 'dataset will be logged.')
+ f"The num_eval_images ({self.num_eval_images}) is "
+ "greater than the total number of validation samples "
+ f"({len(self.val_dataset)}). The complete validation "
+ "dataset will be logged."
+ )
# Check conditions to log checkpoint metadata
if self.log_checkpoint_metadata:
- assert self.ckpt_interval % self.eval_interval == 0, \
- 'To log checkpoint metadata in MMSegWandbHook, the interval ' \
- f'of checkpoint saving ({self.ckpt_interval}) should be ' \
- 'divisible by the interval of evaluation ' \
- f'({self.eval_interval}).'
+ assert self.ckpt_interval % self.eval_interval == 0, (
+ "To log checkpoint metadata in MMSegWandbHook, the interval "
+ f"of checkpoint saving ({self.ckpt_interval}) should be "
+ "divisible by the interval of evaluation "
+ f"({self.eval_interval})."
+ )
# Initialize evaluation table
if self.log_evaluation:
@@ -171,14 +178,14 @@ def before_run(self, runner):
# for the reason of this double-layered structure, refer to
# https://github.com/open-mmlab/mmdetection/issues/8145#issuecomment-1345343076
def after_train_iter(self, runner):
- if self.get_mode(runner) == 'train':
+ if self.get_mode(runner) == "train":
# An ugly patch. The iter-based eval hook will call the
# `after_train_iter` method of all logger hooks before evaluation.
# Use this trick to skip that call.
# Don't call super method at first, it will clear the log_buffer
- return super(MMSegWandbHook, self).after_train_iter(runner)
+ return super().after_train_iter(runner)
else:
- super(MMSegWandbHook, self).after_train_iter(runner)
+ super().after_train_iter(runner)
self._after_train_iter(runner)
@master_only
@@ -187,19 +194,17 @@ def _after_train_iter(self, runner):
return
# Save checkpoint and metadata
- if (self.log_checkpoint
- and self.every_n_iters(runner, self.ckpt_interval)
- or (self.ckpt_hook.save_last and self.is_last_iter(runner))):
+ if (
+ self.log_checkpoint
+ and self.every_n_iters(runner, self.ckpt_interval)
+ or (self.ckpt_hook.save_last and self.is_last_iter(runner))
+ ):
if self.log_checkpoint_metadata and self.eval_hook:
- metadata = {
- 'iter': runner.iter + 1,
- **self._get_eval_results()
- }
+ metadata = {"iter": runner.iter + 1, **self._get_eval_results()}
else:
metadata = None
- aliases = [f'iter_{runner.iter+1}', 'latest']
- model_path = osp.join(self.ckpt_hook.out_dir,
- f'iter_{runner.iter+1}.pth')
+ aliases = [f"iter_{runner.iter+1}", "latest"]
+ model_path = osp.join(self.ckpt_hook.out_dir, f"iter_{runner.iter+1}.pth")
self._log_ckpt_as_artifact(model_path, aliases, metadata)
# Save prediction table
@@ -228,7 +233,8 @@ def _log_ckpt_as_artifact(self, model_path, aliases, metadata=None):
metadata (dict, optional): Metadata associated with this artifact.
"""
model_artifact = self.wandb.Artifact(
- f'run_{self.wandb.run.id}_model', type='model', metadata=metadata)
+ f"run_{self.wandb.run.id}_model", type="model", metadata=metadata
+ )
model_artifact.add_file(model_path)
self.wandb.log_artifact(model_artifact, aliases=aliases)
@@ -236,22 +242,24 @@ def _get_eval_results(self):
"""Get model evaluation results."""
results = self.eval_hook.latest_results
eval_results = self.val_dataset.evaluate(
- results, logger='silent', **self.eval_hook.eval_kwargs)
+ results, logger="silent", **self.eval_hook.eval_kwargs
+ )
return eval_results
def _init_data_table(self):
"""Initialize the W&B Tables for validation data."""
- columns = ['image_name', 'image']
+ columns = ["image_name", "image"]
self.data_table = self.wandb.Table(columns=columns)
def _init_pred_table(self):
"""Initialize the W&B Tables for model evaluation."""
- columns = ['image_name', 'ground_truth', 'prediction']
+ columns = ["image_name", "ground_truth", "prediction"]
self.eval_table = self.wandb.Table(columns=columns)
def _add_ground_truth(self, runner):
# Get image loading pipeline
from mmseg.datasets.pipelines import LoadImageFromFile
+
img_loader = None
for t in self.val_dataset.pipeline.transforms:
if isinstance(t, LoadImageFromFile):
@@ -260,8 +268,8 @@ def _add_ground_truth(self, runner):
if img_loader is None:
self.log_evaluation = False
runner.logger.warning(
- 'LoadImageFromFile is required to add images '
- 'to W&B Tables.')
+ "LoadImageFromFile is required to add images " "to W&B Tables."
+ )
return
# Select the images to be logged.
@@ -269,23 +277,23 @@ def _add_ground_truth(self, runner):
# Set seed so that same validation set is logged each time.
np.random.seed(42)
np.random.shuffle(self.eval_image_indexs)
- self.eval_image_indexs = self.eval_image_indexs[:self.num_eval_images]
+ self.eval_image_indexs = self.eval_image_indexs[: self.num_eval_images]
classes = self.val_dataset.CLASSES
self.class_id_to_label = {id: name for id, name in enumerate(classes)}
- self.class_set = self.wandb.Classes([{
- 'id': id,
- 'name': name
- } for id, name in self.class_id_to_label.items()])
+ self.class_set = self.wandb.Classes(
+ [{"id": id, "name": name} for id, name in self.class_id_to_label.items()]
+ )
for idx in self.eval_image_indexs:
img_info = self.val_dataset.img_infos[idx]
- image_name = img_info['filename']
+ image_name = img_info["filename"]
# Get image and convert from BGR to RGB
img_meta = img_loader(
- dict(img_info=img_info, img_prefix=self.val_dataset.img_dir))
- image = mmcv.bgr2rgb(img_meta['img'])
+ dict(img_info=img_info, img_prefix=self.val_dataset.img_dir)
+ )
+ image = mmcv.bgr2rgb(img_meta["img"])
# Get segmentation mask
seg_mask = self.val_dataset.get_gt_seg_map_by_idx(idx)
@@ -293,21 +301,22 @@ def _add_ground_truth(self, runner):
wandb_masks = None
if seg_mask.ndim == 2:
wandb_masks = {
- 'ground_truth': {
- 'mask_data': seg_mask,
- 'class_labels': self.class_id_to_label
+ "ground_truth": {
+ "mask_data": seg_mask,
+ "class_labels": self.class_id_to_label,
}
}
# Log a row to the data table.
self.data_table.add_data(
image_name,
- self.wandb.Image(
- image, masks=wandb_masks, classes=self.class_set))
+ self.wandb.Image(image, masks=wandb_masks, classes=self.class_set),
+ )
else:
runner.logger.warning(
- f'The segmentation mask is {seg_mask.ndim}D which '
- 'is not supported by W&B.')
+ f"The segmentation mask is {seg_mask.ndim}D which "
+ "is not supported by W&B."
+ )
self.log_evaluation = False
return
@@ -322,9 +331,9 @@ def _log_predictions(self, results, runner):
if pred_mask.ndim == 2:
wandb_masks = {
- 'prediction': {
- 'mask_data': pred_mask,
- 'class_labels': self.class_id_to_label
+ "prediction": {
+ "mask_data": pred_mask,
+ "class_labels": self.class_id_to_label,
}
}
@@ -335,11 +344,14 @@ def _log_predictions(self, results, runner):
self.wandb.Image(
self.data_table_ref.data[ndx][1],
masks=wandb_masks,
- classes=self.class_set))
+ classes=self.class_set,
+ ),
+ )
else:
runner.logger.warning(
- 'The predictio segmentation mask is '
- f'{pred_mask.ndim}D which is not supported by W&B.')
+ "The predictio segmentation mask is "
+ f"{pred_mask.ndim}D which is not supported by W&B."
+ )
self.log_evaluation = False
return
@@ -350,13 +362,13 @@ def _log_data_table(self):
This allows the data to be uploaded just once.
"""
- data_artifact = self.wandb.Artifact('val', type='dataset')
- data_artifact.add(self.data_table, 'val_data')
+ data_artifact = self.wandb.Artifact("val", type="dataset")
+ data_artifact.add(self.data_table, "val_data")
self.wandb.run.use_artifact(data_artifact)
data_artifact.wait()
- self.data_table_ref = data_artifact.get('val_data')
+ self.data_table_ref = data_artifact.get("val_data")
def _log_eval_table(self, iter):
"""Log the W&B Tables for model evaluation.
@@ -365,6 +377,7 @@ def _log_eval_table(self, iter):
to compare models at different intervals interactively.
"""
pred_artifact = self.wandb.Artifact(
- f'run_{self.wandb.run.id}_pred', type='evaluation')
- pred_artifact.add(self.eval_table, 'eval_data')
+ f"run_{self.wandb.run.id}_pred", type="evaluation"
+ )
+ pred_artifact.add(self.eval_table, "eval_data")
self.wandb.run.log_artifact(pred_artifact)
diff --git a/mmsegmentation/mmseg/core/optimizers/__init__.py b/mmsegmentation/mmseg/core/optimizers/__init__.py
index 4fbf4ec..811bdcb 100644
--- a/mmsegmentation/mmseg/core/optimizers/__init__.py
+++ b/mmsegmentation/mmseg/core/optimizers/__init__.py
@@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .layer_decay_optimizer_constructor import (
- LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor)
+ LayerDecayOptimizerConstructor,
+ LearningRateDecayOptimizerConstructor,
+)
-__all__ = [
- 'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor'
-]
+__all__ = ["LearningRateDecayOptimizerConstructor", "LayerDecayOptimizerConstructor"]
diff --git a/mmsegmentation/mmseg/core/optimizers/layer_decay_optimizer_constructor.py b/mmsegmentation/mmseg/core/optimizers/layer_decay_optimizer_constructor.py
index 2b6b8ff..9f261a1 100644
--- a/mmsegmentation/mmseg/core/optimizers/layer_decay_optimizer_constructor.py
+++ b/mmsegmentation/mmseg/core/optimizers/layer_decay_optimizer_constructor.py
@@ -21,11 +21,10 @@ def get_layer_id_for_convnext(var_name, max_layer_id):
``LearningRateDecayOptimizerConstructor``.
"""
- if var_name in ('backbone.cls_token', 'backbone.mask_token',
- 'backbone.pos_embed'):
+ if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
return 0
- elif var_name.startswith('backbone.downsample_layers'):
- stage_id = int(var_name.split('.')[2])
+ elif var_name.startswith("backbone.downsample_layers"):
+ stage_id = int(var_name.split(".")[2])
if stage_id == 0:
layer_id = 0
elif stage_id == 1:
@@ -35,9 +34,9 @@ def get_layer_id_for_convnext(var_name, max_layer_id):
elif stage_id == 3:
layer_id = max_layer_id
return layer_id
- elif var_name.startswith('backbone.stages'):
- stage_id = int(var_name.split('.')[2])
- block_id = int(var_name.split('.')[3])
+ elif var_name.startswith("backbone.stages"):
+ stage_id = int(var_name.split(".")[2])
+ block_id = int(var_name.split(".")[3])
if stage_id == 0:
layer_id = 1
elif stage_id == 1:
@@ -64,13 +63,12 @@ def get_stage_id_for_convnext(var_name, max_stage_id):
``LearningRateDecayOptimizerConstructor``.
"""
- if var_name in ('backbone.cls_token', 'backbone.mask_token',
- 'backbone.pos_embed'):
+ if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
return 0
- elif var_name.startswith('backbone.downsample_layers'):
+ elif var_name.startswith("backbone.downsample_layers"):
return 0
- elif var_name.startswith('backbone.stages'):
- stage_id = int(var_name.split('.')[2])
+ elif var_name.startswith("backbone.stages"):
+ stage_id = int(var_name.split(".")[2])
return stage_id + 1
else:
return max_stage_id - 1
@@ -87,13 +85,12 @@ def get_layer_id_for_vit(var_name, max_layer_id):
int: Returns the layer id of the key.
"""
- if var_name in ('backbone.cls_token', 'backbone.mask_token',
- 'backbone.pos_embed'):
+ if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
return 0
- elif var_name.startswith('backbone.patch_embed'):
+ elif var_name.startswith("backbone.patch_embed"):
return 0
- elif var_name.startswith('backbone.layers'):
- layer_id = int(var_name.split('.')[2])
+ elif var_name.startswith("backbone.layers"):
+ layer_id = int(var_name.split(".")[2])
return layer_id + 1
else:
return max_layer_id - 1
@@ -121,67 +118,75 @@ def add_params(self, params, module, **kwargs):
logger = get_root_logger()
parameter_groups = {}
- logger.info(f'self.paramwise_cfg is {self.paramwise_cfg}')
- num_layers = self.paramwise_cfg.get('num_layers') + 2
- decay_rate = self.paramwise_cfg.get('decay_rate')
- decay_type = self.paramwise_cfg.get('decay_type', 'layer_wise')
- logger.info('Build LearningRateDecayOptimizerConstructor '
- f'{decay_type} {decay_rate} - {num_layers}')
+ logger.info(f"self.paramwise_cfg is {self.paramwise_cfg}")
+ num_layers = self.paramwise_cfg.get("num_layers") + 2
+ decay_rate = self.paramwise_cfg.get("decay_rate")
+ decay_type = self.paramwise_cfg.get("decay_type", "layer_wise")
+ logger.info(
+ "Build LearningRateDecayOptimizerConstructor "
+ f"{decay_type} {decay_rate} - {num_layers}"
+ )
weight_decay = self.base_wd
for name, param in module.named_parameters():
if not param.requires_grad:
continue # frozen weights
- if len(param.shape) == 1 or name.endswith('.bias') or name in (
- 'pos_embed', 'cls_token'):
- group_name = 'no_decay'
- this_weight_decay = 0.
+ if (
+ len(param.shape) == 1
+ or name.endswith(".bias")
+ or name in ("pos_embed", "cls_token")
+ ):
+ group_name = "no_decay"
+ this_weight_decay = 0.0
else:
- group_name = 'decay'
+ group_name = "decay"
this_weight_decay = weight_decay
- if 'layer_wise' in decay_type:
- if 'ConvNeXt' in module.backbone.__class__.__name__:
+ if "layer_wise" in decay_type:
+ if "ConvNeXt" in module.backbone.__class__.__name__:
layer_id = get_layer_id_for_convnext(
- name, self.paramwise_cfg.get('num_layers'))
- logger.info(f'set param {name} as id {layer_id}')
- elif 'BEiT' in module.backbone.__class__.__name__ or \
- 'MAE' in module.backbone.__class__.__name__:
+ name, self.paramwise_cfg.get("num_layers")
+ )
+ logger.info(f"set param {name} as id {layer_id}")
+ elif (
+ "BEiT" in module.backbone.__class__.__name__
+ or "MAE" in module.backbone.__class__.__name__
+ ):
layer_id = get_layer_id_for_vit(name, num_layers)
- logger.info(f'set param {name} as id {layer_id}')
+ logger.info(f"set param {name} as id {layer_id}")
else:
raise NotImplementedError()
- elif decay_type == 'stage_wise':
- if 'ConvNeXt' in module.backbone.__class__.__name__:
+ elif decay_type == "stage_wise":
+ if "ConvNeXt" in module.backbone.__class__.__name__:
layer_id = get_stage_id_for_convnext(name, num_layers)
- logger.info(f'set param {name} as id {layer_id}')
+ logger.info(f"set param {name} as id {layer_id}")
else:
raise NotImplementedError()
- group_name = f'layer_{layer_id}_{group_name}'
+ group_name = f"layer_{layer_id}_{group_name}"
if group_name not in parameter_groups:
- scale = decay_rate**(num_layers - layer_id - 1)
+ scale = decay_rate ** (num_layers - layer_id - 1)
parameter_groups[group_name] = {
- 'weight_decay': this_weight_decay,
- 'params': [],
- 'param_names': [],
- 'lr_scale': scale,
- 'group_name': group_name,
- 'lr': scale * self.base_lr,
+ "weight_decay": this_weight_decay,
+ "params": [],
+ "param_names": [],
+ "lr_scale": scale,
+ "group_name": group_name,
+ "lr": scale * self.base_lr,
}
- parameter_groups[group_name]['params'].append(param)
- parameter_groups[group_name]['param_names'].append(name)
+ parameter_groups[group_name]["params"].append(param)
+ parameter_groups[group_name]["param_names"].append(name)
rank, _ = get_dist_info()
if rank == 0:
to_display = {}
for key in parameter_groups:
to_display[key] = {
- 'param_names': parameter_groups[key]['param_names'],
- 'lr_scale': parameter_groups[key]['lr_scale'],
- 'lr': parameter_groups[key]['lr'],
- 'weight_decay': parameter_groups[key]['weight_decay'],
+ "param_names": parameter_groups[key]["param_names"],
+ "lr_scale": parameter_groups[key]["lr_scale"],
+ "lr": parameter_groups[key]["lr"],
+ "weight_decay": parameter_groups[key]["weight_decay"],
}
- logger.info(f'Param groups = {json.dumps(to_display, indent=2)}')
+ logger.info(f"Param groups = {json.dumps(to_display, indent=2)}")
params.extend(parameter_groups.values())
@@ -195,14 +200,17 @@ class LayerDecayOptimizerConstructor(LearningRateDecayOptimizerConstructor):
"""
def __init__(self, optimizer_cfg, paramwise_cfg):
- warnings.warn('DeprecationWarning: Original '
- 'LayerDecayOptimizerConstructor of BEiT '
- 'will be deprecated. Please use '
- 'LearningRateDecayOptimizerConstructor instead, '
- 'and set decay_type = layer_wise_vit in paramwise_cfg.')
- paramwise_cfg.update({'decay_type': 'layer_wise_vit'})
- warnings.warn('DeprecationWarning: Layer_decay_rate will '
- 'be deleted, please use decay_rate instead.')
- paramwise_cfg['decay_rate'] = paramwise_cfg.pop('layer_decay_rate')
- super(LayerDecayOptimizerConstructor,
- self).__init__(optimizer_cfg, paramwise_cfg)
+ warnings.warn(
+ "DeprecationWarning: Original "
+ "LayerDecayOptimizerConstructor of BEiT "
+ "will be deprecated. Please use "
+ "LearningRateDecayOptimizerConstructor instead, "
+ "and set decay_type = layer_wise_vit in paramwise_cfg."
+ )
+ paramwise_cfg.update({"decay_type": "layer_wise_vit"})
+ warnings.warn(
+ "DeprecationWarning: Layer_decay_rate will "
+ "be deleted, please use decay_rate instead."
+ )
+ paramwise_cfg["decay_rate"] = paramwise_cfg.pop("layer_decay_rate")
+ super().__init__(optimizer_cfg, paramwise_cfg)
diff --git a/mmsegmentation/mmseg/core/seg/__init__.py b/mmsegmentation/mmseg/core/seg/__init__.py
index 5206b96..5bdc6c3 100644
--- a/mmsegmentation/mmseg/core/seg/__init__.py
+++ b/mmsegmentation/mmseg/core/seg/__init__.py
@@ -2,4 +2,4 @@
from .builder import build_pixel_sampler
from .sampler import BasePixelSampler, OHEMPixelSampler
-__all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler']
+__all__ = ["build_pixel_sampler", "BasePixelSampler", "OHEMPixelSampler"]
diff --git a/mmsegmentation/mmseg/core/seg/builder.py b/mmsegmentation/mmseg/core/seg/builder.py
index 1cecd34..525364e 100644
--- a/mmsegmentation/mmseg/core/seg/builder.py
+++ b/mmsegmentation/mmseg/core/seg/builder.py
@@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
-PIXEL_SAMPLERS = Registry('pixel sampler')
+PIXEL_SAMPLERS = Registry("pixel sampler")
def build_pixel_sampler(cfg, **default_args):
diff --git a/mmsegmentation/mmseg/core/seg/sampler/__init__.py b/mmsegmentation/mmseg/core/seg/sampler/__init__.py
index 5a76485..3caa926 100644
--- a/mmsegmentation/mmseg/core/seg/sampler/__init__.py
+++ b/mmsegmentation/mmseg/core/seg/sampler/__init__.py
@@ -2,4 +2,4 @@
from .base_pixel_sampler import BasePixelSampler
from .ohem_pixel_sampler import OHEMPixelSampler
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
+__all__ = ["BasePixelSampler", "OHEMPixelSampler"]
diff --git a/mmsegmentation/mmseg/core/seg/sampler/ohem_pixel_sampler.py b/mmsegmentation/mmseg/core/seg/sampler/ohem_pixel_sampler.py
index 833a287..223cfda 100644
--- a/mmsegmentation/mmseg/core/seg/sampler/ohem_pixel_sampler.py
+++ b/mmsegmentation/mmseg/core/seg/sampler/ohem_pixel_sampler.py
@@ -23,7 +23,7 @@ class OHEMPixelSampler(BasePixelSampler):
"""
def __init__(self, context, thresh=None, min_kept=100000):
- super(OHEMPixelSampler, self).__init__()
+ super().__init__()
self.context = context
assert min_kept > 1
self.thresh = thresh
@@ -56,12 +56,11 @@ def sample(self, seg_logit, seg_label):
sort_prob, sort_indices = seg_prob[valid_mask].sort()
if sort_prob.numel() > 0:
- min_threshold = sort_prob[min(batch_kept,
- sort_prob.numel() - 1)]
+ min_threshold = sort_prob[min(batch_kept, sort_prob.numel() - 1)]
else:
min_threshold = 0.0
threshold = max(min_threshold, self.thresh)
- valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.
+ valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.0
else:
if not isinstance(self.context.loss_decode, nn.ModuleList):
losses_decode = [self.context.loss_decode]
@@ -74,11 +73,12 @@ def sample(self, seg_logit, seg_label):
seg_label,
weight=None,
ignore_index=self.context.ignore_index,
- reduction_override='none')
+ reduction_override="none",
+ )
# faster than topk according to https://github.com/pytorch/pytorch/issues/22812 # noqa
_, sort_indices = losses[valid_mask].sort(descending=True)
- valid_seg_weight[sort_indices[:batch_kept]] = 1.
+ valid_seg_weight[sort_indices[:batch_kept]] = 1.0
seg_weight[valid_mask] = valid_seg_weight
diff --git a/mmsegmentation/mmseg/core/utils/__init__.py b/mmsegmentation/mmseg/core/utils/__init__.py
index 2888289..b9d7ca8 100644
--- a/mmsegmentation/mmseg/core/utils/__init__.py
+++ b/mmsegmentation/mmseg/core/utils/__init__.py
@@ -2,4 +2,4 @@
from .dist_util import check_dist_init, sync_random_seed
from .misc import add_prefix
-__all__ = ['add_prefix', 'check_dist_init', 'sync_random_seed']
+__all__ = ["add_prefix", "check_dist_init", "sync_random_seed"]
diff --git a/mmsegmentation/mmseg/core/utils/dist_util.py b/mmsegmentation/mmseg/core/utils/dist_util.py
index b328851..4511444 100644
--- a/mmsegmentation/mmseg/core/utils/dist_util.py
+++ b/mmsegmentation/mmseg/core/utils/dist_util.py
@@ -9,7 +9,7 @@ def check_dist_init():
return dist.is_available() and dist.is_initialized()
-def sync_random_seed(seed=None, device='cuda'):
+def sync_random_seed(seed=None, device="cuda"):
"""Make sure different ranks share the same seed. All workers must call
this function, otherwise it will deadlock. This method is generally used in
`DistributedSampler`, because the seed should be identical across all
diff --git a/mmsegmentation/mmseg/core/utils/misc.py b/mmsegmentation/mmseg/core/utils/misc.py
index 282bb8d..44d3cfd 100644
--- a/mmsegmentation/mmseg/core/utils/misc.py
+++ b/mmsegmentation/mmseg/core/utils/misc.py
@@ -13,6 +13,6 @@ def add_prefix(inputs, prefix):
outputs = dict()
for name, value in inputs.items():
- outputs[f'{prefix}.{name}'] = value
+ outputs[f"{prefix}.{name}"] = value
return outputs
diff --git a/mmsegmentation/mmseg/datasets/__init__.py b/mmsegmentation/mmseg/datasets/__init__.py
index 4281180..bad2c9a 100644
--- a/mmsegmentation/mmseg/datasets/__init__.py
+++ b/mmsegmentation/mmseg/datasets/__init__.py
@@ -7,8 +7,7 @@
from .coco_trash import COCOTrashDataset
from .custom import CustomDataset
from .dark_zurich import DarkZurichDataset
-from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
- RepeatDataset)
+from .dataset_wrappers import ConcatDataset, MultiImageMixDataset, RepeatDataset
from .drive import DRIVEDataset
from .face import FaceOccludedDataset
from .hrf import HRFDataset
@@ -22,12 +21,30 @@
from .voc import PascalVOCDataset
__all__ = [
- 'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
- 'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset',
- 'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
- 'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
- 'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
- 'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
- 'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset', 'FaceOccludedDataset',
- 'COCOTrashDataset'
+ "CustomDataset",
+ "build_dataloader",
+ "ConcatDataset",
+ "RepeatDataset",
+ "DATASETS",
+ "build_dataset",
+ "PIPELINES",
+ "CityscapesDataset",
+ "PascalVOCDataset",
+ "ADE20KDataset",
+ "PascalContextDataset",
+ "PascalContextDataset59",
+ "ChaseDB1Dataset",
+ "DRIVEDataset",
+ "HRFDataset",
+ "STAREDataset",
+ "DarkZurichDataset",
+ "NightDrivingDataset",
+ "COCOStuffDataset",
+ "LoveDADataset",
+ "MultiImageMixDataset",
+ "iSAIDDataset",
+ "ISPRSDataset",
+ "PotsdamDataset",
+ "FaceOccludedDataset",
+ "COCOTrashDataset",
]
diff --git a/mmsegmentation/mmseg/datasets/ade.py b/mmsegmentation/mmseg/datasets/ade.py
index db94ceb..1fc84d0 100644
--- a/mmsegmentation/mmseg/datasets/ade.py
+++ b/mmsegmentation/mmseg/datasets/ade.py
@@ -18,77 +18,317 @@ class ADE20KDataset(CustomDataset):
The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
'.png'.
"""
+
CLASSES = (
- 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
- 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
- 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
- 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
- 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
- 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
- 'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
- 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
- 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
- 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
- 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
- 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
- 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
- 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
- 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
- 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
- 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
- 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
- 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
- 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
- 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
- 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
- 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
- 'clock', 'flag')
-
- PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
- [102, 255, 0], [92, 0, 255]]
+ "wall",
+ "building",
+ "sky",
+ "floor",
+ "tree",
+ "ceiling",
+ "road",
+ "bed ",
+ "windowpane",
+ "grass",
+ "cabinet",
+ "sidewalk",
+ "person",
+ "earth",
+ "door",
+ "table",
+ "mountain",
+ "plant",
+ "curtain",
+ "chair",
+ "car",
+ "water",
+ "painting",
+ "sofa",
+ "shelf",
+ "house",
+ "sea",
+ "mirror",
+ "rug",
+ "field",
+ "armchair",
+ "seat",
+ "fence",
+ "desk",
+ "rock",
+ "wardrobe",
+ "lamp",
+ "bathtub",
+ "railing",
+ "cushion",
+ "base",
+ "box",
+ "column",
+ "signboard",
+ "chest of drawers",
+ "counter",
+ "sand",
+ "sink",
+ "skyscraper",
+ "fireplace",
+ "refrigerator",
+ "grandstand",
+ "path",
+ "stairs",
+ "runway",
+ "case",
+ "pool table",
+ "pillow",
+ "screen door",
+ "stairway",
+ "river",
+ "bridge",
+ "bookcase",
+ "blind",
+ "coffee table",
+ "toilet",
+ "flower",
+ "book",
+ "hill",
+ "bench",
+ "countertop",
+ "stove",
+ "palm",
+ "kitchen island",
+ "computer",
+ "swivel chair",
+ "boat",
+ "bar",
+ "arcade machine",
+ "hovel",
+ "bus",
+ "towel",
+ "light",
+ "truck",
+ "tower",
+ "chandelier",
+ "awning",
+ "streetlight",
+ "booth",
+ "television receiver",
+ "airplane",
+ "dirt track",
+ "apparel",
+ "pole",
+ "land",
+ "bannister",
+ "escalator",
+ "ottoman",
+ "bottle",
+ "buffet",
+ "poster",
+ "stage",
+ "van",
+ "ship",
+ "fountain",
+ "conveyer belt",
+ "canopy",
+ "washer",
+ "plaything",
+ "swimming pool",
+ "stool",
+ "barrel",
+ "basket",
+ "waterfall",
+ "tent",
+ "bag",
+ "minibike",
+ "cradle",
+ "oven",
+ "ball",
+ "food",
+ "step",
+ "tank",
+ "trade name",
+ "microwave",
+ "pot",
+ "animal",
+ "bicycle",
+ "lake",
+ "dishwasher",
+ "screen",
+ "blanket",
+ "sculpture",
+ "hood",
+ "sconce",
+ "vase",
+ "traffic light",
+ "tray",
+ "ashcan",
+ "fan",
+ "pier",
+ "crt screen",
+ "plate",
+ "monitor",
+ "bulletin board",
+ "shower",
+ "radiator",
+ "glass",
+ "clock",
+ "flag",
+ )
+
+ PALETTE = [
+ [120, 120, 120],
+ [180, 120, 120],
+ [6, 230, 230],
+ [80, 50, 50],
+ [4, 200, 3],
+ [120, 120, 80],
+ [140, 140, 140],
+ [204, 5, 255],
+ [230, 230, 230],
+ [4, 250, 7],
+ [224, 5, 255],
+ [235, 255, 7],
+ [150, 5, 61],
+ [120, 120, 70],
+ [8, 255, 51],
+ [255, 6, 82],
+ [143, 255, 140],
+ [204, 255, 4],
+ [255, 51, 7],
+ [204, 70, 3],
+ [0, 102, 200],
+ [61, 230, 250],
+ [255, 6, 51],
+ [11, 102, 255],
+ [255, 7, 71],
+ [255, 9, 224],
+ [9, 7, 230],
+ [220, 220, 220],
+ [255, 9, 92],
+ [112, 9, 255],
+ [8, 255, 214],
+ [7, 255, 224],
+ [255, 184, 6],
+ [10, 255, 71],
+ [255, 41, 10],
+ [7, 255, 255],
+ [224, 255, 8],
+ [102, 8, 255],
+ [255, 61, 6],
+ [255, 194, 7],
+ [255, 122, 8],
+ [0, 255, 20],
+ [255, 8, 41],
+ [255, 5, 153],
+ [6, 51, 255],
+ [235, 12, 255],
+ [160, 150, 20],
+ [0, 163, 255],
+ [140, 140, 140],
+ [250, 10, 15],
+ [20, 255, 0],
+ [31, 255, 0],
+ [255, 31, 0],
+ [255, 224, 0],
+ [153, 255, 0],
+ [0, 0, 255],
+ [255, 71, 0],
+ [0, 235, 255],
+ [0, 173, 255],
+ [31, 0, 255],
+ [11, 200, 200],
+ [255, 82, 0],
+ [0, 255, 245],
+ [0, 61, 255],
+ [0, 255, 112],
+ [0, 255, 133],
+ [255, 0, 0],
+ [255, 163, 0],
+ [255, 102, 0],
+ [194, 255, 0],
+ [0, 143, 255],
+ [51, 255, 0],
+ [0, 82, 255],
+ [0, 255, 41],
+ [0, 255, 173],
+ [10, 0, 255],
+ [173, 255, 0],
+ [0, 255, 153],
+ [255, 92, 0],
+ [255, 0, 255],
+ [255, 0, 245],
+ [255, 0, 102],
+ [255, 173, 0],
+ [255, 0, 20],
+ [255, 184, 184],
+ [0, 31, 255],
+ [0, 255, 61],
+ [0, 71, 255],
+ [255, 0, 204],
+ [0, 255, 194],
+ [0, 255, 82],
+ [0, 10, 255],
+ [0, 112, 255],
+ [51, 0, 255],
+ [0, 194, 255],
+ [0, 122, 255],
+ [0, 255, 163],
+ [255, 153, 0],
+ [0, 255, 10],
+ [255, 112, 0],
+ [143, 255, 0],
+ [82, 0, 255],
+ [163, 255, 0],
+ [255, 235, 0],
+ [8, 184, 170],
+ [133, 0, 255],
+ [0, 255, 92],
+ [184, 0, 255],
+ [255, 0, 31],
+ [0, 184, 255],
+ [0, 214, 255],
+ [255, 0, 112],
+ [92, 255, 0],
+ [0, 224, 255],
+ [112, 224, 255],
+ [70, 184, 160],
+ [163, 0, 255],
+ [153, 0, 255],
+ [71, 255, 0],
+ [255, 0, 163],
+ [255, 204, 0],
+ [255, 0, 143],
+ [0, 255, 235],
+ [133, 255, 0],
+ [255, 0, 235],
+ [245, 0, 255],
+ [255, 0, 122],
+ [255, 245, 0],
+ [10, 190, 212],
+ [214, 255, 0],
+ [0, 204, 255],
+ [20, 0, 255],
+ [255, 255, 0],
+ [0, 153, 255],
+ [0, 41, 255],
+ [0, 255, 204],
+ [41, 0, 255],
+ [41, 255, 0],
+ [173, 0, 255],
+ [0, 245, 255],
+ [71, 0, 255],
+ [122, 0, 255],
+ [0, 255, 184],
+ [0, 92, 255],
+ [184, 255, 0],
+ [0, 133, 255],
+ [255, 214, 0],
+ [25, 194, 194],
+ [102, 255, 0],
+ [92, 0, 255],
+ ]
def __init__(self, **kwargs):
- super(ADE20KDataset, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
- reduce_zero_label=True,
- **kwargs)
+ super().__init__(
+ img_suffix=".jpg", seg_map_suffix=".png", reduce_zero_label=True, **kwargs
+ )
def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
"""Write the segmentation results to images.
@@ -116,10 +356,10 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
result_files = []
for result, idx in zip(results, indices):
- filename = self.img_infos[idx]['filename']
+ filename = self.img_infos[idx]["filename"]
basename = osp.splitext(osp.basename(filename))[0]
- png_filename = osp.join(imgfile_prefix, f'{basename}.png')
+ png_filename = osp.join(imgfile_prefix, f"{basename}.png")
# The index range of official requirement is from 0 to 150.
# But the index range of output is from 0 to 149.
@@ -132,11 +372,7 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
return result_files
- def format_results(self,
- results,
- imgfile_prefix,
- to_label_id=True,
- indices=None):
+ def format_results(self, results, imgfile_prefix, to_label_id=True, indices=None):
"""Format the results into dir (standard format for ade20k evaluation).
Args:
@@ -159,9 +395,8 @@ def format_results(self,
if indices is None:
indices = list(range(len(self)))
- assert isinstance(results, list), 'results must be a list.'
- assert isinstance(indices, list), 'indices must be a list.'
+ assert isinstance(results, list), "results must be a list."
+ assert isinstance(indices, list), "indices must be a list."
- result_files = self.results2img(results, imgfile_prefix, to_label_id,
- indices)
+ result_files = self.results2img(results, imgfile_prefix, to_label_id, indices)
return result_files
diff --git a/mmsegmentation/mmseg/datasets/builder.py b/mmsegmentation/mmseg/datasets/builder.py
index 49ee633..6b2020c 100644
--- a/mmsegmentation/mmseg/datasets/builder.py
+++ b/mmsegmentation/mmseg/datasets/builder.py
@@ -13,27 +13,29 @@
from .samplers import DistributedSampler
-if platform.system() != 'Windows':
+if platform.system() != "Windows":
# https://github.com/pytorch/pytorch/issues/973
import resource
+
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
base_soft_limit = rlimit[0]
hard_limit = rlimit[1]
soft_limit = min(max(4096, base_soft_limit), hard_limit)
resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
-DATASETS = Registry('dataset')
-PIPELINES = Registry('pipeline')
+DATASETS = Registry("dataset")
+PIPELINES = Registry("pipeline")
def _concat_dataset(cfg, default_args=None):
"""Build :obj:`ConcatDataset by."""
from .dataset_wrappers import ConcatDataset
- img_dir = cfg['img_dir']
- ann_dir = cfg.get('ann_dir', None)
- split = cfg.get('split', None)
+
+ img_dir = cfg["img_dir"]
+ ann_dir = cfg.get("ann_dir", None)
+ split = cfg.get("split", None)
# pop 'separate_eval' since it is not a valid key for common datasets.
- separate_eval = cfg.pop('separate_eval', True)
+ separate_eval = cfg.pop("separate_eval", True)
num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
if ann_dir is not None:
num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
@@ -54,11 +56,11 @@ def _concat_dataset(cfg, default_args=None):
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
if isinstance(img_dir, (list, tuple)):
- data_cfg['img_dir'] = img_dir[i]
+ data_cfg["img_dir"] = img_dir[i]
if isinstance(ann_dir, (list, tuple)):
- data_cfg['ann_dir'] = ann_dir[i]
+ data_cfg["ann_dir"] = ann_dir[i]
if isinstance(split, (list, tuple)):
- data_cfg['split'] = split[i]
+ data_cfg["split"] = split[i]
datasets.append(build_dataset(data_cfg, default_args))
return ConcatDataset(datasets, separate_eval)
@@ -66,20 +68,22 @@ def _concat_dataset(cfg, default_args=None):
def build_dataset(cfg, default_args=None):
"""Build datasets."""
- from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
- RepeatDataset)
+ from .dataset_wrappers import ConcatDataset, MultiImageMixDataset, RepeatDataset
+
if isinstance(cfg, (list, tuple)):
dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
- elif cfg['type'] == 'RepeatDataset':
+ elif cfg["type"] == "RepeatDataset":
dataset = RepeatDataset(
- build_dataset(cfg['dataset'], default_args), cfg['times'])
- elif cfg['type'] == 'MultiImageMixDataset':
+ build_dataset(cfg["dataset"], default_args), cfg["times"]
+ )
+ elif cfg["type"] == "MultiImageMixDataset":
cp_cfg = copy.deepcopy(cfg)
- cp_cfg['dataset'] = build_dataset(cp_cfg['dataset'])
- cp_cfg.pop('type')
+ cp_cfg["dataset"] = build_dataset(cp_cfg["dataset"])
+ cp_cfg.pop("type")
dataset = MultiImageMixDataset(**cp_cfg)
- elif isinstance(cfg.get('img_dir'), (list, tuple)) or isinstance(
- cfg.get('split', None), (list, tuple)):
+ elif isinstance(cfg.get("img_dir"), (list, tuple)) or isinstance(
+ cfg.get("split", None), (list, tuple)
+ ):
dataset = _concat_dataset(cfg, default_args)
else:
dataset = build_from_cfg(cfg, DATASETS, default_args)
@@ -87,17 +91,19 @@ def build_dataset(cfg, default_args=None):
return dataset
-def build_dataloader(dataset,
- samples_per_gpu,
- workers_per_gpu,
- num_gpus=1,
- dist=True,
- shuffle=True,
- seed=None,
- drop_last=False,
- pin_memory=True,
- persistent_workers=True,
- **kwargs):
+def build_dataloader(
+ dataset,
+ samples_per_gpu,
+ workers_per_gpu,
+ num_gpus=1,
+ dist=True,
+ shuffle=True,
+ seed=None,
+ drop_last=False,
+ pin_memory=True,
+ persistent_workers=True,
+ **kwargs,
+):
"""Build PyTorch DataLoader.
In distributed training, each GPU/process has a dataloader.
@@ -131,7 +137,8 @@ def build_dataloader(dataset,
rank, world_size = get_dist_info()
if dist and not isinstance(dataset, IterableDataset):
sampler = DistributedSampler(
- dataset, world_size, rank, shuffle=shuffle, seed=seed)
+ dataset, world_size, rank, shuffle=shuffle, seed=seed
+ )
shuffle = False
batch_size = samples_per_gpu
num_workers = workers_per_gpu
@@ -145,11 +152,13 @@ def build_dataloader(dataset,
batch_size = num_gpus * samples_per_gpu
num_workers = num_gpus * workers_per_gpu
- init_fn = partial(
- worker_init_fn, num_workers=num_workers, rank=rank,
- seed=seed) if seed is not None else None
+ init_fn = (
+ partial(worker_init_fn, num_workers=num_workers, rank=rank, seed=seed)
+ if seed is not None
+ else None
+ )
- if digit_version(torch.__version__) >= digit_version('1.8.0'):
+ if digit_version(torch.__version__) >= digit_version("1.8.0"):
data_loader = DataLoader(
dataset,
batch_size=batch_size,
@@ -161,7 +170,8 @@ def build_dataloader(dataset,
worker_init_fn=init_fn,
drop_last=drop_last,
persistent_workers=persistent_workers,
- **kwargs)
+ **kwargs,
+ )
else:
data_loader = DataLoader(
dataset,
@@ -173,7 +183,8 @@ def build_dataloader(dataset,
shuffle=shuffle,
worker_init_fn=init_fn,
drop_last=drop_last,
- **kwargs)
+ **kwargs,
+ )
return data_loader
diff --git a/mmsegmentation/mmseg/datasets/chase_db1.py b/mmsegmentation/mmseg/datasets/chase_db1.py
index 5cdc8d8..617f151 100644
--- a/mmsegmentation/mmseg/datasets/chase_db1.py
+++ b/mmsegmentation/mmseg/datasets/chase_db1.py
@@ -14,14 +14,15 @@ class ChaseDB1Dataset(CustomDataset):
'_1stHO.png'.
"""
- CLASSES = ('background', 'vessel')
+ CLASSES = ("background", "vessel")
PALETTE = [[120, 120, 120], [6, 230, 230]]
def __init__(self, **kwargs):
- super(ChaseDB1Dataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='_1stHO.png',
+ super().__init__(
+ img_suffix=".png",
+ seg_map_suffix="_1stHO.png",
reduce_zero_label=False,
- **kwargs)
+ **kwargs,
+ )
assert self.file_client.exists(self.img_dir)
diff --git a/mmsegmentation/mmseg/datasets/cityscapes.py b/mmsegmentation/mmseg/datasets/cityscapes.py
index ed633d0..ad90899 100644
--- a/mmsegmentation/mmseg/datasets/cityscapes.py
+++ b/mmsegmentation/mmseg/datasets/cityscapes.py
@@ -18,23 +18,57 @@ class CityscapesDataset(CustomDataset):
fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
"""
- CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
- 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
- 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
- 'bicycle')
-
- PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
- [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
- [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
- [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100],
- [0, 80, 100], [0, 0, 230], [119, 11, 32]]
-
- def __init__(self,
- img_suffix='_leftImg8bit.png',
- seg_map_suffix='_gtFine_labelTrainIds.png',
- **kwargs):
- super(CityscapesDataset, self).__init__(
- img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
+ CLASSES = (
+ "road",
+ "sidewalk",
+ "building",
+ "wall",
+ "fence",
+ "pole",
+ "traffic light",
+ "traffic sign",
+ "vegetation",
+ "terrain",
+ "sky",
+ "person",
+ "rider",
+ "car",
+ "truck",
+ "bus",
+ "train",
+ "motorcycle",
+ "bicycle",
+ )
+
+ PALETTE = [
+ [128, 64, 128],
+ [244, 35, 232],
+ [70, 70, 70],
+ [102, 102, 156],
+ [190, 153, 153],
+ [153, 153, 153],
+ [250, 170, 30],
+ [220, 220, 0],
+ [107, 142, 35],
+ [152, 251, 152],
+ [70, 130, 180],
+ [220, 20, 60],
+ [255, 0, 0],
+ [0, 0, 142],
+ [0, 0, 70],
+ [0, 60, 100],
+ [0, 80, 100],
+ [0, 0, 230],
+ [119, 11, 32],
+ ]
+
+ def __init__(
+ self,
+ img_suffix="_leftImg8bit.png",
+ seg_map_suffix="_gtFine_labelTrainIds.png",
+ **kwargs,
+ ):
+ super().__init__(img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
@staticmethod
def _convert_to_label_id(result):
@@ -42,6 +76,7 @@ def _convert_to_label_id(result):
if isinstance(result, str):
result = np.load(result)
import cityscapesscripts.helpers.labels as CSLabels
+
result_copy = result.copy()
for trainId, label in CSLabels.trainId2label.items():
result_copy[result == trainId] = label.id
@@ -75,13 +110,14 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
for result, idx in zip(results, indices):
if to_label_id:
result = self._convert_to_label_id(result)
- filename = self.img_infos[idx]['filename']
+ filename = self.img_infos[idx]["filename"]
basename = osp.splitext(osp.basename(filename))[0]
- png_filename = osp.join(imgfile_prefix, f'{basename}.png')
+ png_filename = osp.join(imgfile_prefix, f"{basename}.png")
- output = Image.fromarray(result.astype(np.uint8)).convert('P')
+ output = Image.fromarray(result.astype(np.uint8)).convert("P")
import cityscapesscripts.helpers.labels as CSLabels
+
palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8)
for label_id, label in CSLabels.id2label.items():
palette[label_id] = label.color
@@ -92,11 +128,7 @@ def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
return result_files
- def format_results(self,
- results,
- imgfile_prefix,
- to_label_id=True,
- indices=None):
+ def format_results(self, results, imgfile_prefix, to_label_id=True, indices=None):
"""Format the results into dir (standard format for Cityscapes
evaluation).
@@ -119,19 +151,14 @@ def format_results(self,
if indices is None:
indices = list(range(len(self)))
- assert isinstance(results, list), 'results must be a list.'
- assert isinstance(indices, list), 'indices must be a list.'
+ assert isinstance(results, list), "results must be a list."
+ assert isinstance(indices, list), "indices must be a list."
- result_files = self.results2img(results, imgfile_prefix, to_label_id,
- indices)
+ result_files = self.results2img(results, imgfile_prefix, to_label_id, indices)
return result_files
- def evaluate(self,
- results,
- metric='mIoU',
- logger=None,
- imgfile_prefix=None):
+ def evaluate(self, results, metric="mIoU", logger=None, imgfile_prefix=None):
"""Evaluation in Cityscapes/default protocol.
Args:
@@ -155,14 +182,13 @@ def evaluate(self,
eval_results = dict()
metrics = metric.copy() if isinstance(metric, list) else [metric]
- if 'cityscapes' in metrics:
+ if "cityscapes" in metrics:
eval_results.update(
- self._evaluate_cityscapes(results, logger, imgfile_prefix))
- metrics.remove('cityscapes')
+ self._evaluate_cityscapes(results, logger, imgfile_prefix)
+ )
+ metrics.remove("cityscapes")
if len(metrics) > 0:
- eval_results.update(
- super(CityscapesDataset,
- self).evaluate(results, metrics, logger))
+ eval_results.update(super().evaluate(results, metrics, logger))
return eval_results
@@ -181,17 +207,19 @@ def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
try:
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval # noqa
except ImportError:
- raise ImportError('Please run "pip install cityscapesscripts" to '
- 'install cityscapesscripts first.')
- msg = 'Evaluating in Cityscapes style'
+ raise ImportError(
+ 'Please run "pip install cityscapesscripts" to '
+ "install cityscapesscripts first."
+ )
+ msg = "Evaluating in Cityscapes style"
if logger is None:
- msg = '\n' + msg
+ msg = "\n" + msg
print_log(msg, logger=logger)
result_dir = imgfile_prefix
eval_results = dict()
- print_log(f'Evaluating results under {result_dir} ...', logger=logger)
+ print_log(f"Evaluating results under {result_dir} ...", logger=logger)
CSEval.args.evalInstLevelScore = True
CSEval.args.predictionPath = osp.abspath(result_dir)
@@ -204,11 +232,13 @@ def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
# when evaluating with official cityscapesscripts,
# **_gtFine_labelIds.png is used
for seg_map in mmcv.scandir(
- self.ann_dir, 'gtFine_labelIds.png', recursive=True):
+ self.ann_dir, "gtFine_labelIds.png", recursive=True
+ ):
seg_map_list.append(osp.join(self.ann_dir, seg_map))
pred_list.append(CSEval.getPrediction(CSEval.args, seg_map))
eval_results.update(
- CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args))
+ CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args)
+ )
return eval_results
diff --git a/mmsegmentation/mmseg/datasets/coco_stuff.py b/mmsegmentation/mmseg/datasets/coco_stuff.py
index 24d0895..56b4901 100644
--- a/mmsegmentation/mmseg/datasets/coco_stuff.py
+++ b/mmsegmentation/mmseg/datasets/coco_stuff.py
@@ -14,81 +14,356 @@ class COCOStuffDataset(CustomDataset):
10k and 164k versions, respectively. The ``img_suffix`` is fixed to '.jpg',
and ``seg_map_suffix`` is fixed to '.png'.
"""
+
CLASSES = (
- 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
- 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
- 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
- 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
- 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
- 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
- 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
- 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
- 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
- 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
- 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
- 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
- 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
- 'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',
- 'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',
- 'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',
- 'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',
- 'floor-other', 'floor-stone', 'floor-tile', 'floor-wood',
- 'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass',
- 'gravel', 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat',
- 'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',
- 'paper', 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
- 'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
- 'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper',
- 'snow', 'solid-other', 'stairs', 'stone', 'straw', 'structural-other',
- 'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable',
- 'wall-brick', 'wall-concrete', 'wall-other', 'wall-panel',
- 'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
- 'window-blind', 'window-other', 'wood')
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "backpack",
+ "umbrella",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "dining table",
+ "toilet",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush",
+ "banner",
+ "blanket",
+ "branch",
+ "bridge",
+ "building-other",
+ "bush",
+ "cabinet",
+ "cage",
+ "cardboard",
+ "carpet",
+ "ceiling-other",
+ "ceiling-tile",
+ "cloth",
+ "clothes",
+ "clouds",
+ "counter",
+ "cupboard",
+ "curtain",
+ "desk-stuff",
+ "dirt",
+ "door-stuff",
+ "fence",
+ "floor-marble",
+ "floor-other",
+ "floor-stone",
+ "floor-tile",
+ "floor-wood",
+ "flower",
+ "fog",
+ "food-other",
+ "fruit",
+ "furniture-other",
+ "grass",
+ "gravel",
+ "ground-other",
+ "hill",
+ "house",
+ "leaves",
+ "light",
+ "mat",
+ "metal",
+ "mirror-stuff",
+ "moss",
+ "mountain",
+ "mud",
+ "napkin",
+ "net",
+ "paper",
+ "pavement",
+ "pillow",
+ "plant-other",
+ "plastic",
+ "platform",
+ "playingfield",
+ "railing",
+ "railroad",
+ "river",
+ "road",
+ "rock",
+ "roof",
+ "rug",
+ "salad",
+ "sand",
+ "sea",
+ "shelf",
+ "sky-other",
+ "skyscraper",
+ "snow",
+ "solid-other",
+ "stairs",
+ "stone",
+ "straw",
+ "structural-other",
+ "table",
+ "tent",
+ "textile-other",
+ "towel",
+ "tree",
+ "vegetable",
+ "wall-brick",
+ "wall-concrete",
+ "wall-other",
+ "wall-panel",
+ "wall-stone",
+ "wall-tile",
+ "wall-wood",
+ "water-other",
+ "waterdrops",
+ "window-blind",
+ "window-other",
+ "wood",
+ )
- PALETTE = [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],
- [0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],
- [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],
- [0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],
- [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
- [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],
- [64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160],
- [0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0],
- [0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128],
- [64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160],
- [0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128],
- [128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192],
- [0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160],
- [64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0],
- [0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],
- [0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160],
- [64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128],
- [128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128],
- [64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224],
- [64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0],
- [0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128],
- [64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224],
- [64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128],
- [128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192],
- [0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224],
- [0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0],
- [64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192],
- [0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224],
- [0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128],
- [192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128],
- [64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160],
- [0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64],
- [64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128],
- [64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160],
- [0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192],
- [192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192],
- [0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160],
- [64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 224, 64],
- [64, 128, 64], [128, 192, 32], [192, 32, 192], [64, 64, 192],
- [0, 64, 32], [64, 160, 192], [192, 64, 64], [128, 64, 160],
- [64, 32, 192], [192, 192, 192], [0, 64, 160], [192, 160, 192],
- [192, 192, 0], [128, 64, 96], [192, 32, 64], [192, 64, 128],
- [64, 192, 96], [64, 160, 64], [64, 64, 0]]
+ PALETTE = [
+ [0, 192, 64],
+ [0, 192, 64],
+ [0, 64, 96],
+ [128, 192, 192],
+ [0, 64, 64],
+ [0, 192, 224],
+ [0, 192, 192],
+ [128, 192, 64],
+ [0, 192, 96],
+ [128, 192, 64],
+ [128, 32, 192],
+ [0, 0, 224],
+ [0, 0, 64],
+ [0, 160, 192],
+ [128, 0, 96],
+ [128, 0, 192],
+ [0, 32, 192],
+ [128, 128, 224],
+ [0, 0, 192],
+ [128, 160, 192],
+ [128, 128, 0],
+ [128, 0, 32],
+ [128, 32, 0],
+ [128, 0, 128],
+ [64, 128, 32],
+ [0, 160, 0],
+ [0, 0, 0],
+ [192, 128, 160],
+ [0, 32, 0],
+ [0, 128, 128],
+ [64, 128, 160],
+ [128, 160, 0],
+ [0, 128, 0],
+ [192, 128, 32],
+ [128, 96, 128],
+ [0, 0, 128],
+ [64, 0, 32],
+ [0, 224, 128],
+ [128, 0, 0],
+ [192, 0, 160],
+ [0, 96, 128],
+ [128, 128, 128],
+ [64, 0, 160],
+ [128, 224, 128],
+ [128, 128, 64],
+ [192, 0, 32],
+ [128, 96, 0],
+ [128, 0, 192],
+ [0, 128, 32],
+ [64, 224, 0],
+ [0, 0, 64],
+ [128, 128, 160],
+ [64, 96, 0],
+ [0, 128, 192],
+ [0, 128, 160],
+ [192, 224, 0],
+ [0, 128, 64],
+ [128, 128, 32],
+ [192, 32, 128],
+ [0, 64, 192],
+ [0, 0, 32],
+ [64, 160, 128],
+ [128, 64, 64],
+ [128, 0, 160],
+ [64, 32, 128],
+ [128, 192, 192],
+ [0, 0, 160],
+ [192, 160, 128],
+ [128, 192, 0],
+ [128, 0, 96],
+ [192, 32, 0],
+ [128, 64, 128],
+ [64, 128, 96],
+ [64, 160, 0],
+ [0, 64, 0],
+ [192, 128, 224],
+ [64, 32, 0],
+ [0, 192, 128],
+ [64, 128, 224],
+ [192, 160, 0],
+ [0, 192, 0],
+ [192, 128, 96],
+ [192, 96, 128],
+ [0, 64, 128],
+ [64, 0, 96],
+ [64, 224, 128],
+ [128, 64, 0],
+ [192, 0, 224],
+ [64, 96, 128],
+ [128, 192, 128],
+ [64, 0, 224],
+ [192, 224, 128],
+ [128, 192, 64],
+ [192, 0, 96],
+ [192, 96, 0],
+ [128, 64, 192],
+ [0, 128, 96],
+ [0, 224, 0],
+ [64, 64, 64],
+ [128, 128, 224],
+ [0, 96, 0],
+ [64, 192, 192],
+ [0, 128, 224],
+ [128, 224, 0],
+ [64, 192, 64],
+ [128, 128, 96],
+ [128, 32, 128],
+ [64, 0, 192],
+ [0, 64, 96],
+ [0, 160, 128],
+ [192, 0, 64],
+ [128, 64, 224],
+ [0, 32, 128],
+ [192, 128, 192],
+ [0, 64, 224],
+ [128, 160, 128],
+ [192, 128, 0],
+ [128, 64, 32],
+ [128, 32, 64],
+ [192, 0, 128],
+ [64, 192, 32],
+ [0, 160, 64],
+ [64, 0, 0],
+ [192, 192, 160],
+ [0, 32, 64],
+ [64, 128, 128],
+ [64, 192, 160],
+ [128, 160, 64],
+ [64, 128, 0],
+ [192, 192, 32],
+ [128, 96, 192],
+ [64, 0, 128],
+ [64, 64, 32],
+ [0, 224, 192],
+ [192, 0, 0],
+ [192, 64, 160],
+ [0, 96, 192],
+ [192, 128, 128],
+ [64, 64, 160],
+ [128, 224, 192],
+ [192, 128, 64],
+ [192, 64, 32],
+ [128, 96, 64],
+ [192, 0, 192],
+ [0, 192, 32],
+ [64, 224, 64],
+ [64, 0, 64],
+ [128, 192, 160],
+ [64, 96, 64],
+ [64, 128, 192],
+ [0, 192, 160],
+ [192, 224, 64],
+ [64, 128, 64],
+ [128, 192, 32],
+ [192, 32, 192],
+ [64, 64, 192],
+ [0, 64, 32],
+ [64, 160, 192],
+ [192, 64, 64],
+ [128, 64, 160],
+ [64, 32, 192],
+ [192, 192, 192],
+ [0, 64, 160],
+ [192, 160, 192],
+ [192, 192, 0],
+ [128, 64, 96],
+ [192, 32, 64],
+ [192, 64, 128],
+ [64, 192, 96],
+ [64, 160, 64],
+ [64, 64, 0],
+ ]
def __init__(self, **kwargs):
- super(COCOStuffDataset, self).__init__(
- img_suffix='.jpg', seg_map_suffix='_labelTrainIds.png', **kwargs)
+ super().__init__(
+ img_suffix=".jpg", seg_map_suffix="_labelTrainIds.png", **kwargs
+ )
diff --git a/mmsegmentation/mmseg/datasets/coco_trash.py b/mmsegmentation/mmseg/datasets/coco_trash.py
index 383c9a3..0dcc71b 100644
--- a/mmsegmentation/mmseg/datasets/coco_trash.py
+++ b/mmsegmentation/mmseg/datasets/coco_trash.py
@@ -8,16 +8,31 @@ class COCOTrashDataset(CustomDataset):
"""COCO-Trash dataset."""
CLASSES = (
- "Background", "General trash", "Paper", "Paper pack", "Metal", "Glass",
- "Plastic", "Styrofoam", "Plastic bag", "Battery", "Clothing",
+ "Background",
+ "General trash",
+ "Paper",
+ "Paper pack",
+ "Metal",
+ "Glass",
+ "Plastic",
+ "Styrofoam",
+ "Plastic bag",
+ "Battery",
+ "Clothing",
)
PALETTE = (
- (128, 224, 128), (128, 62, 62), (30, 142, 30), (192, 0, 0), (50, 50, 160),
- (0, 224, 224), (0, 0, 224), (192, 224, 0), (192, 224, 224), (192, 96, 0),
+ (128, 224, 128),
+ (128, 62, 62),
+ (30, 142, 30),
+ (192, 0, 0),
+ (50, 50, 160),
+ (0, 224, 224),
+ (0, 0, 224),
+ (192, 224, 0),
+ (192, 224, 224),
+ (192, 96, 0),
(0, 224, 0),
)
def __init__(self, **kwargs) -> None:
- super(COCOTrashDataset, self).__init__(
- img_suffix=".jpg", seg_map_suffix=".png", **kwargs
- )
+ super().__init__(img_suffix=".jpg", seg_map_suffix=".png", **kwargs)
diff --git a/mmsegmentation/mmseg/datasets/custom.py b/mmsegmentation/mmseg/datasets/custom.py
index 4615d41..0d22391 100644
--- a/mmsegmentation/mmseg/datasets/custom.py
+++ b/mmsegmentation/mmseg/datasets/custom.py
@@ -77,21 +77,23 @@ class CustomDataset(Dataset):
PALETTE = None
- def __init__(self,
- pipeline,
- img_dir,
- img_suffix='.jpg',
- ann_dir=None,
- seg_map_suffix='.png',
- split=None,
- data_root=None,
- test_mode=False,
- ignore_index=255,
- reduce_zero_label=False,
- classes=None,
- palette=None,
- gt_seg_map_loader_cfg=None,
- file_client_args=dict(backend='disk')):
+ def __init__(
+ self,
+ pipeline,
+ img_dir,
+ img_suffix=".jpg",
+ ann_dir=None,
+ seg_map_suffix=".png",
+ split=None,
+ data_root=None,
+ test_mode=False,
+ ignore_index=255,
+ reduce_zero_label=False,
+ classes=None,
+ palette=None,
+ gt_seg_map_loader_cfg=None,
+ file_client_args=dict(backend="disk"),
+ ):
self.pipeline = Compose(pipeline)
self.img_dir = img_dir
self.img_suffix = img_suffix
@@ -103,18 +105,20 @@ def __init__(self,
self.ignore_index = ignore_index
self.reduce_zero_label = reduce_zero_label
self.label_map = None
- self.CLASSES, self.PALETTE = self.get_classes_and_palette(
- classes, palette)
- self.gt_seg_map_loader = LoadAnnotations(
- ) if gt_seg_map_loader_cfg is None else LoadAnnotations(
- **gt_seg_map_loader_cfg)
+ self.CLASSES, self.PALETTE = self.get_classes_and_palette(classes, palette)
+ self.gt_seg_map_loader = (
+ LoadAnnotations()
+ if gt_seg_map_loader_cfg is None
+ else LoadAnnotations(**gt_seg_map_loader_cfg)
+ )
self.file_client_args = file_client_args
self.file_client = mmcv.FileClient.infer_client(self.file_client_args)
if test_mode:
- assert self.CLASSES is not None, \
- '`cls.CLASSES` or `classes` should be specified when testing'
+ assert (
+ self.CLASSES is not None
+ ), "`cls.CLASSES` or `classes` should be specified when testing"
# join paths if data_root is specified
if self.data_root is not None:
@@ -126,16 +130,15 @@ def __init__(self,
self.split = osp.join(self.data_root, self.split)
# load annotations
- self.img_infos = self.load_annotations(self.img_dir, self.img_suffix,
- self.ann_dir,
- self.seg_map_suffix, self.split)
+ self.img_infos = self.load_annotations(
+ self.img_dir, self.img_suffix, self.ann_dir, self.seg_map_suffix, self.split
+ )
def __len__(self):
"""Total number of samples of data."""
return len(self.img_infos)
- def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
- split):
+ def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix, split):
"""Load annotation from directory.
Args:
@@ -153,29 +156,26 @@ def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
img_infos = []
if split is not None:
- lines = mmcv.list_from_file(
- split, file_client_args=self.file_client_args)
+ lines = mmcv.list_from_file(split, file_client_args=self.file_client_args)
for line in lines:
img_name = line.strip()
img_info = dict(filename=img_name + img_suffix)
if ann_dir is not None:
seg_map = img_name + seg_map_suffix
- img_info['ann'] = dict(seg_map=seg_map)
+ img_info["ann"] = dict(seg_map=seg_map)
img_infos.append(img_info)
else:
for img in self.file_client.list_dir_or_file(
- dir_path=img_dir,
- list_dir=False,
- suffix=img_suffix,
- recursive=True):
+ dir_path=img_dir, list_dir=False, suffix=img_suffix, recursive=True
+ ):
img_info = dict(filename=img)
if ann_dir is not None:
seg_map = img.replace(img_suffix, seg_map_suffix)
- img_info['ann'] = dict(seg_map=seg_map)
+ img_info["ann"] = dict(seg_map=seg_map)
img_infos.append(img_info)
- img_infos = sorted(img_infos, key=lambda x: x['filename'])
+ img_infos = sorted(img_infos, key=lambda x: x["filename"])
- print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
+ print_log(f"Loaded {len(img_infos)} images", logger=get_root_logger())
return img_infos
def get_ann_info(self, idx):
@@ -188,15 +188,15 @@ def get_ann_info(self, idx):
dict: Annotation info of specified index.
"""
- return self.img_infos[idx]['ann']
+ return self.img_infos[idx]["ann"]
def pre_pipeline(self, results):
"""Prepare results dict for pipeline."""
- results['seg_fields'] = []
- results['img_prefix'] = self.img_dir
- results['seg_prefix'] = self.ann_dir
+ results["seg_fields"] = []
+ results["img_prefix"] = self.img_dir
+ results["seg_prefix"] = self.ann_dir
if self.custom_classes:
- results['label_map'] = self.label_map
+ results["label_map"] = self.label_map
def __getitem__(self, idx):
"""Get training/test data after pipeline.
@@ -257,22 +257,23 @@ def get_gt_seg_map_by_idx(self, index):
results = dict(ann_info=ann_info)
self.pre_pipeline(results)
self.gt_seg_map_loader(results)
- return results['gt_semantic_seg']
+ return results["gt_semantic_seg"]
def get_gt_seg_maps(self, efficient_test=None):
"""Get ground truth segmentation maps for evaluation."""
if efficient_test is not None:
warnings.warn(
- 'DeprecationWarning: ``efficient_test`` has been deprecated '
- 'since MMSeg v0.16, the ``get_gt_seg_maps()`` is CPU memory '
- 'friendly by default. ')
+ "DeprecationWarning: ``efficient_test`` has been deprecated "
+ "since MMSeg v0.16, the ``get_gt_seg_maps()`` is CPU memory "
+ "friendly by default. "
+ )
for idx in range(len(self)):
ann_info = self.get_ann_info(idx)
results = dict(ann_info=ann_info)
self.pre_pipeline(results)
self.gt_seg_map_loader(results)
- yield results['gt_semantic_seg']
+ yield results["gt_semantic_seg"]
def pre_eval(self, preds, indices):
"""Collect eval result from each iteration.
@@ -309,7 +310,9 @@ def pre_eval(self, preds, indices):
# https://github.com/open-mmlab/mmsegmentation/issues/1415
# for more ditails
label_map=dict(),
- reduce_zero_label=self.reduce_zero_label))
+ reduce_zero_label=self.reduce_zero_label,
+ )
+ )
return pre_eval_results
@@ -337,11 +340,11 @@ def get_classes_and_palette(self, classes=None, palette=None):
elif isinstance(classes, (tuple, list)):
class_names = classes
else:
- raise ValueError(f'Unsupported type {type(classes)} of classes.')
+ raise ValueError(f"Unsupported type {type(classes)} of classes.")
if self.CLASSES:
if not set(class_names).issubset(self.CLASSES):
- raise ValueError('classes is not a subset of CLASSES.')
+ raise ValueError("classes is not a subset of CLASSES.")
# dictionary, its keys are the old label ids and its values
# are the new label ids.
@@ -362,8 +365,7 @@ def get_palette_for_custom_classes(self, class_names, palette=None):
if self.label_map is not None:
# return subset of palette
palette = []
- for old_id, new_id in sorted(
- self.label_map.items(), key=lambda x: x[1]):
+ for old_id, new_id in sorted(self.label_map.items(), key=lambda x: x[1]):
if new_id != -1:
palette.append(self.PALETTE[old_id])
palette = type(self.PALETTE)(palette)
@@ -385,12 +387,7 @@ def get_palette_for_custom_classes(self, class_names, palette=None):
return palette
- def evaluate(self,
- results,
- metric='mIoU',
- logger=None,
- gt_seg_maps=None,
- **kwargs):
+ def evaluate(self, results, metric="mIoU", logger=None, gt_seg_maps=None, **kwargs):
"""Evaluate the dataset.
Args:
@@ -409,14 +406,13 @@ def evaluate(self,
"""
if isinstance(metric, str):
metric = [metric]
- allowed_metrics = ['mIoU', 'mDice', 'mFscore']
+ allowed_metrics = ["mIoU", "mDice", "mFscore"]
if not set(metric).issubset(set(allowed_metrics)):
- raise KeyError('metric {} is not supported'.format(metric))
+ raise KeyError(f"metric {metric} is not supported")
eval_results = {}
# test a list of files
- if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
- results, str):
+ if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(results, str):
if gt_seg_maps is None:
gt_seg_maps = self.get_gt_seg_maps()
num_classes = len(self.CLASSES)
@@ -427,7 +423,8 @@ def evaluate(self,
self.ignore_index,
metric,
label_map=dict(),
- reduce_zero_label=self.reduce_zero_label)
+ reduce_zero_label=self.reduce_zero_label,
+ )
# test a list of pre_eval_results
else:
ret_metrics = pre_eval_to_metrics(results, metric)
@@ -439,19 +436,23 @@ def evaluate(self,
class_names = self.CLASSES
# summary table
- ret_metrics_summary = OrderedDict({
- ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
- for ret_metric, ret_metric_value in ret_metrics.items()
- })
+ ret_metrics_summary = OrderedDict(
+ {
+ ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
+ for ret_metric, ret_metric_value in ret_metrics.items()
+ }
+ )
# each class table
- ret_metrics.pop('aAcc', None)
- ret_metrics_class = OrderedDict({
- ret_metric: np.round(ret_metric_value * 100, 2)
- for ret_metric, ret_metric_value in ret_metrics.items()
- })
- ret_metrics_class.update({'Class': class_names})
- ret_metrics_class.move_to_end('Class', last=False)
+ ret_metrics.pop("aAcc", None)
+ ret_metrics_class = OrderedDict(
+ {
+ ret_metric: np.round(ret_metric_value * 100, 2)
+ for ret_metric, ret_metric_value in ret_metrics.items()
+ }
+ )
+ ret_metrics_class.update({"Class": class_names})
+ ret_metrics_class.move_to_end("Class", last=False)
# for logger
class_table_data = PrettyTable()
@@ -460,28 +461,30 @@ def evaluate(self,
summary_table_data = PrettyTable()
for key, val in ret_metrics_summary.items():
- if key == 'aAcc':
+ if key == "aAcc":
summary_table_data.add_column(key, [val])
else:
- summary_table_data.add_column('m' + key, [val])
+ summary_table_data.add_column("m" + key, [val])
- print_log('per class results:', logger)
- print_log('\n' + class_table_data.get_string(), logger=logger)
- print_log('Summary:', logger)
- print_log('\n' + summary_table_data.get_string(), logger=logger)
+ print_log("per class results:", logger)
+ print_log("\n" + class_table_data.get_string(), logger=logger)
+ print_log("Summary:", logger)
+ print_log("\n" + summary_table_data.get_string(), logger=logger)
# each metric dict
for key, value in ret_metrics_summary.items():
- if key == 'aAcc':
+ if key == "aAcc":
eval_results[key] = value / 100.0
else:
- eval_results['m' + key] = value / 100.0
+ eval_results["m" + key] = value / 100.0
- ret_metrics_class.pop('Class', None)
+ ret_metrics_class.pop("Class", None)
for key, value in ret_metrics_class.items():
- eval_results.update({
- key + '.' + str(name): value[idx] / 100.0
- for idx, name in enumerate(class_names)
- })
+ eval_results.update(
+ {
+ key + "." + str(name): value[idx] / 100.0
+ for idx, name in enumerate(class_names)
+ }
+ )
return eval_results
diff --git a/mmsegmentation/mmseg/datasets/dark_zurich.py b/mmsegmentation/mmseg/datasets/dark_zurich.py
index 0b6fda6..59a6e53 100644
--- a/mmsegmentation/mmseg/datasets/dark_zurich.py
+++ b/mmsegmentation/mmseg/datasets/dark_zurich.py
@@ -9,6 +9,5 @@ class DarkZurichDataset(CityscapesDataset):
def __init__(self, **kwargs):
super().__init__(
- img_suffix='_rgb_anon.png',
- seg_map_suffix='_gt_labelTrainIds.png',
- **kwargs)
+ img_suffix="_rgb_anon.png", seg_map_suffix="_gt_labelTrainIds.png", **kwargs
+ )
diff --git a/mmsegmentation/mmseg/datasets/dataset_wrappers.py b/mmsegmentation/mmseg/datasets/dataset_wrappers.py
index 1fb089f..324bddf 100644
--- a/mmsegmentation/mmseg/datasets/dataset_wrappers.py
+++ b/mmsegmentation/mmseg/datasets/dataset_wrappers.py
@@ -27,17 +27,18 @@ class ConcatDataset(_ConcatDataset):
"""
def __init__(self, datasets, separate_eval=True):
- super(ConcatDataset, self).__init__(datasets)
+ super().__init__(datasets)
self.CLASSES = datasets[0].CLASSES
self.PALETTE = datasets[0].PALETTE
self.separate_eval = separate_eval
- assert separate_eval in [True, False], \
- f'separate_eval can only be True or False,' \
- f'but get {separate_eval}'
+ assert separate_eval in [True, False], (
+ f"separate_eval can only be True or False," f"but get {separate_eval}"
+ )
if any([isinstance(ds, CityscapesDataset) for ds in datasets]):
raise NotImplementedError(
- 'Evaluating ConcatDataset containing CityscapesDataset'
- 'is not supported!')
+ "Evaluating ConcatDataset containing CityscapesDataset"
+ "is not supported!"
+ )
def evaluate(self, results, logger=None, **kwargs):
"""Evaluate the results.
@@ -54,53 +55,60 @@ def evaluate(self, results, logger=None, **kwargs):
or each separate
dataset if `self.separate_eval=True`.
"""
- assert len(results) == self.cumulative_sizes[-1], \
- ('Dataset and results have different sizes: '
- f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
+ assert len(results) == self.cumulative_sizes[-1], (
+ "Dataset and results have different sizes: "
+ f"{self.cumulative_sizes[-1]} v.s. {len(results)}"
+ )
# Check whether all the datasets support evaluation
for dataset in self.datasets:
- assert hasattr(dataset, 'evaluate'), \
- f'{type(dataset)} does not implement evaluate function'
+ assert hasattr(
+ dataset, "evaluate"
+ ), f"{type(dataset)} does not implement evaluate function"
if self.separate_eval:
dataset_idx = -1
total_eval_results = dict()
for size, dataset in zip(self.cumulative_sizes, self.datasets):
- start_idx = 0 if dataset_idx == -1 else \
- self.cumulative_sizes[dataset_idx]
+ start_idx = (
+ 0 if dataset_idx == -1 else self.cumulative_sizes[dataset_idx]
+ )
end_idx = self.cumulative_sizes[dataset_idx + 1]
results_per_dataset = results[start_idx:end_idx]
print_log(
- f'\nEvaluateing {dataset.img_dir} with '
- f'{len(results_per_dataset)} images now',
- logger=logger)
+ f"\nEvaluateing {dataset.img_dir} with "
+ f"{len(results_per_dataset)} images now",
+ logger=logger,
+ )
eval_results_per_dataset = dataset.evaluate(
- results_per_dataset, logger=logger, **kwargs)
+ results_per_dataset, logger=logger, **kwargs
+ )
dataset_idx += 1
for k, v in eval_results_per_dataset.items():
- total_eval_results.update({f'{dataset_idx}_{k}': v})
+ total_eval_results.update({f"{dataset_idx}_{k}": v})
return total_eval_results
- if len(set([type(ds) for ds in self.datasets])) != 1:
+ if len({type(ds) for ds in self.datasets}) != 1:
raise NotImplementedError(
- 'All the datasets should have same types when '
- 'self.separate_eval=False')
+ "All the datasets should have same types when "
+ "self.separate_eval=False"
+ )
else:
- if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
- results, str):
+ if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(results, str):
# merge the generators of gt_seg_maps
gt_seg_maps = chain(
- *[dataset.get_gt_seg_maps() for dataset in self.datasets])
+ *[dataset.get_gt_seg_maps() for dataset in self.datasets]
+ )
else:
# if the results are `pre_eval` results,
# we do not need gt_seg_maps to evaluate
gt_seg_maps = None
eval_results = self.datasets[0].evaluate(
- results, gt_seg_maps=gt_seg_maps, logger=logger, **kwargs)
+ results, gt_seg_maps=gt_seg_maps, logger=logger, **kwargs
+ )
return eval_results
def get_dataset_idx_and_sample_idx(self, indice):
@@ -117,7 +125,8 @@ def get_dataset_idx_and_sample_idx(self, indice):
if indice < 0:
if -indice > len(self):
raise ValueError(
- 'absolute value of index should not exceed dataset length')
+ "absolute value of index should not exceed dataset length"
+ )
indice = len(self) + indice
dataset_idx = bisect.bisect_right(self.cumulative_sizes, indice)
if dataset_idx == 0:
@@ -131,18 +140,18 @@ def format_results(self, results, imgfile_prefix, indices=None, **kwargs):
if indices is None:
indices = list(range(len(self)))
- assert isinstance(results, list), 'results must be a list.'
- assert isinstance(indices, list), 'indices must be a list.'
+ assert isinstance(results, list), "results must be a list."
+ assert isinstance(indices, list), "indices must be a list."
ret_res = []
for i, indice in enumerate(indices):
- dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
- indice)
+ dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(indice)
res = self.datasets[dataset_idx].format_results(
[results[i]],
- imgfile_prefix + f'/{dataset_idx}',
+ imgfile_prefix + f"/{dataset_idx}",
indices=[sample_idx],
- **kwargs)
+ **kwargs,
+ )
ret_res.append(res)
return sum(ret_res, [])
@@ -155,15 +164,14 @@ def pre_eval(self, preds, indices):
preds = [preds]
ret_res = []
for i, indice in enumerate(indices):
- dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
- indice)
+ dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(indice)
res = self.datasets[dataset_idx].pre_eval(preds[i], sample_idx)
ret_res.append(res)
return sum(ret_res, [])
@DATASETS.register_module()
-class RepeatDataset(object):
+class RepeatDataset:
"""A wrapper of repeated dataset.
The length of repeated dataset will be `times` larger than the original
@@ -214,21 +222,20 @@ class MultiImageMixDataset:
def __init__(self, dataset, pipeline, skip_type_keys=None):
assert isinstance(pipeline, collections.abc.Sequence)
if skip_type_keys is not None:
- assert all([
- isinstance(skip_type_key, str)
- for skip_type_key in skip_type_keys
- ])
+ assert all(
+ [isinstance(skip_type_key, str) for skip_type_key in skip_type_keys]
+ )
self._skip_type_keys = skip_type_keys
self.pipeline = []
self.pipeline_types = []
for transform in pipeline:
if isinstance(transform, dict):
- self.pipeline_types.append(transform['type'])
+ self.pipeline_types.append(transform["type"])
transform = build_from_cfg(transform, PIPELINES)
self.pipeline.append(transform)
else:
- raise TypeError('pipeline must be a dict')
+ raise TypeError("pipeline must be a dict")
self.dataset = dataset
self.CLASSES = dataset.CLASSES
@@ -240,25 +247,24 @@ def __len__(self):
def __getitem__(self, idx):
results = copy.deepcopy(self.dataset[idx])
- for (transform, transform_type) in zip(self.pipeline,
- self.pipeline_types):
- if self._skip_type_keys is not None and \
- transform_type in self._skip_type_keys:
+ for transform, transform_type in zip(self.pipeline, self.pipeline_types):
+ if (
+ self._skip_type_keys is not None
+ and transform_type in self._skip_type_keys
+ ):
continue
- if hasattr(transform, 'get_indexes'):
+ if hasattr(transform, "get_indexes"):
indexes = transform.get_indexes(self.dataset)
if not isinstance(indexes, collections.abc.Sequence):
indexes = [indexes]
- mix_results = [
- copy.deepcopy(self.dataset[index]) for index in indexes
- ]
- results['mix_results'] = mix_results
+ mix_results = [copy.deepcopy(self.dataset[index]) for index in indexes]
+ results["mix_results"] = mix_results
results = transform(results)
- if 'mix_results' in results:
- results.pop('mix_results')
+ if "mix_results" in results:
+ results.pop("mix_results")
return results
@@ -271,7 +277,5 @@ def update_skip_type_keys(self, skip_type_keys):
skip_type_keys (list[str], optional): Sequence of type
string to be skip pipeline.
"""
- assert all([
- isinstance(skip_type_key, str) for skip_type_key in skip_type_keys
- ])
+ assert all([isinstance(skip_type_key, str) for skip_type_key in skip_type_keys])
self._skip_type_keys = skip_type_keys
diff --git a/mmsegmentation/mmseg/datasets/drive.py b/mmsegmentation/mmseg/datasets/drive.py
index d44fb0d..1f984eb 100644
--- a/mmsegmentation/mmseg/datasets/drive.py
+++ b/mmsegmentation/mmseg/datasets/drive.py
@@ -14,14 +14,15 @@ class DRIVEDataset(CustomDataset):
'_manual1.png'.
"""
- CLASSES = ('background', 'vessel')
+ CLASSES = ("background", "vessel")
PALETTE = [[120, 120, 120], [6, 230, 230]]
def __init__(self, **kwargs):
- super(DRIVEDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='_manual1.png',
+ super().__init__(
+ img_suffix=".png",
+ seg_map_suffix="_manual1.png",
reduce_zero_label=False,
- **kwargs)
+ **kwargs,
+ )
assert self.file_client.exists(self.img_dir)
diff --git a/mmsegmentation/mmseg/datasets/face.py b/mmsegmentation/mmseg/datasets/face.py
index cbc2345..cfbf72e 100755
--- a/mmsegmentation/mmseg/datasets/face.py
+++ b/mmsegmentation/mmseg/datasets/face.py
@@ -13,11 +13,12 @@ class FaceOccludedDataset(CustomDataset):
split (str): Split txt file for Pascal VOC.
"""
- CLASSES = ('background', 'face')
+ CLASSES = ("background", "face")
PALETTE = [[0, 0, 0], [128, 0, 0]]
def __init__(self, split, **kwargs):
- super(FaceOccludedDataset, self).__init__(
- img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
+ super().__init__(
+ img_suffix=".jpg", seg_map_suffix=".png", split=split, **kwargs
+ )
assert osp.exists(self.img_dir) and self.split is not None
diff --git a/mmsegmentation/mmseg/datasets/hrf.py b/mmsegmentation/mmseg/datasets/hrf.py
index cf3ea8d..3400cf6 100644
--- a/mmsegmentation/mmseg/datasets/hrf.py
+++ b/mmsegmentation/mmseg/datasets/hrf.py
@@ -14,14 +14,12 @@ class HRFDataset(CustomDataset):
'.png'.
"""
- CLASSES = ('background', 'vessel')
+ CLASSES = ("background", "vessel")
PALETTE = [[120, 120, 120], [6, 230, 230]]
def __init__(self, **kwargs):
- super(HRFDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- reduce_zero_label=False,
- **kwargs)
+ super().__init__(
+ img_suffix=".png", seg_map_suffix=".png", reduce_zero_label=False, **kwargs
+ )
assert self.file_client.exists(self.img_dir)
diff --git a/mmsegmentation/mmseg/datasets/isaid.py b/mmsegmentation/mmseg/datasets/isaid.py
index db24f93..1a9dd98 100644
--- a/mmsegmentation/mmseg/datasets/isaid.py
+++ b/mmsegmentation/mmseg/datasets/isaid.py
@@ -10,38 +10,60 @@
@DATASETS.register_module()
class iSAIDDataset(CustomDataset):
- """ iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
+ """iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
In segmentation map annotation for iSAID dataset, which is included
in 16 categories. ``reduce_zero_label`` is fixed to False. The
``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
'_manual1.png'.
"""
- CLASSES = ('background', 'ship', 'store_tank', 'baseball_diamond',
- 'tennis_court', 'basketball_court', 'Ground_Track_Field',
- 'Bridge', 'Large_Vehicle', 'Small_Vehicle', 'Helicopter',
- 'Swimming_pool', 'Roundabout', 'Soccer_ball_field', 'plane',
- 'Harbor')
+ CLASSES = (
+ "background",
+ "ship",
+ "store_tank",
+ "baseball_diamond",
+ "tennis_court",
+ "basketball_court",
+ "Ground_Track_Field",
+ "Bridge",
+ "Large_Vehicle",
+ "Small_Vehicle",
+ "Helicopter",
+ "Swimming_pool",
+ "Roundabout",
+ "Soccer_ball_field",
+ "plane",
+ "Harbor",
+ )
- PALETTE = [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
- [0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127, 127],
- [0, 0, 127], [0, 0, 191], [0, 0, 255], [0, 191, 127],
- [0, 127, 191], [0, 127, 255], [0, 100, 155]]
+ PALETTE = [
+ [0, 0, 0],
+ [0, 0, 63],
+ [0, 63, 63],
+ [0, 63, 0],
+ [0, 63, 127],
+ [0, 63, 191],
+ [0, 63, 255],
+ [0, 127, 63],
+ [0, 127, 127],
+ [0, 0, 127],
+ [0, 0, 191],
+ [0, 0, 255],
+ [0, 191, 127],
+ [0, 127, 191],
+ [0, 127, 255],
+ [0, 100, 155],
+ ]
def __init__(self, **kwargs):
- super(iSAIDDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- ignore_index=255,
- **kwargs)
+ super().__init__(
+ img_suffix=".png", seg_map_suffix=".png", ignore_index=255, **kwargs
+ )
assert self.file_client.exists(self.img_dir)
- def load_annotations(self,
- img_dir,
- img_suffix,
- ann_dir,
- seg_map_suffix=None,
- split=None):
+ def load_annotations(
+ self, img_dir, img_suffix, ann_dir, seg_map_suffix=None, split=None
+ ):
"""Load annotation from directory.
Args:
@@ -64,9 +86,9 @@ def load_annotations(self,
name = line.strip()
img_info = dict(filename=name + img_suffix)
if ann_dir is not None:
- ann_name = name + '_instance_color_RGB'
+ ann_name = name + "_instance_color_RGB"
seg_map = ann_name + seg_map_suffix
- img_info['ann'] = dict(seg_map=seg_map)
+ img_info["ann"] = dict(seg_map=seg_map)
img_infos.append(img_info)
else:
for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
@@ -74,9 +96,10 @@ def load_annotations(self,
if ann_dir is not None:
seg_img = img
seg_map = seg_img.replace(
- img_suffix, '_instance_color_RGB' + seg_map_suffix)
- img_info['ann'] = dict(seg_map=seg_map)
+ img_suffix, "_instance_color_RGB" + seg_map_suffix
+ )
+ img_info["ann"] = dict(seg_map=seg_map)
img_infos.append(img_info)
- print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
+ print_log(f"Loaded {len(img_infos)} images", logger=get_root_logger())
return img_infos
diff --git a/mmsegmentation/mmseg/datasets/isprs.py b/mmsegmentation/mmseg/datasets/isprs.py
index 5f23e1a..31bed19 100644
--- a/mmsegmentation/mmseg/datasets/isprs.py
+++ b/mmsegmentation/mmseg/datasets/isprs.py
@@ -11,15 +11,26 @@ class ISPRSDataset(CustomDataset):
``reduce_zero_label`` should be set to True. The ``img_suffix`` and
``seg_map_suffix`` are both fixed to '.png'.
"""
- CLASSES = ('impervious_surface', 'building', 'low_vegetation', 'tree',
- 'car', 'clutter')
- PALETTE = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
- [255, 255, 0], [255, 0, 0]]
+ CLASSES = (
+ "impervious_surface",
+ "building",
+ "low_vegetation",
+ "tree",
+ "car",
+ "clutter",
+ )
+
+ PALETTE = [
+ [255, 255, 255],
+ [0, 0, 255],
+ [0, 255, 255],
+ [0, 255, 0],
+ [255, 255, 0],
+ [255, 0, 0],
+ ]
def __init__(self, **kwargs):
- super(ISPRSDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- reduce_zero_label=True,
- **kwargs)
+ super().__init__(
+ img_suffix=".png", seg_map_suffix=".png", reduce_zero_label=True, **kwargs
+ )
diff --git a/mmsegmentation/mmseg/datasets/loveda.py b/mmsegmentation/mmseg/datasets/loveda.py
index 90d654f..00aab8c 100644
--- a/mmsegmentation/mmseg/datasets/loveda.py
+++ b/mmsegmentation/mmseg/datasets/loveda.py
@@ -17,18 +17,31 @@ class LoveDADataset(CustomDataset):
``reduce_zero_label`` should be set to True. The ``img_suffix`` and
``seg_map_suffix`` are both fixed to '.png'.
"""
- CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest',
- 'agricultural')
- PALETTE = [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
- [159, 129, 183], [0, 255, 0], [255, 195, 128]]
+ CLASSES = (
+ "background",
+ "building",
+ "road",
+ "water",
+ "barren",
+ "forest",
+ "agricultural",
+ )
+
+ PALETTE = [
+ [255, 255, 255],
+ [255, 0, 0],
+ [255, 255, 0],
+ [0, 0, 255],
+ [159, 129, 183],
+ [0, 255, 0],
+ [255, 195, 128],
+ ]
def __init__(self, **kwargs):
- super(LoveDADataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- reduce_zero_label=True,
- **kwargs)
+ super().__init__(
+ img_suffix=".png", seg_map_suffix=".png", reduce_zero_label=True, **kwargs
+ )
def results2img(self, results, imgfile_prefix, indices=None):
"""Write the segmentation results to images.
@@ -52,10 +65,10 @@ def results2img(self, results, imgfile_prefix, indices=None):
result_files = []
for result, idx in zip(results, indices):
- filename = self.img_infos[idx]['filename']
+ filename = self.img_infos[idx]["filename"]
basename = osp.splitext(osp.basename(filename))[0]
- png_filename = osp.join(imgfile_prefix, f'{basename}.png')
+ png_filename = osp.join(imgfile_prefix, f"{basename}.png")
# The index range of official requirement is from 0 to 6.
output = Image.fromarray(result.astype(np.uint8))
@@ -84,8 +97,8 @@ def format_results(self, results, imgfile_prefix, indices=None):
if indices is None:
indices = list(range(len(self)))
- assert isinstance(results, list), 'results must be a list.'
- assert isinstance(indices, list), 'indices must be a list.'
+ assert isinstance(results, list), "results must be a list."
+ assert isinstance(indices, list), "indices must be a list."
result_files = self.results2img(results, imgfile_prefix, indices)
diff --git a/mmsegmentation/mmseg/datasets/night_driving.py b/mmsegmentation/mmseg/datasets/night_driving.py
index 6620586..a00bf53 100644
--- a/mmsegmentation/mmseg/datasets/night_driving.py
+++ b/mmsegmentation/mmseg/datasets/night_driving.py
@@ -9,6 +9,7 @@ class NightDrivingDataset(CityscapesDataset):
def __init__(self, **kwargs):
super().__init__(
- img_suffix='_leftImg8bit.png',
- seg_map_suffix='_gtCoarse_labelTrainIds.png',
- **kwargs)
+ img_suffix="_leftImg8bit.png",
+ seg_map_suffix="_gtCoarse_labelTrainIds.png",
+ **kwargs,
+ )
diff --git a/mmsegmentation/mmseg/datasets/pascal_context.py b/mmsegmentation/mmseg/datasets/pascal_context.py
index 20285d8..fcfebcb 100644
--- a/mmsegmentation/mmseg/datasets/pascal_context.py
+++ b/mmsegmentation/mmseg/datasets/pascal_context.py
@@ -17,40 +17,140 @@ class PascalContextDataset(CustomDataset):
split (str): Split txt file for PascalContext.
"""
- CLASSES = ('background', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench',
- 'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus',
- 'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth',
- 'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence',
- 'floor', 'flower', 'food', 'grass', 'ground', 'horse',
- 'keyboard', 'light', 'motorbike', 'mountain', 'mouse', 'person',
- 'plate', 'platform', 'pottedplant', 'road', 'rock', 'sheep',
- 'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table',
- 'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water',
- 'window', 'wood')
+ CLASSES = (
+ "background",
+ "aeroplane",
+ "bag",
+ "bed",
+ "bedclothes",
+ "bench",
+ "bicycle",
+ "bird",
+ "boat",
+ "book",
+ "bottle",
+ "building",
+ "bus",
+ "cabinet",
+ "car",
+ "cat",
+ "ceiling",
+ "chair",
+ "cloth",
+ "computer",
+ "cow",
+ "cup",
+ "curtain",
+ "dog",
+ "door",
+ "fence",
+ "floor",
+ "flower",
+ "food",
+ "grass",
+ "ground",
+ "horse",
+ "keyboard",
+ "light",
+ "motorbike",
+ "mountain",
+ "mouse",
+ "person",
+ "plate",
+ "platform",
+ "pottedplant",
+ "road",
+ "rock",
+ "sheep",
+ "shelves",
+ "sidewalk",
+ "sign",
+ "sky",
+ "snow",
+ "sofa",
+ "table",
+ "track",
+ "train",
+ "tree",
+ "truck",
+ "tvmonitor",
+ "wall",
+ "water",
+ "window",
+ "wood",
+ )
- PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255]]
+ PALETTE = [
+ [120, 120, 120],
+ [180, 120, 120],
+ [6, 230, 230],
+ [80, 50, 50],
+ [4, 200, 3],
+ [120, 120, 80],
+ [140, 140, 140],
+ [204, 5, 255],
+ [230, 230, 230],
+ [4, 250, 7],
+ [224, 5, 255],
+ [235, 255, 7],
+ [150, 5, 61],
+ [120, 120, 70],
+ [8, 255, 51],
+ [255, 6, 82],
+ [143, 255, 140],
+ [204, 255, 4],
+ [255, 51, 7],
+ [204, 70, 3],
+ [0, 102, 200],
+ [61, 230, 250],
+ [255, 6, 51],
+ [11, 102, 255],
+ [255, 7, 71],
+ [255, 9, 224],
+ [9, 7, 230],
+ [220, 220, 220],
+ [255, 9, 92],
+ [112, 9, 255],
+ [8, 255, 214],
+ [7, 255, 224],
+ [255, 184, 6],
+ [10, 255, 71],
+ [255, 41, 10],
+ [7, 255, 255],
+ [224, 255, 8],
+ [102, 8, 255],
+ [255, 61, 6],
+ [255, 194, 7],
+ [255, 122, 8],
+ [0, 255, 20],
+ [255, 8, 41],
+ [255, 5, 153],
+ [6, 51, 255],
+ [235, 12, 255],
+ [160, 150, 20],
+ [0, 163, 255],
+ [140, 140, 140],
+ [250, 10, 15],
+ [20, 255, 0],
+ [31, 255, 0],
+ [255, 31, 0],
+ [255, 224, 0],
+ [153, 255, 0],
+ [0, 0, 255],
+ [255, 71, 0],
+ [0, 235, 255],
+ [0, 173, 255],
+ [31, 0, 255],
+ ]
def __init__(self, split, **kwargs):
- super(PascalContextDataset, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
+ super().__init__(
+ img_suffix=".jpg",
+ seg_map_suffix=".png",
split=split,
reduce_zero_label=False,
- **kwargs)
+ **kwargs,
+ )
assert self.file_client.exists(self.img_dir) and self.split is not None
@@ -67,37 +167,136 @@ class PascalContextDataset59(CustomDataset):
split (str): Split txt file for PascalContext.
"""
- CLASSES = ('aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle',
- 'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet',
- 'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow',
- 'cup', 'curtain', 'dog', 'door', 'fence', 'floor', 'flower',
- 'food', 'grass', 'ground', 'horse', 'keyboard', 'light',
- 'motorbike', 'mountain', 'mouse', 'person', 'plate', 'platform',
- 'pottedplant', 'road', 'rock', 'sheep', 'shelves', 'sidewalk',
- 'sign', 'sky', 'snow', 'sofa', 'table', 'track', 'train',
- 'tree', 'truck', 'tvmonitor', 'wall', 'water', 'window', 'wood')
+ CLASSES = (
+ "aeroplane",
+ "bag",
+ "bed",
+ "bedclothes",
+ "bench",
+ "bicycle",
+ "bird",
+ "boat",
+ "book",
+ "bottle",
+ "building",
+ "bus",
+ "cabinet",
+ "car",
+ "cat",
+ "ceiling",
+ "chair",
+ "cloth",
+ "computer",
+ "cow",
+ "cup",
+ "curtain",
+ "dog",
+ "door",
+ "fence",
+ "floor",
+ "flower",
+ "food",
+ "grass",
+ "ground",
+ "horse",
+ "keyboard",
+ "light",
+ "motorbike",
+ "mountain",
+ "mouse",
+ "person",
+ "plate",
+ "platform",
+ "pottedplant",
+ "road",
+ "rock",
+ "sheep",
+ "shelves",
+ "sidewalk",
+ "sign",
+ "sky",
+ "snow",
+ "sofa",
+ "table",
+ "track",
+ "train",
+ "tree",
+ "truck",
+ "tvmonitor",
+ "wall",
+ "water",
+ "window",
+ "wood",
+ )
- PALETTE = [[180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3],
- [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230],
- [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61],
- [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140],
- [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200],
- [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71],
- [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92],
- [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6],
- [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8],
- [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8],
- [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255],
- [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140],
- [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0],
- [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0],
- [0, 235, 255], [0, 173, 255], [31, 0, 255]]
+ PALETTE = [
+ [180, 120, 120],
+ [6, 230, 230],
+ [80, 50, 50],
+ [4, 200, 3],
+ [120, 120, 80],
+ [140, 140, 140],
+ [204, 5, 255],
+ [230, 230, 230],
+ [4, 250, 7],
+ [224, 5, 255],
+ [235, 255, 7],
+ [150, 5, 61],
+ [120, 120, 70],
+ [8, 255, 51],
+ [255, 6, 82],
+ [143, 255, 140],
+ [204, 255, 4],
+ [255, 51, 7],
+ [204, 70, 3],
+ [0, 102, 200],
+ [61, 230, 250],
+ [255, 6, 51],
+ [11, 102, 255],
+ [255, 7, 71],
+ [255, 9, 224],
+ [9, 7, 230],
+ [220, 220, 220],
+ [255, 9, 92],
+ [112, 9, 255],
+ [8, 255, 214],
+ [7, 255, 224],
+ [255, 184, 6],
+ [10, 255, 71],
+ [255, 41, 10],
+ [7, 255, 255],
+ [224, 255, 8],
+ [102, 8, 255],
+ [255, 61, 6],
+ [255, 194, 7],
+ [255, 122, 8],
+ [0, 255, 20],
+ [255, 8, 41],
+ [255, 5, 153],
+ [6, 51, 255],
+ [235, 12, 255],
+ [160, 150, 20],
+ [0, 163, 255],
+ [140, 140, 140],
+ [250, 10, 15],
+ [20, 255, 0],
+ [31, 255, 0],
+ [255, 31, 0],
+ [255, 224, 0],
+ [153, 255, 0],
+ [0, 0, 255],
+ [255, 71, 0],
+ [0, 235, 255],
+ [0, 173, 255],
+ [31, 0, 255],
+ ]
def __init__(self, split, **kwargs):
- super(PascalContextDataset59, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
+ super().__init__(
+ img_suffix=".jpg",
+ seg_map_suffix=".png",
split=split,
reduce_zero_label=True,
- **kwargs)
+ **kwargs,
+ )
assert self.file_client.exists(self.img_dir) and self.split is not None
diff --git a/mmsegmentation/mmseg/datasets/pipelines/__init__.py b/mmsegmentation/mmseg/datasets/pipelines/__init__.py
index 8256a6f..e8747ef 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/__init__.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/__init__.py
@@ -1,19 +1,55 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .compose import Compose
-from .formatting import (Collect, ImageToTensor, ToDataContainer, ToTensor,
- Transpose, to_tensor)
+from .formatting import (
+ Collect,
+ ImageToTensor,
+ ToDataContainer,
+ ToTensor,
+ Transpose,
+ to_tensor,
+)
from .loading import LoadAnnotations, LoadImageFromFile
from .test_time_aug import MultiScaleFlipAug
-from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,
- PhotoMetricDistortion, RandomCrop, RandomCutOut,
- RandomFlip, RandomMosaic, RandomRotate, Rerange,
- Resize, RGB2Gray, SegRescale)
+from .transforms import (
+ CLAHE,
+ AdjustGamma,
+ Normalize,
+ Pad,
+ PhotoMetricDistortion,
+ RandomCrop,
+ RandomCutOut,
+ RandomFlip,
+ RandomMosaic,
+ RandomRotate,
+ Rerange,
+ Resize,
+ RGB2Gray,
+ SegRescale,
+)
__all__ = [
- 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
- 'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile',
- 'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop',
- 'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate',
- 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray', 'RandomCutOut',
- 'RandomMosaic'
+ "Compose",
+ "to_tensor",
+ "ToTensor",
+ "ImageToTensor",
+ "ToDataContainer",
+ "Transpose",
+ "Collect",
+ "LoadAnnotations",
+ "LoadImageFromFile",
+ "MultiScaleFlipAug",
+ "Resize",
+ "RandomFlip",
+ "Pad",
+ "RandomCrop",
+ "Normalize",
+ "SegRescale",
+ "PhotoMetricDistortion",
+ "RandomRotate",
+ "AdjustGamma",
+ "CLAHE",
+ "Rerange",
+ "RGB2Gray",
+ "RandomCutOut",
+ "RandomMosaic",
]
diff --git a/mmsegmentation/mmseg/datasets/pipelines/compose.py b/mmsegmentation/mmseg/datasets/pipelines/compose.py
index 30280c1..343e519 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/compose.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/compose.py
@@ -7,7 +7,7 @@
@PIPELINES.register_module()
-class Compose(object):
+class Compose:
"""Compose multiple transforms sequentially.
Args:
@@ -25,7 +25,7 @@ def __init__(self, transforms):
elif callable(transform):
self.transforms.append(transform)
else:
- raise TypeError('transform must be callable or a dict')
+ raise TypeError("transform must be callable or a dict")
def __call__(self, data):
"""Call function to apply transforms sequentially.
@@ -44,9 +44,9 @@ def __call__(self, data):
return data
def __repr__(self):
- format_string = self.__class__.__name__ + '('
+ format_string = self.__class__.__name__ + "("
for t in self.transforms:
- format_string += '\n'
- format_string += f' {t}'
- format_string += '\n)'
+ format_string += "\n"
+ format_string += f" {t}"
+ format_string += "\n)"
return format_string
diff --git a/mmsegmentation/mmseg/datasets/pipelines/formating.py b/mmsegmentation/mmseg/datasets/pipelines/formating.py
index f6e53bf..17abc83 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/formating.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/formating.py
@@ -2,8 +2,8 @@
# flake8: noqa
import warnings
-from .formatting import *
-
-warnings.warn('DeprecationWarning: mmseg.datasets.pipelines.formating will be '
- 'deprecated in 2021, please replace it with '
- 'mmseg.datasets.pipelines.formatting.')
+warnings.warn(
+ "DeprecationWarning: mmseg.datasets.pipelines.formating will be "
+ "deprecated in 2021, please replace it with "
+ "mmseg.datasets.pipelines.formatting."
+)
diff --git a/mmsegmentation/mmseg/datasets/pipelines/formatting.py b/mmsegmentation/mmseg/datasets/pipelines/formatting.py
index 4e057c1..0aa0bdd 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/formatting.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/formatting.py
@@ -31,11 +31,11 @@ def to_tensor(data):
elif isinstance(data, float):
return torch.FloatTensor([data])
else:
- raise TypeError(f'type {type(data)} cannot be converted to tensor.')
+ raise TypeError(f"type {type(data)} cannot be converted to tensor.")
@PIPELINES.register_module()
-class ToTensor(object):
+class ToTensor:
"""Convert some results to :obj:`torch.Tensor` by given keys.
Args:
@@ -61,11 +61,11 @@ def __call__(self, results):
return results
def __repr__(self):
- return self.__class__.__name__ + f'(keys={self.keys})'
+ return self.__class__.__name__ + f"(keys={self.keys})"
@PIPELINES.register_module()
-class ImageToTensor(object):
+class ImageToTensor:
"""Convert image to :obj:`torch.Tensor` by given keys.
The dimension order of input image is (H, W, C). The pipeline will convert
@@ -99,11 +99,11 @@ def __call__(self, results):
return results
def __repr__(self):
- return self.__class__.__name__ + f'(keys={self.keys})'
+ return self.__class__.__name__ + f"(keys={self.keys})"
@PIPELINES.register_module()
-class Transpose(object):
+class Transpose:
"""Transpose some results by given keys.
Args:
@@ -132,12 +132,11 @@ def __call__(self, results):
return results
def __repr__(self):
- return self.__class__.__name__ + \
- f'(keys={self.keys}, order={self.order})'
+ return self.__class__.__name__ + f"(keys={self.keys}, order={self.order})"
@PIPELINES.register_module()
-class ToDataContainer(object):
+class ToDataContainer:
"""Convert results to :obj:`mmcv.DataContainer` by given fields.
Args:
@@ -148,9 +147,9 @@ class ToDataContainer(object):
dict(key='gt_semantic_seg'))``.
"""
- def __init__(self,
- fields=(dict(key='img',
- stack=True), dict(key='gt_semantic_seg'))):
+ def __init__(
+ self, fields=(dict(key="img", stack=True), dict(key="gt_semantic_seg"))
+ ):
self.fields = fields
def __call__(self, results):
@@ -167,16 +166,16 @@ def __call__(self, results):
for field in self.fields:
field = field.copy()
- key = field.pop('key')
+ key = field.pop("key")
results[key] = DC(results[key], **field)
return results
def __repr__(self):
- return self.__class__.__name__ + f'(fields={self.fields})'
+ return self.__class__.__name__ + f"(fields={self.fields})"
@PIPELINES.register_module()
-class DefaultFormatBundle(object):
+class DefaultFormatBundle:
"""Default formatting bundle.
It simplifies the pipeline of formatting common fields, including "img"
@@ -198,18 +197,18 @@ def __call__(self, results):
default bundle.
"""
- if 'img' in results:
- img = results['img']
+ if "img" in results:
+ img = results["img"]
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
img = np.ascontiguousarray(img.transpose(2, 0, 1))
- results['img'] = DC(to_tensor(img), stack=True)
- if 'gt_semantic_seg' in results:
+ results["img"] = DC(to_tensor(img), stack=True)
+ if "gt_semantic_seg" in results:
# convert to long
- results['gt_semantic_seg'] = DC(
- to_tensor(results['gt_semantic_seg'][None,
- ...].astype(np.int64)),
- stack=True)
+ results["gt_semantic_seg"] = DC(
+ to_tensor(results["gt_semantic_seg"][None, ...].astype(np.int64)),
+ stack=True,
+ )
return results
def __repr__(self):
@@ -217,7 +216,7 @@ def __repr__(self):
@PIPELINES.register_module()
-class Collect(object):
+class Collect:
"""Collect data from the loader relevant to the specific task.
This is usually the last stage of the data loader pipeline. Typically keys
@@ -254,11 +253,21 @@ class Collect(object):
``flip_direction``, ``img_norm_cfg``)
"""
- def __init__(self,
- keys,
- meta_keys=('filename', 'ori_filename', 'ori_shape',
- 'img_shape', 'pad_shape', 'scale_factor', 'flip',
- 'flip_direction', 'img_norm_cfg')):
+ def __init__(
+ self,
+ keys,
+ meta_keys=(
+ "filename",
+ "ori_filename",
+ "ori_shape",
+ "img_shape",
+ "pad_shape",
+ "scale_factor",
+ "flip",
+ "flip_direction",
+ "img_norm_cfg",
+ ),
+ ):
self.keys = keys
self.meta_keys = meta_keys
@@ -279,11 +288,12 @@ def __call__(self, results):
img_meta = {}
for key in self.meta_keys:
img_meta[key] = results[key]
- data['img_metas'] = DC(img_meta, cpu_only=True)
+ data["img_metas"] = DC(img_meta, cpu_only=True)
for key in self.keys:
data[key] = results[key]
return data
def __repr__(self):
- return self.__class__.__name__ + \
- f'(keys={self.keys}, meta_keys={self.meta_keys})'
+ return (
+ self.__class__.__name__ + f"(keys={self.keys}, meta_keys={self.meta_keys})"
+ )
diff --git a/mmsegmentation/mmseg/datasets/pipelines/loading.py b/mmsegmentation/mmseg/datasets/pipelines/loading.py
index 572e434..3a8ca44 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/loading.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/loading.py
@@ -8,7 +8,7 @@
@PIPELINES.register_module()
-class LoadImageFromFile(object):
+class LoadImageFromFile:
"""Load an image from file.
Required keys are "img_prefix" and "img_info" (a dict that must contain the
@@ -29,11 +29,13 @@ class LoadImageFromFile(object):
'cv2'
"""
- def __init__(self,
- to_float32=False,
- color_type='color',
- file_client_args=dict(backend='disk'),
- imdecode_backend='cv2'):
+ def __init__(
+ self,
+ to_float32=False,
+ color_type="color",
+ file_client_args=dict(backend="disk"),
+ imdecode_backend="cv2",
+ ):
self.to_float32 = to_float32
self.color_type = color_type
self.file_client_args = file_client_args.copy()
@@ -53,42 +55,43 @@ def __call__(self, results):
if self.file_client is None:
self.file_client = mmcv.FileClient(**self.file_client_args)
- if results.get('img_prefix') is not None:
- filename = osp.join(results['img_prefix'],
- results['img_info']['filename'])
+ if results.get("img_prefix") is not None:
+ filename = osp.join(results["img_prefix"], results["img_info"]["filename"])
else:
- filename = results['img_info']['filename']
+ filename = results["img_info"]["filename"]
img_bytes = self.file_client.get(filename)
img = mmcv.imfrombytes(
- img_bytes, flag=self.color_type, backend=self.imdecode_backend)
+ img_bytes, flag=self.color_type, backend=self.imdecode_backend
+ )
if self.to_float32:
img = img.astype(np.float32)
- results['filename'] = filename
- results['ori_filename'] = results['img_info']['filename']
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["filename"] = filename
+ results["ori_filename"] = results["img_info"]["filename"]
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
num_channels = 1 if len(img.shape) < 3 else img.shape[2]
- results['img_norm_cfg'] = dict(
+ results["img_norm_cfg"] = dict(
mean=np.zeros(num_channels, dtype=np.float32),
std=np.ones(num_channels, dtype=np.float32),
- to_rgb=False)
+ to_rgb=False,
+ )
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(to_float32={self.to_float32},'
+ repr_str += f"(to_float32={self.to_float32},"
repr_str += f"color_type='{self.color_type}',"
repr_str += f"imdecode_backend='{self.imdecode_backend}')"
return repr_str
@PIPELINES.register_module()
-class LoadAnnotations(object):
+class LoadAnnotations:
"""Load annotations for semantic segmentation.
Args:
@@ -102,10 +105,12 @@ class LoadAnnotations(object):
'pillow'
"""
- def __init__(self,
- reduce_zero_label=False,
- file_client_args=dict(backend='disk'),
- imdecode_backend='pillow'):
+ def __init__(
+ self,
+ reduce_zero_label=False,
+ file_client_args=dict(backend="disk"),
+ imdecode_backend="pillow",
+ ):
self.reduce_zero_label = reduce_zero_label
self.file_client_args = file_client_args.copy()
self.file_client = None
@@ -124,22 +129,23 @@ def __call__(self, results):
if self.file_client is None:
self.file_client = mmcv.FileClient(**self.file_client_args)
- if results.get('seg_prefix', None) is not None:
- filename = osp.join(results['seg_prefix'],
- results['ann_info']['seg_map'])
+ if results.get("seg_prefix", None) is not None:
+ filename = osp.join(results["seg_prefix"], results["ann_info"]["seg_map"])
else:
- filename = results['ann_info']['seg_map']
+ filename = results["ann_info"]["seg_map"]
img_bytes = self.file_client.get(filename)
- gt_semantic_seg = mmcv.imfrombytes(
- img_bytes, flag='unchanged',
- backend=self.imdecode_backend).squeeze().astype(np.uint8)
+ gt_semantic_seg = (
+ mmcv.imfrombytes(img_bytes, flag="unchanged", backend=self.imdecode_backend)
+ .squeeze()
+ .astype(np.uint8)
+ )
# modify if custom classes
- if results.get('label_map', None) is not None:
+ if results.get("label_map", None) is not None:
# Add deep copy to solve bug of repeatedly
# replace `gt_semantic_seg`, which is reported in
# https://github.com/open-mmlab/mmsegmentation/pull/1445/
gt_semantic_seg_copy = gt_semantic_seg.copy()
- for old_id, new_id in results['label_map'].items():
+ for old_id, new_id in results["label_map"].items():
gt_semantic_seg[gt_semantic_seg_copy == old_id] = new_id
# reduce zero_label
if self.reduce_zero_label:
@@ -147,12 +153,12 @@ def __call__(self, results):
gt_semantic_seg[gt_semantic_seg == 0] = 255
gt_semantic_seg = gt_semantic_seg - 1
gt_semantic_seg[gt_semantic_seg == 254] = 255
- results['gt_semantic_seg'] = gt_semantic_seg
- results['seg_fields'].append('gt_semantic_seg')
+ results["gt_semantic_seg"] = gt_semantic_seg
+ results["seg_fields"].append("gt_semantic_seg")
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(reduce_zero_label={self.reduce_zero_label},'
+ repr_str += f"(reduce_zero_label={self.reduce_zero_label},"
repr_str += f"imdecode_backend='{self.imdecode_backend}')"
return repr_str
diff --git a/mmsegmentation/mmseg/datasets/pipelines/test_time_aug.py b/mmsegmentation/mmseg/datasets/pipelines/test_time_aug.py
index 4964087..b4ef940 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/test_time_aug.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/test_time_aug.py
@@ -8,7 +8,7 @@
@PIPELINES.register_module()
-class MultiScaleFlipAug(object):
+class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
@@ -51,53 +51,49 @@ class MultiScaleFlipAug(object):
It has no effect when flip == False. Default: "horizontal".
"""
- def __init__(self,
- transforms,
- img_scale,
- img_ratios=None,
- flip=False,
- flip_direction='horizontal'):
+ def __init__(
+ self,
+ transforms,
+ img_scale,
+ img_ratios=None,
+ flip=False,
+ flip_direction="horizontal",
+ ):
if flip:
- trans_index = {
- key['type']: index
- for index, key in enumerate(transforms)
- }
- if 'RandomFlip' in trans_index and 'Pad' in trans_index:
- assert trans_index['RandomFlip'] < trans_index['Pad'], \
- 'Pad must be executed after RandomFlip when flip is True'
+ trans_index = {key["type"]: index for index, key in enumerate(transforms)}
+ if "RandomFlip" in trans_index and "Pad" in trans_index:
+ assert (
+ trans_index["RandomFlip"] < trans_index["Pad"]
+ ), "Pad must be executed after RandomFlip when flip is True"
self.transforms = Compose(transforms)
if img_ratios is not None:
- img_ratios = img_ratios if isinstance(img_ratios,
- list) else [img_ratios]
+ img_ratios = img_ratios if isinstance(img_ratios, list) else [img_ratios]
assert mmcv.is_list_of(img_ratios, float)
if img_scale is None:
# mode 1: given img_scale=None and a range of image ratio
self.img_scale = None
assert mmcv.is_list_of(img_ratios, float)
- elif isinstance(img_scale, tuple) and mmcv.is_list_of(
- img_ratios, float):
+ elif isinstance(img_scale, tuple) and mmcv.is_list_of(img_ratios, float):
assert len(img_scale) == 2
# mode 2: given a scale and a range of image ratio
- self.img_scale = [(int(img_scale[0] * ratio),
- int(img_scale[1] * ratio))
- for ratio in img_ratios]
+ self.img_scale = [
+ (int(img_scale[0] * ratio), int(img_scale[1] * ratio))
+ for ratio in img_ratios
+ ]
else:
# mode 3: given multiple scales
- self.img_scale = img_scale if isinstance(img_scale,
- list) else [img_scale]
+ self.img_scale = img_scale if isinstance(img_scale, list) else [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None
self.flip = flip
self.img_ratios = img_ratios
- self.flip_direction = flip_direction if isinstance(
- flip_direction, list) else [flip_direction]
+ self.flip_direction = (
+ flip_direction if isinstance(flip_direction, list) else [flip_direction]
+ )
assert mmcv.is_list_of(self.flip_direction, str)
- if not self.flip and self.flip_direction != ['horizontal']:
- warnings.warn(
- 'flip_direction has no effect when flip is set to False')
- if (self.flip
- and not any([t['type'] == 'RandomFlip' for t in transforms])):
- warnings.warn(
- 'flip has no effect when RandomFlip is not in transforms')
+ if not self.flip and self.flip_direction != ["horizontal"]:
+ warnings.warn("flip_direction has no effect when flip is set to False")
+ if self.flip and not any([t["type"] == "RandomFlip" for t in transforms]):
+ warnings.warn("flip has no effect when RandomFlip is not in transforms")
def __call__(self, results):
"""Call function to apply test time augment transforms on results.
@@ -112,9 +108,8 @@ def __call__(self, results):
aug_data = []
if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float):
- h, w = results['img'].shape[:2]
- img_scale = [(int(w * ratio), int(h * ratio))
- for ratio in self.img_ratios]
+ h, w = results["img"].shape[:2]
+ img_scale = [(int(w * ratio), int(h * ratio)) for ratio in self.img_ratios]
else:
img_scale = self.img_scale
flip_aug = [False, True] if self.flip else [False]
@@ -122,9 +117,9 @@ def __call__(self, results):
for flip in flip_aug:
for direction in self.flip_direction:
_results = results.copy()
- _results['scale'] = scale
- _results['flip'] = flip
- _results['flip_direction'] = direction
+ _results["scale"] = scale
+ _results["flip"] = flip
+ _results["flip_direction"] = direction
data = self.transforms(_results)
aug_data.append(data)
# list of dict to dict of list
@@ -136,7 +131,7 @@ def __call__(self, results):
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(transforms={self.transforms}, '
- repr_str += f'img_scale={self.img_scale}, flip={self.flip})'
- repr_str += f'flip_direction={self.flip_direction}'
+ repr_str += f"(transforms={self.transforms}, "
+ repr_str += f"img_scale={self.img_scale}, flip={self.flip})"
+ repr_str += f"flip_direction={self.flip_direction}"
return repr_str
diff --git a/mmsegmentation/mmseg/datasets/pipelines/transforms.py b/mmsegmentation/mmseg/datasets/pipelines/transforms.py
index 5673b64..af14744 100644
--- a/mmsegmentation/mmseg/datasets/pipelines/transforms.py
+++ b/mmsegmentation/mmseg/datasets/pipelines/transforms.py
@@ -10,7 +10,7 @@
@PIPELINES.register_module()
-class ResizeToMultiple(object):
+class ResizeToMultiple:
"""Resize images & seg to multiple of divisor.
Args:
@@ -35,39 +35,39 @@ def __call__(self, results):
dict: Resized results, 'img_shape', 'pad_shape' keys are updated.
"""
# Align image to multiple of size divisor.
- img = results['img']
+ img = results["img"]
img = mmcv.imresize_to_multiple(
img,
self.size_divisor,
scale_factor=1,
- interpolation=self.interpolation
- if self.interpolation else 'bilinear')
+ interpolation=self.interpolation if self.interpolation else "bilinear",
+ )
- results['img'] = img
- results['img_shape'] = img.shape
- results['pad_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["pad_shape"] = img.shape
# Align segmentation map to multiple of size divisor.
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
gt_seg = results[key]
gt_seg = mmcv.imresize_to_multiple(
- gt_seg,
- self.size_divisor,
- scale_factor=1,
- interpolation='nearest')
+ gt_seg, self.size_divisor, scale_factor=1, interpolation="nearest"
+ )
results[key] = gt_seg
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += (f'(size_divisor={self.size_divisor}, '
- f'interpolation={self.interpolation})')
+ repr_str += (
+ f"(size_divisor={self.size_divisor}, "
+ f"interpolation={self.interpolation})"
+ )
return repr_str
@PIPELINES.register_module()
-class Resize(object):
+class Resize:
"""Resize images & seg.
This transform resizes the input image to some scale. If the input dict
@@ -106,12 +106,14 @@ class Resize(object):
bigger than the crop size in ``slide_inference``. Default: None
"""
- def __init__(self,
- img_scale=None,
- multiscale_mode='range',
- ratio_range=None,
- keep_ratio=True,
- min_size=None):
+ def __init__(
+ self,
+ img_scale=None,
+ multiscale_mode="range",
+ ratio_range=None,
+ keep_ratio=True,
+ min_size=None,
+ ):
if img_scale is None:
self.img_scale = None
else:
@@ -127,7 +129,7 @@ def __init__(self,
assert self.img_scale is None or len(self.img_scale) == 1
else:
# mode 3 and 4: given multiple scales or a range of scales
- assert multiscale_mode in ['value', 'range']
+ assert multiscale_mode in ["value", "range"]
self.multiscale_mode = multiscale_mode
self.ratio_range = ratio_range
@@ -170,12 +172,8 @@ def random_sample(img_scales):
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
- long_edge = np.random.randint(
- min(img_scale_long),
- max(img_scale_long) + 1)
- short_edge = np.random.randint(
- min(img_scale_short),
- max(img_scale_short) + 1)
+ long_edge = np.random.randint(min(img_scale_long), max(img_scale_long) + 1)
+ short_edge = np.random.randint(min(img_scale_short), max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
@@ -226,23 +224,23 @@ def _random_scale(self, results):
if self.ratio_range is not None:
if self.img_scale is None:
- h, w = results['img'].shape[:2]
- scale, scale_idx = self.random_sample_ratio((w, h),
- self.ratio_range)
+ h, w = results["img"].shape[:2]
+ scale, scale_idx = self.random_sample_ratio((w, h), self.ratio_range)
else:
scale, scale_idx = self.random_sample_ratio(
- self.img_scale[0], self.ratio_range)
+ self.img_scale[0], self.ratio_range
+ )
elif len(self.img_scale) == 1:
scale, scale_idx = self.img_scale[0], 0
- elif self.multiscale_mode == 'range':
+ elif self.multiscale_mode == "range":
scale, scale_idx = self.random_sample(self.img_scale)
- elif self.multiscale_mode == 'value':
+ elif self.multiscale_mode == "value":
scale, scale_idx = self.random_select(self.img_scale)
else:
raise NotImplementedError
- results['scale'] = scale
- results['scale_idx'] = scale_idx
+ results["scale"] = scale
+ results["scale_idx"] = scale_idx
def _resize_img(self, results):
"""Resize images with ``results['scale']``."""
@@ -252,46 +250,49 @@ def _resize_img(self, results):
# shape of images is (min_size, min_size, 3). 'min_size'
# with tuple type will be supported, i.e. the width and
# height are not equal.
- if min(results['scale']) < self.min_size:
+ if min(results["scale"]) < self.min_size:
new_short = self.min_size
else:
- new_short = min(results['scale'])
+ new_short = min(results["scale"])
- h, w = results['img'].shape[:2]
+ h, w = results["img"].shape[:2]
if h > w:
new_h, new_w = new_short * h / w, new_short
else:
new_h, new_w = new_short, new_short * w / h
- results['scale'] = (new_h, new_w)
+ results["scale"] = (new_h, new_w)
img, scale_factor = mmcv.imrescale(
- results['img'], results['scale'], return_scale=True)
+ results["img"], results["scale"], return_scale=True
+ )
# the w_scale and h_scale has minor difference
# a real fix should be done in the mmcv.imrescale in the future
new_h, new_w = img.shape[:2]
- h, w = results['img'].shape[:2]
+ h, w = results["img"].shape[:2]
w_scale = new_w / w
h_scale = new_h / h
else:
img, w_scale, h_scale = mmcv.imresize(
- results['img'], results['scale'], return_scale=True)
- scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
- dtype=np.float32)
- results['img'] = img
- results['img_shape'] = img.shape
- results['pad_shape'] = img.shape # in case that there is no padding
- results['scale_factor'] = scale_factor
- results['keep_ratio'] = self.keep_ratio
+ results["img"], results["scale"], return_scale=True
+ )
+ scale_factor = np.array([w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["pad_shape"] = img.shape # in case that there is no padding
+ results["scale_factor"] = scale_factor
+ results["keep_ratio"] = self.keep_ratio
def _resize_seg(self, results):
"""Resize semantic segmentation map with ``results['scale']``."""
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
if self.keep_ratio:
gt_seg = mmcv.imrescale(
- results[key], results['scale'], interpolation='nearest')
+ results[key], results["scale"], interpolation="nearest"
+ )
else:
gt_seg = mmcv.imresize(
- results[key], results['scale'], interpolation='nearest')
+ results[key], results["scale"], interpolation="nearest"
+ )
results[key] = gt_seg
def __call__(self, results):
@@ -306,7 +307,7 @@ def __call__(self, results):
'keep_ratio' keys are added into result dict.
"""
- if 'scale' not in results:
+ if "scale" not in results:
self._random_scale(results)
self._resize_img(results)
self._resize_seg(results)
@@ -314,15 +315,17 @@ def __call__(self, results):
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += (f'(img_scale={self.img_scale}, '
- f'multiscale_mode={self.multiscale_mode}, '
- f'ratio_range={self.ratio_range}, '
- f'keep_ratio={self.keep_ratio})')
+ repr_str += (
+ f"(img_scale={self.img_scale}, "
+ f"multiscale_mode={self.multiscale_mode}, "
+ f"ratio_range={self.ratio_range}, "
+ f"keep_ratio={self.keep_ratio})"
+ )
return repr_str
@PIPELINES.register_module()
-class RandomFlip(object):
+class RandomFlip:
"""Flip the image & seg.
If the input dict contains the key "flip", then the flag will be used,
@@ -335,13 +338,13 @@ class RandomFlip(object):
'horizontal' and 'vertical'. Default: 'horizontal'.
"""
- @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
- def __init__(self, prob=None, direction='horizontal'):
+ @deprecated_api_warning({"flip_ratio": "prob"}, cls_name="RandomFlip")
+ def __init__(self, prob=None, direction="horizontal"):
self.prob = prob
self.direction = direction
if prob is not None:
assert prob >= 0 and prob <= 1
- assert direction in ['horizontal', 'vertical']
+ assert direction in ["horizontal", "vertical"]
def __call__(self, results):
"""Call function to flip bounding boxes, masks, semantic segmentation
@@ -355,29 +358,31 @@ def __call__(self, results):
result dict.
"""
- if 'flip' not in results:
+ if "flip" not in results:
flip = True if np.random.rand() < self.prob else False
- results['flip'] = flip
- if 'flip_direction' not in results:
- results['flip_direction'] = self.direction
- if results['flip']:
+ results["flip"] = flip
+ if "flip_direction" not in results:
+ results["flip_direction"] = self.direction
+ if results["flip"]:
# flip image
- results['img'] = mmcv.imflip(
- results['img'], direction=results['flip_direction'])
+ results["img"] = mmcv.imflip(
+ results["img"], direction=results["flip_direction"]
+ )
# flip segs
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
# use copy() to make numpy stride positive
results[key] = mmcv.imflip(
- results[key], direction=results['flip_direction']).copy()
+ results[key], direction=results["flip_direction"]
+ ).copy()
return results
def __repr__(self):
- return self.__class__.__name__ + f'(prob={self.prob})'
+ return self.__class__.__name__ + f"(prob={self.prob})"
@PIPELINES.register_module()
-class Pad(object):
+class Pad:
"""Pad the image & mask.
There are two padding modes: (1) pad to a fixed size and (2) pad to the
@@ -392,11 +397,7 @@ class Pad(object):
Default: 255.
"""
- def __init__(self,
- size=None,
- size_divisor=None,
- pad_val=0,
- seg_pad_val=255):
+ def __init__(self, size=None, size_divisor=None, pad_val=0, seg_pad_val=255):
self.size = size
self.size_divisor = size_divisor
self.pad_val = pad_val
@@ -409,22 +410,23 @@ def _pad_img(self, results):
"""Pad images according to ``self.size``."""
if self.size is not None:
padded_img = mmcv.impad(
- results['img'], shape=self.size, pad_val=self.pad_val)
+ results["img"], shape=self.size, pad_val=self.pad_val
+ )
elif self.size_divisor is not None:
padded_img = mmcv.impad_to_multiple(
- results['img'], self.size_divisor, pad_val=self.pad_val)
- results['img'] = padded_img
- results['pad_shape'] = padded_img.shape
- results['pad_fixed_size'] = self.size
- results['pad_size_divisor'] = self.size_divisor
+ results["img"], self.size_divisor, pad_val=self.pad_val
+ )
+ results["img"] = padded_img
+ results["pad_shape"] = padded_img.shape
+ results["pad_fixed_size"] = self.size
+ results["pad_size_divisor"] = self.size_divisor
def _pad_seg(self, results):
"""Pad masks according to ``results['pad_shape']``."""
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
results[key] = mmcv.impad(
- results[key],
- shape=results['pad_shape'][:2],
- pad_val=self.seg_pad_val)
+ results[key], shape=results["pad_shape"][:2], pad_val=self.seg_pad_val
+ )
def __call__(self, results):
"""Call function to pad images, masks, semantic segmentation maps.
@@ -442,13 +444,15 @@ def __call__(self, results):
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
- f'pad_val={self.pad_val})'
+ repr_str += (
+ f"(size={self.size}, size_divisor={self.size_divisor}, "
+ f"pad_val={self.pad_val})"
+ )
return repr_str
@PIPELINES.register_module()
-class Normalize(object):
+class Normalize:
"""Normalize the image.
Added key is "img_norm_cfg".
@@ -476,21 +480,20 @@ def __call__(self, results):
result dict.
"""
- results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
- self.to_rgb)
- results['img_norm_cfg'] = dict(
- mean=self.mean, std=self.std, to_rgb=self.to_rgb)
+ results["img"] = mmcv.imnormalize(
+ results["img"], self.mean, self.std, self.to_rgb
+ )
+ results["img_norm_cfg"] = dict(mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
- f'{self.to_rgb})'
+ repr_str += f"(mean={self.mean}, std={self.std}, to_rgb=" f"{self.to_rgb})"
return repr_str
@PIPELINES.register_module()
-class Rerange(object):
+class Rerange:
"""Rerange the image pixel value.
Args:
@@ -516,7 +519,7 @@ def __call__(self, results):
dict: Reranged results.
"""
- img = results['img']
+ img = results["img"]
img_min_value = np.min(img)
img_max_value = np.max(img)
@@ -525,18 +528,18 @@ def __call__(self, results):
img = (img - img_min_value) / (img_max_value - img_min_value)
# rerange to [min_value, max_value]
img = img * (self.max_value - self.min_value) + self.min_value
- results['img'] = img
+ results["img"] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
+ repr_str += f"(min_value={self.min_value}, max_value={self.max_value})"
return repr_str
@PIPELINES.register_module()
-class CLAHE(object):
+class CLAHE:
"""Use CLAHE method to process the image.
See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
@@ -566,22 +569,25 @@ def __call__(self, results):
dict: Processed results.
"""
- for i in range(results['img'].shape[2]):
- results['img'][:, :, i] = mmcv.clahe(
- np.array(results['img'][:, :, i], dtype=np.uint8),
- self.clip_limit, self.tile_grid_size)
+ for i in range(results["img"].shape[2]):
+ results["img"][:, :, i] = mmcv.clahe(
+ np.array(results["img"][:, :, i], dtype=np.uint8),
+ self.clip_limit,
+ self.tile_grid_size,
+ )
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(clip_limit={self.clip_limit}, '\
- f'tile_grid_size={self.tile_grid_size})'
+ repr_str += (
+ f"(clip_limit={self.clip_limit}, " f"tile_grid_size={self.tile_grid_size})"
+ )
return repr_str
@PIPELINES.register_module()
-class RandomCrop(object):
+class RandomCrop:
"""Random crop the image & seg.
Args:
@@ -590,7 +596,7 @@ class RandomCrop(object):
occupy.
"""
- def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
+ def __init__(self, crop_size, cat_max_ratio=1.0, ignore_index=255):
assert crop_size[0] > 0 and crop_size[1] > 0
self.crop_size = crop_size
self.cat_max_ratio = cat_max_ratio
@@ -624,37 +630,36 @@ def __call__(self, results):
updated according to crop size.
"""
- img = results['img']
+ img = results["img"]
crop_bbox = self.get_crop_bbox(img)
- if self.cat_max_ratio < 1.:
+ if self.cat_max_ratio < 1.0:
# Repeat 10 times
for _ in range(10):
- seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
+ seg_temp = self.crop(results["gt_semantic_seg"], crop_bbox)
labels, cnt = np.unique(seg_temp, return_counts=True)
cnt = cnt[labels != self.ignore_index]
- if len(cnt) > 1 and np.max(cnt) / np.sum(
- cnt) < self.cat_max_ratio:
+ if len(cnt) > 1 and np.max(cnt) / np.sum(cnt) < self.cat_max_ratio:
break
crop_bbox = self.get_crop_bbox(img)
# crop the image
img = self.crop(img, crop_bbox)
img_shape = img.shape
- results['img'] = img
- results['img_shape'] = img_shape
+ results["img"] = img
+ results["img_shape"] = img_shape
# crop semantic seg
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
results[key] = self.crop(results[key], crop_bbox)
return results
def __repr__(self):
- return self.__class__.__name__ + f'(crop_size={self.crop_size})'
+ return self.__class__.__name__ + f"(crop_size={self.crop_size})"
@PIPELINES.register_module()
-class RandomRotate(object):
+class RandomRotate:
"""Rotate the image & seg.
Args:
@@ -672,22 +677,19 @@ class RandomRotate(object):
rotated image. Default: False
"""
- def __init__(self,
- prob,
- degree,
- pad_val=0,
- seg_pad_val=255,
- center=None,
- auto_bound=False):
+ def __init__(
+ self, prob, degree, pad_val=0, seg_pad_val=255, center=None, auto_bound=False
+ ):
self.prob = prob
assert prob >= 0 and prob <= 1
if isinstance(degree, (float, int)):
- assert degree > 0, f'degree {degree} should be positive'
+ assert degree > 0, f"degree {degree} should be positive"
self.degree = (-degree, degree)
else:
self.degree = degree
- assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
- f'tuple of (min, max)'
+ assert len(self.degree) == 2, (
+ f"degree {self.degree} should be a " f"tuple of (min, max)"
+ )
self.pal_val = pad_val
self.seg_pad_val = seg_pad_val
self.center = center
@@ -707,37 +709,41 @@ def __call__(self, results):
degree = np.random.uniform(min(*self.degree), max(*self.degree))
if rotate:
# rotate image
- results['img'] = mmcv.imrotate(
- results['img'],
+ results["img"] = mmcv.imrotate(
+ results["img"],
angle=degree,
border_value=self.pal_val,
center=self.center,
- auto_bound=self.auto_bound)
+ auto_bound=self.auto_bound,
+ )
# rotate segs
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
results[key] = mmcv.imrotate(
results[key],
angle=degree,
border_value=self.seg_pad_val,
center=self.center,
auto_bound=self.auto_bound,
- interpolation='nearest')
+ interpolation="nearest",
+ )
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(prob={self.prob}, ' \
- f'degree={self.degree}, ' \
- f'pad_val={self.pal_val}, ' \
- f'seg_pad_val={self.seg_pad_val}, ' \
- f'center={self.center}, ' \
- f'auto_bound={self.auto_bound})'
+ repr_str += (
+ f"(prob={self.prob}, "
+ f"degree={self.degree}, "
+ f"pad_val={self.pal_val}, "
+ f"seg_pad_val={self.seg_pad_val}, "
+ f"center={self.center}, "
+ f"auto_bound={self.auto_bound})"
+ )
return repr_str
@PIPELINES.register_module()
-class RGB2Gray(object):
+class RGB2Gray:
"""Convert RGB image to grayscale image.
This transform calculate the weighted mean of input image channels with
@@ -769,7 +775,7 @@ def __call__(self, results):
Returns:
dict: Result dict with grayscale image.
"""
- img = results['img']
+ img = results["img"]
assert len(img.shape) == 3
assert img.shape[2] == len(self.weights)
weights = np.array(self.weights).reshape((1, 1, -1))
@@ -779,20 +785,19 @@ def __call__(self, results):
else:
img = img.repeat(self.out_channels, axis=2)
- results['img'] = img
- results['img_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(out_channels={self.out_channels}, ' \
- f'weights={self.weights})'
+ repr_str += f"(out_channels={self.out_channels}, " f"weights={self.weights})"
return repr_str
@PIPELINES.register_module()
-class AdjustGamma(object):
+class AdjustGamma:
"""Using gamma correction to process the image.
Args:
@@ -805,8 +810,9 @@ def __init__(self, gamma=1.0):
assert gamma > 0
self.gamma = gamma
inv_gamma = 1.0 / gamma
- self.table = np.array([(i / 255.0)**inv_gamma * 255
- for i in np.arange(256)]).astype('uint8')
+ self.table = np.array(
+ [(i / 255.0) ** inv_gamma * 255 for i in np.arange(256)]
+ ).astype("uint8")
def __call__(self, results):
"""Call function to process the image with gamma correction.
@@ -818,17 +824,18 @@ def __call__(self, results):
dict: Processed results.
"""
- results['img'] = mmcv.lut_transform(
- np.array(results['img'], dtype=np.uint8), self.table)
+ results["img"] = mmcv.lut_transform(
+ np.array(results["img"], dtype=np.uint8), self.table
+ )
return results
def __repr__(self):
- return self.__class__.__name__ + f'(gamma={self.gamma})'
+ return self.__class__.__name__ + f"(gamma={self.gamma})"
@PIPELINES.register_module()
-class SegRescale(object):
+class SegRescale:
"""Rescale semantic segmentation maps.
Args:
@@ -847,18 +854,19 @@ def __call__(self, results):
Returns:
dict: Result dict with semantic segmentation map scaled.
"""
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
if self.scale_factor != 1:
results[key] = mmcv.imrescale(
- results[key], self.scale_factor, interpolation='nearest')
+ results[key], self.scale_factor, interpolation="nearest"
+ )
return results
def __repr__(self):
- return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
+ return self.__class__.__name__ + f"(scale_factor={self.scale_factor})"
@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
+class PhotoMetricDistortion:
"""Apply photometric distortion to image sequentially, every transformation
is applied with a probability of 0.5. The position of random contrast is in
second or second to last.
@@ -878,11 +886,13 @@ class PhotoMetricDistortion(object):
hue_delta (int): delta of hue.
"""
- def __init__(self,
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18):
+ def __init__(
+ self,
+ brightness_delta=32,
+ contrast_range=(0.5, 1.5),
+ saturation_range=(0.5, 1.5),
+ hue_delta=18,
+ ):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
@@ -898,17 +908,16 @@ def brightness(self, img):
"""Brightness distortion."""
if random.randint(2):
return self.convert(
- img,
- beta=random.uniform(-self.brightness_delta,
- self.brightness_delta))
+ img, beta=random.uniform(-self.brightness_delta, self.brightness_delta)
+ )
return img
def contrast(self, img):
"""Contrast distortion."""
if random.randint(2):
return self.convert(
- img,
- alpha=random.uniform(self.contrast_lower, self.contrast_upper))
+ img, alpha=random.uniform(self.contrast_lower, self.contrast_upper)
+ )
return img
def saturation(self, img):
@@ -917,8 +926,8 @@ def saturation(self, img):
img = mmcv.bgr2hsv(img)
img[:, :, 1] = self.convert(
img[:, :, 1],
- alpha=random.uniform(self.saturation_lower,
- self.saturation_upper))
+ alpha=random.uniform(self.saturation_lower, self.saturation_upper),
+ )
img = mmcv.hsv2bgr(img)
return img
@@ -926,9 +935,10 @@ def hue(self, img):
"""Hue distortion."""
if random.randint(2):
img = mmcv.bgr2hsv(img)
- img[:, :,
- 0] = (img[:, :, 0].astype(int) +
- random.randint(-self.hue_delta, self.hue_delta)) % 180
+ img[:, :, 0] = (
+ img[:, :, 0].astype(int)
+ + random.randint(-self.hue_delta, self.hue_delta)
+ ) % 180
img = mmcv.hsv2bgr(img)
return img
@@ -942,7 +952,7 @@ def __call__(self, results):
dict: Result dict with images distorted.
"""
- img = results['img']
+ img = results["img"]
# random brightness
img = self.brightness(img)
@@ -962,22 +972,24 @@ def __call__(self, results):
if mode == 0:
img = self.contrast(img)
- results['img'] = img
+ results["img"] = img
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += (f'(brightness_delta={self.brightness_delta}, '
- f'contrast_range=({self.contrast_lower}, '
- f'{self.contrast_upper}), '
- f'saturation_range=({self.saturation_lower}, '
- f'{self.saturation_upper}), '
- f'hue_delta={self.hue_delta})')
+ repr_str += (
+ f"(brightness_delta={self.brightness_delta}, "
+ f"contrast_range=({self.contrast_lower}, "
+ f"{self.contrast_upper}), "
+ f"saturation_range=({self.saturation_lower}, "
+ f"{self.saturation_upper}), "
+ f"hue_delta={self.hue_delta})"
+ )
return repr_str
@PIPELINES.register_module()
-class RandomCutOut(object):
+class RandomCutOut:
"""CutOut operation.
Randomly drop some regions of image used in
@@ -1002,26 +1014,31 @@ class RandomCutOut(object):
If seg_fill_in is None, skip. Default: None.
"""
- def __init__(self,
- prob,
- n_holes,
- cutout_shape=None,
- cutout_ratio=None,
- fill_in=(0, 0, 0),
- seg_fill_in=None):
+ def __init__(
+ self,
+ prob,
+ n_holes,
+ cutout_shape=None,
+ cutout_ratio=None,
+ fill_in=(0, 0, 0),
+ seg_fill_in=None,
+ ):
assert 0 <= prob and prob <= 1
- assert (cutout_shape is None) ^ (cutout_ratio is None), \
- 'Either cutout_shape or cutout_ratio should be specified.'
- assert (isinstance(cutout_shape, (list, tuple))
- or isinstance(cutout_ratio, (list, tuple)))
+ assert (cutout_shape is None) ^ (
+ cutout_ratio is None
+ ), "Either cutout_shape or cutout_ratio should be specified."
+ assert isinstance(cutout_shape, (list, tuple)) or isinstance(
+ cutout_ratio, (list, tuple)
+ )
if isinstance(n_holes, tuple):
assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
else:
n_holes = (n_holes, n_holes)
if seg_fill_in is not None:
- assert (isinstance(seg_fill_in, int) and 0 <= seg_fill_in
- and seg_fill_in <= 255)
+ assert (
+ isinstance(seg_fill_in, int) and 0 <= seg_fill_in and seg_fill_in <= 255
+ )
self.prob = prob
self.n_holes = n_holes
self.fill_in = fill_in
@@ -1035,7 +1052,7 @@ def __call__(self, results):
"""Call function to drop some regions of image."""
cutout = True if np.random.rand() < self.prob else False
if cutout:
- h, w, c = results['img'].shape
+ h, w, c = results["img"].shape
n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
for _ in range(n_holes):
x1 = np.random.randint(0, w)
@@ -1049,27 +1066,30 @@ def __call__(self, results):
x2 = np.clip(x1 + cutout_w, 0, w)
y2 = np.clip(y1 + cutout_h, 0, h)
- results['img'][y1:y2, x1:x2, :] = self.fill_in
+ results["img"][y1:y2, x1:x2, :] = self.fill_in
if self.seg_fill_in is not None:
- for key in results.get('seg_fields', []):
+ for key in results.get("seg_fields", []):
results[key][y1:y2, x1:x2] = self.seg_fill_in
return results
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(prob={self.prob}, '
- repr_str += f'n_holes={self.n_holes}, '
- repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
- else f'cutout_shape={self.candidates}, ')
- repr_str += f'fill_in={self.fill_in}, '
- repr_str += f'seg_fill_in={self.seg_fill_in})'
+ repr_str += f"(prob={self.prob}, "
+ repr_str += f"n_holes={self.n_holes}, "
+ repr_str += (
+ f"cutout_ratio={self.candidates}, "
+ if self.with_ratio
+ else f"cutout_shape={self.candidates}, "
+ )
+ repr_str += f"fill_in={self.fill_in}, "
+ repr_str += f"seg_fill_in={self.seg_fill_in})"
return repr_str
@PIPELINES.register_module()
-class RandomMosaic(object):
+class RandomMosaic:
"""Mosaic augmentation. Given 4 images, mosaic transform combines them into
one output image. The output image is composed of the parts from each sub-
image.
@@ -1111,12 +1131,14 @@ class RandomMosaic(object):
seg_pad_val (int): Pad value of segmentation map. Default: 255.
"""
- def __init__(self,
- prob,
- img_scale=(640, 640),
- center_ratio_range=(0.5, 1.5),
- pad_val=0,
- seg_pad_val=255):
+ def __init__(
+ self,
+ prob,
+ img_scale=(640, 640),
+ center_ratio_range=(0.5, 1.5),
+ pad_val=0,
+ seg_pad_val=255,
+ ):
assert 0 <= prob and prob <= 1
assert isinstance(img_scale, tuple)
self.prob = prob
@@ -1163,52 +1185,57 @@ def _mosaic_transform_img(self, results):
dict: Updated result dict.
"""
- assert 'mix_results' in results
- if len(results['img'].shape) == 3:
+ assert "mix_results" in results
+ if len(results["img"].shape) == 3:
mosaic_img = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2), 3),
self.pad_val,
- dtype=results['img'].dtype)
+ dtype=results["img"].dtype,
+ )
else:
mosaic_img = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
self.pad_val,
- dtype=results['img'].dtype)
+ dtype=results["img"].dtype,
+ )
# mosaic center x, y
self.center_x = int(
- random.uniform(*self.center_ratio_range) * self.img_scale[1])
+ random.uniform(*self.center_ratio_range) * self.img_scale[1]
+ )
self.center_y = int(
- random.uniform(*self.center_ratio_range) * self.img_scale[0])
+ random.uniform(*self.center_ratio_range) * self.img_scale[0]
+ )
center_position = (self.center_x, self.center_y)
- loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+ loc_strs = ("top_left", "top_right", "bottom_left", "bottom_right")
for i, loc in enumerate(loc_strs):
- if loc == 'top_left':
+ if loc == "top_left":
result_patch = copy.deepcopy(results)
else:
- result_patch = copy.deepcopy(results['mix_results'][i - 1])
+ result_patch = copy.deepcopy(results["mix_results"][i - 1])
- img_i = result_patch['img']
+ img_i = result_patch["img"]
h_i, w_i = img_i.shape[:2]
# keep_ratio resize
- scale_ratio_i = min(self.img_scale[0] / h_i,
- self.img_scale[1] / w_i)
+ scale_ratio_i = min(self.img_scale[0] / h_i, self.img_scale[1] / w_i)
img_i = mmcv.imresize(
- img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
+ img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i))
+ )
# compute the combine parameters
paste_coord, crop_coord = self._mosaic_combine(
- loc, center_position, img_i.shape[:2][::-1])
+ loc, center_position, img_i.shape[:2][::-1]
+ )
x1_p, y1_p, x2_p, y2_p = paste_coord
x1_c, y1_c, x2_c, y2_c = crop_coord
# crop and paste image
mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
- results['img'] = mosaic_img
- results['img_shape'] = mosaic_img.shape
- results['ori_shape'] = mosaic_img.shape
+ results["img"] = mosaic_img
+ results["img_shape"] = mosaic_img.shape
+ results["ori_shape"] = mosaic_img.shape
return results
@@ -1222,42 +1249,43 @@ def _mosaic_transform_seg(self, results):
dict: Updated result dict.
"""
- assert 'mix_results' in results
- for key in results.get('seg_fields', []):
+ assert "mix_results" in results
+ for key in results.get("seg_fields", []):
mosaic_seg = np.full(
(int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
self.seg_pad_val,
- dtype=results[key].dtype)
+ dtype=results[key].dtype,
+ )
# mosaic center x, y
center_position = (self.center_x, self.center_y)
- loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+ loc_strs = ("top_left", "top_right", "bottom_left", "bottom_right")
for i, loc in enumerate(loc_strs):
- if loc == 'top_left':
+ if loc == "top_left":
result_patch = copy.deepcopy(results)
else:
- result_patch = copy.deepcopy(results['mix_results'][i - 1])
+ result_patch = copy.deepcopy(results["mix_results"][i - 1])
gt_seg_i = result_patch[key]
h_i, w_i = gt_seg_i.shape[:2]
# keep_ratio resize
- scale_ratio_i = min(self.img_scale[0] / h_i,
- self.img_scale[1] / w_i)
+ scale_ratio_i = min(self.img_scale[0] / h_i, self.img_scale[1] / w_i)
gt_seg_i = mmcv.imresize(
gt_seg_i,
(int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)),
- interpolation='nearest')
+ interpolation="nearest",
+ )
# compute the combine parameters
paste_coord, crop_coord = self._mosaic_combine(
- loc, center_position, gt_seg_i.shape[:2][::-1])
+ loc, center_position, gt_seg_i.shape[:2][::-1]
+ )
x1_p, y1_p, x2_p, y2_p = paste_coord
x1_c, y1_c, x2_c, y2_c = crop_coord
# crop and paste image
- mosaic_seg[y1_p:y2_p, x1_p:x2_p] = gt_seg_i[y1_c:y2_c,
- x1_c:x2_c]
+ mosaic_seg[y1_p:y2_p, x1_p:x2_p] = gt_seg_i[y1_c:y2_c, x1_c:x2_c]
results[key] = mosaic_seg
@@ -1281,55 +1309,75 @@ def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
- crop_coord (tuple): crop corner coordinate in mosaic image.
"""
- assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
- if loc == 'top_left':
+ assert loc in ("top_left", "top_right", "bottom_left", "bottom_right")
+ if loc == "top_left":
# index0 to top left part of image
- x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
- max(center_position_xy[1] - img_shape_wh[1], 0), \
- center_position_xy[0], \
- center_position_xy[1]
- crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
- y2 - y1), img_shape_wh[0], img_shape_wh[1]
-
- elif loc == 'top_right':
+ x1, y1, x2, y2 = (
+ max(center_position_xy[0] - img_shape_wh[0], 0),
+ max(center_position_xy[1] - img_shape_wh[1], 0),
+ center_position_xy[0],
+ center_position_xy[1],
+ )
+ crop_coord = (
+ img_shape_wh[0] - (x2 - x1),
+ img_shape_wh[1] - (y2 - y1),
+ img_shape_wh[0],
+ img_shape_wh[1],
+ )
+
+ elif loc == "top_right":
# index1 to top right part of image
- x1, y1, x2, y2 = center_position_xy[0], \
- max(center_position_xy[1] - img_shape_wh[1], 0), \
- min(center_position_xy[0] + img_shape_wh[0],
- self.img_scale[1] * 2), \
- center_position_xy[1]
- crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
- img_shape_wh[0], x2 - x1), img_shape_wh[1]
-
- elif loc == 'bottom_left':
+ x1, y1, x2, y2 = (
+ center_position_xy[0],
+ max(center_position_xy[1] - img_shape_wh[1], 0),
+ min(center_position_xy[0] + img_shape_wh[0], self.img_scale[1] * 2),
+ center_position_xy[1],
+ )
+ crop_coord = (
+ 0,
+ img_shape_wh[1] - (y2 - y1),
+ min(img_shape_wh[0], x2 - x1),
+ img_shape_wh[1],
+ )
+
+ elif loc == "bottom_left":
# index2 to bottom left part of image
- x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
- center_position_xy[1], \
- center_position_xy[0], \
- min(self.img_scale[0] * 2, center_position_xy[1] +
- img_shape_wh[1])
- crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
- y2 - y1, img_shape_wh[1])
+ x1, y1, x2, y2 = (
+ max(center_position_xy[0] - img_shape_wh[0], 0),
+ center_position_xy[1],
+ center_position_xy[0],
+ min(self.img_scale[0] * 2, center_position_xy[1] + img_shape_wh[1]),
+ )
+ crop_coord = (
+ img_shape_wh[0] - (x2 - x1),
+ 0,
+ img_shape_wh[0],
+ min(y2 - y1, img_shape_wh[1]),
+ )
else:
# index3 to bottom right part of image
- x1, y1, x2, y2 = center_position_xy[0], \
- center_position_xy[1], \
- min(center_position_xy[0] + img_shape_wh[0],
- self.img_scale[1] * 2), \
- min(self.img_scale[0] * 2, center_position_xy[1] +
- img_shape_wh[1])
- crop_coord = 0, 0, min(img_shape_wh[0],
- x2 - x1), min(y2 - y1, img_shape_wh[1])
+ x1, y1, x2, y2 = (
+ center_position_xy[0],
+ center_position_xy[1],
+ min(center_position_xy[0] + img_shape_wh[0], self.img_scale[1] * 2),
+ min(self.img_scale[0] * 2, center_position_xy[1] + img_shape_wh[1]),
+ )
+ crop_coord = (
+ 0,
+ 0,
+ min(img_shape_wh[0], x2 - x1),
+ min(y2 - y1, img_shape_wh[1]),
+ )
paste_coord = x1, y1, x2, y2
return paste_coord, crop_coord
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(prob={self.prob}, '
- repr_str += f'img_scale={self.img_scale}, '
- repr_str += f'center_ratio_range={self.center_ratio_range}, '
- repr_str += f'pad_val={self.pad_val}, '
- repr_str += f'seg_pad_val={self.pad_val})'
+ repr_str += f"(prob={self.prob}, "
+ repr_str += f"img_scale={self.img_scale}, "
+ repr_str += f"center_ratio_range={self.center_ratio_range}, "
+ repr_str += f"pad_val={self.pad_val}, "
+ repr_str += f"seg_pad_val={self.pad_val})"
return repr_str
diff --git a/mmsegmentation/mmseg/datasets/potsdam.py b/mmsegmentation/mmseg/datasets/potsdam.py
index 2986b8f..44dd4e7 100644
--- a/mmsegmentation/mmseg/datasets/potsdam.py
+++ b/mmsegmentation/mmseg/datasets/potsdam.py
@@ -11,15 +11,26 @@ class PotsdamDataset(CustomDataset):
``reduce_zero_label`` should be set to True. The ``img_suffix`` and
``seg_map_suffix`` are both fixed to '.png'.
"""
- CLASSES = ('impervious_surface', 'building', 'low_vegetation', 'tree',
- 'car', 'clutter')
- PALETTE = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
- [255, 255, 0], [255, 0, 0]]
+ CLASSES = (
+ "impervious_surface",
+ "building",
+ "low_vegetation",
+ "tree",
+ "car",
+ "clutter",
+ )
+
+ PALETTE = [
+ [255, 255, 255],
+ [0, 0, 255],
+ [0, 255, 255],
+ [0, 255, 0],
+ [255, 255, 0],
+ [255, 0, 0],
+ ]
def __init__(self, **kwargs):
- super(PotsdamDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.png',
- reduce_zero_label=True,
- **kwargs)
+ super().__init__(
+ img_suffix=".png", seg_map_suffix=".png", reduce_zero_label=True, **kwargs
+ )
diff --git a/mmsegmentation/mmseg/datasets/samplers/__init__.py b/mmsegmentation/mmseg/datasets/samplers/__init__.py
index da09eff..c2c3d98 100644
--- a/mmsegmentation/mmseg/datasets/samplers/__init__.py
+++ b/mmsegmentation/mmseg/datasets/samplers/__init__.py
@@ -1,4 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
-__all__ = ['DistributedSampler']
+__all__ = ["DistributedSampler"]
diff --git a/mmsegmentation/mmseg/datasets/samplers/distributed_sampler.py b/mmsegmentation/mmseg/datasets/samplers/distributed_sampler.py
index 4f9bf35..ece8b57 100644
--- a/mmsegmentation/mmseg/datasets/samplers/distributed_sampler.py
+++ b/mmsegmentation/mmseg/datasets/samplers/distributed_sampler.py
@@ -1,5 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
from typing import Iterator, Optional
import torch
@@ -27,14 +26,15 @@ class DistributedSampler(_DistributedSampler):
processes in the distributed group. Default: ``0``.
"""
- def __init__(self,
- dataset: Dataset,
- num_replicas: Optional[int] = None,
- rank: Optional[int] = None,
- shuffle: bool = True,
- seed=0) -> None:
- super().__init__(
- dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+ def __init__(
+ self,
+ dataset: Dataset,
+ num_replicas: Optional[int] = None,
+ rank: Optional[int] = None,
+ shuffle: bool = True,
+ seed=0,
+ ) -> None:
+ super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
# In distributed sampling, different ranks should sample
# non-overlapped data in the dataset. Therefore, this function
@@ -47,8 +47,8 @@ def __init__(self,
def __iter__(self) -> Iterator:
"""
- Yields:
- Iterator: iterator of indices for rank.
+ Yields:
+ Iterator: iterator of indices for rank.
"""
# deterministically shuffle based on epoch
if self.shuffle:
@@ -63,11 +63,11 @@ def __iter__(self) -> Iterator:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
- indices += indices[:(self.total_size - len(indices))]
+ indices += indices[: (self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
- indices = indices[self.rank:self.total_size:self.num_replicas]
+ indices = indices[self.rank : self.total_size : self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
diff --git a/mmsegmentation/mmseg/datasets/stare.py b/mmsegmentation/mmseg/datasets/stare.py
index a24d1d9..2eec5ba 100644
--- a/mmsegmentation/mmseg/datasets/stare.py
+++ b/mmsegmentation/mmseg/datasets/stare.py
@@ -15,14 +15,15 @@ class STAREDataset(CustomDataset):
'.ah.png'.
"""
- CLASSES = ('background', 'vessel')
+ CLASSES = ("background", "vessel")
PALETTE = [[120, 120, 120], [6, 230, 230]]
def __init__(self, **kwargs):
- super(STAREDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.ah.png',
+ super().__init__(
+ img_suffix=".png",
+ seg_map_suffix=".ah.png",
reduce_zero_label=False,
- **kwargs)
+ **kwargs,
+ )
assert osp.exists(self.img_dir)
diff --git a/mmsegmentation/mmseg/datasets/voc.py b/mmsegmentation/mmseg/datasets/voc.py
index 3cec9e3..149bf37 100644
--- a/mmsegmentation/mmseg/datasets/voc.py
+++ b/mmsegmentation/mmseg/datasets/voc.py
@@ -13,18 +13,56 @@ class PascalVOCDataset(CustomDataset):
split (str): Split txt file for Pascal VOC.
"""
- CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
- 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
- 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
- 'train', 'tvmonitor')
+ CLASSES = (
+ "background",
+ "aeroplane",
+ "bicycle",
+ "bird",
+ "boat",
+ "bottle",
+ "bus",
+ "car",
+ "cat",
+ "chair",
+ "cow",
+ "diningtable",
+ "dog",
+ "horse",
+ "motorbike",
+ "person",
+ "pottedplant",
+ "sheep",
+ "sofa",
+ "train",
+ "tvmonitor",
+ )
- PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
- [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
- [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
- [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
- [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
+ PALETTE = [
+ [0, 0, 0],
+ [128, 0, 0],
+ [0, 128, 0],
+ [128, 128, 0],
+ [0, 0, 128],
+ [128, 0, 128],
+ [0, 128, 128],
+ [128, 128, 128],
+ [64, 0, 0],
+ [192, 0, 0],
+ [64, 128, 0],
+ [192, 128, 0],
+ [64, 0, 128],
+ [192, 0, 128],
+ [64, 128, 128],
+ [192, 128, 128],
+ [0, 64, 0],
+ [128, 64, 0],
+ [0, 192, 0],
+ [128, 192, 0],
+ [0, 64, 128],
+ ]
def __init__(self, split, **kwargs):
- super(PascalVOCDataset, self).__init__(
- img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
+ super().__init__(
+ img_suffix=".jpg", seg_map_suffix=".png", split=split, **kwargs
+ )
assert osp.exists(self.img_dir) and self.split is not None
diff --git a/mmsegmentation/mmseg/models/__init__.py b/mmsegmentation/mmseg/models/__init__.py
index 87d8108..05765bc 100644
--- a/mmsegmentation/mmseg/models/__init__.py
+++ b/mmsegmentation/mmseg/models/__init__.py
@@ -1,13 +1,27 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
-from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone,
- build_head, build_loss, build_segmentor)
+from .builder import (
+ BACKBONES,
+ HEADS,
+ LOSSES,
+ SEGMENTORS,
+ build_backbone,
+ build_head,
+ build_loss,
+ build_segmentor,
+)
from .decode_heads import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
from .necks import * # noqa: F401,F403
from .segmentors import * # noqa: F401,F403
__all__ = [
- 'BACKBONES', 'HEADS', 'LOSSES', 'SEGMENTORS', 'build_backbone',
- 'build_head', 'build_loss', 'build_segmentor'
+ "BACKBONES",
+ "HEADS",
+ "LOSSES",
+ "SEGMENTORS",
+ "build_backbone",
+ "build_head",
+ "build_loss",
+ "build_segmentor",
]
diff --git a/mmsegmentation/mmseg/models/backbones/__init__.py b/mmsegmentation/mmseg/models/backbones/__init__.py
index bda42bb..4e686e2 100644
--- a/mmsegmentation/mmseg/models/backbones/__init__.py
+++ b/mmsegmentation/mmseg/models/backbones/__init__.py
@@ -22,9 +22,29 @@
from .vit import VisionTransformer
__all__ = [
- 'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
- 'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
- 'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
- 'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'TIMMBackbone', 'ERFNet', 'PCPVT',
- 'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE'
+ "ResNet",
+ "ResNetV1c",
+ "ResNetV1d",
+ "ResNeXt",
+ "HRNet",
+ "FastSCNN",
+ "ResNeSt",
+ "MobileNetV2",
+ "UNet",
+ "CGNet",
+ "MobileNetV3",
+ "VisionTransformer",
+ "SwinTransformer",
+ "MixVisionTransformer",
+ "BiSeNetV1",
+ "BiSeNetV2",
+ "ICNet",
+ "TIMMBackbone",
+ "ERFNet",
+ "PCPVT",
+ "SVT",
+ "STDCNet",
+ "STDCContextPathNet",
+ "BEiT",
+ "MAE",
]
diff --git a/mmsegmentation/mmseg/models/backbones/beit.py b/mmsegmentation/mmseg/models/backbones/beit.py
index fade601..817196d 100644
--- a/mmsegmentation/mmseg/models/backbones/beit.py
+++ b/mmsegmentation/mmseg/models/backbones/beit.py
@@ -7,8 +7,7 @@
import torch.nn.functional as F
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
-from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
- trunc_normal_)
+from mmcv.cnn.utils.weight_init import constant_init, kaiming_init, trunc_normal_
from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _pair as to_2tuple
@@ -45,16 +44,18 @@ class BEiTAttention(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- window_size,
- bias='qv_bias',
- qk_scale=None,
- attn_drop_rate=0.,
- proj_drop_rate=0.,
- init_cfg=None,
- **kwargs):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ window_size,
+ bias="qv_bias",
+ qk_scale=None,
+ attn_drop_rate=0.0,
+ proj_drop_rate=0.0,
+ init_cfg=None,
+ **kwargs,
+ ):
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
self.num_heads = num_heads
@@ -63,7 +64,7 @@ def __init__(self,
self.scale = qk_scale or head_embed_dims**-0.5
qkv_bias = bias
- if bias == 'qv_bias':
+ if bias == "qv_bias":
self._init_qv_bias()
qkv_bias = False
@@ -85,7 +86,8 @@ def _init_rel_pos_embedding(self):
self.num_relative_distance = (2 * Wh - 1) * (2 * Ww - 1) + 3
# relative_position_bias_table shape is (2*Wh-1 * 2*Ww-1 + 3, nH)
self.relative_position_bias_table = nn.Parameter(
- torch.zeros(self.num_relative_distance, self.num_heads))
+ torch.zeros(self.num_relative_distance, self.num_heads)
+ )
# get pair-wise relative position index for
# each token inside the window
@@ -95,8 +97,7 @@ def _init_rel_pos_embedding(self):
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
# coords_flatten shape is (2, Wh*Ww)
coords_flatten = torch.flatten(coords, 1)
- relative_coords = (
- coords_flatten[:, :, None] - coords_flatten[:, None, :])
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
# relative_coords shape is (Wh*Ww, Wh*Ww, 2)
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
# shift to start from 0
@@ -104,15 +105,15 @@ def _init_rel_pos_embedding(self):
relative_coords[:, :, 1] += Ww - 1
relative_coords[:, :, 0] *= 2 * Ww - 1
relative_position_index = torch.zeros(
- size=(Wh * Ww + 1, ) * 2, dtype=relative_coords.dtype)
+ size=(Wh * Ww + 1,) * 2, dtype=relative_coords.dtype
+ )
# relative_position_index shape is (Wh*Ww, Wh*Ww)
relative_position_index[1:, 1:] = relative_coords.sum(-1)
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
- self.register_buffer('relative_position_index',
- relative_position_index)
+ self.register_buffer("relative_position_index", relative_position_index)
def init_weights(self):
trunc_normal_(self.relative_position_bias_table, std=0.02)
@@ -124,7 +125,7 @@ def forward(self, x):
"""
B, N, C = x.shape
- if self.bias == 'qv_bias':
+ if self.bias == "qv_bias":
k_bias = torch.zeros_like(self.v_bias, requires_grad=False)
qkv_bias = torch.cat((self.q_bias, k_bias, self.v_bias))
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
@@ -134,15 +135,16 @@ def forward(self, x):
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
+ attn = q @ k.transpose(-2, -1)
if self.relative_position_bias_table is not None:
Wh = self.window_size[0]
Ww = self.window_size[1]
relative_position_bias = self.relative_position_bias_table[
- self.relative_position_index.view(-1)].view(
- Wh * Ww + 1, Wh * Ww + 1, -1)
+ self.relative_position_index.view(-1)
+ ].view(Wh * Ww + 1, Wh * Ww + 1, -1)
relative_position_bias = relative_position_bias.permute(
- 2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ 2, 0, 1
+ ).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
@@ -178,45 +180,51 @@ class BEiTTransformerEncoderLayer(VisionTransformerEncoderLayer):
and FFN with learnable scaling. Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- num_fcs=2,
- bias='qv_bias',
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- window_size=None,
- attn_cfg=dict(),
- ffn_cfg=dict(add_identity=False),
- init_values=None):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ num_fcs=2,
+ bias="qv_bias",
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ window_size=None,
+ attn_cfg=dict(),
+ ffn_cfg=dict(add_identity=False),
+ init_values=None,
+ ):
attn_cfg.update(dict(window_size=window_size, qk_scale=None))
- super(BEiTTransformerEncoderLayer, self).__init__(
+ super().__init__(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=feedforward_channels,
attn_drop_rate=attn_drop_rate,
- drop_path_rate=0.,
- drop_rate=0.,
+ drop_path_rate=0.0,
+ drop_rate=0.0,
num_fcs=num_fcs,
qkv_bias=bias,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
attn_cfg=attn_cfg,
- ffn_cfg=ffn_cfg)
+ ffn_cfg=ffn_cfg,
+ )
# NOTE: drop path for stochastic depth, we shall see if
# this is better than dropout here
- dropout_layer = dict(type='DropPath', drop_prob=drop_path_rate)
- self.drop_path = build_dropout(
- dropout_layer) if dropout_layer else nn.Identity()
+ dropout_layer = dict(type="DropPath", drop_prob=drop_path_rate)
+ self.drop_path = (
+ build_dropout(dropout_layer) if dropout_layer else nn.Identity()
+ )
self.gamma_1 = nn.Parameter(
- init_values * torch.ones((embed_dims)), requires_grad=True)
+ init_values * torch.ones(embed_dims), requires_grad=True
+ )
self.gamma_2 = nn.Parameter(
- init_values * torch.ones((embed_dims)), requires_grad=True)
+ init_values * torch.ones(embed_dims), requires_grad=True
+ )
def build_attn(self, attn_cfg):
self.attn = BEiTAttention(**attn_cfg)
@@ -266,45 +274,51 @@ class BEiT(BaseModule):
Default: None.
"""
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_channels=3,
- embed_dims=768,
- num_layers=12,
- num_heads=12,
- mlp_ratio=4,
- out_indices=-1,
- qv_bias=True,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- norm_cfg=dict(type='LN'),
- act_cfg=dict(type='GELU'),
- patch_norm=False,
- final_norm=False,
- num_fcs=2,
- norm_eval=False,
- pretrained=None,
- init_values=0.1,
- init_cfg=None):
- super(BEiT, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ img_size=224,
+ patch_size=16,
+ in_channels=3,
+ embed_dims=768,
+ num_layers=12,
+ num_heads=12,
+ mlp_ratio=4,
+ out_indices=-1,
+ qv_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ norm_cfg=dict(type="LN"),
+ act_cfg=dict(type="GELU"),
+ patch_norm=False,
+ final_norm=False,
+ num_fcs=2,
+ norm_eval=False,
+ pretrained=None,
+ init_values=0.1,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
if isinstance(img_size, int):
img_size = to_2tuple(img_size)
elif isinstance(img_size, tuple):
if len(img_size) == 1:
img_size = to_2tuple(img_size[0])
- assert len(img_size) == 2, \
- f'The size of image should have length 1 or 2, ' \
- f'but got {len(img_size)}'
-
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be set at the same time'
+ assert len(img_size) == 2, (
+ f"The size of image should have length 1 or 2, "
+ f"but got {len(img_size)}"
+ )
+
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be set at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is not None:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.in_channels = in_channels
self.img_size = img_size
@@ -323,8 +337,7 @@ def __init__(self,
self.norm_cfg = norm_cfg
self.patch_norm = patch_norm
self.init_values = init_values
- self.window_size = (img_size[0] // patch_size,
- img_size[1] // patch_size)
+ self.window_size = (img_size[0] // patch_size, img_size[1] // patch_size)
self.patch_shape = self.window_size
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
@@ -338,12 +351,11 @@ def __init__(self,
elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
self.out_indices = out_indices
else:
- raise TypeError('out_indices must be type of int, list or tuple')
+ raise TypeError("out_indices must be type of int, list or tuple")
self.final_norm = final_norm
if final_norm:
- self.norm1_name, norm1 = build_norm_layer(
- norm_cfg, embed_dims, postfix=1)
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
def _build_patch_embedding(self):
@@ -351,19 +363,19 @@ def _build_patch_embedding(self):
self.patch_embed = PatchEmbed(
in_channels=self.in_channels,
embed_dims=self.embed_dims,
- conv_type='Conv2d',
+ conv_type="Conv2d",
kernel_size=self.patch_size,
stride=self.patch_size,
padding=0,
norm_cfg=self.norm_cfg if self.patch_norm else None,
- init_cfg=None)
+ init_cfg=None,
+ )
def _build_layers(self):
"""Build transformer encoding layers."""
dpr = [
- x.item()
- for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
+ x.item() for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
]
self.layers = ModuleList()
for i in range(self.num_layers):
@@ -375,18 +387,19 @@ def _build_layers(self):
attn_drop_rate=self.attn_drop_rate,
drop_path_rate=dpr[i],
num_fcs=self.num_fcs,
- bias='qv_bias' if self.qv_bias else False,
+ bias="qv_bias" if self.qv_bias else False,
act_cfg=self.act_cfg,
norm_cfg=self.norm_cfg,
window_size=self.window_size,
- init_values=self.init_values))
+ init_values=self.init_values,
+ )
+ )
@property
def norm1(self):
return getattr(self, self.norm1_name)
- def _geometric_sequence_interpolation(self, src_size, dst_size, sequence,
- num):
+ def _geometric_sequence_interpolation(self, src_size, dst_size, sequence, num):
"""Get new sequence via geometric sequence interpolation.
Args:
@@ -419,7 +432,7 @@ def geometric_progression(a, r, n):
cur = 1
for i in range(src_size // 2):
dis.append(cur)
- cur += q**(i + 1)
+ cur += q ** (i + 1)
r_ids = [-_ for _ in reversed(dis)]
x = r_ids + [0] + dis
y = r_ids + [0] + dis
@@ -430,9 +443,10 @@ def geometric_progression(a, r, n):
new_sequence = []
for i in range(num):
z = sequence[:, i].view(src_size, src_size).float().numpy()
- f = interpolate.interp2d(x, y, z, kind='cubic')
+ f = interpolate.interp2d(x, y, z, kind="cubic")
new_sequence.append(
- torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(sequence))
+ torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(sequence)
+ )
new_sequence = torch.cat(new_sequence, dim=-1)
return new_sequence
@@ -449,19 +463,19 @@ def resize_rel_pos_embed(self, checkpoint):
state_dict (dict): Interpolate the relative pos_embed weights
in the pre-train model to the current model size.
"""
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
else:
state_dict = checkpoint
all_keys = list(state_dict.keys())
for key in all_keys:
- if 'relative_position_index' in key:
+ if "relative_position_index" in key:
state_dict.pop(key)
# In order to keep the center of pos_bias as consistent as
# possible after interpolation, and vice versa in the edge
# area, the geometric sequence interpolation method is adopted.
- if 'relative_position_bias_table' in key:
+ if "relative_position_bias_table" in key:
rel_pos_bias = state_dict[key]
src_num_pos, num_attn_heads = rel_pos_bias.size()
dst_num_pos, _ = self.state_dict()[key].size()
@@ -469,18 +483,20 @@ def resize_rel_pos_embed(self, checkpoint):
if dst_patch_shape[0] != dst_patch_shape[1]:
raise NotImplementedError()
# Count the number of extra tokens.
- num_extra_tokens = dst_num_pos - (
- dst_patch_shape[0] * 2 - 1) * (
- dst_patch_shape[1] * 2 - 1)
- src_size = int((src_num_pos - num_extra_tokens)**0.5)
- dst_size = int((dst_num_pos - num_extra_tokens)**0.5)
+ num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (
+ dst_patch_shape[1] * 2 - 1
+ )
+ src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
+ dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
if src_size != dst_size:
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
new_rel_pos_bias = self._geometric_sequence_interpolation(
- src_size, dst_size, rel_pos_bias, num_attn_heads)
+ src_size, dst_size, rel_pos_bias, num_attn_heads
+ )
new_rel_pos_bias = torch.cat(
- (new_rel_pos_bias, extra_tokens), dim=0)
+ (new_rel_pos_bias, extra_tokens), dim=0
+ )
state_dict[key] = new_rel_pos_bias
return state_dict
@@ -489,7 +505,7 @@ def init_weights(self):
def _init_weights(m):
if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
+ trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
@@ -498,33 +514,36 @@ def _init_weights(m):
self.apply(_init_weights)
- if (isinstance(self.init_cfg, dict)
- and self.init_cfg.get('type') == 'Pretrained'):
+ if (
+ isinstance(self.init_cfg, dict)
+ and self.init_cfg.get("type") == "Pretrained"
+ ):
logger = get_root_logger()
checkpoint = _load_checkpoint(
- self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
+ self.init_cfg["checkpoint"], logger=logger, map_location="cpu"
+ )
state_dict = self.resize_rel_pos_embed(checkpoint)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
- super(BEiT, self).init_weights()
+ super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
# Copyright 2019 Ross Wightman
# Licensed under the Apache License, Version 2.0 (the "License")
- trunc_normal_(self.cls_token, std=.02)
+ trunc_normal_(self.cls_token, std=0.02)
for n, m in self.named_modules():
if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
+ trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
- if 'ffn' in n:
- nn.init.normal_(m.bias, mean=0., std=1e-6)
+ if "ffn" in n:
+ nn.init.normal_(m.bias, mean=0.0, std=1e-6)
else:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
- kaiming_init(m, mode='fan_in', bias=0.)
+ kaiming_init(m, mode="fan_in", bias=0.0)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
def forward(self, inputs):
B = inputs.shape[0]
@@ -545,14 +564,17 @@ def forward(self, inputs):
# Remove class token and reshape token for decoder head
out = x[:, 1:]
B, _, C = out.shape
- out = out.reshape(B, hw_shape[0], hw_shape[1],
- C).permute(0, 3, 1, 2).contiguous()
+ out = (
+ out.reshape(B, hw_shape[0], hw_shape[1], C)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
outs.append(out)
return tuple(outs)
def train(self, mode=True):
- super(BEiT, self).train(mode)
+ super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, nn.LayerNorm):
diff --git a/mmsegmentation/mmseg/models/backbones/bisenetv1.py b/mmsegmentation/mmseg/models/backbones/bisenetv1.py
index 4beb7b3..86bf30b 100644
--- a/mmsegmentation/mmseg/models/backbones/bisenetv1.py
+++ b/mmsegmentation/mmseg/models/backbones/bisenetv1.py
@@ -22,20 +22,24 @@ class SpatialPath(BaseModule):
x (torch.Tensor): Feature map for Feature Fusion Module.
"""
- def __init__(self,
- in_channels=3,
- num_channels=(64, 64, 64, 128),
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(SpatialPath, self).__init__(init_cfg=init_cfg)
- assert len(num_channels) == 4, 'Length of input channels \
- of Spatial Path must be 4!'
+ def __init__(
+ self,
+ in_channels=3,
+ num_channels=(64, 64, 64, 128),
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ len(num_channels) == 4
+ ), "Length of input channels \
+ of Spatial Path must be 4!"
self.layers = []
for i in range(len(num_channels)):
- layer_name = f'layer{i + 1}'
+ layer_name = f"layer{i + 1}"
self.layers.append(layer_name)
if i == 0:
self.add_module(
@@ -48,7 +52,9 @@ def __init__(self,
padding=3,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ ),
+ )
elif i == len(num_channels) - 1:
self.add_module(
layer_name,
@@ -60,7 +66,9 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ ),
+ )
else:
self.add_module(
layer_name,
@@ -72,7 +80,9 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ ),
+ )
def forward(self, x):
for i, layer_name in enumerate(self.layers):
@@ -91,14 +101,16 @@ class AttentionRefinementModule(BaseModule):
x_out (torch.Tensor): Feature map of Attention Refinement Module.
"""
- def __init__(self,
- in_channels,
- out_channel,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(AttentionRefinementModule, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channel,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv_layer = ConvModule(
in_channels=in_channels,
out_channels=out_channel,
@@ -107,7 +119,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.atten_conv_layer = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
ConvModule(
@@ -117,7 +130,10 @@ def __init__(self,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None), nn.Sigmoid())
+ act_cfg=None,
+ ),
+ nn.Sigmoid(),
+ )
def forward(self, x):
x = self.conv_layer(x)
@@ -144,25 +160,27 @@ class ContextPath(BaseModule):
Fusion Module and Auxiliary Head.
"""
- def __init__(self,
- backbone_cfg,
- context_channels=(128, 256, 512),
- align_corners=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(ContextPath, self).__init__(init_cfg=init_cfg)
- assert len(context_channels) == 3, 'Length of input channels \
- of Context Path must be 3!'
+ def __init__(
+ self,
+ backbone_cfg,
+ context_channels=(128, 256, 512),
+ align_corners=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ len(context_channels) == 3
+ ), "Length of input channels \
+ of Context Path must be 3!"
self.backbone = build_backbone(backbone_cfg)
self.align_corners = align_corners
- self.arm16 = AttentionRefinementModule(context_channels[1],
- context_channels[0])
- self.arm32 = AttentionRefinementModule(context_channels[2],
- context_channels[0])
+ self.arm16 = AttentionRefinementModule(context_channels[1], context_channels[0])
+ self.arm32 = AttentionRefinementModule(context_channels[2], context_channels[0])
self.conv_head32 = ConvModule(
in_channels=context_channels[0],
out_channels=context_channels[0],
@@ -171,7 +189,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.conv_head16 = ConvModule(
in_channels=context_channels[0],
out_channels=context_channels[0],
@@ -180,7 +199,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.gap_conv = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
ConvModule(
@@ -191,7 +211,9 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ ),
+ )
def forward(self, x):
x_4, x_8, x_16, x_32 = self.backbone(x)
@@ -199,12 +221,12 @@ def forward(self, x):
x_32_arm = self.arm32(x_32)
x_32_sum = x_32_arm + x_gap
- x_32_up = resize(input=x_32_sum, size=x_16.shape[2:], mode='nearest')
+ x_32_up = resize(input=x_32_sum, size=x_16.shape[2:], mode="nearest")
x_32_up = self.conv_head32(x_32_up)
x_16_arm = self.arm16(x_16)
x_16_sum = x_16_arm + x_32_up
- x_16_up = resize(input=x_16_sum, size=x_8.shape[2:], mode='nearest')
+ x_16_up = resize(input=x_16_sum, size=x_8.shape[2:], mode="nearest")
x_16_up = self.conv_head16(x_16_up)
return x_16_up, x_32_up
@@ -221,14 +243,16 @@ class FeatureFusionModule(BaseModule):
x_out (torch.Tensor): Feature map of Feature Fusion Module.
"""
- def __init__(self,
- in_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(FeatureFusionModule, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv1 = ConvModule(
in_channels=in_channels,
out_channels=out_channels,
@@ -237,7 +261,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.gap = nn.AdaptiveAvgPool2d((1, 1))
self.conv_atten = nn.Sequential(
ConvModule(
@@ -249,7 +274,10 @@ def __init__(self,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg), nn.Sigmoid())
+ act_cfg=act_cfg,
+ ),
+ nn.Sigmoid(),
+ )
def forward(self, x_sp, x_cp):
x_concat = torch.cat([x_sp, x_cp], dim=1)
@@ -291,30 +319,37 @@ class BiSeNetV1(BaseModule):
Default: 256.
"""
- def __init__(self,
- backbone_cfg,
- in_channels=3,
- spatial_channels=(64, 64, 64, 128),
- context_channels=(128, 256, 512),
- out_indices=(0, 1, 2),
- align_corners=False,
- out_channels=256,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
-
- super(BiSeNetV1, self).__init__(init_cfg=init_cfg)
- assert len(spatial_channels) == 4, 'Length of input channels \
- of Spatial Path must be 4!'
-
- assert len(context_channels) == 3, 'Length of input channels \
- of Context Path must be 3!'
+ def __init__(
+ self,
+ backbone_cfg,
+ in_channels=3,
+ spatial_channels=(64, 64, 64, 128),
+ context_channels=(128, 256, 512),
+ out_indices=(0, 1, 2),
+ align_corners=False,
+ out_channels=256,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ len(spatial_channels) == 4
+ ), "Length of input channels \
+ of Spatial Path must be 4!"
+
+ assert (
+ len(context_channels) == 3
+ ), "Length of input channels \
+ of Context Path must be 3!"
self.out_indices = out_indices
self.align_corners = align_corners
- self.context_path = ContextPath(backbone_cfg, context_channels,
- self.align_corners)
+ self.context_path = ContextPath(
+ backbone_cfg, context_channels, self.align_corners
+ )
self.spatial_path = SpatialPath(in_channels, spatial_channels)
self.ffm = FeatureFusionModule(context_channels[1], out_channels)
self.conv_cfg = conv_cfg
diff --git a/mmsegmentation/mmseg/models/backbones/bisenetv2.py b/mmsegmentation/mmseg/models/backbones/bisenetv2.py
index d908b32..c97928c 100644
--- a/mmsegmentation/mmseg/models/backbones/bisenetv2.py
+++ b/mmsegmentation/mmseg/models/backbones/bisenetv2.py
@@ -1,8 +1,12 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
-from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule,
- build_activation_layer, build_norm_layer)
+from mmcv.cnn import (
+ ConvModule,
+ DepthwiseSeparableConvModule,
+ build_activation_layer,
+ build_norm_layer,
+)
from mmcv.runner import BaseModule
from mmseg.ops import resize
@@ -30,14 +34,16 @@ class DetailBranch(BaseModule):
x (torch.Tensor): Feature map of Detail Branch.
"""
- def __init__(self,
- detail_channels=(64, 64, 128),
- in_channels=3,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(DetailBranch, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ detail_channels=(64, 64, 128),
+ in_channels=3,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
detail_branch = []
for i in range(len(detail_channels)):
if i == 0:
@@ -51,7 +57,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg),
+ act_cfg=act_cfg,
+ ),
ConvModule(
in_channels=detail_channels[i],
out_channels=detail_channels[i],
@@ -60,7 +67,10 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)))
+ act_cfg=act_cfg,
+ ),
+ )
+ )
else:
detail_branch.append(
nn.Sequential(
@@ -72,7 +82,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg),
+ act_cfg=act_cfg,
+ ),
ConvModule(
in_channels=detail_channels[i],
out_channels=detail_channels[i],
@@ -81,7 +92,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg),
+ act_cfg=act_cfg,
+ ),
ConvModule(
in_channels=detail_channels[i],
out_channels=detail_channels[i],
@@ -90,7 +102,10 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)))
+ act_cfg=act_cfg,
+ ),
+ )
+ )
self.detail_branch = nn.ModuleList(detail_branch)
def forward(self, x):
@@ -119,14 +134,16 @@ class StemBlock(BaseModule):
x (torch.Tensor): First feature map in Semantic Branch.
"""
- def __init__(self,
- in_channels=3,
- out_channels=16,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(StemBlock, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ out_channels=16,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv_first = ConvModule(
in_channels=in_channels,
@@ -136,7 +153,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.convs = nn.Sequential(
ConvModule(
in_channels=out_channels,
@@ -146,7 +164,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg),
+ act_cfg=act_cfg,
+ ),
ConvModule(
in_channels=out_channels // 2,
out_channels=out_channels,
@@ -155,9 +174,10 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
- self.pool = nn.MaxPool2d(
- kernel_size=3, stride=2, padding=1, ceil_mode=False)
+ act_cfg=act_cfg,
+ ),
+ )
+ self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False)
self.fuse_last = ConvModule(
in_channels=out_channels * 2,
out_channels=out_channels,
@@ -166,7 +186,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, x):
x = self.conv_first(x)
@@ -198,16 +219,18 @@ class GELayer(BaseModule):
Semantic Branch.
"""
- def __init__(self,
- in_channels,
- out_channels,
- exp_ratio=6,
- stride=1,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(GELayer, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ exp_ratio=6,
+ stride=1,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
mid_channel = in_channels * exp_ratio
self.conv1 = ConvModule(
in_channels=in_channels,
@@ -217,7 +240,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
if stride == 1:
self.dwconv = nn.Sequential(
# ReLU in ConvModule not shown in paper
@@ -230,7 +254,9 @@ def __init__(self,
groups=in_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.shortcut = None
else:
self.dwconv = nn.Sequential(
@@ -244,7 +270,8 @@ def __init__(self,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None),
+ act_cfg=None,
+ ),
# ReLU in ConvModule not shown in paper
ConvModule(
in_channels=mid_channel,
@@ -255,7 +282,8 @@ def __init__(self,
groups=mid_channel,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg),
+ act_cfg=act_cfg,
+ ),
)
self.shortcut = nn.Sequential(
DepthwiseSeparableConvModule(
@@ -268,7 +296,8 @@ def __init__(self,
dw_act_cfg=None,
pw_norm_cfg=norm_cfg,
pw_act_cfg=None,
- ))
+ )
+ )
self.conv2 = nn.Sequential(
ConvModule(
@@ -281,7 +310,8 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None,
- ))
+ )
+ )
self.act = build_activation_layer(act_cfg)
@@ -319,19 +349,22 @@ class CEBlock(BaseModule):
x (torch.Tensor): Last feature map in Semantic Branch.
"""
- def __init__(self,
- in_channels=3,
- out_channels=16,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(CEBlock, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ out_channels=16,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
self.gap = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
- build_norm_layer(norm_cfg, self.in_channels)[1])
+ build_norm_layer(norm_cfg, self.in_channels)[1],
+ )
self.conv_gap = ConvModule(
in_channels=self.in_channels,
out_channels=self.out_channels,
@@ -340,7 +373,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
# Note: in paper here is naive conv2d, no bn-relu
self.conv_last = ConvModule(
in_channels=self.out_channels,
@@ -350,7 +384,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, x):
identity = x
@@ -380,46 +415,60 @@ class SemanticBranch(BaseModule):
Guided Aggregation Layer.
"""
- def __init__(self,
- semantic_channels=(16, 32, 64, 128),
- in_channels=3,
- exp_ratio=6,
- init_cfg=None):
- super(SemanticBranch, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ semantic_channels=(16, 32, 64, 128),
+ in_channels=3,
+ exp_ratio=6,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.semantic_channels = semantic_channels
self.semantic_stages = []
for i in range(len(semantic_channels)):
- stage_name = f'stage{i + 1}'
+ stage_name = f"stage{i + 1}"
self.semantic_stages.append(stage_name)
if i == 0:
self.add_module(
- stage_name,
- StemBlock(self.in_channels, semantic_channels[i]))
+ stage_name, StemBlock(self.in_channels, semantic_channels[i])
+ )
elif i == (len(semantic_channels) - 1):
self.add_module(
stage_name,
nn.Sequential(
- GELayer(semantic_channels[i - 1], semantic_channels[i],
- exp_ratio, 2),
- GELayer(semantic_channels[i], semantic_channels[i],
- exp_ratio, 1),
- GELayer(semantic_channels[i], semantic_channels[i],
- exp_ratio, 1),
- GELayer(semantic_channels[i], semantic_channels[i],
- exp_ratio, 1)))
+ GELayer(
+ semantic_channels[i - 1], semantic_channels[i], exp_ratio, 2
+ ),
+ GELayer(
+ semantic_channels[i], semantic_channels[i], exp_ratio, 1
+ ),
+ GELayer(
+ semantic_channels[i], semantic_channels[i], exp_ratio, 1
+ ),
+ GELayer(
+ semantic_channels[i], semantic_channels[i], exp_ratio, 1
+ ),
+ ),
+ )
else:
self.add_module(
stage_name,
nn.Sequential(
- GELayer(semantic_channels[i - 1], semantic_channels[i],
- exp_ratio, 2),
- GELayer(semantic_channels[i], semantic_channels[i],
- exp_ratio, 1)))
-
- self.add_module(f'stage{len(semantic_channels)}_CEBlock',
- CEBlock(semantic_channels[-1], semantic_channels[-1]))
- self.semantic_stages.append(f'stage{len(semantic_channels)}_CEBlock')
+ GELayer(
+ semantic_channels[i - 1], semantic_channels[i], exp_ratio, 2
+ ),
+ GELayer(
+ semantic_channels[i], semantic_channels[i], exp_ratio, 1
+ ),
+ ),
+ )
+
+ self.add_module(
+ f"stage{len(semantic_channels)}_CEBlock",
+ CEBlock(semantic_channels[-1], semantic_channels[-1]),
+ )
+ self.semantic_stages.append(f"stage{len(semantic_channels)}_CEBlock")
def forward(self, x):
semantic_outs = []
@@ -451,14 +500,16 @@ class BGALayer(BaseModule):
output (torch.Tensor): Output feature map for Segment heads.
"""
- def __init__(self,
- out_channels=128,
- align_corners=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(BGALayer, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ out_channels=128,
+ align_corners=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.out_channels = out_channels
self.align_corners = align_corners
self.detail_dwconv = nn.Sequential(
@@ -472,7 +523,8 @@ def __init__(self,
dw_act_cfg=None,
pw_norm_cfg=None,
pw_act_cfg=None,
- ))
+ )
+ )
self.detail_down = nn.Sequential(
ConvModule(
in_channels=self.out_channels,
@@ -483,8 +535,10 @@ def __init__(self,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None),
- nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False))
+ act_cfg=None,
+ ),
+ nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False),
+ )
self.semantic_conv = nn.Sequential(
ConvModule(
in_channels=self.out_channels,
@@ -495,7 +549,9 @@ def __init__(self,
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None))
+ act_cfg=None,
+ )
+ )
self.semantic_dwconv = nn.Sequential(
DepthwiseSeparableConvModule(
in_channels=self.out_channels,
@@ -507,7 +563,8 @@ def __init__(self,
dw_act_cfg=None,
pw_norm_cfg=None,
pw_act_cfg=None,
- ))
+ )
+ )
self.conv = ConvModule(
in_channels=self.out_channels,
out_channels=self.out_channels,
@@ -528,15 +585,17 @@ def forward(self, x_d, x_s):
semantic_conv = resize(
input=semantic_conv,
size=detail_dwconv.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
fuse_1 = detail_dwconv * torch.sigmoid(semantic_conv)
fuse_2 = detail_down * torch.sigmoid(semantic_dwconv)
fuse_2 = resize(
input=fuse_2,
size=fuse_1.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
output = self.conv(fuse_1 + fuse_2)
return output
@@ -576,25 +635,26 @@ class BiSeNetV2(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels=3,
- detail_channels=(64, 64, 128),
- semantic_channels=(16, 32, 64, 128),
- semantic_expansion_ratio=6,
- bga_channels=128,
- out_indices=(0, 1, 2, 3, 4),
- align_corners=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
+ def __init__(
+ self,
+ in_channels=3,
+ detail_channels=(64, 64, 128),
+ semantic_channels=(16, 32, 64, 128),
+ semantic_expansion_ratio=6,
+ bga_channels=128,
+ out_indices=(0, 1, 2, 3, 4),
+ align_corners=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
if init_cfg is None:
init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
- super(BiSeNetV2, self).__init__(init_cfg=init_cfg)
+ super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_indices = out_indices
self.detail_channels = detail_channels
@@ -607,9 +667,9 @@ def __init__(self,
self.act_cfg = act_cfg
self.detail = DetailBranch(self.detail_channels, self.in_channels)
- self.semantic = SemanticBranch(self.semantic_channels,
- self.in_channels,
- self.semantic_expansion_ratio)
+ self.semantic = SemanticBranch(
+ self.semantic_channels, self.in_channels, self.semantic_expansion_ratio
+ )
self.bga = BGALayer(self.bga_channels, self.align_corners)
def forward(self, x):
diff --git a/mmsegmentation/mmseg/models/backbones/cgnet.py b/mmsegmentation/mmseg/models/backbones/cgnet.py
index 168194c..4de6ac9 100644
--- a/mmsegmentation/mmseg/models/backbones/cgnet.py
+++ b/mmsegmentation/mmseg/models/backbones/cgnet.py
@@ -25,15 +25,18 @@ class GlobalContextExtractor(nn.Module):
"""
def __init__(self, channel, reduction=16, with_cp=False):
- super(GlobalContextExtractor, self).__init__()
+ super().__init__()
self.channel = channel
self.reduction = reduction
assert reduction >= 1 and channel >= reduction
self.with_cp = with_cp
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
- nn.Linear(channel, channel // reduction), nn.ReLU(inplace=True),
- nn.Linear(channel // reduction, channel), nn.Sigmoid())
+ nn.Linear(channel, channel // reduction),
+ nn.ReLU(inplace=True),
+ nn.Linear(channel // reduction, channel),
+ nn.Sigmoid(),
+ )
def forward(self, x):
@@ -76,24 +79,26 @@ class ContextGuidedBlock(nn.Module):
memory while slowing down the training speed. Default: False.
"""
- def __init__(self,
- in_channels,
- out_channels,
- dilation=2,
- reduction=16,
- skip_connect=True,
- downsample=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='PReLU'),
- with_cp=False):
- super(ContextGuidedBlock, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ dilation=2,
+ reduction=16,
+ skip_connect=True,
+ downsample=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="PReLU"),
+ with_cp=False,
+ ):
+ super().__init__()
self.with_cp = with_cp
self.downsample = downsample
channels = out_channels if downsample else out_channels // 2
- if 'type' in act_cfg and act_cfg['type'] == 'PReLU':
- act_cfg['num_parameters'] = channels
+ if "type" in act_cfg and act_cfg["type"] == "PReLU":
+ act_cfg["num_parameters"] = channels
kernel_size = 3 if downsample else 1
stride = 2 if downsample else 1
padding = (kernel_size - 1) // 2
@@ -106,7 +111,8 @@ def __init__(self,
padding,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.f_loc = build_conv_layer(
conv_cfg,
@@ -115,7 +121,8 @@ def __init__(self,
kernel_size=3,
padding=1,
groups=channels,
- bias=False)
+ bias=False,
+ )
self.f_sur = build_conv_layer(
conv_cfg,
channels,
@@ -124,18 +131,16 @@ def __init__(self,
padding=dilation,
groups=channels,
dilation=dilation,
- bias=False)
+ bias=False,
+ )
self.bn = build_norm_layer(norm_cfg, 2 * channels)[1]
self.activate = nn.PReLU(2 * channels)
if downsample:
self.bottleneck = build_conv_layer(
- conv_cfg,
- 2 * channels,
- out_channels,
- kernel_size=1,
- bias=False)
+ conv_cfg, 2 * channels, out_channels, kernel_size=1, bias=False
+ )
self.skip_connect = skip_connect and not downsample
self.f_glo = GlobalContextExtractor(out_channels, reduction, with_cp)
@@ -172,7 +177,7 @@ class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
- super(InputInjection, self).__init__()
+ super().__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling):
self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))
@@ -216,45 +221,46 @@ class CGNet(BaseModule):
Default: None
"""
- def __init__(self,
- in_channels=3,
- num_channels=(32, 64, 128),
- num_blocks=(3, 21),
- dilations=(2, 4),
- reductions=(8, 16),
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='PReLU'),
- norm_eval=False,
- with_cp=False,
- pretrained=None,
- init_cfg=None):
-
- super(CGNet, self).__init__(init_cfg)
-
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ def __init__(
+ self,
+ in_channels=3,
+ num_channels=(32, 64, 128),
+ num_blocks=(3, 21),
+ dilations=(2, 4),
+ reductions=(8, 16),
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="PReLU"),
+ norm_eval=False,
+ with_cp=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg)
+
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is a deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is a deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer=['Conv2d', 'Linear']),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm']),
- dict(type='Constant', val=0, layer='PReLU')
+ dict(type="Kaiming", layer=["Conv2d", "Linear"]),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
+ dict(type="Constant", val=0, layer="PReLU"),
]
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.in_channels = in_channels
self.num_channels = num_channels
- assert isinstance(self.num_channels, tuple) and len(
- self.num_channels) == 3
+ assert isinstance(self.num_channels, tuple) and len(self.num_channels) == 3
self.num_blocks = num_blocks
assert isinstance(self.num_blocks, tuple) and len(self.num_blocks) == 2
self.dilations = dilations
@@ -264,8 +270,8 @@ def __init__(self,
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
- if 'type' in self.act_cfg and self.act_cfg['type'] == 'PReLU':
- self.act_cfg['num_parameters'] = num_channels[0]
+ if "type" in self.act_cfg and self.act_cfg["type"] == "PReLU":
+ self.act_cfg["num_parameters"] = num_channels[0]
self.norm_eval = norm_eval
self.with_cp = with_cp
@@ -281,7 +287,9 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
cur_channels = num_channels[0]
self.inject_2x = InputInjection(1) # down-sample for Input, factor=2
@@ -289,8 +297,8 @@ def __init__(self,
cur_channels += in_channels
self.norm_prelu_0 = nn.Sequential(
- build_norm_layer(norm_cfg, cur_channels)[1],
- nn.PReLU(cur_channels))
+ build_norm_layer(norm_cfg, cur_channels)[1], nn.PReLU(cur_channels)
+ )
# stage 1
self.level1 = nn.ModuleList()
@@ -305,12 +313,14 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- with_cp=with_cp)) # CG block
+ with_cp=with_cp,
+ )
+ ) # CG block
cur_channels = 2 * num_channels[1] + in_channels
self.norm_prelu_1 = nn.Sequential(
- build_norm_layer(norm_cfg, cur_channels)[1],
- nn.PReLU(cur_channels))
+ build_norm_layer(norm_cfg, cur_channels)[1], nn.PReLU(cur_channels)
+ )
# stage 2
self.level2 = nn.ModuleList()
@@ -325,12 +335,14 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- with_cp=with_cp)) # CG block
+ with_cp=with_cp,
+ )
+ ) # CG block
cur_channels = 2 * num_channels[2]
self.norm_prelu_2 = nn.Sequential(
- build_norm_layer(norm_cfg, cur_channels)[1],
- nn.PReLU(cur_channels))
+ build_norm_layer(norm_cfg, cur_channels)[1], nn.PReLU(cur_channels)
+ )
def forward(self, x):
output = []
@@ -364,7 +376,7 @@ def forward(self, x):
def train(self, mode=True):
"""Convert the model into training mode will keeping the normalization
layer freezed."""
- super(CGNet, self).train(mode)
+ super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
diff --git a/mmsegmentation/mmseg/models/backbones/erfnet.py b/mmsegmentation/mmseg/models/backbones/erfnet.py
index 8921c18..53477db 100644
--- a/mmsegmentation/mmseg/models/backbones/erfnet.py
+++ b/mmsegmentation/mmseg/models/backbones/erfnet.py
@@ -28,14 +28,16 @@ class DownsamplerBlock(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN', eps=1e-3),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(DownsamplerBlock, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", eps=1e-3),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@@ -46,7 +48,8 @@ def __init__(self,
out_channels - in_channels,
kernel_size=3,
stride=2,
- padding=1)
+ padding=1,
+ )
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn = build_norm_layer(self.norm_cfg, out_channels)[1]
self.act = build_activation_layer(self.act_cfg)
@@ -57,8 +60,9 @@ def forward(self, input):
pool_out = resize(
input=pool_out,
size=conv_out.size()[2:],
- mode='bilinear',
- align_corners=False)
+ mode="bilinear",
+ align_corners=False,
+ )
output = torch.cat([conv_out, pool_out], 1)
output = self.bn(output)
output = self.act(output)
@@ -86,16 +90,18 @@ class NonBottleneck1d(BaseModule):
Default: None.
"""
- def __init__(self,
- channels,
- drop_rate=0,
- dilation=1,
- num_conv_layer=2,
- conv_cfg=None,
- norm_cfg=dict(type='BN', eps=1e-3),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(NonBottleneck1d, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ channels,
+ drop_rate=0,
+ dilation=1,
+ num_conv_layer=2,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", eps=1e-3),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
@@ -118,7 +124,9 @@ def __init__(self,
stride=1,
padding=first_conv_padding,
bias=True,
- dilation=first_conv_dilation))
+ dilation=first_conv_dilation,
+ )
+ )
self.convs_layers.append(self.act)
self.convs_layers.append(
build_conv_layer(
@@ -129,9 +137,10 @@ def __init__(self,
stride=1,
padding=second_conv_padding,
bias=True,
- dilation=second_conv_dilation))
- self.convs_layers.append(
- build_norm_layer(self.norm_cfg, channels)[1])
+ dilation=second_conv_dilation,
+ )
+ )
+ self.convs_layers.append(build_norm_layer(self.norm_cfg, channels)[1])
if conv_layer == 0:
self.convs_layers.append(self.act)
else:
@@ -161,14 +170,16 @@ class UpsamplerBlock(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN', eps=1e-3),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(UpsamplerBlock, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", eps=1e-3),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@@ -180,7 +191,8 @@ def __init__(self,
stride=2,
padding=1,
output_padding=1,
- bias=True)
+ bias=True,
+ )
self.bn = build_norm_layer(self.norm_cfg, out_channels)[1]
self.act = build_activation_layer(self.act_cfg)
@@ -227,46 +239,54 @@ class ERFNet(BaseModule):
Default 0.1.
"""
- def __init__(self,
- in_channels=3,
- enc_downsample_channels=(16, 64, 128),
- enc_stage_non_bottlenecks=(5, 8),
- enc_non_bottleneck_dilations=(2, 4, 8, 16),
- enc_non_bottleneck_channels=(64, 128),
- dec_upsample_channels=(64, 16),
- dec_stages_non_bottleneck=(2, 2),
- dec_non_bottleneck_channels=(64, 16),
- dropout_ratio=0.1,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
-
- super(ERFNet, self).__init__(init_cfg=init_cfg)
- assert len(enc_downsample_channels) \
- == len(dec_upsample_channels)+1, 'Number of downsample\
+ def __init__(
+ self,
+ in_channels=3,
+ enc_downsample_channels=(16, 64, 128),
+ enc_stage_non_bottlenecks=(5, 8),
+ enc_non_bottleneck_dilations=(2, 4, 8, 16),
+ enc_non_bottleneck_channels=(64, 128),
+ dec_upsample_channels=(64, 16),
+ dec_stages_non_bottleneck=(2, 2),
+ dec_non_bottleneck_channels=(64, 16),
+ dropout_ratio=0.1,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ len(enc_downsample_channels) == len(dec_upsample_channels) + 1
+ ), "Number of downsample\
block of encoder does not \
- match number of upsample block of decoder!'
- assert len(enc_downsample_channels) \
- == len(enc_stage_non_bottlenecks)+1, 'Number of \
+ match number of upsample block of decoder!"
+ assert (
+ len(enc_downsample_channels) == len(enc_stage_non_bottlenecks) + 1
+ ), "Number of \
downsample block of encoder does not match \
- number of Non-bottleneck block of encoder!'
- assert len(enc_downsample_channels) \
- == len(enc_non_bottleneck_channels)+1, 'Number of \
+ number of Non-bottleneck block of encoder!"
+ assert (
+ len(enc_downsample_channels) == len(enc_non_bottleneck_channels) + 1
+ ), "Number of \
downsample block of encoder does not match \
- number of channels of Non-bottleneck block of encoder!'
- assert enc_stage_non_bottlenecks[-1] \
- % len(enc_non_bottleneck_dilations) == 0, 'Number of \
+ number of channels of Non-bottleneck block of encoder!"
+ assert (
+ enc_stage_non_bottlenecks[-1] % len(enc_non_bottleneck_dilations) == 0
+ ), "Number of \
Non-bottleneck block of encoder does not match \
- number of Non-bottleneck block of encoder!'
- assert len(dec_upsample_channels) \
- == len(dec_stages_non_bottleneck), 'Number of \
+ number of Non-bottleneck block of encoder!"
+ assert len(dec_upsample_channels) == len(
+ dec_stages_non_bottleneck
+ ), "Number of \
upsample block of decoder does not match \
- number of Non-bottleneck block of decoder!'
- assert len(dec_stages_non_bottleneck) \
- == len(dec_non_bottleneck_channels), 'Number of \
+ number of Non-bottleneck block of decoder!"
+ assert len(dec_stages_non_bottleneck) == len(
+ dec_non_bottleneck_channels
+ ), "Number of \
Non-bottleneck block of decoder does not match \
- number of channels of Non-bottleneck block of decoder!'
+ number of channels of Non-bottleneck block of decoder!"
self.in_channels = in_channels
self.enc_downsample_channels = enc_downsample_channels
@@ -286,40 +306,53 @@ def __init__(self,
self.act_cfg = act_cfg
self.encoder.append(
- DownsamplerBlock(self.in_channels, enc_downsample_channels[0]))
+ DownsamplerBlock(self.in_channels, enc_downsample_channels[0])
+ )
for i in range(len(enc_downsample_channels) - 1):
self.encoder.append(
- DownsamplerBlock(enc_downsample_channels[i],
- enc_downsample_channels[i + 1]))
+ DownsamplerBlock(
+ enc_downsample_channels[i], enc_downsample_channels[i + 1]
+ )
+ )
# Last part of encoder is some dilated NonBottleneck1d blocks.
if i == len(enc_downsample_channels) - 2:
- iteration_times = int(enc_stage_non_bottlenecks[-1] /
- len(enc_non_bottleneck_dilations))
+ iteration_times = int(
+ enc_stage_non_bottlenecks[-1] / len(enc_non_bottleneck_dilations)
+ )
for j in range(iteration_times):
for k in range(len(enc_non_bottleneck_dilations)):
self.encoder.append(
- NonBottleneck1d(enc_downsample_channels[-1],
- self.dropout_ratio,
- enc_non_bottleneck_dilations[k]))
+ NonBottleneck1d(
+ enc_downsample_channels[-1],
+ self.dropout_ratio,
+ enc_non_bottleneck_dilations[k],
+ )
+ )
else:
for j in range(enc_stage_non_bottlenecks[i]):
self.encoder.append(
- NonBottleneck1d(enc_downsample_channels[i + 1],
- self.dropout_ratio))
+ NonBottleneck1d(
+ enc_downsample_channels[i + 1], self.dropout_ratio
+ )
+ )
for i in range(len(dec_upsample_channels)):
if i == 0:
self.decoder.append(
- UpsamplerBlock(enc_downsample_channels[-1],
- dec_non_bottleneck_channels[i]))
+ UpsamplerBlock(
+ enc_downsample_channels[-1], dec_non_bottleneck_channels[i]
+ )
+ )
else:
self.decoder.append(
- UpsamplerBlock(dec_non_bottleneck_channels[i - 1],
- dec_non_bottleneck_channels[i]))
+ UpsamplerBlock(
+ dec_non_bottleneck_channels[i - 1],
+ dec_non_bottleneck_channels[i],
+ )
+ )
for j in range(dec_stages_non_bottleneck[i]):
- self.decoder.append(
- NonBottleneck1d(dec_non_bottleneck_channels[i]))
+ self.decoder.append(NonBottleneck1d(dec_non_bottleneck_channels[i]))
def forward(self, x):
for enc in self.encoder:
diff --git a/mmsegmentation/mmseg/models/backbones/fast_scnn.py b/mmsegmentation/mmseg/models/backbones/fast_scnn.py
index cbfbcaf..e6c80c7 100644
--- a/mmsegmentation/mmseg/models/backbones/fast_scnn.py
+++ b/mmsegmentation/mmseg/models/backbones/fast_scnn.py
@@ -29,15 +29,17 @@ class LearningToDownsample(nn.Module):
as `act_cfg`. Default: None.
"""
- def __init__(self,
- in_channels,
- dw_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- dw_act_cfg=None):
- super(LearningToDownsample, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ dw_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ dw_act_cfg=None,
+ ):
+ super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@@ -53,7 +55,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.dsconv1 = DepthwiseSeparableConvModule(
dw_channels1,
@@ -62,7 +65,8 @@ def __init__(self,
stride=2,
padding=1,
norm_cfg=self.norm_cfg,
- dw_act_cfg=self.dw_act_cfg)
+ dw_act_cfg=self.dw_act_cfg,
+ )
self.dsconv2 = DepthwiseSeparableConvModule(
dw_channels2,
@@ -71,7 +75,8 @@ def __init__(self,
stride=2,
padding=1,
norm_cfg=self.norm_cfg,
- dw_act_cfg=self.dw_act_cfg)
+ dw_act_cfg=self.dw_act_cfg,
+ )
def forward(self, x):
x = self.conv(x)
@@ -113,32 +118,42 @@ class GlobalFeatureExtractor(nn.Module):
Default: False
"""
- def __init__(self,
- in_channels=64,
- block_channels=(64, 96, 128),
- out_channels=128,
- expand_ratio=6,
- num_blocks=(3, 3, 3),
- strides=(2, 2, 1),
- pool_scales=(1, 2, 3, 6),
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- align_corners=False):
- super(GlobalFeatureExtractor, self).__init__()
+ def __init__(
+ self,
+ in_channels=64,
+ block_channels=(64, 96, 128),
+ out_channels=128,
+ expand_ratio=6,
+ num_blocks=(3, 3, 3),
+ strides=(2, 2, 1),
+ pool_scales=(1, 2, 3, 6),
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ align_corners=False,
+ ):
+ super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
assert len(block_channels) == len(num_blocks) == 3
- self.bottleneck1 = self._make_layer(in_channels, block_channels[0],
- num_blocks[0], strides[0],
- expand_ratio)
- self.bottleneck2 = self._make_layer(block_channels[0],
- block_channels[1], num_blocks[1],
- strides[1], expand_ratio)
- self.bottleneck3 = self._make_layer(block_channels[1],
- block_channels[2], num_blocks[2],
- strides[2], expand_ratio)
+ self.bottleneck1 = self._make_layer(
+ in_channels, block_channels[0], num_blocks[0], strides[0], expand_ratio
+ )
+ self.bottleneck2 = self._make_layer(
+ block_channels[0],
+ block_channels[1],
+ num_blocks[1],
+ strides[1],
+ expand_ratio,
+ )
+ self.bottleneck3 = self._make_layer(
+ block_channels[1],
+ block_channels[2],
+ num_blocks[2],
+ strides[2],
+ expand_ratio,
+ )
self.ppm = PPM(
pool_scales,
block_channels[2],
@@ -146,7 +161,8 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=align_corners)
+ align_corners=align_corners,
+ )
self.out = ConvModule(
block_channels[2] * 2,
@@ -155,14 +171,10 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
- def _make_layer(self,
- in_channels,
- out_channels,
- blocks,
- stride=1,
- expand_ratio=6):
+ def _make_layer(self, in_channels, out_channels, blocks, stride=1, expand_ratio=6):
layers = [
InvertedResidual(
in_channels,
@@ -170,7 +182,8 @@ def _make_layer(self,
stride,
expand_ratio,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
]
for i in range(1, blocks):
layers.append(
@@ -180,7 +193,9 @@ def _make_layer(self,
1,
expand_ratio,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
return nn.Sequential(*layers)
def forward(self, x):
@@ -212,16 +227,18 @@ class FeatureFusionModule(nn.Module):
Default: False.
"""
- def __init__(self,
- higher_in_channels,
- lower_in_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dwconv_act_cfg=dict(type='ReLU'),
- conv_act_cfg=None,
- align_corners=False):
- super(FeatureFusionModule, self).__init__()
+ def __init__(
+ self,
+ higher_in_channels,
+ lower_in_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ dwconv_act_cfg=dict(type="ReLU"),
+ conv_act_cfg=None,
+ align_corners=False,
+ ):
+ super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dwconv_act_cfg = dwconv_act_cfg
@@ -235,14 +252,16 @@ def __init__(self,
groups=out_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.dwconv_act_cfg)
+ act_cfg=self.dwconv_act_cfg,
+ )
self.conv_lower_res = ConvModule(
out_channels,
out_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.conv_act_cfg)
+ act_cfg=self.conv_act_cfg,
+ )
self.conv_higher_res = ConvModule(
higher_in_channels,
@@ -250,7 +269,8 @@ def __init__(self,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.conv_act_cfg)
+ act_cfg=self.conv_act_cfg,
+ )
self.relu = nn.ReLU(True)
@@ -258,8 +278,9 @@ def forward(self, higher_res_feature, lower_res_feature):
lower_res_feature = resize(
lower_res_feature,
size=higher_res_feature.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
lower_res_feature = self.dwconv(lower_res_feature)
lower_res_feature = self.conv_lower_res(lower_res_feature)
@@ -323,39 +344,44 @@ class FastSCNN(BaseModule):
Default: None
"""
- def __init__(self,
- in_channels=3,
- downsample_dw_channels=(32, 48),
- global_in_channels=64,
- global_block_channels=(64, 96, 128),
- global_block_strides=(2, 2, 1),
- global_out_channels=128,
- higher_in_channels=64,
- lower_in_channels=128,
- fusion_out_channels=128,
- out_indices=(0, 1, 2),
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- align_corners=False,
- dw_act_cfg=None,
- init_cfg=None):
-
- super(FastSCNN, self).__init__(init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ downsample_dw_channels=(32, 48),
+ global_in_channels=64,
+ global_block_channels=(64, 96, 128),
+ global_block_strides=(2, 2, 1),
+ global_out_channels=128,
+ higher_in_channels=64,
+ lower_in_channels=128,
+ fusion_out_channels=128,
+ out_indices=(0, 1, 2),
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ align_corners=False,
+ dw_act_cfg=None,
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg)
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
if global_in_channels != higher_in_channels:
- raise AssertionError('Global Input Channels must be the same \
- with Higher Input Channels!')
+ raise AssertionError(
+ "Global Input Channels must be the same \
+ with Higher Input Channels!"
+ )
elif global_out_channels != lower_in_channels:
- raise AssertionError('Global Output Channels must be the same \
- with Lower Input Channels!')
+ raise AssertionError(
+ "Global Output Channels must be the same \
+ with Lower Input Channels!"
+ )
self.in_channels = in_channels
self.downsample_dw_channels1 = downsample_dw_channels[0]
@@ -379,7 +405,8 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- dw_act_cfg=dw_act_cfg)
+ dw_act_cfg=dw_act_cfg,
+ )
self.global_feature_extractor = GlobalFeatureExtractor(
global_in_channels,
global_block_channels,
@@ -388,7 +415,8 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
self.feature_fusion = FeatureFusionModule(
higher_in_channels,
lower_in_channels,
@@ -396,13 +424,13 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
dwconv_act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
def forward(self, x):
higher_res_features = self.learning_to_downsample(x)
lower_res_features = self.global_feature_extractor(higher_res_features)
- fusion_output = self.feature_fusion(higher_res_features,
- lower_res_features)
+ fusion_output = self.feature_fusion(higher_res_features, lower_res_features)
outs = [higher_res_features, lower_res_features, fusion_output]
outs = [outs[i] for i in self.out_indices]
diff --git a/mmsegmentation/mmseg/models/backbones/hrnet.py b/mmsegmentation/mmseg/models/backbones/hrnet.py
index 90feadc..3a1d0b4 100644
--- a/mmsegmentation/mmseg/models/backbones/hrnet.py
+++ b/mmsegmentation/mmseg/models/backbones/hrnet.py
@@ -18,22 +18,23 @@ class HRModule(BaseModule):
is in this module.
"""
- def __init__(self,
- num_branches,
- blocks,
- num_blocks,
- in_channels,
- num_channels,
- multiscale_output=True,
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- block_init_cfg=None,
- init_cfg=None):
- super(HRModule, self).__init__(init_cfg)
+ def __init__(
+ self,
+ num_branches,
+ blocks,
+ num_blocks,
+ in_channels,
+ num_channels,
+ multiscale_output=True,
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ block_init_cfg=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.block_init_cfg = block_init_cfg
- self._check_branches(num_branches, num_blocks, in_channels,
- num_channels)
+ self._check_branches(num_branches, num_blocks, in_channels, num_channels)
self.in_channels = in_channels
self.num_branches = num_branches
@@ -42,40 +43,41 @@ def __init__(self,
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.with_cp = with_cp
- self.branches = self._make_branches(num_branches, blocks, num_blocks,
- num_channels)
+ self.branches = self._make_branches(
+ num_branches, blocks, num_blocks, num_channels
+ )
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=False)
- def _check_branches(self, num_branches, num_blocks, in_channels,
- num_channels):
+ def _check_branches(self, num_branches, num_blocks, in_channels, num_channels):
"""Check branches configuration."""
if num_branches != len(num_blocks):
- error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_BLOCKS(' \
- f'{len(num_blocks)})'
+ error_msg = (
+ f"NUM_BRANCHES({num_branches}) <> NUM_BLOCKS(" f"{len(num_blocks)})"
+ )
raise ValueError(error_msg)
if num_branches != len(num_channels):
- error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_CHANNELS(' \
- f'{len(num_channels)})'
+ error_msg = (
+ f"NUM_BRANCHES({num_branches}) <> NUM_CHANNELS(" f"{len(num_channels)})"
+ )
raise ValueError(error_msg)
if num_branches != len(in_channels):
- error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_INCHANNELS(' \
- f'{len(in_channels)})'
+ error_msg = (
+ f"NUM_BRANCHES({num_branches}) <> NUM_INCHANNELS("
+ f"{len(in_channels)})"
+ )
raise ValueError(error_msg)
- def _make_one_branch(self,
- branch_index,
- block,
- num_blocks,
- num_channels,
- stride=1):
+ def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
"""Build one branch."""
downsample = None
- if stride != 1 or \
- self.in_channels[branch_index] != \
- num_channels[branch_index] * block.expansion:
+ if (
+ stride != 1
+ or self.in_channels[branch_index]
+ != num_channels[branch_index] * block.expansion
+ ):
downsample = nn.Sequential(
build_conv_layer(
self.conv_cfg,
@@ -83,9 +85,12 @@ def _make_one_branch(self,
num_channels[branch_index] * block.expansion,
kernel_size=1,
stride=stride,
- bias=False),
- build_norm_layer(self.norm_cfg, num_channels[branch_index] *
- block.expansion)[1])
+ bias=False,
+ ),
+ build_norm_layer(
+ self.norm_cfg, num_channels[branch_index] * block.expansion
+ )[1],
+ )
layers = []
layers.append(
@@ -97,9 +102,10 @@ def _make_one_branch(self,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
- init_cfg=self.block_init_cfg))
- self.in_channels[branch_index] = \
- num_channels[branch_index] * block.expansion
+ init_cfg=self.block_init_cfg,
+ )
+ )
+ self.in_channels[branch_index] = num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(
block(
@@ -108,7 +114,9 @@ def _make_one_branch(self,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
- init_cfg=self.block_init_cfg))
+ init_cfg=self.block_init_cfg,
+ )
+ )
return Sequential(*layers)
@@ -117,8 +125,7 @@ def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
- branches.append(
- self._make_one_branch(i, block, num_blocks, num_channels))
+ branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
return ModuleList(branches)
@@ -144,13 +151,17 @@ def _make_fuse_layers(self):
kernel_size=1,
stride=1,
padding=0,
- bias=False),
+ bias=False,
+ ),
build_norm_layer(self.norm_cfg, in_channels[i])[1],
# we set align_corners=False for HRNet
Upsample(
- scale_factor=2**(j - i),
- mode='bilinear',
- align_corners=False)))
+ scale_factor=2 ** (j - i),
+ mode="bilinear",
+ align_corners=False,
+ ),
+ )
+ )
elif j == i:
fuse_layer.append(None)
else:
@@ -166,9 +177,11 @@ def _make_fuse_layers(self):
kernel_size=3,
stride=2,
padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg,
- in_channels[i])[1]))
+ bias=False,
+ ),
+ build_norm_layer(self.norm_cfg, in_channels[i])[1],
+ )
+ )
else:
conv_downsamples.append(
nn.Sequential(
@@ -179,10 +192,12 @@ def _make_fuse_layers(self):
kernel_size=3,
stride=2,
padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg,
- in_channels[j])[1],
- nn.ReLU(inplace=False)))
+ bias=False,
+ ),
+ build_norm_layer(self.norm_cfg, in_channels[j])[1],
+ nn.ReLU(inplace=False),
+ )
+ )
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.ModuleList(fuse_layer))
@@ -206,8 +221,9 @@ def forward(self, x):
y = y + resize(
self.fuse_layers[i][j](x[j]),
size=x[i].shape[2:],
- mode='bilinear',
- align_corners=False)
+ mode="bilinear",
+ align_corners=False,
+ )
else:
y += self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
@@ -294,51 +310,59 @@ class HRNet(BaseModule):
(1, 256, 1, 1)
"""
- blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
-
- def __init__(self,
- extra,
- in_channels=3,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False,
- with_cp=False,
- frozen_stages=-1,
- zero_init_residual=False,
- multiscale_output=True,
- pretrained=None,
- init_cfg=None):
- super(HRNet, self).__init__(init_cfg)
+ blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck}
+
+ def __init__(
+ self,
+ extra,
+ in_channels=3,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ norm_eval=False,
+ with_cp=False,
+ frozen_stages=-1,
+ zero_init_residual=False,
+ multiscale_output=True,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.pretrained = pretrained
self.zero_init_residual = zero_init_residual
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
# Assert configurations of 4 stages are in extra
- assert 'stage1' in extra and 'stage2' in extra \
- and 'stage3' in extra and 'stage4' in extra
+ assert (
+ "stage1" in extra
+ and "stage2" in extra
+ and "stage3" in extra
+ and "stage4" in extra
+ )
# Assert whether the length of `num_blocks` and `num_channels` are
# equal to `num_branches`
for i in range(4):
- cfg = extra[f'stage{i + 1}']
- assert len(cfg['num_blocks']) == cfg['num_branches'] and \
- len(cfg['num_channels']) == cfg['num_branches']
+ cfg = extra[f"stage{i + 1}"]
+ assert (
+ len(cfg["num_blocks"]) == cfg["num_branches"]
+ and len(cfg["num_channels"]) == cfg["num_branches"]
+ )
self.extra = extra
self.conv_cfg = conv_cfg
@@ -358,66 +382,64 @@ def __init__(self,
kernel_size=3,
stride=2,
padding=1,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
- self.conv_cfg,
- 64,
- 64,
- kernel_size=3,
- stride=2,
- padding=1,
- bias=False)
+ self.conv_cfg, 64, 64, kernel_size=3, stride=2, padding=1, bias=False
+ )
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# stage 1
- self.stage1_cfg = self.extra['stage1']
- num_channels = self.stage1_cfg['num_channels'][0]
- block_type = self.stage1_cfg['block']
- num_blocks = self.stage1_cfg['num_blocks'][0]
+ self.stage1_cfg = self.extra["stage1"]
+ num_channels = self.stage1_cfg["num_channels"][0]
+ block_type = self.stage1_cfg["block"]
+ num_blocks = self.stage1_cfg["num_blocks"][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * block.expansion
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
# stage 2
- self.stage2_cfg = self.extra['stage2']
- num_channels = self.stage2_cfg['num_channels']
- block_type = self.stage2_cfg['block']
+ self.stage2_cfg = self.extra["stage2"]
+ num_channels = self.stage2_cfg["num_channels"]
+ block_type = self.stage2_cfg["block"]
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
- self.transition1 = self._make_transition_layer([stage1_out_channels],
- num_channels)
+ self.transition1 = self._make_transition_layer(
+ [stage1_out_channels], num_channels
+ )
self.stage2, pre_stage_channels = self._make_stage(
- self.stage2_cfg, num_channels)
+ self.stage2_cfg, num_channels
+ )
# stage 3
- self.stage3_cfg = self.extra['stage3']
- num_channels = self.stage3_cfg['num_channels']
- block_type = self.stage3_cfg['block']
+ self.stage3_cfg = self.extra["stage3"]
+ num_channels = self.stage3_cfg["num_channels"]
+ block_type = self.stage3_cfg["block"]
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
- self.transition2 = self._make_transition_layer(pre_stage_channels,
- num_channels)
+ self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
self.stage3, pre_stage_channels = self._make_stage(
- self.stage3_cfg, num_channels)
+ self.stage3_cfg, num_channels
+ )
# stage 4
- self.stage4_cfg = self.extra['stage4']
- num_channels = self.stage4_cfg['num_channels']
- block_type = self.stage4_cfg['block']
+ self.stage4_cfg = self.extra["stage4"]
+ num_channels = self.stage4_cfg["num_channels"]
+ block_type = self.stage4_cfg["block"]
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
- self.transition3 = self._make_transition_layer(pre_stage_channels,
- num_channels)
+ self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
self.stage4, pre_stage_channels = self._make_stage(
- self.stage4_cfg, num_channels, multiscale_output=multiscale_output)
+ self.stage4_cfg, num_channels, multiscale_output=multiscale_output
+ )
self._freeze_stages()
@@ -431,8 +453,7 @@ def norm2(self):
"""nn.Module: the normalization layer named "norm2" """
return getattr(self, self.norm2_name)
- def _make_transition_layer(self, num_channels_pre_layer,
- num_channels_cur_layer):
+ def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
"""Make transition layer."""
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
@@ -450,18 +471,25 @@ def _make_transition_layer(self, num_channels_pre_layer,
kernel_size=3,
stride=1,
padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg,
- num_channels_cur_layer[i])[1],
- nn.ReLU(inplace=True)))
+ bias=False,
+ ),
+ build_norm_layer(self.norm_cfg, num_channels_cur_layer[i])[
+ 1
+ ],
+ nn.ReLU(inplace=True),
+ )
+ )
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
in_channels = num_channels_pre_layer[-1]
- out_channels = num_channels_cur_layer[i] \
- if j == i - num_branches_pre else in_channels
+ out_channels = (
+ num_channels_cur_layer[i]
+ if j == i - num_branches_pre
+ else in_channels
+ )
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
@@ -471,9 +499,12 @@ def _make_transition_layer(self, num_channels_pre_layer,
kernel_size=3,
stride=2,
padding=1,
- bias=False),
+ bias=False,
+ ),
build_norm_layer(self.norm_cfg, out_channels)[1],
- nn.ReLU(inplace=True)))
+ nn.ReLU(inplace=True),
+ )
+ )
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.ModuleList(transition_layers)
@@ -489,19 +520,26 @@ def _make_layer(self, block, inplanes, planes, blocks, stride=1):
planes * block.expansion,
kernel_size=1,
stride=stride,
- bias=False),
- build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
+ bias=False,
+ ),
+ build_norm_layer(self.norm_cfg, planes * block.expansion)[1],
+ )
layers = []
block_init_cfg = None
- if self.pretrained is None and not hasattr(
- self, 'init_cfg') and self.zero_init_residual:
+ if (
+ self.pretrained is None
+ and not hasattr(self, "init_cfg")
+ and self.zero_init_residual
+ ):
if block is BasicBlock:
block_init_cfg = dict(
- type='Constant', val=0, override=dict(name='norm2'))
+ type="Constant", val=0, override=dict(name="norm2")
+ )
elif block is Bottleneck:
block_init_cfg = dict(
- type='Constant', val=0, override=dict(name='norm3'))
+ type="Constant", val=0, override=dict(name="norm3")
+ )
layers.append(
block(
@@ -512,7 +550,9 @@ def _make_layer(self, block, inplanes, planes, blocks, stride=1):
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
- init_cfg=block_init_cfg))
+ init_cfg=block_init_cfg,
+ )
+ )
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
@@ -522,28 +562,35 @@ def _make_layer(self, block, inplanes, planes, blocks, stride=1):
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
- init_cfg=block_init_cfg))
+ init_cfg=block_init_cfg,
+ )
+ )
return Sequential(*layers)
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
"""Make each stage."""
- num_modules = layer_config['num_modules']
- num_branches = layer_config['num_branches']
- num_blocks = layer_config['num_blocks']
- num_channels = layer_config['num_channels']
- block = self.blocks_dict[layer_config['block']]
+ num_modules = layer_config["num_modules"]
+ num_branches = layer_config["num_branches"]
+ num_blocks = layer_config["num_blocks"]
+ num_channels = layer_config["num_channels"]
+ block = self.blocks_dict[layer_config["block"]]
hr_modules = []
block_init_cfg = None
- if self.pretrained is None and not hasattr(
- self, 'init_cfg') and self.zero_init_residual:
+ if (
+ self.pretrained is None
+ and not hasattr(self, "init_cfg")
+ and self.zero_init_residual
+ ):
if block is BasicBlock:
block_init_cfg = dict(
- type='Constant', val=0, override=dict(name='norm2'))
+ type="Constant", val=0, override=dict(name="norm2")
+ )
elif block is Bottleneck:
block_init_cfg = dict(
- type='Constant', val=0, override=dict(name='norm3'))
+ type="Constant", val=0, override=dict(name="norm3")
+ )
for i in range(num_modules):
# multi_scale_output is only used for the last module
@@ -563,7 +610,9 @@ def _make_stage(self, layer_config, in_channels, multiscale_output=True):
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
- block_init_cfg=block_init_cfg))
+ block_init_cfg=block_init_cfg,
+ )
+ )
return Sequential(*hr_modules), in_channels
@@ -579,13 +628,13 @@ def _freeze_stages(self):
for i in range(1, self.frozen_stages + 1):
if i == 1:
- m = getattr(self, f'layer{i}')
- t = getattr(self, f'transition{i}')
+ m = getattr(self, f"layer{i}")
+ t = getattr(self, f"transition{i}")
elif i == 4:
- m = getattr(self, f'stage{i}')
+ m = getattr(self, f"stage{i}")
else:
- m = getattr(self, f'stage{i}')
- t = getattr(self, f'transition{i}')
+ m = getattr(self, f"stage{i}")
+ t = getattr(self, f"transition{i}")
m.eval()
for param in m.parameters():
param.requires_grad = False
@@ -605,7 +654,7 @@ def forward(self, x):
x = self.layer1(x)
x_list = []
- for i in range(self.stage2_cfg['num_branches']):
+ for i in range(self.stage2_cfg["num_branches"]):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
@@ -613,7 +662,7 @@ def forward(self, x):
y_list = self.stage2(x_list)
x_list = []
- for i in range(self.stage3_cfg['num_branches']):
+ for i in range(self.stage3_cfg["num_branches"]):
if self.transition2[i] is not None:
x_list.append(self.transition2[i](y_list[-1]))
else:
@@ -621,7 +670,7 @@ def forward(self, x):
y_list = self.stage3(x_list)
x_list = []
- for i in range(self.stage4_cfg['num_branches']):
+ for i in range(self.stage4_cfg["num_branches"]):
if self.transition3[i] is not None:
x_list.append(self.transition3[i](y_list[-1]))
else:
@@ -633,7 +682,7 @@ def forward(self, x):
def train(self, mode=True):
"""Convert the model into training mode will keeping the normalization
layer freezed."""
- super(HRNet, self).train(mode)
+ super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
diff --git a/mmsegmentation/mmseg/models/backbones/icnet.py b/mmsegmentation/mmseg/models/backbones/icnet.py
index 6faaeab..6413a16 100644
--- a/mmsegmentation/mmseg/models/backbones/icnet.py
+++ b/mmsegmentation/mmseg/models/backbones/icnet.py
@@ -43,35 +43,38 @@ class ICNet(BaseModule):
Default: None.
"""
- def __init__(self,
- backbone_cfg,
- in_channels=3,
- layer_channels=(512, 2048),
- light_branch_middle_channels=32,
- psp_out_channels=512,
- out_channels=(64, 256, 256),
- pool_scales=(1, 2, 3, 6),
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- align_corners=False,
- init_cfg=None):
+ def __init__(
+ self,
+ backbone_cfg,
+ in_channels=3,
+ layer_channels=(512, 2048),
+ light_branch_middle_channels=32,
+ psp_out_channels=512,
+ out_channels=(64, 256, 256),
+ pool_scales=(1, 2, 3, 6),
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ align_corners=False,
+ init_cfg=None,
+ ):
if backbone_cfg is None:
- raise TypeError('backbone_cfg must be passed from config file!')
+ raise TypeError("backbone_cfg must be passed from config file!")
if init_cfg is None:
init_cfg = [
- dict(type='Kaiming', mode='fan_out', layer='Conv2d'),
- dict(type='Constant', val=1, layer='_BatchNorm'),
- dict(type='Normal', mean=0.01, layer='Linear')
+ dict(type="Kaiming", mode="fan_out", layer="Conv2d"),
+ dict(type="Constant", val=1, layer="_BatchNorm"),
+ dict(type="Normal", mean=0.01, layer="Linear"),
]
- super(ICNet, self).__init__(init_cfg=init_cfg)
+ super().__init__(init_cfg=init_cfg)
self.align_corners = align_corners
self.backbone = build_backbone(backbone_cfg)
# Note: Default `ceil_mode` is false in nn.MaxPool2d, set
# `ceil_mode=True` to keep information in the corner of feature map.
self.backbone.maxpool = nn.MaxPool2d(
- kernel_size=3, stride=2, padding=1, ceil_mode=True)
+ kernel_size=3, stride=2, padding=1, ceil_mode=True
+ )
self.psp_modules = PPM(
pool_scales=pool_scales,
@@ -80,7 +83,8 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- align_corners=align_corners)
+ align_corners=align_corners,
+ )
self.psp_bottleneck = ConvModule(
layer_channels[1] + len(pool_scales) * psp_out_channels,
@@ -89,7 +93,8 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.conv_sub1 = nn.Sequential(
ConvModule(
@@ -99,7 +104,8 @@ def __init__(self,
stride=2,
padding=1,
conv_cfg=conv_cfg,
- norm_cfg=norm_cfg),
+ norm_cfg=norm_cfg,
+ ),
ConvModule(
in_channels=light_branch_middle_channels,
out_channels=light_branch_middle_channels,
@@ -107,7 +113,8 @@ def __init__(self,
stride=2,
padding=1,
conv_cfg=conv_cfg,
- norm_cfg=norm_cfg),
+ norm_cfg=norm_cfg,
+ ),
ConvModule(
in_channels=light_branch_middle_channels,
out_channels=out_channels[0],
@@ -115,21 +122,17 @@ def __init__(self,
stride=2,
padding=1,
conv_cfg=conv_cfg,
- norm_cfg=norm_cfg))
+ norm_cfg=norm_cfg,
+ ),
+ )
self.conv_sub2 = ConvModule(
- layer_channels[0],
- out_channels[1],
- 1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg)
+ layer_channels[0], out_channels[1], 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg
+ )
self.conv_sub4 = ConvModule(
- psp_out_channels,
- out_channels[2],
- 1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg)
+ psp_out_channels, out_channels[2], 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg
+ )
def forward(self, x):
output = []
@@ -139,10 +142,8 @@ def forward(self, x):
# sub 2
x = resize(
- x,
- scale_factor=0.5,
- mode='bilinear',
- align_corners=self.align_corners)
+ x, scale_factor=0.5, mode="bilinear", align_corners=self.align_corners
+ )
x = self.backbone.stem(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
@@ -151,10 +152,8 @@ def forward(self, x):
# sub 4
x = resize(
- x,
- scale_factor=0.5,
- mode='bilinear',
- align_corners=self.align_corners)
+ x, scale_factor=0.5, mode="bilinear", align_corners=self.align_corners
+ )
x = self.backbone.layer3(x)
x = self.backbone.layer4(x)
psp_outs = self.psp_modules(x) + [x]
diff --git a/mmsegmentation/mmseg/models/backbones/mae.py b/mmsegmentation/mmseg/models/backbones/mae.py
index d3e8754..5ba4707 100644
--- a/mmsegmentation/mmseg/models/backbones/mae.py
+++ b/mmsegmentation/mmseg/models/backbones/mae.py
@@ -3,8 +3,7 @@
import torch
import torch.nn as nn
-from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
- trunc_normal_)
+from mmcv.cnn.utils.weight_init import constant_init, kaiming_init, trunc_normal_
from mmcv.runner import ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
@@ -28,8 +27,6 @@ def init_weights(self):
# with `trunc_normal`, `init_weights` here does
# nothing and just passes directly
- pass
-
class MAETransformerEncoderLayer(BEiTTransformerEncoderLayer):
"""Implements one encoder layer in Vision Transformer.
@@ -80,27 +77,29 @@ class MAE(BEiT):
Default: None.
"""
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_channels=3,
- embed_dims=768,
- num_layers=12,
- num_heads=12,
- mlp_ratio=4,
- out_indices=-1,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- norm_cfg=dict(type='LN'),
- act_cfg=dict(type='GELU'),
- patch_norm=False,
- final_norm=False,
- num_fcs=2,
- norm_eval=False,
- pretrained=None,
- init_values=0.1,
- init_cfg=None):
- super(MAE, self).__init__(
+ def __init__(
+ self,
+ img_size=224,
+ patch_size=16,
+ in_channels=3,
+ embed_dims=768,
+ num_layers=12,
+ num_heads=12,
+ mlp_ratio=4,
+ out_indices=-1,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ norm_cfg=dict(type="LN"),
+ act_cfg=dict(type="GELU"),
+ patch_norm=False,
+ final_norm=False,
+ num_fcs=2,
+ norm_eval=False,
+ pretrained=None,
+ init_values=0.1,
+ init_cfg=None,
+ ):
+ super().__init__(
img_size=img_size,
patch_size=patch_size,
in_channels=in_channels,
@@ -120,18 +119,17 @@ def __init__(self,
norm_eval=norm_eval,
pretrained=pretrained,
init_values=init_values,
- init_cfg=init_cfg)
+ init_cfg=init_cfg,
+ )
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
self.num_patches = self.patch_shape[0] * self.patch_shape[1]
- self.pos_embed = nn.Parameter(
- torch.zeros(1, self.num_patches + 1, embed_dims))
+ self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_dims))
def _build_layers(self):
dpr = [
- x.item()
- for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
+ x.item() for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
]
self.layers = ModuleList()
for i in range(self.num_layers):
@@ -147,7 +145,9 @@ def _build_layers(self):
act_cfg=self.act_cfg,
norm_cfg=self.norm_cfg,
window_size=self.patch_shape,
- init_values=self.init_values))
+ init_values=self.init_values,
+ )
+ )
def fix_init_weight(self):
"""Rescale the initialization according to layer id.
@@ -168,7 +168,7 @@ def init_weights(self):
def _init_weights(m):
if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
+ trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
@@ -178,43 +178,45 @@ def _init_weights(m):
self.apply(_init_weights)
self.fix_init_weight()
- if (isinstance(self.init_cfg, dict)
- and self.init_cfg.get('type') == 'Pretrained'):
+ if (
+ isinstance(self.init_cfg, dict)
+ and self.init_cfg.get("type") == "Pretrained"
+ ):
logger = get_root_logger()
checkpoint = _load_checkpoint(
- self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
+ self.init_cfg["checkpoint"], logger=logger, map_location="cpu"
+ )
state_dict = self.resize_rel_pos_embed(checkpoint)
state_dict = self.resize_abs_pos_embed(state_dict)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
- super(MAE, self).init_weights()
+ super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
# Copyright 2019 Ross Wightman
# Licensed under the Apache License, Version 2.0 (the "License")
- trunc_normal_(self.cls_token, std=.02)
+ trunc_normal_(self.cls_token, std=0.02)
for n, m in self.named_modules():
if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
+ trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
- if 'ffn' in n:
- nn.init.normal_(m.bias, mean=0., std=1e-6)
+ if "ffn" in n:
+ nn.init.normal_(m.bias, mean=0.0, std=1e-6)
else:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
- kaiming_init(m, mode='fan_in', bias=0.)
+ kaiming_init(m, mode="fan_in", bias=0.0)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
def resize_abs_pos_embed(self, state_dict):
- if 'pos_embed' in state_dict:
- pos_embed_checkpoint = state_dict['pos_embed']
+ if "pos_embed" in state_dict:
+ pos_embed_checkpoint = state_dict["pos_embed"]
embedding_size = pos_embed_checkpoint.shape[-1]
num_extra_tokens = self.pos_embed.shape[-2] - self.num_patches
# height (== width) for the checkpoint position embedding
- orig_size = int(
- (pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
+ orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# height (== width) for the new position embedding
new_size = int(self.num_patches**0.5)
# class_token and dist_token are kept unchanged
@@ -222,17 +224,18 @@ def resize_abs_pos_embed(self, state_dict):
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
- pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
- embedding_size).permute(
- 0, 3, 1, 2)
+ pos_tokens = pos_tokens.reshape(
+ -1, orig_size, orig_size, embedding_size
+ ).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens,
size=(new_size, new_size),
- mode='bicubic',
- align_corners=False)
+ mode="bicubic",
+ align_corners=False,
+ )
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
- state_dict['pos_embed'] = new_pos_embed
+ state_dict["pos_embed"] = new_pos_embed
return state_dict
def forward(self, inputs):
@@ -254,8 +257,11 @@ def forward(self, inputs):
if i in self.out_indices:
out = x[:, 1:]
B, _, C = out.shape
- out = out.reshape(B, hw_shape[0], hw_shape[1],
- C).permute(0, 3, 1, 2).contiguous()
+ out = (
+ out.reshape(B, hw_shape[0], hw_shape[1], C)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
outs.append(out)
return tuple(outs)
diff --git a/mmsegmentation/mmseg/models/backbones/mit.py b/mmsegmentation/mmseg/models/backbones/mit.py
index 4417cf1..d4f23d8 100644
--- a/mmsegmentation/mmseg/models/backbones/mit.py
+++ b/mmsegmentation/mmseg/models/backbones/mit.py
@@ -8,8 +8,7 @@
from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import MultiheadAttention
-from mmcv.cnn.utils.weight_init import (constant_init, normal_init,
- trunc_normal_init)
+from mmcv.cnn.utils.weight_init import constant_init, normal_init, trunc_normal_init
from mmcv.runner import BaseModule, ModuleList, Sequential
from ..builder import BACKBONES
@@ -37,14 +36,16 @@ class MixFFN(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- feedforward_channels,
- act_cfg=dict(type='GELU'),
- ffn_drop=0.,
- dropout_layer=None,
- init_cfg=None):
- super(MixFFN, self).__init__(init_cfg)
+ def __init__(
+ self,
+ embed_dims,
+ feedforward_channels,
+ act_cfg=dict(type="GELU"),
+ ffn_drop=0.0,
+ dropout_layer=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.embed_dims = embed_dims
self.feedforward_channels = feedforward_channels
@@ -57,7 +58,8 @@ def __init__(self,
out_channels=feedforward_channels,
kernel_size=1,
stride=1,
- bias=True)
+ bias=True,
+ )
# 3x3 depth wise conv to provide positional encode information
pe_conv = Conv2d(
in_channels=feedforward_channels,
@@ -66,18 +68,21 @@ def __init__(self,
stride=1,
padding=(3 - 1) // 2,
bias=True,
- groups=feedforward_channels)
+ groups=feedforward_channels,
+ )
fc2 = Conv2d(
in_channels=feedforward_channels,
out_channels=in_channels,
kernel_size=1,
stride=1,
- bias=True)
+ bias=True,
+ )
drop = nn.Dropout(ffn_drop)
layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
self.layers = Sequential(*layers)
- self.dropout_layer = build_dropout(
- dropout_layer) if dropout_layer else torch.nn.Identity()
+ self.dropout_layer = (
+ build_dropout(dropout_layer) if dropout_layer else torch.nn.Identity()
+ )
def forward(self, x, hw_shape, identity=None):
out = nlc_to_nchw(x, hw_shape)
@@ -114,17 +119,19 @@ class EfficientMultiheadAttention(MultiheadAttention):
Attention of Segformer. Default: 1.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- attn_drop=0.,
- proj_drop=0.,
- dropout_layer=None,
- init_cfg=None,
- batch_first=True,
- qkv_bias=False,
- norm_cfg=dict(type='LN'),
- sr_ratio=1):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ dropout_layer=None,
+ init_cfg=None,
+ batch_first=True,
+ qkv_bias=False,
+ norm_cfg=dict(type="LN"),
+ sr_ratio=1,
+ ):
super().__init__(
embed_dims,
num_heads,
@@ -133,7 +140,8 @@ def __init__(self,
dropout_layer=dropout_layer,
init_cfg=init_cfg,
batch_first=batch_first,
- bias=qkv_bias)
+ bias=qkv_bias,
+ )
self.sr_ratio = sr_ratio
if sr_ratio > 1:
@@ -141,17 +149,21 @@ def __init__(self,
in_channels=embed_dims,
out_channels=embed_dims,
kernel_size=sr_ratio,
- stride=sr_ratio)
+ stride=sr_ratio,
+ )
# The ret[0] of build_norm_layer is norm name.
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
# handle the BC-breaking from https://github.com/open-mmlab/mmcv/pull/1418 # noqa
from mmseg import digit_version, mmcv_version
- if mmcv_version < digit_version('1.3.17'):
- warnings.warn('The legacy version of forward function in'
- 'EfficientMultiheadAttention is deprecated in'
- 'mmcv>=1.3.17 and will no longer support in the'
- 'future. Please upgrade your mmcv.')
+
+ if mmcv_version < digit_version("1.3.17"):
+ warnings.warn(
+ "The legacy version of forward function in"
+ "EfficientMultiheadAttention is deprecated in"
+ "mmcv>=1.3.17 and will no longer support in the"
+ "future. Please upgrade your mmcv."
+ )
self.forward = self.legacy_forward
def forward(self, x, hw_shape, identity=None):
@@ -240,20 +252,22 @@ class TransformerEncoderLayer(BaseModule):
some memory while slowing down the training speed. Default: False.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- qkv_bias=True,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- batch_first=True,
- sr_ratio=1,
- with_cp=False):
- super(TransformerEncoderLayer, self).__init__()
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ qkv_bias=True,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ batch_first=True,
+ sr_ratio=1,
+ with_cp=False,
+ ):
+ super().__init__()
# The ret[0] of build_norm_layer is norm name.
self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
@@ -263,11 +277,12 @@ def __init__(self,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
batch_first=batch_first,
qkv_bias=qkv_bias,
norm_cfg=norm_cfg,
- sr_ratio=sr_ratio)
+ sr_ratio=sr_ratio,
+ )
# The ret[0] of build_norm_layer is norm name.
self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
@@ -276,8 +291,9 @@ def __init__(self,
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
ffn_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
- act_cfg=act_cfg)
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
+ act_cfg=act_cfg,
+ )
self.with_cp = with_cp
@@ -337,36 +353,41 @@ class MixVisionTransformer(BaseModule):
some memory while slowing down the training speed. Default: False.
"""
- def __init__(self,
- in_channels=3,
- embed_dims=64,
- num_stages=4,
- num_layers=[3, 4, 6, 3],
- num_heads=[1, 2, 4, 8],
- patch_sizes=[7, 3, 3, 3],
- strides=[4, 2, 2, 2],
- sr_ratios=[8, 4, 2, 1],
- out_indices=(0, 1, 2, 3),
- mlp_ratio=4,
- qkv_bias=True,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN', eps=1e-6),
- pretrained=None,
- init_cfg=None,
- with_cp=False):
- super(MixVisionTransformer, self).__init__(init_cfg=init_cfg)
-
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be set at the same time'
+ def __init__(
+ self,
+ in_channels=3,
+ embed_dims=64,
+ num_stages=4,
+ num_layers=[3, 4, 6, 3],
+ num_heads=[1, 2, 4, 8],
+ patch_sizes=[7, 3, 3, 3],
+ strides=[4, 2, 2, 2],
+ sr_ratios=[8, 4, 2, 1],
+ out_indices=(0, 1, 2, 3),
+ mlp_ratio=4,
+ qkv_bias=True,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN", eps=1e-6),
+ pretrained=None,
+ init_cfg=None,
+ with_cp=False,
+ ):
+ super().__init__(init_cfg=init_cfg)
+
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be set at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is not None:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.embed_dims = embed_dims
self.num_stages = num_stages
@@ -376,16 +397,21 @@ def __init__(self,
self.strides = strides
self.sr_ratios = sr_ratios
self.with_cp = with_cp
- assert num_stages == len(num_layers) == len(num_heads) \
- == len(patch_sizes) == len(strides) == len(sr_ratios)
+ assert (
+ num_stages
+ == len(num_layers)
+ == len(num_heads)
+ == len(patch_sizes)
+ == len(strides)
+ == len(sr_ratios)
+ )
self.out_indices = out_indices
assert max(out_indices) < self.num_stages
# transformer encoder
dpr = [
- x.item()
- for x in torch.linspace(0, drop_path_rate, sum(num_layers))
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(num_layers))
] # stochastic num_layer decay rule
cur = 0
@@ -398,21 +424,26 @@ def __init__(self,
kernel_size=patch_sizes[i],
stride=strides[i],
padding=patch_sizes[i] // 2,
- norm_cfg=norm_cfg)
- layer = ModuleList([
- TransformerEncoderLayer(
- embed_dims=embed_dims_i,
- num_heads=num_heads[i],
- feedforward_channels=mlp_ratio * embed_dims_i,
- drop_rate=drop_rate,
- attn_drop_rate=attn_drop_rate,
- drop_path_rate=dpr[cur + idx],
- qkv_bias=qkv_bias,
- act_cfg=act_cfg,
- norm_cfg=norm_cfg,
- with_cp=with_cp,
- sr_ratio=sr_ratios[i]) for idx in range(num_layer)
- ])
+ norm_cfg=norm_cfg,
+ )
+ layer = ModuleList(
+ [
+ TransformerEncoderLayer(
+ embed_dims=embed_dims_i,
+ num_heads=num_heads[i],
+ feedforward_channels=mlp_ratio * embed_dims_i,
+ drop_rate=drop_rate,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dpr[cur + idx],
+ qkv_bias=qkv_bias,
+ act_cfg=act_cfg,
+ norm_cfg=norm_cfg,
+ with_cp=with_cp,
+ sr_ratio=sr_ratios[i],
+ )
+ for idx in range(num_layer)
+ ]
+ )
in_channels = embed_dims_i
# The ret[0] of build_norm_layer is norm name.
norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
@@ -423,17 +454,15 @@ def init_weights(self):
if self.init_cfg is None:
for m in self.modules():
if isinstance(m, nn.Linear):
- trunc_normal_init(m, std=.02, bias=0.)
+ trunc_normal_init(m, std=0.02, bias=0.0)
elif isinstance(m, nn.LayerNorm):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[
- 1] * m.out_channels
+ fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
- normal_init(
- m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
+ normal_init(m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
else:
- super(MixVisionTransformer, self).init_weights()
+ super().init_weights()
def forward(self, x):
outs = []
diff --git a/mmsegmentation/mmseg/models/backbones/mobilenet_v2.py b/mmsegmentation/mmseg/models/backbones/mobilenet_v2.py
index cbb9c6c..6621305 100644
--- a/mmsegmentation/mmseg/models/backbones/mobilenet_v2.py
+++ b/mmsegmentation/mmseg/models/backbones/mobilenet_v2.py
@@ -47,42 +47,51 @@ class MobileNetV2(BaseModule):
# Parameters to build layers. 3 parameters are needed to construct a
# layer, from left to right: expand_ratio, channel, num_blocks.
- arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
- [6, 96, 3], [6, 160, 3], [6, 320, 1]]
-
- def __init__(self,
- widen_factor=1.,
- strides=(1, 2, 2, 2, 1, 2, 1),
- dilations=(1, 1, 1, 1, 1, 1, 1),
- out_indices=(1, 2, 4, 6),
- frozen_stages=-1,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU6'),
- norm_eval=False,
- with_cp=False,
- pretrained=None,
- init_cfg=None):
- super(MobileNetV2, self).__init__(init_cfg)
+ arch_settings = [
+ [1, 16, 1],
+ [6, 24, 2],
+ [6, 32, 3],
+ [6, 64, 4],
+ [6, 96, 3],
+ [6, 160, 3],
+ [6, 320, 1],
+ ]
+
+ def __init__(
+ self,
+ widen_factor=1.0,
+ strides=(1, 2, 2, 2, 1, 2, 1),
+ dilations=(1, 1, 1, 1, 1, 1, 1),
+ out_indices=(1, 2, 4, 6),
+ frozen_stages=-1,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU6"),
+ norm_eval=False,
+ with_cp=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.pretrained = pretrained
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is a deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is a deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.widen_factor = widen_factor
self.strides = strides
@@ -91,12 +100,16 @@ def __init__(self,
self.out_indices = out_indices
for index in out_indices:
if index not in range(0, 7):
- raise ValueError('the item in out_indices must in '
- f'range(0, 7). But received {index}')
+ raise ValueError(
+ "the item in out_indices must in "
+ f"range(0, 7). But received {index}"
+ )
if frozen_stages not in range(-1, 7):
- raise ValueError('frozen_stages must be in range(-1, 7). '
- f'But received {frozen_stages}')
+ raise ValueError(
+ "frozen_stages must be in range(-1, 7). "
+ f"But received {frozen_stages}"
+ )
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
@@ -115,7 +128,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.layers = []
@@ -129,13 +143,13 @@ def __init__(self,
num_blocks=num_blocks,
stride=stride,
dilation=dilation,
- expand_ratio=expand_ratio)
- layer_name = f'layer{i + 1}'
+ expand_ratio=expand_ratio,
+ )
+ layer_name = f"layer{i + 1}"
self.add_module(layer_name, inverted_res_layer)
self.layers.append(layer_name)
- def make_layer(self, out_channels, num_blocks, stride, dilation,
- expand_ratio):
+ def make_layer(self, out_channels, num_blocks, stride, dilation, expand_ratio):
"""Stack InvertedResidual blocks to build a layer for MobileNetV2.
Args:
@@ -158,7 +172,9 @@ def make_layer(self, out_channels, num_blocks, stride, dilation,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- with_cp=self.with_cp))
+ with_cp=self.with_cp,
+ )
+ )
self.in_channels = out_channels
return nn.Sequential(*layers)
@@ -183,13 +199,13 @@ def _freeze_stages(self):
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
- layer = getattr(self, f'layer{i}')
+ layer = getattr(self, f"layer{i}")
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def train(self, mode=True):
- super(MobileNetV2, self).train(mode)
+ super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
diff --git a/mmsegmentation/mmseg/models/backbones/mobilenet_v3.py b/mmsegmentation/mmseg/models/backbones/mobilenet_v3.py
index dd3d6eb..0670a43 100644
--- a/mmsegmentation/mmseg/models/backbones/mobilenet_v3.py
+++ b/mmsegmentation/mmseg/models/backbones/mobilenet_v3.py
@@ -39,68 +39,75 @@ class MobileNetV3(BaseModule):
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
+
# Parameters to build each block:
# [kernel size, mid channels, out channels, with_se, act type, stride]
arch_settings = {
- 'small': [[3, 16, 16, True, 'ReLU', 2], # block0 layer1 os=4
- [3, 72, 24, False, 'ReLU', 2], # block1 layer2 os=8
- [3, 88, 24, False, 'ReLU', 1],
- [5, 96, 40, True, 'HSwish', 2], # block2 layer4 os=16
- [5, 240, 40, True, 'HSwish', 1],
- [5, 240, 40, True, 'HSwish', 1],
- [5, 120, 48, True, 'HSwish', 1], # block3 layer7 os=16
- [5, 144, 48, True, 'HSwish', 1],
- [5, 288, 96, True, 'HSwish', 2], # block4 layer9 os=32
- [5, 576, 96, True, 'HSwish', 1],
- [5, 576, 96, True, 'HSwish', 1]],
- 'large': [[3, 16, 16, False, 'ReLU', 1], # block0 layer1 os=2
- [3, 64, 24, False, 'ReLU', 2], # block1 layer2 os=4
- [3, 72, 24, False, 'ReLU', 1],
- [5, 72, 40, True, 'ReLU', 2], # block2 layer4 os=8
- [5, 120, 40, True, 'ReLU', 1],
- [5, 120, 40, True, 'ReLU', 1],
- [3, 240, 80, False, 'HSwish', 2], # block3 layer7 os=16
- [3, 200, 80, False, 'HSwish', 1],
- [3, 184, 80, False, 'HSwish', 1],
- [3, 184, 80, False, 'HSwish', 1],
- [3, 480, 112, True, 'HSwish', 1], # block4 layer11 os=16
- [3, 672, 112, True, 'HSwish', 1],
- [5, 672, 160, True, 'HSwish', 2], # block5 layer13 os=32
- [5, 960, 160, True, 'HSwish', 1],
- [5, 960, 160, True, 'HSwish', 1]]
+ "small": [
+ [3, 16, 16, True, "ReLU", 2], # block0 layer1 os=4
+ [3, 72, 24, False, "ReLU", 2], # block1 layer2 os=8
+ [3, 88, 24, False, "ReLU", 1],
+ [5, 96, 40, True, "HSwish", 2], # block2 layer4 os=16
+ [5, 240, 40, True, "HSwish", 1],
+ [5, 240, 40, True, "HSwish", 1],
+ [5, 120, 48, True, "HSwish", 1], # block3 layer7 os=16
+ [5, 144, 48, True, "HSwish", 1],
+ [5, 288, 96, True, "HSwish", 2], # block4 layer9 os=32
+ [5, 576, 96, True, "HSwish", 1],
+ [5, 576, 96, True, "HSwish", 1],
+ ],
+ "large": [
+ [3, 16, 16, False, "ReLU", 1], # block0 layer1 os=2
+ [3, 64, 24, False, "ReLU", 2], # block1 layer2 os=4
+ [3, 72, 24, False, "ReLU", 1],
+ [5, 72, 40, True, "ReLU", 2], # block2 layer4 os=8
+ [5, 120, 40, True, "ReLU", 1],
+ [5, 120, 40, True, "ReLU", 1],
+ [3, 240, 80, False, "HSwish", 2], # block3 layer7 os=16
+ [3, 200, 80, False, "HSwish", 1],
+ [3, 184, 80, False, "HSwish", 1],
+ [3, 184, 80, False, "HSwish", 1],
+ [3, 480, 112, True, "HSwish", 1], # block4 layer11 os=16
+ [3, 672, 112, True, "HSwish", 1],
+ [5, 672, 160, True, "HSwish", 2], # block5 layer13 os=32
+ [5, 960, 160, True, "HSwish", 1],
+ [5, 960, 160, True, "HSwish", 1],
+ ],
} # yapf: disable
- def __init__(self,
- arch='small',
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- out_indices=(0, 1, 12),
- frozen_stages=-1,
- reduction_factor=1,
- norm_eval=False,
- with_cp=False,
- pretrained=None,
- init_cfg=None):
- super(MobileNetV3, self).__init__(init_cfg)
+ def __init__(
+ self,
+ arch="small",
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ out_indices=(0, 1, 12),
+ frozen_stages=-1,
+ reduction_factor=1,
+ norm_eval=False,
+ with_cp=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.pretrained = pretrained
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is a deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is a deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
assert arch in self.arch_settings
assert isinstance(reduction_factor, int) and reduction_factor > 0
@@ -108,14 +115,17 @@ def __init__(self,
for index in out_indices:
if index not in range(0, len(self.arch_settings[arch]) + 2):
raise ValueError(
- 'the item in out_indices must in '
- f'range(0, {len(self.arch_settings[arch])+2}). '
- f'But received {index}')
+ "the item in out_indices must in "
+ f"range(0, {len(self.arch_settings[arch])+2}). "
+ f"But received {index}"
+ )
if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
- raise ValueError('frozen_stages must be in range(-1, '
- f'{len(self.arch_settings[arch])+2}). '
- f'But received {frozen_stages}')
+ raise ValueError(
+ "frozen_stages must be in range(-1, "
+ f"{len(self.arch_settings[arch])+2}). "
+ f"But received {frozen_stages}"
+ )
self.arch = arch
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
@@ -137,19 +147,18 @@ def _make_layer(self):
kernel_size=3,
stride=2,
padding=1,
- conv_cfg=dict(type='Conv2dAdaptivePadding'),
+ conv_cfg=dict(type="Conv2dAdaptivePadding"),
norm_cfg=self.norm_cfg,
- act_cfg=dict(type='HSwish'))
- self.add_module('layer0', layer)
- layers.append('layer0')
+ act_cfg=dict(type="HSwish"),
+ )
+ self.add_module("layer0", layer)
+ layers.append("layer0")
layer_setting = self.arch_settings[self.arch]
for i, params in enumerate(layer_setting):
- (kernel_size, mid_channels, out_channels, with_se, act,
- stride) = params
+ (kernel_size, mid_channels, out_channels, with_se, act, stride) = params
- if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
- i >= 8:
+ if self.arch == "large" and i >= 12 or self.arch == "small" and i >= 8:
mid_channels = mid_channels // self.reduction_factor
out_channels = out_channels // self.reduction_factor
@@ -157,8 +166,11 @@ def _make_layer(self):
se_cfg = dict(
channels=mid_channels,
ratio=4,
- act_cfg=(dict(type='ReLU'),
- dict(type='HSigmoid', bias=3.0, divisor=6.0)))
+ act_cfg=(
+ dict(type="ReLU"),
+ dict(type="HSigmoid", bias=3.0, divisor=6.0),
+ ),
+ )
else:
se_cfg = None
@@ -173,9 +185,10 @@ def _make_layer(self):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type=act),
- with_cp=self.with_cp)
+ with_cp=self.with_cp,
+ )
in_channels = out_channels
- layer_name = 'layer{}'.format(i + 1)
+ layer_name = f"layer{i + 1}"
self.add_module(layer_name, layer)
layers.append(layer_name)
@@ -184,20 +197,21 @@ def _make_layer(self):
# block6 layer16 os=32 for large model
layer = ConvModule(
in_channels=in_channels,
- out_channels=576 if self.arch == 'small' else 960,
+ out_channels=576 if self.arch == "small" else 960,
kernel_size=1,
stride=1,
dilation=4,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=dict(type='HSwish'))
- layer_name = 'layer{}'.format(len(layer_setting) + 1)
+ act_cfg=dict(type="HSwish"),
+ )
+ layer_name = f"layer{len(layer_setting) + 1}"
self.add_module(layer_name, layer)
layers.append(layer_name)
# next, convert backbone MobileNetV3 to a semantic segmentation version
- if self.arch == 'small':
+ if self.arch == "small":
self.layer4.depthwise_conv.conv.stride = (1, 1)
self.layer9.depthwise_conv.conv.stride = (1, 1)
for i in range(4, len(layers)):
@@ -253,13 +267,13 @@ def forward(self, x):
def _freeze_stages(self):
for i in range(self.frozen_stages + 1):
- layer = getattr(self, f'layer{i}')
+ layer = getattr(self, f"layer{i}")
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def train(self, mode=True):
- super(MobileNetV3, self).train(mode)
+ super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
diff --git a/mmsegmentation/mmseg/models/backbones/resnest.py b/mmsegmentation/mmseg/models/backbones/resnest.py
index 91952c2..6ad178c 100644
--- a/mmsegmentation/mmseg/models/backbones/resnest.py
+++ b/mmsegmentation/mmseg/models/backbones/resnest.py
@@ -56,20 +56,22 @@ class SplitAttentionConv2d(nn.Module):
dcn (dict): Config dict for DCN. Default: None.
"""
- def __init__(self,
- in_channels,
- channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- radix=2,
- reduction_factor=4,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dcn=None):
- super(SplitAttentionConv2d, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ channels,
+ kernel_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ groups=1,
+ radix=2,
+ reduction_factor=4,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ dcn=None,
+ ):
+ super().__init__()
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.groups = groups
@@ -78,9 +80,9 @@ def __init__(self,
self.dcn = dcn
fallback_on_stride = False
if self.with_dcn:
- fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+ fallback_on_stride = self.dcn.pop("fallback_on_stride", False)
if self.with_dcn and not fallback_on_stride:
- assert conv_cfg is None, 'conv_cfg must be None for DCN'
+ assert conv_cfg is None, "conv_cfg must be None for DCN"
conv_cfg = dcn
self.conv = build_conv_layer(
conv_cfg,
@@ -91,18 +93,19 @@ def __init__(self,
padding=padding,
dilation=dilation,
groups=groups * radix,
- bias=False)
- self.norm0_name, norm0 = build_norm_layer(
- norm_cfg, channels * radix, postfix=0)
+ bias=False,
+ )
+ self.norm0_name, norm0 = build_norm_layer(norm_cfg, channels * radix, postfix=0)
self.add_module(self.norm0_name, norm0)
self.relu = nn.ReLU(inplace=True)
self.fc1 = build_conv_layer(
- None, channels, inter_channels, 1, groups=self.groups)
- self.norm1_name, norm1 = build_norm_layer(
- norm_cfg, inter_channels, postfix=1)
+ None, channels, inter_channels, 1, groups=self.groups
+ )
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, inter_channels, postfix=1)
self.add_module(self.norm1_name, norm1)
self.fc2 = build_conv_layer(
- None, inter_channels, channels * radix, 1, groups=self.groups)
+ None, inter_channels, channels * radix, 1, groups=self.groups
+ )
self.rsoftmax = RSoftmax(radix, groups)
@property
@@ -161,33 +164,35 @@ class Bottleneck(_Bottleneck):
Bottleneck. Default: True.
kwargs (dict): Key word arguments for base class.
"""
+
expansion = 4
- def __init__(self,
- inplanes,
- planes,
- groups=1,
- base_width=4,
- base_channels=64,
- radix=2,
- reduction_factor=4,
- avg_down_stride=True,
- **kwargs):
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ groups=1,
+ base_width=4,
+ base_channels=64,
+ radix=2,
+ reduction_factor=4,
+ avg_down_stride=True,
+ **kwargs,
+ ):
"""Bottleneck block for ResNeSt."""
- super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
+ super().__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
- width = math.floor(self.planes *
- (base_width / base_channels)) * groups
+ width = math.floor(self.planes * (base_width / base_channels)) * groups
self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
- self.norm1_name, norm1 = build_norm_layer(
- self.norm_cfg, width, postfix=1)
+ self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, width, postfix=1)
self.norm3_name, norm3 = build_norm_layer(
- self.norm_cfg, self.planes * self.expansion, postfix=3)
+ self.norm_cfg, self.planes * self.expansion, postfix=3
+ )
self.conv1 = build_conv_layer(
self.conv_cfg,
@@ -195,7 +200,8 @@ def __init__(self,
width,
kernel_size=1,
stride=self.conv1_stride,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm1_name, norm1)
self.with_modulated_dcn = False
self.conv2 = SplitAttentionConv2d(
@@ -210,7 +216,8 @@ def __init__(self,
reduction_factor=reduction_factor,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- dcn=self.dcn)
+ dcn=self.dcn,
+ )
delattr(self, self.norm2_name)
if self.avg_down_stride:
@@ -221,7 +228,8 @@ def __init__(self,
width,
self.planes * self.expansion,
kernel_size=1,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm3_name, norm3)
def forward(self, x):
@@ -289,22 +297,24 @@ class ResNeSt(ResNetV1d):
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3)),
- 200: (Bottleneck, (3, 24, 36, 3))
+ 200: (Bottleneck, (3, 24, 36, 3)),
}
- def __init__(self,
- groups=1,
- base_width=4,
- radix=2,
- reduction_factor=4,
- avg_down_stride=True,
- **kwargs):
+ def __init__(
+ self,
+ groups=1,
+ base_width=4,
+ radix=2,
+ reduction_factor=4,
+ avg_down_stride=True,
+ **kwargs,
+ ):
self.groups = groups
self.base_width = base_width
self.radix = radix
self.reduction_factor = reduction_factor
self.avg_down_stride = avg_down_stride
- super(ResNeSt, self).__init__(**kwargs)
+ super().__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``."""
@@ -315,4 +325,5 @@ def make_res_layer(self, **kwargs):
radix=self.radix,
reduction_factor=self.reduction_factor,
avg_down_stride=self.avg_down_stride,
- **kwargs)
+ **kwargs,
+ )
diff --git a/mmsegmentation/mmseg/models/backbones/resnet.py b/mmsegmentation/mmseg/models/backbones/resnet.py
index e8b961d..357b0c3 100644
--- a/mmsegmentation/mmseg/models/backbones/resnet.py
+++ b/mmsegmentation/mmseg/models/backbones/resnet.py
@@ -16,22 +16,24 @@ class BasicBlock(BaseModule):
expansion = 1
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- dilation=1,
- downsample=None,
- style='pytorch',
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dcn=None,
- plugins=None,
- init_cfg=None):
- super(BasicBlock, self).__init__(init_cfg)
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ style="pytorch",
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ dcn=None,
+ plugins=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
+ assert dcn is None, "Not implemented yet."
+ assert plugins is None, "Not implemented yet."
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
@@ -44,10 +46,12 @@ def __init__(self,
stride=stride,
padding=dilation,
dilation=dilation,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
- conv_cfg, planes, planes, 3, padding=1, bias=False)
+ conv_cfg, planes, planes, 3, padding=1, bias=False
+ )
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
@@ -105,26 +109,28 @@ class Bottleneck(BaseModule):
expansion = 4
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- dilation=1,
- downsample=None,
- style='pytorch',
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dcn=None,
- plugins=None,
- init_cfg=None):
- super(Bottleneck, self).__init__(init_cfg)
- assert style in ['pytorch', 'caffe']
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ style="pytorch",
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ dcn=None,
+ plugins=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
+ assert style in ["pytorch", "caffe"]
assert dcn is None or isinstance(dcn, dict)
assert plugins is None or isinstance(plugins, list)
if plugins is not None:
- allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
- assert all(p['position'] in allowed_position for p in plugins)
+ allowed_position = ["after_conv1", "after_conv2", "after_conv3"]
+ assert all(p["position"] in allowed_position for p in plugins)
self.inplanes = inplanes
self.planes = planes
@@ -142,19 +148,22 @@ def __init__(self,
if self.with_plugins:
# collect plugins for conv1/conv2/conv3
self.after_conv1_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv1'
+ plugin["cfg"]
+ for plugin in plugins
+ if plugin["position"] == "after_conv1"
]
self.after_conv2_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv2'
+ plugin["cfg"]
+ for plugin in plugins
+ if plugin["position"] == "after_conv2"
]
self.after_conv3_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv3'
+ plugin["cfg"]
+ for plugin in plugins
+ if plugin["position"] == "after_conv3"
]
- if self.style == 'pytorch':
+ if self.style == "pytorch":
self.conv1_stride = 1
self.conv2_stride = stride
else:
@@ -164,7 +173,8 @@ def __init__(self,
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
- norm_cfg, planes * self.expansion, postfix=3)
+ norm_cfg, planes * self.expansion, postfix=3
+ )
self.conv1 = build_conv_layer(
conv_cfg,
@@ -172,11 +182,12 @@ def __init__(self,
planes,
kernel_size=1,
stride=self.conv1_stride,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
if self.with_dcn:
- fallback_on_stride = dcn.pop('fallback_on_stride', False)
+ fallback_on_stride = dcn.pop("fallback_on_stride", False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
conv_cfg,
@@ -186,9 +197,10 @@ def __init__(self,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
- bias=False)
+ bias=False,
+ )
else:
- assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+ assert self.conv_cfg is None, "conv_cfg must be None for DCN"
self.conv2 = build_conv_layer(
dcn,
planes,
@@ -197,15 +209,13 @@ def __init__(self,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
- conv_cfg,
- planes,
- planes * self.expansion,
- kernel_size=1,
- bias=False)
+ conv_cfg, planes, planes * self.expansion, kernel_size=1, bias=False
+ )
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
@@ -213,11 +223,14 @@ def __init__(self,
if self.with_plugins:
self.after_conv1_plugin_names = self.make_block_plugins(
- planes, self.after_conv1_plugins)
+ planes, self.after_conv1_plugins
+ )
self.after_conv2_plugin_names = self.make_block_plugins(
- planes, self.after_conv2_plugins)
+ planes, self.after_conv2_plugins
+ )
self.after_conv3_plugin_names = self.make_block_plugins(
- planes * self.expansion, self.after_conv3_plugins)
+ planes * self.expansion, self.after_conv3_plugins
+ )
def make_block_plugins(self, in_channels, plugins):
"""make plugins for block.
@@ -234,10 +247,9 @@ def make_block_plugins(self, in_channels, plugins):
for plugin in plugins:
plugin = plugin.copy()
name, layer = build_plugin_layer(
- plugin,
- in_channels=in_channels,
- postfix=plugin.pop('postfix', ''))
- assert not hasattr(self, name), f'duplicate plugin {name}'
+ plugin, in_channels=in_channels, postfix=plugin.pop("postfix", "")
+ )
+ assert not hasattr(self, name), f"duplicate plugin {name}"
self.add_module(name, layer)
plugin_names.append(name)
return plugin_names
@@ -390,70 +402,70 @@ class ResNet(BaseModule):
34: (BasicBlock, (3, 4, 6, 3)),
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
- 152: (Bottleneck, (3, 8, 36, 3))
+ 152: (Bottleneck, (3, 8, 36, 3)),
}
- def __init__(self,
- depth,
- in_channels=3,
- stem_channels=64,
- base_channels=64,
- num_stages=4,
- strides=(1, 2, 2, 2),
- dilations=(1, 1, 1, 1),
- out_indices=(0, 1, 2, 3),
- style='pytorch',
- deep_stem=False,
- avg_down=False,
- frozen_stages=-1,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False,
- dcn=None,
- stage_with_dcn=(False, False, False, False),
- plugins=None,
- multi_grid=None,
- contract_dilation=False,
- with_cp=False,
- zero_init_residual=True,
- pretrained=None,
- init_cfg=None):
- super(ResNet, self).__init__(init_cfg)
+ def __init__(
+ self,
+ depth,
+ in_channels=3,
+ stem_channels=64,
+ base_channels=64,
+ num_stages=4,
+ strides=(1, 2, 2, 2),
+ dilations=(1, 1, 1, 1),
+ out_indices=(0, 1, 2, 3),
+ style="pytorch",
+ deep_stem=False,
+ avg_down=False,
+ frozen_stages=-1,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN", requires_grad=True),
+ norm_eval=False,
+ dcn=None,
+ stage_with_dcn=(False, False, False, False),
+ plugins=None,
+ multi_grid=None,
+ contract_dilation=False,
+ with_cp=False,
+ zero_init_residual=True,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
if depth not in self.arch_settings:
- raise KeyError(f'invalid depth {depth} for resnet')
+ raise KeyError(f"invalid depth {depth} for resnet")
self.pretrained = pretrained
self.zero_init_residual = zero_init_residual
block_init_cfg = None
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is a deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is a deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
block = self.arch_settings[depth][0]
if self.zero_init_residual:
if block is BasicBlock:
block_init_cfg = dict(
- type='Constant',
- val=0,
- override=dict(name='norm2'))
+ type="Constant", val=0, override=dict(name="norm2")
+ )
elif block is Bottleneck:
block_init_cfg = dict(
- type='Constant',
- val=0,
- override=dict(name='norm3'))
+ type="Constant", val=0, override=dict(name="norm3")
+ )
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.depth = depth
self.stem_channels = stem_channels
@@ -496,8 +508,7 @@ def __init__(self,
else:
stage_plugins = None
# multi grid is applied to last layer only
- stage_multi_grid = multi_grid if i == len(
- self.stage_blocks) - 1 else None
+ stage_multi_grid = multi_grid if i == len(self.stage_blocks) - 1 else None
planes = base_channels * 2**i
res_layer = self.make_res_layer(
block=self.block,
@@ -515,16 +526,18 @@ def __init__(self,
plugins=stage_plugins,
multi_grid=stage_multi_grid,
contract_dilation=contract_dilation,
- init_cfg=block_init_cfg)
+ init_cfg=block_init_cfg,
+ )
self.inplanes = planes * self.block.expansion
- layer_name = f'layer{i+1}'
+ layer_name = f"layer{i+1}"
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self._freeze_stages()
- self.feat_dim = self.block.expansion * base_channels * 2**(
- len(self.stage_blocks) - 1)
+ self.feat_dim = (
+ self.block.expansion * base_channels * 2 ** (len(self.stage_blocks) - 1)
+ )
def make_stage_plugins(self, plugins, stage_idx):
"""make plugins for ResNet 'stage_idx'th stage .
@@ -571,7 +584,7 @@ def make_stage_plugins(self, plugins, stage_idx):
stage_plugins = []
for plugin in plugins:
plugin = plugin.copy()
- stages = plugin.pop('stages', None)
+ stages = plugin.pop("stages", None)
assert stages is None or len(stages) == self.num_stages
# whether to insert plugin into current stage
if stages is None or stages[stage_idx]:
@@ -599,7 +612,8 @@ def _make_stem_layer(self, in_channels, stem_channels):
kernel_size=3,
stride=2,
padding=1,
- bias=False),
+ bias=False,
+ ),
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
nn.ReLU(inplace=True),
build_conv_layer(
@@ -609,7 +623,8 @@ def _make_stem_layer(self, in_channels, stem_channels):
kernel_size=3,
stride=1,
padding=1,
- bias=False),
+ bias=False,
+ ),
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
nn.ReLU(inplace=True),
build_conv_layer(
@@ -619,9 +634,11 @@ def _make_stem_layer(self, in_channels, stem_channels):
kernel_size=3,
stride=1,
padding=1,
- bias=False),
+ bias=False,
+ ),
build_norm_layer(self.norm_cfg, stem_channels)[1],
- nn.ReLU(inplace=True))
+ nn.ReLU(inplace=True),
+ )
else:
self.conv1 = build_conv_layer(
self.conv_cfg,
@@ -630,9 +647,11 @@ def _make_stem_layer(self, in_channels, stem_channels):
kernel_size=7,
stride=2,
padding=3,
- bias=False)
+ bias=False,
+ )
self.norm1_name, norm1 = build_norm_layer(
- self.norm_cfg, stem_channels, postfix=1)
+ self.norm_cfg, stem_channels, postfix=1
+ )
self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -651,7 +670,7 @@ def _freeze_stages(self):
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
- m = getattr(self, f'layer{i}')
+ m = getattr(self, f"layer{i}")
m.eval()
for param in m.parameters():
param.requires_grad = False
@@ -676,7 +695,7 @@ def forward(self, x):
def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
- super(ResNet, self).train(mode)
+ super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
@@ -696,8 +715,7 @@ class ResNetV1c(ResNet):
"""
def __init__(self, **kwargs):
- super(ResNetV1c, self).__init__(
- deep_stem=True, avg_down=False, **kwargs)
+ super().__init__(deep_stem=True, avg_down=False, **kwargs)
@BACKBONES.register_module()
@@ -710,5 +728,4 @@ class ResNetV1d(ResNet):
"""
def __init__(self, **kwargs):
- super(ResNetV1d, self).__init__(
- deep_stem=True, avg_down=True, **kwargs)
+ super().__init__(deep_stem=True, avg_down=True, **kwargs)
diff --git a/mmsegmentation/mmseg/models/backbones/resnext.py b/mmsegmentation/mmseg/models/backbones/resnext.py
index 805c27b..a612b93 100644
--- a/mmsegmentation/mmseg/models/backbones/resnext.py
+++ b/mmsegmentation/mmseg/models/backbones/resnext.py
@@ -16,27 +16,21 @@ class Bottleneck(_Bottleneck):
"caffe", the stride-two layer is the first 1x1 conv layer.
"""
- def __init__(self,
- inplanes,
- planes,
- groups=1,
- base_width=4,
- base_channels=64,
- **kwargs):
- super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
+ def __init__(
+ self, inplanes, planes, groups=1, base_width=4, base_channels=64, **kwargs
+ ):
+ super().__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
- width = math.floor(self.planes *
- (base_width / base_channels)) * groups
+ width = math.floor(self.planes * (base_width / base_channels)) * groups
- self.norm1_name, norm1 = build_norm_layer(
- self.norm_cfg, width, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(
- self.norm_cfg, width, postfix=2)
+ self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, width, postfix=1)
+ self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, width, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
- self.norm_cfg, self.planes * self.expansion, postfix=3)
+ self.norm_cfg, self.planes * self.expansion, postfix=3
+ )
self.conv1 = build_conv_layer(
self.conv_cfg,
@@ -44,12 +38,13 @@ def __init__(self,
width,
kernel_size=1,
stride=self.conv1_stride,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
- fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+ fallback_on_stride = self.dcn.pop("fallback_on_stride", False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
@@ -60,9 +55,10 @@ def __init__(self,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
- bias=False)
+ bias=False,
+ )
else:
- assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+ assert self.conv_cfg is None, "conv_cfg must be None for DCN"
self.conv2 = build_conv_layer(
self.dcn,
width,
@@ -72,7 +68,8 @@ def __init__(self,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
@@ -80,7 +77,8 @@ def __init__(self,
width,
self.planes * self.expansion,
kernel_size=1,
- bias=False)
+ bias=False,
+ )
self.add_module(self.norm3_name, norm3)
@@ -133,13 +131,13 @@ class ResNeXt(ResNet):
arch_settings = {
50: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
- 152: (Bottleneck, (3, 8, 36, 3))
+ 152: (Bottleneck, (3, 8, 36, 3)),
}
def __init__(self, groups=1, base_width=4, **kwargs):
self.groups = groups
self.base_width = base_width
- super(ResNeXt, self).__init__(**kwargs)
+ super().__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``"""
@@ -147,4 +145,5 @@ def make_res_layer(self, **kwargs):
groups=self.groups,
base_width=self.base_width,
base_channels=self.base_channels,
- **kwargs)
+ **kwargs,
+ )
diff --git a/mmsegmentation/mmseg/models/backbones/stdc.py b/mmsegmentation/mmseg/models/backbones/stdc.py
index 04f2f7a..81e5b7a 100644
--- a/mmsegmentation/mmseg/models/backbones/stdc.py
+++ b/mmsegmentation/mmseg/models/backbones/stdc.py
@@ -26,25 +26,28 @@ class STDCModule(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- stride,
- norm_cfg=None,
- act_cfg=None,
- num_convs=4,
- fusion_type='add',
- init_cfg=None):
- super(STDCModule, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ stride,
+ norm_cfg=None,
+ act_cfg=None,
+ num_convs=4,
+ fusion_type="add",
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
assert num_convs > 1
- assert fusion_type in ['add', 'cat']
+ assert fusion_type in ["add", "cat"]
self.stride = stride
self.with_downsample = True if self.stride == 2 else False
self.fusion_type = fusion_type
self.layers = ModuleList()
conv_0 = ConvModule(
- in_channels, out_channels // 2, kernel_size=1, norm_cfg=norm_cfg)
+ in_channels, out_channels // 2, kernel_size=1, norm_cfg=norm_cfg
+ )
if self.with_downsample:
self.downsample = ConvModule(
@@ -55,9 +58,10 @@ def __init__(self,
padding=1,
groups=out_channels // 2,
norm_cfg=norm_cfg,
- act_cfg=None)
+ act_cfg=None,
+ )
- if self.fusion_type == 'add':
+ if self.fusion_type == "add":
self.layers.append(nn.Sequential(conv_0, self.downsample))
self.skip = Sequential(
ConvModule(
@@ -68,13 +72,12 @@ def __init__(self,
padding=1,
groups=in_channels,
norm_cfg=norm_cfg,
- act_cfg=None),
+ act_cfg=None,
+ ),
ConvModule(
- in_channels,
- out_channels,
- 1,
- norm_cfg=norm_cfg,
- act_cfg=None))
+ in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None
+ ),
+ )
else:
self.layers.append(conv_0)
self.skip = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
@@ -82,7 +85,7 @@ def __init__(self,
self.layers.append(conv_0)
for i in range(1, num_convs):
- out_factor = 2**(i + 1) if i != num_convs - 1 else 2**i
+ out_factor = 2 ** (i + 1) if i != num_convs - 1 else 2**i
self.layers.append(
ConvModule(
out_channels // 2**i,
@@ -91,10 +94,12 @@ def __init__(self,
stride=1,
padding=1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
def forward(self, inputs):
- if self.fusion_type == 'add':
+ if self.fusion_type == "add":
out = self.forward_add(inputs)
else:
out = self.forward_cat(inputs)
@@ -148,33 +153,30 @@ class FeatureFusionModule(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- scale_factor=4,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(FeatureFusionModule, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ scale_factor=4,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
channels = out_channels // scale_factor
self.conv0 = ConvModule(
- in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=act_cfg)
+ in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=act_cfg
+ )
self.attention = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
ConvModule(
- out_channels,
- channels,
- 1,
- norm_cfg=None,
- bias=False,
- act_cfg=act_cfg),
+ out_channels, channels, 1, norm_cfg=None, bias=False, act_cfg=act_cfg
+ ),
ConvModule(
- channels,
- out_channels,
- 1,
- norm_cfg=None,
- bias=False,
- act_cfg=None), nn.Sigmoid())
+ channels, out_channels, 1, norm_cfg=None, bias=False, act_cfg=None
+ ),
+ nn.Sigmoid(),
+ )
def forward(self, spatial_inputs, context_inputs):
inputs = torch.cat([spatial_inputs, context_inputs], dim=1)
@@ -225,29 +227,35 @@ class STDCNet(BaseModule):
"""
arch_settings = {
- 'STDCNet1': [(2, 1), (2, 1), (2, 1)],
- 'STDCNet2': [(2, 1, 1, 1), (2, 1, 1, 1, 1), (2, 1, 1)]
+ "STDCNet1": [(2, 1), (2, 1), (2, 1)],
+ "STDCNet2": [(2, 1, 1, 1), (2, 1, 1, 1, 1), (2, 1, 1)],
}
- def __init__(self,
- stdc_type,
- in_channels,
- channels,
- bottleneck_type,
- norm_cfg,
- act_cfg,
- num_convs=4,
- with_final_conv=False,
- pretrained=None,
- init_cfg=None):
- super(STDCNet, self).__init__(init_cfg=init_cfg)
- assert stdc_type in self.arch_settings, \
- f'invalid structure {stdc_type} for STDCNet.'
- assert bottleneck_type in ['add', 'cat'],\
- f'bottleneck_type must be `add` or `cat`, got {bottleneck_type}'
-
- assert len(channels) == 5,\
- f'invalid channels length {len(channels)} for STDCNet.'
+ def __init__(
+ self,
+ stdc_type,
+ in_channels,
+ channels,
+ bottleneck_type,
+ norm_cfg,
+ act_cfg,
+ num_convs=4,
+ with_final_conv=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ stdc_type in self.arch_settings
+ ), f"invalid structure {stdc_type} for STDCNet."
+ assert bottleneck_type in [
+ "add",
+ "cat",
+ ], f"bottleneck_type must be `add` or `cat`, got {bottleneck_type}"
+
+ assert (
+ len(channels) == 5
+ ), f"invalid channels length {len(channels)} for STDCNet."
self.in_channels = in_channels
self.channels = channels
@@ -256,24 +264,28 @@ def __init__(self,
self.num_convs = num_convs
self.with_final_conv = with_final_conv
- self.stages = ModuleList([
- ConvModule(
- self.in_channels,
- self.channels[0],
- kernel_size=3,
- stride=2,
- padding=1,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg),
- ConvModule(
- self.channels[0],
- self.channels[1],
- kernel_size=3,
- stride=2,
- padding=1,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg)
- ])
+ self.stages = ModuleList(
+ [
+ ConvModule(
+ self.in_channels,
+ self.channels[0],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ norm_cfg=norm_cfg,
+ act_cfg=act_cfg,
+ ),
+ ConvModule(
+ self.channels[0],
+ self.channels[1],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ norm_cfg=norm_cfg,
+ act_cfg=act_cfg,
+ ),
+ ]
+ )
# `self.num_shallow_features` is the number of shallow modules in
# `STDCNet`, which is noted as `Stage1` and `Stage2` in original paper.
# They are both not used for following modules like Attention
@@ -285,8 +297,15 @@ def __init__(self,
for strides in self.stage_strides:
idx = len(self.stages) - 1
self.stages.append(
- self._make_stage(self.channels[idx], self.channels[idx + 1],
- strides, norm_cfg, act_cfg, bottleneck_type))
+ self._make_stage(
+ self.channels[idx],
+ self.channels[idx + 1],
+ strides,
+ norm_cfg,
+ act_cfg,
+ bottleneck_type,
+ )
+ )
# After appending, `self.stages` is a ModuleList including several
# shallow modules and STDCModules.
# (len(self.stages) ==
@@ -297,10 +316,12 @@ def __init__(self,
max(1024, self.channels[-1]),
1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
- def _make_stage(self, in_channels, out_channels, strides, norm_cfg,
- act_cfg, bottleneck_type):
+ def _make_stage(
+ self, in_channels, out_channels, strides, norm_cfg, act_cfg, bottleneck_type
+ ):
layers = []
for i, stride in enumerate(strides):
layers.append(
@@ -311,7 +332,9 @@ def _make_stage(self, in_channels, out_channels, strides, norm_cfg,
norm_cfg,
act_cfg,
num_convs=self.num_convs,
- fusion_type=bottleneck_type))
+ fusion_type=bottleneck_type,
+ )
+ )
return Sequential(*layers)
def forward(self, x):
@@ -321,7 +344,7 @@ def forward(self, x):
outs.append(x)
if self.with_final_conv:
outs[-1] = self.final_conv(outs[-1])
- outs = outs[self.num_shallow_features:]
+ outs = outs[self.num_shallow_features :]
return tuple(outs)
@@ -360,31 +383,29 @@ class STDCContextPathNet(BaseModule):
auxiliary heads and decoder head.
"""
- def __init__(self,
- backbone_cfg,
- last_in_channels=(1024, 512),
- out_channels=128,
- ffm_cfg=dict(
- in_channels=512, out_channels=256, scale_factor=4),
- upsample_mode='nearest',
- align_corners=None,
- norm_cfg=dict(type='BN'),
- init_cfg=None):
- super(STDCContextPathNet, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ backbone_cfg,
+ last_in_channels=(1024, 512),
+ out_channels=128,
+ ffm_cfg=dict(in_channels=512, out_channels=256, scale_factor=4),
+ upsample_mode="nearest",
+ align_corners=None,
+ norm_cfg=dict(type="BN"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.backbone = build_backbone(backbone_cfg)
self.arms = ModuleList()
self.convs = ModuleList()
for channels in last_in_channels:
self.arms.append(AttentionRefinementModule(channels, out_channels))
self.convs.append(
- ConvModule(
- out_channels,
- out_channels,
- 3,
- padding=1,
- norm_cfg=norm_cfg))
+ ConvModule(out_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg)
+ )
self.conv_avg = ConvModule(
- last_in_channels[0], out_channels, 1, norm_cfg=norm_cfg)
+ last_in_channels[0], out_channels, 1, norm_cfg=norm_cfg
+ )
self.ffm = FeatureFusionModule(**ffm_cfg)
@@ -400,7 +421,8 @@ def forward(self, x):
avg_feat,
size=outs[-1].shape[2:],
mode=self.upsample_mode,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
arms_out = []
for i in range(len(self.arms)):
x_arm = self.arms[i](outs[len(outs) - 1 - i]) + feature_up
@@ -408,7 +430,8 @@ def forward(self, x):
x_arm,
size=outs[len(outs) - 1 - i - 1].shape[2:],
mode=self.upsample_mode,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
feature_up = self.convs[i](feature_up)
arms_out.append(feature_up)
diff --git a/mmsegmentation/mmseg/models/backbones/swin.py b/mmsegmentation/mmseg/models/backbones/swin.py
index cbf1328..2cd4304 100644
--- a/mmsegmentation/mmseg/models/backbones/swin.py
+++ b/mmsegmentation/mmseg/models/backbones/swin.py
@@ -9,10 +9,8 @@
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, build_dropout
-from mmcv.cnn.utils.weight_init import (constant_init, trunc_normal_,
- trunc_normal_init)
-from mmcv.runner import (BaseModule, CheckpointLoader, ModuleList,
- load_state_dict)
+from mmcv.cnn.utils.weight_init import constant_init, trunc_normal_, trunc_normal_init
+from mmcv.runner import BaseModule, CheckpointLoader, ModuleList, load_state_dict
from mmcv.utils import to_2tuple
from ...utils import get_root_logger
@@ -39,15 +37,17 @@ class WindowMSA(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- window_size,
- qkv_bias=True,
- qk_scale=None,
- attn_drop_rate=0.,
- proj_drop_rate=0.,
- init_cfg=None):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ window_size,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop_rate=0.0,
+ proj_drop_rate=0.0,
+ init_cfg=None,
+ ):
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
@@ -58,15 +58,15 @@ def __init__(self,
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
- num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+ ) # 2*Wh-1 * 2*Ww-1, nH
# About 2x faster than original impl
Wh, Ww = self.window_size
rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
rel_position_index = rel_index_coords + rel_index_coords.T
rel_position_index = rel_position_index.flip(1).contiguous()
- self.register_buffer('relative_position_index', rel_position_index)
+ self.register_buffer("relative_position_index", rel_position_index)
self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop_rate)
@@ -87,27 +87,34 @@ def forward(self, x, mask=None):
Wh*Ww, Wh*Ww), value should be between (-inf, 0].
"""
B, N, C = x.shape
- qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
- C // self.num_heads).permute(2, 0, 3, 1, 4)
+ qkv = (
+ self.qkv(x)
+ .reshape(B, N, 3, self.num_heads, C // self.num_heads)
+ .permute(2, 0, 3, 1, 4)
+ )
# make torchscript happy (cannot use tensor as tuple)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
+ attn = q @ k.transpose(-2, -1)
relative_position_bias = self.relative_position_bias_table[
- self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1],
- self.window_size[0] * self.window_size[1],
- -1) # Wh*Ww,Wh*Ww,nH
+ self.relative_position_index.view(-1)
+ ].view(
+ self.window_size[0] * self.window_size[1],
+ self.window_size[0] * self.window_size[1],
+ -1,
+ ) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
- 2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ 2, 0, 1
+ ).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
- attn = attn.view(B // nW, nW, self.num_heads, N,
- N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(B // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
+ 1
+ ).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
@@ -148,17 +155,19 @@ class ShiftWindowMSA(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- window_size,
- shift_size=0,
- qkv_bias=True,
- qk_scale=None,
- attn_drop_rate=0,
- proj_drop_rate=0,
- dropout_layer=dict(type='DropPath', drop_prob=0.),
- init_cfg=None):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ window_size,
+ shift_size=0,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop_rate=0,
+ proj_drop_rate=0,
+ dropout_layer=dict(type="DropPath", drop_prob=0.0),
+ init_cfg=None,
+ ):
super().__init__(init_cfg=init_cfg)
self.window_size = window_size
@@ -173,14 +182,15 @@ def __init__(self,
qk_scale=qk_scale,
attn_drop_rate=attn_drop_rate,
proj_drop_rate=proj_drop_rate,
- init_cfg=None)
+ init_cfg=None,
+ )
self.drop = build_dropout(dropout_layer)
def forward(self, query, hw_shape):
B, L, C = query.shape
H, W = hw_shape
- assert L == H * W, 'input feature has wrong size'
+ assert L == H * W, "input feature has wrong size"
query = query.view(B, H, W, C)
# pad feature maps to multiples of window size
@@ -192,18 +202,21 @@ def forward(self, query, hw_shape):
# cyclic shift
if self.shift_size > 0:
shifted_query = torch.roll(
- query,
- shifts=(-self.shift_size, -self.shift_size),
- dims=(1, 2))
+ query, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+ )
# calculate attention mask for SW-MSA
img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device)
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size,
- -self.shift_size), slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size,
- -self.shift_size), slice(-self.shift_size, None))
+ h_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ w_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
cnt = 0
for h in h_slices:
for w in w_slices:
@@ -212,12 +225,11 @@ def forward(self, query, hw_shape):
# nW, window_size, window_size, 1
mask_windows = self.window_partition(img_mask)
- mask_windows = mask_windows.view(
- -1, self.window_size * self.window_size)
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0,
- float(-100.0)).masked_fill(
- attn_mask == 0, float(0.0))
+ attn_mask = attn_mask.masked_fill(
+ attn_mask != 0, float(-100.0)
+ ).masked_fill(attn_mask == 0, float(0.0))
else:
shifted_query = query
attn_mask = None
@@ -231,17 +243,15 @@ def forward(self, query, hw_shape):
attn_windows = self.w_msa(query_windows, mask=attn_mask)
# merge windows
- attn_windows = attn_windows.view(-1, self.window_size,
- self.window_size, C)
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
# B H' W' C
shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(
- shifted_x,
- shifts=(self.shift_size, self.shift_size),
- dims=(1, 2))
+ shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
+ )
else:
x = shifted_x
@@ -264,8 +274,9 @@ def window_reverse(self, windows, H, W):
"""
window_size = self.window_size
B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size,
- window_size, -1)
+ x = windows.view(
+ B, H // window_size, W // window_size, window_size, window_size, -1
+ )
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
@@ -278,15 +289,14 @@ def window_partition(self, x):
"""
B, H, W, C = x.shape
window_size = self.window_size
- x = x.view(B, H // window_size, window_size, W // window_size,
- window_size, C)
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
windows = windows.view(-1, window_size, window_size, C)
return windows
class SwinBlock(BaseModule):
- """"
+ """ "
Args:
embed_dims (int): The feature dimension.
num_heads (int): Parallel attention heads.
@@ -310,23 +320,25 @@ class SwinBlock(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- window_size=7,
- shift=False,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- with_cp=False,
- init_cfg=None):
-
- super(SwinBlock, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ window_size=7,
+ shift=False,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ with_cp=False,
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg=init_cfg)
self.with_cp = with_cp
@@ -340,8 +352,9 @@ def __init__(self,
qk_scale=qk_scale,
attn_drop_rate=attn_drop_rate,
proj_drop_rate=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
- init_cfg=None)
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
+ init_cfg=None,
+ )
self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
self.ffn = FFN(
@@ -349,10 +362,11 @@ def __init__(self,
feedforward_channels=feedforward_channels,
num_fcs=2,
ffn_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
act_cfg=act_cfg,
add_identity=True,
- init_cfg=None)
+ init_cfg=None,
+ )
def forward(self, x, hw_shape):
@@ -406,22 +420,24 @@ class SwinBlockSequence(BaseModule):
Default: None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- depth,
- window_size=7,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- downsample=None,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- with_cp=False,
- init_cfg=None):
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ depth,
+ window_size=7,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ downsample=None,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ with_cp=False,
+ init_cfg=None,
+ ):
super().__init__(init_cfg=init_cfg)
if isinstance(drop_path_rate, list):
@@ -446,7 +462,8 @@ def __init__(self,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
with_cp=with_cp,
- init_cfg=None)
+ init_cfg=None,
+ )
self.blocks.append(block)
self.downsample = downsample
@@ -515,30 +532,32 @@ class SwinTransformer(BaseModule):
Defaults to None.
"""
- def __init__(self,
- pretrain_img_size=224,
- in_channels=3,
- embed_dims=96,
- patch_size=4,
- window_size=7,
- mlp_ratio=4,
- depths=(2, 2, 6, 2),
- num_heads=(3, 6, 12, 24),
- strides=(4, 2, 2, 2),
- out_indices=(0, 1, 2, 3),
- qkv_bias=True,
- qk_scale=None,
- patch_norm=True,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.1,
- use_abs_pos_embed=False,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- with_cp=False,
- pretrained=None,
- frozen_stages=-1,
- init_cfg=None):
+ def __init__(
+ self,
+ pretrain_img_size=224,
+ in_channels=3,
+ embed_dims=96,
+ patch_size=4,
+ window_size=7,
+ mlp_ratio=4,
+ depths=(2, 2, 6, 2),
+ num_heads=(3, 6, 12, 24),
+ strides=(4, 2, 2, 2),
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=True,
+ qk_scale=None,
+ patch_norm=True,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.1,
+ use_abs_pos_embed=False,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ with_cp=False,
+ pretrained=None,
+ frozen_stages=-1,
+ init_cfg=None,
+ ):
self.frozen_stages = frozen_stages
if isinstance(pretrain_img_size, int):
@@ -546,53 +565,57 @@ def __init__(self,
elif isinstance(pretrain_img_size, tuple):
if len(pretrain_img_size) == 1:
pretrain_img_size = to_2tuple(pretrain_img_size[0])
- assert len(pretrain_img_size) == 2, \
- f'The size of image should have length 1 or 2, ' \
- f'but got {len(pretrain_img_size)}'
-
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be specified at the same time'
+ assert len(pretrain_img_size) == 2, (
+ f"The size of image should have length 1 or 2, "
+ f"but got {len(pretrain_img_size)}"
+ )
+
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be specified at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
init_cfg = init_cfg
else:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
- super(SwinTransformer, self).__init__(init_cfg=init_cfg)
+ super().__init__(init_cfg=init_cfg)
num_layers = len(depths)
self.out_indices = out_indices
self.use_abs_pos_embed = use_abs_pos_embed
- assert strides[0] == patch_size, 'Use non-overlapping patch embed.'
+ assert strides[0] == patch_size, "Use non-overlapping patch embed."
self.patch_embed = PatchEmbed(
in_channels=in_channels,
embed_dims=embed_dims,
- conv_type='Conv2d',
+ conv_type="Conv2d",
kernel_size=patch_size,
stride=strides[0],
- padding='corner',
+ padding="corner",
norm_cfg=norm_cfg if patch_norm else None,
- init_cfg=None)
+ init_cfg=None,
+ )
if self.use_abs_pos_embed:
patch_row = pretrain_img_size[0] // patch_size
patch_col = pretrain_img_size[1] // patch_size
num_patches = patch_row * patch_col
self.absolute_pos_embed = nn.Parameter(
- torch.zeros((1, num_patches, embed_dims)))
+ torch.zeros((1, num_patches, embed_dims))
+ )
self.drop_after_pos = nn.Dropout(p=drop_rate)
# set stochastic depth decay rule
total_depth = sum(depths)
- dpr = [
- x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
- ]
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)]
self.stages = ModuleList()
in_channels = embed_dims
@@ -603,7 +626,8 @@ def __init__(self,
out_channels=2 * in_channels,
stride=strides[i + 1],
norm_cfg=norm_cfg if patch_norm else None,
- init_cfg=None)
+ init_cfg=None,
+ )
else:
downsample = None
@@ -617,12 +641,13 @@ def __init__(self,
qk_scale=qk_scale,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
- drop_path_rate=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+ drop_path_rate=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
downsample=downsample,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
with_cp=with_cp,
- init_cfg=None)
+ init_cfg=None,
+ )
self.stages.append(stage)
if downsample:
in_channels = downsample.out_channels
@@ -631,12 +656,12 @@ def __init__(self,
# Add a norm layer for each output
for i in out_indices:
layer = build_norm_layer(norm_cfg, self.num_features[i])[1]
- layer_name = f'norm{i}'
+ layer_name = f"norm{i}"
self.add_module(layer_name, layer)
def train(self, mode=True):
"""Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
+ super().train(mode)
self._freeze_stages()
def _freeze_stages(self):
@@ -651,7 +676,7 @@ def _freeze_stages(self):
for i in range(1, self.frozen_stages + 1):
if (i - 1) in self.out_indices:
- norm_layer = getattr(self, f'norm{i-1}')
+ norm_layer = getattr(self, f"norm{i-1}")
norm_layer.eval()
for param in norm_layer.parameters():
param.requires_grad = False
@@ -664,56 +689,63 @@ def _freeze_stages(self):
def init_weights(self):
logger = get_root_logger()
if self.init_cfg is None:
- logger.warn(f'No pre-trained weights for '
- f'{self.__class__.__name__}, '
- f'training start from scratch')
+ logger.warn(
+ f"No pre-trained weights for "
+ f"{self.__class__.__name__}, "
+ f"training start from scratch"
+ )
if self.use_abs_pos_embed:
trunc_normal_(self.absolute_pos_embed, std=0.02)
for m in self.modules():
if isinstance(m, nn.Linear):
- trunc_normal_init(m, std=.02, bias=0.)
+ trunc_normal_init(m, std=0.02, bias=0.0)
elif isinstance(m, nn.LayerNorm):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
else:
- assert 'checkpoint' in self.init_cfg, f'Only support ' \
- f'specify `Pretrained` in ' \
- f'`init_cfg` in ' \
- f'{self.__class__.__name__} '
+ assert "checkpoint" in self.init_cfg, (
+ f"Only support "
+ f"specify `Pretrained` in "
+ f"`init_cfg` in "
+ f"{self.__class__.__name__} "
+ )
ckpt = CheckpointLoader.load_checkpoint(
- self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
- if 'state_dict' in ckpt:
- _state_dict = ckpt['state_dict']
- elif 'model' in ckpt:
- _state_dict = ckpt['model']
+ self.init_cfg["checkpoint"], logger=logger, map_location="cpu"
+ )
+ if "state_dict" in ckpt:
+ _state_dict = ckpt["state_dict"]
+ elif "model" in ckpt:
+ _state_dict = ckpt["model"]
else:
_state_dict = ckpt
state_dict = OrderedDict()
for k, v in _state_dict.items():
- if k.startswith('backbone.'):
+ if k.startswith("backbone."):
state_dict[k[9:]] = v
else:
state_dict[k] = v
# strip prefix of state_dict
- if list(state_dict.keys())[0].startswith('module.'):
+ if list(state_dict.keys())[0].startswith("module."):
state_dict = {k[7:]: v for k, v in state_dict.items()}
# reshape absolute position embedding
- if state_dict.get('absolute_pos_embed') is not None:
- absolute_pos_embed = state_dict['absolute_pos_embed']
+ if state_dict.get("absolute_pos_embed") is not None:
+ absolute_pos_embed = state_dict["absolute_pos_embed"]
N1, L, C1 = absolute_pos_embed.size()
N2, C2, H, W = self.absolute_pos_embed.size()
if N1 != N2 or C1 != C2 or L != H * W:
- logger.warning('Error in loading absolute_pos_embed, pass')
+ logger.warning("Error in loading absolute_pos_embed, pass")
else:
- state_dict['absolute_pos_embed'] = absolute_pos_embed.view(
- N2, H, W, C2).permute(0, 3, 1, 2).contiguous()
+ state_dict["absolute_pos_embed"] = (
+ absolute_pos_embed.view(N2, H, W, C2)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
# interpolate position bias table if needed
relative_position_bias_table_keys = [
- k for k in state_dict.keys()
- if 'relative_position_bias_table' in k
+ k for k in state_dict.keys() if "relative_position_bias_table" in k
]
for table_key in relative_position_bias_table_keys:
table_pretrained = state_dict[table_key]
@@ -721,16 +753,20 @@ def init_weights(self):
L1, nH1 = table_pretrained.size()
L2, nH2 = table_current.size()
if nH1 != nH2:
- logger.warning(f'Error in loading {table_key}, pass')
+ logger.warning(f"Error in loading {table_key}, pass")
elif L1 != L2:
S1 = int(L1**0.5)
S2 = int(L2**0.5)
table_pretrained_resized = F.interpolate(
table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1),
size=(S2, S2),
- mode='bicubic')
- state_dict[table_key] = table_pretrained_resized.view(
- nH2, L2).permute(1, 0).contiguous()
+ mode="bicubic",
+ )
+ state_dict[table_key] = (
+ table_pretrained_resized.view(nH2, L2)
+ .permute(1, 0)
+ .contiguous()
+ )
# load state_dict
load_state_dict(self, state_dict, strict=False, logger=logger)
@@ -746,11 +782,13 @@ def forward(self, x):
for i, stage in enumerate(self.stages):
x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
if i in self.out_indices:
- norm_layer = getattr(self, f'norm{i}')
+ norm_layer = getattr(self, f"norm{i}")
out = norm_layer(out)
- out = out.view(-1, *out_hw_shape,
- self.num_features[i]).permute(0, 3, 1,
- 2).contiguous()
+ out = (
+ out.view(-1, *out_hw_shape, self.num_features[i])
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
outs.append(out)
return outs
diff --git a/mmsegmentation/mmseg/models/backbones/timm_backbone.py b/mmsegmentation/mmseg/models/backbones/timm_backbone.py
index 01b29fc..1cd7638 100644
--- a/mmsegmentation/mmseg/models/backbones/timm_backbone.py
+++ b/mmsegmentation/mmseg/models/backbones/timm_backbone.py
@@ -1,14 +1,14 @@
# Copyright (c) OpenMMLab. All rights reserved.
-try:
- import timm
-except ImportError:
- timm = None
-
from mmcv.cnn.bricks.registry import NORM_LAYERS
from mmcv.runner import BaseModule
from ..builder import BACKBONES
+try:
+ import timm
+except ImportError:
+ timm = None
+
@BACKBONES.register_module()
class TIMMBackbone(BaseModule):
@@ -30,16 +30,16 @@ def __init__(
model_name,
features_only=True,
pretrained=True,
- checkpoint_path='',
+ checkpoint_path="",
in_channels=3,
init_cfg=None,
**kwargs,
):
if timm is None:
- raise RuntimeError('timm is not installed')
- super(TIMMBackbone, self).__init__(init_cfg)
- if 'norm_layer' in kwargs:
- kwargs['norm_layer'] = NORM_LAYERS.get(kwargs['norm_layer'])
+ raise RuntimeError("timm is not installed")
+ super().__init__(init_cfg)
+ if "norm_layer" in kwargs:
+ kwargs["norm_layer"] = NORM_LAYERS.get(kwargs["norm_layer"])
self.timm_model = timm.create_model(
model_name=model_name,
features_only=features_only,
diff --git a/mmsegmentation/mmseg/models/backbones/twins.py b/mmsegmentation/mmseg/models/backbones/twins.py
index 6bd9469..575b54d 100644
--- a/mmsegmentation/mmseg/models/backbones/twins.py
+++ b/mmsegmentation/mmseg/models/backbones/twins.py
@@ -8,8 +8,7 @@
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import FFN
-from mmcv.cnn.utils.weight_init import (constant_init, normal_init,
- trunc_normal_init)
+from mmcv.cnn.utils.weight_init import constant_init, normal_init, trunc_normal_init
from mmcv.runner import BaseModule, ModuleList
from torch.nn.modules.batchnorm import _BatchNorm
@@ -51,18 +50,20 @@ class GlobalSubsampledAttention(EfficientMultiheadAttention):
Defaults to None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- attn_drop=0.,
- proj_drop=0.,
- dropout_layer=None,
- batch_first=True,
- qkv_bias=True,
- norm_cfg=dict(type='LN'),
- sr_ratio=1,
- init_cfg=None):
- super(GlobalSubsampledAttention, self).__init__(
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ dropout_layer=None,
+ batch_first=True,
+ qkv_bias=True,
+ norm_cfg=dict(type="LN"),
+ sr_ratio=1,
+ init_cfg=None,
+ ):
+ super().__init__(
embed_dims,
num_heads,
attn_drop=attn_drop,
@@ -72,7 +73,8 @@ def __init__(self,
qkv_bias=qkv_bias,
norm_cfg=norm_cfg,
sr_ratio=sr_ratio,
- init_cfg=init_cfg)
+ init_cfg=init_cfg,
+ )
class GSAEncoderLayer(BaseModule):
@@ -99,20 +101,22 @@ class GSAEncoderLayer(BaseModule):
Defaults to None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- num_fcs=2,
- qkv_bias=True,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- sr_ratio=1.,
- init_cfg=None):
- super(GSAEncoderLayer, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ num_fcs=2,
+ qkv_bias=True,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ sr_ratio=1.0,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
self.attn = GlobalSubsampledAttention(
@@ -120,10 +124,11 @@ def __init__(self,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
qkv_bias=qkv_bias,
norm_cfg=norm_cfg,
- sr_ratio=sr_ratio)
+ sr_ratio=sr_ratio,
+ )
self.norm2 = build_norm_layer(norm_cfg, embed_dims, postfix=2)[1]
self.ffn = FFN(
@@ -131,16 +136,19 @@ def __init__(self,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
act_cfg=act_cfg,
- add_identity=False)
+ add_identity=False,
+ )
- self.drop_path = build_dropout(
- dict(type='DropPath', drop_prob=drop_path_rate)
- ) if drop_path_rate > 0. else nn.Identity()
+ self.drop_path = (
+ build_dropout(dict(type="DropPath", drop_prob=drop_path_rate))
+ if drop_path_rate > 0.0
+ else nn.Identity()
+ )
def forward(self, x, hw_shape):
- x = x + self.drop_path(self.attn(self.norm1(x), hw_shape, identity=0.))
+ x = x + self.drop_path(self.attn(self.norm1(x), hw_shape, identity=0.0))
x = x + self.drop_path(self.ffn(self.norm2(x)))
return x
@@ -163,20 +171,22 @@ class LocallyGroupedSelfAttention(BaseModule):
Defaults to None.
"""
- def __init__(self,
- embed_dims,
- num_heads=8,
- qkv_bias=False,
- qk_scale=None,
- attn_drop_rate=0.,
- proj_drop_rate=0.,
- window_size=1,
- init_cfg=None):
- super(LocallyGroupedSelfAttention, self).__init__(init_cfg=init_cfg)
-
- assert embed_dims % num_heads == 0, f'dim {embed_dims} should be ' \
- f'divided by num_heads ' \
- f'{num_heads}.'
+ def __init__(
+ self,
+ embed_dims,
+ num_heads=8,
+ qkv_bias=False,
+ qk_scale=None,
+ attn_drop_rate=0.0,
+ proj_drop_rate=0.0,
+ window_size=1,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+
+ assert embed_dims % num_heads == 0, (
+ f"dim {embed_dims} should be " f"divided by num_heads " f"{num_heads}."
+ )
self.embed_dims = embed_dims
self.num_heads = num_heads
head_dim = embed_dims // num_heads
@@ -207,33 +217,45 @@ def forward(self, x, hw_shape):
mask[:, :, -pad_r:].fill_(1)
# [B, _h, _w, window_size, window_size, C]
- x = x.reshape(b, _h, self.window_size, _w, self.window_size,
- c).transpose(2, 3)
- mask = mask.reshape(1, _h, self.window_size, _w,
- self.window_size).transpose(2, 3).reshape(
- 1, _h * _w,
- self.window_size * self.window_size)
+ x = x.reshape(b, _h, self.window_size, _w, self.window_size, c).transpose(2, 3)
+ mask = (
+ mask.reshape(1, _h, self.window_size, _w, self.window_size)
+ .transpose(2, 3)
+ .reshape(1, _h * _w, self.window_size * self.window_size)
+ )
# [1, _h*_w, window_size*window_size, window_size*window_size]
attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3)
- attn_mask = attn_mask.masked_fill(attn_mask != 0,
- float(-1000.0)).masked_fill(
- attn_mask == 0, float(0.0))
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(
+ attn_mask == 0, float(0.0)
+ )
# [3, B, _w*_h, nhead, window_size*window_size, dim]
- qkv = self.qkv(x).reshape(b, _h * _w,
- self.window_size * self.window_size, 3,
- self.num_heads, c // self.num_heads).permute(
- 3, 0, 1, 4, 2, 5)
+ qkv = (
+ self.qkv(x)
+ .reshape(
+ b,
+ _h * _w,
+ self.window_size * self.window_size,
+ 3,
+ self.num_heads,
+ c // self.num_heads,
+ )
+ .permute(3, 0, 1, 4, 2, 5)
+ )
q, k, v = qkv[0], qkv[1], qkv[2]
# [B, _h*_w, n_head, window_size*window_size, window_size*window_size]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn + attn_mask.unsqueeze(2)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
- attn = (attn @ v).transpose(2, 3).reshape(b, _h, _w, self.window_size,
- self.window_size, c)
- x = attn.transpose(2, 3).reshape(b, _h * self.window_size,
- _w * self.window_size, c)
+ attn = (
+ (attn @ v)
+ .transpose(2, 3)
+ .reshape(b, _h, _w, self.window_size, self.window_size, c)
+ )
+ x = attn.transpose(2, 3).reshape(
+ b, _h * self.window_size, _w * self.window_size, c
+ )
if pad_r > 0 or pad_b > 0:
x = x[:, :h, :w, :].contiguous()
@@ -269,28 +291,35 @@ class LSAEncoderLayer(BaseModule):
Defaults to None.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- num_fcs=2,
- qkv_bias=True,
- qk_scale=None,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- window_size=1,
- init_cfg=None):
-
- super(LSAEncoderLayer, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ num_fcs=2,
+ qkv_bias=True,
+ qk_scale=None,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ window_size=1,
+ init_cfg=None,
+ ):
+
+ super().__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
- self.attn = LocallyGroupedSelfAttention(embed_dims, num_heads,
- qkv_bias, qk_scale,
- attn_drop_rate, drop_rate,
- window_size)
+ self.attn = LocallyGroupedSelfAttention(
+ embed_dims,
+ num_heads,
+ qkv_bias,
+ qk_scale,
+ attn_drop_rate,
+ drop_rate,
+ window_size,
+ )
self.norm2 = build_norm_layer(norm_cfg, embed_dims, postfix=2)[1]
self.ffn = FFN(
@@ -298,13 +327,16 @@ def __init__(self,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+ dropout_layer=dict(type="DropPath", drop_prob=drop_path_rate),
act_cfg=act_cfg,
- add_identity=False)
+ add_identity=False,
+ )
- self.drop_path = build_dropout(
- dict(type='DropPath', drop_prob=drop_path_rate)
- ) if drop_path_rate > 0. else nn.Identity()
+ self.drop_path = (
+ build_dropout(dict(type="DropPath", drop_prob=drop_path_rate))
+ if drop_path_rate > 0.0
+ else nn.Identity()
+ )
def forward(self, x, hw_shape):
x = x + self.drop_path(self.attn(self.norm1(x), hw_shape))
@@ -325,7 +357,7 @@ class ConditionalPositionEncoding(BaseModule):
"""
def __init__(self, in_channels, embed_dims=768, stride=1, init_cfg=None):
- super(ConditionalPositionEncoding, self).__init__(init_cfg=init_cfg)
+ super().__init__(init_cfg=init_cfg)
self.proj = nn.Conv2d(
in_channels,
embed_dims,
@@ -333,7 +365,8 @@ def __init__(self, in_channels, embed_dims=768, stride=1, init_cfg=None):
stride=stride,
padding=1,
bias=True,
- groups=embed_dims)
+ groups=embed_dims,
+ )
self.stride = stride
def forward(self, x, hw_shape):
@@ -383,33 +416,38 @@ class PCPVT(BaseModule):
Defaults to None.
"""
- def __init__(self,
- in_channels=3,
- embed_dims=[64, 128, 256, 512],
- patch_sizes=[4, 2, 2, 2],
- strides=[4, 2, 2, 2],
- num_heads=[1, 2, 4, 8],
- mlp_ratios=[4, 4, 4, 4],
- out_indices=(0, 1, 2, 3),
- qkv_bias=False,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- norm_cfg=dict(type='LN'),
- depths=[3, 4, 6, 3],
- sr_ratios=[8, 4, 2, 1],
- norm_after_stage=False,
- pretrained=None,
- init_cfg=None):
- super(PCPVT, self).__init__(init_cfg=init_cfg)
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be set at the same time'
+ def __init__(
+ self,
+ in_channels=3,
+ embed_dims=[64, 128, 256, 512],
+ patch_sizes=[4, 2, 2, 2],
+ strides=[4, 2, 2, 2],
+ num_heads=[1, 2, 4, 8],
+ mlp_ratios=[4, 4, 4, 4],
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=False,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ norm_cfg=dict(type="LN"),
+ depths=[3, 4, 6, 3],
+ sr_ratios=[8, 4, 2, 1],
+ norm_after_stage=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be set at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is not None:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.depths = depths
# patch_embed
@@ -422,18 +460,22 @@ def __init__(self,
PatchEmbed(
in_channels=in_channels if i == 0 else embed_dims[i - 1],
embed_dims=embed_dims[i],
- conv_type='Conv2d',
+ conv_type="Conv2d",
kernel_size=patch_sizes[i],
stride=strides[i],
- padding='corner',
- norm_cfg=norm_cfg))
+ padding="corner",
+ norm_cfg=norm_cfg,
+ )
+ )
self.position_encoding_drops.append(nn.Dropout(p=drop_rate))
- self.position_encodings = ModuleList([
- ConditionalPositionEncoding(embed_dim, embed_dim)
- for embed_dim in embed_dims
- ])
+ self.position_encodings = ModuleList(
+ [
+ ConditionalPositionEncoding(embed_dim, embed_dim)
+ for embed_dim in embed_dims
+ ]
+ )
# transformer encoder
dpr = [
@@ -442,25 +484,28 @@ def __init__(self,
cur = 0
for k in range(len(depths)):
- _block = ModuleList([
- GSAEncoderLayer(
- embed_dims=embed_dims[k],
- num_heads=num_heads[k],
- feedforward_channels=mlp_ratios[k] * embed_dims[k],
- attn_drop_rate=attn_drop_rate,
- drop_rate=drop_rate,
- drop_path_rate=dpr[cur + i],
- num_fcs=2,
- qkv_bias=qkv_bias,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- sr_ratio=sr_ratios[k]) for i in range(depths[k])
- ])
+ _block = ModuleList(
+ [
+ GSAEncoderLayer(
+ embed_dims=embed_dims[k],
+ num_heads=num_heads[k],
+ feedforward_channels=mlp_ratios[k] * embed_dims[k],
+ attn_drop_rate=attn_drop_rate,
+ drop_rate=drop_rate,
+ drop_path_rate=dpr[cur + i],
+ num_fcs=2,
+ qkv_bias=qkv_bias,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ sr_ratio=sr_ratios[k],
+ )
+ for i in range(depths[k])
+ ]
+ )
self.layers.append(_block)
cur += depths[k]
- self.norm_name, norm = build_norm_layer(
- norm_cfg, embed_dims[-1], postfix=1)
+ self.norm_name, norm = build_norm_layer(norm_cfg, embed_dims[-1], postfix=1)
self.out_indices = out_indices
self.norm_after_stage = norm_after_stage
@@ -471,19 +516,17 @@ def __init__(self,
def init_weights(self):
if self.init_cfg is not None:
- super(PCPVT, self).init_weights()
+ super().init_weights()
else:
for m in self.modules():
if isinstance(m, nn.Linear):
- trunc_normal_init(m, std=.02, bias=0.)
+ trunc_normal_init(m, std=0.02, bias=0.0)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[
- 1] * m.out_channels
+ fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
- normal_init(
- m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
+ normal_init(m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
def forward(self, x):
outputs = list()
@@ -544,30 +587,46 @@ class SVT(PCPVT):
Defaults to None.
"""
- def __init__(self,
- in_channels=3,
- embed_dims=[64, 128, 256],
- patch_sizes=[4, 2, 2, 2],
- strides=[4, 2, 2, 2],
- num_heads=[1, 2, 4],
- mlp_ratios=[4, 4, 4],
- out_indices=(0, 1, 2, 3),
- qkv_bias=False,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- norm_cfg=dict(type='LN'),
- depths=[4, 4, 4],
- sr_ratios=[4, 2, 1],
- windiow_sizes=[7, 7, 7],
- norm_after_stage=True,
- pretrained=None,
- init_cfg=None):
- super(SVT, self).__init__(in_channels, embed_dims, patch_sizes,
- strides, num_heads, mlp_ratios, out_indices,
- qkv_bias, drop_rate, attn_drop_rate,
- drop_path_rate, norm_cfg, depths, sr_ratios,
- norm_after_stage, pretrained, init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ embed_dims=[64, 128, 256],
+ patch_sizes=[4, 2, 2, 2],
+ strides=[4, 2, 2, 2],
+ num_heads=[1, 2, 4],
+ mlp_ratios=[4, 4, 4],
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=False,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.2,
+ norm_cfg=dict(type="LN"),
+ depths=[4, 4, 4],
+ sr_ratios=[4, 2, 1],
+ windiow_sizes=[7, 7, 7],
+ norm_after_stage=True,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(
+ in_channels,
+ embed_dims,
+ patch_sizes,
+ strides,
+ num_heads,
+ mlp_ratios,
+ out_indices,
+ qkv_bias,
+ drop_rate,
+ attn_drop_rate,
+ drop_path_rate,
+ norm_cfg,
+ depths,
+ sr_ratios,
+ norm_after_stage,
+ pretrained,
+ init_cfg,
+ )
# transformer encoder
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
@@ -576,13 +635,13 @@ def __init__(self,
for k in range(len(depths)):
for i in range(depths[k]):
if i % 2 == 0:
- self.layers[k][i] = \
- LSAEncoderLayer(
- embed_dims=embed_dims[k],
- num_heads=num_heads[k],
- feedforward_channels=mlp_ratios[k] * embed_dims[k],
- drop_rate=drop_rate,
- attn_drop_rate=attn_drop_rate,
- drop_path_rate=dpr[sum(depths[:k])+i],
- qkv_bias=qkv_bias,
- window_size=windiow_sizes[k])
+ self.layers[k][i] = LSAEncoderLayer(
+ embed_dims=embed_dims[k],
+ num_heads=num_heads[k],
+ feedforward_channels=mlp_ratios[k] * embed_dims[k],
+ drop_rate=drop_rate,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dpr[sum(depths[:k]) + i],
+ qkv_bias=qkv_bias,
+ window_size=windiow_sizes[k],
+ )
diff --git a/mmsegmentation/mmseg/models/backbones/unet.py b/mmsegmentation/mmseg/models/backbones/unet.py
index c2d3366..51597b3 100644
--- a/mmsegmentation/mmseg/models/backbones/unet.py
+++ b/mmsegmentation/mmseg/models/backbones/unet.py
@@ -3,8 +3,12 @@
import torch.nn as nn
import torch.utils.checkpoint as cp
-from mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
- build_norm_layer)
+from mmcv.cnn import (
+ UPSAMPLE_LAYERS,
+ ConvModule,
+ build_activation_layer,
+ build_norm_layer,
+)
from mmcv.runner import BaseModule
from mmcv.utils.parrots_wrapper import _BatchNorm
@@ -43,21 +47,23 @@ class BasicConvBlock(nn.Module):
plugins (dict): plugins for convolutional layers. Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- num_convs=2,
- stride=1,
- dilation=1,
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- dcn=None,
- plugins=None):
- super(BasicConvBlock, self).__init__()
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_convs=2,
+ stride=1,
+ dilation=1,
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ dcn=None,
+ plugins=None,
+ ):
+ super().__init__()
+ assert dcn is None, "Not implemented yet."
+ assert plugins is None, "Not implemented yet."
self.with_cp = with_cp
convs = []
@@ -72,7 +78,9 @@ def __init__(self,
padding=1 if i == 0 else dilation,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.convs = nn.Sequential(*convs)
@@ -105,23 +113,27 @@ class DeconvModule(nn.Module):
kernel_size (int): Kernel size of the convolutional layer. Default: 4.
"""
- def __init__(self,
- in_channels,
- out_channels,
- with_cp=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- *,
- kernel_size=4,
- scale_factor=2):
- super(DeconvModule, self).__init__()
-
- assert (kernel_size - scale_factor >= 0) and\
- (kernel_size - scale_factor) % 2 == 0,\
- f'kernel_size should be greater than or equal to scale_factor '\
- f'and (kernel_size - scale_factor) should be even numbers, '\
- f'while the kernel size is {kernel_size} and scale_factor is '\
- f'{scale_factor}.'
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ with_cp=False,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ *,
+ kernel_size=4,
+ scale_factor=2,
+ ):
+ super().__init__()
+
+ assert (kernel_size - scale_factor >= 0) and (
+ kernel_size - scale_factor
+ ) % 2 == 0, (
+ f"kernel_size should be greater than or equal to scale_factor "
+ f"and (kernel_size - scale_factor) should be even numbers, "
+ f"while the kernel size is {kernel_size} and scale_factor is "
+ f"{scale_factor}."
+ )
stride = scale_factor
padding = (kernel_size - scale_factor) // 2
@@ -131,7 +143,8 @@ def __init__(self,
out_channels,
kernel_size=kernel_size,
stride=stride,
- padding=padding)
+ padding=padding,
+ )
norm_name, norm = build_norm_layer(norm_cfg, out_channels)
activate = build_activation_layer(act_cfg)
@@ -179,21 +192,22 @@ class InterpConv(nn.Module):
scale_factor=2, mode='bilinear', align_corners=False).
"""
- def __init__(self,
- in_channels,
- out_channels,
- with_cp=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- *,
- conv_cfg=None,
- conv_first=False,
- kernel_size=1,
- stride=1,
- padding=0,
- upsample_cfg=dict(
- scale_factor=2, mode='bilinear', align_corners=False)):
- super(InterpConv, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ with_cp=False,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ *,
+ conv_cfg=None,
+ conv_first=False,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ upsample_cfg=dict(scale_factor=2, mode="bilinear", align_corners=False),
+ ):
+ super().__init__()
self.with_cp = with_cp
conv = ConvModule(
@@ -204,7 +218,8 @@ def __init__(self,
padding=padding,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
upsample = Upsample(**upsample_cfg)
if conv_first:
self.interp_upsample = nn.Sequential(conv, upsample)
@@ -280,79 +295,87 @@ class UNet(BaseModule):
in UNet._check_input_divisible.
"""
- def __init__(self,
- in_channels=3,
- base_channels=64,
- num_stages=5,
- strides=(1, 1, 1, 1, 1),
- enc_num_convs=(2, 2, 2, 2, 2),
- dec_num_convs=(2, 2, 2, 2),
- downsamples=(True, True, True, True),
- enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1),
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- upsample_cfg=dict(type='InterpConv'),
- norm_eval=False,
- dcn=None,
- plugins=None,
- pretrained=None,
- init_cfg=None):
- super(UNet, self).__init__(init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ base_channels=64,
+ num_stages=5,
+ strides=(1, 1, 1, 1, 1),
+ enc_num_convs=(2, 2, 2, 2, 2),
+ dec_num_convs=(2, 2, 2, 2),
+ downsamples=(True, True, True, True),
+ enc_dilations=(1, 1, 1, 1, 1),
+ dec_dilations=(1, 1, 1, 1),
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ upsample_cfg=dict(type="InterpConv"),
+ norm_eval=False,
+ dcn=None,
+ plugins=None,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.pretrained = pretrained
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be setting at the same time'
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be setting at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is a deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is a deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
- dict(type='Kaiming', layer='Conv2d'),
- dict(
- type='Constant',
- val=1,
- layer=['_BatchNorm', 'GroupNorm'])
+ dict(type="Kaiming", layer="Conv2d"),
+ dict(type="Constant", val=1, layer=["_BatchNorm", "GroupNorm"]),
]
else:
- raise TypeError('pretrained must be a str or None')
-
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
- assert len(strides) == num_stages, \
- 'The length of strides should be equal to num_stages, '\
- f'while the strides is {strides}, the length of '\
- f'strides is {len(strides)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(enc_num_convs) == num_stages, \
- 'The length of enc_num_convs should be equal to num_stages, '\
- f'while the enc_num_convs is {enc_num_convs}, the length of '\
- f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(dec_num_convs) == (num_stages-1), \
- 'The length of dec_num_convs should be equal to (num_stages-1), '\
- f'while the dec_num_convs is {dec_num_convs}, the length of '\
- f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(downsamples) == (num_stages-1), \
- 'The length of downsamples should be equal to (num_stages-1), '\
- f'while the downsamples is {downsamples}, the length of '\
- f'downsamples is {len(downsamples)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(enc_dilations) == num_stages, \
- 'The length of enc_dilations should be equal to num_stages, '\
- f'while the enc_dilations is {enc_dilations}, the length of '\
- f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\
- f'{num_stages}.'
- assert len(dec_dilations) == (num_stages-1), \
- 'The length of dec_dilations should be equal to (num_stages-1), '\
- f'while the dec_dilations is {dec_dilations}, the length of '\
- f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\
- f'{num_stages}.'
+ raise TypeError("pretrained must be a str or None")
+
+ assert dcn is None, "Not implemented yet."
+ assert plugins is None, "Not implemented yet."
+ assert len(strides) == num_stages, (
+ "The length of strides should be equal to num_stages, "
+ f"while the strides is {strides}, the length of "
+ f"strides is {len(strides)}, and the num_stages is "
+ f"{num_stages}."
+ )
+ assert len(enc_num_convs) == num_stages, (
+ "The length of enc_num_convs should be equal to num_stages, "
+ f"while the enc_num_convs is {enc_num_convs}, the length of "
+ f"enc_num_convs is {len(enc_num_convs)}, and the num_stages is "
+ f"{num_stages}."
+ )
+ assert len(dec_num_convs) == (num_stages - 1), (
+ "The length of dec_num_convs should be equal to (num_stages-1), "
+ f"while the dec_num_convs is {dec_num_convs}, the length of "
+ f"dec_num_convs is {len(dec_num_convs)}, and the num_stages is "
+ f"{num_stages}."
+ )
+ assert len(downsamples) == (num_stages - 1), (
+ "The length of downsamples should be equal to (num_stages-1), "
+ f"while the downsamples is {downsamples}, the length of "
+ f"downsamples is {len(downsamples)}, and the num_stages is "
+ f"{num_stages}."
+ )
+ assert len(enc_dilations) == num_stages, (
+ "The length of enc_dilations should be equal to num_stages, "
+ f"while the enc_dilations is {enc_dilations}, the length of "
+ f"enc_dilations is {len(enc_dilations)}, and the num_stages is "
+ f"{num_stages}."
+ )
+ assert len(dec_dilations) == (num_stages - 1), (
+ "The length of dec_dilations should be equal to (num_stages-1), "
+ f"while the dec_dilations is {dec_dilations}, the length of "
+ f"dec_dilations is {len(dec_dilations)}, and the num_stages is "
+ f"{num_stages}."
+ )
self.num_stages = num_stages
self.strides = strides
self.downsamples = downsamples
@@ -367,13 +390,13 @@ def __init__(self,
if i != 0:
if strides[i] == 1 and downsamples[i - 1]:
enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
- upsample = (strides[i] != 1 or downsamples[i - 1])
+ upsample = strides[i] != 1 or downsamples[i - 1]
self.decoder.append(
UpConvBlock(
conv_block=BasicConvBlock,
in_channels=base_channels * 2**i,
- skip_channels=base_channels * 2**(i - 1),
- out_channels=base_channels * 2**(i - 1),
+ skip_channels=base_channels * 2 ** (i - 1),
+ out_channels=base_channels * 2 ** (i - 1),
num_convs=dec_num_convs[i - 1],
stride=1,
dilation=dec_dilations[i - 1],
@@ -383,7 +406,9 @@ def __init__(self,
act_cfg=act_cfg,
upsample_cfg=upsample_cfg if upsample else None,
dcn=None,
- plugins=None))
+ plugins=None,
+ )
+ )
enc_conv_block.append(
BasicConvBlock(
@@ -397,8 +422,10 @@ def __init__(self,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
dcn=None,
- plugins=None))
- self.encoder.append((nn.Sequential(*enc_conv_block)))
+ plugins=None,
+ )
+ )
+ self.encoder.append(nn.Sequential(*enc_conv_block))
in_channels = base_channels * 2**i
def forward(self, x):
@@ -417,7 +444,7 @@ def forward(self, x):
def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
- super(UNet, self).train(mode)
+ super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
@@ -430,9 +457,9 @@ def _check_input_divisible(self, x):
for i in range(1, self.num_stages):
if self.strides[i] == 2 or self.downsamples[i - 1]:
whole_downsample_rate *= 2
- assert (h % whole_downsample_rate == 0) \
- and (w % whole_downsample_rate == 0),\
- f'The input image size {(h, w)} should be divisible by the whole '\
- f'downsample rate {whole_downsample_rate}, when num_stages is '\
- f'{self.num_stages}, strides is {self.strides}, and downsamples '\
- f'is {self.downsamples}.'
+ assert (h % whole_downsample_rate == 0) and (w % whole_downsample_rate == 0), (
+ f"The input image size {(h, w)} should be divisible by the whole "
+ f"downsample rate {whole_downsample_rate}, when num_stages is "
+ f"{self.num_stages}, strides is {self.strides}, and downsamples "
+ f"is {self.downsamples}."
+ )
diff --git a/mmsegmentation/mmseg/models/backbones/vit.py b/mmsegmentation/mmseg/models/backbones/vit.py
index 37b9a4f..42b4661 100644
--- a/mmsegmentation/mmseg/models/backbones/vit.py
+++ b/mmsegmentation/mmseg/models/backbones/vit.py
@@ -7,10 +7,8 @@
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
-from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
- trunc_normal_)
-from mmcv.runner import (BaseModule, CheckpointLoader, ModuleList,
- load_state_dict)
+from mmcv.cnn.utils.weight_init import constant_init, kaiming_init, trunc_normal_
+from mmcv.runner import BaseModule, CheckpointLoader, ModuleList, load_state_dict
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _pair as to_2tuple
@@ -46,25 +44,26 @@ class TransformerEncoderLayer(BaseModule):
some memory while slowing down the training speed. Default: False.
"""
- def __init__(self,
- embed_dims,
- num_heads,
- feedforward_channels,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- num_fcs=2,
- qkv_bias=True,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- batch_first=True,
- attn_cfg=dict(),
- ffn_cfg=dict(),
- with_cp=False):
- super(TransformerEncoderLayer, self).__init__()
-
- self.norm1_name, norm1 = build_norm_layer(
- norm_cfg, embed_dims, postfix=1)
+ def __init__(
+ self,
+ embed_dims,
+ num_heads,
+ feedforward_channels,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ num_fcs=2,
+ qkv_bias=True,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ batch_first=True,
+ attn_cfg=dict(),
+ ffn_cfg=dict(),
+ with_cp=False,
+ ):
+ super().__init__()
+
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
attn_cfg.update(
@@ -74,12 +73,13 @@ def __init__(self,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
batch_first=batch_first,
- bias=qkv_bias))
+ bias=qkv_bias,
+ )
+ )
self.build_attn(attn_cfg)
- self.norm2_name, norm2 = build_norm_layer(
- norm_cfg, embed_dims, postfix=2)
+ self.norm2_name, norm2 = build_norm_layer(norm_cfg, embed_dims, postfix=2)
self.add_module(self.norm2_name, norm2)
ffn_cfg.update(
@@ -88,9 +88,14 @@ def __init__(self,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
- dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate)
- if drop_path_rate > 0 else None,
- act_cfg=act_cfg))
+ dropout_layer=(
+ dict(type="DropPath", drop_prob=drop_path_rate)
+ if drop_path_rate > 0
+ else None
+ ),
+ act_cfg=act_cfg,
+ )
+ )
self.build_ffn(ffn_cfg)
self.with_cp = with_cp
@@ -173,54 +178,62 @@ class VisionTransformer(BaseModule):
Default: None.
"""
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_channels=3,
- embed_dims=768,
- num_layers=12,
- num_heads=12,
- mlp_ratio=4,
- out_indices=-1,
- qkv_bias=True,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- with_cls_token=True,
- output_cls_token=False,
- norm_cfg=dict(type='LN'),
- act_cfg=dict(type='GELU'),
- patch_norm=False,
- final_norm=False,
- interpolate_mode='bicubic',
- num_fcs=2,
- norm_eval=False,
- with_cp=False,
- pretrained=None,
- init_cfg=None):
- super(VisionTransformer, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ img_size=224,
+ patch_size=16,
+ in_channels=3,
+ embed_dims=768,
+ num_layers=12,
+ num_heads=12,
+ mlp_ratio=4,
+ out_indices=-1,
+ qkv_bias=True,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ with_cls_token=True,
+ output_cls_token=False,
+ norm_cfg=dict(type="LN"),
+ act_cfg=dict(type="GELU"),
+ patch_norm=False,
+ final_norm=False,
+ interpolate_mode="bicubic",
+ num_fcs=2,
+ norm_eval=False,
+ with_cp=False,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
if isinstance(img_size, int):
img_size = to_2tuple(img_size)
elif isinstance(img_size, tuple):
if len(img_size) == 1:
img_size = to_2tuple(img_size[0])
- assert len(img_size) == 2, \
- f'The size of image should have length 1 or 2, ' \
- f'but got {len(img_size)}'
+ assert len(img_size) == 2, (
+ f"The size of image should have length 1 or 2, "
+ f"but got {len(img_size)}"
+ )
if output_cls_token:
- assert with_cls_token is True, f'with_cls_token must be True if' \
- f'set output_cls_token to True, but got {with_cls_token}'
-
- assert not (init_cfg and pretrained), \
- 'init_cfg and pretrained cannot be set at the same time'
+ assert with_cls_token is True, (
+ f"with_cls_token must be True if"
+ f"set output_cls_token to True, but got {with_cls_token}"
+ )
+
+ assert not (
+ init_cfg and pretrained
+ ), "init_cfg and pretrained cannot be set at the same time"
if isinstance(pretrained, str):
- warnings.warn('DeprecationWarning: pretrained is deprecated, '
- 'please use "init_cfg" instead')
- self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+ warnings.warn(
+ "DeprecationWarning: pretrained is deprecated, "
+ 'please use "init_cfg" instead'
+ )
+ self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
elif pretrained is not None:
- raise TypeError('pretrained must be a str or None')
+ raise TypeError("pretrained must be a str or None")
self.img_size = img_size
self.patch_size = patch_size
@@ -232,22 +245,20 @@ def __init__(self,
self.patch_embed = PatchEmbed(
in_channels=in_channels,
embed_dims=embed_dims,
- conv_type='Conv2d',
+ conv_type="Conv2d",
kernel_size=patch_size,
stride=patch_size,
- padding='corner',
+ padding="corner",
norm_cfg=norm_cfg if patch_norm else None,
init_cfg=None,
)
- num_patches = (img_size[0] // patch_size) * \
- (img_size[1] // patch_size)
+ num_patches = (img_size[0] // patch_size) * (img_size[1] // patch_size)
self.with_cls_token = with_cls_token
self.output_cls_token = output_cls_token
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
- self.pos_embed = nn.Parameter(
- torch.zeros(1, num_patches + 1, embed_dims))
+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dims))
self.drop_after_pos = nn.Dropout(p=drop_rate)
if isinstance(out_indices, int):
@@ -257,7 +268,7 @@ def __init__(self,
elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
self.out_indices = out_indices
else:
- raise TypeError('out_indices must be type of int, list or tuple')
+ raise TypeError("out_indices must be type of int, list or tuple")
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, num_layers)
@@ -278,12 +289,13 @@ def __init__(self,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
with_cp=with_cp,
- batch_first=True))
+ batch_first=True,
+ )
+ )
self.final_norm = final_norm
if final_norm:
- self.norm1_name, norm1 = build_norm_layer(
- norm_cfg, embed_dims, postfix=1)
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
@property
@@ -291,50 +303,56 @@ def norm1(self):
return getattr(self, self.norm1_name)
def init_weights(self):
- if (isinstance(self.init_cfg, dict)
- and self.init_cfg.get('type') == 'Pretrained'):
+ if (
+ isinstance(self.init_cfg, dict)
+ and self.init_cfg.get("type") == "Pretrained"
+ ):
logger = get_root_logger()
checkpoint = CheckpointLoader.load_checkpoint(
- self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
+ self.init_cfg["checkpoint"], logger=logger, map_location="cpu"
+ )
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
else:
state_dict = checkpoint
- if 'pos_embed' in state_dict.keys():
- if self.pos_embed.shape != state_dict['pos_embed'].shape:
- logger.info(msg=f'Resize the pos_embed shape from '
- f'{state_dict["pos_embed"].shape} to '
- f'{self.pos_embed.shape}')
+ if "pos_embed" in state_dict.keys():
+ if self.pos_embed.shape != state_dict["pos_embed"].shape:
+ logger.info(
+ msg=f"Resize the pos_embed shape from "
+ f'{state_dict["pos_embed"].shape} to '
+ f"{self.pos_embed.shape}"
+ )
h, w = self.img_size
- pos_size = int(
- math.sqrt(state_dict['pos_embed'].shape[1] - 1))
- state_dict['pos_embed'] = self.resize_pos_embed(
- state_dict['pos_embed'],
+ pos_size = int(math.sqrt(state_dict["pos_embed"].shape[1] - 1))
+ state_dict["pos_embed"] = self.resize_pos_embed(
+ state_dict["pos_embed"],
(h // self.patch_size, w // self.patch_size),
- (pos_size, pos_size), self.interpolate_mode)
+ (pos_size, pos_size),
+ self.interpolate_mode,
+ )
load_state_dict(self, state_dict, strict=False, logger=logger)
elif self.init_cfg is not None:
- super(VisionTransformer, self).init_weights()
+ super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
- trunc_normal_(self.pos_embed, std=.02)
- trunc_normal_(self.cls_token, std=.02)
+ trunc_normal_(self.pos_embed, std=0.02)
+ trunc_normal_(self.cls_token, std=0.02)
for n, m in self.named_modules():
if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
+ trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
- if 'ffn' in n:
- nn.init.normal_(m.bias, mean=0., std=1e-6)
+ if "ffn" in n:
+ nn.init.normal_(m.bias, mean=0.0, std=1e-6)
else:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
- kaiming_init(m, mode='fan_in', bias=0.)
+ kaiming_init(m, mode="fan_in", bias=0.0)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
- constant_init(m, val=1.0, bias=0.)
+ constant_init(m, val=1.0, bias=0.0)
def _pos_embeding(self, patched_img, hw_shape, pos_embed):
"""Positioning embeding method.
@@ -350,21 +368,26 @@ def _pos_embeding(self, patched_img, hw_shape, pos_embed):
Return:
torch.Tensor: The pos encoded image feature.
"""
- assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
- 'the shapes of patched_img and pos_embed must be [B, L, C]'
+ assert (
+ patched_img.ndim == 3 and pos_embed.ndim == 3
+ ), "the shapes of patched_img and pos_embed must be [B, L, C]"
x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
if x_len != pos_len:
- if pos_len == (self.img_size[0] // self.patch_size) * (
- self.img_size[1] // self.patch_size) + 1:
+ if (
+ pos_len
+ == (self.img_size[0] // self.patch_size)
+ * (self.img_size[1] // self.patch_size)
+ + 1
+ ):
pos_h = self.img_size[0] // self.patch_size
pos_w = self.img_size[1] // self.patch_size
else:
raise ValueError(
- 'Unexpected shape of pos_embed, got {}.'.format(
- pos_embed.shape))
- pos_embed = self.resize_pos_embed(pos_embed, hw_shape,
- (pos_h, pos_w),
- self.interpolate_mode)
+ "Unexpected shape of pos_embed, got {}.".format(pos_embed.shape)
+ )
+ pos_embed = self.resize_pos_embed(
+ pos_embed, hw_shape, (pos_h, pos_w), self.interpolate_mode
+ )
return self.drop_after_pos(patched_img + pos_embed)
@staticmethod
@@ -384,15 +407,17 @@ def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode):
Return:
torch.Tensor: The resized pos_embed of shape [B, L_new, C]
"""
- assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
+ assert pos_embed.ndim == 3, "shape of pos_embed must be [B, L, C]"
pos_h, pos_w = pos_shape
# keep dim for easy deployment
cls_token_weight = pos_embed[:, 0:1]
- pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
+ pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w) :]
pos_embed_weight = pos_embed_weight.reshape(
- 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
+ 1, pos_h, pos_w, pos_embed.shape[2]
+ ).permute(0, 3, 1, 2)
pos_embed_weight = resize(
- pos_embed_weight, size=input_shpae, align_corners=False, mode=mode)
+ pos_embed_weight, size=input_shpae, align_corners=False, mode=mode
+ )
pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
return pos_embed
@@ -424,8 +449,11 @@ def forward(self, inputs):
else:
out = x
B, _, C = out.shape
- out = out.reshape(B, hw_shape[0], hw_shape[1],
- C).permute(0, 3, 1, 2).contiguous()
+ out = (
+ out.reshape(B, hw_shape[0], hw_shape[1], C)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
if self.output_cls_token:
out = [out, x[:, 0]]
outs.append(out)
@@ -433,7 +461,7 @@ def forward(self, inputs):
return tuple(outs)
def train(self, mode=True):
- super(VisionTransformer, self).train(mode)
+ super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, nn.LayerNorm):
diff --git a/mmsegmentation/mmseg/models/builder.py b/mmsegmentation/mmseg/models/builder.py
index 5e18e4e..0a8a648 100644
--- a/mmsegmentation/mmseg/models/builder.py
+++ b/mmsegmentation/mmseg/models/builder.py
@@ -5,8 +5,8 @@
from mmcv.cnn.bricks.registry import ATTENTION as MMCV_ATTENTION
from mmcv.utils import Registry
-MODELS = Registry('models', parent=MMCV_MODELS)
-ATTENTION = Registry('attention', parent=MMCV_ATTENTION)
+MODELS = Registry("models", parent=MMCV_MODELS)
+ATTENTION = Registry("attention", parent=MMCV_ATTENTION)
BACKBONES = MODELS
NECKS = MODELS
@@ -39,11 +39,15 @@ def build_segmentor(cfg, train_cfg=None, test_cfg=None):
"""Build segmentor."""
if train_cfg is not None or test_cfg is not None:
warnings.warn(
- 'train_cfg and test_cfg is deprecated, '
- 'please specify them in model', UserWarning)
- assert cfg.get('train_cfg') is None or train_cfg is None, \
- 'train_cfg specified in both outer field and model field '
- assert cfg.get('test_cfg') is None or test_cfg is None, \
- 'test_cfg specified in both outer field and model field '
+ "train_cfg and test_cfg is deprecated, " "please specify them in model",
+ UserWarning,
+ )
+ assert (
+ cfg.get("train_cfg") is None or train_cfg is None
+ ), "train_cfg specified in both outer field and model field "
+ assert (
+ cfg.get("test_cfg") is None or test_cfg is None
+ ), "test_cfg specified in both outer field and model field "
return SEGMENTORS.build(
- cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
+ cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)
+ )
diff --git a/mmsegmentation/mmseg/models/decode_heads/__init__.py b/mmsegmentation/mmseg/models/decode_heads/__init__.py
index 8add761..7878051 100644
--- a/mmsegmentation/mmseg/models/decode_heads/__init__.py
+++ b/mmsegmentation/mmseg/models/decode_heads/__init__.py
@@ -30,11 +30,36 @@
from .uper_head import UPerHead
__all__ = [
- 'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
- 'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
- 'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
- 'PointHead', 'APCHead', 'DMHead', 'LRASPPHead', 'SETRUPHead',
- 'SETRMLAHead', 'DPTHead', 'SETRMLAHead', 'SegmenterMaskTransformerHead',
- 'SegformerHead', 'ISAHead', 'STDCHead', 'IterativeDecodeHead',
- 'KernelUpdateHead', 'KernelUpdator'
+ "FCNHead",
+ "PSPHead",
+ "ASPPHead",
+ "PSAHead",
+ "NLHead",
+ "GCHead",
+ "CCHead",
+ "UPerHead",
+ "DepthwiseSeparableASPPHead",
+ "ANNHead",
+ "DAHead",
+ "OCRHead",
+ "EncHead",
+ "DepthwiseSeparableFCNHead",
+ "FPNHead",
+ "EMAHead",
+ "DNLHead",
+ "PointHead",
+ "APCHead",
+ "DMHead",
+ "LRASPPHead",
+ "SETRUPHead",
+ "SETRMLAHead",
+ "DPTHead",
+ "SETRMLAHead",
+ "SegmenterMaskTransformerHead",
+ "SegformerHead",
+ "ISAHead",
+ "STDCHead",
+ "IterativeDecodeHead",
+ "KernelUpdateHead",
+ "KernelUpdator",
]
diff --git a/mmsegmentation/mmseg/models/decode_heads/ann_head.py b/mmsegmentation/mmseg/models/decode_heads/ann_head.py
index c8d882e..48b3caf 100644
--- a/mmsegmentation/mmseg/models/decode_heads/ann_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/ann_head.py
@@ -17,8 +17,9 @@ class PPMConcat(nn.ModuleList):
"""
def __init__(self, pool_scales=(1, 3, 6, 8)):
- super(PPMConcat, self).__init__(
- [nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales])
+ super().__init__(
+ [nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales]
+ )
def forward(self, feats):
"""Forward function."""
@@ -50,15 +51,25 @@ class SelfAttentionBlock(_SelfAttentionBlock):
act_cfg (dict|None): Config of activation layers.
"""
- def __init__(self, low_in_channels, high_in_channels, channels,
- out_channels, share_key_query, query_scale, key_pool_scales,
- conv_cfg, norm_cfg, act_cfg):
+ def __init__(
+ self,
+ low_in_channels,
+ high_in_channels,
+ channels,
+ out_channels,
+ share_key_query,
+ query_scale,
+ key_pool_scales,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ ):
key_psp = PPMConcat(key_pool_scales)
if query_scale > 1:
query_downsample = nn.MaxPool2d(kernel_size=query_scale)
else:
query_downsample = None
- super(SelfAttentionBlock, self).__init__(
+ super().__init__(
key_in_channels=low_in_channels,
query_in_channels=high_in_channels,
channels=channels,
@@ -74,7 +85,8 @@ def __init__(self, low_in_channels, high_in_channels, channels,
with_out=True,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
class AFNB(nn.Module):
@@ -97,10 +109,19 @@ class AFNB(nn.Module):
act_cfg (dict|None): Config of activation layers.
"""
- def __init__(self, low_in_channels, high_in_channels, channels,
- out_channels, query_scales, key_pool_scales, conv_cfg,
- norm_cfg, act_cfg):
- super(AFNB, self).__init__()
+ def __init__(
+ self,
+ low_in_channels,
+ high_in_channels,
+ channels,
+ out_channels,
+ query_scales,
+ key_pool_scales,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ ):
+ super().__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
@@ -114,14 +135,17 @@ def __init__(self, low_in_channels, high_in_channels, channels,
key_pool_scales=key_pool_scales,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.bottleneck = ConvModule(
out_channels + high_in_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None)
+ act_cfg=None,
+ )
def forward(self, low_feats, high_feats):
"""Forward function."""
@@ -148,9 +172,18 @@ class APNB(nn.Module):
act_cfg (dict|None): Config of activation layers.
"""
- def __init__(self, in_channels, channels, out_channels, query_scales,
- key_pool_scales, conv_cfg, norm_cfg, act_cfg):
- super(APNB, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ channels,
+ out_channels,
+ query_scales,
+ key_pool_scales,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ ):
+ super().__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
@@ -164,14 +197,17 @@ def __init__(self, in_channels, channels, out_channels, query_scales,
key_pool_scales=key_pool_scales,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.bottleneck = ConvModule(
2 * in_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, feats):
"""Forward function."""
@@ -196,13 +232,14 @@ class ANNHead(BaseDecodeHead):
Default: (1, 3, 6, 8).
"""
- def __init__(self,
- project_channels,
- query_scales=(1, ),
- key_pool_scales=(1, 3, 6, 8),
- **kwargs):
- super(ANNHead, self).__init__(
- input_transform='multiple_select', **kwargs)
+ def __init__(
+ self,
+ project_channels,
+ query_scales=(1,),
+ key_pool_scales=(1, 3, 6, 8),
+ **kwargs,
+ ):
+ super().__init__(input_transform="multiple_select", **kwargs)
assert len(self.in_channels) == 2
low_in_channels, high_in_channels = self.in_channels
self.project_channels = project_channels
@@ -215,7 +252,8 @@ def __init__(self,
key_pool_scales=key_pool_scales,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.bottleneck = ConvModule(
high_in_channels,
self.channels,
@@ -223,7 +261,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.context = APNB(
in_channels=self.channels,
out_channels=self.channels,
@@ -232,7 +271,8 @@ def __init__(self,
key_pool_scales=key_pool_scales,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/apc_head.py b/mmsegmentation/mmseg/models/decode_heads/apc_head.py
index 3198fd1..ad1cc76 100644
--- a/mmsegmentation/mmseg/models/decode_heads/apc_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/apc_head.py
@@ -23,9 +23,10 @@ class ACM(nn.Module):
act_cfg (dict): Config of activation layers.
"""
- def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
- norm_cfg, act_cfg):
- super(ACM, self).__init__()
+ def __init__(
+ self, pool_scale, fusion, in_channels, channels, conv_cfg, norm_cfg, act_cfg
+ ):
+ super().__init__()
self.pool_scale = pool_scale
self.fusion = fusion
self.in_channels = in_channels
@@ -39,7 +40,8 @@ def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.input_redu_conv = ConvModule(
self.in_channels,
@@ -47,7 +49,8 @@ def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.global_info = ConvModule(
self.channels,
@@ -55,7 +58,8 @@ def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)
@@ -65,7 +69,8 @@ def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if self.fusion:
self.fusion_conv = ConvModule(
@@ -74,7 +79,8 @@ def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, x):
"""Forward function."""
@@ -85,13 +91,20 @@ def forward(self, x):
pooled_x = self.pooled_redu_conv(pooled_x)
batch_size = x.size(0)
# [batch_size, pool_scale * pool_scale, channels]
- pooled_x = pooled_x.view(batch_size, self.channels,
- -1).permute(0, 2, 1).contiguous()
+ pooled_x = (
+ pooled_x.view(batch_size, self.channels, -1).permute(0, 2, 1).contiguous()
+ )
# [batch_size, h * w, pool_scale * pool_scale]
- affinity_matrix = self.gla(x + resize(
- self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])
- ).permute(0, 2, 3, 1).reshape(
- batch_size, -1, self.pool_scale**2)
+ affinity_matrix = (
+ self.gla(
+ x
+ + resize(
+ self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:]
+ )
+ )
+ .permute(0, 2, 3, 1)
+ .reshape(batch_size, -1, self.pool_scale**2)
+ )
affinity_matrix = F.sigmoid(affinity_matrix)
# [batch_size, h * w, channels]
z_out = torch.matmul(affinity_matrix, pooled_x)
@@ -123,20 +136,23 @@ class APCHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
- super(APCHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
assert isinstance(pool_scales, (list, tuple))
self.pool_scales = pool_scales
self.fusion = fusion
acm_modules = []
for pool_scale in self.pool_scales:
acm_modules.append(
- ACM(pool_scale,
+ ACM(
+ pool_scale,
self.fusion,
self.in_channels,
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
self.acm_modules = nn.ModuleList(acm_modules)
self.bottleneck = ConvModule(
self.in_channels + len(pool_scales) * self.channels,
@@ -145,7 +161,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/aspp_head.py b/mmsegmentation/mmseg/models/decode_heads/aspp_head.py
index 7059aee..088d1f0 100644
--- a/mmsegmentation/mmseg/models/decode_heads/aspp_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/aspp_head.py
@@ -20,9 +20,8 @@ class ASPPModule(nn.ModuleList):
act_cfg (dict): Config of activation layers.
"""
- def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg,
- act_cfg):
- super(ASPPModule, self).__init__()
+ def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg, act_cfg):
+ super().__init__()
self.dilations = dilations
self.in_channels = in_channels
self.channels = channels
@@ -39,7 +38,9 @@ def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg,
padding=0 if dilation == 1 else dilation,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
def forward(self, x):
"""Forward function."""
@@ -63,7 +64,7 @@ class ASPPHead(BaseDecodeHead):
"""
def __init__(self, dilations=(1, 6, 12, 18), **kwargs):
- super(ASPPHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
assert isinstance(dilations, (list, tuple))
self.dilations = dilations
self.image_pool = nn.Sequential(
@@ -74,14 +75,17 @@ def __init__(self, dilations=(1, 6, 12, 18), **kwargs):
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ ),
+ )
self.aspp_modules = ASPPModule(
dilations,
self.in_channels,
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.bottleneck = ConvModule(
(len(dilations) + 1) * self.channels,
self.channels,
@@ -89,7 +93,8 @@ def __init__(self, dilations=(1, 6, 12, 18), **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def _forward_feature(self, inputs):
"""Forward function for feature maps before classifying each pixel with
@@ -107,8 +112,9 @@ def _forward_feature(self, inputs):
resize(
self.image_pool(x),
size=x.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
]
aspp_outs.extend(self.aspp_modules(x))
aspp_outs = torch.cat(aspp_outs, dim=1)
diff --git a/mmsegmentation/mmseg/models/decode_heads/cascade_decode_head.py b/mmsegmentation/mmseg/models/decode_heads/cascade_decode_head.py
index f7c3da0..08c434f 100644
--- a/mmsegmentation/mmseg/models/decode_heads/cascade_decode_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/cascade_decode_head.py
@@ -9,15 +9,13 @@ class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta):
:class:`CascadeEncoderDecoder."""
def __init__(self, *args, **kwargs):
- super(BaseCascadeDecodeHead, self).__init__(*args, **kwargs)
+ super().__init__(*args, **kwargs)
@abstractmethod
def forward(self, inputs, prev_output):
"""Placeholder of forward function."""
- pass
- def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
- train_cfg):
+ def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
diff --git a/mmsegmentation/mmseg/models/decode_heads/cc_head.py b/mmsegmentation/mmseg/models/decode_heads/cc_head.py
index ed19eb4..6de4f97 100644
--- a/mmsegmentation/mmseg/models/decode_heads/cc_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/cc_head.py
@@ -24,9 +24,10 @@ class CCHead(FCNHead):
def __init__(self, recurrence=2, **kwargs):
if CrissCrossAttention is None:
- raise RuntimeError('Please install mmcv-full for '
- 'CrissCrossAttention ops')
- super(CCHead, self).__init__(num_convs=2, **kwargs)
+ raise RuntimeError(
+ "Please install mmcv-full for " "CrissCrossAttention ops"
+ )
+ super().__init__(num_convs=2, **kwargs)
self.recurrence = recurrence
self.cca = CrissCrossAttention(self.channels)
diff --git a/mmsegmentation/mmseg/models/decode_heads/da_head.py b/mmsegmentation/mmseg/models/decode_heads/da_head.py
index 77fd663..8c0ca7c 100644
--- a/mmsegmentation/mmseg/models/decode_heads/da_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/da_head.py
@@ -19,7 +19,7 @@ class PAM(_SelfAttentionBlock):
"""
def __init__(self, in_channels, channels):
- super(PAM, self).__init__(
+ super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@@ -35,13 +35,14 @@ def __init__(self, in_channels, channels):
with_out=False,
conv_cfg=None,
norm_cfg=None,
- act_cfg=None)
+ act_cfg=None,
+ )
self.gamma = Scale(0)
def forward(self, x):
"""Forward function."""
- out = super(PAM, self).forward(x, x)
+ out = super().forward(x, x)
out = self.gamma(out) + x
return out
@@ -51,7 +52,7 @@ class CAM(nn.Module):
"""Channel Attention Module (CAM)"""
def __init__(self):
- super(CAM, self).__init__()
+ super().__init__()
self.gamma = Scale(0)
def forward(self, x):
@@ -60,8 +61,7 @@ def forward(self, x):
proj_query = x.view(batch_size, channels, -1)
proj_key = x.view(batch_size, channels, -1).permute(0, 2, 1)
energy = torch.bmm(proj_query, proj_key)
- energy_new = torch.max(
- energy, -1, keepdim=True)[0].expand_as(energy) - energy
+ energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy) - energy
attention = F.softmax(energy_new, dim=-1)
proj_value = x.view(batch_size, channels, -1)
@@ -84,7 +84,7 @@ class DAHead(BaseDecodeHead):
"""
def __init__(self, pam_channels, **kwargs):
- super(DAHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
self.pam_channels = pam_channels
self.pam_in_conv = ConvModule(
self.in_channels,
@@ -93,7 +93,8 @@ def __init__(self, pam_channels, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.pam = PAM(self.channels, pam_channels)
self.pam_out_conv = ConvModule(
self.channels,
@@ -102,9 +103,9 @@ def __init__(self, pam_channels, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
- self.pam_conv_seg = nn.Conv2d(
- self.channels, self.num_classes, kernel_size=1)
+ act_cfg=self.act_cfg,
+ )
+ self.pam_conv_seg = nn.Conv2d(self.channels, self.num_classes, kernel_size=1)
self.cam_in_conv = ConvModule(
self.in_channels,
@@ -113,7 +114,8 @@ def __init__(self, pam_channels, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.cam = CAM()
self.cam_out_conv = ConvModule(
self.channels,
@@ -122,9 +124,9 @@ def __init__(self, pam_channels, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
- self.cam_conv_seg = nn.Conv2d(
- self.channels, self.num_classes, kernel_size=1)
+ act_cfg=self.act_cfg,
+ )
+ self.cam_conv_seg = nn.Conv2d(self.channels, self.num_classes, kernel_size=1)
def pam_cls_seg(self, feat):
"""PAM feature classification."""
@@ -166,14 +168,7 @@ def losses(self, seg_logit, seg_label):
"""Compute ``pam_cam``, ``pam``, ``cam`` loss."""
pam_cam_seg_logit, pam_seg_logit, cam_seg_logit = seg_logit
loss = dict()
- loss.update(
- add_prefix(
- super(DAHead, self).losses(pam_cam_seg_logit, seg_label),
- 'pam_cam'))
- loss.update(
- add_prefix(
- super(DAHead, self).losses(pam_seg_logit, seg_label), 'pam'))
- loss.update(
- add_prefix(
- super(DAHead, self).losses(cam_seg_logit, seg_label), 'cam'))
+ loss.update(add_prefix(super().losses(pam_cam_seg_logit, seg_label), "pam_cam"))
+ loss.update(add_prefix(super().losses(pam_seg_logit, seg_label), "pam"))
+ loss.update(add_prefix(super().losses(cam_seg_logit, seg_label), "cam"))
return loss
diff --git a/mmsegmentation/mmseg/models/decode_heads/decode_head.py b/mmsegmentation/mmseg/models/decode_heads/decode_head.py
index c893f76..87da02d 100644
--- a/mmsegmentation/mmseg/models/decode_heads/decode_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/decode_head.py
@@ -55,29 +55,27 @@ class BaseDecodeHead(BaseModule, metaclass=ABCMeta):
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
- def __init__(self,
- in_channels,
- channels,
- *,
- num_classes,
- out_channels=None,
- threshold=None,
- dropout_ratio=0.1,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- in_index=-1,
- input_transform=None,
- loss_decode=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- ignore_index=255,
- sampler=None,
- align_corners=False,
- init_cfg=dict(
- type='Normal', std=0.01, override=dict(name='conv_seg'))):
- super(BaseDecodeHead, self).__init__(init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ channels,
+ *,
+ num_classes,
+ out_channels=None,
+ threshold=None,
+ dropout_ratio=0.1,
+ conv_cfg=None,
+ norm_cfg=None,
+ act_cfg=dict(type="ReLU"),
+ in_index=-1,
+ input_transform=None,
+ loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+ ignore_index=255,
+ sampler=None,
+ align_corners=False,
+ init_cfg=dict(type="Normal", std=0.01, override=dict(name="conv_seg")),
+ ):
+ super().__init__(init_cfg)
self._init_inputs(in_channels, in_index, input_transform)
self.channels = channels
self.dropout_ratio = dropout_ratio
@@ -91,24 +89,26 @@ def __init__(self,
if out_channels is None:
if num_classes == 2:
- warnings.warn('For binary segmentation, we suggest using'
- '`out_channels = 1` to define the output'
- 'channels of segmentor, and use `threshold`'
- 'to convert seg_logist into a prediction'
- 'applying a threshold')
+ warnings.warn(
+ "For binary segmentation, we suggest using"
+ "`out_channels = 1` to define the output"
+ "channels of segmentor, and use `threshold`"
+ "to convert seg_logist into a prediction"
+ "applying a threshold"
+ )
out_channels = num_classes
if out_channels != num_classes and out_channels != 1:
raise ValueError(
- 'out_channels should be equal to num_classes,'
- 'except binary segmentation set out_channels == 1 and'
- f'num_classes == 2, but got out_channels={out_channels}'
- f'and num_classes={num_classes}')
+ "out_channels should be equal to num_classes,"
+ "except binary segmentation set out_channels == 1 and"
+ f"num_classes == 2, but got out_channels={out_channels}"
+ f"and num_classes={num_classes}"
+ )
if out_channels == 1 and threshold is None:
threshold = 0.3
- warnings.warn('threshold is not defined for binary, and defaults'
- 'to 0.3')
+ warnings.warn("threshold is not defined for binary, and defaults" "to 0.3")
self.num_classes = num_classes
self.out_channels = out_channels
self.threshold = threshold
@@ -120,8 +120,10 @@ def __init__(self,
for loss in loss_decode:
self.loss_decode.append(build_loss(loss))
else:
- raise TypeError(f'loss_decode must be a dict or sequence of dict,\
- but got {type(loss_decode)}')
+ raise TypeError(
+ f"loss_decode must be a dict or sequence of dict,\
+ but got {type(loss_decode)}"
+ )
if sampler is not None:
self.sampler = build_pixel_sampler(sampler, context=self)
@@ -137,9 +139,11 @@ def __init__(self,
def extra_repr(self):
"""Extra repr."""
- s = f'input_transform={self.input_transform}, ' \
- f'ignore_index={self.ignore_index}, ' \
- f'align_corners={self.align_corners}'
+ s = (
+ f"input_transform={self.input_transform}, "
+ f"ignore_index={self.ignore_index}, "
+ f"align_corners={self.align_corners}"
+ )
return s
def _init_inputs(self, in_channels, in_index, input_transform):
@@ -164,14 +168,14 @@ def _init_inputs(self, in_channels, in_index, input_transform):
"""
if input_transform is not None:
- assert input_transform in ['resize_concat', 'multiple_select']
+ assert input_transform in ["resize_concat", "multiple_select"]
self.input_transform = input_transform
self.in_index = in_index
if input_transform is not None:
assert isinstance(in_channels, (list, tuple))
assert isinstance(in_index, (list, tuple))
assert len(in_channels) == len(in_index)
- if input_transform == 'resize_concat':
+ if input_transform == "resize_concat":
self.in_channels = sum(in_channels)
else:
self.in_channels = in_channels
@@ -190,17 +194,19 @@ def _transform_inputs(self, inputs):
Tensor: The transformed inputs
"""
- if self.input_transform == 'resize_concat':
+ if self.input_transform == "resize_concat":
inputs = [inputs[i] for i in self.in_index]
upsampled_inputs = [
resize(
input=x,
size=inputs[0].shape[2:],
- mode='bilinear',
- align_corners=self.align_corners) for x in inputs
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
+ for x in inputs
]
inputs = torch.cat(upsampled_inputs, dim=1)
- elif self.input_transform == 'multiple_select':
+ elif self.input_transform == "multiple_select":
inputs = [inputs[i] for i in self.in_index]
else:
inputs = inputs[self.in_index]
@@ -211,7 +217,6 @@ def _transform_inputs(self, inputs):
@abstractmethod
def forward(self, inputs):
"""Placeholder of forward function."""
- pass
def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
@@ -257,15 +262,16 @@ def cls_seg(self, feat):
output = self.conv_seg(feat)
return output
- @force_fp32(apply_to=('seg_logit', ))
+ @force_fp32(apply_to=("seg_logit",))
def losses(self, seg_logit, seg_label):
"""Compute segmentation loss."""
loss = dict()
seg_logit = resize(
input=seg_logit,
size=seg_label.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
if self.sampler is not None:
seg_weight = self.sampler.sample(seg_logit, seg_label)
else:
@@ -282,14 +288,15 @@ def losses(self, seg_logit, seg_label):
seg_logit,
seg_label,
weight=seg_weight,
- ignore_index=self.ignore_index)
+ ignore_index=self.ignore_index,
+ )
else:
loss[loss_decode.loss_name] += loss_decode(
seg_logit,
seg_label,
weight=seg_weight,
- ignore_index=self.ignore_index)
+ ignore_index=self.ignore_index,
+ )
- loss['acc_seg'] = accuracy(
- seg_logit, seg_label, ignore_index=self.ignore_index)
+ loss["acc_seg"] = accuracy(seg_logit, seg_label, ignore_index=self.ignore_index)
return loss
diff --git a/mmsegmentation/mmseg/models/decode_heads/dm_head.py b/mmsegmentation/mmseg/models/decode_heads/dm_head.py
index ffaa870..742af3c 100644
--- a/mmsegmentation/mmseg/models/decode_heads/dm_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/dm_head.py
@@ -22,9 +22,10 @@ class DCM(nn.Module):
act_cfg (dict): Config of activation layers.
"""
- def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
- norm_cfg, act_cfg):
- super(DCM, self).__init__()
+ def __init__(
+ self, filter_size, fusion, in_channels, channels, conv_cfg, norm_cfg, act_cfg
+ ):
+ super().__init__()
self.filter_size = filter_size
self.fusion = fusion
self.in_channels = in_channels
@@ -32,8 +33,7 @@ def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
- self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
- 0)
+ self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1, 0)
self.input_redu_conv = ConvModule(
self.in_channels,
@@ -41,7 +41,8 @@ def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if self.norm_cfg is not None:
self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
@@ -56,25 +57,28 @@ def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, x):
"""Forward function."""
generated_filter = self.filter_gen_conv(
- F.adaptive_avg_pool2d(x, self.filter_size))
+ F.adaptive_avg_pool2d(x, self.filter_size)
+ )
x = self.input_redu_conv(x)
b, c, h, w = x.shape
# [1, b * c, h, w], c = self.channels
x = x.view(1, b * c, h, w)
# [b * c, 1, filter_size, filter_size]
- generated_filter = generated_filter.view(b * c, 1, self.filter_size,
- self.filter_size)
+ generated_filter = generated_filter.view(
+ b * c, 1, self.filter_size, self.filter_size
+ )
pad = (self.filter_size - 1) // 2
if (self.filter_size - 1) % 2 == 0:
p2d = (pad, pad, pad, pad)
else:
p2d = (pad + 1, pad, pad + 1, pad)
- x = F.pad(input=x, pad=p2d, mode='constant', value=0)
+ x = F.pad(input=x, pad=p2d, mode="constant", value=0)
# [1, b * c, h, w]
output = F.conv2d(input=x, weight=generated_filter, groups=b * c)
# [b, c, h, w]
@@ -105,20 +109,23 @@ class DMHead(BaseDecodeHead):
"""
def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
- super(DMHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
assert isinstance(filter_sizes, (list, tuple))
self.filter_sizes = filter_sizes
self.fusion = fusion
dcm_modules = []
for filter_size in self.filter_sizes:
dcm_modules.append(
- DCM(filter_size,
+ DCM(
+ filter_size,
self.fusion,
self.in_channels,
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
self.dcm_modules = nn.ModuleList(dcm_modules)
self.bottleneck = ConvModule(
self.in_channels + len(filter_sizes) * self.channels,
@@ -127,7 +134,8 @@ def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/dnl_head.py b/mmsegmentation/mmseg/models/decode_heads/dnl_head.py
index dabf154..da01954 100644
--- a/mmsegmentation/mmseg/models/decode_heads/dnl_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/dnl_head.py
@@ -27,12 +27,9 @@ def embedded_gaussian(self, theta_x, phi_x):
if self.use_scale:
# theta_x.shape[-1] is `self.inter_channels`
pairwise_weight /= torch.tensor(
- theta_x.shape[-1],
- dtype=torch.float,
- device=pairwise_weight.device)**torch.tensor(
- 0.5, device=pairwise_weight.device)
- pairwise_weight /= torch.tensor(
- self.temperature, device=pairwise_weight.device)
+ theta_x.shape[-1], dtype=torch.float, device=pairwise_weight.device
+ ) ** torch.tensor(0.5, device=pairwise_weight.device)
+ pairwise_weight /= torch.tensor(self.temperature, device=pairwise_weight.device)
pairwise_weight = pairwise_weight.softmax(dim=-1)
return pairwise_weight
@@ -45,14 +42,14 @@ def forward(self, x):
g_x = g_x.permute(0, 2, 1)
# theta_x: [N, HxW, C], phi_x: [N, C, HxW]
- if self.mode == 'gaussian':
+ if self.mode == "gaussian":
theta_x = x.view(n, self.in_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
if self.sub_sample:
phi_x = self.phi(x).view(n, self.in_channels, -1)
else:
phi_x = x.view(n, self.in_channels, -1)
- elif self.mode == 'concatenation':
+ elif self.mode == "concatenation":
theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
else:
@@ -71,8 +68,11 @@ def forward(self, x):
# y: [N, HxW, C]
y = torch.matmul(pairwise_weight, g_x)
# y: [N, C, H, W]
- y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
- *x.size()[2:])
+ y = (
+ y.permute(0, 2, 1)
+ .contiguous()
+ .reshape(n, self.inter_channels, *x.size()[2:])
+ )
# unary_mask: [N, 1, HxW]
unary_mask = self.conv_mask(x)
@@ -81,8 +81,9 @@ def forward(self, x):
# unary_x: [N, 1, C]
unary_x = torch.matmul(unary_mask, g_x)
# unary_x: [N, C, 1, 1]
- unary_x = unary_x.permute(0, 2, 1).contiguous().reshape(
- n, self.inter_channels, 1, 1)
+ unary_x = (
+ unary_x.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, 1, 1)
+ )
output = x + self.conv_out(y + unary_x)
@@ -105,13 +106,15 @@ class DNLHead(FCNHead):
temperature (float): Temperature to adjust attention. Default: 0.05
"""
- def __init__(self,
- reduction=2,
- use_scale=True,
- mode='embedded_gaussian',
- temperature=0.05,
- **kwargs):
- super(DNLHead, self).__init__(num_convs=2, **kwargs)
+ def __init__(
+ self,
+ reduction=2,
+ use_scale=True,
+ mode="embedded_gaussian",
+ temperature=0.05,
+ **kwargs,
+ ):
+ super().__init__(num_convs=2, **kwargs)
self.reduction = reduction
self.use_scale = use_scale
self.mode = mode
@@ -123,7 +126,8 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
mode=self.mode,
- temperature=self.temperature)
+ temperature=self.temperature,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/dpt_head.py b/mmsegmentation/mmseg/models/decode_heads/dpt_head.py
index 6c895d0..cee39f8 100644
--- a/mmsegmentation/mmseg/models/decode_heads/dpt_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/dpt_head.py
@@ -24,55 +24,67 @@ class ReassembleBlocks(BaseModule):
init_cfg (dict, optional): Initialization config dict. Default: None.
"""
- def __init__(self,
- in_channels=768,
- out_channels=[96, 192, 384, 768],
- readout_type='ignore',
- patch_size=16,
- init_cfg=None):
- super(ReassembleBlocks, self).__init__(init_cfg)
-
- assert readout_type in ['ignore', 'add', 'project']
+ def __init__(
+ self,
+ in_channels=768,
+ out_channels=[96, 192, 384, 768],
+ readout_type="ignore",
+ patch_size=16,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
+
+ assert readout_type in ["ignore", "add", "project"]
self.readout_type = readout_type
self.patch_size = patch_size
- self.projects = nn.ModuleList([
- ConvModule(
- in_channels=in_channels,
- out_channels=out_channel,
- kernel_size=1,
- act_cfg=None,
- ) for out_channel in out_channels
- ])
-
- self.resize_layers = nn.ModuleList([
- nn.ConvTranspose2d(
- in_channels=out_channels[0],
- out_channels=out_channels[0],
- kernel_size=4,
- stride=4,
- padding=0),
- nn.ConvTranspose2d(
- in_channels=out_channels[1],
- out_channels=out_channels[1],
- kernel_size=2,
- stride=2,
- padding=0),
- nn.Identity(),
- nn.Conv2d(
- in_channels=out_channels[3],
- out_channels=out_channels[3],
- kernel_size=3,
- stride=2,
- padding=1)
- ])
- if self.readout_type == 'project':
+ self.projects = nn.ModuleList(
+ [
+ ConvModule(
+ in_channels=in_channels,
+ out_channels=out_channel,
+ kernel_size=1,
+ act_cfg=None,
+ )
+ for out_channel in out_channels
+ ]
+ )
+
+ self.resize_layers = nn.ModuleList(
+ [
+ nn.ConvTranspose2d(
+ in_channels=out_channels[0],
+ out_channels=out_channels[0],
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=out_channels[1],
+ out_channels=out_channels[1],
+ kernel_size=2,
+ stride=2,
+ padding=0,
+ ),
+ nn.Identity(),
+ nn.Conv2d(
+ in_channels=out_channels[3],
+ out_channels=out_channels[3],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ ]
+ )
+ if self.readout_type == "project":
self.readout_projects = nn.ModuleList()
for _ in range(len(self.projects)):
self.readout_projects.append(
nn.Sequential(
Linear(2 * in_channels, in_channels),
- build_activation_layer(dict(type='GELU'))))
+ build_activation_layer(dict(type="GELU")),
+ )
+ )
def forward(self, inputs):
assert isinstance(inputs, list)
@@ -81,12 +93,12 @@ def forward(self, inputs):
assert len(x) == 2
x, cls_token = x[0], x[1]
feature_shape = x.shape
- if self.readout_type == 'project':
+ if self.readout_type == "project":
x = x.flatten(2).permute((0, 2, 1))
readout = cls_token.unsqueeze(1).expand_as(x)
x = self.readout_projects[i](torch.cat((x, readout), -1))
x = x.permute(0, 2, 1).reshape(feature_shape)
- elif self.readout_type == 'add':
+ elif self.readout_type == "add":
x = x.flatten(2) + cls_token.unsqueeze(-1)
x = x.reshape(feature_shape)
else:
@@ -109,14 +121,10 @@ class PreActResidualConvUnit(BaseModule):
init_cfg (dict, optional): Initialization config dict. Default: None.
"""
- def __init__(self,
- in_channels,
- act_cfg,
- norm_cfg,
- stride=1,
- dilation=1,
- init_cfg=None):
- super(PreActResidualConvUnit, self).__init__(init_cfg)
+ def __init__(
+ self, in_channels, act_cfg, norm_cfg, stride=1, dilation=1, init_cfg=None
+ ):
+ super().__init__(init_cfg)
self.conv1 = ConvModule(
in_channels,
@@ -128,7 +136,8 @@ def __init__(self,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
bias=False,
- order=('act', 'conv', 'norm'))
+ order=("act", "conv", "norm"),
+ )
self.conv2 = ConvModule(
in_channels,
@@ -138,7 +147,8 @@ def __init__(self,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
bias=False,
- order=('act', 'conv', 'norm'))
+ order=("act", "conv", "norm"),
+ )
def forward(self, inputs):
inputs_ = inputs.clone()
@@ -161,14 +171,16 @@ class FeatureFusionBlock(BaseModule):
init_cfg (dict, optional): Initialization config dict. Default: None.
"""
- def __init__(self,
- in_channels,
- act_cfg,
- norm_cfg,
- expand=False,
- align_corners=True,
- init_cfg=None):
- super(FeatureFusionBlock, self).__init__(init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ act_cfg,
+ norm_cfg,
+ expand=False,
+ align_corners=True,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
self.in_channels = in_channels
self.expand = expand
@@ -179,16 +191,15 @@ def __init__(self,
self.out_channels = in_channels // 2
self.project = ConvModule(
- self.in_channels,
- self.out_channels,
- kernel_size=1,
- act_cfg=None,
- bias=True)
+ self.in_channels, self.out_channels, kernel_size=1, act_cfg=None, bias=True
+ )
self.res_conv_unit1 = PreActResidualConvUnit(
- in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg)
+ in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg
+ )
self.res_conv_unit2 = PreActResidualConvUnit(
- in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg)
+ in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg
+ )
def forward(self, *inputs):
x = inputs[0]
@@ -197,17 +208,14 @@ def forward(self, *inputs):
res = resize(
inputs[1],
size=(x.shape[2], x.shape[3]),
- mode='bilinear',
- align_corners=False)
+ mode="bilinear",
+ align_corners=False,
+ )
else:
res = inputs[1]
x = x + self.res_conv_unit1(res)
x = self.res_conv_unit2(x)
- x = resize(
- x,
- scale_factor=2,
- mode='bilinear',
- align_corners=self.align_corners)
+ x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
x = self.project(x)
return x
@@ -233,22 +241,24 @@ class DPTHead(BaseDecodeHead):
Default: dict(type='BN').
"""
- def __init__(self,
- embed_dims=768,
- post_process_channels=[96, 192, 384, 768],
- readout_type='ignore',
- patch_size=16,
- expand_channels=False,
- act_cfg=dict(type='ReLU'),
- norm_cfg=dict(type='BN'),
- **kwargs):
- super(DPTHead, self).__init__(**kwargs)
+ def __init__(
+ self,
+ embed_dims=768,
+ post_process_channels=[96, 192, 384, 768],
+ readout_type="ignore",
+ patch_size=16,
+ expand_channels=False,
+ act_cfg=dict(type="ReLU"),
+ norm_cfg=dict(type="BN"),
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
self.in_channels = self.in_channels
self.expand_channels = expand_channels
- self.reassemble_blocks = ReassembleBlocks(embed_dims,
- post_process_channels,
- readout_type, patch_size)
+ self.reassemble_blocks = ReassembleBlocks(
+ embed_dims, post_process_channels, readout_type, patch_size
+ )
self.post_process_channels = [
channel * math.pow(2, i) if expand_channels else channel
@@ -263,18 +273,18 @@ def __init__(self,
kernel_size=3,
padding=1,
act_cfg=None,
- bias=False))
+ bias=False,
+ )
+ )
self.fusion_blocks = nn.ModuleList()
for _ in range(len(self.convs)):
self.fusion_blocks.append(
- FeatureFusionBlock(self.channels, act_cfg, norm_cfg))
+ FeatureFusionBlock(self.channels, act_cfg, norm_cfg)
+ )
self.fusion_blocks[0].res_conv_unit1 = None
self.project = ConvModule(
- self.channels,
- self.channels,
- kernel_size=3,
- padding=1,
- norm_cfg=norm_cfg)
+ self.channels, self.channels, kernel_size=3, padding=1, norm_cfg=norm_cfg
+ )
self.num_fusion_blocks = len(self.fusion_blocks)
self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
self.num_post_process_channels = len(self.post_process_channels)
diff --git a/mmsegmentation/mmseg/models/decode_heads/ema_head.py b/mmsegmentation/mmseg/models/decode_heads/ema_head.py
index f6de167..c588b7e 100644
--- a/mmsegmentation/mmseg/models/decode_heads/ema_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/ema_head.py
@@ -30,17 +30,17 @@ class EMAModule(nn.Module):
"""
def __init__(self, channels, num_bases, num_stages, momentum):
- super(EMAModule, self).__init__()
- assert num_stages >= 1, 'num_stages must be at least 1!'
+ super().__init__()
+ assert num_stages >= 1, "num_stages must be at least 1!"
self.num_bases = num_bases
self.num_stages = num_stages
self.momentum = momentum
bases = torch.zeros(1, channels, self.num_bases)
- bases.normal_(0, math.sqrt(2. / self.num_bases))
+ bases.normal_(0, math.sqrt(2.0 / self.num_bases))
# [1, channels, num_bases]
bases = F.normalize(bases, dim=1, p=2)
- self.register_buffer('bases', bases)
+ self.register_buffer("bases", bases)
def forward(self, feats):
"""Forward function."""
@@ -53,16 +53,16 @@ def forward(self, feats):
with torch.no_grad():
for i in range(self.num_stages):
# [batch_size, height*width, num_bases]
- attention = torch.einsum('bcn,bck->bnk', feats, bases)
+ attention = torch.einsum("bcn,bck->bnk", feats, bases)
attention = F.softmax(attention, dim=2)
# l1 norm
attention_normed = F.normalize(attention, dim=1, p=1)
# [batch_size, channels, num_bases]
- bases = torch.einsum('bcn,bnk->bck', feats, attention_normed)
+ bases = torch.einsum("bcn,bnk->bck", feats, attention_normed)
# l2 norm
bases = F.normalize(bases, dim=1, p=2)
- feats_recon = torch.einsum('bck,bnk->bcn', bases, attention)
+ feats_recon = torch.einsum("bck,bnk->bcn", bases, attention)
feats_recon = feats_recon.view(batch_size, channels, height, width)
if self.training:
@@ -70,8 +70,7 @@ def forward(self, feats):
bases = reduce_mean(bases)
# l2 norm
bases = F.normalize(bases, dim=1, p=2)
- self.bases = (1 -
- self.momentum) * self.bases + self.momentum * bases
+ self.bases = (1 - self.momentum) * self.bases + self.momentum * bases
return feats_recon
@@ -92,21 +91,24 @@ class EMAHead(BaseDecodeHead):
momentum (float): Momentum to update the base. Default: 0.1.
"""
- def __init__(self,
- ema_channels,
- num_bases,
- num_stages,
- concat_input=True,
- momentum=0.1,
- **kwargs):
- super(EMAHead, self).__init__(**kwargs)
+ def __init__(
+ self,
+ ema_channels,
+ num_bases,
+ num_stages,
+ concat_input=True,
+ momentum=0.1,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
self.ema_channels = ema_channels
self.num_bases = num_bases
self.num_stages = num_stages
self.concat_input = concat_input
self.momentum = momentum
- self.ema_module = EMAModule(self.ema_channels, self.num_bases,
- self.num_stages, self.momentum)
+ self.ema_module = EMAModule(
+ self.ema_channels, self.num_bases, self.num_stages, self.momentum
+ )
self.ema_in_conv = ConvModule(
self.in_channels,
@@ -115,7 +117,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
# project (0, inf) -> (-inf, inf)
self.ema_mid_conv = ConvModule(
self.ema_channels,
@@ -123,7 +126,8 @@ def __init__(self,
1,
conv_cfg=self.conv_cfg,
norm_cfg=None,
- act_cfg=None)
+ act_cfg=None,
+ )
for param in self.ema_mid_conv.parameters():
param.requires_grad = False
@@ -133,7 +137,8 @@ def __init__(self,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=None)
+ act_cfg=None,
+ )
self.bottleneck = ConvModule(
self.ema_channels,
self.channels,
@@ -141,7 +146,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if self.concat_input:
self.conv_cat = ConvModule(
self.in_channels + self.channels,
@@ -150,7 +156,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/enc_head.py b/mmsegmentation/mmseg/models/decode_heads/enc_head.py
index 648c890..5ddd1b9 100644
--- a/mmsegmentation/mmseg/models/decode_heads/enc_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/enc_head.py
@@ -21,32 +21,34 @@ class EncModule(nn.Module):
"""
def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg):
- super(EncModule, self).__init__()
+ super().__init__()
self.encoding_project = ConvModule(
in_channels,
in_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
# TODO: resolve this hack
# change to 1d
if norm_cfg is not None:
encoding_norm_cfg = norm_cfg.copy()
- if encoding_norm_cfg['type'] in ['BN', 'IN']:
- encoding_norm_cfg['type'] += '1d'
+ if encoding_norm_cfg["type"] in ["BN", "IN"]:
+ encoding_norm_cfg["type"] += "1d"
else:
- encoding_norm_cfg['type'] = encoding_norm_cfg['type'].replace(
- '2d', '1d')
+ encoding_norm_cfg["type"] = encoding_norm_cfg["type"].replace(
+ "2d", "1d"
+ )
else:
# fallback to BN1d
- encoding_norm_cfg = dict(type='BN1d')
+ encoding_norm_cfg = dict(type="BN1d")
self.encoding = nn.Sequential(
Encoding(channels=in_channels, num_codes=num_codes),
build_norm_layer(encoding_norm_cfg, num_codes)[1],
- nn.ReLU(inplace=True))
- self.fc = nn.Sequential(
- nn.Linear(in_channels, in_channels), nn.Sigmoid())
+ nn.ReLU(inplace=True),
+ )
+ self.fc = nn.Sequential(nn.Linear(in_channels, in_channels), nn.Sigmoid())
def forward(self, x):
"""Forward function."""
@@ -76,17 +78,15 @@ class EncHead(BaseDecodeHead):
Default: dict(type='CrossEntropyLoss', use_sigmoid=True).
"""
- def __init__(self,
- num_codes=32,
- use_se_loss=True,
- add_lateral=False,
- loss_se_decode=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=0.2),
- **kwargs):
- super(EncHead, self).__init__(
- input_transform='multiple_select', **kwargs)
+ def __init__(
+ self,
+ num_codes=32,
+ use_se_loss=True,
+ add_lateral=False,
+ loss_se_decode=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=0.2),
+ **kwargs,
+ ):
+ super().__init__(input_transform="multiple_select", **kwargs)
self.use_se_loss = use_se_loss
self.add_lateral = add_lateral
self.num_codes = num_codes
@@ -97,7 +97,8 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if add_lateral:
self.lateral_convs = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the last one
@@ -108,7 +109,9 @@ def __init__(self,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
self.fusion = ConvModule(
len(self.in_channels) * self.channels,
self.channels,
@@ -116,13 +119,15 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.enc_module = EncModule(
self.channels,
num_codes=num_codes,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if self.use_se_loss:
self.loss_se_decode = build_loss(loss_se_decode)
self.se_layer = nn.Linear(self.channels, self.num_classes)
@@ -136,8 +141,9 @@ def forward(self, inputs):
resize(
lateral_conv(inputs[i]),
size=feat.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
for i, lateral_conv in enumerate(self.lateral_convs)
]
feat = self.fusion(torch.cat([feat, *laterals], 1))
@@ -171,8 +177,9 @@ def _convert_to_onehot_labels(seg_label, num_classes):
batch_size = seg_label.size(0)
onehot_labels = seg_label.new_zeros((batch_size, num_classes))
for i in range(batch_size):
- hist = seg_label[i].float().histc(
- bins=num_classes, min=0, max=num_classes - 1)
+ hist = (
+ seg_label[i].float().histc(bins=num_classes, min=0, max=num_classes - 1)
+ )
onehot_labels[i] = hist > 0
return onehot_labels
@@ -180,9 +187,9 @@ def losses(self, seg_logit, seg_label):
"""Compute segmentation and semantic encoding loss."""
seg_logit, se_seg_logit = seg_logit
loss = dict()
- loss.update(super(EncHead, self).losses(seg_logit, seg_label))
+ loss.update(super().losses(seg_logit, seg_label))
se_loss = self.loss_se_decode(
- se_seg_logit,
- self._convert_to_onehot_labels(seg_label, self.num_classes))
- loss['loss_se'] = se_loss
+ se_seg_logit, self._convert_to_onehot_labels(seg_label, self.num_classes)
+ )
+ loss["loss_se"] = se_loss
return loss
diff --git a/mmsegmentation/mmseg/models/decode_heads/fcn_head.py b/mmsegmentation/mmseg/models/decode_heads/fcn_head.py
index e27be69..6dd9d1b 100644
--- a/mmsegmentation/mmseg/models/decode_heads/fcn_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/fcn_head.py
@@ -21,17 +21,14 @@ class FCNHead(BaseDecodeHead):
dilation (int): The dilation rate for convs in the head. Default: 1.
"""
- def __init__(self,
- num_convs=2,
- kernel_size=3,
- concat_input=True,
- dilation=1,
- **kwargs):
+ def __init__(
+ self, num_convs=2, kernel_size=3, concat_input=True, dilation=1, **kwargs
+ ):
assert num_convs >= 0 and dilation > 0 and isinstance(dilation, int)
self.num_convs = num_convs
self.concat_input = concat_input
self.kernel_size = kernel_size
- super(FCNHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
if num_convs == 0:
assert self.in_channels == self.channels
@@ -48,7 +45,9 @@ def __init__(self,
dilation=dilation,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
if len(convs) == 0:
self.convs = nn.Identity()
@@ -62,7 +61,8 @@ def __init__(self,
padding=kernel_size // 2,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def _forward_feature(self, inputs):
"""Forward function for feature maps before classifying each pixel with
diff --git a/mmsegmentation/mmseg/models/decode_heads/fpn_head.py b/mmsegmentation/mmseg/models/decode_heads/fpn_head.py
index e41f324..e4e0605 100644
--- a/mmsegmentation/mmseg/models/decode_heads/fpn_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/fpn_head.py
@@ -22,8 +22,7 @@ class FPNHead(BaseDecodeHead):
"""
def __init__(self, feature_strides, **kwargs):
- super(FPNHead, self).__init__(
- input_transform='multiple_select', **kwargs)
+ super().__init__(input_transform="multiple_select", **kwargs)
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides
@@ -31,8 +30,8 @@ def __init__(self, feature_strides, **kwargs):
self.scale_heads = nn.ModuleList()
for i in range(len(feature_strides)):
head_length = max(
- 1,
- int(np.log2(feature_strides[i]) - np.log2(feature_strides[0])))
+ 1, int(np.log2(feature_strides[i]) - np.log2(feature_strides[0]))
+ )
scale_head = []
for k in range(head_length):
scale_head.append(
@@ -43,13 +42,17 @@ def __init__(self, feature_strides, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
if feature_strides[i] != feature_strides[0]:
scale_head.append(
Upsample(
scale_factor=2,
- mode='bilinear',
- align_corners=self.align_corners))
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
+ )
self.scale_heads.append(nn.Sequential(*scale_head))
def forward(self, inputs):
@@ -62,8 +65,9 @@ def forward(self, inputs):
output = output + resize(
self.scale_heads[i](x[i]),
size=output.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
output = self.cls_seg(output)
return output
diff --git a/mmsegmentation/mmseg/models/decode_heads/gc_head.py b/mmsegmentation/mmseg/models/decode_heads/gc_head.py
index eed5074..695019f 100644
--- a/mmsegmentation/mmseg/models/decode_heads/gc_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/gc_head.py
@@ -21,12 +21,10 @@ class GCHead(FCNHead):
Options are 'channel_add', 'channel_mul'. Default: ('channel_add',)
"""
- def __init__(self,
- ratio=1 / 4.,
- pooling_type='att',
- fusion_types=('channel_add', ),
- **kwargs):
- super(GCHead, self).__init__(num_convs=2, **kwargs)
+ def __init__(
+ self, ratio=1 / 4.0, pooling_type="att", fusion_types=("channel_add",), **kwargs
+ ):
+ super().__init__(num_convs=2, **kwargs)
self.ratio = ratio
self.pooling_type = pooling_type
self.fusion_types = fusion_types
@@ -34,7 +32,8 @@ def __init__(self,
in_channels=self.channels,
ratio=self.ratio,
pooling_type=self.pooling_type,
- fusion_types=self.fusion_types)
+ fusion_types=self.fusion_types,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/isa_head.py b/mmsegmentation/mmseg/models/decode_heads/isa_head.py
index 0bf3455..bcd86a7 100644
--- a/mmsegmentation/mmseg/models/decode_heads/isa_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/isa_head.py
@@ -22,7 +22,7 @@ class SelfAttentionBlock(_SelfAttentionBlock):
"""
def __init__(self, in_channels, channels, conv_cfg, norm_cfg, act_cfg):
- super(SelfAttentionBlock, self).__init__(
+ super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@@ -38,7 +38,8 @@ def __init__(self, in_channels, channels, conv_cfg, norm_cfg, act_cfg):
with_out=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.output_project = self.build_project(
in_channels,
@@ -47,11 +48,12 @@ def __init__(self, in_channels, channels, conv_cfg, norm_cfg, act_cfg):
use_conv_module=True,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, x):
"""Forward function."""
- context = super(SelfAttentionBlock, self).forward(x, x)
+ context = super().forward(x, x)
return self.output_project(context)
@@ -68,7 +70,7 @@ class ISAHead(BaseDecodeHead):
"""
def __init__(self, isa_channels, down_factor=(8, 8), **kwargs):
- super(ISAHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
self.down_factor = down_factor
self.in_conv = ConvModule(
@@ -78,26 +80,30 @@ def __init__(self, isa_channels, down_factor=(8, 8), **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.global_relation = SelfAttentionBlock(
self.channels,
isa_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.local_relation = SelfAttentionBlock(
self.channels,
isa_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.out_conv = ConvModule(
self.channels * 2,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
@@ -110,8 +116,7 @@ def forward(self, inputs):
glb_h, glb_w = math.ceil(h / loc_h), math.ceil(w / loc_w)
pad_h, pad_w = glb_h * loc_h - h, glb_w * loc_w - w
if pad_h > 0 or pad_w > 0: # pad if the size is not divisible
- padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
- pad_h - pad_h // 2)
+ padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
x = F.pad(x, padding)
# global relation
@@ -135,7 +140,7 @@ def forward(self, inputs):
x = x.permute(0, 3, 1, 4, 2, 5) # (n, c, glb_h, loc_h, glb_w, loc_w)
x = x.reshape(n, c, glb_h * loc_h, glb_w * loc_w)
if pad_h > 0 or pad_w > 0: # remove padding
- x = x[:, :, pad_h // 2:pad_h // 2 + h, pad_w // 2:pad_w // 2 + w]
+ x = x[:, :, pad_h // 2 : pad_h // 2 + h, pad_w // 2 : pad_w // 2 + w]
x = self.out_conv(torch.cat([x, residual], dim=1))
out = self.cls_seg(x)
diff --git a/mmsegmentation/mmseg/models/decode_heads/knet_head.py b/mmsegmentation/mmseg/models/decode_heads/knet_head.py
index 78a2702..1e082c1 100644
--- a/mmsegmentation/mmseg/models/decode_heads/knet_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/knet_head.py
@@ -3,9 +3,12 @@
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
-from mmcv.cnn.bricks.transformer import (FFN, TRANSFORMER_LAYER,
- MultiheadAttention,
- build_transformer_layer)
+from mmcv.cnn.bricks.transformer import (
+ FFN,
+ TRANSFORMER_LAYER,
+ MultiheadAttention,
+ build_transformer_layer,
+)
from mmseg.models.builder import HEADS, build_head
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
@@ -35,17 +38,17 @@ class KernelUpdator(nn.Module):
"""
def __init__(
- self,
- in_channels=256,
- feat_channels=64,
- out_channels=None,
- gate_sigmoid=True,
- gate_norm_act=False,
- activate_out=False,
- norm_cfg=dict(type='LN'),
- act_cfg=dict(type='ReLU', inplace=True),
+ self,
+ in_channels=256,
+ feat_channels=64,
+ out_channels=None,
+ gate_sigmoid=True,
+ gate_norm_act=False,
+ activate_out=False,
+ norm_cfg=dict(type="LN"),
+ act_cfg=dict(type="ReLU", inplace=True),
):
- super(KernelUpdator, self).__init__()
+ super().__init__()
self.in_channels = in_channels
self.feat_channels = feat_channels
self.out_channels_raw = out_channels
@@ -59,10 +62,11 @@ def __init__(
self.num_params_in = self.feat_channels
self.num_params_out = self.feat_channels
self.dynamic_layer = nn.Linear(
- self.in_channels, self.num_params_in + self.num_params_out)
- self.input_layer = nn.Linear(self.in_channels,
- self.num_params_in + self.num_params_out,
- 1)
+ self.in_channels, self.num_params_in + self.num_params_out
+ )
+ self.input_layer = nn.Linear(
+ self.in_channels, self.num_params_in + self.num_params_out, 1
+ )
self.input_gate = nn.Linear(self.in_channels, self.feat_channels, 1)
self.update_gate = nn.Linear(self.in_channels, self.feat_channels, 1)
if self.gate_norm_act:
@@ -98,17 +102,16 @@ def forward(self, update_feature, input_feature):
# dynamic_layer works for
# phi_1 and psi_3 in Eq.(4) and (5) of K-Net paper
parameters = self.dynamic_layer(update_feature)
- param_in = parameters[:, :self.num_params_in].view(
- -1, self.feat_channels)
- param_out = parameters[:, -self.num_params_out:].view(
- -1, self.feat_channels)
+ param_in = parameters[:, : self.num_params_in].view(-1, self.feat_channels)
+ param_out = parameters[:, -self.num_params_out :].view(-1, self.feat_channels)
# input_layer works for
# phi_2 and psi_4 in Eq.(4) and (5) of K-Net paper
input_feats = self.input_layer(
- input_feature.reshape(num_proposals, -1, self.feat_channels))
- input_in = input_feats[..., :self.num_params_in]
- input_out = input_feats[..., -self.num_params_out:]
+ input_feature.reshape(num_proposals, -1, self.feat_channels)
+ )
+ input_in = input_feats[..., : self.num_params_in]
+ input_out = input_feats[..., -self.num_params_out :]
# `gate_feats` is F^G in K-Net paper
gate_feats = input_in * param_in.unsqueeze(-2)
@@ -129,8 +132,7 @@ def forward(self, update_feature, input_feature):
# Gate mechanism. Eq.(5) in original paper.
# param_out has shape (batch_size, feat_channels, out_channels)
- features = update_gate * param_out.unsqueeze(
- -2) + input_gate * input_out
+ features = update_gate * param_out.unsqueeze(-2) + input_gate * input_out
features = self.fc_layer(features)
features = self.fc_norm(features)
@@ -186,31 +188,34 @@ class KernelUpdateHead(nn.Module):
norm_cfg=dict(type='LN')).
"""
- def __init__(self,
- num_classes=150,
- num_ffn_fcs=2,
- num_heads=8,
- num_mask_fcs=3,
- feedforward_channels=2048,
- in_channels=256,
- out_channels=256,
- dropout=0.0,
- act_cfg=dict(type='ReLU', inplace=True),
- ffn_act_cfg=dict(type='ReLU', inplace=True),
- conv_kernel_size=1,
- feat_transform_cfg=None,
- kernel_init=False,
- with_ffn=True,
- feat_gather_stride=1,
- mask_transform_stride=1,
- kernel_updator_cfg=dict(
- type='DynamicConv',
- in_channels=256,
- feat_channels=64,
- out_channels=256,
- act_cfg=dict(type='ReLU', inplace=True),
- norm_cfg=dict(type='LN'))):
- super(KernelUpdateHead, self).__init__()
+ def __init__(
+ self,
+ num_classes=150,
+ num_ffn_fcs=2,
+ num_heads=8,
+ num_mask_fcs=3,
+ feedforward_channels=2048,
+ in_channels=256,
+ out_channels=256,
+ dropout=0.0,
+ act_cfg=dict(type="ReLU", inplace=True),
+ ffn_act_cfg=dict(type="ReLU", inplace=True),
+ conv_kernel_size=1,
+ feat_transform_cfg=None,
+ kernel_init=False,
+ with_ffn=True,
+ feat_gather_stride=1,
+ mask_transform_stride=1,
+ kernel_updator_cfg=dict(
+ type="DynamicConv",
+ in_channels=256,
+ feat_channels=64,
+ out_channels=256,
+ act_cfg=dict(type="ReLU", inplace=True),
+ norm_cfg=dict(type="LN"),
+ ),
+ ):
+ super().__init__()
self.num_classes = num_classes
self.in_channels = in_channels
self.out_channels = out_channels
@@ -223,14 +228,16 @@ def __init__(self,
self.feat_gather_stride = feat_gather_stride
self.mask_transform_stride = mask_transform_stride
- self.attention = MultiheadAttention(in_channels * conv_kernel_size**2,
- num_heads, dropout)
+ self.attention = MultiheadAttention(
+ in_channels * conv_kernel_size**2, num_heads, dropout
+ )
self.attention_norm = build_norm_layer(
- dict(type='LN'), in_channels * conv_kernel_size**2)[1]
+ dict(type="LN"), in_channels * conv_kernel_size**2
+ )[1]
self.kernel_update_conv = build_transformer_layer(kernel_updator_cfg)
if feat_transform_cfg is not None:
- kernel_size = feat_transform_cfg.pop('kernel_size', 1)
+ kernel_size = feat_transform_cfg.pop("kernel_size", 1)
transform_channels = in_channels
self.feat_transform = ConvModule(
transform_channels,
@@ -238,7 +245,8 @@ def __init__(self,
kernel_size,
stride=feat_gather_stride,
padding=int(feat_gather_stride // 2),
- **feat_transform_cfg)
+ **feat_transform_cfg,
+ )
else:
self.feat_transform = None
@@ -248,15 +256,14 @@ def __init__(self,
feedforward_channels,
num_ffn_fcs,
act_cfg=ffn_act_cfg,
- dropout=dropout)
- self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
+ dropout=dropout,
+ )
+ self.ffn_norm = build_norm_layer(dict(type="LN"), in_channels)[1]
self.mask_fcs = nn.ModuleList()
for _ in range(num_mask_fcs):
- self.mask_fcs.append(
- nn.Linear(in_channels, in_channels, bias=False))
- self.mask_fcs.append(
- build_norm_layer(dict(type='LN'), in_channels)[1])
+ self.mask_fcs.append(nn.Linear(in_channels, in_channels, bias=False))
+ self.mask_fcs.append(build_norm_layer(dict(type="LN"), in_channels)[1])
self.mask_fcs.append(build_activation_layer(act_cfg))
self.fc_mask = nn.Linear(in_channels, out_channels)
@@ -273,8 +280,7 @@ def init_weights(self):
pass
if self.kernel_init:
logger = get_root_logger()
- logger.info(
- 'mask kernel in mask head is normal initialized by std 0.01')
+ logger.info("mask kernel in mask head is normal initialized by std 0.01")
nn.init.normal_(self.fc_mask.weight, mean=0, std=0.01)
def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
@@ -303,7 +309,8 @@ def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
mask_h, mask_w = mask_preds.shape[-2:]
if mask_h != H or mask_w != W:
gather_mask = F.interpolate(
- mask_preds, (H, W), align_corners=False, mode='bilinear')
+ mask_preds, (H, W), align_corners=False, mode="bilinear"
+ )
else:
gather_mask = mask_preds
@@ -311,12 +318,12 @@ def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
# Group Feature Assembling. Eq.(3) in original paper.
# einsum is faster than bmm by 30%
- x_feat = torch.einsum('bnhw,bchw->bnc', sigmoid_masks, x)
+ x_feat = torch.einsum("bnhw,bchw->bnc", sigmoid_masks, x)
# obj_feat in shape [B, N, C, K, K] -> [B, N, C, K*K] -> [B, N, K*K, C]
- proposal_feat = proposal_feat.reshape(N, num_proposals,
- self.in_channels,
- -1).permute(0, 1, 3, 2)
+ proposal_feat = proposal_feat.reshape(
+ N, num_proposals, self.in_channels, -1
+ ).permute(0, 1, 3, 2)
obj_feat = self.kernel_update_conv(x_feat, proposal_feat)
# [B, N, K*K, C] -> [B, N, K*K*C] -> [N, B, K*K*C]
@@ -340,9 +347,10 @@ def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
# [B, N, K*K, C] -> [B, N, C, K*K]
mask_feat = self.fc_mask(mask_feat).permute(0, 1, 3, 2)
- if (self.mask_transform_stride == 2 and self.feat_gather_stride == 1):
+ if self.mask_transform_stride == 2 and self.feat_gather_stride == 1:
mask_x = F.interpolate(
- x, scale_factor=0.5, mode='bilinear', align_corners=False)
+ x, scale_factor=0.5, mode="bilinear", align_corners=False
+ )
H, W = mask_x.shape[-2:]
else:
mask_x = x
@@ -358,37 +366,39 @@ def forward(self, x, proposal_feat, mask_preds, mask_shape=None):
# mask_feat = mask_feat.reshape(N, num_proposals, -1)
# new_mask_preds = torch.einsum('bnc,bcl->bnl', mask_feat, fold_x)
# [B, N, C, K*K] -> [B*N, C, K, K]
- mask_feat = mask_feat.reshape(N, num_proposals, C,
- self.conv_kernel_size,
- self.conv_kernel_size)
+ mask_feat = mask_feat.reshape(
+ N, num_proposals, C, self.conv_kernel_size, self.conv_kernel_size
+ )
# [B, C, H, W] -> [1, B*C, H, W]
new_mask_preds = []
for i in range(N):
new_mask_preds.append(
F.conv2d(
- mask_x[i:i + 1],
+ mask_x[i : i + 1],
mask_feat[i],
- padding=int(self.conv_kernel_size // 2)))
+ padding=int(self.conv_kernel_size // 2),
+ )
+ )
new_mask_preds = torch.cat(new_mask_preds, dim=0)
new_mask_preds = new_mask_preds.reshape(N, num_proposals, H, W)
if self.mask_transform_stride == 2:
new_mask_preds = F.interpolate(
- new_mask_preds,
- scale_factor=2,
- mode='bilinear',
- align_corners=False)
+ new_mask_preds, scale_factor=2, mode="bilinear", align_corners=False
+ )
if mask_shape is not None and mask_shape[0] != H:
new_mask_preds = F.interpolate(
- new_mask_preds,
- mask_shape,
- align_corners=False,
- mode='bilinear')
+ new_mask_preds, mask_shape, align_corners=False, mode="bilinear"
+ )
return new_mask_preds, obj_feat.permute(0, 1, 3, 2).reshape(
- N, num_proposals, self.in_channels, self.conv_kernel_size,
- self.conv_kernel_size)
+ N,
+ num_proposals,
+ self.in_channels,
+ self.conv_kernel_size,
+ self.conv_kernel_size,
+ )
@HEADS.register_module()
@@ -409,8 +419,7 @@ class IterativeDecodeHead(BaseDecodeHead):
"""
- def __init__(self, num_stages, kernel_generate_head, kernel_update_head,
- **kwargs):
+ def __init__(self, num_stages, kernel_generate_head, kernel_update_head, **kwargs):
# ``IterativeDecodeHead`` would skip initialization of
# ``BaseDecodeHead`` which would be called when building
# ``self.kernel_generate_head``.
@@ -433,14 +442,13 @@ def forward(self, inputs):
feats = self.kernel_generate_head._forward_feature(inputs)
sem_seg = self.kernel_generate_head.cls_seg(feats)
seg_kernels = self.kernel_generate_head.conv_seg.weight.clone()
- seg_kernels = seg_kernels[None].expand(
- feats.size(0), *seg_kernels.size())
+ seg_kernels = seg_kernels[None].expand(feats.size(0), *seg_kernels.size())
stage_segs = [sem_seg]
for i in range(self.num_stages):
- sem_seg, seg_kernels = self.kernel_update_head[i](feats,
- seg_kernels,
- sem_seg)
+ sem_seg, seg_kernels = self.kernel_update_head[i](
+ feats, seg_kernels, sem_seg
+ )
stage_segs.append(sem_seg)
if self.training:
return stage_segs
@@ -452,6 +460,6 @@ def losses(self, seg_logit, seg_label):
for i, logit in enumerate(seg_logit):
loss = self.kernel_generate_head.losses(logit, seg_label)
for k, v in loss.items():
- losses[f'{k}.s{i}'] = v
+ losses[f"{k}.s{i}"] = v
return losses
diff --git a/mmsegmentation/mmseg/models/decode_heads/lraspp_head.py b/mmsegmentation/mmseg/models/decode_heads/lraspp_head.py
index c10ff0d..5101c28 100644
--- a/mmsegmentation/mmseg/models/decode_heads/lraspp_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/lraspp_head.py
@@ -22,11 +22,13 @@ class LRASPPHead(BaseDecodeHead):
"""
def __init__(self, branch_channels=(32, 64), **kwargs):
- super(LRASPPHead, self).__init__(**kwargs)
- if self.input_transform != 'multiple_select':
- raise ValueError('in Lite R-ASPP (LRASPP) head, input_transform '
- f'must be \'multiple_select\'. But received '
- f'\'{self.input_transform}\'')
+ super().__init__(**kwargs)
+ if self.input_transform != "multiple_select":
+ raise ValueError(
+ "in Lite R-ASPP (LRASPP) head, input_transform "
+ f"must be 'multiple_select'. But received "
+ f"'{self.input_transform}'"
+ )
assert is_tuple_of(branch_channels, int)
assert len(branch_channels) == len(self.in_channels) - 1
self.branch_channels = branch_channels
@@ -35,18 +37,20 @@ def __init__(self, branch_channels=(32, 64), **kwargs):
self.conv_ups = nn.Sequential()
for i in range(len(branch_channels)):
self.convs.add_module(
- f'conv{i}',
- nn.Conv2d(
- self.in_channels[i], branch_channels[i], 1, bias=False))
+ f"conv{i}",
+ nn.Conv2d(self.in_channels[i], branch_channels[i], 1, bias=False),
+ )
self.conv_ups.add_module(
- f'conv_up{i}',
+ f"conv_up{i}",
ConvModule(
self.channels + branch_channels[i],
self.channels,
1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- bias=False))
+ bias=False,
+ ),
+ )
self.conv_up_input = nn.Conv2d(self.channels, self.channels, 1)
@@ -56,15 +60,18 @@ def __init__(self, branch_channels=(32, 64), **kwargs):
1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- bias=False)
+ bias=False,
+ )
self.image_pool = nn.Sequential(
nn.AvgPool2d(kernel_size=49, stride=(16, 20)),
ConvModule(
self.in_channels[2],
self.channels,
1,
- act_cfg=dict(type='Sigmoid'),
- bias=False))
+ act_cfg=dict(type="Sigmoid"),
+ bias=False,
+ ),
+ )
def forward(self, inputs):
"""Forward function."""
@@ -75,16 +82,18 @@ def forward(self, inputs):
x = self.aspp_conv(x) * resize(
self.image_pool(x),
size=x.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
x = self.conv_up_input(x)
for i in range(len(self.branch_channels) - 1, -1, -1):
x = resize(
x,
size=inputs[i].size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
x = torch.cat([x, self.convs[i](inputs[i])], 1)
x = self.conv_ups[i](x)
diff --git a/mmsegmentation/mmseg/models/decode_heads/nl_head.py b/mmsegmentation/mmseg/models/decode_heads/nl_head.py
index 637517e..50df90a 100644
--- a/mmsegmentation/mmseg/models/decode_heads/nl_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/nl_head.py
@@ -21,12 +21,8 @@ class NLHead(FCNHead):
'dot_product'. Default: 'embedded_gaussian.'.
"""
- def __init__(self,
- reduction=2,
- use_scale=True,
- mode='embedded_gaussian',
- **kwargs):
- super(NLHead, self).__init__(num_convs=2, **kwargs)
+ def __init__(self, reduction=2, use_scale=True, mode="embedded_gaussian", **kwargs):
+ super().__init__(num_convs=2, **kwargs)
self.reduction = reduction
self.use_scale = use_scale
self.mode = mode
@@ -36,7 +32,8 @@ def __init__(self,
use_scale=self.use_scale,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- mode=self.mode)
+ mode=self.mode,
+ )
def forward(self, inputs):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/ocr_head.py b/mmsegmentation/mmseg/models/decode_heads/ocr_head.py
index 09eadfb..e289c88 100644
--- a/mmsegmentation/mmseg/models/decode_heads/ocr_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/ocr_head.py
@@ -18,7 +18,7 @@ class SpatialGatherModule(nn.Module):
"""
def __init__(self, scale):
- super(SpatialGatherModule, self).__init__()
+ super().__init__()
self.scale = scale
def forward(self, feats, probs):
@@ -40,13 +40,12 @@ def forward(self, feats, probs):
class ObjectAttentionBlock(_SelfAttentionBlock):
"""Make a OCR used SelfAttentionBlock."""
- def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg,
- act_cfg):
+ def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg, act_cfg):
if scale > 1:
query_downsample = nn.MaxPool2d(kernel_size=scale)
else:
query_downsample = None
- super(ObjectAttentionBlock, self).__init__(
+ super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@@ -62,19 +61,20 @@ def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg,
with_out=True,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.bottleneck = ConvModule(
in_channels * 2,
in_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, query_feats, key_feats):
"""Forward function."""
- context = super(ObjectAttentionBlock,
- self).forward(query_feats, key_feats)
+ context = super().forward(query_feats, key_feats)
output = self.bottleneck(torch.cat([context, query_feats], dim=1))
if self.query_downsample is not None:
output = resize(query_feats)
@@ -96,7 +96,7 @@ class OCRHead(BaseCascadeDecodeHead):
"""
def __init__(self, ocr_channels, scale=1, **kwargs):
- super(OCRHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
self.ocr_channels = ocr_channels
self.scale = scale
self.object_context_block = ObjectAttentionBlock(
@@ -105,7 +105,8 @@ def __init__(self, ocr_channels, scale=1, **kwargs):
self.scale,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.spatial_gather_module = SpatialGatherModule(self.scale)
self.bottleneck = ConvModule(
@@ -115,7 +116,8 @@ def __init__(self, ocr_channels, scale=1, **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs, prev_output):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/models/decode_heads/point_head.py b/mmsegmentation/mmseg/models/decode_heads/point_head.py
index 5e60527..0e14b62 100644
--- a/mmsegmentation/mmseg/models/decode_heads/point_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/point_head.py
@@ -5,16 +5,16 @@
import torch.nn as nn
from mmcv.cnn import ConvModule
-try:
- from mmcv.ops import point_sample
-except ModuleNotFoundError:
- point_sample = None
-
from mmseg.models.builder import HEADS
from mmseg.ops import resize
from ..losses import accuracy
from .cascade_decode_head import BaseCascadeDecodeHead
+try:
+ from mmcv.ops import point_sample
+except ModuleNotFoundError:
+ point_sample = None
+
def calculate_uncertainty(seg_logits):
"""Estimate uncertainty based on seg logits.
@@ -64,24 +64,25 @@ class PointHead(BaseCascadeDecodeHead):
loss_weight=1.0).
"""
- def __init__(self,
- num_fcs=3,
- coarse_pred_each_layer=True,
- conv_cfg=dict(type='Conv1d'),
- norm_cfg=None,
- act_cfg=dict(type='ReLU', inplace=False),
- **kwargs):
- super(PointHead, self).__init__(
- input_transform='multiple_select',
+ def __init__(
+ self,
+ num_fcs=3,
+ coarse_pred_each_layer=True,
+ conv_cfg=dict(type="Conv1d"),
+ norm_cfg=None,
+ act_cfg=dict(type="ReLU", inplace=False),
+ **kwargs,
+ ):
+ super().__init__(
+ input_transform="multiple_select",
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- init_cfg=dict(
- type='Normal', std=0.01, override=dict(name='fc_seg')),
- **kwargs)
+ init_cfg=dict(type="Normal", std=0.01, override=dict(name="fc_seg")),
+ **kwargs,
+ )
if point_sample is None:
- raise RuntimeError('Please install mmcv-full for '
- 'point_sample ops')
+ raise RuntimeError("Please install mmcv-full for " "point_sample ops")
self.num_fcs = num_fcs
self.coarse_pred_each_layer = coarse_pred_each_layer
@@ -98,20 +99,17 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.fcs.append(fc)
fc_in_channels = fc_channels
- fc_in_channels += self.num_classes if self.coarse_pred_each_layer \
- else 0
+ fc_in_channels += self.num_classes if self.coarse_pred_each_layer else 0
self.fc_seg = nn.Conv1d(
- fc_in_channels,
- self.num_classes,
- kernel_size=1,
- stride=1,
- padding=0)
+ fc_in_channels, self.num_classes, kernel_size=1, stride=1, padding=0
+ )
if self.dropout_ratio > 0:
self.dropout = nn.Dropout(self.dropout_ratio)
- delattr(self, 'conv_seg')
+ delattr(self, "conv_seg")
def cls_seg(self, feat):
"""Classify each pixel with fc."""
@@ -142,8 +140,7 @@ def _get_fine_grained_point_feats(self, x, points):
"""
fine_grained_feats_list = [
- point_sample(_, points, align_corners=self.align_corners)
- for _ in x
+ point_sample(_, points, align_corners=self.align_corners) for _ in x
]
if len(fine_grained_feats_list) > 1:
fine_grained_feats = torch.cat(fine_grained_feats_list, dim=1)
@@ -166,12 +163,12 @@ def _get_coarse_point_feats(self, prev_output, points):
"""
coarse_feats = point_sample(
- prev_output, points, align_corners=self.align_corners)
+ prev_output, points, align_corners=self.align_corners
+ )
return coarse_feats
- def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
- train_cfg):
+ def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
@@ -191,17 +188,17 @@ def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
x = self._transform_inputs(inputs)
with torch.no_grad():
points = self.get_points_train(
- prev_output, calculate_uncertainty, cfg=train_cfg)
- fine_grained_point_feats = self._get_fine_grained_point_feats(
- x, points)
+ prev_output, calculate_uncertainty, cfg=train_cfg
+ )
+ fine_grained_point_feats = self._get_fine_grained_point_feats(x, points)
coarse_point_feats = self._get_coarse_point_feats(prev_output, points)
- point_logits = self.forward(fine_grained_point_feats,
- coarse_point_feats)
+ point_logits = self.forward(fine_grained_point_feats, coarse_point_feats)
point_label = point_sample(
gt_semantic_seg.float(),
points,
- mode='nearest',
- align_corners=self.align_corners)
+ mode="nearest",
+ align_corners=self.align_corners,
+ )
point_label = point_label.squeeze(1).long()
losses = self.losses(point_logits, point_label)
@@ -231,25 +228,27 @@ def forward_test(self, inputs, prev_output, img_metas, test_cfg):
refined_seg_logits = resize(
refined_seg_logits,
scale_factor=test_cfg.scale_factor,
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
batch_size, channels, height, width = refined_seg_logits.shape
point_indices, points = self.get_points_test(
- refined_seg_logits, calculate_uncertainty, cfg=test_cfg)
- fine_grained_point_feats = self._get_fine_grained_point_feats(
- x, points)
- coarse_point_feats = self._get_coarse_point_feats(
- prev_output, points)
- point_logits = self.forward(fine_grained_point_feats,
- coarse_point_feats)
+ refined_seg_logits, calculate_uncertainty, cfg=test_cfg
+ )
+ fine_grained_point_feats = self._get_fine_grained_point_feats(x, points)
+ coarse_point_feats = self._get_coarse_point_feats(prev_output, points)
+ point_logits = self.forward(fine_grained_point_feats, coarse_point_feats)
point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
refined_seg_logits = refined_seg_logits.reshape(
- batch_size, channels, height * width)
+ batch_size, channels, height * width
+ )
refined_seg_logits = refined_seg_logits.scatter_(
- 2, point_indices, point_logits)
+ 2, point_indices, point_logits
+ )
refined_seg_logits = refined_seg_logits.view(
- batch_size, channels, height, width)
+ batch_size, channels, height, width
+ )
return refined_seg_logits
@@ -261,11 +260,13 @@ def losses(self, point_logits, point_label):
else:
losses_decode = self.loss_decode
for loss_module in losses_decode:
- loss['point' + loss_module.loss_name] = loss_module(
- point_logits, point_label, ignore_index=self.ignore_index)
+ loss["point" + loss_module.loss_name] = loss_module(
+ point_logits, point_label, ignore_index=self.ignore_index
+ )
- loss['acc_point'] = accuracy(
- point_logits, point_label, ignore_index=self.ignore_index)
+ loss["acc_point"] = accuracy(
+ point_logits, point_label, ignore_index=self.ignore_index
+ )
return loss
def get_points_train(self, seg_logits, uncertainty_func, cfg):
@@ -294,8 +295,7 @@ def get_points_train(self, seg_logits, uncertainty_func, cfg):
assert 0 <= importance_sample_ratio <= 1
batch_size = seg_logits.shape[0]
num_sampled = int(num_points * oversample_ratio)
- point_coords = torch.rand(
- batch_size, num_sampled, 2, device=seg_logits.device)
+ point_coords = torch.rand(batch_size, num_sampled, 2, device=seg_logits.device)
point_logits = point_sample(seg_logits, point_coords)
# It is crucial to calculate uncertainty based on the sampled
# prediction value for the points. Calculating uncertainties of the
@@ -309,16 +309,18 @@ def get_points_train(self, seg_logits, uncertainty_func, cfg):
point_uncertainties = uncertainty_func(point_logits)
num_uncertain_points = int(importance_sample_ratio * num_points)
num_random_points = num_points - num_uncertain_points
- idx = torch.topk(
- point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+ idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
shift = num_sampled * torch.arange(
- batch_size, dtype=torch.long, device=seg_logits.device)
+ batch_size, dtype=torch.long, device=seg_logits.device
+ )
idx += shift[:, None]
point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
- batch_size, num_uncertain_points, 2)
+ batch_size, num_uncertain_points, 2
+ )
if num_random_points > 0:
rand_point_coords = torch.rand(
- batch_size, num_random_points, 2, device=seg_logits.device)
+ batch_size, num_random_points, 2, device=seg_logits.device
+ )
point_coords = torch.cat((point_coords, rand_point_coords), dim=1)
return point_coords
@@ -352,13 +354,8 @@ def get_points_test(self, seg_logits, uncertainty_func, cfg):
num_points = min(height * width, num_points)
point_indices = uncertainty_map.topk(num_points, dim=1)[1]
point_coords = torch.zeros(
- batch_size,
- num_points,
- 2,
- dtype=torch.float,
- device=seg_logits.device)
- point_coords[:, :, 0] = w_step / 2.0 + (point_indices %
- width).float() * w_step
- point_coords[:, :, 1] = h_step / 2.0 + (point_indices //
- width).float() * h_step
+ batch_size, num_points, 2, dtype=torch.float, device=seg_logits.device
+ )
+ point_coords[:, :, 0] = w_step / 2.0 + (point_indices % width).float() * w_step
+ point_coords[:, :, 1] = h_step / 2.0 + (point_indices // width).float() * h_step
return point_indices, point_coords
diff --git a/mmsegmentation/mmseg/models/decode_heads/psa_head.py b/mmsegmentation/mmseg/models/decode_heads/psa_head.py
index df7593c..f4390eb 100644
--- a/mmsegmentation/mmseg/models/decode_heads/psa_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/psa_head.py
@@ -33,18 +33,20 @@ class PSAHead(BaseDecodeHead):
psa_softmax (bool): Whether use softmax for attention.
"""
- def __init__(self,
- mask_size,
- psa_type='bi-direction',
- compact=False,
- shrink_factor=2,
- normalization_factor=1.0,
- psa_softmax=True,
- **kwargs):
+ def __init__(
+ self,
+ mask_size,
+ psa_type="bi-direction",
+ compact=False,
+ shrink_factor=2,
+ normalization_factor=1.0,
+ psa_softmax=True,
+ **kwargs,
+ ):
if PSAMask is None:
- raise RuntimeError('Please install mmcv-full for PSAMask ops')
- super(PSAHead, self).__init__(**kwargs)
- assert psa_type in ['collect', 'distribute', 'bi-direction']
+ raise RuntimeError("Please install mmcv-full for PSAMask ops")
+ super().__init__(**kwargs)
+ assert psa_type in ["collect", "distribute", "bi-direction"]
self.psa_type = psa_type
self.compact = compact
self.shrink_factor = shrink_factor
@@ -61,7 +63,8 @@ def __init__(self,
kernel_size=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.attention = nn.Sequential(
ConvModule(
self.channels,
@@ -69,17 +72,19 @@ def __init__(self,
kernel_size=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
- nn.Conv2d(
- self.channels, mask_h * mask_w, kernel_size=1, bias=False))
- if psa_type == 'bi-direction':
+ act_cfg=self.act_cfg,
+ ),
+ nn.Conv2d(self.channels, mask_h * mask_w, kernel_size=1, bias=False),
+ )
+ if psa_type == "bi-direction":
self.reduce_p = ConvModule(
self.in_channels,
self.channels,
kernel_size=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.attention_p = nn.Sequential(
ConvModule(
self.channels,
@@ -87,21 +92,23 @@ def __init__(self,
kernel_size=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
- nn.Conv2d(
- self.channels, mask_h * mask_w, kernel_size=1, bias=False))
- self.psamask_collect = PSAMask('collect', mask_size)
- self.psamask_distribute = PSAMask('distribute', mask_size)
+ act_cfg=self.act_cfg,
+ ),
+ nn.Conv2d(self.channels, mask_h * mask_w, kernel_size=1, bias=False),
+ )
+ self.psamask_collect = PSAMask("collect", mask_size)
+ self.psamask_distribute = PSAMask("distribute", mask_size)
else:
self.psamask = PSAMask(psa_type, mask_size)
self.proj = ConvModule(
- self.channels * (2 if psa_type == 'bi-direction' else 1),
+ self.channels * (2 if psa_type == "bi-direction" else 1),
self.in_channels,
kernel_size=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
self.bottleneck = ConvModule(
self.in_channels * 2,
self.channels,
@@ -109,14 +116,15 @@ def __init__(self,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def forward(self, inputs):
"""Forward function."""
x = self._transform_inputs(inputs)
identity = x
align_corners = self.align_corners
- if self.psa_type in ['collect', 'distribute']:
+ if self.psa_type in ["collect", "distribute"]:
out = self.reduce(x)
n, c, h, w = out.size()
if self.shrink_factor != 1:
@@ -129,22 +137,19 @@ def forward(self, inputs):
w = w // self.shrink_factor
align_corners = False
out = resize(
- out,
- size=(h, w),
- mode='bilinear',
- align_corners=align_corners)
+ out, size=(h, w), mode="bilinear", align_corners=align_corners
+ )
y = self.attention(out)
if self.compact:
- if self.psa_type == 'collect':
- y = y.view(n, h * w,
- h * w).transpose(1, 2).view(n, h * w, h, w)
+ if self.psa_type == "collect":
+ y = y.view(n, h * w, h * w).transpose(1, 2).view(n, h * w, h, w)
else:
y = self.psamask(y)
if self.psa_softmax:
y = F.softmax(y, dim=1)
- out = torch.bmm(
- out.view(n, c, h * w), y.view(n, h * w, h * w)).view(
- n, c, h, w) * (1.0 / self.normalization_factor)
+ out = torch.bmm(out.view(n, c, h * w), y.view(n, h * w, h * w)).view(
+ n, c, h, w
+ ) * (1.0 / self.normalization_factor)
else:
x_col = self.reduce(x)
x_dis = self.reduce_p(x)
@@ -159,20 +164,15 @@ def forward(self, inputs):
w = w // self.shrink_factor
align_corners = False
x_col = resize(
- x_col,
- size=(h, w),
- mode='bilinear',
- align_corners=align_corners)
+ x_col, size=(h, w), mode="bilinear", align_corners=align_corners
+ )
x_dis = resize(
- x_dis,
- size=(h, w),
- mode='bilinear',
- align_corners=align_corners)
+ x_dis, size=(h, w), mode="bilinear", align_corners=align_corners
+ )
y_col = self.attention(x_col)
y_dis = self.attention_p(x_dis)
if self.compact:
- y_dis = y_dis.view(n, h * w,
- h * w).transpose(1, 2).view(n, h * w, h, w)
+ y_dis = y_dis.view(n, h * w, h * w).transpose(1, 2).view(n, h * w, h, w)
else:
y_col = self.psamask_collect(y_col)
y_dis = self.psamask_distribute(y_dis)
@@ -180,18 +180,16 @@ def forward(self, inputs):
y_col = F.softmax(y_col, dim=1)
y_dis = F.softmax(y_dis, dim=1)
x_col = torch.bmm(
- x_col.view(n, c, h * w), y_col.view(n, h * w, h * w)).view(
- n, c, h, w) * (1.0 / self.normalization_factor)
+ x_col.view(n, c, h * w), y_col.view(n, h * w, h * w)
+ ).view(n, c, h, w) * (1.0 / self.normalization_factor)
x_dis = torch.bmm(
- x_dis.view(n, c, h * w), y_dis.view(n, h * w, h * w)).view(
- n, c, h, w) * (1.0 / self.normalization_factor)
+ x_dis.view(n, c, h * w), y_dis.view(n, h * w, h * w)
+ ).view(n, c, h, w) * (1.0 / self.normalization_factor)
out = torch.cat([x_col, x_dis], 1)
out = self.proj(out)
out = resize(
- out,
- size=identity.shape[2:],
- mode='bilinear',
- align_corners=align_corners)
+ out, size=identity.shape[2:], mode="bilinear", align_corners=align_corners
+ )
out = self.bottleneck(torch.cat((identity, out), dim=1))
out = self.cls_seg(out)
return out
diff --git a/mmsegmentation/mmseg/models/decode_heads/psp_head.py b/mmsegmentation/mmseg/models/decode_heads/psp_head.py
index 6990676..20898b5 100644
--- a/mmsegmentation/mmseg/models/decode_heads/psp_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/psp_head.py
@@ -22,9 +22,18 @@ class PPM(nn.ModuleList):
align_corners (bool): align_corners argument of F.interpolate.
"""
- def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
- act_cfg, align_corners, **kwargs):
- super(PPM, self).__init__()
+ def __init__(
+ self,
+ pool_scales,
+ in_channels,
+ channels,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ align_corners,
+ **kwargs,
+ ):
+ super().__init__()
self.pool_scales = pool_scales
self.align_corners = align_corners
self.in_channels = in_channels
@@ -43,7 +52,10 @@ def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- **kwargs)))
+ **kwargs,
+ ),
+ )
+ )
def forward(self, x):
"""Forward function."""
@@ -53,8 +65,9 @@ def forward(self, x):
upsampled_ppm_out = resize(
ppm_out,
size=x.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
ppm_outs.append(upsampled_ppm_out)
return ppm_outs
@@ -72,7 +85,7 @@ class PSPHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
- super(PSPHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
assert isinstance(pool_scales, (list, tuple))
self.pool_scales = pool_scales
self.psp_modules = PPM(
@@ -82,7 +95,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
self.bottleneck = ConvModule(
self.in_channels + len(pool_scales) * self.channels,
self.channels,
@@ -90,7 +104,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def _forward_feature(self, inputs):
"""Forward function for feature maps before classifying each pixel with
diff --git a/mmsegmentation/mmseg/models/decode_heads/segformer_head.py b/mmsegmentation/mmseg/models/decode_heads/segformer_head.py
index d6e172e..1c2b53d 100644
--- a/mmsegmentation/mmseg/models/decode_heads/segformer_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/segformer_head.py
@@ -98,8 +98,8 @@ class SegformerHead(BaseDecodeHead):
Default: 'bilinear'.
"""
- def __init__(self, interpolate_mode='bilinear', **kwargs):
- super().__init__(input_transform='multiple_select', **kwargs)
+ def __init__(self, interpolate_mode="bilinear", **kwargs):
+ super().__init__(input_transform="multiple_select", **kwargs)
self.interpolate_mode = interpolate_mode
num_inputs = len(self.in_channels)
@@ -115,13 +115,16 @@ def __init__(self, interpolate_mode='bilinear', **kwargs):
kernel_size=1,
stride=1,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ )
+ )
self.fusion_conv = ConvModule(
in_channels=self.channels * num_inputs,
out_channels=self.channels,
kernel_size=1,
- norm_cfg=self.norm_cfg)
+ norm_cfg=self.norm_cfg,
+ )
def forward(self, inputs):
# Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
@@ -135,7 +138,9 @@ def forward(self, inputs):
input=conv(x),
size=inputs[0].shape[2:],
mode=self.interpolate_mode,
- align_corners=self.align_corners))
+ align_corners=self.align_corners,
+ )
+ )
out = self.fusion_conv(torch.cat(outs, dim=1))
diff --git a/mmsegmentation/mmseg/models/decode_heads/segmenter_mask_head.py b/mmsegmentation/mmseg/models/decode_heads/segmenter_mask_head.py
index 6a9b3d4..d330a32 100644
--- a/mmsegmentation/mmseg/models/decode_heads/segmenter_mask_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/segmenter_mask_head.py
@@ -3,8 +3,7 @@
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_norm_layer
-from mmcv.cnn.utils.weight_init import (constant_init, trunc_normal_,
- trunc_normal_init)
+from mmcv.cnn.utils.weight_init import constant_init, trunc_normal_, trunc_normal_init
from mmcv.runner import ModuleList
from mmseg.models.backbones.vit import TransformerEncoderLayer
@@ -45,24 +44,23 @@ class SegmenterMaskTransformerHead(BaseDecodeHead):
"""
def __init__(
- self,
- in_channels,
- num_layers,
- num_heads,
- embed_dims,
- mlp_ratio=4,
- drop_path_rate=0.1,
- drop_rate=0.0,
- attn_drop_rate=0.0,
- num_fcs=2,
- qkv_bias=True,
- act_cfg=dict(type='GELU'),
- norm_cfg=dict(type='LN'),
- init_std=0.02,
- **kwargs,
+ self,
+ in_channels,
+ num_layers,
+ num_heads,
+ embed_dims,
+ mlp_ratio=4,
+ drop_path_rate=0.1,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ num_fcs=2,
+ qkv_bias=True,
+ act_cfg=dict(type="GELU"),
+ norm_cfg=dict(type="LN"),
+ init_std=0.02,
+ **kwargs,
):
- super(SegmenterMaskTransformerHead, self).__init__(
- in_channels=in_channels, **kwargs)
+ super().__init__(in_channels=in_channels, **kwargs)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_layers)]
self.layers = ModuleList()
@@ -80,23 +78,21 @@ def __init__(
act_cfg=act_cfg,
norm_cfg=norm_cfg,
batch_first=True,
- ))
+ )
+ )
self.dec_proj = nn.Linear(in_channels, embed_dims)
- self.cls_emb = nn.Parameter(
- torch.randn(1, self.num_classes, embed_dims))
+ self.cls_emb = nn.Parameter(torch.randn(1, self.num_classes, embed_dims))
self.patch_proj = nn.Linear(embed_dims, embed_dims, bias=False)
self.classes_proj = nn.Linear(embed_dims, embed_dims, bias=False)
- self.decoder_norm = build_norm_layer(
- norm_cfg, embed_dims, postfix=1)[1]
- self.mask_norm = build_norm_layer(
- norm_cfg, self.num_classes, postfix=2)[1]
+ self.decoder_norm = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
+ self.mask_norm = build_norm_layer(norm_cfg, self.num_classes, postfix=2)[1]
self.init_std = init_std
- delattr(self, 'conv_seg')
+ delattr(self, "conv_seg")
def init_weights(self):
trunc_normal_(self.cls_emb, std=self.init_std)
@@ -120,8 +116,8 @@ def forward(self, inputs):
x = layer(x)
x = self.decoder_norm(x)
- patches = self.patch_proj(x[:, :-self.num_classes])
- cls_seg_feat = self.classes_proj(x[:, -self.num_classes:])
+ patches = self.patch_proj(x[:, : -self.num_classes])
+ cls_seg_feat = self.classes_proj(x[:, -self.num_classes :])
patches = F.normalize(patches, dim=2, p=2)
cls_seg_feat = F.normalize(cls_seg_feat, dim=2, p=2)
diff --git a/mmsegmentation/mmseg/models/decode_heads/sep_aspp_head.py b/mmsegmentation/mmseg/models/decode_heads/sep_aspp_head.py
index 4e894e2..c13a66c 100644
--- a/mmsegmentation/mmseg/models/decode_heads/sep_aspp_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/sep_aspp_head.py
@@ -13,7 +13,7 @@ class DepthwiseSeparableASPPModule(ASPPModule):
conv."""
def __init__(self, **kwargs):
- super(DepthwiseSeparableASPPModule, self).__init__(**kwargs)
+ super().__init__(**kwargs)
for i, dilation in enumerate(self.dilations):
if dilation > 1:
self[i] = DepthwiseSeparableConvModule(
@@ -23,7 +23,8 @@ def __init__(self, **kwargs):
dilation=dilation,
padding=dilation,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
@HEADS.register_module()
@@ -41,7 +42,7 @@ class DepthwiseSeparableASPPHead(ASPPHead):
"""
def __init__(self, c1_in_channels, c1_channels, **kwargs):
- super(DepthwiseSeparableASPPHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
assert c1_in_channels >= 0
self.aspp_modules = DepthwiseSeparableASPPModule(
dilations=self.dilations,
@@ -49,7 +50,8 @@ def __init__(self, c1_in_channels, c1_channels, **kwargs):
channels=self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
if c1_in_channels > 0:
self.c1_bottleneck = ConvModule(
c1_in_channels,
@@ -57,7 +59,8 @@ def __init__(self, c1_in_channels, c1_channels, **kwargs):
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
else:
self.c1_bottleneck = None
self.sep_bottleneck = nn.Sequential(
@@ -67,14 +70,17 @@ def __init__(self, c1_in_channels, c1_channels, **kwargs):
3,
padding=1,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
+ act_cfg=self.act_cfg,
+ ),
DepthwiseSeparableConvModule(
self.channels,
self.channels,
3,
padding=1,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
+ act_cfg=self.act_cfg,
+ ),
+ )
def forward(self, inputs):
"""Forward function."""
@@ -83,8 +89,9 @@ def forward(self, inputs):
resize(
self.image_pool(x),
size=x.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
]
aspp_outs.extend(self.aspp_modules(x))
aspp_outs = torch.cat(aspp_outs, dim=1)
@@ -94,8 +101,9 @@ def forward(self, inputs):
output = resize(
input=output,
size=c1_output.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
output = torch.cat([output, c1_output], dim=1)
output = self.sep_bottleneck(output)
output = self.cls_seg(output)
diff --git a/mmsegmentation/mmseg/models/decode_heads/sep_fcn_head.py b/mmsegmentation/mmseg/models/decode_heads/sep_fcn_head.py
index 7f9658e..ffa5718 100644
--- a/mmsegmentation/mmseg/models/decode_heads/sep_fcn_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/sep_fcn_head.py
@@ -32,14 +32,15 @@ class DepthwiseSeparableFCNHead(FCNHead):
"""
def __init__(self, dw_act_cfg=None, **kwargs):
- super(DepthwiseSeparableFCNHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
self.convs[0] = DepthwiseSeparableConvModule(
self.in_channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
- dw_act_cfg=dw_act_cfg)
+ dw_act_cfg=dw_act_cfg,
+ )
for i in range(1, self.num_convs):
self.convs[i] = DepthwiseSeparableConvModule(
@@ -48,7 +49,8 @@ def __init__(self, dw_act_cfg=None, **kwargs):
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
- dw_act_cfg=dw_act_cfg)
+ dw_act_cfg=dw_act_cfg,
+ )
if self.concat_input:
self.conv_cat = DepthwiseSeparableConvModule(
@@ -57,4 +59,5 @@ def __init__(self, dw_act_cfg=None, **kwargs):
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg,
- dw_act_cfg=dw_act_cfg)
+ dw_act_cfg=dw_act_cfg,
+ )
diff --git a/mmsegmentation/mmseg/models/decode_heads/setr_mla_head.py b/mmsegmentation/mmseg/models/decode_heads/setr_mla_head.py
index 6bb94ae..c7fed60 100644
--- a/mmsegmentation/mmseg/models/decode_heads/setr_mla_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/setr_mla_head.py
@@ -21,8 +21,7 @@ class SETRMLAHead(BaseDecodeHead):
"""
def __init__(self, mla_channels=128, up_scale=4, **kwargs):
- super(SETRMLAHead, self).__init__(
- input_transform='multiple_select', **kwargs)
+ super().__init__(input_transform="multiple_select", **kwargs)
self.mla_channels = mla_channels
num_inputs = len(self.in_channels)
@@ -40,18 +39,23 @@ def __init__(self, mla_channels=128, up_scale=4, **kwargs):
kernel_size=3,
padding=1,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
+ act_cfg=self.act_cfg,
+ ),
ConvModule(
in_channels=mla_channels,
out_channels=mla_channels,
kernel_size=3,
padding=1,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
+ act_cfg=self.act_cfg,
+ ),
Upsample(
scale_factor=up_scale,
- mode='bilinear',
- align_corners=self.align_corners)))
+ mode="bilinear",
+ align_corners=self.align_corners,
+ ),
+ )
+ )
def forward(self, inputs):
inputs = self._transform_inputs(inputs)
diff --git a/mmsegmentation/mmseg/models/decode_heads/setr_up_head.py b/mmsegmentation/mmseg/models/decode_heads/setr_up_head.py
index 87e7ea7..3bc0453 100644
--- a/mmsegmentation/mmseg/models/decode_heads/setr_up_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/setr_up_head.py
@@ -25,23 +25,22 @@ class SETRUPHead(BaseDecodeHead):
type='Constant', val=1.0, bias=0, layer='LayerNorm').
"""
- def __init__(self,
- norm_layer=dict(type='LN', eps=1e-6, requires_grad=True),
- num_convs=1,
- up_scale=4,
- kernel_size=3,
- init_cfg=[
- dict(type='Constant', val=1.0, bias=0, layer='LayerNorm'),
- dict(
- type='Normal',
- std=0.01,
- override=dict(name='conv_seg'))
- ],
- **kwargs):
+ def __init__(
+ self,
+ norm_layer=dict(type="LN", eps=1e-6, requires_grad=True),
+ num_convs=1,
+ up_scale=4,
+ kernel_size=3,
+ init_cfg=[
+ dict(type="Constant", val=1.0, bias=0, layer="LayerNorm"),
+ dict(type="Normal", std=0.01, override=dict(name="conv_seg")),
+ ],
+ **kwargs,
+ ):
- assert kernel_size in [1, 3], 'kernel_size must be 1 or 3.'
+ assert kernel_size in [1, 3], "kernel_size must be 1 or 3."
- super(SETRUPHead, self).__init__(init_cfg=init_cfg, **kwargs)
+ super().__init__(init_cfg=init_cfg, **kwargs)
assert isinstance(self.in_channels, int)
@@ -60,11 +59,15 @@ def __init__(self,
stride=1,
padding=int(kernel_size - 1) // 2,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg),
+ act_cfg=self.act_cfg,
+ ),
Upsample(
scale_factor=up_scale,
- mode='bilinear',
- align_corners=self.align_corners)))
+ mode="bilinear",
+ align_corners=self.align_corners,
+ ),
+ )
+ )
in_channels = out_channels
def forward(self, x):
diff --git a/mmsegmentation/mmseg/models/decode_heads/stdc_head.py b/mmsegmentation/mmseg/models/decode_heads/stdc_head.py
index bddf1eb..995e072 100644
--- a/mmsegmentation/mmseg/models/decode_heads/stdc_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/stdc_head.py
@@ -17,19 +17,24 @@ class STDCHead(FCNHead):
"""
def __init__(self, boundary_threshold=0.1, **kwargs):
- super(STDCHead, self).__init__(**kwargs)
+ super().__init__(**kwargs)
self.boundary_threshold = boundary_threshold
# Using register buffer to make laplacian kernel on the same
# device of `seg_label`.
self.register_buffer(
- 'laplacian_kernel',
- torch.tensor([-1, -1, -1, -1, 8, -1, -1, -1, -1],
- dtype=torch.float32,
- requires_grad=False).reshape((1, 1, 3, 3)))
+ "laplacian_kernel",
+ torch.tensor(
+ [-1, -1, -1, -1, 8, -1, -1, -1, -1],
+ dtype=torch.float32,
+ requires_grad=False,
+ ).reshape((1, 1, 3, 3)),
+ )
self.fusion_kernel = torch.nn.Parameter(
- torch.tensor([[6. / 10], [3. / 10], [1. / 10]],
- dtype=torch.float32).reshape(1, 3, 1, 1),
- requires_grad=False)
+ torch.tensor(
+ [[6.0 / 10], [3.0 / 10], [1.0 / 10]], dtype=torch.float32
+ ).reshape(1, 3, 1, 1),
+ requires_grad=False,
+ )
def losses(self, seg_logit, seg_label):
"""Compute Detail Aggregation Loss."""
@@ -38,48 +43,47 @@ def losses(self, seg_logit, seg_label):
# codebase because it would not be added into computation graph
# after threshold operation.
seg_label = seg_label.to(self.laplacian_kernel)
- boundary_targets = F.conv2d(
- seg_label, self.laplacian_kernel, padding=1)
+ boundary_targets = F.conv2d(seg_label, self.laplacian_kernel, padding=1)
boundary_targets = boundary_targets.clamp(min=0)
boundary_targets[boundary_targets > self.boundary_threshold] = 1
boundary_targets[boundary_targets <= self.boundary_threshold] = 0
boundary_targets_x2 = F.conv2d(
- seg_label, self.laplacian_kernel, stride=2, padding=1)
+ seg_label, self.laplacian_kernel, stride=2, padding=1
+ )
boundary_targets_x2 = boundary_targets_x2.clamp(min=0)
boundary_targets_x4 = F.conv2d(
- seg_label, self.laplacian_kernel, stride=4, padding=1)
+ seg_label, self.laplacian_kernel, stride=4, padding=1
+ )
boundary_targets_x4 = boundary_targets_x4.clamp(min=0)
boundary_targets_x4_up = F.interpolate(
- boundary_targets_x4, boundary_targets.shape[2:], mode='nearest')
+ boundary_targets_x4, boundary_targets.shape[2:], mode="nearest"
+ )
boundary_targets_x2_up = F.interpolate(
- boundary_targets_x2, boundary_targets.shape[2:], mode='nearest')
+ boundary_targets_x2, boundary_targets.shape[2:], mode="nearest"
+ )
- boundary_targets_x2_up[
- boundary_targets_x2_up > self.boundary_threshold] = 1
- boundary_targets_x2_up[
- boundary_targets_x2_up <= self.boundary_threshold] = 0
+ boundary_targets_x2_up[boundary_targets_x2_up > self.boundary_threshold] = 1
+ boundary_targets_x2_up[boundary_targets_x2_up <= self.boundary_threshold] = 0
- boundary_targets_x4_up[
- boundary_targets_x4_up > self.boundary_threshold] = 1
- boundary_targets_x4_up[
- boundary_targets_x4_up <= self.boundary_threshold] = 0
+ boundary_targets_x4_up[boundary_targets_x4_up > self.boundary_threshold] = 1
+ boundary_targets_x4_up[boundary_targets_x4_up <= self.boundary_threshold] = 0
boundary_targets_pyramids = torch.stack(
- (boundary_targets, boundary_targets_x2_up, boundary_targets_x4_up),
- dim=1)
+ (boundary_targets, boundary_targets_x2_up, boundary_targets_x4_up), dim=1
+ )
boundary_targets_pyramids = boundary_targets_pyramids.squeeze(2)
- boundary_targets_pyramid = F.conv2d(boundary_targets_pyramids,
- self.fusion_kernel)
+ boundary_targets_pyramid = F.conv2d(
+ boundary_targets_pyramids, self.fusion_kernel
+ )
+ boundary_targets_pyramid[boundary_targets_pyramid > self.boundary_threshold] = 1
boundary_targets_pyramid[
- boundary_targets_pyramid > self.boundary_threshold] = 1
- boundary_targets_pyramid[
- boundary_targets_pyramid <= self.boundary_threshold] = 0
+ boundary_targets_pyramid <= self.boundary_threshold
+ ] = 0
- loss = super(STDCHead, self).losses(seg_logit,
- boundary_targets_pyramid.long())
+ loss = super().losses(seg_logit, boundary_targets_pyramid.long())
return loss
diff --git a/mmsegmentation/mmseg/models/decode_heads/uper_head.py b/mmsegmentation/mmseg/models/decode_heads/uper_head.py
index 06b152a..ee21a2c 100644
--- a/mmsegmentation/mmseg/models/decode_heads/uper_head.py
+++ b/mmsegmentation/mmseg/models/decode_heads/uper_head.py
@@ -22,8 +22,7 @@ class UPerHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
- super(UPerHead, self).__init__(
- input_transform='multiple_select', **kwargs)
+ super().__init__(input_transform="multiple_select", **kwargs)
# PSP Module
self.psp_modules = PPM(
pool_scales,
@@ -32,7 +31,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
self.bottleneck = ConvModule(
self.in_channels[-1] + len(pool_scales) * self.channels,
self.channels,
@@ -40,7 +40,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
# FPN Module
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
@@ -52,7 +53,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- inplace=False)
+ inplace=False,
+ )
fpn_conv = ConvModule(
self.channels,
self.channels,
@@ -61,7 +63,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- inplace=False)
+ inplace=False,
+ )
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
@@ -72,7 +75,8 @@ def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
+ act_cfg=self.act_cfg,
+ )
def psp_forward(self, inputs):
"""Forward function of PSP module."""
@@ -99,8 +103,7 @@ def _forward_feature(self, inputs):
# build laterals
laterals = [
- lateral_conv(inputs[i])
- for i, lateral_conv in enumerate(self.lateral_convs)
+ lateral_conv(inputs[i]) for i, lateral_conv in enumerate(self.lateral_convs)
]
laterals.append(self.psp_forward(inputs))
@@ -112,13 +115,13 @@ def _forward_feature(self, inputs):
laterals[i - 1] = laterals[i - 1] + resize(
laterals[i],
size=prev_shape,
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
# build outputs
fpn_outs = [
- self.fpn_convs[i](laterals[i])
- for i in range(used_backbone_levels - 1)
+ self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)
]
# append psp feature
fpn_outs.append(laterals[-1])
@@ -127,8 +130,9 @@ def _forward_feature(self, inputs):
fpn_outs[i] = resize(
fpn_outs[i],
size=fpn_outs[0].shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
fpn_outs = torch.cat(fpn_outs, dim=1)
feats = self.fpn_bottleneck(fpn_outs)
return feats
diff --git a/mmsegmentation/mmseg/models/losses/__init__.py b/mmsegmentation/mmseg/models/losses/__init__.py
index d7e0197..84bfbff 100644
--- a/mmsegmentation/mmseg/models/losses/__init__.py
+++ b/mmsegmentation/mmseg/models/losses/__init__.py
@@ -1,7 +1,11 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
-from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
- cross_entropy, mask_cross_entropy)
+from .cross_entropy_loss import (
+ CrossEntropyLoss,
+ binary_cross_entropy,
+ cross_entropy,
+ mask_cross_entropy,
+)
from .dice_loss import DiceLoss
from .focal_loss import FocalLoss
from .lovasz_loss import LovaszLoss
@@ -9,8 +13,17 @@
from .utils import reduce_loss, weight_reduce_loss, weighted_loss
__all__ = [
- 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
- 'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
- 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss',
- 'FocalLoss', 'TverskyLoss'
+ "accuracy",
+ "Accuracy",
+ "cross_entropy",
+ "binary_cross_entropy",
+ "mask_cross_entropy",
+ "CrossEntropyLoss",
+ "reduce_loss",
+ "weight_reduce_loss",
+ "weighted_loss",
+ "LovaszLoss",
+ "DiceLoss",
+ "FocalLoss",
+ "TverskyLoss",
]
diff --git a/mmsegmentation/mmseg/models/losses/accuracy.py b/mmsegmentation/mmseg/models/losses/accuracy.py
index 1d9e2d7..9900ce9 100644
--- a/mmsegmentation/mmseg/models/losses/accuracy.py
+++ b/mmsegmentation/mmseg/models/losses/accuracy.py
@@ -25,19 +25,18 @@ def accuracy(pred, target, topk=1, thresh=None, ignore_index=None):
"""
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
- topk = (topk, )
+ topk = (topk,)
return_single = True
else:
return_single = False
maxk = max(topk)
if pred.size(0) == 0:
- accu = [pred.new_tensor(0.) for i in range(len(topk))]
+ accu = [pred.new_tensor(0.0) for i in range(len(topk))]
return accu[0] if return_single else accu
assert pred.ndim == target.ndim + 1
assert pred.size(0) == target.size(0)
- assert maxk <= pred.size(1), \
- f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
+ assert maxk <= pred.size(1), f"maxk {maxk} exceeds pred dimension {pred.size(1)}"
pred_value, pred_label = pred.topk(maxk, dim=1)
# transpose to shape (maxk, N, ...)
pred_label = pred_label.transpose(0, 1)
@@ -64,7 +63,7 @@ def accuracy(pred, target, topk=1, thresh=None, ignore_index=None):
class Accuracy(nn.Module):
"""Accuracy calculation module."""
- def __init__(self, topk=(1, ), thresh=None, ignore_index=None):
+ def __init__(self, topk=(1,), thresh=None, ignore_index=None):
"""Module to calculate the accuracy.
Args:
@@ -88,5 +87,4 @@ def forward(self, pred, target):
Returns:
tuple[float]: The accuracies under different topk criterions.
"""
- return accuracy(pred, target, self.topk, self.thresh,
- self.ignore_index)
+ return accuracy(pred, target, self.topk, self.thresh, self.ignore_index)
diff --git a/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py b/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py
index fe7b4a2..37441b2 100644
--- a/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py
+++ b/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py
@@ -9,14 +9,16 @@
from .utils import get_class_weight, weight_reduce_loss
-def cross_entropy(pred,
- label,
- weight=None,
- class_weight=None,
- reduction='mean',
- avg_factor=None,
- ignore_index=-100,
- avg_non_ignore=False):
+def cross_entropy(
+ pred,
+ label,
+ weight=None,
+ class_weight=None,
+ reduction="mean",
+ avg_factor=None,
+ ignore_index=-100,
+ avg_non_ignore=False,
+):
"""cross_entropy. The wrapper function for :func:`F.cross_entropy`
Args:
@@ -43,22 +45,20 @@ def cross_entropy(pred,
# class_weight is a manual rescaling weight given to each class.
# If given, has to be a Tensor of size C element-wise losses
loss = F.cross_entropy(
- pred,
- label,
- weight=class_weight,
- reduction='none',
- ignore_index=ignore_index)
+ pred, label, weight=class_weight, reduction="none", ignore_index=ignore_index
+ )
# apply weights and do the reduction
# average loss over non-ignored elements
# pytorch's official cross_entropy average loss over non-ignored elements
# refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660 # noqa
- if (avg_factor is None) and avg_non_ignore and reduction == 'mean':
+ if (avg_factor is None) and avg_non_ignore and reduction == "mean":
avg_factor = label.numel() - (label == ignore_index).sum().item()
if weight is not None:
weight = weight.float()
loss = weight_reduce_loss(
- loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
+ loss, weight=weight, reduction=reduction, avg_factor=avg_factor
+ )
return loss
@@ -86,15 +86,17 @@ def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
return bin_labels, bin_label_weights, valid_mask
-def binary_cross_entropy(pred,
- label,
- weight=None,
- reduction='mean',
- avg_factor=None,
- class_weight=None,
- ignore_index=-100,
- avg_non_ignore=False,
- **kwargs):
+def binary_cross_entropy(
+ pred,
+ label,
+ weight=None,
+ reduction="mean",
+ avg_factor=None,
+ class_weight=None,
+ ignore_index=-100,
+ avg_non_ignore=False,
+ **kwargs,
+):
"""Calculate the binary CrossEntropy loss.
Args:
@@ -121,19 +123,22 @@ def binary_cross_entropy(pred,
# As the ignore_index often set as 255, so the
# binary class label check should mask out
# ignore_index
- assert label[label != ignore_index].max() <= 1, \
- 'For pred with shape [N, 1, H, W], its label must have at ' \
- 'most 2 classes'
+ assert label[label != ignore_index].max() <= 1, (
+ "For pred with shape [N, 1, H, W], its label must have at " "most 2 classes"
+ )
pred = pred.squeeze(1)
if pred.dim() != label.dim():
assert (pred.dim() == 2 and label.dim() == 1) or (
- pred.dim() == 4 and label.dim() == 3), \
- 'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
- 'H, W], label shape [N, H, W] are supported'
+ pred.dim() == 4 and label.dim() == 3
+ ), (
+ "Only pred shape [N, C], label shape [N] or pred shape [N, C, "
+ "H, W], label shape [N, H, W] are supported"
+ )
# `weight` returned from `_expand_onehot_labels`
# has been treated for valid (non-ignore) pixels
label, weight, valid_mask = _expand_onehot_labels(
- label, weight, pred.shape, ignore_index)
+ label, weight, pred.shape, ignore_index
+ )
else:
# should mask out the ignored elements
valid_mask = ((label >= 0) & (label != ignore_index)).float()
@@ -142,26 +147,28 @@ def binary_cross_entropy(pred,
else:
weight = valid_mask
# average loss over non-ignored and valid elements
- if reduction == 'mean' and avg_factor is None and avg_non_ignore:
+ if reduction == "mean" and avg_factor is None and avg_non_ignore:
avg_factor = valid_mask.sum().item()
loss = F.binary_cross_entropy_with_logits(
- pred, label.float(), pos_weight=class_weight, reduction='none')
+ pred, label.float(), pos_weight=class_weight, reduction="none"
+ )
# do the reduction for the weighted loss
- loss = weight_reduce_loss(
- loss, weight, reduction=reduction, avg_factor=avg_factor)
+ loss = weight_reduce_loss(loss, weight, reduction=reduction, avg_factor=avg_factor)
return loss
-def mask_cross_entropy(pred,
- target,
- label,
- reduction='mean',
- avg_factor=None,
- class_weight=None,
- ignore_index=None,
- **kwargs):
+def mask_cross_entropy(
+ pred,
+ target,
+ label,
+ reduction="mean",
+ avg_factor=None,
+ class_weight=None,
+ ignore_index=None,
+ **kwargs,
+):
"""Calculate the CrossEntropy loss for masks.
Args:
@@ -183,14 +190,15 @@ def mask_cross_entropy(pred,
Returns:
torch.Tensor: The calculated loss
"""
- assert ignore_index is None, 'BCE loss does not support ignore_index'
+ assert ignore_index is None, "BCE loss does not support ignore_index"
# TODO: handle these two reserved arguments
- assert reduction == 'mean' and avg_factor is None
+ assert reduction == "mean" and avg_factor is None
num_rois = pred.size()[0]
inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
pred_slice = pred[inds, label].squeeze(1)
return F.binary_cross_entropy_with_logits(
- pred_slice, target, weight=class_weight, reduction='mean')[None]
+ pred_slice, target, weight=class_weight, reduction="mean"
+ )[None]
@LOSSES.register_module()
@@ -215,15 +223,17 @@ class CrossEntropyLoss(nn.Module):
`New in version 0.23.0.`
"""
- def __init__(self,
- use_sigmoid=False,
- use_mask=False,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0,
- loss_name='loss_ce',
- avg_non_ignore=False):
- super(CrossEntropyLoss, self).__init__()
+ def __init__(
+ self,
+ use_sigmoid=False,
+ use_mask=False,
+ reduction="mean",
+ class_weight=None,
+ loss_weight=1.0,
+ loss_name="loss_ce",
+ avg_non_ignore=False,
+ ):
+ super().__init__()
assert (use_sigmoid is False) or (use_mask is False)
self.use_sigmoid = use_sigmoid
self.use_mask = use_mask
@@ -231,12 +241,13 @@ def __init__(self,
self.loss_weight = loss_weight
self.class_weight = get_class_weight(class_weight)
self.avg_non_ignore = avg_non_ignore
- if not self.avg_non_ignore and self.reduction == 'mean':
+ if not self.avg_non_ignore and self.reduction == "mean":
warnings.warn(
- 'Default ``avg_non_ignore`` is False, if you would like to '
- 'ignore the certain label and average loss over non-ignore '
- 'labels, which is the same with PyTorch official '
- 'cross_entropy, set ``avg_non_ignore=True``.')
+ "Default ``avg_non_ignore`` is False, if you would like to "
+ "ignore the certain label and average loss over non-ignore "
+ "labels, which is the same with PyTorch official "
+ "cross_entropy, set ``avg_non_ignore=True``."
+ )
if self.use_sigmoid:
self.cls_criterion = binary_cross_entropy
@@ -248,21 +259,22 @@ def __init__(self,
def extra_repr(self):
"""Extra repr."""
- s = f'avg_non_ignore={self.avg_non_ignore}'
+ s = f"avg_non_ignore={self.avg_non_ignore}"
return s
- def forward(self,
- cls_score,
- label,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- ignore_index=-100,
- **kwargs):
+ def forward(
+ self,
+ cls_score,
+ label,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ ignore_index=-100,
+ **kwargs,
+ ):
"""Forward function."""
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
+ assert reduction_override in (None, "none", "mean", "sum")
+ reduction = reduction_override if reduction_override else self.reduction
if self.class_weight is not None:
class_weight = cls_score.new_tensor(self.class_weight)
else:
@@ -277,7 +289,8 @@ def forward(self,
avg_factor=avg_factor,
avg_non_ignore=self.avg_non_ignore,
ignore_index=ignore_index,
- **kwargs)
+ **kwargs,
+ )
return loss_cls
@property
diff --git a/mmsegmentation/mmseg/models/losses/dice_loss.py b/mmsegmentation/mmseg/models/losses/dice_loss.py
index a294bc2..5975157 100644
--- a/mmsegmentation/mmseg/models/losses/dice_loss.py
+++ b/mmsegmentation/mmseg/models/losses/dice_loss.py
@@ -10,13 +10,9 @@
@weighted_loss
-def dice_loss(pred,
- target,
- valid_mask,
- smooth=1,
- exponent=2,
- class_weight=None,
- ignore_index=255):
+def dice_loss(
+ pred, target, valid_mask, smooth=1, exponent=2, class_weight=None, ignore_index=255
+):
assert pred.shape[0] == target.shape[0]
total_loss = 0
num_classes = pred.shape[1]
@@ -27,7 +23,8 @@ def dice_loss(pred,
target[..., i],
valid_mask=valid_mask,
smooth=smooth,
- exponent=exponent)
+ exponent=exponent,
+ )
if class_weight is not None:
dice_loss *= class_weight[i]
total_loss += dice_loss
@@ -71,16 +68,18 @@ class DiceLoss(nn.Module):
prefix of the name. Defaults to 'loss_dice'.
"""
- def __init__(self,
- smooth=1,
- exponent=2,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0,
- ignore_index=255,
- loss_name='loss_dice',
- **kwargs):
- super(DiceLoss, self).__init__()
+ def __init__(
+ self,
+ smooth=1,
+ exponent=2,
+ reduction="mean",
+ class_weight=None,
+ loss_weight=1.0,
+ ignore_index=255,
+ loss_name="loss_dice",
+ **kwargs,
+ ):
+ super().__init__()
self.smooth = smooth
self.exponent = exponent
self.reduction = reduction
@@ -89,15 +88,9 @@ def __init__(self,
self.ignore_index = ignore_index
self._loss_name = loss_name
- def forward(self,
- pred,
- target,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
+ def forward(self, pred, target, avg_factor=None, reduction_override=None, **kwargs):
+ assert reduction_override in (None, "none", "mean", "sum")
+ reduction = reduction_override if reduction_override else self.reduction
if self.class_weight is not None:
class_weight = pred.new_tensor(self.class_weight)
else:
@@ -106,8 +99,8 @@ def forward(self,
pred = F.softmax(pred, dim=1)
num_classes = pred.shape[1]
one_hot_target = F.one_hot(
- torch.clamp(target.long(), 0, num_classes - 1),
- num_classes=num_classes)
+ torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes
+ )
valid_mask = (target != self.ignore_index).long()
loss = self.loss_weight * dice_loss(
@@ -119,7 +112,8 @@ def forward(self,
smooth=self.smooth,
exponent=self.exponent,
class_weight=class_weight,
- ignore_index=self.ignore_index)
+ ignore_index=self.ignore_index,
+ )
return loss
@property
diff --git a/mmsegmentation/mmseg/models/losses/focal_loss.py b/mmsegmentation/mmseg/models/losses/focal_loss.py
index cd43ce5..9276b62 100644
--- a/mmsegmentation/mmseg/models/losses/focal_loss.py
+++ b/mmsegmentation/mmseg/models/losses/focal_loss.py
@@ -10,16 +10,18 @@
# This method is used when cuda is not available
-def py_sigmoid_focal_loss(pred,
- target,
- one_hot_target=None,
- weight=None,
- gamma=2.0,
- alpha=0.5,
- class_weight=None,
- valid_mask=None,
- reduction='mean',
- avg_factor=None):
+def py_sigmoid_focal_loss(
+ pred,
+ target,
+ one_hot_target=None,
+ weight=None,
+ gamma=2.0,
+ alpha=0.5,
+ class_weight=None,
+ valid_mask=None,
+ reduction="mean",
+ avg_factor=None,
+):
"""PyTorch version of `Focal Loss `_.
Args:
@@ -47,11 +49,14 @@ def py_sigmoid_focal_loss(pred,
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
one_minus_pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
- focal_weight = (alpha * target + (1 - alpha) *
- (1 - target)) * one_minus_pt.pow(gamma)
+ focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * one_minus_pt.pow(
+ gamma
+ )
- loss = F.binary_cross_entropy_with_logits(
- pred, target, reduction='none') * focal_weight
+ loss = (
+ F.binary_cross_entropy_with_logits(pred, target, reduction="none")
+ * focal_weight
+ )
final_weight = torch.ones(1, pred.size(1)).type_as(loss)
if weight is not None:
if weight.shape != loss.shape and weight.size(0) == loss.size(0):
@@ -68,16 +73,18 @@ def py_sigmoid_focal_loss(pred,
return loss
-def sigmoid_focal_loss(pred,
- target,
- one_hot_target,
- weight=None,
- gamma=2.0,
- alpha=0.5,
- class_weight=None,
- valid_mask=None,
- reduction='mean',
- avg_factor=None):
+def sigmoid_focal_loss(
+ pred,
+ target,
+ one_hot_target,
+ weight=None,
+ gamma=2.0,
+ alpha=0.5,
+ class_weight=None,
+ valid_mask=None,
+ reduction="mean",
+ avg_factor=None,
+):
r"""A wrapper of cuda version `Focal Loss
`_.
Args:
@@ -110,14 +117,20 @@ def sigmoid_focal_loss(pred,
# multiplying the loss by 2, the effect of setting alpha as 0.5 is
# undone. The alpha of type list is used to regulate the loss in the
# post-processing process.
- loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(),
- gamma, 0.5, None, 'none') * 2
+ loss = (
+ _sigmoid_focal_loss(
+ pred.contiguous(), target.contiguous(), gamma, 0.5, None, "none"
+ )
+ * 2
+ )
alpha = pred.new_tensor(alpha)
final_weight = final_weight * (
- alpha * one_hot_target + (1 - alpha) * (1 - one_hot_target))
+ alpha * one_hot_target + (1 - alpha) * (1 - one_hot_target)
+ )
else:
- loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(),
- gamma, alpha, None, 'none')
+ loss = _sigmoid_focal_loss(
+ pred.contiguous(), target.contiguous(), gamma, alpha, None, "none"
+ )
if weight is not None:
if weight.shape != loss.shape and weight.size(0) == loss.size(0):
# For most cases, weight is of shape (N, ),
@@ -136,14 +149,16 @@ def sigmoid_focal_loss(pred,
@LOSSES.register_module()
class FocalLoss(nn.Module):
- def __init__(self,
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.5,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0,
- loss_name='loss_focal'):
+ def __init__(
+ self,
+ use_sigmoid=True,
+ gamma=2.0,
+ alpha=0.5,
+ reduction="mean",
+ class_weight=None,
+ loss_weight=1.0,
+ loss_name="loss_focal",
+ ):
"""`Focal Loss `_
Args:
use_sigmoid (bool, optional): Whether to the prediction is
@@ -172,22 +187,26 @@ def __init__(self,
loss item to be included into the backward graph, `loss_` must
be the prefix of the name. Defaults to 'loss_focal'.
"""
- super(FocalLoss, self).__init__()
- assert use_sigmoid is True, \
- 'AssertionError: Only sigmoid focal loss supported now.'
- assert reduction in ('none', 'mean', 'sum'), \
- "AssertionError: reduction should be 'none', 'mean' or " \
- "'sum'"
- assert isinstance(alpha, (float, list)), \
- 'AssertionError: alpha should be of type float'
- assert isinstance(gamma, float), \
- 'AssertionError: gamma should be of type float'
- assert isinstance(loss_weight, float), \
- 'AssertionError: loss_weight should be of type float'
- assert isinstance(loss_name, str), \
- 'AssertionError: loss_name should be of type str'
- assert isinstance(class_weight, list) or class_weight is None, \
- 'AssertionError: class_weight must be None or of type list'
+ super().__init__()
+ assert (
+ use_sigmoid is True
+ ), "AssertionError: Only sigmoid focal loss supported now."
+ assert reduction in ("none", "mean", "sum"), (
+ "AssertionError: reduction should be 'none', 'mean' or " "'sum'"
+ )
+ assert isinstance(
+ alpha, (float, list)
+ ), "AssertionError: alpha should be of type float"
+ assert isinstance(gamma, float), "AssertionError: gamma should be of type float"
+ assert isinstance(
+ loss_weight, float
+ ), "AssertionError: loss_weight should be of type float"
+ assert isinstance(
+ loss_name, str
+ ), "AssertionError: loss_name should be of type str"
+ assert (
+ isinstance(class_weight, list) or class_weight is None
+ ), "AssertionError: class_weight must be None or of type list"
self.use_sigmoid = use_sigmoid
self.gamma = gamma
self.alpha = alpha
@@ -196,14 +215,16 @@ def __init__(self,
self.loss_weight = loss_weight
self._loss_name = loss_name
- def forward(self,
- pred,
- target,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- ignore_index=255,
- **kwargs):
+ def forward(
+ self,
+ pred,
+ target,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ ignore_index=255,
+ **kwargs,
+ ):
"""Forward function.
Args:
@@ -228,15 +249,13 @@ def forward(self,
Returns:
torch.Tensor: The calculated loss
"""
- assert isinstance(ignore_index, int), \
- 'ignore_index must be of type int'
- assert reduction_override in (None, 'none', 'mean', 'sum'), \
- "AssertionError: reduction should be 'none', 'mean' or " \
- "'sum'"
- assert pred.shape == target.shape or \
- (pred.size(0) == target.size(0) and
- pred.shape[2:] == target.shape[1:]), \
- "The shape of pred doesn't match the shape of target"
+ assert isinstance(ignore_index, int), "ignore_index must be of type int"
+ assert reduction_override in (None, "none", "mean", "sum"), (
+ "AssertionError: reduction should be 'none', 'mean' or " "'sum'"
+ )
+ assert pred.shape == target.shape or (
+ pred.size(0) == target.size(0) and pred.shape[2:] == target.shape[1:]
+ ), "The shape of pred doesn't match the shape of target"
original_shape = pred.shape
@@ -262,11 +281,9 @@ def forward(self,
target = target.view(-1).contiguous()
valid_mask = (target != ignore_index).view(-1, 1)
# avoid raising error when using F.one_hot()
- target = torch.where(target == ignore_index, target.new_tensor(0),
- target)
+ target = torch.where(target == ignore_index, target.new_tensor(0), target)
- reduction = (
- reduction_override if reduction_override else self.reduction)
+ reduction = reduction_override if reduction_override else self.reduction
if self.use_sigmoid:
num_classes = pred.size(1)
if torch.cuda.is_available() and pred.is_cuda:
@@ -282,8 +299,7 @@ def forward(self,
if target.dim() == 1:
target = F.one_hot(target, num_classes=num_classes)
else:
- valid_mask = (target.argmax(dim=1) != ignore_index).view(
- -1, 1)
+ valid_mask = (target.argmax(dim=1) != ignore_index).view(-1, 1)
calculate_loss_func = py_sigmoid_focal_loss
loss_cls = self.loss_weight * calculate_loss_func(
@@ -296,16 +312,17 @@ def forward(self,
class_weight=self.class_weight,
valid_mask=valid_mask,
reduction=reduction,
- avg_factor=avg_factor)
+ avg_factor=avg_factor,
+ )
- if reduction == 'none':
+ if reduction == "none":
# [N, C] -> [C, N]
loss_cls = loss_cls.transpose(0, 1)
# [C, N] -> [C, B, d1, d2, ...]
# original_shape: [B, C, d1, d2, ...]
- loss_cls = loss_cls.reshape(original_shape[1],
- original_shape[0],
- *original_shape[2:])
+ loss_cls = loss_cls.reshape(
+ original_shape[1], original_shape[0], *original_shape[2:]
+ )
# [C, B, d1, d2, ...] -> [B, C, d1, d2, ...]
loss_cls = loss_cls.transpose(0, 1).contiguous()
else:
diff --git a/mmsegmentation/mmseg/models/losses/lovasz_loss.py b/mmsegmentation/mmseg/models/losses/lovasz_loss.py
index 2bb0fad..2c8e930 100644
--- a/mmsegmentation/mmseg/models/losses/lovasz_loss.py
+++ b/mmsegmentation/mmseg/models/losses/lovasz_loss.py
@@ -21,7 +21,7 @@ def lovasz_grad(gt_sorted):
gts = gt_sorted.sum()
intersection = gts - gt_sorted.float().cumsum(0)
union = gts + (1 - gt_sorted).float().cumsum(0)
- jaccard = 1. - intersection / union
+ jaccard = 1.0 - intersection / union
if p > 1: # cover 1-pixel case
jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
return jaccard
@@ -34,7 +34,7 @@ def flatten_binary_logits(logits, labels, ignore_index=None):
labels = labels.view(-1)
if ignore_index is None:
return logits, labels
- valid = (labels != ignore_index)
+ valid = labels != ignore_index
vlogits = logits[valid]
vlabels = labels[valid]
return vlogits, vlabels
@@ -51,7 +51,7 @@ def flatten_probs(probs, labels, ignore_index=None):
labels = labels.view(-1)
if ignore_index is None:
return probs, labels
- valid = (labels != ignore_index)
+ valid = labels != ignore_index
vprobs = probs[valid.nonzero().squeeze()]
vlabels = labels[valid]
return vprobs, vlabels
@@ -70,9 +70,9 @@ def lovasz_hinge_flat(logits, labels):
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
- return logits.sum() * 0.
- signs = 2. * labels.float() - 1.
- errors = (1. - logits * signs)
+ return logits.sum() * 0.0
+ signs = 2.0 * labels.float() - 1.0
+ errors = 1.0 - logits * signs
errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
perm = perm.data
gt_sorted = labels[perm]
@@ -81,14 +81,16 @@ def lovasz_hinge_flat(logits, labels):
return loss
-def lovasz_hinge(logits,
- labels,
- classes='present',
- per_image=False,
- class_weight=None,
- reduction='mean',
- avg_factor=None,
- ignore_index=255):
+def lovasz_hinge(
+ logits,
+ labels,
+ classes="present",
+ per_image=False,
+ class_weight=None,
+ reduction="mean",
+ avg_factor=None,
+ ignore_index=255,
+):
"""Binary Lovasz hinge loss.
Args:
@@ -114,19 +116,20 @@ def lovasz_hinge(logits,
"""
if per_image:
loss = [
- lovasz_hinge_flat(*flatten_binary_logits(
- logit.unsqueeze(0), label.unsqueeze(0), ignore_index))
+ lovasz_hinge_flat(
+ *flatten_binary_logits(
+ logit.unsqueeze(0), label.unsqueeze(0), ignore_index
+ )
+ )
for logit, label in zip(logits, labels)
]
- loss = weight_reduce_loss(
- torch.stack(loss), None, reduction, avg_factor)
+ loss = weight_reduce_loss(torch.stack(loss), None, reduction, avg_factor)
else:
- loss = lovasz_hinge_flat(
- *flatten_binary_logits(logits, labels, ignore_index))
+ loss = lovasz_hinge_flat(*flatten_binary_logits(logits, labels, ignore_index))
return loss
-def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
+def lovasz_softmax_flat(probs, labels, classes="present", class_weight=None):
"""Multi-class Lovasz-Softmax loss.
Args:
@@ -144,17 +147,17 @@ def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
"""
if probs.numel() == 0:
# only void pixels, the gradients should be 0
- return probs * 0.
+ return probs * 0.0
C = probs.size(1)
losses = []
- class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
+ class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
for c in class_to_sum:
fg = (labels == c).float() # foreground for class c
- if (classes == 'present' and fg.sum() == 0):
+ if classes == "present" and fg.sum() == 0:
continue
if C == 1:
if len(classes) > 1:
- raise ValueError('Sigmoid output possible only with 1 class')
+ raise ValueError("Sigmoid output possible only with 1 class")
class_pred = probs[:, 0]
else:
class_pred = probs[:, c]
@@ -169,14 +172,16 @@ def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
return torch.stack(losses).mean()
-def lovasz_softmax(probs,
- labels,
- classes='present',
- per_image=False,
- class_weight=None,
- reduction='mean',
- avg_factor=None,
- ignore_index=255):
+def lovasz_softmax(
+ probs,
+ labels,
+ classes="present",
+ per_image=False,
+ class_weight=None,
+ reduction="mean",
+ avg_factor=None,
+ ignore_index=255,
+):
"""Multi-class Lovasz-Softmax loss.
Args:
@@ -206,19 +211,19 @@ def lovasz_softmax(probs,
if per_image:
loss = [
lovasz_softmax_flat(
- *flatten_probs(
- prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
+ *flatten_probs(prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
classes=classes,
- class_weight=class_weight)
+ class_weight=class_weight,
+ )
for prob, label in zip(probs, labels)
]
- loss = weight_reduce_loss(
- torch.stack(loss), None, reduction, avg_factor)
+ loss = weight_reduce_loss(torch.stack(loss), None, reduction, avg_factor)
else:
loss = lovasz_softmax_flat(
*flatten_probs(probs, labels, ignore_index),
classes=classes,
- class_weight=class_weight)
+ class_weight=class_weight,
+ )
return loss
@@ -249,25 +254,32 @@ class LovaszLoss(nn.Module):
prefix of the name. Defaults to 'loss_lovasz'.
"""
- def __init__(self,
- loss_type='multi_class',
- classes='present',
- per_image=False,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0,
- loss_name='loss_lovasz'):
- super(LovaszLoss, self).__init__()
- assert loss_type in ('binary', 'multi_class'), "loss_type should be \
+ def __init__(
+ self,
+ loss_type="multi_class",
+ classes="present",
+ per_image=False,
+ reduction="mean",
+ class_weight=None,
+ loss_weight=1.0,
+ loss_name="loss_lovasz",
+ ):
+ super().__init__()
+ assert loss_type in (
+ "binary",
+ "multi_class",
+ ), "loss_type should be \
'binary' or 'multi_class'."
- if loss_type == 'binary':
+ if loss_type == "binary":
self.cls_criterion = lovasz_hinge
else:
self.cls_criterion = lovasz_softmax
- assert classes in ('all', 'present') or mmcv.is_list_of(classes, int)
+ assert classes in ("all", "present") or mmcv.is_list_of(classes, int)
if not per_image:
- assert reduction == 'none', "reduction should be 'none' when \
+ assert (
+ reduction == "none"
+ ), "reduction should be 'none' when \
per_image is False."
self.classes = classes
@@ -277,17 +289,18 @@ def __init__(self,
self.class_weight = get_class_weight(class_weight)
self._loss_name = loss_name
- def forward(self,
- cls_score,
- label,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
+ def forward(
+ self,
+ cls_score,
+ label,
+ weight=None,
+ avg_factor=None,
+ reduction_override=None,
+ **kwargs,
+ ):
"""Forward function."""
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
+ assert reduction_override in (None, "none", "mean", "sum")
+ reduction = reduction_override if reduction_override else self.reduction
if self.class_weight is not None:
class_weight = cls_score.new_tensor(self.class_weight)
else:
@@ -305,7 +318,8 @@ def forward(self,
class_weight=class_weight,
reduction=reduction,
avg_factor=avg_factor,
- **kwargs)
+ **kwargs,
+ )
return loss_cls
@property
diff --git a/mmsegmentation/mmseg/models/losses/tversky_loss.py b/mmsegmentation/mmseg/models/losses/tversky_loss.py
index 4ad14f7..6f01d38 100644
--- a/mmsegmentation/mmseg/models/losses/tversky_loss.py
+++ b/mmsegmentation/mmseg/models/losses/tversky_loss.py
@@ -11,14 +11,16 @@
@weighted_loss
-def tversky_loss(pred,
- target,
- valid_mask,
- alpha=0.3,
- beta=0.7,
- smooth=1,
- class_weight=None,
- ignore_index=255):
+def tversky_loss(
+ pred,
+ target,
+ valid_mask,
+ alpha=0.3,
+ beta=0.7,
+ smooth=1,
+ class_weight=None,
+ ignore_index=255,
+):
assert pred.shape[0] == target.shape[0]
total_loss = 0
num_classes = pred.shape[1]
@@ -30,7 +32,8 @@ def tversky_loss(pred,
valid_mask=valid_mask,
alpha=alpha,
beta=beta,
- smooth=smooth)
+ smooth=smooth,
+ )
if class_weight is not None:
tversky_loss *= class_weight[i]
total_loss += tversky_loss
@@ -38,12 +41,7 @@ def tversky_loss(pred,
@weighted_loss
-def binary_tversky_loss(pred,
- target,
- valid_mask,
- alpha=0.3,
- beta=0.7,
- smooth=1):
+def binary_tversky_loss(pred, target, valid_mask, alpha=0.3, beta=0.7, smooth=1):
assert pred.shape[0] == target.shape[0]
pred = pred.reshape(pred.shape[0], -1)
target = target.reshape(target.shape[0], -1)
@@ -80,20 +78,22 @@ class TverskyLoss(nn.Module):
prefix of the name. Defaults to 'loss_tversky'.
"""
- def __init__(self,
- smooth=1,
- class_weight=None,
- loss_weight=1.0,
- ignore_index=255,
- alpha=0.3,
- beta=0.7,
- loss_name='loss_tversky'):
- super(TverskyLoss, self).__init__()
+ def __init__(
+ self,
+ smooth=1,
+ class_weight=None,
+ loss_weight=1.0,
+ ignore_index=255,
+ alpha=0.3,
+ beta=0.7,
+ loss_name="loss_tversky",
+ ):
+ super().__init__()
self.smooth = smooth
self.class_weight = get_class_weight(class_weight)
self.loss_weight = loss_weight
self.ignore_index = ignore_index
- assert (alpha + beta == 1.0), 'Sum of alpha and beta but be 1.0!'
+ assert alpha + beta == 1.0, "Sum of alpha and beta but be 1.0!"
self.alpha = alpha
self.beta = beta
self._loss_name = loss_name
@@ -107,8 +107,8 @@ def forward(self, pred, target, **kwargs):
pred = F.softmax(pred, dim=1)
num_classes = pred.shape[1]
one_hot_target = F.one_hot(
- torch.clamp(target.long(), 0, num_classes - 1),
- num_classes=num_classes)
+ torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes
+ )
valid_mask = (target != self.ignore_index).long()
loss = self.loss_weight * tversky_loss(
@@ -119,7 +119,8 @@ def forward(self, pred, target, **kwargs):
beta=self.beta,
smooth=self.smooth,
class_weight=class_weight,
- ignore_index=self.ignore_index)
+ ignore_index=self.ignore_index,
+ )
return loss
@property
diff --git a/mmsegmentation/mmseg/models/losses/utils.py b/mmsegmentation/mmseg/models/losses/utils.py
index 621f57c..3c136fb 100644
--- a/mmsegmentation/mmseg/models/losses/utils.py
+++ b/mmsegmentation/mmseg/models/losses/utils.py
@@ -16,7 +16,7 @@ def get_class_weight(class_weight):
"""
if isinstance(class_weight, str):
# take it as a file path
- if class_weight.endswith('.npy'):
+ if class_weight.endswith(".npy"):
class_weight = np.load(class_weight)
else:
# pkl, json or yaml
@@ -45,7 +45,7 @@ def reduce_loss(loss, reduction):
return loss.sum()
-def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
+def weight_reduce_loss(loss, weight=None, reduction="mean", avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
@@ -69,13 +69,13 @@ def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
loss = reduce_loss(loss, reduction)
else:
# if reduction is mean, then average the loss by avg_factor
- if reduction == 'mean':
+ if reduction == "mean":
# Avoid causing ZeroDivisionError when avg_factor is 0.0,
# i.e., all labels of an image belong to ignore index.
eps = torch.finfo(torch.float32).eps
loss = loss.sum() / (avg_factor + eps)
# if reduction is 'none', then do nothing, otherwise raise an error
- elif reduction != 'none':
+ elif reduction != "none":
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
@@ -112,12 +112,7 @@ def weighted_loss(loss_func):
"""
@functools.wraps(loss_func)
- def wrapper(pred,
- target,
- weight=None,
- reduction='mean',
- avg_factor=None,
- **kwargs):
+ def wrapper(pred, target, weight=None, reduction="mean", avg_factor=None, **kwargs):
# get element-wise loss
loss = loss_func(pred, target, **kwargs)
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
diff --git a/mmsegmentation/mmseg/models/necks/__init__.py b/mmsegmentation/mmseg/models/necks/__init__.py
index ff03186..58ee82b 100644
--- a/mmsegmentation/mmseg/models/necks/__init__.py
+++ b/mmsegmentation/mmseg/models/necks/__init__.py
@@ -6,6 +6,4 @@
from .mla_neck import MLANeck
from .multilevel_neck import MultiLevelNeck
-__all__ = [
- 'FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU', 'Feature2Pyramid'
-]
+__all__ = ["FPN", "MultiLevelNeck", "MLANeck", "ICNeck", "JPU", "Feature2Pyramid"]
diff --git a/mmsegmentation/mmseg/models/necks/featurepyramid.py b/mmsegmentation/mmseg/models/necks/featurepyramid.py
index 82a00ce..b82a27c 100644
--- a/mmsegmentation/mmseg/models/necks/featurepyramid.py
+++ b/mmsegmentation/mmseg/models/necks/featurepyramid.py
@@ -19,27 +19,27 @@ class Feature2Pyramid(nn.Module):
Default: dict(type='SyncBN', requires_grad=True).
"""
- def __init__(self,
- embed_dim,
- rescales=[4, 2, 1, 0.5],
- norm_cfg=dict(type='SyncBN', requires_grad=True)):
- super(Feature2Pyramid, self).__init__()
+ def __init__(
+ self,
+ embed_dim,
+ rescales=[4, 2, 1, 0.5],
+ norm_cfg=dict(type="SyncBN", requires_grad=True),
+ ):
+ super().__init__()
self.rescales = rescales
self.upsample_4x = None
for k in self.rescales:
if k == 4:
self.upsample_4x = nn.Sequential(
- nn.ConvTranspose2d(
- embed_dim, embed_dim, kernel_size=2, stride=2),
+ nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
build_norm_layer(norm_cfg, embed_dim)[1],
nn.GELU(),
- nn.ConvTranspose2d(
- embed_dim, embed_dim, kernel_size=2, stride=2),
+ nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
)
elif k == 2:
self.upsample_2x = nn.Sequential(
- nn.ConvTranspose2d(
- embed_dim, embed_dim, kernel_size=2, stride=2))
+ nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2)
+ )
elif k == 1:
self.identity = nn.Identity()
elif k == 0.5:
@@ -47,20 +47,24 @@ def __init__(self,
elif k == 0.25:
self.downsample_4x = nn.MaxPool2d(kernel_size=4, stride=4)
else:
- raise KeyError(f'invalid {k} for feature2pyramid')
+ raise KeyError(f"invalid {k} for feature2pyramid")
def forward(self, inputs):
assert len(inputs) == len(self.rescales)
outputs = []
if self.upsample_4x is not None:
ops = [
- self.upsample_4x, self.upsample_2x, self.identity,
- self.downsample_2x
+ self.upsample_4x,
+ self.upsample_2x,
+ self.identity,
+ self.downsample_2x,
]
else:
ops = [
- self.upsample_2x, self.identity, self.downsample_2x,
- self.downsample_4x
+ self.upsample_2x,
+ self.identity,
+ self.downsample_2x,
+ self.downsample_4x,
]
for i in range(len(inputs)):
outputs.append(ops[i](inputs[i]))
diff --git a/mmsegmentation/mmseg/models/necks/fpn.py b/mmsegmentation/mmseg/models/necks/fpn.py
index 6997de9..ca73ab0 100644
--- a/mmsegmentation/mmseg/models/necks/fpn.py
+++ b/mmsegmentation/mmseg/models/necks/fpn.py
@@ -64,23 +64,24 @@ class FPN(BaseModule):
outputs[3].shape = torch.Size([1, 11, 43, 43])
"""
- def __init__(self,
- in_channels,
- out_channels,
- num_outs,
- start_level=0,
- end_level=-1,
- add_extra_convs=False,
- extra_convs_on_inputs=False,
- relu_before_extra_convs=False,
- no_norm_on_lateral=False,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=None,
- upsample_cfg=dict(mode='nearest'),
- init_cfg=dict(
- type='Xavier', layer='Conv2d', distribution='uniform')):
- super(FPN, self).__init__(init_cfg)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_outs,
+ start_level=0,
+ end_level=-1,
+ add_extra_convs=False,
+ extra_convs_on_inputs=False,
+ relu_before_extra_convs=False,
+ no_norm_on_lateral=False,
+ conv_cfg=None,
+ norm_cfg=None,
+ act_cfg=None,
+ upsample_cfg=dict(mode="nearest"),
+ init_cfg=dict(type="Xavier", layer="Conv2d", distribution="uniform"),
+ ):
+ super().__init__(init_cfg)
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
@@ -105,14 +106,14 @@ def __init__(self,
assert isinstance(add_extra_convs, (str, bool))
if isinstance(add_extra_convs, str):
# Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
- assert add_extra_convs in ('on_input', 'on_lateral', 'on_output')
+ assert add_extra_convs in ("on_input", "on_lateral", "on_output")
elif add_extra_convs: # True
if extra_convs_on_inputs:
# For compatibility with previous release
# TODO: deprecate `extra_convs_on_inputs`
- self.add_extra_convs = 'on_input'
+ self.add_extra_convs = "on_input"
else:
- self.add_extra_convs = 'on_output'
+ self.add_extra_convs = "on_output"
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
@@ -125,7 +126,8 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
act_cfg=act_cfg,
- inplace=False)
+ inplace=False,
+ )
fpn_conv = ConvModule(
out_channels,
out_channels,
@@ -134,7 +136,8 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- inplace=False)
+ inplace=False,
+ )
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
@@ -143,7 +146,7 @@ def __init__(self,
extra_levels = num_outs - self.backbone_end_level + self.start_level
if self.add_extra_convs and extra_levels >= 1:
for i in range(extra_levels):
- if i == 0 and self.add_extra_convs == 'on_input':
+ if i == 0 and self.add_extra_convs == "on_input":
in_channels = self.in_channels[self.backbone_end_level - 1]
else:
in_channels = out_channels
@@ -156,7 +159,8 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- inplace=False)
+ inplace=False,
+ )
self.fpn_convs.append(extra_fpn_conv)
@auto_fp16()
@@ -174,19 +178,19 @@ def forward(self, inputs):
for i in range(used_backbone_levels - 1, 0, -1):
# In some cases, fixing `scale factor` (e.g. 2) is preferred, but
# it cannot co-exist with `size` in `F.interpolate`.
- if 'scale_factor' in self.upsample_cfg:
+ if "scale_factor" in self.upsample_cfg:
laterals[i - 1] = laterals[i - 1] + resize(
- laterals[i], **self.upsample_cfg)
+ laterals[i], **self.upsample_cfg
+ )
else:
prev_shape = laterals[i - 1].shape[2:]
laterals[i - 1] = laterals[i - 1] + resize(
- laterals[i], size=prev_shape, **self.upsample_cfg)
+ laterals[i], size=prev_shape, **self.upsample_cfg
+ )
# build outputs
# part 1: from original levels
- outs = [
- self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
- ]
+ outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)]
# part 2: add extra levels
if self.num_outs > len(outs):
# use max pool to get more levels on top of outputs
@@ -196,11 +200,11 @@ def forward(self, inputs):
outs.append(F.max_pool2d(outs[-1], 1, stride=2))
# add conv layers on top of original feature maps (RetinaNet)
else:
- if self.add_extra_convs == 'on_input':
+ if self.add_extra_convs == "on_input":
extra_source = inputs[self.backbone_end_level - 1]
- elif self.add_extra_convs == 'on_lateral':
+ elif self.add_extra_convs == "on_lateral":
extra_source = laterals[-1]
- elif self.add_extra_convs == 'on_output':
+ elif self.add_extra_convs == "on_output":
extra_source = outs[-1]
else:
raise NotImplementedError
diff --git a/mmsegmentation/mmseg/models/necks/ic_neck.py b/mmsegmentation/mmseg/models/necks/ic_neck.py
index a5d81ce..8ed1c55 100644
--- a/mmsegmentation/mmseg/models/necks/ic_neck.py
+++ b/mmsegmentation/mmseg/models/necks/ic_neck.py
@@ -33,16 +33,18 @@ class CascadeFeatureFusion(BaseModule):
for Cascade Label Guidance in auxiliary heads.
"""
- def __init__(self,
- low_channels,
- high_channels,
- out_channels,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- align_corners=False,
- init_cfg=None):
- super(CascadeFeatureFusion, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ low_channels,
+ high_channels,
+ out_channels,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ align_corners=False,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.align_corners = align_corners
self.conv_low = ConvModule(
low_channels,
@@ -52,21 +54,24 @@ def __init__(self,
dilation=2,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.conv_high = ConvModule(
high_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, x_low, x_high):
x_low = resize(
x_low,
size=x_high.size()[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
# Note: Different from original paper, `x_low` is underwent
# `self.conv_low` rather than another 1x1 conv classifier
# before being used for auxiliary head.
@@ -100,17 +105,21 @@ class ICNeck(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels=(64, 256, 256),
- out_channels=128,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- align_corners=False,
- init_cfg=None):
- super(ICNeck, self).__init__(init_cfg=init_cfg)
- assert len(in_channels) == 3, 'Length of input channels \
- must be 3!'
+ def __init__(
+ self,
+ in_channels=(64, 256, 256),
+ out_channels=128,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ align_corners=False,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
+ assert (
+ len(in_channels) == 3
+ ), "Length of input channels \
+ must be 3!"
self.in_channels = in_channels
self.out_channels = out_channels
@@ -125,7 +134,8 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
self.cff_12 = CascadeFeatureFusion(
self.out_channels,
@@ -134,11 +144,14 @@ def __init__(self,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
- align_corners=self.align_corners)
+ align_corners=self.align_corners,
+ )
def forward(self, inputs):
- assert len(inputs) == 3, 'Length of input feature \
- maps must be 3!'
+ assert (
+ len(inputs) == 3
+ ), "Length of input feature \
+ maps must be 3!"
x_sub1, x_sub2, x_sub4 = inputs
x_cff_24, x_24 = self.cff_24(x_sub4, x_sub2)
diff --git a/mmsegmentation/mmseg/models/necks/jpu.py b/mmsegmentation/mmseg/models/necks/jpu.py
index 3cc6b9f..350cde7 100644
--- a/mmsegmentation/mmseg/models/necks/jpu.py
+++ b/mmsegmentation/mmseg/models/necks/jpu.py
@@ -40,18 +40,20 @@ class JPU(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels=(512, 1024, 2048),
- mid_channels=512,
- start_level=0,
- end_level=-1,
- dilations=(1, 2, 4, 8),
- align_corners=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- init_cfg=None):
- super(JPU, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels=(512, 1024, 2048),
+ mid_channels=512,
+ start_level=0,
+ end_level=-1,
+ dilations=(1, 2, 4, 8),
+ align_corners=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
assert isinstance(in_channels, tuple)
assert isinstance(dilations, tuple)
self.in_channels = in_channels
@@ -78,13 +80,15 @@ def __init__(self,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.conv_layers.append(conv_layer)
for i in range(len(dilations)):
dilation_layer = nn.Sequential(
DepthwiseSeparableConvModule(
- in_channels=(self.backbone_end_level - self.start_level) *
- self.mid_channels,
+ in_channels=(self.backbone_end_level - self.start_level)
+ * self.mid_channels,
out_channels=self.mid_channels,
kernel_size=3,
stride=1,
@@ -93,13 +97,17 @@ def __init__(self,
dw_norm_cfg=norm_cfg,
dw_act_cfg=None,
pw_norm_cfg=norm_cfg,
- pw_act_cfg=act_cfg))
+ pw_act_cfg=act_cfg,
+ )
+ )
self.dilation_layers.append(dilation_layer)
def forward(self, inputs):
"""Forward function."""
- assert len(inputs) == len(self.in_channels), 'Length of inputs must \
- be the same with self.in_channels!'
+ assert len(inputs) == len(
+ self.in_channels
+ ), "Length of inputs must \
+ be the same with self.in_channels!"
feats = [
self.conv_layers[i - self.start_level](inputs[i])
@@ -109,16 +117,13 @@ def forward(self, inputs):
h, w = feats[0].shape[2:]
for i in range(1, len(feats)):
feats[i] = resize(
- feats[i],
- size=(h, w),
- mode='bilinear',
- align_corners=self.align_corners)
+ feats[i], size=(h, w), mode="bilinear", align_corners=self.align_corners
+ )
feat = torch.cat(feats, dim=1)
- concat_feat = torch.cat([
- self.dilation_layers[i](feat) for i in range(len(self.dilations))
- ],
- dim=1)
+ concat_feat = torch.cat(
+ [self.dilation_layers[i](feat) for i in range(len(self.dilations))], dim=1
+ )
outs = []
diff --git a/mmsegmentation/mmseg/models/necks/mla_neck.py b/mmsegmentation/mmseg/models/necks/mla_neck.py
index 1513e29..86b6851 100644
--- a/mmsegmentation/mmseg/models/necks/mla_neck.py
+++ b/mmsegmentation/mmseg/models/necks/mla_neck.py
@@ -7,12 +7,14 @@
class MLAModule(nn.Module):
- def __init__(self,
- in_channels=[1024, 1024, 1024, 1024],
- out_channels=256,
- norm_cfg=None,
- act_cfg=None):
- super(MLAModule, self).__init__()
+ def __init__(
+ self,
+ in_channels=[1024, 1024, 1024, 1024],
+ out_channels=256,
+ norm_cfg=None,
+ act_cfg=None,
+ ):
+ super().__init__()
self.channel_proj = nn.ModuleList()
for i in range(len(in_channels)):
self.channel_proj.append(
@@ -21,7 +23,9 @@ def __init__(self,
out_channels=out_channels,
kernel_size=1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
self.feat_extract = nn.ModuleList()
for i in range(len(in_channels)):
self.feat_extract.append(
@@ -31,7 +35,9 @@ def __init__(self,
kernel_size=3,
padding=1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
def forward(self, inputs):
@@ -77,29 +83,34 @@ class MLANeck(nn.Module):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- norm_layer=dict(type='LN', eps=1e-6, requires_grad=True),
- norm_cfg=None,
- act_cfg=None):
- super(MLANeck, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ norm_layer=dict(type="LN", eps=1e-6, requires_grad=True),
+ norm_cfg=None,
+ act_cfg=None,
+ ):
+ super().__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
# In order to build general vision transformer backbone, we have to
# move MLA to neck.
- self.norm = nn.ModuleList([
- build_norm_layer(norm_layer, in_channels[i])[1]
- for i in range(len(in_channels))
- ])
+ self.norm = nn.ModuleList(
+ [
+ build_norm_layer(norm_layer, in_channels[i])[1]
+ for i in range(len(in_channels))
+ ]
+ )
self.mla = MLAModule(
in_channels=in_channels,
out_channels=out_channels,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, inputs):
assert len(inputs) == len(self.in_channels)
diff --git a/mmsegmentation/mmseg/models/necks/multilevel_neck.py b/mmsegmentation/mmseg/models/necks/multilevel_neck.py
index 5151f87..26345aa 100644
--- a/mmsegmentation/mmseg/models/necks/multilevel_neck.py
+++ b/mmsegmentation/mmseg/models/necks/multilevel_neck.py
@@ -22,13 +22,15 @@ class MultiLevelNeck(nn.Module):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- scales=[0.5, 1, 2, 4],
- norm_cfg=None,
- act_cfg=None):
- super(MultiLevelNeck, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ scales=[0.5, 1, 2, 4],
+ norm_cfg=None,
+ act_cfg=None,
+ ):
+ super().__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
@@ -43,7 +45,9 @@ def __init__(self,
out_channels,
kernel_size=1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
for _ in range(self.num_outs):
self.convs.append(
ConvModule(
@@ -53,26 +57,26 @@ def __init__(self,
padding=1,
stride=1,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
# default init_weights for conv(msra) and norm in ConvModule
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
+ xavier_init(m, distribution="uniform")
def forward(self, inputs):
assert len(inputs) == len(self.in_channels)
inputs = [
- lateral_conv(inputs[i])
- for i, lateral_conv in enumerate(self.lateral_convs)
+ lateral_conv(inputs[i]) for i, lateral_conv in enumerate(self.lateral_convs)
]
# for len(inputs) not equal to self.num_outs
if len(inputs) == 1:
inputs = [inputs[0] for _ in range(self.num_outs)]
outs = []
for i in range(self.num_outs):
- x_resize = resize(
- inputs[i], scale_factor=self.scales[i], mode='bilinear')
+ x_resize = resize(inputs[i], scale_factor=self.scales[i], mode="bilinear")
outs.append(self.convs[i](x_resize))
return tuple(outs)
diff --git a/mmsegmentation/mmseg/models/segmentors/__init__.py b/mmsegmentation/mmseg/models/segmentors/__init__.py
index 387c858..659bb16 100644
--- a/mmsegmentation/mmseg/models/segmentors/__init__.py
+++ b/mmsegmentation/mmseg/models/segmentors/__init__.py
@@ -3,4 +3,4 @@
from .cascade_encoder_decoder import CascadeEncoderDecoder
from .encoder_decoder import EncoderDecoder
-__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
+__all__ = ["BaseSegmentor", "EncoderDecoder", "CascadeEncoderDecoder"]
diff --git a/mmsegmentation/mmseg/models/segmentors/base.py b/mmsegmentation/mmseg/models/segmentors/base.py
index 76dc8f0..5033889 100644
--- a/mmsegmentation/mmseg/models/segmentors/base.py
+++ b/mmsegmentation/mmseg/models/segmentors/base.py
@@ -14,50 +14,44 @@ class BaseSegmentor(BaseModule, metaclass=ABCMeta):
"""Base class for segmentors."""
def __init__(self, init_cfg=None):
- super(BaseSegmentor, self).__init__(init_cfg)
+ super().__init__(init_cfg)
self.fp16_enabled = False
@property
def with_neck(self):
"""bool: whether the segmentor has neck"""
- return hasattr(self, 'neck') and self.neck is not None
+ return hasattr(self, "neck") and self.neck is not None
@property
def with_auxiliary_head(self):
"""bool: whether the segmentor has auxiliary head"""
- return hasattr(self,
- 'auxiliary_head') and self.auxiliary_head is not None
+ return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None
@property
def with_decode_head(self):
"""bool: whether the segmentor has decode head"""
- return hasattr(self, 'decode_head') and self.decode_head is not None
+ return hasattr(self, "decode_head") and self.decode_head is not None
@abstractmethod
def extract_feat(self, imgs):
"""Placeholder for extract features from images."""
- pass
@abstractmethod
def encode_decode(self, img, img_metas):
"""Placeholder for encode images with backbone and decode into a
semantic segmentation map of the same size as input."""
- pass
@abstractmethod
def forward_train(self, imgs, img_metas, **kwargs):
"""Placeholder for Forward function for training."""
- pass
@abstractmethod
def simple_test(self, img, img_meta, **kwargs):
"""Placeholder for single image test."""
- pass
@abstractmethod
def aug_test(self, imgs, img_metas, **kwargs):
"""Placeholder for augmentation test."""
- pass
def forward_test(self, imgs, img_metas, **kwargs):
"""
@@ -69,23 +63,24 @@ def forward_test(self, imgs, img_metas, **kwargs):
augs (multiscale, flip, etc.) and the inner list indicates
images in a batch.
"""
- for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+ for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
if not isinstance(var, list):
- raise TypeError(f'{name} must be a list, but got '
- f'{type(var)}')
+ raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
num_augs = len(imgs)
if num_augs != len(img_metas):
- raise ValueError(f'num of augmentations ({len(imgs)}) != '
- f'num of image meta ({len(img_metas)})')
+ raise ValueError(
+ f"num of augmentations ({len(imgs)}) != "
+ f"num of image meta ({len(img_metas)})"
+ )
# all images in the same aug batch all of the same ori_shape and pad
# shape
for img_meta in img_metas:
- ori_shapes = [_['ori_shape'] for _ in img_meta]
+ ori_shapes = [_["ori_shape"] for _ in img_meta]
assert all(shape == ori_shapes[0] for shape in ori_shapes)
- img_shapes = [_['img_shape'] for _ in img_meta]
+ img_shapes = [_["img_shape"] for _ in img_meta]
assert all(shape == img_shapes[0] for shape in img_shapes)
- pad_shapes = [_['pad_shape'] for _ in img_meta]
+ pad_shapes = [_["pad_shape"] for _ in img_meta]
assert all(shape == pad_shapes[0] for shape in pad_shapes)
if num_augs == 1:
@@ -93,7 +88,7 @@ def forward_test(self, imgs, img_metas, **kwargs):
else:
return self.aug_test(imgs, img_metas, **kwargs)
- @auto_fp16(apply_to=('img', ))
+ @auto_fp16(apply_to=("img",))
def forward(self, img, img_metas, return_loss=True, **kwargs):
"""Calls either :func:`forward_train` or :func:`forward_test` depending
on whether ``return_loss`` is ``True``.
@@ -139,9 +134,8 @@ def train_step(self, data_batch, optimizer, **kwargs):
loss, log_vars = self._parse_losses(losses)
outputs = dict(
- loss=loss,
- log_vars=log_vars,
- num_samples=len(data_batch['img_metas']))
+ loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"])
+ )
return outputs
@@ -157,13 +151,12 @@ def val_step(self, data_batch, optimizer=None, **kwargs):
log_vars_ = dict()
for loss_name, loss_value in log_vars.items():
- k = loss_name + '_val'
+ k = loss_name + "_val"
log_vars_[k] = loss_value
outputs = dict(
- loss=loss,
- log_vars=log_vars_,
- num_samples=len(data_batch['img_metas']))
+ loss=loss, log_vars=log_vars_, num_samples=len(data_batch["img_metas"])
+ )
return outputs
@@ -187,24 +180,27 @@ def _parse_losses(losses):
elif isinstance(loss_value, list):
log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
else:
- raise TypeError(
- f'{loss_name} is not a tensor or list of tensors')
+ raise TypeError(f"{loss_name} is not a tensor or list of tensors")
- loss = sum(_value for _key, _value in log_vars.items()
- if 'loss' in _key)
+ loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
# If the loss_vars has different length, raise assertion error
# to prevent GPUs from infinite waiting.
if dist.is_available() and dist.is_initialized():
log_var_length = torch.tensor(len(log_vars), device=loss.device)
dist.all_reduce(log_var_length)
- message = (f'rank {dist.get_rank()}' +
- f' len(log_vars): {len(log_vars)}' + ' keys: ' +
- ','.join(log_vars.keys()) + '\n')
- assert log_var_length == len(log_vars) * dist.get_world_size(), \
- 'loss log variables are different across GPUs!\n' + message
-
- log_vars['loss'] = loss
+ message = (
+ f"rank {dist.get_rank()}"
+ + f" len(log_vars): {len(log_vars)}"
+ + " keys: "
+ + ",".join(log_vars.keys())
+ + "\n"
+ )
+ assert log_var_length == len(log_vars) * dist.get_world_size(), (
+ "loss log variables are different across GPUs!\n" + message
+ )
+
+ log_vars["loss"] = loss
for loss_name, loss_value in log_vars.items():
# reduce loss when distributed training
if dist.is_available() and dist.is_initialized():
@@ -214,15 +210,17 @@ def _parse_losses(losses):
return loss, log_vars
- def show_result(self,
- img,
- result,
- palette=None,
- win_name='',
- show=False,
- wait_time=0,
- out_file=None,
- opacity=0.5):
+ def show_result(
+ self,
+ img,
+ result,
+ palette=None,
+ win_name="",
+ show=False,
+ wait_time=0,
+ out_file=None,
+ opacity=0.5,
+ ):
"""Draw `result` over `img`.
Args:
@@ -258,8 +256,7 @@ def show_result(self,
state = np.random.get_state()
np.random.seed(42)
# random palette
- palette = np.random.randint(
- 0, 255, size=(len(self.CLASSES), 3))
+ palette = np.random.randint(0, 255, size=(len(self.CLASSES), 3))
np.random.set_state(state)
else:
palette = self.PALETTE
@@ -286,6 +283,8 @@ def show_result(self,
mmcv.imwrite(img, out_file)
if not (show or out_file):
- warnings.warn('show==False and out_file is not specified, only '
- 'result image will be returned')
+ warnings.warn(
+ "show==False and out_file is not specified, only "
+ "result image will be returned"
+ )
return img
diff --git a/mmsegmentation/mmseg/models/segmentors/cascade_encoder_decoder.py b/mmsegmentation/mmseg/models/segmentors/cascade_encoder_decoder.py
index e9a9127..ba688f4 100644
--- a/mmsegmentation/mmseg/models/segmentors/cascade_encoder_decoder.py
+++ b/mmsegmentation/mmseg/models/segmentors/cascade_encoder_decoder.py
@@ -17,18 +17,20 @@ class CascadeEncoderDecoder(EncoderDecoder):
will be the input of next decoder_head.
"""
- def __init__(self,
- num_stages,
- backbone,
- decode_head,
- neck=None,
- auxiliary_head=None,
- train_cfg=None,
- test_cfg=None,
- pretrained=None,
- init_cfg=None):
+ def __init__(
+ self,
+ num_stages,
+ backbone,
+ decode_head,
+ neck=None,
+ auxiliary_head=None,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None,
+ init_cfg=None,
+ ):
self.num_stages = num_stages
- super(CascadeEncoderDecoder, self).__init__(
+ super().__init__(
backbone=backbone,
decode_head=decode_head,
neck=neck,
@@ -36,7 +38,8 @@ def __init__(self,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained,
- init_cfg=init_cfg)
+ init_cfg=init_cfg,
+ )
def _init_decode_head(self, decode_head):
"""Initialize ``decode_head``"""
@@ -55,13 +58,13 @@ def encode_decode(self, img, img_metas):
x = self.extract_feat(img)
out = self.decode_head[0].forward_test(x, img_metas, self.test_cfg)
for i in range(1, self.num_stages):
- out = self.decode_head[i].forward_test(x, out, img_metas,
- self.test_cfg)
+ out = self.decode_head[i].forward_test(x, out, img_metas, self.test_cfg)
out = resize(
input=out,
size=img.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
return out
def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
@@ -70,20 +73,24 @@ def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
losses = dict()
loss_decode = self.decode_head[0].forward_train(
- x, img_metas, gt_semantic_seg, self.train_cfg)
+ x, img_metas, gt_semantic_seg, self.train_cfg
+ )
- losses.update(add_prefix(loss_decode, 'decode_0'))
+ losses.update(add_prefix(loss_decode, "decode_0"))
for i in range(1, self.num_stages):
# forward test again, maybe unnecessary for most methods.
if i == 1:
prev_outputs = self.decode_head[0].forward_test(
- x, img_metas, self.test_cfg)
+ x, img_metas, self.test_cfg
+ )
else:
prev_outputs = self.decode_head[i - 1].forward_test(
- x, prev_outputs, img_metas, self.test_cfg)
+ x, prev_outputs, img_metas, self.test_cfg
+ )
loss_decode = self.decode_head[i].forward_train(
- x, prev_outputs, img_metas, gt_semantic_seg, self.train_cfg)
- losses.update(add_prefix(loss_decode, f'decode_{i}'))
+ x, prev_outputs, img_metas, gt_semantic_seg, self.train_cfg
+ )
+ losses.update(add_prefix(loss_decode, f"decode_{i}"))
return losses
diff --git a/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py b/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py
index e0ce8df..237501d 100644
--- a/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py
+++ b/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py
@@ -19,19 +19,22 @@ class EncoderDecoder(BaseSegmentor):
which could be dumped during inference.
"""
- def __init__(self,
- backbone,
- decode_head,
- neck=None,
- auxiliary_head=None,
- train_cfg=None,
- test_cfg=None,
- pretrained=None,
- init_cfg=None):
- super(EncoderDecoder, self).__init__(init_cfg)
+ def __init__(
+ self,
+ backbone,
+ decode_head,
+ neck=None,
+ auxiliary_head=None,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg)
if pretrained is not None:
- assert backbone.get('pretrained') is None, \
- 'both backbone and segmentor set pretrained weight'
+ assert (
+ backbone.get("pretrained") is None
+ ), "both backbone and segmentor set pretrained weight"
backbone.pretrained = pretrained
self.backbone = builder.build_backbone(backbone)
if neck is not None:
@@ -76,19 +79,20 @@ def encode_decode(self, img, img_metas):
out = resize(
input=out,
size=img.shape[2:],
- mode='bilinear',
- align_corners=self.align_corners)
+ mode="bilinear",
+ align_corners=self.align_corners,
+ )
return out
def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
"""Run forward function and calculate loss for decode head in
training."""
losses = dict()
- loss_decode = self.decode_head.forward_train(x, img_metas,
- gt_semantic_seg,
- self.train_cfg)
+ loss_decode = self.decode_head.forward_train(
+ x, img_metas, gt_semantic_seg, self.train_cfg
+ )
- losses.update(add_prefix(loss_decode, 'decode'))
+ losses.update(add_prefix(loss_decode, "decode"))
return losses
def _decode_head_forward_test(self, x, img_metas):
@@ -103,14 +107,15 @@ def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg):
losses = dict()
if isinstance(self.auxiliary_head, nn.ModuleList):
for idx, aux_head in enumerate(self.auxiliary_head):
- loss_aux = aux_head.forward_train(x, img_metas,
- gt_semantic_seg,
- self.train_cfg)
- losses.update(add_prefix(loss_aux, f'aux_{idx}'))
+ loss_aux = aux_head.forward_train(
+ x, img_metas, gt_semantic_seg, self.train_cfg
+ )
+ losses.update(add_prefix(loss_aux, f"aux_{idx}"))
else:
loss_aux = self.auxiliary_head.forward_train(
- x, img_metas, gt_semantic_seg, self.train_cfg)
- losses.update(add_prefix(loss_aux, 'aux'))
+ x, img_metas, gt_semantic_seg, self.train_cfg
+ )
+ losses.update(add_prefix(loss_aux, "aux"))
return losses
@@ -141,13 +146,11 @@ def forward_train(self, img, img_metas, gt_semantic_seg):
losses = dict()
- loss_decode = self._decode_head_forward_train(x, img_metas,
- gt_semantic_seg)
+ loss_decode = self._decode_head_forward_train(x, img_metas, gt_semantic_seg)
losses.update(loss_decode)
if self.with_auxiliary_head:
- loss_aux = self._auxiliary_head_forward_train(
- x, img_metas, gt_semantic_seg)
+ loss_aux = self._auxiliary_head_forward_train(x, img_metas, gt_semantic_seg)
losses.update(loss_aux)
return losses
@@ -178,27 +181,35 @@ def slide_inference(self, img, img_meta, rescale):
x1 = max(x2 - w_crop, 0)
crop_img = img[:, :, y1:y2, x1:x2]
crop_seg_logit = self.encode_decode(crop_img, img_meta)
- preds += F.pad(crop_seg_logit,
- (int(x1), int(preds.shape[3] - x2), int(y1),
- int(preds.shape[2] - y2)))
+ preds += F.pad(
+ crop_seg_logit,
+ (
+ int(x1),
+ int(preds.shape[3] - x2),
+ int(y1),
+ int(preds.shape[2] - y2),
+ ),
+ )
count_mat[:, :, y1:y2, x1:x2] += 1
assert (count_mat == 0).sum() == 0
if torch.onnx.is_in_onnx_export():
# cast count_mat to constant while exporting to ONNX
- count_mat = torch.from_numpy(
- count_mat.cpu().detach().numpy()).to(device=img.device)
+ count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(
+ device=img.device
+ )
preds = preds / count_mat
if rescale:
# remove padding area
- resize_shape = img_meta[0]['img_shape'][:2]
- preds = preds[:, :, :resize_shape[0], :resize_shape[1]]
+ resize_shape = img_meta[0]["img_shape"][:2]
+ preds = preds[:, :, : resize_shape[0], : resize_shape[1]]
preds = resize(
preds,
- size=img_meta[0]['ori_shape'][:2],
- mode='bilinear',
+ size=img_meta[0]["ori_shape"][:2],
+ mode="bilinear",
align_corners=self.align_corners,
- warning=False)
+ warning=False,
+ )
return preds
def whole_inference(self, img, img_meta, rescale):
@@ -211,15 +222,16 @@ def whole_inference(self, img, img_meta, rescale):
size = img.shape[2:]
else:
# remove padding area
- resize_shape = img_meta[0]['img_shape'][:2]
- seg_logit = seg_logit[:, :, :resize_shape[0], :resize_shape[1]]
- size = img_meta[0]['ori_shape'][:2]
+ resize_shape = img_meta[0]["img_shape"][:2]
+ seg_logit = seg_logit[:, :, : resize_shape[0], : resize_shape[1]]
+ size = img_meta[0]["ori_shape"][:2]
seg_logit = resize(
seg_logit,
size=size,
- mode='bilinear',
+ mode="bilinear",
align_corners=self.align_corners,
- warning=False)
+ warning=False,
+ )
return seg_logit
@@ -239,10 +251,10 @@ def inference(self, img, img_meta, rescale):
Tensor: The output segmentation map.
"""
- assert self.test_cfg.mode in ['slide', 'whole']
- ori_shape = img_meta[0]['ori_shape']
- assert all(_['ori_shape'] == ori_shape for _ in img_meta)
- if self.test_cfg.mode == 'slide':
+ assert self.test_cfg.mode in ["slide", "whole"]
+ ori_shape = img_meta[0]["ori_shape"]
+ assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+ if self.test_cfg.mode == "slide":
seg_logit = self.slide_inference(img, img_meta, rescale)
else:
seg_logit = self.whole_inference(img, img_meta, rescale)
@@ -250,14 +262,14 @@ def inference(self, img, img_meta, rescale):
output = F.sigmoid(seg_logit)
else:
output = F.softmax(seg_logit, dim=1)
- flip = img_meta[0]['flip']
+ flip = img_meta[0]["flip"]
if flip:
- flip_direction = img_meta[0]['flip_direction']
- assert flip_direction in ['horizontal', 'vertical']
- if flip_direction == 'horizontal':
- output = output.flip(dims=(3, ))
- elif flip_direction == 'vertical':
- output = output.flip(dims=(2, ))
+ flip_direction = img_meta[0]["flip_direction"]
+ assert flip_direction in ["horizontal", "vertical"]
+ if flip_direction == "horizontal":
+ output = output.flip(dims=(3,))
+ elif flip_direction == "vertical":
+ output = output.flip(dims=(2,))
return output
@@ -265,8 +277,7 @@ def simple_test(self, img, img_meta, rescale=True):
"""Simple test with single image."""
seg_logit = self.inference(img, img_meta, rescale)
if self.out_channels == 1:
- seg_pred = (seg_logit >
- self.decode_head.threshold).to(seg_logit).squeeze(1)
+ seg_pred = (seg_logit > self.decode_head.threshold).to(seg_logit).squeeze(1)
else:
seg_pred = seg_logit.argmax(dim=1)
if torch.onnx.is_in_onnx_export():
@@ -301,8 +312,7 @@ def aug_test(self, imgs, img_metas, rescale=True):
seg_logit += cur_seg_logit
seg_logit /= len(imgs)
if self.out_channels == 1:
- seg_pred = (seg_logit >
- self.decode_head.threshold).to(seg_logit).squeeze(1)
+ seg_pred = (seg_logit > self.decode_head.threshold).to(seg_logit).squeeze(1)
else:
seg_pred = seg_logit.argmax(dim=1)
seg_pred = seg_pred.cpu().numpy()
diff --git a/mmsegmentation/mmseg/models/utils/__init__.py b/mmsegmentation/mmseg/models/utils/__init__.py
index 6d83290..feb5e30 100644
--- a/mmsegmentation/mmseg/models/utils/__init__.py
+++ b/mmsegmentation/mmseg/models/utils/__init__.py
@@ -5,12 +5,20 @@
from .res_layer import ResLayer
from .se_layer import SELayer
from .self_attention_block import SelfAttentionBlock
-from .shape_convert import (nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc,
- nlc_to_nchw)
+from .shape_convert import nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc, nlc_to_nchw
from .up_conv_block import UpConvBlock
__all__ = [
- 'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
- 'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'PatchEmbed',
- 'nchw_to_nlc', 'nlc_to_nchw', 'nchw2nlc2nchw', 'nlc2nchw2nlc'
+ "ResLayer",
+ "SelfAttentionBlock",
+ "make_divisible",
+ "InvertedResidual",
+ "UpConvBlock",
+ "InvertedResidualV3",
+ "SELayer",
+ "PatchEmbed",
+ "nchw_to_nlc",
+ "nlc_to_nchw",
+ "nchw2nlc2nchw",
+ "nlc2nchw2nlc",
]
diff --git a/mmsegmentation/mmseg/models/utils/embed.py b/mmsegmentation/mmseg/models/utils/embed.py
index 1515675..247e802 100644
--- a/mmsegmentation/mmseg/models/utils/embed.py
+++ b/mmsegmentation/mmseg/models/utils/embed.py
@@ -40,11 +40,11 @@ class AdaptivePadding(nn.Module):
>>> assert (out.shape[2], out.shape[3]) == (16, 32)
"""
- def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
+ def __init__(self, kernel_size=1, stride=1, dilation=1, padding="corner"):
- super(AdaptivePadding, self).__init__()
+ super().__init__()
- assert padding in ('same', 'corner')
+ assert padding in ("same", "corner")
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
@@ -61,22 +61,25 @@ def get_pad_shape(self, input_shape):
stride_h, stride_w = self.stride
output_h = math.ceil(input_h / stride_h)
output_w = math.ceil(input_w / stride_w)
- pad_h = max((output_h - 1) * stride_h +
- (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
- pad_w = max((output_w - 1) * stride_w +
- (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+ pad_h = max(
+ (output_h - 1) * stride_h + (kernel_h - 1) * self.dilation[0] + 1 - input_h,
+ 0,
+ )
+ pad_w = max(
+ (output_w - 1) * stride_w + (kernel_w - 1) * self.dilation[1] + 1 - input_w,
+ 0,
+ )
return pad_h, pad_w
def forward(self, x):
pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
if pad_h > 0 or pad_w > 0:
- if self.padding == 'corner':
+ if self.padding == "corner":
x = F.pad(x, [0, pad_w, 0, pad_h])
- elif self.padding == 'same':
- x = F.pad(x, [
- pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
- pad_h - pad_h // 2
- ])
+ elif self.padding == "same":
+ x = F.pad(
+ x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
+ )
return x
@@ -108,19 +111,21 @@ class PatchEmbed(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels=3,
- embed_dims=768,
- conv_type='Conv2d',
- kernel_size=16,
- stride=None,
- padding='corner',
- dilation=1,
- bias=True,
- norm_cfg=None,
- input_size=None,
- init_cfg=None):
- super(PatchEmbed, self).__init__(init_cfg=init_cfg)
+ def __init__(
+ self,
+ in_channels=3,
+ embed_dims=768,
+ conv_type="Conv2d",
+ kernel_size=16,
+ stride=None,
+ padding="corner",
+ dilation=1,
+ bias=True,
+ norm_cfg=None,
+ input_size=None,
+ init_cfg=None,
+ ):
+ super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
if stride is None:
@@ -135,7 +140,8 @@ def __init__(self,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
- padding=padding)
+ padding=padding,
+ )
# disable the padding of conv
padding = 0
else:
@@ -150,7 +156,8 @@ def __init__(self,
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
if norm_cfg is not None:
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
@@ -171,10 +178,12 @@ def __init__(self,
input_size = (input_h, input_w)
# https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
- h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
- (kernel_size[0] - 1) - 1) // stride[0] + 1
- w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
- (kernel_size[1] - 1) - 1) // stride[1] + 1
+ h_out = (
+ input_size[0] + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1
+ ) // stride[0] + 1
+ w_out = (
+ input_size[1] + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1
+ ) // stride[1] + 1
self.init_out_size = (h_out, w_out)
else:
self.init_input_size = None
@@ -233,16 +242,18 @@ class PatchMerging(BaseModule):
Default: None.
"""
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=2,
- stride=None,
- padding='corner',
- dilation=1,
- bias=False,
- norm_cfg=dict(type='LN'),
- init_cfg=None):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size=2,
+ stride=None,
+ padding="corner",
+ dilation=1,
+ bias=False,
+ norm_cfg=dict(type="LN"),
+ init_cfg=None,
+ ):
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
@@ -260,7 +271,8 @@ def __init__(self,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
- padding=padding)
+ padding=padding,
+ )
# disable the padding of unfold
padding = 0
else:
@@ -268,10 +280,8 @@ def __init__(self,
padding = to_2tuple(padding)
self.sampler = nn.Unfold(
- kernel_size=kernel_size,
- dilation=dilation,
- padding=padding,
- stride=stride)
+ kernel_size=kernel_size, dilation=dilation, padding=padding, stride=stride
+ )
sample_dim = kernel_size[0] * kernel_size[1] * in_channels
@@ -297,13 +307,12 @@ def forward(self, x, input_size):
(Merged_H, Merged_W).
"""
B, L, C = x.shape
- assert isinstance(input_size, Sequence), f'Expect ' \
- f'input_size is ' \
- f'`Sequence` ' \
- f'but get {input_size}'
+ assert isinstance(input_size, Sequence), (
+ f"Expect " f"input_size is " f"`Sequence` " f"but get {input_size}"
+ )
H, W = input_size
- assert L == H * W, 'input feature has wrong size'
+ assert L == H * W, "input feature has wrong size"
x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W
# Use nn.Unfold to merge patch. About 25% faster than original method,
@@ -316,12 +325,18 @@ def forward(self, x, input_size):
x = self.sampler(x)
# if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
- out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
- (self.sampler.kernel_size[0] - 1) -
- 1) // self.sampler.stride[0] + 1
- out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
- (self.sampler.kernel_size[1] - 1) -
- 1) // self.sampler.stride[1] + 1
+ out_h = (
+ H
+ + 2 * self.sampler.padding[0]
+ - self.sampler.dilation[0] * (self.sampler.kernel_size[0] - 1)
+ - 1
+ ) // self.sampler.stride[0] + 1
+ out_w = (
+ W
+ + 2 * self.sampler.padding[1]
+ - self.sampler.dilation[1] * (self.sampler.kernel_size[1] - 1)
+ - 1
+ ) // self.sampler.stride[1] + 1
output_size = (out_h, out_w)
x = x.transpose(1, 2) # B, H/2*W/2, 4*C
diff --git a/mmsegmentation/mmseg/models/utils/inverted_residual.py b/mmsegmentation/mmseg/models/utils/inverted_residual.py
index c9cda76..e3be49c 100644
--- a/mmsegmentation/mmseg/models/utils/inverted_residual.py
+++ b/mmsegmentation/mmseg/models/utils/inverted_residual.py
@@ -29,21 +29,22 @@ class InvertedResidual(nn.Module):
Tensor: The output tensor.
"""
- def __init__(self,
- in_channels,
- out_channels,
- stride,
- expand_ratio,
- dilation=1,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU6'),
- with_cp=False,
- **kwargs):
- super(InvertedResidual, self).__init__()
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ stride,
+ expand_ratio,
+ dilation=1,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU6"),
+ with_cp=False,
+ **kwargs,
+ ):
+ super().__init__()
self.stride = stride
- assert stride in [1, 2], f'stride must in [1, 2]. ' \
- f'But received {stride}.'
+ assert stride in [1, 2], f"stride must in [1, 2]. " f"But received {stride}."
self.with_cp = with_cp
self.use_res_connect = self.stride == 1 and in_channels == out_channels
hidden_dim = int(round(in_channels * expand_ratio))
@@ -58,29 +59,35 @@ def __init__(self,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
- **kwargs))
- layers.extend([
- ConvModule(
- in_channels=hidden_dim,
- out_channels=hidden_dim,
- kernel_size=3,
- stride=stride,
- padding=dilation,
- dilation=dilation,
- groups=hidden_dim,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg,
- **kwargs),
- ConvModule(
- in_channels=hidden_dim,
- out_channels=out_channels,
- kernel_size=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=None,
- **kwargs)
- ])
+ **kwargs,
+ )
+ )
+ layers.extend(
+ [
+ ConvModule(
+ in_channels=hidden_dim,
+ out_channels=hidden_dim,
+ kernel_size=3,
+ stride=stride,
+ padding=dilation,
+ dilation=dilation,
+ groups=hidden_dim,
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ act_cfg=act_cfg,
+ **kwargs,
+ ),
+ ConvModule(
+ in_channels=hidden_dim,
+ out_channels=out_channels,
+ kernel_size=1,
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ act_cfg=None,
+ **kwargs,
+ ),
+ ]
+ )
self.conv = nn.Sequential(*layers)
def forward(self, x):
@@ -126,20 +133,22 @@ class InvertedResidualV3(nn.Module):
Tensor: The output tensor.
"""
- def __init__(self,
- in_channels,
- out_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- se_cfg=None,
- with_expand_conv=True,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- with_cp=False):
- super(InvertedResidualV3, self).__init__()
- self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ mid_channels,
+ kernel_size=3,
+ stride=1,
+ se_cfg=None,
+ with_expand_conv=True,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ with_cp=False,
+ ):
+ super().__init__()
+ self.with_res_shortcut = stride == 1 and in_channels == out_channels
assert stride in [1, 2]
self.with_cp = with_cp
self.with_se = se_cfg is not None
@@ -159,7 +168,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.depthwise_conv = ConvModule(
in_channels=mid_channels,
out_channels=mid_channels,
@@ -167,10 +177,10 @@ def __init__(self,
stride=stride,
padding=kernel_size // 2,
groups=mid_channels,
- conv_cfg=dict(
- type='Conv2dAdaptivePadding') if stride == 2 else conv_cfg,
+ conv_cfg=dict(type="Conv2dAdaptivePadding") if stride == 2 else conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
if self.with_se:
self.se = SELayer(**se_cfg)
@@ -183,7 +193,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=None)
+ act_cfg=None,
+ )
def forward(self, x):
diff --git a/mmsegmentation/mmseg/models/utils/res_layer.py b/mmsegmentation/mmseg/models/utils/res_layer.py
index 190a0c5..f53f063 100644
--- a/mmsegmentation/mmseg/models/utils/res_layer.py
+++ b/mmsegmentation/mmseg/models/utils/res_layer.py
@@ -25,19 +25,21 @@ class ResLayer(Sequential):
Default: False
"""
- def __init__(self,
- block,
- inplanes,
- planes,
- num_blocks,
- stride=1,
- dilation=1,
- avg_down=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- multi_grid=None,
- contract_dilation=False,
- **kwargs):
+ def __init__(
+ self,
+ block,
+ inplanes,
+ planes,
+ num_blocks,
+ stride=1,
+ dilation=1,
+ avg_down=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ multi_grid=None,
+ contract_dilation=False,
+ **kwargs,
+ ):
self.block = block
downsample = None
@@ -51,17 +53,22 @@ def __init__(self,
kernel_size=stride,
stride=stride,
ceil_mode=True,
- count_include_pad=False))
- downsample.extend([
- build_conv_layer(
- conv_cfg,
- inplanes,
- planes * block.expansion,
- kernel_size=1,
- stride=conv_stride,
- bias=False),
- build_norm_layer(norm_cfg, planes * block.expansion)[1]
- ])
+ count_include_pad=False,
+ )
+ )
+ downsample.extend(
+ [
+ build_conv_layer(
+ conv_cfg,
+ inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=conv_stride,
+ bias=False,
+ ),
+ build_norm_layer(norm_cfg, planes * block.expansion)[1],
+ ]
+ )
downsample = nn.Sequential(*downsample)
layers = []
@@ -81,7 +88,9 @@ def __init__(self,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- **kwargs))
+ **kwargs,
+ )
+ )
inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(
@@ -92,5 +101,7 @@ def __init__(self,
dilation=dilation if multi_grid is None else multi_grid[i],
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- **kwargs))
- super(ResLayer, self).__init__(*layers)
+ **kwargs,
+ )
+ )
+ super().__init__(*layers)
diff --git a/mmsegmentation/mmseg/models/utils/se_layer.py b/mmsegmentation/mmseg/models/utils/se_layer.py
index 16f52aa..ce86abe 100644
--- a/mmsegmentation/mmseg/models/utils/se_layer.py
+++ b/mmsegmentation/mmseg/models/utils/se_layer.py
@@ -24,13 +24,14 @@ class SELayer(nn.Module):
divisor=6.0)).
"""
- def __init__(self,
- channels,
- ratio=16,
- conv_cfg=None,
- act_cfg=(dict(type='ReLU'),
- dict(type='HSigmoid', bias=3.0, divisor=6.0))):
- super(SELayer, self).__init__()
+ def __init__(
+ self,
+ channels,
+ ratio=16,
+ conv_cfg=None,
+ act_cfg=(dict(type="ReLU"), dict(type="HSigmoid", bias=3.0, divisor=6.0)),
+ ):
+ super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
@@ -42,14 +43,16 @@ def __init__(self,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
- act_cfg=act_cfg[0])
+ act_cfg=act_cfg[0],
+ )
self.conv2 = ConvModule(
in_channels=make_divisible(channels // ratio, 8),
out_channels=channels,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
- act_cfg=act_cfg[1])
+ act_cfg=act_cfg[1],
+ )
def forward(self, x):
out = self.global_avgpool(x)
diff --git a/mmsegmentation/mmseg/models/utils/self_attention_block.py b/mmsegmentation/mmseg/models/utils/self_attention_block.py
index c945fa7..3679470 100644
--- a/mmsegmentation/mmseg/models/utils/self_attention_block.py
+++ b/mmsegmentation/mmseg/models/utils/self_attention_block.py
@@ -30,12 +30,26 @@ class SelfAttentionBlock(nn.Module):
act_cfg (dict|None): Config of activation layers.
"""
- def __init__(self, key_in_channels, query_in_channels, channels,
- out_channels, share_key_query, query_downsample,
- key_downsample, key_query_num_convs, value_out_num_convs,
- key_query_norm, value_out_norm, matmul_norm, with_out,
- conv_cfg, norm_cfg, act_cfg):
- super(SelfAttentionBlock, self).__init__()
+ def __init__(
+ self,
+ key_in_channels,
+ query_in_channels,
+ channels,
+ out_channels,
+ share_key_query,
+ query_downsample,
+ key_downsample,
+ key_query_num_convs,
+ value_out_num_convs,
+ key_query_norm,
+ value_out_norm,
+ matmul_norm,
+ with_out,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ ):
+ super().__init__()
if share_key_query:
assert key_in_channels == query_in_channels
self.key_in_channels = key_in_channels
@@ -53,7 +67,8 @@ def __init__(self, key_in_channels, query_in_channels, channels,
use_conv_module=key_query_norm,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
if share_key_query:
self.query_project = self.key_project
else:
@@ -64,7 +79,8 @@ def __init__(self, key_in_channels, query_in_channels, channels,
use_conv_module=key_query_norm,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
self.value_project = self.build_project(
key_in_channels,
channels if with_out else out_channels,
@@ -72,7 +88,8 @@ def __init__(self, key_in_channels, query_in_channels, channels,
use_conv_module=value_out_norm,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
if with_out:
self.out_project = self.build_project(
channels,
@@ -81,7 +98,8 @@ def __init__(self, key_in_channels, query_in_channels, channels,
use_conv_module=value_out_norm,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
else:
self.out_project = None
@@ -97,8 +115,16 @@ def init_weights(self):
if not isinstance(self.out_project, ConvModule):
constant_init(self.out_project, 0)
- def build_project(self, in_channels, channels, num_convs, use_conv_module,
- conv_cfg, norm_cfg, act_cfg):
+ def build_project(
+ self,
+ in_channels,
+ channels,
+ num_convs,
+ use_conv_module,
+ conv_cfg,
+ norm_cfg,
+ act_cfg,
+ ):
"""Build projection layer for key/query/value/out."""
if use_conv_module:
convs = [
@@ -108,7 +134,8 @@ def build_project(self, in_channels, channels, num_convs, use_conv_module,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
]
for _ in range(num_convs - 1):
convs.append(
@@ -118,7 +145,9 @@ def build_project(self, in_channels, channels, num_convs, use_conv_module,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg))
+ act_cfg=act_cfg,
+ )
+ )
else:
convs = [nn.Conv2d(in_channels, channels, 1)]
for _ in range(num_convs - 1):
@@ -149,7 +178,7 @@ def forward(self, query_feats, key_feats):
sim_map = torch.matmul(query, key)
if self.matmul_norm:
- sim_map = (self.channels**-.5) * sim_map
+ sim_map = (self.channels**-0.5) * sim_map
sim_map = F.softmax(sim_map, dim=-1)
context = torch.matmul(sim_map, value)
diff --git a/mmsegmentation/mmseg/models/utils/shape_convert.py b/mmsegmentation/mmseg/models/utils/shape_convert.py
index cce1e22..8aa8fa6 100644
--- a/mmsegmentation/mmseg/models/utils/shape_convert.py
+++ b/mmsegmentation/mmseg/models/utils/shape_convert.py
@@ -12,7 +12,7 @@ def nlc_to_nchw(x, hw_shape):
H, W = hw_shape
assert len(x.shape) == 3
B, L, C = x.shape
- assert L == H * W, 'The seq_len doesn\'t match H, W'
+ assert L == H * W, "The seq_len doesn't match H, W"
return x.transpose(1, 2).reshape(B, C, H, W)
@@ -95,7 +95,7 @@ def nlc2nchw2nlc(module, x, hw_shape, contiguous=False, **kwargs):
H, W = hw_shape
assert len(x.shape) == 3
B, L, C = x.shape
- assert L == H * W, 'The seq_len doesn\'t match H, W'
+ assert L == H * W, "The seq_len doesn't match H, W"
if not contiguous:
x = x.transpose(1, 2).reshape(B, C, H, W)
x = module(x, **kwargs)
diff --git a/mmsegmentation/mmseg/models/utils/up_conv_block.py b/mmsegmentation/mmseg/models/utils/up_conv_block.py
index d8396d9..6564b79 100644
--- a/mmsegmentation/mmseg/models/utils/up_conv_block.py
+++ b/mmsegmentation/mmseg/models/utils/up_conv_block.py
@@ -42,24 +42,26 @@ class UpConvBlock(nn.Module):
plugins (dict): plugins for convolutional layers. Default: None.
"""
- def __init__(self,
- conv_block,
- in_channels,
- skip_channels,
- out_channels,
- num_convs=2,
- stride=1,
- dilation=1,
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
- upsample_cfg=dict(type='InterpConv'),
- dcn=None,
- plugins=None):
- super(UpConvBlock, self).__init__()
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
+ def __init__(
+ self,
+ conv_block,
+ in_channels,
+ skip_channels,
+ out_channels,
+ num_convs=2,
+ stride=1,
+ dilation=1,
+ with_cp=False,
+ conv_cfg=None,
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
+ upsample_cfg=dict(type="InterpConv"),
+ dcn=None,
+ plugins=None,
+ ):
+ super().__init__()
+ assert dcn is None, "Not implemented yet."
+ assert plugins is None, "Not implemented yet."
self.conv_block = conv_block(
in_channels=2 * skip_channels,
@@ -72,7 +74,8 @@ def __init__(self,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
dcn=None,
- plugins=None)
+ plugins=None,
+ )
if upsample_cfg is not None:
self.upsample = build_upsample_layer(
cfg=upsample_cfg,
@@ -80,7 +83,8 @@ def __init__(self,
out_channels=skip_channels,
with_cp=with_cp,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
else:
self.upsample = ConvModule(
in_channels,
@@ -90,7 +94,8 @@ def __init__(self,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
- act_cfg=act_cfg)
+ act_cfg=act_cfg,
+ )
def forward(self, skip, x):
"""Forward function."""
diff --git a/mmsegmentation/mmseg/ops/__init__.py b/mmsegmentation/mmseg/ops/__init__.py
index bc075cd..a6a4761 100644
--- a/mmsegmentation/mmseg/ops/__init__.py
+++ b/mmsegmentation/mmseg/ops/__init__.py
@@ -2,4 +2,4 @@
from .encoding import Encoding
from .wrappers import Upsample, resize
-__all__ = ['Upsample', 'resize', 'Encoding']
+__all__ = ["Upsample", "resize", "Encoding"]
diff --git a/mmsegmentation/mmseg/ops/encoding.py b/mmsegmentation/mmseg/ops/encoding.py
index f397cc5..c8d944c 100644
--- a/mmsegmentation/mmseg/ops/encoding.py
+++ b/mmsegmentation/mmseg/ops/encoding.py
@@ -16,31 +16,32 @@ class Encoding(nn.Module):
"""
def __init__(self, channels, num_codes):
- super(Encoding, self).__init__()
+ super().__init__()
# init codewords and smoothing factor
self.channels, self.num_codes = channels, num_codes
- std = 1. / ((num_codes * channels)**0.5)
+ std = 1.0 / ((num_codes * channels) ** 0.5)
# [num_codes, channels]
self.codewords = nn.Parameter(
- torch.empty(num_codes, channels,
- dtype=torch.float).uniform_(-std, std),
- requires_grad=True)
+ torch.empty(num_codes, channels, dtype=torch.float).uniform_(-std, std),
+ requires_grad=True,
+ )
# [num_codes]
self.scale = nn.Parameter(
torch.empty(num_codes, dtype=torch.float).uniform_(-1, 0),
- requires_grad=True)
+ requires_grad=True,
+ )
@staticmethod
def scaled_l2(x, codewords, scale):
num_codes, channels = codewords.size()
batch_size = x.size(0)
reshaped_scale = scale.view((1, 1, num_codes))
- expanded_x = x.unsqueeze(2).expand(
- (batch_size, x.size(1), num_codes, channels))
+ expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1), num_codes, channels))
reshaped_codewords = codewords.view((1, 1, num_codes, channels))
- scaled_l2_norm = reshaped_scale * (
- expanded_x - reshaped_codewords).pow(2).sum(dim=3)
+ scaled_l2_norm = reshaped_scale * (expanded_x - reshaped_codewords).pow(2).sum(
+ dim=3
+ )
return scaled_l2_norm
@staticmethod
@@ -49,10 +50,10 @@ def aggregate(assignment_weights, x, codewords):
reshaped_codewords = codewords.view((1, 1, num_codes, channels))
batch_size = x.size(0)
- expanded_x = x.unsqueeze(2).expand(
- (batch_size, x.size(1), num_codes, channels))
- encoded_feat = (assignment_weights.unsqueeze(3) *
- (expanded_x - reshaped_codewords)).sum(dim=1)
+ expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1), num_codes, channels))
+ encoded_feat = (
+ assignment_weights.unsqueeze(3) * (expanded_x - reshaped_codewords)
+ ).sum(dim=1)
return encoded_feat
def forward(self, x):
@@ -63,13 +64,13 @@ def forward(self, x):
x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous()
# assignment_weights: [batch_size, channels, num_codes]
assignment_weights = F.softmax(
- self.scaled_l2(x, self.codewords, self.scale), dim=2)
+ self.scaled_l2(x, self.codewords, self.scale), dim=2
+ )
# aggregate
encoded_feat = self.aggregate(assignment_weights, x, self.codewords)
return encoded_feat
def __repr__(self):
repr_str = self.__class__.__name__
- repr_str += f'(Nx{self.channels}xHxW =>Nx{self.num_codes}' \
- f'x{self.channels})'
+ repr_str += f"(Nx{self.channels}xHxW =>Nx{self.num_codes}" f"x{self.channels})"
return repr_str
diff --git a/mmsegmentation/mmseg/ops/wrappers.py b/mmsegmentation/mmseg/ops/wrappers.py
index bcababd..98bb47f 100644
--- a/mmsegmentation/mmseg/ops/wrappers.py
+++ b/mmsegmentation/mmseg/ops/wrappers.py
@@ -5,36 +5,39 @@
import torch.nn.functional as F
-def resize(input,
- size=None,
- scale_factor=None,
- mode='nearest',
- align_corners=None,
- warning=True):
+def resize(
+ input,
+ size=None,
+ scale_factor=None,
+ mode="nearest",
+ align_corners=None,
+ warning=True,
+):
if warning:
if size is not None and align_corners:
input_h, input_w = tuple(int(x) for x in input.shape[2:])
output_h, output_w = tuple(int(x) for x in size)
if output_h > input_h or output_w > input_w:
- if ((output_h > 1 and output_w > 1 and input_h > 1
- and input_w > 1) and (output_h - 1) % (input_h - 1)
- and (output_w - 1) % (input_w - 1)):
+ if (
+ (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
+ and (output_h - 1) % (input_h - 1)
+ and (output_w - 1) % (input_w - 1)
+ ):
warnings.warn(
- f'When align_corners={align_corners}, '
- 'the output would more aligned if '
- f'input size {(input_h, input_w)} is `x+1` and '
- f'out size {(output_h, output_w)} is `nx+1`')
+ f"When align_corners={align_corners}, "
+ "the output would more aligned if "
+ f"input size {(input_h, input_w)} is `x+1` and "
+ f"out size {(output_h, output_w)} is `nx+1`"
+ )
return F.interpolate(input, size, scale_factor, mode, align_corners)
class Upsample(nn.Module):
- def __init__(self,
- size=None,
- scale_factor=None,
- mode='nearest',
- align_corners=None):
- super(Upsample, self).__init__()
+ def __init__(
+ self, size=None, scale_factor=None, mode="nearest", align_corners=None
+ ):
+ super().__init__()
self.size = size
if isinstance(scale_factor, tuple):
self.scale_factor = tuple(float(factor) for factor in scale_factor)
diff --git a/mmsegmentation/mmseg/utils/__init__.py b/mmsegmentation/mmseg/utils/__init__.py
index e3ef4b3..6589b41 100644
--- a/mmsegmentation/mmseg/utils/__init__.py
+++ b/mmsegmentation/mmseg/utils/__init__.py
@@ -6,6 +6,11 @@
from .util_distribution import build_ddp, build_dp, get_device
__all__ = [
- 'get_root_logger', 'collect_env', 'find_latest_checkpoint',
- 'setup_multi_processes', 'build_ddp', 'build_dp', 'get_device'
+ "get_root_logger",
+ "collect_env",
+ "find_latest_checkpoint",
+ "setup_multi_processes",
+ "build_ddp",
+ "build_dp",
+ "get_device",
]
diff --git a/mmsegmentation/mmseg/utils/collect_env.py b/mmsegmentation/mmseg/utils/collect_env.py
index 3379ecb..55a8e00 100644
--- a/mmsegmentation/mmseg/utils/collect_env.py
+++ b/mmsegmentation/mmseg/utils/collect_env.py
@@ -8,11 +8,11 @@
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
- env_info['MMSegmentation'] = f'{mmseg.__version__}+{get_git_hash()[:7]}'
+ env_info["MMSegmentation"] = f"{mmseg.__version__}+{get_git_hash()[:7]}"
return env_info
-if __name__ == '__main__':
+if __name__ == "__main__":
for name, val in collect_env().items():
- print('{}: {}'.format(name, val))
+ print(f"{name}: {val}")
diff --git a/mmsegmentation/mmseg/utils/logger.py b/mmsegmentation/mmseg/utils/logger.py
index 0cb3c78..00019e4 100644
--- a/mmsegmentation/mmseg/utils/logger.py
+++ b/mmsegmentation/mmseg/utils/logger.py
@@ -23,6 +23,6 @@ def get_root_logger(log_file=None, log_level=logging.INFO):
logging.Logger: The root logger.
"""
- logger = get_logger(name='mmseg', log_file=log_file, log_level=log_level)
+ logger = get_logger(name="mmseg", log_file=log_file, log_level=log_level)
return logger
diff --git a/mmsegmentation/mmseg/utils/misc.py b/mmsegmentation/mmseg/utils/misc.py
index bd1b6b1..e669ce2 100644
--- a/mmsegmentation/mmseg/utils/misc.py
+++ b/mmsegmentation/mmseg/utils/misc.py
@@ -4,7 +4,7 @@
import warnings
-def find_latest_checkpoint(path, suffix='pth'):
+def find_latest_checkpoint(path, suffix="pth"):
"""This function is for finding the latest checkpoint.
It will be used when automatically resume, modified from
@@ -20,21 +20,21 @@ def find_latest_checkpoint(path, suffix='pth'):
if not osp.exists(path):
warnings.warn("The path of the checkpoints doesn't exist.")
return None
- if osp.exists(osp.join(path, f'latest.{suffix}')):
- return osp.join(path, f'latest.{suffix}')
+ if osp.exists(osp.join(path, f"latest.{suffix}")):
+ return osp.join(path, f"latest.{suffix}")
- checkpoints = glob.glob(osp.join(path, f'*.{suffix}'))
+ checkpoints = glob.glob(osp.join(path, f"*.{suffix}"))
if len(checkpoints) == 0:
- warnings.warn('The are no checkpoints in the path')
+ warnings.warn("The are no checkpoints in the path")
return None
latest = -1
- latest_path = ''
+ latest_path = ""
for checkpoint in checkpoints:
if len(checkpoint) < len(latest_path):
continue
# `count` is iteration number, as checkpoints are saved as
# 'iter_xx.pth' or 'epoch_xx.pth' and xx is iteration number.
- count = int(osp.basename(checkpoint).split('_')[-1].split('.')[0])
+ count = int(osp.basename(checkpoint).split("_")[-1].split(".")[0])
if count > latest:
latest = count
latest_path = checkpoint
diff --git a/mmsegmentation/mmseg/utils/set_env.py b/mmsegmentation/mmseg/utils/set_env.py
index bf18453..855275f 100644
--- a/mmsegmentation/mmseg/utils/set_env.py
+++ b/mmsegmentation/mmseg/utils/set_env.py
@@ -13,43 +13,43 @@ def setup_multi_processes(cfg):
logger = get_root_logger()
# set multi-process start method
- if platform.system() != 'Windows':
- mp_start_method = cfg.get('mp_start_method', None)
+ if platform.system() != "Windows":
+ mp_start_method = cfg.get("mp_start_method", None)
current_method = mp.get_start_method(allow_none=True)
- if mp_start_method in ('fork', 'spawn', 'forkserver'):
+ if mp_start_method in ("fork", "spawn", "forkserver"):
logger.info(
- f'Multi-processing start method `{mp_start_method}` is '
- f'different from the previous setting `{current_method}`.'
- f'It will be force set to `{mp_start_method}`.')
+ f"Multi-processing start method `{mp_start_method}` is "
+ f"different from the previous setting `{current_method}`."
+ f"It will be force set to `{mp_start_method}`."
+ )
mp.set_start_method(mp_start_method, force=True)
else:
- logger.info(
- f'Multi-processing start method is `{mp_start_method}`')
+ logger.info(f"Multi-processing start method is `{mp_start_method}`")
# disable opencv multithreading to avoid system being overloaded
- opencv_num_threads = cfg.get('opencv_num_threads', None)
+ opencv_num_threads = cfg.get("opencv_num_threads", None)
if isinstance(opencv_num_threads, int):
- logger.info(f'OpenCV num_threads is `{opencv_num_threads}`')
+ logger.info(f"OpenCV num_threads is `{opencv_num_threads}`")
cv2.setNumThreads(opencv_num_threads)
else:
- logger.info(f'OpenCV num_threads is `{cv2.getNumThreads()}')
+ logger.info(f"OpenCV num_threads is `{cv2.getNumThreads()}")
if cfg.data.workers_per_gpu > 1:
# setup OMP threads
# This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py # noqa
- omp_num_threads = cfg.get('omp_num_threads', None)
- if 'OMP_NUM_THREADS' not in os.environ:
+ omp_num_threads = cfg.get("omp_num_threads", None)
+ if "OMP_NUM_THREADS" not in os.environ:
if isinstance(omp_num_threads, int):
- logger.info(f'OMP num threads is {omp_num_threads}')
- os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
+ logger.info(f"OMP num threads is {omp_num_threads}")
+ os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
else:
logger.info(f'OMP num threads is {os.environ["OMP_NUM_THREADS"] }')
# setup MKL threads
- if 'MKL_NUM_THREADS' not in os.environ:
- mkl_num_threads = cfg.get('mkl_num_threads', None)
+ if "MKL_NUM_THREADS" not in os.environ:
+ mkl_num_threads = cfg.get("mkl_num_threads", None)
if isinstance(mkl_num_threads, int):
- logger.info(f'MKL num threads is {mkl_num_threads}')
- os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
+ logger.info(f"MKL num threads is {mkl_num_threads}")
+ os.environ["MKL_NUM_THREADS"] = str(mkl_num_threads)
else:
logger.info(f'MKL num threads is {os.environ["MKL_NUM_THREADS"]}')
diff --git a/mmsegmentation/mmseg/utils/util_distribution.py b/mmsegmentation/mmseg/utils/util_distribution.py
index 16651c2..051435e 100644
--- a/mmsegmentation/mmseg/utils/util_distribution.py
+++ b/mmsegmentation/mmseg/utils/util_distribution.py
@@ -5,12 +5,12 @@
from mmseg import digit_version
-dp_factory = {'cuda': MMDataParallel, 'cpu': MMDataParallel}
+dp_factory = {"cuda": MMDataParallel, "cpu": MMDataParallel}
-ddp_factory = {'cuda': MMDistributedDataParallel}
+ddp_factory = {"cuda": MMDistributedDataParallel}
-def build_dp(model, device='cuda', dim=0, *args, **kwargs):
+def build_dp(model, device="cuda", dim=0, *args, **kwargs):
"""build DataParallel module by device type.
if device is cuda, return a MMDataParallel module; if device is mlu,
@@ -24,19 +24,21 @@ def build_dp(model, device='cuda', dim=0, *args, **kwargs):
Returns:
:class:`nn.Module`: parallelized module.
"""
- if device == 'cuda':
+ if device == "cuda":
model = model.cuda()
- elif device == 'mlu':
- assert digit_version(mmcv.__version__) >= digit_version('1.5.0'), \
- 'Please use MMCV >= 1.5.0 for MLU training!'
+ elif device == "mlu":
+ assert digit_version(mmcv.__version__) >= digit_version(
+ "1.5.0"
+ ), "Please use MMCV >= 1.5.0 for MLU training!"
from mmcv.device.mlu import MLUDataParallel
- dp_factory['mlu'] = MLUDataParallel
+
+ dp_factory["mlu"] = MLUDataParallel
model = model.mlu()
return dp_factory[device](model, dim=dim, *args, **kwargs)
-def build_ddp(model, device='cuda', *args, **kwargs):
+def build_ddp(model, device="cuda", *args, **kwargs):
"""Build DistributedDataParallel module by device type.
If device is cuda, return a MMDistributedDataParallel module;
@@ -53,14 +55,16 @@ def build_ddp(model, device='cuda', *args, **kwargs):
.. [1] https://pytorch.org/docs/stable/generated/torch.nn.parallel.
DistributedDataParallel.html
"""
- assert device in ['cuda', 'mlu'], 'Only available for cuda or mlu devices.'
- if device == 'cuda':
+ assert device in ["cuda", "mlu"], "Only available for cuda or mlu devices."
+ if device == "cuda":
model = model.cuda()
- elif device == 'mlu':
- assert digit_version(mmcv.__version__) >= digit_version('1.5.0'), \
- 'Please use MMCV >= 1.5.0 for MLU training!'
+ elif device == "mlu":
+ assert digit_version(mmcv.__version__) >= digit_version(
+ "1.5.0"
+ ), "Please use MMCV >= 1.5.0 for MLU training!"
from mmcv.device.mlu import MLUDistributedDataParallel
- ddp_factory['mlu'] = MLUDistributedDataParallel
+
+ ddp_factory["mlu"] = MLUDistributedDataParallel
model = model.mlu()
return ddp_factory[device](model, *args, **kwargs)
@@ -68,14 +72,11 @@ def build_ddp(model, device='cuda', *args, **kwargs):
def is_mlu_available():
"""Returns a bool indicating if MLU is currently available."""
- return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()
+ return hasattr(torch, "is_mlu_available") and torch.is_mlu_available()
def get_device():
"""Returns an available device, cpu, cuda or mlu."""
- is_device_available = {
- 'cuda': torch.cuda.is_available(),
- 'mlu': is_mlu_available()
- }
+ is_device_available = {"cuda": torch.cuda.is_available(), "mlu": is_mlu_available()}
device_list = [k for k, v in is_device_available.items() if v]
- return device_list[0] if len(device_list) == 1 else 'cpu'
+ return device_list[0] if len(device_list) == 1 else "cpu"
diff --git a/mmsegmentation/mmseg/version.py b/mmsegmentation/mmseg/version.py
index 9f27ecb..4cd1f71 100644
--- a/mmsegmentation/mmseg/version.py
+++ b/mmsegmentation/mmseg/version.py
@@ -1,17 +1,17 @@
# Copyright (c) Open-MMLab. All rights reserved.
-__version__ = '0.29.1'
+__version__ = "0.29.1"
def parse_version_info(version_str):
version_info = []
- for x in version_str.split('.'):
+ for x in version_str.split("."):
if x.isdigit():
version_info.append(int(x))
- elif x.find('rc') != -1:
- patch_version = x.split('rc')
+ elif x.find("rc") != -1:
+ patch_version = x.split("rc")
version_info.append(int(patch_version[0]))
- version_info.append(f'rc{patch_version[1]}')
+ version_info.append(f"rc{patch_version[1]}")
return tuple(version_info)
diff --git a/mmsegmentation/setup.py b/mmsegmentation/setup.py
index 7461e76..a0e3069 100755
--- a/mmsegmentation/setup.py
+++ b/mmsegmentation/setup.py
@@ -9,21 +9,21 @@
def readme():
- with open('README.md', encoding='utf-8') as f:
+ with open("README.md", encoding="utf-8") as f:
content = f.read()
return content
-version_file = 'mmseg/version.py'
+version_file = "mmseg/version.py"
def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
+ with open(version_file) as f:
+ exec(compile(f.read(), version_file, "exec"))
+ return locals()["__version__"]
-def parse_requirements(fname='requirements.txt', with_version=True):
+def parse_requirements(fname="requirements.txt", with_version=True):
"""Parse the package dependencies listed in a requirements file but strips
specific versioning information.
@@ -40,56 +40,56 @@ def parse_requirements(fname='requirements.txt', with_version=True):
import re
import sys
from os.path import exists
+
require_fpath = fname
def parse_line(line):
"""Parse information from a line in a requirements text file."""
- if line.startswith('-r '):
+ if line.startswith("-r "):
# Allow specifying requirements in other files
- target = line.split(' ')[1]
+ target = line.split(" ")[1]
yield from parse_require_file(target)
else:
- info = {'line': line}
- if line.startswith('-e '):
- info['package'] = line.split('#egg=')[1]
+ info = {"line": line}
+ if line.startswith("-e "):
+ info["package"] = line.split("#egg=")[1]
else:
# Remove versioning from the package
- pat = '(' + '|'.join(['>=', '==', '>']) + ')'
+ pat = "(" + "|".join([">=", "==", ">"]) + ")"
parts = re.split(pat, line, maxsplit=1)
parts = [p.strip() for p in parts]
- info['package'] = parts[0]
+ info["package"] = parts[0]
if len(parts) > 1:
op, rest = parts[1:]
- if ';' in rest:
+ if ";" in rest:
# Handle platform specific dependencies
# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
- version, platform_deps = map(str.strip,
- rest.split(';'))
- info['platform_deps'] = platform_deps
+ version, platform_deps = map(str.strip, rest.split(";"))
+ info["platform_deps"] = platform_deps
else:
version = rest
- info['version'] = op, version
+ info["version"] = op, version
yield info
def parse_require_file(fpath):
- with open(fpath, 'r') as f:
+ with open(fpath) as f:
for line in f.readlines():
line = line.strip()
- if line and not line.startswith('#'):
+ if line and not line.startswith("#"):
yield from parse_line(line)
def gen_packages_items():
if not exists(require_fpath):
return
for info in parse_require_file(require_fpath):
- parts = [info['package']]
- if with_version and 'version' in info:
- parts.extend(info['version'])
- if not sys.version.startswith('3.4'):
- platform_deps = info.get('platform_deps')
+ parts = [info["package"]]
+ if with_version and "version" in info:
+ parts.extend(info["version"])
+ if not sys.version.startswith("3.4"):
+ platform_deps = info.get("platform_deps")
if platform_deps is not None:
- parts.append(f';{platform_deps}')
- item = ''.join(parts)
+ parts.append(f";{platform_deps}")
+ item = "".join(parts)
yield item
packages = list(gen_packages_items())
@@ -105,21 +105,24 @@ def add_mim_extension():
"""
# parse installment mode
- if 'develop' in sys.argv:
+ if "develop" in sys.argv:
# installed by `pip install -e .`
# set `copy` mode here since symlink fails on Windows.
- mode = 'copy' if platform.system() == 'Windows' else 'symlink'
- elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv or platform.system(
- ) == 'Windows':
+ mode = "copy" if platform.system() == "Windows" else "symlink"
+ elif (
+ "sdist" in sys.argv
+ or "bdist_wheel" in sys.argv
+ or platform.system() == "Windows"
+ ):
# installed by `pip install .`
# or create source distribution by `python setup.py sdist`
# set `copy` mode here since symlink fails with WinError on Windows.
- mode = 'copy'
+ mode = "copy"
else:
return
- filenames = ['tools', 'configs', 'model-index.yml']
+ filenames = ["tools", "configs", "model-index.yml"]
repo_path = osp.dirname(__file__)
- mim_path = osp.join(repo_path, 'mmseg', '.mim')
+ mim_path = osp.join(repo_path, "mmseg", ".mim")
os.makedirs(mim_path, exist_ok=True)
for filename in filenames:
if osp.exists(filename):
@@ -129,7 +132,7 @@ def add_mim_extension():
os.remove(tar_path)
elif osp.isdir(tar_path):
shutil.rmtree(tar_path)
- if mode == 'symlink':
+ if mode == "symlink":
src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
try:
os.symlink(src_relpath, tar_path)
@@ -137,54 +140,56 @@ def add_mim_extension():
# Creating a symbolic link on windows may raise an
# `OSError: [WinError 1314]` due to privilege. If
# the error happens, the src file will be copied
- mode = 'copy'
+ mode = "copy"
warnings.warn(
- f'Failed to create a symbolic link for {src_relpath},'
- f' and it will be copied to {tar_path}')
+ f"Failed to create a symbolic link for {src_relpath},"
+ f" and it will be copied to {tar_path}"
+ )
else:
continue
- if mode != 'copy':
- raise ValueError(f'Invalid mode {mode}')
+ if mode != "copy":
+ raise ValueError(f"Invalid mode {mode}")
if osp.isfile(src_path):
shutil.copyfile(src_path, tar_path)
elif osp.isdir(src_path):
shutil.copytree(src_path, tar_path)
else:
- warnings.warn(f'Cannot copy file {src_path}.')
+ warnings.warn(f"Cannot copy file {src_path}.")
-if __name__ == '__main__':
+if __name__ == "__main__":
add_mim_extension()
setup(
- name='mmsegmentation',
+ name="mmsegmentation",
version=get_version(),
- description='Open MMLab Semantic Segmentation Toolbox and Benchmark',
+ description="Open MMLab Semantic Segmentation Toolbox and Benchmark",
long_description=readme(),
- long_description_content_type='text/markdown',
- author='MMSegmentation Contributors',
- author_email='openmmlab@gmail.com',
- keywords='computer vision, semantic segmentation',
- url='http://github.com/open-mmlab/mmsegmentation',
- packages=find_packages(exclude=('configs', 'tools', 'demo')),
+ long_description_content_type="text/markdown",
+ author="MMSegmentation Contributors",
+ author_email="openmmlab@gmail.com",
+ keywords="computer vision, semantic segmentation",
+ url="http://github.com/open-mmlab/mmsegmentation",
+ packages=find_packages(exclude=("configs", "tools", "demo")),
include_package_data=True,
classifiers=[
- 'Development Status :: 4 - Beta',
- 'License :: OSI Approved :: Apache Software License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 3.6',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- 'Programming Language :: Python :: 3.9',
+ "Development Status :: 4 - Beta",
+ "License :: OSI Approved :: Apache Software License",
+ "Operating System :: OS Independent",
+ "Programming Language :: Python :: 3.6",
+ "Programming Language :: Python :: 3.7",
+ "Programming Language :: Python :: 3.8",
+ "Programming Language :: Python :: 3.9",
],
- license='Apache License 2.0',
- install_requires=parse_requirements('requirements/runtime.txt'),
+ license="Apache License 2.0",
+ install_requires=parse_requirements("requirements/runtime.txt"),
extras_require={
- 'all': parse_requirements('requirements.txt'),
- 'tests': parse_requirements('requirements/tests.txt'),
- 'build': parse_requirements('requirements/build.txt'),
- 'optional': parse_requirements('requirements/optional.txt'),
- 'mim': parse_requirements('requirements/mminstall.txt'),
+ "all": parse_requirements("requirements.txt"),
+ "tests": parse_requirements("requirements/tests.txt"),
+ "build": parse_requirements("requirements/build.txt"),
+ "optional": parse_requirements("requirements/optional.txt"),
+ "mim": parse_requirements("requirements/mminstall.txt"),
},
ext_modules=[],
- zip_safe=False)
+ zip_safe=False,
+ )
diff --git a/mmsegmentation/tests/test_apis/test_single_gpu.py b/mmsegmentation/tests/test_apis/test_single_gpu.py
index 0b484f2..fd4e0a4 100644
--- a/mmsegmentation/tests/test_apis/test_single_gpu.py
+++ b/mmsegmentation/tests/test_apis/test_single_gpu.py
@@ -24,7 +24,7 @@ def __len__(self):
class ExampleModel(nn.Module):
def __init__(self):
- super(ExampleModel, self).__init__()
+ super().__init__()
self.test_cfg = None
self.conv = nn.Conv2d(3, 3, 3)
@@ -48,26 +48,23 @@ def test_single_gpu():
assert len(results) == 1
pred = np.load(results[0])
assert isinstance(pred, np.ndarray)
- assert pred.shape == (1, )
+ assert pred.shape == (1,)
assert pred[0] == 1
- shutil.rmtree('.efficient_test')
+ shutil.rmtree(".efficient_test")
# Test pre_eval
- test_dataset.pre_eval = MagicMock(return_value=['success'])
+ test_dataset.pre_eval = MagicMock(return_value=["success"])
results = single_gpu_test(model, data_loader, pre_eval=True)
- assert results == ['success']
+ assert results == ["success"]
# Test format_only
- test_dataset.format_results = MagicMock(return_value=['success'])
+ test_dataset.format_results = MagicMock(return_value=["success"])
results = single_gpu_test(model, data_loader, format_only=True)
- assert results == ['success']
+ assert results == ["success"]
# efficient_test, pre_eval and format_only are mutually exclusive
with pytest.raises(AssertionError):
single_gpu_test(
- model,
- dataloader,
- efficient_test=True,
- format_only=True,
- pre_eval=True)
+ model, dataloader, efficient_test=True, format_only=True, pre_eval=True
+ )
diff --git a/mmsegmentation/tests/test_config.py b/mmsegmentation/tests/test_config.py
index 2482144..43dee1f 100644
--- a/mmsegmentation/tests/test_config.py
+++ b/mmsegmentation/tests/test_config.py
@@ -17,10 +17,11 @@ def _get_config_directory():
except NameError:
# For IPython development when this __file__ is not defined
import mmseg
+
repo_dpath = dirname(dirname(mmseg.__file__))
- config_dpath = join(repo_dpath, 'configs')
+ config_dpath = join(repo_dpath, "configs")
if not exists(config_dpath):
- raise Exception('Cannot find config path')
+ raise Exception("Cannot find config path")
return config_dpath
@@ -28,35 +29,36 @@ def test_config_build_segmentor():
"""Test that all segmentation models defined in the configs can be
initialized."""
config_dpath = _get_config_directory()
- print('Found config_dpath = {!r}'.format(config_dpath))
+ print(f"Found config_dpath = {config_dpath!r}")
config_fpaths = []
# one config each sub folder
for sub_folder in os.listdir(config_dpath):
if isdir(sub_folder):
config_fpaths.append(
- list(glob.glob(join(config_dpath, sub_folder, '*.py')))[0])
- config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
+ list(glob.glob(join(config_dpath, sub_folder, "*.py")))[0]
+ )
+ config_fpaths = [p for p in config_fpaths if p.find("_base_") == -1]
config_names = [relpath(p, config_dpath) for p in config_fpaths]
- print('Using {} config files'.format(len(config_names)))
+ print(f"Using {len(config_names)} config files")
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = Config.fromfile(config_fpath)
config_mod.model
- print('Building segmentor, config_fpath = {!r}'.format(config_fpath))
+ print(f"Building segmentor, config_fpath = {config_fpath!r}")
# Remove pretrained keys to allow for testing in an offline environment
- if 'pretrained' in config_mod.model:
- config_mod.model['pretrained'] = None
+ if "pretrained" in config_mod.model:
+ config_mod.model["pretrained"] = None
- print('building {}'.format(config_fname))
+ print(f"building {config_fname}")
segmentor = build_segmentor(config_mod.model)
assert segmentor is not None
- head_config = config_mod.model['decode_head']
+ head_config = config_mod.model["decode_head"]
_check_decode_head(head_config, segmentor.decode_head)
@@ -72,24 +74,24 @@ def test_config_data_pipeline():
from mmseg.datasets.pipelines import Compose
config_dpath = _get_config_directory()
- print('Found config_dpath = {!r}'.format(config_dpath))
+ print(f"Found config_dpath = {config_dpath!r}")
import glob
- config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
- config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
+
+ config_fpaths = list(glob.glob(join(config_dpath, "**", "*.py")))
+ config_fpaths = [p for p in config_fpaths if p.find("_base_") == -1]
config_names = [relpath(p, config_dpath) for p in config_fpaths]
- print('Using {} config files'.format(len(config_names)))
+ print(f"Using {len(config_names)} config files")
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
- print(
- 'Building data pipeline, config_fpath = {!r}'.format(config_fpath))
+ print(f"Building data pipeline, config_fpath = {config_fpath!r}")
config_mod = Config.fromfile(config_fpath)
# remove loading pipeline
load_img_pipeline = config_mod.train_pipeline.pop(0)
- to_float32 = load_img_pipeline.get('to_float32', False)
+ to_float32 = load_img_pipeline.get("to_float32", False)
config_mod.train_pipeline.pop(0)
config_mod.test_pipeline.pop(0)
@@ -102,26 +104,27 @@ def test_config_data_pipeline():
seg = np.random.randint(0, 255, size=(1024, 2048, 1), dtype=np.uint8)
results = dict(
- filename='test_img.png',
- ori_filename='test_img.png',
+ filename="test_img.png",
+ ori_filename="test_img.png",
img=img,
img_shape=img.shape,
ori_shape=img.shape,
- gt_semantic_seg=seg)
- results['seg_fields'] = ['gt_semantic_seg']
+ gt_semantic_seg=seg,
+ )
+ results["seg_fields"] = ["gt_semantic_seg"]
- print('Test training data pipeline: \n{!r}'.format(train_pipeline))
+ print(f"Test training data pipeline: \n{train_pipeline!r}")
output_results = train_pipeline(results)
assert output_results is not None
results = dict(
- filename='test_img.png',
- ori_filename='test_img.png',
+ filename="test_img.png",
+ ori_filename="test_img.png",
img=img,
img_shape=img.shape,
ori_shape=img.shape,
)
- print('Test testing data pipeline: \n{!r}'.format(test_pipeline))
+ print(f"Test testing data pipeline: \n{test_pipeline!r}")
output_results = test_pipeline(results)
assert output_results is not None
@@ -135,27 +138,29 @@ def _check_decode_head(decode_head_cfg, decode_head):
_check_decode_head(decode_head_cfg[i], decode_head[i])
return
# check consistency between head_config and roi_head
- assert decode_head_cfg['type'] == decode_head.__class__.__name__
+ assert decode_head_cfg["type"] == decode_head.__class__.__name__
- assert decode_head_cfg['type'] == decode_head.__class__.__name__
+ assert decode_head_cfg["type"] == decode_head.__class__.__name__
in_channels = decode_head_cfg.in_channels
input_transform = decode_head.input_transform
- assert input_transform in ['resize_concat', 'multiple_select', None]
+ assert input_transform in ["resize_concat", "multiple_select", None]
if input_transform is not None:
assert isinstance(in_channels, (list, tuple))
assert isinstance(decode_head.in_index, (list, tuple))
assert len(in_channels) == len(decode_head.in_index)
- elif input_transform == 'resize_concat':
+ elif input_transform == "resize_concat":
assert sum(in_channels) == decode_head.in_channels
else:
assert isinstance(in_channels, int)
assert in_channels == decode_head.in_channels
assert isinstance(decode_head.in_index, int)
- if decode_head_cfg['type'] == 'PointHead':
- assert decode_head_cfg.channels+decode_head_cfg.num_classes == \
- decode_head.fc_seg.in_channels
+ if decode_head_cfg["type"] == "PointHead":
+ assert (
+ decode_head_cfg.channels + decode_head_cfg.num_classes
+ == decode_head.fc_seg.in_channels
+ )
assert decode_head.fc_seg.out_channels == decode_head_cfg.num_classes
else:
assert decode_head_cfg.channels == decode_head.conv_seg.in_channels
diff --git a/mmsegmentation/tests/test_core/test_layer_decay_optimizer_constructor.py b/mmsegmentation/tests/test_core/test_layer_decay_optimizer_constructor.py
index 4911f3b..50cafe0 100644
--- a/mmsegmentation/tests/test_core/test_layer_decay_optimizer_constructor.py
+++ b/mmsegmentation/tests/test_core/test_layer_decay_optimizer_constructor.py
@@ -5,117 +5,56 @@
from mmcv.cnn import ConvModule
from mmseg.core.optimizers.layer_decay_optimizer_constructor import (
- LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor)
+ LayerDecayOptimizerConstructor,
+ LearningRateDecayOptimizerConstructor,
+)
base_lr = 1
decay_rate = 2
base_wd = 0.05
weight_decay = 0.05
-expected_stage_wise_lr_wd_convnext = [{
- 'weight_decay': 0.0,
- 'lr_scale': 128
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 1
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 64
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 64
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 32
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 32
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 16
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 16
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 8
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 8
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 128
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 1
-}]
-
-expected_layer_wise_lr_wd_convnext = [{
- 'weight_decay': 0.0,
- 'lr_scale': 128
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 1
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 64
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 64
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 32
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 32
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 16
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 16
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 2
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 2
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 128
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 1
-}]
-
-expected_layer_wise_wd_lr_beit = [{
- 'weight_decay': 0.0,
- 'lr_scale': 16
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 8
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 8
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 4
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 4
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 2
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 2
-}, {
- 'weight_decay': 0.05,
- 'lr_scale': 1
-}, {
- 'weight_decay': 0.0,
- 'lr_scale': 1
-}]
+expected_stage_wise_lr_wd_convnext = [
+ {"weight_decay": 0.0, "lr_scale": 128},
+ {"weight_decay": 0.0, "lr_scale": 1},
+ {"weight_decay": 0.05, "lr_scale": 64},
+ {"weight_decay": 0.0, "lr_scale": 64},
+ {"weight_decay": 0.05, "lr_scale": 32},
+ {"weight_decay": 0.0, "lr_scale": 32},
+ {"weight_decay": 0.05, "lr_scale": 16},
+ {"weight_decay": 0.0, "lr_scale": 16},
+ {"weight_decay": 0.05, "lr_scale": 8},
+ {"weight_decay": 0.0, "lr_scale": 8},
+ {"weight_decay": 0.05, "lr_scale": 128},
+ {"weight_decay": 0.05, "lr_scale": 1},
+]
+
+expected_layer_wise_lr_wd_convnext = [
+ {"weight_decay": 0.0, "lr_scale": 128},
+ {"weight_decay": 0.0, "lr_scale": 1},
+ {"weight_decay": 0.05, "lr_scale": 64},
+ {"weight_decay": 0.0, "lr_scale": 64},
+ {"weight_decay": 0.05, "lr_scale": 32},
+ {"weight_decay": 0.0, "lr_scale": 32},
+ {"weight_decay": 0.05, "lr_scale": 16},
+ {"weight_decay": 0.0, "lr_scale": 16},
+ {"weight_decay": 0.05, "lr_scale": 2},
+ {"weight_decay": 0.0, "lr_scale": 2},
+ {"weight_decay": 0.05, "lr_scale": 128},
+ {"weight_decay": 0.05, "lr_scale": 1},
+]
+
+expected_layer_wise_wd_lr_beit = [
+ {"weight_decay": 0.0, "lr_scale": 16},
+ {"weight_decay": 0.05, "lr_scale": 8},
+ {"weight_decay": 0.0, "lr_scale": 8},
+ {"weight_decay": 0.05, "lr_scale": 4},
+ {"weight_decay": 0.0, "lr_scale": 4},
+ {"weight_decay": 0.05, "lr_scale": 2},
+ {"weight_decay": 0.0, "lr_scale": 2},
+ {"weight_decay": 0.05, "lr_scale": 1},
+ {"weight_decay": 0.0, "lr_scale": 1},
+]
class ToyConvNeXt(nn.Module):
@@ -193,15 +132,15 @@ def __init__(self):
def check_optimizer_lr_wd(optimizer, gt_lr_wd):
assert isinstance(optimizer, torch.optim.AdamW)
- assert optimizer.defaults['lr'] == base_lr
- assert optimizer.defaults['weight_decay'] == base_wd
+ assert optimizer.defaults["lr"] == base_lr
+ assert optimizer.defaults["weight_decay"] == base_wd
param_groups = optimizer.param_groups
print(param_groups)
assert len(param_groups) == len(gt_lr_wd)
for i, param_dict in enumerate(param_groups):
- assert param_dict['weight_decay'] == gt_lr_wd[i]['weight_decay']
- assert param_dict['lr_scale'] == gt_lr_wd[i]['lr_scale']
- assert param_dict['lr_scale'] == param_dict['lr']
+ assert param_dict["weight_decay"] == gt_lr_wd[i]["weight_decay"]
+ assert param_dict["lr_scale"] == gt_lr_wd[i]["lr_scale"]
+ assert param_dict["lr_scale"] == param_dict["lr"]
def test_learning_rate_decay_optimizer_constructor():
@@ -210,19 +149,24 @@ def test_learning_rate_decay_optimizer_constructor():
backbone = ToyConvNeXt()
model = PseudoDataParallel(ToySegmentor(backbone))
optimizer_cfg = dict(
- type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05)
+ type="AdamW", lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05
+ )
# stagewise decay
stagewise_paramwise_cfg = dict(
- decay_rate=decay_rate, decay_type='stage_wise', num_layers=6)
+ decay_rate=decay_rate, decay_type="stage_wise", num_layers=6
+ )
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, stagewise_paramwise_cfg)
+ optimizer_cfg, stagewise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_stage_wise_lr_wd_convnext)
# layerwise decay
layerwise_paramwise_cfg = dict(
- decay_rate=decay_rate, decay_type='layer_wise', num_layers=6)
+ decay_rate=decay_rate, decay_type="layer_wise", num_layers=6
+ )
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, layerwise_paramwise_cfg)
+ optimizer_cfg, layerwise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_layer_wise_lr_wd_convnext)
@@ -231,9 +175,11 @@ def test_learning_rate_decay_optimizer_constructor():
model = PseudoDataParallel(ToySegmentor(backbone))
layerwise_paramwise_cfg = dict(
- decay_rate=decay_rate, decay_type='layer_wise', num_layers=3)
+ decay_rate=decay_rate, decay_type="layer_wise", num_layers=3
+ )
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, layerwise_paramwise_cfg)
+ optimizer_cfg, layerwise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_layer_wise_wd_lr_beit)
@@ -242,11 +188,13 @@ def test_learning_rate_decay_optimizer_constructor():
model = PseudoDataParallel(ToySegmentor(backbone))
with pytest.raises(NotImplementedError):
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, layerwise_paramwise_cfg)
+ optimizer_cfg, layerwise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
with pytest.raises(NotImplementedError):
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, stagewise_paramwise_cfg)
+ optimizer_cfg, stagewise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
# Test lr wd for MAE
@@ -254,9 +202,11 @@ def test_learning_rate_decay_optimizer_constructor():
model = PseudoDataParallel(ToySegmentor(backbone))
layerwise_paramwise_cfg = dict(
- decay_rate=decay_rate, decay_type='layer_wise', num_layers=3)
+ decay_rate=decay_rate, decay_type="layer_wise", num_layers=3
+ )
optim_constructor = LearningRateDecayOptimizerConstructor(
- optimizer_cfg, layerwise_paramwise_cfg)
+ optimizer_cfg, layerwise_paramwise_cfg
+ )
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_layer_wise_wd_lr_beit)
@@ -266,10 +216,8 @@ def test_beit_layer_decay_optimizer_constructor():
# paramwise_cfg with BEiTExampleModel
backbone = ToyBEiT()
model = PseudoDataParallel(ToySegmentor(backbone))
- optimizer_cfg = dict(
- type='AdamW', lr=1, betas=(0.9, 0.999), weight_decay=0.05)
+ optimizer_cfg = dict(type="AdamW", lr=1, betas=(0.9, 0.999), weight_decay=0.05)
paramwise_cfg = dict(layer_decay_rate=2, num_layers=3)
- optim_constructor = LayerDecayOptimizerConstructor(optimizer_cfg,
- paramwise_cfg)
+ optim_constructor = LayerDecayOptimizerConstructor(optimizer_cfg, paramwise_cfg)
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_layer_wise_wd_lr_beit)
diff --git a/mmsegmentation/tests/test_core/test_optimizer.py b/mmsegmentation/tests/test_core/test_optimizer.py
index 247f9fe..5134d66 100644
--- a/mmsegmentation/tests/test_core/test_optimizer.py
+++ b/mmsegmentation/tests/test_core/test_optimizer.py
@@ -4,8 +4,11 @@
import torch.nn as nn
from mmcv.runner import DefaultOptimizerConstructor
-from mmseg.core.builder import (OPTIMIZER_BUILDERS, build_optimizer,
- build_optimizer_constructor)
+from mmseg.core.builder import (
+ OPTIMIZER_BUILDERS,
+ build_optimizer,
+ build_optimizer_constructor,
+)
class ExampleModel(nn.Module):
@@ -28,9 +31,11 @@ def forward(self, x):
def test_build_optimizer_constructor():
optimizer_cfg = dict(
- type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
+ type="SGD", lr=base_lr, weight_decay=base_wd, momentum=momentum
+ )
optim_constructor_cfg = dict(
- type='DefaultOptimizerConstructor', optimizer_cfg=optimizer_cfg)
+ type="DefaultOptimizerConstructor", optimizer_cfg=optimizer_cfg
+ )
optim_constructor = build_optimizer_constructor(optim_constructor_cfg)
# Test whether optimizer constructor can be built from parent.
assert type(optim_constructor) is DefaultOptimizerConstructor
@@ -40,20 +45,22 @@ class MyOptimizerConstructor(DefaultOptimizerConstructor):
pass
optim_constructor_cfg = dict(
- type='MyOptimizerConstructor', optimizer_cfg=optimizer_cfg)
+ type="MyOptimizerConstructor", optimizer_cfg=optimizer_cfg
+ )
optim_constructor = build_optimizer_constructor(optim_constructor_cfg)
# Test optimizer constructor can be built from child registry.
assert type(optim_constructor) is MyOptimizerConstructor
# Test unregistered constructor cannot be built
with pytest.raises(KeyError):
- build_optimizer_constructor(dict(type='A'))
+ build_optimizer_constructor(dict(type="A"))
def test_build_optimizer():
model = ExampleModel()
optimizer_cfg = dict(
- type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
+ type="SGD", lr=base_lr, weight_decay=base_wd, momentum=momentum
+ )
optimizer = build_optimizer(model, optimizer_cfg)
# test whether optimizer is successfully built from parent.
assert isinstance(optimizer, torch.optim.SGD)
diff --git a/mmsegmentation/tests/test_data/test_dataset.py b/mmsegmentation/tests/test_data/test_dataset.py
index 6ea6eb9..92b2817 100644
--- a/mmsegmentation/tests/test_data/test_dataset.py
+++ b/mmsegmentation/tests/test_data/test_dataset.py
@@ -12,51 +12,64 @@
from PIL import Image
from mmseg.core.evaluation import get_classes, get_palette
-from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
- COCOStuffDataset, ConcatDataset, CustomDataset,
- ISPRSDataset, LoveDADataset, MultiImageMixDataset,
- PascalVOCDataset, PotsdamDataset, RepeatDataset,
- build_dataset, iSAIDDataset)
+from mmseg.datasets import (
+ DATASETS,
+ ADE20KDataset,
+ CityscapesDataset,
+ COCOStuffDataset,
+ ConcatDataset,
+ CustomDataset,
+ ISPRSDataset,
+ LoveDADataset,
+ MultiImageMixDataset,
+ PascalVOCDataset,
+ PotsdamDataset,
+ RepeatDataset,
+ build_dataset,
+ iSAIDDataset,
+)
def test_classes():
- assert list(CityscapesDataset.CLASSES) == get_classes('cityscapes')
- assert list(PascalVOCDataset.CLASSES) == get_classes('voc') == get_classes(
- 'pascal_voc')
- assert list(
- ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
- assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
- assert list(LoveDADataset.CLASSES) == get_classes('loveda')
- assert list(PotsdamDataset.CLASSES) == get_classes('potsdam')
- assert list(ISPRSDataset.CLASSES) == get_classes('vaihingen')
- assert list(iSAIDDataset.CLASSES) == get_classes('isaid')
+ assert list(CityscapesDataset.CLASSES) == get_classes("cityscapes")
+ assert (
+ list(PascalVOCDataset.CLASSES)
+ == get_classes("voc")
+ == get_classes("pascal_voc")
+ )
+ assert list(ADE20KDataset.CLASSES) == get_classes("ade") == get_classes("ade20k")
+ assert list(COCOStuffDataset.CLASSES) == get_classes("cocostuff")
+ assert list(LoveDADataset.CLASSES) == get_classes("loveda")
+ assert list(PotsdamDataset.CLASSES) == get_classes("potsdam")
+ assert list(ISPRSDataset.CLASSES) == get_classes("vaihingen")
+ assert list(iSAIDDataset.CLASSES) == get_classes("isaid")
with pytest.raises(ValueError):
- get_classes('unsupported')
+ get_classes("unsupported")
def test_classes_file_path():
tmp_file = tempfile.NamedTemporaryFile()
- classes_path = f'{tmp_file.name}.txt'
- train_pipeline = [dict(type='LoadImageFromFile')]
- kwargs = dict(pipeline=train_pipeline, img_dir='./', classes=classes_path)
+ classes_path = f"{tmp_file.name}.txt"
+ train_pipeline = [dict(type="LoadImageFromFile")]
+ kwargs = dict(pipeline=train_pipeline, img_dir="./", classes=classes_path)
# classes.txt with full categories
- categories = get_classes('cityscapes')
- with open(classes_path, 'w') as f:
- f.write('\n'.join(categories))
+ categories = get_classes("cityscapes")
+ with open(classes_path, "w") as f:
+ f.write("\n".join(categories))
assert list(CityscapesDataset(**kwargs).CLASSES) == categories
# classes.txt with sub categories
- categories = ['road', 'sidewalk', 'building']
- with open(classes_path, 'w') as f:
- f.write('\n'.join(categories))
+ categories = ["road", "sidewalk", "building"]
+ with open(classes_path, "w") as f:
+ f.write("\n".join(categories))
assert list(CityscapesDataset(**kwargs).CLASSES) == categories
# classes.txt with unknown categories
- categories = ['road', 'sidewalk', 'unknown']
- with open(classes_path, 'w') as f:
- f.write('\n'.join(categories))
+ categories = ["road", "sidewalk", "unknown"]
+ with open(classes_path, "w") as f:
+ f.write("\n".join(categories))
with pytest.raises(ValueError):
CityscapesDataset(**kwargs)
@@ -67,22 +80,22 @@ def test_classes_file_path():
def test_palette():
- assert CityscapesDataset.PALETTE == get_palette('cityscapes')
- assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
- 'pascal_voc')
- assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
- assert LoveDADataset.PALETTE == get_palette('loveda')
- assert PotsdamDataset.PALETTE == get_palette('potsdam')
- assert COCOStuffDataset.PALETTE == get_palette('cocostuff')
- assert iSAIDDataset.PALETTE == get_palette('isaid')
+ assert CityscapesDataset.PALETTE == get_palette("cityscapes")
+ assert PascalVOCDataset.PALETTE == get_palette("voc") == get_palette("pascal_voc")
+ assert ADE20KDataset.PALETTE == get_palette("ade") == get_palette("ade20k")
+ assert LoveDADataset.PALETTE == get_palette("loveda")
+ assert PotsdamDataset.PALETTE == get_palette("potsdam")
+ assert COCOStuffDataset.PALETTE == get_palette("cocostuff")
+ assert iSAIDDataset.PALETTE == get_palette("isaid")
with pytest.raises(ValueError):
- get_palette('unsupported')
+ get_palette("unsupported")
-@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
-@patch('mmseg.datasets.CustomDataset.__getitem__',
- MagicMock(side_effect=lambda idx: idx))
+@patch("mmseg.datasets.CustomDataset.load_annotations", MagicMock)
+@patch(
+ "mmseg.datasets.CustomDataset.__getitem__", MagicMock(side_effect=lambda idx: idx)
+)
def test_dataset_wrapper():
# CustomDataset.load_annotations = MagicMock()
# CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
@@ -108,9 +121,9 @@ def test_dataset_wrapper():
img_scale = (60, 60)
pipeline = [
- dict(type='RandomMosaic', prob=1, img_scale=img_scale),
- dict(type='RandomFlip', prob=0.5),
- dict(type='Resize', img_scale=img_scale, keep_ratio=False),
+ dict(type="RandomMosaic", prob=1, img_scale=img_scale),
+ dict(type="RandomFlip", prob=0.5),
+ dict(type="Resize", img_scale=img_scale, keep_ratio=False),
]
CustomDataset.load_annotations = MagicMock()
@@ -122,7 +135,7 @@ def test_dataset_wrapper():
gt_semantic_seg = np.random.randint(5, size=(height, weight))
results.append(dict(gt_semantic_seg=gt_semantic_seg, img=img))
- classes = ['0', '1', '2', '3', '4']
+ classes = ["0", "1", "2", "3", "4"]
palette = [(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]
CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx])
dataset_a = CustomDataset(
@@ -130,7 +143,8 @@ def test_dataset_wrapper():
pipeline=[],
test_mode=True,
classes=classes,
- palette=palette)
+ palette=palette,
+ )
len_a = 2
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
@@ -143,106 +157,114 @@ def test_dataset_wrapper():
# test skip_type_keys
multi_image_mix_dataset = MultiImageMixDataset(
- dataset_a, pipeline, skip_type_keys=('RandomFlip'))
+ dataset_a, pipeline, skip_type_keys=("RandomFlip")
+ )
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
- assert results_['img'].shape == (img_scale[0], img_scale[1], 3)
+ assert results_["img"].shape == (img_scale[0], img_scale[1], 3)
- skip_type_keys = ('RandomFlip', 'Resize')
+ skip_type_keys = ("RandomFlip", "Resize")
multi_image_mix_dataset.update_skip_type_keys(skip_type_keys)
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
- assert results_['img'].shape[:2] != img_scale
+ assert results_["img"].shape[:2] != img_scale
# test pipeline
with pytest.raises(TypeError):
- pipeline = [['Resize']]
+ pipeline = [["Resize"]]
multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
def test_custom_dataset():
img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True)
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
+ )
crop_size = (512, 1024)
train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=(128, 256), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+ dict(type="LoadImageFromFile"),
+ dict(type="LoadAnnotations"),
+ dict(type="Resize", img_scale=(128, 256), ratio_range=(0.5, 2.0)),
+ dict(type="RandomCrop", crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type="RandomFlip", prob=0.5),
+ dict(type="PhotoMetricDistortion"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="Pad", size=crop_size, pad_val=0, seg_pad_val=255),
+ dict(type="DefaultFormatBundle"),
+ dict(type="Collect", keys=["img", "gt_semantic_seg"]),
]
test_pipeline = [
- dict(type='LoadImageFromFile'),
+ dict(type="LoadImageFromFile"),
dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="ImageToTensor", keys=["img"]),
+ dict(type="Collect", keys=["img"]),
+ ],
+ ),
]
# with img_dir and ann_dir
train_dataset = CustomDataset(
train_pipeline,
- data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
- img_dir='imgs/',
- ann_dir='gts/',
- img_suffix='img.jpg',
- seg_map_suffix='gt.png')
+ data_root=osp.join(osp.dirname(__file__), "../data/pseudo_dataset"),
+ img_dir="imgs/",
+ ann_dir="gts/",
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ )
assert len(train_dataset) == 5
# with img_dir, ann_dir, split
train_dataset = CustomDataset(
train_pipeline,
- data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
- img_dir='imgs/',
- ann_dir='gts/',
- img_suffix='img.jpg',
- seg_map_suffix='gt.png',
- split='splits/train.txt')
+ data_root=osp.join(osp.dirname(__file__), "../data/pseudo_dataset"),
+ img_dir="imgs/",
+ ann_dir="gts/",
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ split="splits/train.txt",
+ )
assert len(train_dataset) == 4
# no data_root
train_dataset = CustomDataset(
train_pipeline,
- img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
- ann_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts'),
- img_suffix='img.jpg',
- seg_map_suffix='gt.png')
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs"),
+ ann_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/gts"),
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ )
assert len(train_dataset) == 5
# with data_root but img_dir/ann_dir are abs path
train_dataset = CustomDataset(
train_pipeline,
- data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+ data_root=osp.join(osp.dirname(__file__), "../data/pseudo_dataset"),
img_dir=osp.abspath(
- osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs')),
+ osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs")
+ ),
ann_dir=osp.abspath(
- osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts')),
- img_suffix='img.jpg',
- seg_map_suffix='gt.png')
+ osp.join(osp.dirname(__file__), "../data/pseudo_dataset/gts")
+ ),
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ )
assert len(train_dataset) == 5
# test_mode=True
test_dataset = CustomDataset(
test_pipeline,
- img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
- img_suffix='img.jpg',
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs"),
+ img_suffix="img.jpg",
test_mode=True,
- classes=('pseudo_class', ))
+ classes=("pseudo_class",),
+ )
assert len(test_dataset) == 5
# training data get
@@ -261,7 +283,7 @@ def test_custom_dataset():
# format_results not implemented
with pytest.raises(NotImplementedError):
- test_dataset.format_results([], '')
+ test_dataset.format_results([], "")
pseudo_results = []
for gt_seg_map in gt_seg_maps:
@@ -270,135 +292,137 @@ def test_custom_dataset():
# test past evaluation without CLASSES
with pytest.raises(TypeError):
- eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
+ eval_results = train_dataset.evaluate(pseudo_results, metric=["mIoU"])
with pytest.raises(TypeError):
- eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mDice")
with pytest.raises(TypeError):
- eval_results = train_dataset.evaluate(
- pseudo_results, metric=['mDice', 'mIoU'])
+ eval_results = train_dataset.evaluate(pseudo_results, metric=["mDice", "mIoU"])
# test past evaluation with CLASSES
- train_dataset.CLASSES = tuple(['a'] * 7)
- eval_results = train_dataset.evaluate(pseudo_results, metric='mIoU')
+ train_dataset.CLASSES = tuple(["a"] * 7)
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mIoU")
assert isinstance(eval_results, dict)
- assert 'mIoU' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
+ assert "mIoU" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
- eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mDice")
assert isinstance(eval_results, dict)
- assert 'mDice' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
+ assert "mDice" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
- eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mFscore")
assert isinstance(eval_results, dict)
- assert 'mRecall' in eval_results
- assert 'mPrecision' in eval_results
- assert 'mFscore' in eval_results
- assert 'aAcc' in eval_results
+ assert "mRecall" in eval_results
+ assert "mPrecision" in eval_results
+ assert "mFscore" in eval_results
+ assert "aAcc" in eval_results
eval_results = train_dataset.evaluate(
- pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+ pseudo_results, metric=["mIoU", "mDice", "mFscore"]
+ )
assert isinstance(eval_results, dict)
- assert 'mIoU' in eval_results
- assert 'mDice' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
- assert 'mFscore' in eval_results
- assert 'mPrecision' in eval_results
- assert 'mRecall' in eval_results
-
- assert not np.isnan(eval_results['mIoU'])
- assert not np.isnan(eval_results['mDice'])
- assert not np.isnan(eval_results['mAcc'])
- assert not np.isnan(eval_results['aAcc'])
- assert not np.isnan(eval_results['mFscore'])
- assert not np.isnan(eval_results['mPrecision'])
- assert not np.isnan(eval_results['mRecall'])
+ assert "mIoU" in eval_results
+ assert "mDice" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
+ assert "mFscore" in eval_results
+ assert "mPrecision" in eval_results
+ assert "mRecall" in eval_results
+
+ assert not np.isnan(eval_results["mIoU"])
+ assert not np.isnan(eval_results["mDice"])
+ assert not np.isnan(eval_results["mAcc"])
+ assert not np.isnan(eval_results["aAcc"])
+ assert not np.isnan(eval_results["mFscore"])
+ assert not np.isnan(eval_results["mPrecision"])
+ assert not np.isnan(eval_results["mRecall"])
# test evaluation with pre-eval and the dataset.CLASSES is necessary
- train_dataset.CLASSES = tuple(['a'] * 7)
+ train_dataset.CLASSES = tuple(["a"] * 7)
pseudo_results = []
for idx in range(len(train_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
pseudo_results.extend(train_dataset.pre_eval(pseudo_result, idx))
- eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
+ eval_results = train_dataset.evaluate(pseudo_results, metric=["mIoU"])
assert isinstance(eval_results, dict)
- assert 'mIoU' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
+ assert "mIoU" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
- eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mDice")
assert isinstance(eval_results, dict)
- assert 'mDice' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
+ assert "mDice" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
- eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
+ eval_results = train_dataset.evaluate(pseudo_results, metric="mFscore")
assert isinstance(eval_results, dict)
- assert 'mRecall' in eval_results
- assert 'mPrecision' in eval_results
- assert 'mFscore' in eval_results
- assert 'aAcc' in eval_results
+ assert "mRecall" in eval_results
+ assert "mPrecision" in eval_results
+ assert "mFscore" in eval_results
+ assert "aAcc" in eval_results
eval_results = train_dataset.evaluate(
- pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+ pseudo_results, metric=["mIoU", "mDice", "mFscore"]
+ )
assert isinstance(eval_results, dict)
- assert 'mIoU' in eval_results
- assert 'mDice' in eval_results
- assert 'mAcc' in eval_results
- assert 'aAcc' in eval_results
- assert 'mFscore' in eval_results
- assert 'mPrecision' in eval_results
- assert 'mRecall' in eval_results
-
- assert not np.isnan(eval_results['mIoU'])
- assert not np.isnan(eval_results['mDice'])
- assert not np.isnan(eval_results['mAcc'])
- assert not np.isnan(eval_results['aAcc'])
- assert not np.isnan(eval_results['mFscore'])
- assert not np.isnan(eval_results['mPrecision'])
- assert not np.isnan(eval_results['mRecall'])
-
-
-@pytest.mark.parametrize('separate_eval', [True, False])
+ assert "mIoU" in eval_results
+ assert "mDice" in eval_results
+ assert "mAcc" in eval_results
+ assert "aAcc" in eval_results
+ assert "mFscore" in eval_results
+ assert "mPrecision" in eval_results
+ assert "mRecall" in eval_results
+
+ assert not np.isnan(eval_results["mIoU"])
+ assert not np.isnan(eval_results["mDice"])
+ assert not np.isnan(eval_results["mAcc"])
+ assert not np.isnan(eval_results["aAcc"])
+ assert not np.isnan(eval_results["mFscore"])
+ assert not np.isnan(eval_results["mPrecision"])
+ assert not np.isnan(eval_results["mRecall"])
+
+
+@pytest.mark.parametrize("separate_eval", [True, False])
def test_eval_concat_custom_dataset(separate_eval):
img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True)
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
+ )
test_pipeline = [
- dict(type='LoadImageFromFile'),
+ dict(type="LoadImageFromFile"),
dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Normalize", **img_norm_cfg),
+ dict(type="ImageToTensor", keys=["img"]),
+ dict(type="Collect", keys=["img"]),
+ ],
+ ),
]
- data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
- img_dir = 'imgs/'
- ann_dir = 'gts/'
+ data_root = osp.join(osp.dirname(__file__), "../data/pseudo_dataset")
+ img_dir = "imgs/"
+ ann_dir = "gts/"
cfg1 = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=test_pipeline,
data_root=data_root,
img_dir=img_dir,
ann_dir=ann_dir,
- img_suffix='img.jpg',
- seg_map_suffix='gt.png',
- classes=tuple(['a'] * 7))
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ classes=tuple(["a"] * 7),
+ )
dataset1 = build_dataset(cfg1)
assert len(dataset1) == 5
# get gt seg map
@@ -413,50 +437,68 @@ def test_eval_concat_custom_dataset(separate_eval):
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
eval_results1 = dataset1.evaluate(
- pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+ pseudo_results, metric=["mIoU", "mDice", "mFscore"]
+ )
# We use same dir twice for simplicity
# with ann_dir
cfg2 = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=test_pipeline,
data_root=data_root,
img_dir=[img_dir, img_dir],
ann_dir=[ann_dir, ann_dir],
- img_suffix='img.jpg',
- seg_map_suffix='gt.png',
- classes=tuple(['a'] * 7),
- separate_eval=separate_eval)
+ img_suffix="img.jpg",
+ seg_map_suffix="gt.png",
+ classes=tuple(["a"] * 7),
+ separate_eval=separate_eval,
+ )
dataset2 = build_dataset(cfg2)
assert isinstance(dataset2, ConcatDataset)
assert len(dataset2) == 10
eval_results2 = dataset2.evaluate(
- pseudo_results * 2, metric=['mIoU', 'mDice', 'mFscore'])
+ pseudo_results * 2, metric=["mIoU", "mDice", "mFscore"]
+ )
if separate_eval:
- assert eval_results1['mIoU'] == eval_results2[
- '0_mIoU'] == eval_results2['1_mIoU']
- assert eval_results1['mDice'] == eval_results2[
- '0_mDice'] == eval_results2['1_mDice']
- assert eval_results1['mAcc'] == eval_results2[
- '0_mAcc'] == eval_results2['1_mAcc']
- assert eval_results1['aAcc'] == eval_results2[
- '0_aAcc'] == eval_results2['1_aAcc']
- assert eval_results1['mFscore'] == eval_results2[
- '0_mFscore'] == eval_results2['1_mFscore']
- assert eval_results1['mPrecision'] == eval_results2[
- '0_mPrecision'] == eval_results2['1_mPrecision']
- assert eval_results1['mRecall'] == eval_results2[
- '0_mRecall'] == eval_results2['1_mRecall']
+ assert (
+ eval_results1["mIoU"] == eval_results2["0_mIoU"] == eval_results2["1_mIoU"]
+ )
+ assert (
+ eval_results1["mDice"]
+ == eval_results2["0_mDice"]
+ == eval_results2["1_mDice"]
+ )
+ assert (
+ eval_results1["mAcc"] == eval_results2["0_mAcc"] == eval_results2["1_mAcc"]
+ )
+ assert (
+ eval_results1["aAcc"] == eval_results2["0_aAcc"] == eval_results2["1_aAcc"]
+ )
+ assert (
+ eval_results1["mFscore"]
+ == eval_results2["0_mFscore"]
+ == eval_results2["1_mFscore"]
+ )
+ assert (
+ eval_results1["mPrecision"]
+ == eval_results2["0_mPrecision"]
+ == eval_results2["1_mPrecision"]
+ )
+ assert (
+ eval_results1["mRecall"]
+ == eval_results2["0_mRecall"]
+ == eval_results2["1_mRecall"]
+ )
else:
- assert eval_results1['mIoU'] == eval_results2['mIoU']
- assert eval_results1['mDice'] == eval_results2['mDice']
- assert eval_results1['mAcc'] == eval_results2['mAcc']
- assert eval_results1['aAcc'] == eval_results2['aAcc']
- assert eval_results1['mFscore'] == eval_results2['mFscore']
- assert eval_results1['mPrecision'] == eval_results2['mPrecision']
- assert eval_results1['mRecall'] == eval_results2['mRecall']
+ assert eval_results1["mIoU"] == eval_results2["mIoU"]
+ assert eval_results1["mDice"] == eval_results2["mDice"]
+ assert eval_results1["mAcc"] == eval_results2["mAcc"]
+ assert eval_results1["aAcc"] == eval_results2["aAcc"]
+ assert eval_results1["mFscore"] == eval_results2["mFscore"]
+ assert eval_results1["mPrecision"] == eval_results2["mPrecision"]
+ assert eval_results1["mRecall"] == eval_results2["mRecall"]
# test get dataset_idx and sample_idx from ConcateDataset
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(3)
@@ -479,7 +521,8 @@ def test_eval_concat_custom_dataset(separate_eval):
indice = -6
dataset_idx1, sample_idx1 = dataset2.get_dataset_idx_and_sample_idx(indice)
dataset_idx2, sample_idx2 = dataset2.get_dataset_idx_and_sample_idx(
- len(dataset2) + indice)
+ len(dataset2) + indice
+ )
assert dataset_idx1 == dataset_idx2
assert sample_idx1 == sample_idx2
@@ -498,7 +541,8 @@ def test_eval_concat_custom_dataset(separate_eval):
assert isinstance(eval_results1[0][0], torch.Tensor)
eval_results1 = dataset1.evaluate(
- eval_results1, metric=['mIoU', 'mDice', 'mFscore'])
+ eval_results1, metric=["mIoU", "mDice", "mFscore"]
+ )
pseudo_results = pseudo_results * 2
eval_results2 = []
@@ -511,35 +555,50 @@ def test_eval_concat_custom_dataset(separate_eval):
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(
- eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
+ eval_results2, metric=["mIoU", "mDice", "mFscore"]
+ )
if separate_eval:
- assert eval_results1['mIoU'] == eval_results2[
- '0_mIoU'] == eval_results2['1_mIoU']
- assert eval_results1['mDice'] == eval_results2[
- '0_mDice'] == eval_results2['1_mDice']
- assert eval_results1['mAcc'] == eval_results2[
- '0_mAcc'] == eval_results2['1_mAcc']
- assert eval_results1['aAcc'] == eval_results2[
- '0_aAcc'] == eval_results2['1_aAcc']
- assert eval_results1['mFscore'] == eval_results2[
- '0_mFscore'] == eval_results2['1_mFscore']
- assert eval_results1['mPrecision'] == eval_results2[
- '0_mPrecision'] == eval_results2['1_mPrecision']
- assert eval_results1['mRecall'] == eval_results2[
- '0_mRecall'] == eval_results2['1_mRecall']
+ assert (
+ eval_results1["mIoU"] == eval_results2["0_mIoU"] == eval_results2["1_mIoU"]
+ )
+ assert (
+ eval_results1["mDice"]
+ == eval_results2["0_mDice"]
+ == eval_results2["1_mDice"]
+ )
+ assert (
+ eval_results1["mAcc"] == eval_results2["0_mAcc"] == eval_results2["1_mAcc"]
+ )
+ assert (
+ eval_results1["aAcc"] == eval_results2["0_aAcc"] == eval_results2["1_aAcc"]
+ )
+ assert (
+ eval_results1["mFscore"]
+ == eval_results2["0_mFscore"]
+ == eval_results2["1_mFscore"]
+ )
+ assert (
+ eval_results1["mPrecision"]
+ == eval_results2["0_mPrecision"]
+ == eval_results2["1_mPrecision"]
+ )
+ assert (
+ eval_results1["mRecall"]
+ == eval_results2["0_mRecall"]
+ == eval_results2["1_mRecall"]
+ )
else:
- assert eval_results1['mIoU'] == eval_results2['mIoU']
- assert eval_results1['mDice'] == eval_results2['mDice']
- assert eval_results1['mAcc'] == eval_results2['mAcc']
- assert eval_results1['aAcc'] == eval_results2['aAcc']
- assert eval_results1['mFscore'] == eval_results2['mFscore']
- assert eval_results1['mPrecision'] == eval_results2['mPrecision']
- assert eval_results1['mRecall'] == eval_results2['mRecall']
+ assert eval_results1["mIoU"] == eval_results2["mIoU"]
+ assert eval_results1["mDice"] == eval_results2["mDice"]
+ assert eval_results1["mAcc"] == eval_results2["mAcc"]
+ assert eval_results1["aAcc"] == eval_results2["aAcc"]
+ assert eval_results1["mFscore"] == eval_results2["mFscore"]
+ assert eval_results1["mPrecision"] == eval_results2["mPrecision"]
+ assert eval_results1["mRecall"] == eval_results2["mRecall"]
# test batch_indices for pre eval
- eval_results2 = dataset2.pre_eval(pseudo_results,
- list(range(len(pseudo_results))))
+ eval_results2 = dataset2.pre_eval(pseudo_results, list(range(len(pseudo_results))))
assert len(eval_results2) == len(dataset2)
assert isinstance(eval_results2[0], tuple)
@@ -547,37 +606,54 @@ def test_eval_concat_custom_dataset(separate_eval):
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(
- eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
+ eval_results2, metric=["mIoU", "mDice", "mFscore"]
+ )
if separate_eval:
- assert eval_results1['mIoU'] == eval_results2[
- '0_mIoU'] == eval_results2['1_mIoU']
- assert eval_results1['mDice'] == eval_results2[
- '0_mDice'] == eval_results2['1_mDice']
- assert eval_results1['mAcc'] == eval_results2[
- '0_mAcc'] == eval_results2['1_mAcc']
- assert eval_results1['aAcc'] == eval_results2[
- '0_aAcc'] == eval_results2['1_aAcc']
- assert eval_results1['mFscore'] == eval_results2[
- '0_mFscore'] == eval_results2['1_mFscore']
- assert eval_results1['mPrecision'] == eval_results2[
- '0_mPrecision'] == eval_results2['1_mPrecision']
- assert eval_results1['mRecall'] == eval_results2[
- '0_mRecall'] == eval_results2['1_mRecall']
+ assert (
+ eval_results1["mIoU"] == eval_results2["0_mIoU"] == eval_results2["1_mIoU"]
+ )
+ assert (
+ eval_results1["mDice"]
+ == eval_results2["0_mDice"]
+ == eval_results2["1_mDice"]
+ )
+ assert (
+ eval_results1["mAcc"] == eval_results2["0_mAcc"] == eval_results2["1_mAcc"]
+ )
+ assert (
+ eval_results1["aAcc"] == eval_results2["0_aAcc"] == eval_results2["1_aAcc"]
+ )
+ assert (
+ eval_results1["mFscore"]
+ == eval_results2["0_mFscore"]
+ == eval_results2["1_mFscore"]
+ )
+ assert (
+ eval_results1["mPrecision"]
+ == eval_results2["0_mPrecision"]
+ == eval_results2["1_mPrecision"]
+ )
+ assert (
+ eval_results1["mRecall"]
+ == eval_results2["0_mRecall"]
+ == eval_results2["1_mRecall"]
+ )
else:
- assert eval_results1['mIoU'] == eval_results2['mIoU']
- assert eval_results1['mDice'] == eval_results2['mDice']
- assert eval_results1['mAcc'] == eval_results2['mAcc']
- assert eval_results1['aAcc'] == eval_results2['aAcc']
- assert eval_results1['mFscore'] == eval_results2['mFscore']
- assert eval_results1['mPrecision'] == eval_results2['mPrecision']
- assert eval_results1['mRecall'] == eval_results2['mRecall']
+ assert eval_results1["mIoU"] == eval_results2["mIoU"]
+ assert eval_results1["mDice"] == eval_results2["mDice"]
+ assert eval_results1["mAcc"] == eval_results2["mAcc"]
+ assert eval_results1["aAcc"] == eval_results2["aAcc"]
+ assert eval_results1["mFscore"] == eval_results2["mFscore"]
+ assert eval_results1["mPrecision"] == eval_results2["mPrecision"]
+ assert eval_results1["mRecall"] == eval_results2["mRecall"]
def test_ade():
test_dataset = ADE20KDataset(
pipeline=[],
- img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs"),
+ )
assert len(test_dataset) == 5
# Test format_results
@@ -586,23 +662,25 @@ def test_ade():
h, w = (2, 2)
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
- file_paths = test_dataset.format_results(pseudo_results, '.format_ade')
+ file_paths = test_dataset.format_results(pseudo_results, ".format_ade")
assert len(file_paths) == len(test_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
- shutil.rmtree('.format_ade')
+ shutil.rmtree(".format_ade")
-@pytest.mark.parametrize('separate_eval', [True, False])
+@pytest.mark.parametrize("separate_eval", [True, False])
def test_concat_ade(separate_eval):
test_dataset = ADE20KDataset(
pipeline=[],
- img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs"),
+ )
assert len(test_dataset) == 5
- concat_dataset = ConcatDataset([test_dataset, test_dataset],
- separate_eval=separate_eval)
+ concat_dataset = ConcatDataset(
+ [test_dataset, test_dataset], separate_eval=separate_eval
+ )
assert len(concat_dataset) == 10
# Test format_results
pseudo_results = []
@@ -614,32 +692,35 @@ def test_concat_ade(separate_eval):
file_paths = []
for i in range(len(pseudo_results)):
file_paths.extend(
- concat_dataset.format_results([pseudo_results[i]],
- '.format_ade',
- indices=[i]))
+ concat_dataset.format_results(
+ [pseudo_results[i]], ".format_ade", indices=[i]
+ )
+ )
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
- shutil.rmtree('.format_ade')
+ shutil.rmtree(".format_ade")
# test default argument
- file_paths = concat_dataset.format_results(pseudo_results, '.format_ade')
+ file_paths = concat_dataset.format_results(pseudo_results, ".format_ade")
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
- shutil.rmtree('.format_ade')
+ shutil.rmtree(".format_ade")
def test_cityscapes():
test_dataset = CityscapesDataset(
pipeline=[],
img_dir=osp.join(
- osp.dirname(__file__),
- '../data/pseudo_cityscapes_dataset/leftImg8bit'),
+ osp.dirname(__file__), "../data/pseudo_cityscapes_dataset/leftImg8bit"
+ ),
ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
+ osp.dirname(__file__), "../data/pseudo_cityscapes_dataset/gtFine"
+ ),
+ )
assert len(test_dataset) == 1
gt_seg_maps = list(test_dataset.get_gt_seg_maps())
@@ -650,49 +731,55 @@ def test_cityscapes():
h, w = gt_seg_maps[idx].shape
pseudo_results.append(np.random.randint(low=0, high=19, size=(h, w)))
- file_paths = test_dataset.format_results(pseudo_results, '.format_city')
+ file_paths = test_dataset.format_results(pseudo_results, ".format_city")
assert len(file_paths) == len(test_dataset)
temp = np.array(Image.open(file_paths[0]))
- assert np.allclose(temp,
- test_dataset._convert_to_label_id(pseudo_results[0]))
+ assert np.allclose(temp, test_dataset._convert_to_label_id(pseudo_results[0]))
# Test cityscapes evaluate
test_dataset.evaluate(
- pseudo_results, metric='cityscapes', imgfile_prefix='.format_city')
+ pseudo_results, metric="cityscapes", imgfile_prefix=".format_city"
+ )
- shutil.rmtree('.format_city')
+ shutil.rmtree(".format_city")
-@pytest.mark.parametrize('separate_eval', [True, False])
+@pytest.mark.parametrize("separate_eval", [True, False])
def test_concat_cityscapes(separate_eval):
cityscape_dataset = CityscapesDataset(
pipeline=[],
img_dir=osp.join(
- osp.dirname(__file__),
- '../data/pseudo_cityscapes_dataset/leftImg8bit'),
+ osp.dirname(__file__), "../data/pseudo_cityscapes_dataset/leftImg8bit"
+ ),
ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
+ osp.dirname(__file__), "../data/pseudo_cityscapes_dataset/gtFine"
+ ),
+ )
assert len(cityscape_dataset) == 1
with pytest.raises(NotImplementedError):
- _ = ConcatDataset([cityscape_dataset, cityscape_dataset],
- separate_eval=separate_eval)
+ _ = ConcatDataset(
+ [cityscape_dataset, cityscape_dataset], separate_eval=separate_eval
+ )
ade_dataset = ADE20KDataset(
pipeline=[],
- img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_dataset/imgs"),
+ )
assert len(ade_dataset) == 5
with pytest.raises(NotImplementedError):
- _ = ConcatDataset([cityscape_dataset, ade_dataset],
- separate_eval=separate_eval)
+ _ = ConcatDataset([cityscape_dataset, ade_dataset], separate_eval=separate_eval)
def test_loveda():
test_dataset = LoveDADataset(
pipeline=[],
img_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_loveda_dataset/img_dir'),
+ osp.dirname(__file__), "../data/pseudo_loveda_dataset/img_dir"
+ ),
ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_loveda_dataset/ann_dir'))
+ osp.dirname(__file__), "../data/pseudo_loveda_dataset/ann_dir"
+ ),
+ )
assert len(test_dataset) == 3
gt_seg_maps = list(test_dataset.get_gt_seg_maps())
@@ -702,23 +789,27 @@ def test_loveda():
for idx in range(len(test_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
- file_paths = test_dataset.format_results(pseudo_results, '.format_loveda')
+ file_paths = test_dataset.format_results(pseudo_results, ".format_loveda")
assert len(file_paths) == len(test_dataset)
# Test loveda evaluate
test_dataset.evaluate(
- pseudo_results, metric='mIoU', imgfile_prefix='.format_loveda')
+ pseudo_results, metric="mIoU", imgfile_prefix=".format_loveda"
+ )
- shutil.rmtree('.format_loveda')
+ shutil.rmtree(".format_loveda")
def test_potsdam():
test_dataset = PotsdamDataset(
pipeline=[],
img_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_potsdam_dataset/img_dir'),
+ osp.dirname(__file__), "../data/pseudo_potsdam_dataset/img_dir"
+ ),
ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_potsdam_dataset/ann_dir'))
+ osp.dirname(__file__), "../data/pseudo_potsdam_dataset/ann_dir"
+ ),
+ )
assert len(test_dataset) == 1
@@ -726,42 +817,47 @@ def test_vaihingen():
test_dataset = ISPRSDataset(
pipeline=[],
img_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_vaihingen_dataset/img_dir'),
+ osp.dirname(__file__), "../data/pseudo_vaihingen_dataset/img_dir"
+ ),
ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_vaihingen_dataset/ann_dir'))
+ osp.dirname(__file__), "../data/pseudo_vaihingen_dataset/ann_dir"
+ ),
+ )
assert len(test_dataset) == 1
def test_isaid():
test_dataset = iSAIDDataset(
pipeline=[],
- img_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
- ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'))
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_isaid_dataset/img_dir"),
+ ann_dir=osp.join(osp.dirname(__file__), "../data/pseudo_isaid_dataset/ann_dir"),
+ )
assert len(test_dataset) == 2
isaid_info = test_dataset.load_annotations(
- img_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
- img_suffix='.png',
- ann_dir=osp.join(
- osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'),
- seg_map_suffix='.png',
+ img_dir=osp.join(osp.dirname(__file__), "../data/pseudo_isaid_dataset/img_dir"),
+ img_suffix=".png",
+ ann_dir=osp.join(osp.dirname(__file__), "../data/pseudo_isaid_dataset/ann_dir"),
+ seg_map_suffix=".png",
split=osp.join(
- osp.dirname(__file__),
- '../data/pseudo_isaid_dataset/splits/train.txt'))
+ osp.dirname(__file__), "../data/pseudo_isaid_dataset/splits/train.txt"
+ ),
+ )
assert len(isaid_info) == 1
-@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
-@patch('mmseg.datasets.CustomDataset.__getitem__',
- MagicMock(side_effect=lambda idx: idx))
-@pytest.mark.parametrize('dataset, classes', [
- ('ADE20KDataset', ('wall', 'building')),
- ('CityscapesDataset', ('road', 'sidewalk')),
- ('CustomDataset', ('bus', 'car')),
- ('PascalVOCDataset', ('aeroplane', 'bicycle')),
-])
+@patch("mmseg.datasets.CustomDataset.load_annotations", MagicMock)
+@patch(
+ "mmseg.datasets.CustomDataset.__getitem__", MagicMock(side_effect=lambda idx: idx)
+)
+@pytest.mark.parametrize(
+ "dataset, classes",
+ [
+ ("ADE20KDataset", ("wall", "building")),
+ ("CityscapesDataset", ("road", "sidewalk")),
+ ("CustomDataset", ("bus", "car")),
+ ("PascalVOCDataset", ("aeroplane", "bicycle")),
+ ],
+)
def test_custom_classes_override_default(dataset, classes):
dataset_class = DATASETS.get(dataset)
@@ -774,7 +870,8 @@ def test_custom_classes_override_default(dataset, classes):
img_dir=MagicMock(),
split=MagicMock(),
classes=classes,
- test_mode=True)
+ test_mode=True,
+ )
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == classes
@@ -785,7 +882,8 @@ def test_custom_classes_override_default(dataset, classes):
img_dir=MagicMock(),
split=MagicMock(),
classes=list(classes),
- test_mode=True)
+ test_mode=True,
+ )
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == list(classes)
@@ -796,7 +894,8 @@ def test_custom_classes_override_default(dataset, classes):
img_dir=MagicMock(),
split=MagicMock(),
classes=[classes[0]],
- test_mode=True)
+ test_mode=True,
+ )
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == [classes[0]]
@@ -809,43 +908,49 @@ def test_custom_classes_override_default(dataset, classes):
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
- test_mode=True)
+ test_mode=True,
+ )
else:
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
- test_mode=True)
+ test_mode=True,
+ )
assert custom_dataset.CLASSES == original_classes
-@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
-@patch('mmseg.datasets.CustomDataset.__getitem__',
- MagicMock(side_effect=lambda idx: idx))
+@patch("mmseg.datasets.CustomDataset.load_annotations", MagicMock)
+@patch(
+ "mmseg.datasets.CustomDataset.__getitem__", MagicMock(side_effect=lambda idx: idx)
+)
def test_custom_dataset_random_palette_is_generated():
dataset = CustomDataset(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
- classes=('bus', 'car'),
- test_mode=True)
+ classes=("bus", "car"),
+ test_mode=True,
+ )
assert len(dataset.PALETTE) == 2
for class_color in dataset.PALETTE:
assert len(class_color) == 3
assert all(x >= 0 and x <= 255 for x in class_color)
-@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
-@patch('mmseg.datasets.CustomDataset.__getitem__',
- MagicMock(side_effect=lambda idx: idx))
+@patch("mmseg.datasets.CustomDataset.load_annotations", MagicMock)
+@patch(
+ "mmseg.datasets.CustomDataset.__getitem__", MagicMock(side_effect=lambda idx: idx)
+)
def test_custom_dataset_custom_palette():
dataset = CustomDataset(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
- classes=('bus', 'car'),
+ classes=("bus", "car"),
palette=[[100, 100, 100], [200, 200, 200]],
- test_mode=True)
+ test_mode=True,
+ )
assert tuple(dataset.PALETTE) == tuple([[100, 100, 100], [200, 200, 200]])
diff --git a/mmsegmentation/tests/test_data/test_dataset_builder.py b/mmsegmentation/tests/test_data/test_dataset_builder.py
index 30910b0..c747aea 100644
--- a/mmsegmentation/tests/test_data/test_dataset_builder.py
+++ b/mmsegmentation/tests/test_data/test_dataset_builder.py
@@ -3,15 +3,19 @@
import os.path as osp
import pytest
-from torch.utils.data import (DistributedSampler, RandomSampler,
- SequentialSampler)
+from torch.utils.data import DistributedSampler, RandomSampler, SequentialSampler
-from mmseg.datasets import (DATASETS, ConcatDataset, MultiImageMixDataset,
- build_dataloader, build_dataset)
+from mmseg.datasets import (
+ DATASETS,
+ ConcatDataset,
+ MultiImageMixDataset,
+ build_dataloader,
+ build_dataset,
+)
@DATASETS.register_module()
-class ToyDataset(object):
+class ToyDataset:
def __init__(self, cnt=0):
self.cnt = cnt
@@ -24,7 +28,7 @@ def __len__(self):
def test_build_dataset():
- cfg = dict(type='ToyDataset')
+ cfg = dict(type="ToyDataset")
dataset = build_dataset(cfg)
assert isinstance(dataset, ToyDataset)
assert dataset.cnt == 0
@@ -32,72 +36,77 @@ def test_build_dataset():
assert isinstance(dataset, ToyDataset)
assert dataset.cnt == 1
- data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
- img_dir = 'imgs/'
- ann_dir = 'gts/'
+ data_root = osp.join(osp.dirname(__file__), "../data/pseudo_dataset")
+ img_dir = "imgs/"
+ ann_dir = "gts/"
# We use same dir twice for simplicity
# with ann_dir
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
- ann_dir=[ann_dir, ann_dir])
+ ann_dir=[ann_dir, ann_dir],
+ )
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 10
- cfg = dict(type='MultiImageMixDataset', dataset=cfg, pipeline=[])
+ cfg = dict(type="MultiImageMixDataset", dataset=cfg, pipeline=[])
dataset = build_dataset(cfg)
assert isinstance(dataset, MultiImageMixDataset)
assert len(dataset) == 10
# with ann_dir, split
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=ann_dir,
- split=['splits/train.txt', 'splits/val.txt'])
+ split=["splits/train.txt", "splits/val.txt"],
+ )
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 5
# with ann_dir, split
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=[ann_dir, ann_dir],
- split=['splits/train.txt', 'splits/val.txt'])
+ split=["splits/train.txt", "splits/val.txt"],
+ )
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 5
# test mode
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
test_mode=True,
- classes=('pseudo_class', ))
+ classes=("pseudo_class",),
+ )
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 10
# test mode with splits
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
- split=['splits/val.txt', 'splits/val.txt'],
+ split=["splits/val.txt", "splits/val.txt"],
test_mode=True,
- classes=('pseudo_class', ))
+ classes=("pseudo_class",),
+ )
dataset = build_dataset(cfg)
assert isinstance(dataset, ConcatDataset)
assert len(dataset) == 2
@@ -105,33 +114,36 @@ def test_build_dataset():
# len(ann_dir) should be zero or len(img_dir) when len(img_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
- ann_dir=[ann_dir, ann_dir, ann_dir])
+ ann_dir=[ann_dir, ann_dir, ann_dir],
+ )
build_dataset(cfg)
# len(splits) should be zero or len(img_dir) when len(img_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=[img_dir, img_dir],
- split=['splits/val.txt', 'splits/val.txt', 'splits/val.txt'])
+ split=["splits/val.txt", "splits/val.txt", "splits/val.txt"],
+ )
build_dataset(cfg)
# len(splits) == len(ann_dir) when only len(img_dir) == 1 and len(
# ann_dir) > 1
with pytest.raises(AssertionError):
cfg = dict(
- type='CustomDataset',
+ type="CustomDataset",
pipeline=[],
data_root=data_root,
img_dir=img_dir,
ann_dir=[ann_dir, ann_dir],
- split=['splits/val.txt', 'splits/val.txt', 'splits/val.txt'])
+ split=["splits/val.txt", "splits/val.txt", "splits/val.txt"],
+ )
build_dataset(cfg)
@@ -140,7 +152,8 @@ def test_build_dataloader():
samples_per_gpu = 3
# dist=True, shuffle=True, 1GPU
dataloader = build_dataloader(
- dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2)
+ dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2
+ )
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, DistributedSampler)
@@ -148,10 +161,8 @@ def test_build_dataloader():
# dist=True, shuffle=False, 1GPU
dataloader = build_dataloader(
- dataset,
- samples_per_gpu=samples_per_gpu,
- workers_per_gpu=2,
- shuffle=False)
+ dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2, shuffle=False
+ )
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, DistributedSampler)
@@ -159,20 +170,16 @@ def test_build_dataloader():
# dist=True, shuffle=True, 8GPU
dataloader = build_dataloader(
- dataset,
- samples_per_gpu=samples_per_gpu,
- workers_per_gpu=2,
- num_gpus=8)
+ dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2, num_gpus=8
+ )
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert dataloader.num_workers == 2
# dist=False, shuffle=True, 1GPU
dataloader = build_dataloader(
- dataset,
- samples_per_gpu=samples_per_gpu,
- workers_per_gpu=2,
- dist=False)
+ dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=2, dist=False
+ )
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, RandomSampler)
@@ -180,11 +187,8 @@ def test_build_dataloader():
# dist=False, shuffle=False, 1GPU
dataloader = build_dataloader(
- dataset,
- samples_per_gpu=3,
- workers_per_gpu=2,
- shuffle=False,
- dist=False)
+ dataset, samples_per_gpu=3, workers_per_gpu=2, shuffle=False, dist=False
+ )
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, SequentialSampler)
@@ -192,9 +196,9 @@ def test_build_dataloader():
# dist=False, shuffle=True, 8GPU
dataloader = build_dataloader(
- dataset, samples_per_gpu=3, workers_per_gpu=2, num_gpus=8, dist=False)
+ dataset, samples_per_gpu=3, workers_per_gpu=2, num_gpus=8, dist=False
+ )
assert dataloader.batch_size == samples_per_gpu * 8
- assert len(dataloader) == int(
- math.ceil(len(dataset) / samples_per_gpu / 8))
+ assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu / 8))
assert isinstance(dataloader.sampler, RandomSampler)
assert dataloader.num_workers == 16
diff --git a/mmsegmentation/tests/test_data/test_loading.py b/mmsegmentation/tests/test_data/test_loading.py
index fdda93e..6c3c081 100644
--- a/mmsegmentation/tests/test_data/test_loading.py
+++ b/mmsegmentation/tests/test_data/test_loading.py
@@ -9,98 +9,100 @@
from mmseg.datasets.pipelines import LoadAnnotations, LoadImageFromFile
-class TestLoading(object):
+class TestLoading:
@classmethod
def setup_class(cls):
- cls.data_prefix = osp.join(osp.dirname(__file__), '../data')
+ cls.data_prefix = osp.join(osp.dirname(__file__), "../data")
def test_load_img(self):
- results = dict(
- img_prefix=self.data_prefix, img_info=dict(filename='color.jpg'))
+ results = dict(img_prefix=self.data_prefix, img_info=dict(filename="color.jpg"))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
- assert results['filename'] == osp.join(self.data_prefix, 'color.jpg')
- assert results['ori_filename'] == 'color.jpg'
- assert results['img'].shape == (288, 512, 3)
- assert results['img'].dtype == np.uint8
- assert results['img_shape'] == (288, 512, 3)
- assert results['ori_shape'] == (288, 512, 3)
- assert results['pad_shape'] == (288, 512, 3)
- assert results['scale_factor'] == 1.0
- np.testing.assert_equal(results['img_norm_cfg']['mean'],
- np.zeros(3, dtype=np.float32))
- assert repr(transform) == transform.__class__.__name__ + \
- "(to_float32=False,color_type='color',imdecode_backend='cv2')"
+ assert results["filename"] == osp.join(self.data_prefix, "color.jpg")
+ assert results["ori_filename"] == "color.jpg"
+ assert results["img"].shape == (288, 512, 3)
+ assert results["img"].dtype == np.uint8
+ assert results["img_shape"] == (288, 512, 3)
+ assert results["ori_shape"] == (288, 512, 3)
+ assert results["pad_shape"] == (288, 512, 3)
+ assert results["scale_factor"] == 1.0
+ np.testing.assert_equal(
+ results["img_norm_cfg"]["mean"], np.zeros(3, dtype=np.float32)
+ )
+ assert (
+ repr(transform)
+ == transform.__class__.__name__
+ + "(to_float32=False,color_type='color',imdecode_backend='cv2')"
+ )
# no img_prefix
- results = dict(
- img_prefix=None, img_info=dict(filename='tests/data/color.jpg'))
+ results = dict(img_prefix=None, img_info=dict(filename="tests/data/color.jpg"))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
- assert results['filename'] == 'tests/data/color.jpg'
- assert results['ori_filename'] == 'tests/data/color.jpg'
- assert results['img'].shape == (288, 512, 3)
+ assert results["filename"] == "tests/data/color.jpg"
+ assert results["ori_filename"] == "tests/data/color.jpg"
+ assert results["img"].shape == (288, 512, 3)
# to_float32
transform = LoadImageFromFile(to_float32=True)
results = transform(copy.deepcopy(results))
- assert results['img'].dtype == np.float32
+ assert results["img"].dtype == np.float32
# gray image
- results = dict(
- img_prefix=self.data_prefix, img_info=dict(filename='gray.jpg'))
+ results = dict(img_prefix=self.data_prefix, img_info=dict(filename="gray.jpg"))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
- assert results['img'].shape == (288, 512, 3)
- assert results['img'].dtype == np.uint8
+ assert results["img"].shape == (288, 512, 3)
+ assert results["img"].dtype == np.uint8
- transform = LoadImageFromFile(color_type='unchanged')
+ transform = LoadImageFromFile(color_type="unchanged")
results = transform(copy.deepcopy(results))
- assert results['img'].shape == (288, 512)
- assert results['img'].dtype == np.uint8
- np.testing.assert_equal(results['img_norm_cfg']['mean'],
- np.zeros(1, dtype=np.float32))
+ assert results["img"].shape == (288, 512)
+ assert results["img"].dtype == np.uint8
+ np.testing.assert_equal(
+ results["img_norm_cfg"]["mean"], np.zeros(1, dtype=np.float32)
+ )
def test_load_seg(self):
results = dict(
- seg_prefix=self.data_prefix,
- ann_info=dict(seg_map='seg.png'),
- seg_fields=[])
+ seg_prefix=self.data_prefix, ann_info=dict(seg_map="seg.png"), seg_fields=[]
+ )
transform = LoadAnnotations()
results = transform(copy.deepcopy(results))
- assert results['seg_fields'] == ['gt_semantic_seg']
- assert results['gt_semantic_seg'].shape == (288, 512)
- assert results['gt_semantic_seg'].dtype == np.uint8
- assert repr(transform) == transform.__class__.__name__ + \
- "(reduce_zero_label=False,imdecode_backend='pillow')"
+ assert results["seg_fields"] == ["gt_semantic_seg"]
+ assert results["gt_semantic_seg"].shape == (288, 512)
+ assert results["gt_semantic_seg"].dtype == np.uint8
+ assert (
+ repr(transform)
+ == transform.__class__.__name__
+ + "(reduce_zero_label=False,imdecode_backend='pillow')"
+ )
# no img_prefix
results = dict(
- seg_prefix=None,
- ann_info=dict(seg_map='tests/data/seg.png'),
- seg_fields=[])
+ seg_prefix=None, ann_info=dict(seg_map="tests/data/seg.png"), seg_fields=[]
+ )
transform = LoadAnnotations()
results = transform(copy.deepcopy(results))
- assert results['gt_semantic_seg'].shape == (288, 512)
- assert results['gt_semantic_seg'].dtype == np.uint8
+ assert results["gt_semantic_seg"].shape == (288, 512)
+ assert results["gt_semantic_seg"].dtype == np.uint8
# reduce_zero_label
transform = LoadAnnotations(reduce_zero_label=True)
results = transform(copy.deepcopy(results))
- assert results['gt_semantic_seg'].shape == (288, 512)
- assert results['gt_semantic_seg'].dtype == np.uint8
+ assert results["gt_semantic_seg"].shape == (288, 512)
+ assert results["gt_semantic_seg"].dtype == np.uint8
# mmcv backend
results = dict(
- seg_prefix=self.data_prefix,
- ann_info=dict(seg_map='seg.png'),
- seg_fields=[])
- transform = LoadAnnotations(imdecode_backend='pillow')
+ seg_prefix=self.data_prefix, ann_info=dict(seg_map="seg.png"), seg_fields=[]
+ )
+ transform = LoadAnnotations(imdecode_backend="pillow")
results = transform(copy.deepcopy(results))
# this image is saved by PIL
- assert results['gt_semantic_seg'].shape == (288, 512)
- assert results['gt_semantic_seg'].dtype == np.uint8
+ assert results["gt_semantic_seg"].shape == (288, 512)
+ assert results["gt_semantic_seg"].dtype == np.uint8
def test_load_seg_custom_classes(self):
@@ -112,8 +114,8 @@ def test_load_seg_custom_classes(self):
test_gt[6:8, 6:8] = 4
tmp_dir = tempfile.TemporaryDirectory()
- img_path = osp.join(tmp_dir.name, 'img.jpg')
- gt_path = osp.join(tmp_dir.name, 'gt.png')
+ img_path = osp.join(tmp_dir.name, "img.jpg")
+ gt_path = osp.join(tmp_dir.name, "gt.png")
mmcv.imwrite(test_img, img_path)
mmcv.imwrite(test_gt, gt_path)
@@ -122,14 +124,9 @@ def test_load_seg_custom_classes(self):
results = dict(
img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
- label_map={
- 0: 0,
- 1: 0,
- 2: 0,
- 3: 1,
- 4: 0
- },
- seg_fields=[])
+ label_map={0: 0, 1: 0, 2: 0, 3: 1, 4: 0},
+ seg_fields=[],
+ )
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
@@ -137,12 +134,12 @@ def test_load_seg_custom_classes(self):
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
- gt_array = results['gt_semantic_seg']
+ gt_array = results["gt_semantic_seg"]
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 1
- assert results['seg_fields'] == ['gt_semantic_seg']
+ assert results["seg_fields"] == ["gt_semantic_seg"]
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
@@ -151,14 +148,9 @@ def test_load_seg_custom_classes(self):
results = dict(
img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
- label_map={
- 0: 0,
- 1: 0,
- 2: 0,
- 3: 2,
- 4: 1
- },
- seg_fields=[])
+ label_map={0: 0, 1: 0, 2: 0, 3: 2, 4: 1},
+ seg_fields=[],
+ )
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
@@ -166,13 +158,13 @@ def test_load_seg_custom_classes(self):
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
- gt_array = results['gt_semantic_seg']
+ gt_array = results["gt_semantic_seg"]
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 2
true_mask[6:8, 6:8] = 1
- assert results['seg_fields'] == ['gt_semantic_seg']
+ assert results["seg_fields"] == ["gt_semantic_seg"]
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
@@ -181,7 +173,8 @@ def test_load_seg_custom_classes(self):
results = dict(
img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
- seg_fields=[])
+ seg_fields=[],
+ )
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
@@ -189,9 +182,9 @@ def test_load_seg_custom_classes(self):
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
- gt_array = results['gt_semantic_seg']
+ gt_array = results["gt_semantic_seg"]
- assert results['seg_fields'] == ['gt_semantic_seg']
+ assert results["seg_fields"] == ["gt_semantic_seg"]
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, test_gt)
diff --git a/mmsegmentation/tests/test_data/test_transform.py b/mmsegmentation/tests/test_data/test_transform.py
index fcc46e7..a4d6abf 100644
--- a/mmsegmentation/tests/test_data/test_transform.py
+++ b/mmsegmentation/tests/test_data/test_transform.py
@@ -12,242 +12,236 @@
def test_resize_to_multiple():
- transform = dict(type='ResizeToMultiple', size_divisor=32)
+ transform = dict(type="ResizeToMultiple", size_divisor=32)
transform = build_from_cfg(transform, PIPELINES)
img = np.random.randn(213, 232, 3)
seg = np.random.randint(0, 19, (213, 232))
results = dict()
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['pad_shape'] = img.shape
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["pad_shape"] = img.shape
results = transform(results)
- assert results['img'].shape == (224, 256, 3)
- assert results['gt_semantic_seg'].shape == (224, 256)
- assert results['img_shape'] == (224, 256, 3)
- assert results['pad_shape'] == (224, 256, 3)
+ assert results["img"].shape == (224, 256, 3)
+ assert results["gt_semantic_seg"].shape == (224, 256)
+ assert results["img_shape"] == (224, 256, 3)
+ assert results["pad_shape"] == (224, 256, 3)
def test_resize():
# test assertion if img_scale is a list
with pytest.raises(AssertionError):
- transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True)
+ transform = dict(type="Resize", img_scale=[1333, 800], keep_ratio=True)
build_from_cfg(transform, PIPELINES)
# test assertion if len(img_scale) while ratio_range is not None
with pytest.raises(AssertionError):
transform = dict(
- type='Resize',
+ type="Resize",
img_scale=[(1333, 800), (1333, 600)],
ratio_range=(0.9, 1.1),
- keep_ratio=True)
+ keep_ratio=True,
+ )
build_from_cfg(transform, PIPELINES)
# test assertion for invalid multiscale_mode
with pytest.raises(AssertionError):
transform = dict(
- type='Resize',
+ type="Resize",
img_scale=[(1333, 800), (1333, 600)],
keep_ratio=True,
- multiscale_mode='2333')
+ multiscale_mode="2333",
+ )
build_from_cfg(transform, PIPELINES)
- transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
+ transform = dict(type="Resize", img_scale=(1333, 800), keep_ratio=True)
resize_module = build_from_cfg(transform, PIPELINES)
results = dict()
# (288, 512, 3)
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] == (750, 1333, 3)
+ assert resized_results["img_shape"] == (750, 1333, 3)
# test keep_ratio=False
transform = dict(
- type='Resize',
- img_scale=(1280, 800),
- multiscale_mode='value',
- keep_ratio=False)
+ type="Resize", img_scale=(1280, 800), multiscale_mode="value", keep_ratio=False
+ )
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] == (800, 1280, 3)
+ assert resized_results["img_shape"] == (800, 1280, 3)
# test multiscale_mode='range'
transform = dict(
- type='Resize',
+ type="Resize",
img_scale=[(1333, 400), (1333, 1200)],
- multiscale_mode='range',
- keep_ratio=True)
+ multiscale_mode="range",
+ keep_ratio=True,
+ )
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert max(resized_results['img_shape'][:2]) <= 1333
- assert min(resized_results['img_shape'][:2]) >= 400
- assert min(resized_results['img_shape'][:2]) <= 1200
+ assert max(resized_results["img_shape"][:2]) <= 1333
+ assert min(resized_results["img_shape"][:2]) >= 400
+ assert min(resized_results["img_shape"][:2]) <= 1200
# test multiscale_mode='value'
transform = dict(
- type='Resize',
+ type="Resize",
img_scale=[(1333, 800), (1333, 400)],
- multiscale_mode='value',
- keep_ratio=True)
+ multiscale_mode="value",
+ keep_ratio=True,
+ )
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] in [(750, 1333, 3), (400, 711, 3)]
+ assert resized_results["img_shape"] in [(750, 1333, 3), (400, 711, 3)]
# test multiscale_mode='range'
transform = dict(
- type='Resize',
- img_scale=(1333, 800),
- ratio_range=(0.9, 1.1),
- keep_ratio=True)
+ type="Resize", img_scale=(1333, 800), ratio_range=(0.9, 1.1), keep_ratio=True
+ )
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
+ assert max(resized_results["img_shape"][:2]) <= 1333 * 1.1
# test img_scale=None and ratio_range is tuple.
# img shape: (288, 512, 3)
transform = dict(
- type='Resize', img_scale=None, ratio_range=(0.5, 2.0), keep_ratio=True)
+ type="Resize", img_scale=None, ratio_range=(0.5, 2.0), keep_ratio=True
+ )
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert int(288 * 0.5) <= resized_results['img_shape'][0] <= 288 * 2.0
- assert int(512 * 0.5) <= resized_results['img_shape'][1] <= 512 * 2.0
+ assert int(288 * 0.5) <= resized_results["img_shape"][0] <= 288 * 2.0
+ assert int(512 * 0.5) <= resized_results["img_shape"][1] <= 512 * 2.0
# test min_size=640
- transform = dict(type='Resize', img_scale=(2560, 640), min_size=640)
+ transform = dict(type="Resize", img_scale=(2560, 640), min_size=640)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] == (640, 1138, 3)
+ assert resized_results["img_shape"] == (640, 1138, 3)
# test min_size=640 and img_scale=(512, 640)
- transform = dict(type='Resize', img_scale=(512, 640), min_size=640)
+ transform = dict(type="Resize", img_scale=(512, 640), min_size=640)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] == (640, 1138, 3)
+ assert resized_results["img_shape"] == (640, 1138, 3)
# test h > w
img = np.random.randn(512, 288, 3)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
- transform = dict(type='Resize', img_scale=(2560, 640), min_size=640)
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
+ transform = dict(type="Resize", img_scale=(2560, 640), min_size=640)
resize_module = build_from_cfg(transform, PIPELINES)
resized_results = resize_module(results.copy())
- assert resized_results['img_shape'] == (1138, 640, 3)
+ assert resized_results["img_shape"] == (1138, 640, 3)
def test_flip():
# test assertion for invalid prob
with pytest.raises(AssertionError):
- transform = dict(type='RandomFlip', prob=1.5)
+ transform = dict(type="RandomFlip", prob=1.5)
build_from_cfg(transform, PIPELINES)
# test assertion for invalid direction
with pytest.raises(AssertionError):
- transform = dict(type='RandomFlip', prob=1, direction='horizonta')
+ transform = dict(type="RandomFlip", prob=1, direction="horizonta")
build_from_cfg(transform, PIPELINES)
- transform = dict(type='RandomFlip', prob=1)
+ transform = dict(type="RandomFlip", prob=1)
flip_module = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
original_seg = copy.deepcopy(seg)
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = flip_module(results)
flip_module = build_from_cfg(transform, PIPELINES)
results = flip_module(results)
- assert np.equal(original_img, results['img']).all()
- assert np.equal(original_seg, results['gt_semantic_seg']).all()
+ assert np.equal(original_img, results["img"]).all()
+ assert np.equal(original_seg, results["gt_semantic_seg"]).all()
def test_random_crop():
# test assertion for invalid random crop
with pytest.raises(AssertionError):
- transform = dict(type='RandomCrop', crop_size=(-1, 0))
+ transform = dict(type="RandomCrop", crop_size=(-1, 0))
build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
h, w, _ = img.shape
- transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20))
+ transform = dict(type="RandomCrop", crop_size=(h - 20, w - 20))
crop_module = build_from_cfg(transform, PIPELINES)
results = crop_module(results)
- assert results['img'].shape[:2] == (h - 20, w - 20)
- assert results['img_shape'][:2] == (h - 20, w - 20)
- assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
+ assert results["img"].shape[:2] == (h - 20, w - 20)
+ assert results["img_shape"][:2] == (h - 20, w - 20)
+ assert results["gt_semantic_seg"].shape[:2] == (h - 20, w - 20)
def test_pad():
# test assertion if both size_divisor and size is None
with pytest.raises(AssertionError):
- transform = dict(type='Pad')
+ transform = dict(type="Pad")
build_from_cfg(transform, PIPELINES)
- transform = dict(type='Pad', size_divisor=32)
+ transform = dict(type="Pad", size_divisor=32)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
# original img already divisible by 32
- assert np.equal(results['img'], original_img).all()
- img_shape = results['img'].shape
+ assert np.equal(results["img"], original_img).all()
+ img_shape = results["img"].shape
assert img_shape[0] % 32 == 0
assert img_shape[1] % 32 == 0
- resize_transform = dict(
- type='Resize', img_scale=(1333, 800), keep_ratio=True)
+ resize_transform = dict(type="Resize", img_scale=(1333, 800), keep_ratio=True)
resize_module = build_from_cfg(resize_transform, PIPELINES)
results = resize_module(results)
results = transform(results)
- img_shape = results['img'].shape
+ img_shape = results["img"].shape
assert img_shape[0] % 32 == 0
assert img_shape[1] % 32 == 0
@@ -255,205 +249,201 @@ def test_pad():
def test_rotate():
# test assertion degree should be tuple[float] or float
with pytest.raises(AssertionError):
- transform = dict(type='RandomRotate', prob=0.5, degree=-10)
+ transform = dict(type="RandomRotate", prob=0.5, degree=-10)
build_from_cfg(transform, PIPELINES)
# test assertion degree should be tuple[float] or float
with pytest.raises(AssertionError):
- transform = dict(type='RandomRotate', prob=0.5, degree=(10., 20., 30.))
+ transform = dict(type="RandomRotate", prob=0.5, degree=(10.0, 20.0, 30.0))
build_from_cfg(transform, PIPELINES)
- transform = dict(type='RandomRotate', degree=10., prob=1.)
+ transform = dict(type="RandomRotate", degree=10.0, prob=1.0)
transform = build_from_cfg(transform, PIPELINES)
- assert str(transform) == f'RandomRotate(' \
- f'prob={1.}, ' \
- f'degree=({-10.}, {10.}), ' \
- f'pad_val={0}, ' \
- f'seg_pad_val={255}, ' \
- f'center={None}, ' \
- f'auto_bound={False})'
+ assert (
+ str(transform) == f"RandomRotate("
+ f"prob={1.}, "
+ f"degree=({-10.}, {10.}), "
+ f"pad_val={0}, "
+ f"seg_pad_val={255}, "
+ f"center={None}, "
+ f"auto_bound={False})"
+ )
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
h, w, _ = img.shape
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
- assert results['img'].shape[:2] == (h, w)
- assert results['gt_semantic_seg'].shape[:2] == (h, w)
+ assert results["img"].shape[:2] == (h, w)
+ assert results["gt_semantic_seg"].shape[:2] == (h, w)
def test_normalize():
img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True)
- transform = dict(type='Normalize', **img_norm_cfg)
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
+ )
+ transform = dict(type="Normalize", **img_norm_cfg)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
- mean = np.array(img_norm_cfg['mean'])
- std = np.array(img_norm_cfg['std'])
+ mean = np.array(img_norm_cfg["mean"])
+ std = np.array(img_norm_cfg["std"])
converted_img = (original_img[..., ::-1] - mean) / std
- assert np.allclose(results['img'], converted_img)
+ assert np.allclose(results["img"], converted_img)
def test_rgb2gray():
# test assertion out_channels should be greater than 0
with pytest.raises(AssertionError):
- transform = dict(type='RGB2Gray', out_channels=-1)
+ transform = dict(type="RGB2Gray", out_channels=-1)
build_from_cfg(transform, PIPELINES)
# test assertion weights should be tuple[float]
with pytest.raises(AssertionError):
- transform = dict(type='RGB2Gray', out_channels=1, weights=1.1)
+ transform = dict(type="RGB2Gray", out_channels=1, weights=1.1)
build_from_cfg(transform, PIPELINES)
# test out_channels is None
- transform = dict(type='RGB2Gray')
+ transform = dict(type="RGB2Gray")
transform = build_from_cfg(transform, PIPELINES)
- assert str(transform) == f'RGB2Gray(' \
- f'out_channels={None}, ' \
- f'weights={(0.299, 0.587, 0.114)})'
+ assert (
+ str(transform) == f"RGB2Gray("
+ f"out_channels={None}, "
+ f"weights={(0.299, 0.587, 0.114)})"
+ )
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
h, w, c = img.shape
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
- assert results['img'].shape == (h, w, c)
- assert results['img_shape'] == (h, w, c)
- assert results['ori_shape'] == (h, w, c)
+ assert results["img"].shape == (h, w, c)
+ assert results["img_shape"] == (h, w, c)
+ assert results["ori_shape"] == (h, w, c)
# test out_channels = 2
- transform = dict(type='RGB2Gray', out_channels=2)
+ transform = dict(type="RGB2Gray", out_channels=2)
transform = build_from_cfg(transform, PIPELINES)
- assert str(transform) == f'RGB2Gray(' \
- f'out_channels={2}, ' \
- f'weights={(0.299, 0.587, 0.114)})'
+ assert (
+ str(transform) == f"RGB2Gray("
+ f"out_channels={2}, "
+ f"weights={(0.299, 0.587, 0.114)})"
+ )
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
h, w, c = img.shape
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
- assert results['img'].shape == (h, w, 2)
- assert results['img_shape'] == (h, w, 2)
- assert results['ori_shape'] == (h, w, c)
+ assert results["img"].shape == (h, w, 2)
+ assert results["img_shape"] == (h, w, 2)
+ assert results["ori_shape"] == (h, w, c)
def test_adjust_gamma():
# test assertion if gamma <= 0
with pytest.raises(AssertionError):
- transform = dict(type='AdjustGamma', gamma=0)
+ transform = dict(type="AdjustGamma", gamma=0)
build_from_cfg(transform, PIPELINES)
# test assertion if gamma is list
with pytest.raises(AssertionError):
- transform = dict(type='AdjustGamma', gamma=[1.2])
+ transform = dict(type="AdjustGamma", gamma=[1.2])
build_from_cfg(transform, PIPELINES)
# test with gamma = 1.2
- transform = dict(type='AdjustGamma', gamma=1.2)
+ transform = dict(type="AdjustGamma", gamma=1.2)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
inv_gamma = 1.0 / 1.2
- table = np.array([((i / 255.0)**inv_gamma) * 255
- for i in np.arange(0, 256)]).astype('uint8')
- converted_img = mmcv.lut_transform(
- np.array(original_img, dtype=np.uint8), table)
- assert np.allclose(results['img'], converted_img)
- assert str(transform) == f'AdjustGamma(gamma={1.2})'
+ table = np.array(
+ [((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)]
+ ).astype("uint8")
+ converted_img = mmcv.lut_transform(np.array(original_img, dtype=np.uint8), table)
+ assert np.allclose(results["img"], converted_img)
+ assert str(transform) == f"AdjustGamma(gamma={1.2})"
def test_rerange():
# test assertion if min_value or max_value is illegal
with pytest.raises(AssertionError):
- transform = dict(type='Rerange', min_value=[0], max_value=[255])
+ transform = dict(type="Rerange", min_value=[0], max_value=[255])
build_from_cfg(transform, PIPELINES)
# test assertion if min_value >= max_value
with pytest.raises(AssertionError):
- transform = dict(type='Rerange', min_value=1, max_value=1)
+ transform = dict(type="Rerange", min_value=1, max_value=1)
build_from_cfg(transform, PIPELINES)
# test assertion if img_min_value == img_max_value
with pytest.raises(AssertionError):
- transform = dict(type='Rerange', min_value=0, max_value=1)
+ transform = dict(type="Rerange", min_value=0, max_value=1)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- results['img'] = np.array([[1, 1], [1, 1]])
+ results["img"] = np.array([[1, 1], [1, 1]])
transform(results)
img_rerange_cfg = dict()
- transform = dict(type='Rerange', **img_rerange_cfg)
+ transform = dict(type="Rerange", **img_rerange_cfg)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
@@ -461,230 +451,224 @@ def test_rerange():
max_value = np.max(original_img)
converted_img = (original_img - min_value) / (max_value - min_value) * 255
- assert np.allclose(results['img'], converted_img)
- assert str(transform) == f'Rerange(min_value={0}, max_value={255})'
+ assert np.allclose(results["img"], converted_img)
+ assert str(transform) == f"Rerange(min_value={0}, max_value={255})"
def test_CLAHE():
# test assertion if clip_limit is None
with pytest.raises(AssertionError):
- transform = dict(type='CLAHE', clip_limit=None)
+ transform = dict(type="CLAHE", clip_limit=None)
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
- transform = dict(type='CLAHE', tile_grid_size=(8.0, 8.0))
+ transform = dict(type="CLAHE", tile_grid_size=(8.0, 8.0))
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
- transform = dict(type='CLAHE', tile_grid_size=(9, 9, 9))
+ transform = dict(type="CLAHE", tile_grid_size=(9, 9, 9))
build_from_cfg(transform, PIPELINES)
- transform = dict(type='CLAHE', clip_limit=2)
+ transform = dict(type="CLAHE", clip_limit=2)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
original_img = copy.deepcopy(img)
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
results = transform(results)
converted_img = np.empty(original_img.shape)
for i in range(original_img.shape[2]):
converted_img[:, :, i] = mmcv.clahe(
- np.array(original_img[:, :, i], dtype=np.uint8), 2, (8, 8))
+ np.array(original_img[:, :, i], dtype=np.uint8), 2, (8, 8)
+ )
- assert np.allclose(results['img'], converted_img)
- assert str(transform) == f'CLAHE(clip_limit={2}, tile_grid_size={(8, 8)})'
+ assert np.allclose(results["img"], converted_img)
+ assert str(transform) == f"CLAHE(clip_limit={2}, tile_grid_size={(8, 8)})"
def test_seg_rescale():
results = dict()
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
h, w = seg.shape
- transform = dict(type='SegRescale', scale_factor=1. / 2)
+ transform = dict(type="SegRescale", scale_factor=1.0 / 2)
rescale_module = build_from_cfg(transform, PIPELINES)
rescale_results = rescale_module(results.copy())
- assert rescale_results['gt_semantic_seg'].shape == (h // 2, w // 2)
+ assert rescale_results["gt_semantic_seg"].shape == (h // 2, w // 2)
- transform = dict(type='SegRescale', scale_factor=1)
+ transform = dict(type="SegRescale", scale_factor=1)
rescale_module = build_from_cfg(transform, PIPELINES)
rescale_results = rescale_module(results.copy())
- assert rescale_results['gt_semantic_seg'].shape == (h, w)
+ assert rescale_results["gt_semantic_seg"].shape == (h, w)
def test_cutout():
# test prob
with pytest.raises(AssertionError):
- transform = dict(type='RandomCutOut', prob=1.5, n_holes=1)
+ transform = dict(type="RandomCutOut", prob=1.5, n_holes=1)
build_from_cfg(transform, PIPELINES)
# test n_holes
with pytest.raises(AssertionError):
transform = dict(
- type='RandomCutOut', prob=0.5, n_holes=(5, 3), cutout_shape=(8, 8))
+ type="RandomCutOut", prob=0.5, n_holes=(5, 3), cutout_shape=(8, 8)
+ )
build_from_cfg(transform, PIPELINES)
with pytest.raises(AssertionError):
transform = dict(
- type='RandomCutOut',
- prob=0.5,
- n_holes=(3, 4, 5),
- cutout_shape=(8, 8))
+ type="RandomCutOut", prob=0.5, n_holes=(3, 4, 5), cutout_shape=(8, 8)
+ )
build_from_cfg(transform, PIPELINES)
# test cutout_shape and cutout_ratio
with pytest.raises(AssertionError):
- transform = dict(
- type='RandomCutOut', prob=0.5, n_holes=1, cutout_shape=8)
+ transform = dict(type="RandomCutOut", prob=0.5, n_holes=1, cutout_shape=8)
build_from_cfg(transform, PIPELINES)
with pytest.raises(AssertionError):
- transform = dict(
- type='RandomCutOut', prob=0.5, n_holes=1, cutout_ratio=0.2)
+ transform = dict(type="RandomCutOut", prob=0.5, n_holes=1, cutout_ratio=0.2)
build_from_cfg(transform, PIPELINES)
# either of cutout_shape and cutout_ratio should be given
with pytest.raises(AssertionError):
- transform = dict(type='RandomCutOut', prob=0.5, n_holes=1)
+ transform = dict(type="RandomCutOut", prob=0.5, n_holes=1)
build_from_cfg(transform, PIPELINES)
with pytest.raises(AssertionError):
transform = dict(
- type='RandomCutOut',
+ type="RandomCutOut",
prob=0.5,
n_holes=1,
cutout_shape=(2, 2),
- cutout_ratio=(0.4, 0.4))
+ cutout_ratio=(0.4, 0.4),
+ )
build_from_cfg(transform, PIPELINES)
# test seg_fill_in
with pytest.raises(AssertionError):
transform = dict(
- type='RandomCutOut',
+ type="RandomCutOut",
prob=0.5,
n_holes=1,
cutout_shape=(8, 8),
- seg_fill_in='a')
+ seg_fill_in="a",
+ )
build_from_cfg(transform, PIPELINES)
with pytest.raises(AssertionError):
transform = dict(
- type='RandomCutOut',
+ type="RandomCutOut",
prob=0.5,
n_holes=1,
cutout_shape=(8, 8),
- seg_fill_in=256)
+ seg_fill_in=256,
+ )
build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
- results['pad_shape'] = img.shape
- results['img_fields'] = ['img']
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
+ results["pad_shape"] = img.shape
+ results["img_fields"] = ["img"]
- transform = dict(
- type='RandomCutOut', prob=1, n_holes=1, cutout_shape=(10, 10))
+ transform = dict(type="RandomCutOut", prob=1, n_holes=1, cutout_shape=(10, 10))
cutout_module = build_from_cfg(transform, PIPELINES)
- assert 'cutout_shape' in repr(cutout_module)
+ assert "cutout_shape" in repr(cutout_module)
cutout_result = cutout_module(copy.deepcopy(results))
- assert cutout_result['img'].sum() < img.sum()
+ assert cutout_result["img"].sum() < img.sum()
- transform = dict(
- type='RandomCutOut', prob=1, n_holes=1, cutout_ratio=(0.8, 0.8))
+ transform = dict(type="RandomCutOut", prob=1, n_holes=1, cutout_ratio=(0.8, 0.8))
cutout_module = build_from_cfg(transform, PIPELINES)
- assert 'cutout_ratio' in repr(cutout_module)
+ assert "cutout_ratio" in repr(cutout_module)
cutout_result = cutout_module(copy.deepcopy(results))
- assert cutout_result['img'].sum() < img.sum()
+ assert cutout_result["img"].sum() < img.sum()
- transform = dict(
- type='RandomCutOut', prob=0, n_holes=1, cutout_ratio=(0.8, 0.8))
+ transform = dict(type="RandomCutOut", prob=0, n_holes=1, cutout_ratio=(0.8, 0.8))
cutout_module = build_from_cfg(transform, PIPELINES)
cutout_result = cutout_module(copy.deepcopy(results))
- assert cutout_result['img'].sum() == img.sum()
- assert cutout_result['gt_semantic_seg'].sum() == seg.sum()
+ assert cutout_result["img"].sum() == img.sum()
+ assert cutout_result["gt_semantic_seg"].sum() == seg.sum()
transform = dict(
- type='RandomCutOut',
+ type="RandomCutOut",
prob=1,
n_holes=(2, 4),
cutout_shape=[(10, 10), (15, 15)],
fill_in=(255, 255, 255),
- seg_fill_in=None)
+ seg_fill_in=None,
+ )
cutout_module = build_from_cfg(transform, PIPELINES)
cutout_result = cutout_module(copy.deepcopy(results))
- assert cutout_result['img'].sum() > img.sum()
- assert cutout_result['gt_semantic_seg'].sum() == seg.sum()
+ assert cutout_result["img"].sum() > img.sum()
+ assert cutout_result["gt_semantic_seg"].sum() == seg.sum()
transform = dict(
- type='RandomCutOut',
+ type="RandomCutOut",
prob=1,
n_holes=1,
cutout_ratio=(0.8, 0.8),
fill_in=(255, 255, 255),
- seg_fill_in=255)
+ seg_fill_in=255,
+ )
cutout_module = build_from_cfg(transform, PIPELINES)
cutout_result = cutout_module(copy.deepcopy(results))
- assert cutout_result['img'].sum() > img.sum()
- assert cutout_result['gt_semantic_seg'].sum() > seg.sum()
+ assert cutout_result["img"].sum() > img.sum()
+ assert cutout_result["gt_semantic_seg"].sum() > seg.sum()
def test_mosaic():
# test prob
with pytest.raises(AssertionError):
- transform = dict(type='RandomMosaic', prob=1.5)
+ transform = dict(type="RandomMosaic", prob=1.5)
build_from_cfg(transform, PIPELINES)
# test assertion for invalid img_scale
with pytest.raises(AssertionError):
- transform = dict(type='RandomMosaic', prob=1, img_scale=640)
+ transform = dict(type="RandomMosaic", prob=1, img_scale=640)
build_from_cfg(transform, PIPELINES)
results = dict()
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
- seg = np.array(
- Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
+ seg = np.array(Image.open(osp.join(osp.dirname(__file__), "../data/seg.png")))
- results['img'] = img
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
+ results["img"] = img
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
- transform = dict(type='RandomMosaic', prob=1, img_scale=(10, 12))
+ transform = dict(type="RandomMosaic", prob=1, img_scale=(10, 12))
mosaic_module = build_from_cfg(transform, PIPELINES)
- assert 'Mosaic' in repr(mosaic_module)
+ assert "Mosaic" in repr(mosaic_module)
# test assertion for invalid mix_results
with pytest.raises(AssertionError):
mosaic_module(results)
- results['mix_results'] = [copy.deepcopy(results)] * 3
+ results["mix_results"] = [copy.deepcopy(results)] * 3
results = mosaic_module(results)
- assert results['img'].shape[:2] == (20, 24)
+ assert results["img"].shape[:2] == (20, 24)
results = dict()
- results['img'] = img[:, :, 0]
- results['gt_semantic_seg'] = seg
- results['seg_fields'] = ['gt_semantic_seg']
+ results["img"] = img[:, :, 0]
+ results["gt_semantic_seg"] = seg
+ results["seg_fields"] = ["gt_semantic_seg"]
- transform = dict(type='RandomMosaic', prob=0, img_scale=(10, 12))
+ transform = dict(type="RandomMosaic", prob=0, img_scale=(10, 12))
mosaic_module = build_from_cfg(transform, PIPELINES)
- results['mix_results'] = [copy.deepcopy(results)] * 3
+ results["mix_results"] = [copy.deepcopy(results)] * 3
results = mosaic_module(results)
- assert results['img'].shape[:2] == img.shape[:2]
+ assert results["img"].shape[:2] == img.shape[:2]
- transform = dict(type='RandomMosaic', prob=1, img_scale=(10, 12))
+ transform = dict(type="RandomMosaic", prob=1, img_scale=(10, 12))
mosaic_module = build_from_cfg(transform, PIPELINES)
results = mosaic_module(results)
- assert results['img'].shape[:2] == (20, 24)
+ assert results["img"].shape[:2] == (20, 24)
diff --git a/mmsegmentation/tests/test_data/test_tta.py b/mmsegmentation/tests/test_data/test_tta.py
index 9373e2b..91ed14c 100644
--- a/mmsegmentation/tests/test_data/test_tta.py
+++ b/mmsegmentation/tests/test_data/test_tta.py
@@ -12,178 +12,213 @@ def test_multi_scale_flip_aug():
# test assertion if img_scale=None, img_ratios=1 (not float).
with pytest.raises(AssertionError):
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=None,
img_ratios=1,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
build_from_cfg(tta_transform, PIPELINES)
# test assertion if img_scale=None, img_ratios=None.
with pytest.raises(AssertionError):
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=None,
img_ratios=None,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
build_from_cfg(tta_transform, PIPELINES)
# test assertion if img_scale=(512, 512), img_ratios=1 (not float).
with pytest.raises(AssertionError):
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(512, 512),
img_ratios=1,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
build_from_cfg(tta_transform, PIPELINES)
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(512, 512),
img_ratios=[0.5, 1.0, 2.0],
flip=False,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
results = dict()
# (288, 512, 3)
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "../data/color.jpg"), "color")
+ results["img"] = img
+ results["img_shape"] = img.shape
+ results["ori_shape"] = img.shape
# Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
+ results["pad_shape"] = img.shape
+ results["scale_factor"] = 1.0
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)]
- assert tta_results['flip'] == [False, False, False]
+ assert tta_results["scale"] == [(256, 256), (512, 512), (1024, 1024)]
+ assert tta_results["flip"] == [False, False, False]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(512, 512),
img_ratios=[0.5, 1.0, 2.0],
flip=True,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512),
- (512, 512), (1024, 1024), (1024, 1024)]
- assert tta_results['flip'] == [False, True, False, True, False, True]
+ assert tta_results["scale"] == [
+ (256, 256),
+ (256, 256),
+ (512, 512),
+ (512, 512),
+ (1024, 1024),
+ (1024, 1024),
+ ]
+ assert tta_results["flip"] == [False, True, False, True, False, True]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(512, 512),
img_ratios=1.0,
flip=False,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(512, 512)]
- assert tta_results['flip'] == [False]
+ assert tta_results["scale"] == [(512, 512)]
+ assert tta_results["flip"] == [False]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=(512, 512),
img_ratios=1.0,
flip=True,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(512, 512), (512, 512)]
- assert tta_results['flip'] == [False, True]
+ assert tta_results["scale"] == [(512, 512), (512, 512)]
+ assert tta_results["flip"] == [False, True]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=None,
img_ratios=[0.5, 1.0, 2.0],
flip=False,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 144), (512, 288), (1024, 576)]
- assert tta_results['flip'] == [False, False, False]
+ assert tta_results["scale"] == [(256, 144), (512, 288), (1024, 576)]
+ assert tta_results["flip"] == [False, False, False]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=None,
img_ratios=[0.5, 1.0, 2.0],
flip=True,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 144), (256, 144), (512, 288),
- (512, 288), (1024, 576), (1024, 576)]
- assert tta_results['flip'] == [False, True, False, True, False, True]
+ assert tta_results["scale"] == [
+ (256, 144),
+ (256, 144),
+ (512, 288),
+ (512, 288),
+ (1024, 576),
+ (1024, 576),
+ ]
+ assert tta_results["flip"] == [False, True, False, True, False, True]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=[(256, 256), (512, 512), (1024, 1024)],
img_ratios=None,
flip=False,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)]
- assert tta_results['flip'] == [False, False, False]
+ assert tta_results["scale"] == [(256, 256), (512, 512), (1024, 1024)]
+ assert tta_results["flip"] == [False, False, False]
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=[(256, 256), (512, 512), (1024, 1024)],
img_ratios=None,
flip=True,
- transforms=[dict(type='Resize', keep_ratio=False)],
+ transforms=[dict(type="Resize", keep_ratio=False)],
)
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512),
- (512, 512), (1024, 1024), (1024, 1024)]
- assert tta_results['flip'] == [False, True, False, True, False, True]
+ assert tta_results["scale"] == [
+ (256, 256),
+ (256, 256),
+ (512, 512),
+ (512, 512),
+ (1024, 1024),
+ (1024, 1024),
+ ]
+ assert tta_results["flip"] == [False, True, False, True, False, True]
# test assertion if flip is True and Pad executed before RandomFlip
with pytest.raises(AssertionError):
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=[(256, 256), (512, 512), (1024, 1024)],
img_ratios=None,
flip=True,
transforms=[
- dict(type='Resize', keep_ratio=False),
- dict(type='Pad', size_divisor=32),
- dict(type='RandomFlip'),
- ])
+ dict(type="Resize", keep_ratio=False),
+ dict(type="Pad", size_divisor=32),
+ dict(type="RandomFlip"),
+ ],
+ )
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_transform = dict(
- type='MultiScaleFlipAug',
+ type="MultiScaleFlipAug",
img_scale=[(256, 256), (512, 512), (1024, 1024)],
img_ratios=None,
flip=True,
transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Pad', size_divisor=32),
- ])
+ dict(type="Resize", keep_ratio=True),
+ dict(type="RandomFlip"),
+ dict(type="Pad", size_divisor=32),
+ ],
+ )
tta_module = build_from_cfg(tta_transform, PIPELINES)
tta_results = tta_module(results.copy())
- assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512),
- (512, 512), (1024, 1024), (1024, 1024)]
- assert tta_results['flip'] == [False, True, False, True, False, True]
- assert tta_results['img_shape'] == [(144, 256, 3), (144, 256, 3),
- (288, 512, 3), (288, 512, 3),
- (576, 1024, 3), (576, 1024, 3)]
- assert tta_results['pad_shape'] == [(160, 256, 3), (160, 256, 3),
- (288, 512, 3), (288, 512, 3),
- (576, 1024, 3), (576, 1024, 3)]
- for i in range(len(tta_results['img'])):
- assert tta_results['img'][i].shape == tta_results['pad_shape'][i]
+ assert tta_results["scale"] == [
+ (256, 256),
+ (256, 256),
+ (512, 512),
+ (512, 512),
+ (1024, 1024),
+ (1024, 1024),
+ ]
+ assert tta_results["flip"] == [False, True, False, True, False, True]
+ assert tta_results["img_shape"] == [
+ (144, 256, 3),
+ (144, 256, 3),
+ (288, 512, 3),
+ (288, 512, 3),
+ (576, 1024, 3),
+ (576, 1024, 3),
+ ]
+ assert tta_results["pad_shape"] == [
+ (160, 256, 3),
+ (160, 256, 3),
+ (288, 512, 3),
+ (288, 512, 3),
+ (576, 1024, 3),
+ (576, 1024, 3),
+ ]
+ for i in range(len(tta_results["img"])):
+ assert tta_results["img"][i].shape == tta_results["pad_shape"][i]
diff --git a/mmsegmentation/tests/test_digit_version.py b/mmsegmentation/tests/test_digit_version.py
index 45daf09..f5df808 100644
--- a/mmsegmentation/tests/test_digit_version.py
+++ b/mmsegmentation/tests/test_digit_version.py
@@ -3,19 +3,19 @@
def test_digit_version():
- assert digit_version('0.2.16') == (0, 2, 16, 0, 0, 0)
- assert digit_version('1.2.3') == (1, 2, 3, 0, 0, 0)
- assert digit_version('1.2.3rc0') == (1, 2, 3, 0, -1, 0)
- assert digit_version('1.2.3rc1') == (1, 2, 3, 0, -1, 1)
- assert digit_version('1.0rc0') == (1, 0, 0, 0, -1, 0)
- assert digit_version('1.0') == digit_version('1.0.0')
- assert digit_version('1.5.0+cuda90_cudnn7.6.3_lms') == digit_version('1.5')
- assert digit_version('1.0.0dev') < digit_version('1.0.0a')
- assert digit_version('1.0.0a') < digit_version('1.0.0a1')
- assert digit_version('1.0.0a') < digit_version('1.0.0b')
- assert digit_version('1.0.0b') < digit_version('1.0.0rc')
- assert digit_version('1.0.0rc1') < digit_version('1.0.0')
- assert digit_version('1.0.0') < digit_version('1.0.0post')
- assert digit_version('1.0.0post') < digit_version('1.0.0post1')
- assert digit_version('v1') == (1, 0, 0, 0, 0, 0)
- assert digit_version('v1.1.5') == (1, 1, 5, 0, 0, 0)
+ assert digit_version("0.2.16") == (0, 2, 16, 0, 0, 0)
+ assert digit_version("1.2.3") == (1, 2, 3, 0, 0, 0)
+ assert digit_version("1.2.3rc0") == (1, 2, 3, 0, -1, 0)
+ assert digit_version("1.2.3rc1") == (1, 2, 3, 0, -1, 1)
+ assert digit_version("1.0rc0") == (1, 0, 0, 0, -1, 0)
+ assert digit_version("1.0") == digit_version("1.0.0")
+ assert digit_version("1.5.0+cuda90_cudnn7.6.3_lms") == digit_version("1.5")
+ assert digit_version("1.0.0dev") < digit_version("1.0.0a")
+ assert digit_version("1.0.0a") < digit_version("1.0.0a1")
+ assert digit_version("1.0.0a") < digit_version("1.0.0b")
+ assert digit_version("1.0.0b") < digit_version("1.0.0rc")
+ assert digit_version("1.0.0rc1") < digit_version("1.0.0")
+ assert digit_version("1.0.0") < digit_version("1.0.0post")
+ assert digit_version("1.0.0post") < digit_version("1.0.0post1")
+ assert digit_version("v1") == (1, 0, 0, 0, 0, 0)
+ assert digit_version("v1.1.5") == (1, 1, 5, 0, 0, 0)
diff --git a/mmsegmentation/tests/test_eval_hook.py b/mmsegmentation/tests/test_eval_hook.py
index 5267438..2b457bd 100644
--- a/mmsegmentation/tests/test_eval_hook.py
+++ b/mmsegmentation/tests/test_eval_hook.py
@@ -27,7 +27,7 @@ def __len__(self):
class ExampleModel(nn.Module):
def __init__(self):
- super(ExampleModel, self).__init__()
+ super().__init__()
self.test_cfg = None
self.conv = nn.Conv2d(3, 3, 3)
@@ -44,24 +44,21 @@ def test_iter_eval_hook():
test_dataset = ExampleModel()
data_loader = [
DataLoader(
- test_dataset,
- batch_size=1,
- sampler=None,
- num_worker=0,
- shuffle=False)
+ test_dataset, batch_size=1, sampler=None, num_worker=0, shuffle=False
+ )
]
EvalHook(data_loader)
test_dataset = ExampleDataset()
test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
- test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+ test_dataset.evaluate = MagicMock(return_value=dict(test="success"))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
- test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
- optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
- optimizer = obj_from_dict(optim_cfg, torch.optim,
- dict(params=model.parameters()))
+ test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False
+ )
+ optim_cfg = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0005)
+ optimizer = obj_from_dict(optim_cfg, torch.optim, dict(params=model.parameters()))
# test EvalHook
with tempfile.TemporaryDirectory() as tmpdir:
@@ -70,11 +67,13 @@ def test_iter_eval_hook():
model=model,
optimizer=optimizer,
work_dir=tmpdir,
- logger=logging.getLogger())
+ logger=logging.getLogger(),
+ )
runner.register_hook(eval_hook)
- runner.run([loader], [('train', 1)], 1)
- test_dataset.evaluate.assert_called_with([torch.tensor([1])],
- logger=runner.logger)
+ runner.run([loader], [("train", 1)], 1)
+ test_dataset.evaluate.assert_called_with(
+ [torch.tensor([1])], logger=runner.logger
+ )
def test_epoch_eval_hook():
@@ -82,24 +81,21 @@ def test_epoch_eval_hook():
test_dataset = ExampleModel()
data_loader = [
DataLoader(
- test_dataset,
- batch_size=1,
- sampler=None,
- num_worker=0,
- shuffle=False)
+ test_dataset, batch_size=1, sampler=None, num_worker=0, shuffle=False
+ )
]
EvalHook(data_loader, by_epoch=True)
test_dataset = ExampleDataset()
test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
- test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+ test_dataset.evaluate = MagicMock(return_value=dict(test="success"))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
- test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
- optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
- optimizer = obj_from_dict(optim_cfg, torch.optim,
- dict(params=model.parameters()))
+ test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False
+ )
+ optim_cfg = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0005)
+ optimizer = obj_from_dict(optim_cfg, torch.optim, dict(params=model.parameters()))
# test EvalHook with interval
with tempfile.TemporaryDirectory() as tmpdir:
@@ -108,87 +104,80 @@ def test_epoch_eval_hook():
model=model,
optimizer=optimizer,
work_dir=tmpdir,
- logger=logging.getLogger())
+ logger=logging.getLogger(),
+ )
runner.register_hook(eval_hook)
- runner.run([loader], [('train', 1)], 2)
- test_dataset.evaluate.assert_called_once_with([torch.tensor([1])],
- logger=runner.logger)
+ runner.run([loader], [("train", 1)], 2)
+ test_dataset.evaluate.assert_called_once_with(
+ [torch.tensor([1])], logger=runner.logger
+ )
-def multi_gpu_test(model,
- data_loader,
- tmpdir=None,
- gpu_collect=False,
- pre_eval=False):
+def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False, pre_eval=False):
# Pre eval is set by default when training.
results = single_gpu_test(model, data_loader, pre_eval=True)
return results
-@patch('mmseg.apis.multi_gpu_test', multi_gpu_test)
+@patch("mmseg.apis.multi_gpu_test", multi_gpu_test)
def test_dist_eval_hook():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [
DataLoader(
- test_dataset,
- batch_size=1,
- sampler=None,
- num_worker=0,
- shuffle=False)
+ test_dataset, batch_size=1, sampler=None, num_worker=0, shuffle=False
+ )
]
DistEvalHook(data_loader)
test_dataset = ExampleDataset()
test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
- test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+ test_dataset.evaluate = MagicMock(return_value=dict(test="success"))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
- test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
- optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
- optimizer = obj_from_dict(optim_cfg, torch.optim,
- dict(params=model.parameters()))
+ test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False
+ )
+ optim_cfg = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0005)
+ optimizer = obj_from_dict(optim_cfg, torch.optim, dict(params=model.parameters()))
# test DistEvalHook
with tempfile.TemporaryDirectory() as tmpdir:
- eval_hook = DistEvalHook(
- data_loader, by_epoch=False, efficient_test=True)
+ eval_hook = DistEvalHook(data_loader, by_epoch=False, efficient_test=True)
runner = mmcv.runner.IterBasedRunner(
model=model,
optimizer=optimizer,
work_dir=tmpdir,
- logger=logging.getLogger())
+ logger=logging.getLogger(),
+ )
runner.register_hook(eval_hook)
- runner.run([loader], [('train', 1)], 1)
- test_dataset.evaluate.assert_called_with([torch.tensor([1])],
- logger=runner.logger)
+ runner.run([loader], [("train", 1)], 1)
+ test_dataset.evaluate.assert_called_with(
+ [torch.tensor([1])], logger=runner.logger
+ )
-@patch('mmseg.apis.multi_gpu_test', multi_gpu_test)
+@patch("mmseg.apis.multi_gpu_test", multi_gpu_test)
def test_dist_eval_hook_epoch():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [
DataLoader(
- test_dataset,
- batch_size=1,
- sampler=None,
- num_worker=0,
- shuffle=False)
+ test_dataset, batch_size=1, sampler=None, num_worker=0, shuffle=False
+ )
]
DistEvalHook(data_loader)
test_dataset = ExampleDataset()
test_dataset.pre_eval = MagicMock(return_value=[torch.tensor([1])])
- test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
+ test_dataset.evaluate = MagicMock(return_value=dict(test="success"))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
- test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
- optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
- optimizer = obj_from_dict(optim_cfg, torch.optim,
- dict(params=model.parameters()))
+ test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False
+ )
+ optim_cfg = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0005)
+ optimizer = obj_from_dict(optim_cfg, torch.optim, dict(params=model.parameters()))
# test DistEvalHook
with tempfile.TemporaryDirectory() as tmpdir:
@@ -197,8 +186,10 @@ def test_dist_eval_hook_epoch():
model=model,
optimizer=optimizer,
work_dir=tmpdir,
- logger=logging.getLogger())
+ logger=logging.getLogger(),
+ )
runner.register_hook(eval_hook)
- runner.run([loader], [('train', 1)], 2)
- test_dataset.evaluate.assert_called_with([torch.tensor([1])],
- logger=runner.logger)
+ runner.run([loader], [("train", 1)], 2)
+ test_dataset.evaluate.assert_called_with(
+ [torch.tensor([1])], logger=runner.logger
+ )
diff --git a/mmsegmentation/tests/test_inference.py b/mmsegmentation/tests/test_inference.py
index f71a7ea..c754a0e 100644
--- a/mmsegmentation/tests/test_inference.py
+++ b/mmsegmentation/tests/test_inference.py
@@ -7,13 +7,13 @@
def test_test_time_augmentation_on_cpu():
- config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
+ config_file = "configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py"
config = mmcv.Config.fromfile(config_file)
# Remove pretrain model download for testing
config.model.pretrained = None
# Replace SyncBN with BN to inference on CPU
- norm_cfg = dict(type='BN', requires_grad=True)
+ norm_cfg = dict(type="BN", requires_grad=True)
config.model.backbone.norm_cfg = norm_cfg
config.model.decode_head.norm_cfg = norm_cfg
config.model.auxiliary_head.norm_cfg = norm_cfg
@@ -22,9 +22,8 @@ def test_test_time_augmentation_on_cpu():
config.data.test.pipeline[1].flip = True
checkpoint_file = None
- model = init_segmentor(config, checkpoint_file, device='cpu')
+ model = init_segmentor(config, checkpoint_file, device="cpu")
- img = mmcv.imread(
- osp.join(osp.dirname(__file__), 'data/color.jpg'), 'color')
+ img = mmcv.imread(osp.join(osp.dirname(__file__), "data/color.jpg"), "color")
result = inference_segmentor(model, img)
assert result[0].shape == (288, 512)
diff --git a/mmsegmentation/tests/test_metrics.py b/mmsegmentation/tests/test_metrics.py
index adb09ae..2f5791e 100644
--- a/mmsegmentation/tests/test_metrics.py
+++ b/mmsegmentation/tests/test_metrics.py
@@ -1,21 +1,20 @@
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
-from mmseg.core.evaluation import (eval_metrics, mean_dice, mean_fscore,
- mean_iou)
+from mmseg.core.evaluation import eval_metrics, mean_dice, mean_fscore, mean_iou
from mmseg.core.evaluation.metrics import f_score
def get_confusion_matrix(pred_label, label, num_classes, ignore_index):
"""Intersection over Union
- Args:
- pred_label (np.ndarray): 2D predict map
- label (np.ndarray): label 2D label map
- num_classes (int): number of categories
- ignore_index (int): index ignore in evaluation
- """
-
- mask = (label != ignore_index)
+ Args:
+ pred_label (np.ndarray): 2D predict map
+ label (np.ndarray): label 2D label map
+ num_classes (int): number of categories
+ ignore_index (int): index ignore in evaluation
+ """
+
+ mask = label != ignore_index
pred_label = pred_label[mask]
label = label[mask]
@@ -34,12 +33,14 @@ def legacy_mean_iou(results, gt_seg_maps, num_classes, ignore_index):
total_mat = np.zeros((num_classes, num_classes), dtype=np.float32)
for i in range(num_imgs):
mat = get_confusion_matrix(
- results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
+ results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index
+ )
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
acc = np.diag(total_mat) / total_mat.sum(axis=1)
iou = np.diag(total_mat) / (
- total_mat.sum(axis=1) + total_mat.sum(axis=0) - np.diag(total_mat))
+ total_mat.sum(axis=1) + total_mat.sum(axis=0) - np.diag(total_mat)
+ )
return all_acc, acc, iou
@@ -51,28 +52,25 @@ def legacy_mean_dice(results, gt_seg_maps, num_classes, ignore_index):
total_mat = np.zeros((num_classes, num_classes), dtype=np.float32)
for i in range(num_imgs):
mat = get_confusion_matrix(
- results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
+ results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index
+ )
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
acc = np.diag(total_mat) / total_mat.sum(axis=1)
- dice = 2 * np.diag(total_mat) / (
- total_mat.sum(axis=1) + total_mat.sum(axis=0))
+ dice = 2 * np.diag(total_mat) / (total_mat.sum(axis=1) + total_mat.sum(axis=0))
return all_acc, acc, dice
# This func is deprecated since it's not memory efficient
-def legacy_mean_fscore(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- beta=1):
+def legacy_mean_fscore(results, gt_seg_maps, num_classes, ignore_index, beta=1):
num_imgs = len(results)
assert len(gt_seg_maps) == num_imgs
total_mat = np.zeros((num_classes, num_classes), dtype=np.float32)
for i in range(num_imgs):
mat = get_confusion_matrix(
- results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
+ results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index
+ )
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
recall = np.diag(total_mat) / total_mat.sum(axis=1)
@@ -95,46 +93,54 @@ def test_metrics():
# Test the correctness of the implementation of mIoU calculation.
ret_metrics = eval_metrics(
- results, label, num_classes, ignore_index, metrics='mIoU')
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'IoU']
- all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
- ignore_index)
+ results, label, num_classes, ignore_index, metrics="mIoU"
+ )
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
+ all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes, ignore_index)
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
# Test the correctness of the implementation of mDice calculation.
ret_metrics = eval_metrics(
- results, label, num_classes, ignore_index, metrics='mDice')
- all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'Dice']
- all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
- ignore_index)
+ results, label, num_classes, ignore_index, metrics="mDice"
+ )
+ all_acc, acc, dice = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["Dice"]
+ all_acc_l, acc_l, dice_l = legacy_mean_dice(
+ results, label, num_classes, ignore_index
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(dice, dice_l)
# Test the correctness of the implementation of mDice calculation.
ret_metrics = eval_metrics(
- results, label, num_classes, ignore_index, metrics='mFscore')
- all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
- 'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
+ results, label, num_classes, ignore_index, metrics="mFscore"
+ )
+ all_acc, recall, precision, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Recall"],
+ ret_metrics["Precision"],
+ ret_metrics["Fscore"],
+ )
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
- results, label, num_classes, ignore_index)
+ results, label, num_classes, ignore_index
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
assert np.allclose(fscore, fscore_l)
# Test the correctness of the implementation of joint calculation.
ret_metrics = eval_metrics(
- results,
- label,
- num_classes,
- ignore_index,
- metrics=['mIoU', 'mDice', 'mFscore'])
- all_acc, acc, iou, dice, precision, recall, fscore = ret_metrics[
- 'aAcc'], ret_metrics['Acc'], ret_metrics['IoU'], ret_metrics[
- 'Dice'], ret_metrics['Precision'], ret_metrics[
- 'Recall'], ret_metrics['Fscore']
+ results, label, num_classes, ignore_index, metrics=["mIoU", "mDice", "mFscore"]
+ )
+ all_acc, acc, iou, dice, precision, recall, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Acc"],
+ ret_metrics["IoU"],
+ ret_metrics["Dice"],
+ ret_metrics["Precision"],
+ ret_metrics["Recall"],
+ ret_metrics["Fscore"],
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
@@ -148,38 +154,28 @@ def test_metrics():
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
ret_metrics = eval_metrics(
- results,
- label,
- num_classes,
- ignore_index=255,
- metrics='mIoU',
- nan_to_num=-1)
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'IoU']
+ results, label, num_classes, ignore_index=255, metrics="mIoU", nan_to_num=-1
+ )
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
assert acc[-1] == -1
assert iou[-1] == -1
ret_metrics = eval_metrics(
- results,
- label,
- num_classes,
- ignore_index=255,
- metrics='mDice',
- nan_to_num=-1)
- all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'Dice']
+ results, label, num_classes, ignore_index=255, metrics="mDice", nan_to_num=-1
+ )
+ all_acc, acc, dice = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["Dice"]
assert acc[-1] == -1
assert dice[-1] == -1
ret_metrics = eval_metrics(
- results,
- label,
- num_classes,
- ignore_index=255,
- metrics='mFscore',
- nan_to_num=-1)
- all_acc, precision, recall, fscore = ret_metrics['aAcc'], ret_metrics[
- 'Precision'], ret_metrics['Recall'], ret_metrics['Fscore']
+ results, label, num_classes, ignore_index=255, metrics="mFscore", nan_to_num=-1
+ )
+ all_acc, precision, recall, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Precision"],
+ ret_metrics["Recall"],
+ ret_metrics["Fscore"],
+ )
assert precision[-1] == -1
assert recall[-1] == -1
assert fscore[-1] == -1
@@ -189,12 +185,18 @@ def test_metrics():
label,
num_classes,
ignore_index=255,
- metrics=['mDice', 'mIoU', 'mFscore'],
- nan_to_num=-1)
- all_acc, acc, iou, dice, precision, recall, fscore = ret_metrics[
- 'aAcc'], ret_metrics['Acc'], ret_metrics['IoU'], ret_metrics[
- 'Dice'], ret_metrics['Precision'], ret_metrics[
- 'Recall'], ret_metrics['Fscore']
+ metrics=["mDice", "mIoU", "mFscore"],
+ nan_to_num=-1,
+ )
+ all_acc, acc, iou, dice, precision, recall, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Acc"],
+ ret_metrics["IoU"],
+ ret_metrics["Dice"],
+ ret_metrics["Precision"],
+ ret_metrics["Recall"],
+ ret_metrics["Fscore"],
+ )
assert acc[-1] == -1
assert dice[-1] == -1
assert iou[-1] == -1
@@ -210,9 +212,9 @@ def test_metrics():
label = np.array([np.arange(59)])
num_classes = 59
ret_metrics = eval_metrics(
- results, label, num_classes, ignore_index=255, metrics='mIoU')
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'IoU']
+ results, label, num_classes, ignore_index=255, metrics="mIoU"
+ )
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
assert not np.any(np.isnan(iou))
@@ -224,20 +226,16 @@ def test_mean_iou():
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
ret_metrics = mean_iou(results, label, num_classes, ignore_index)
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'IoU']
- all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
- ignore_index)
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
+ all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes, ignore_index)
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
- ret_metrics = mean_iou(
- results, label, num_classes, ignore_index=255, nan_to_num=-1)
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'IoU']
+ ret_metrics = mean_iou(results, label, num_classes, ignore_index=255, nan_to_num=-1)
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
assert acc[-1] == -1
assert acc[-1] == -1
@@ -250,10 +248,10 @@ def test_mean_dice():
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
ret_metrics = mean_dice(results, label, num_classes, ignore_index)
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'Dice']
- all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
- ignore_index)
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["Dice"]
+ all_acc_l, acc_l, dice_l = legacy_mean_dice(
+ results, label, num_classes, ignore_index
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(iou, dice_l)
@@ -261,9 +259,9 @@ def test_mean_dice():
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
ret_metrics = mean_dice(
- results, label, num_classes, ignore_index=255, nan_to_num=-1)
- all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
- 'Dice']
+ results, label, num_classes, ignore_index=255, nan_to_num=-1
+ )
+ all_acc, acc, dice = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["Dice"]
assert acc[-1] == -1
assert dice[-1] == -1
@@ -276,21 +274,30 @@ def test_mean_fscore():
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
ret_metrics = mean_fscore(results, label, num_classes, ignore_index)
- all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
- 'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
+ all_acc, recall, precision, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Recall"],
+ ret_metrics["Precision"],
+ ret_metrics["Fscore"],
+ )
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
- results, label, num_classes, ignore_index)
+ results, label, num_classes, ignore_index
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
assert np.allclose(fscore, fscore_l)
- ret_metrics = mean_fscore(
- results, label, num_classes, ignore_index, beta=2)
- all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
- 'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
+ ret_metrics = mean_fscore(results, label, num_classes, ignore_index, beta=2)
+ all_acc, recall, precision, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Recall"],
+ ret_metrics["Precision"],
+ ret_metrics["Fscore"],
+ )
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
- results, label, num_classes, ignore_index, beta=2)
+ results, label, num_classes, ignore_index, beta=2
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
@@ -299,9 +306,14 @@ def test_mean_fscore():
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
ret_metrics = mean_fscore(
- results, label, num_classes, ignore_index=255, nan_to_num=-1)
- all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
- 'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
+ results, label, num_classes, ignore_index=255, nan_to_num=-1
+ )
+ all_acc, recall, precision, fscore = (
+ ret_metrics["aAcc"],
+ ret_metrics["Recall"],
+ ret_metrics["Precision"],
+ ret_metrics["Fscore"],
+ )
assert recall[-1] == -1
assert precision[-1] == -1
assert fscore[-1] == -1
@@ -314,9 +326,9 @@ def test_filename_inputs():
def save_arr(input_arrays: list, title: str, is_image: bool, dir: str):
filenames = []
- SUFFIX = '.png' if is_image else '.npy'
+ SUFFIX = ".png" if is_image else ".npy"
for idx, arr in enumerate(input_arrays):
- filename = '{}/{}-{}{}'.format(dir, title, idx, SUFFIX)
+ filename = f"{dir}/{title}-{idx}{SUFFIX}"
if is_image:
cv2.imwrite(filename, arr)
else:
@@ -333,19 +345,16 @@ def save_arr(input_arrays: list, title: str, is_image: bool, dir: str):
with tempfile.TemporaryDirectory() as temp_dir:
- result_files = save_arr(results, 'pred', False, temp_dir)
- label_files = save_arr(labels, 'label', True, temp_dir)
+ result_files = save_arr(results, "pred", False, temp_dir)
+ label_files = save_arr(labels, "label", True, temp_dir)
ret_metrics = eval_metrics(
- result_files,
- label_files,
- num_classes,
- ignore_index,
- metrics='mIoU')
- all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics[
- 'Acc'], ret_metrics['IoU']
- all_acc_l, acc_l, iou_l = legacy_mean_iou(results, labels, num_classes,
- ignore_index)
+ result_files, label_files, num_classes, ignore_index, metrics="mIoU"
+ )
+ all_acc, acc, iou = ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
+ all_acc_l, acc_l, iou_l = legacy_mean_iou(
+ results, labels, num_classes, ignore_index
+ )
assert np.allclose(all_acc, all_acc_l)
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
diff --git a/mmsegmentation/tests/test_models/test_backbones/__init__.py b/mmsegmentation/tests/test_models/test_backbones/__init__.py
index 8b673fa..d78e0f2 100644
--- a/mmsegmentation/tests/test_models/test_backbones/__init__.py
+++ b/mmsegmentation/tests/test_models/test_backbones/__init__.py
@@ -1,4 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .utils import all_zeros, check_norm_state, is_block, is_norm
-__all__ = ['is_norm', 'is_block', 'all_zeros', 'check_norm_state']
+__all__ = ["is_norm", "is_block", "all_zeros", "check_norm_state"]
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_beit.py b/mmsegmentation/tests/test_models/test_backbones/test_beit.py
index cf39608..118d1f3 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_beit.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_beit.py
@@ -18,7 +18,7 @@ def test_beit_backbone():
with pytest.raises(TypeError):
# out_indices must be int ,list or tuple
- model = BEiT(out_indices=1.)
+ model = BEiT(out_indices=1.0)
with pytest.raises(AssertionError):
# The length of img_size tuple must be lower than 3.
@@ -30,7 +30,7 @@ def test_beit_backbone():
# Test img_size isinstance tuple
imgs = torch.randn(1, 3, 224, 224)
- model = BEiT(img_size=(224, ))
+ model = BEiT(img_size=(224,))
model.init_weights()
model(imgs)
@@ -115,7 +115,7 @@ def test_beit_backbone():
def test_beit_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = BEiT(pretrained=None, init_cfg=None)
@@ -124,9 +124,8 @@ def test_beit_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
- model = BEiT(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ model = BEiT(pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path))
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -134,9 +133,9 @@ def test_beit_init():
# test resize_rel_pos_embed
value = torch.randn(732, 16)
ckpt = {
- 'state_dict': {
- 'layers.0.attn.relative_position_index': 0,
- 'layers.0.attn.relative_position_bias_table': value
+ "state_dict": {
+ "layers.0.attn.relative_position_index": 0,
+ "layers.0.attn.relative_position_bias_table": value,
}
}
model = BEiT(img_size=(512, 512))
@@ -152,7 +151,7 @@ def test_beit_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = BEiT(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -160,8 +159,7 @@ def test_beit_init():
# pretrained loads pretrain from an non-existent file
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = BEiT(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = BEiT(pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path))
with pytest.raises(AssertionError):
model = BEiT(pretrained=path, init_cfg=123)
@@ -173,8 +171,7 @@ def test_beit_init():
# pretrain=123, whose type is unsupported
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = BEiT(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = BEiT(pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path))
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_bisenetv1.py b/mmsegmentation/tests/test_models/test_backbones/test_bisenetv1.py
index c067749..436f945 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_bisenetv1.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_bisenetv1.py
@@ -3,15 +3,18 @@
import torch
from mmseg.models.backbones import BiSeNetV1
-from mmseg.models.backbones.bisenetv1 import (AttentionRefinementModule,
- ContextPath, FeatureFusionModule,
- SpatialPath)
+from mmseg.models.backbones.bisenetv1 import (
+ AttentionRefinementModule,
+ ContextPath,
+ FeatureFusionModule,
+ SpatialPath,
+)
def test_bisenetv1_backbone():
# Test BiSeNetV1 Standard Forward
backbone_cfg = dict(
- type='ResNet',
+ type="ResNet",
in_channels=3,
depth=18,
num_stages=4,
@@ -19,8 +22,9 @@ def test_bisenetv1_backbone():
dilations=(1, 1, 1, 1),
strides=(1, 2, 2, 2),
norm_eval=False,
- style='pytorch',
- contract_dilation=True)
+ style="pytorch",
+ contract_dilation=True,
+ )
model = BiSeNetV1(in_channels=3, backbone_cfg=backbone_cfg)
model.init_weights()
model.train()
@@ -45,16 +49,14 @@ def test_bisenetv1_backbone():
with pytest.raises(AssertionError):
# BiSeNetV1 spatial path channel constraints.
BiSeNetV1(
- backbone_cfg=backbone_cfg,
- in_channels=3,
- spatial_channels=(16, 16, 16))
+ backbone_cfg=backbone_cfg, in_channels=3, spatial_channels=(16, 16, 16)
+ )
with pytest.raises(AssertionError):
# BiSeNetV1 context path constraints.
BiSeNetV1(
- backbone_cfg=backbone_cfg,
- in_channels=3,
- context_channels=(16, 32, 64, 128))
+ backbone_cfg=backbone_cfg, in_channels=3, context_channels=(16, 32, 64, 128)
+ )
def test_bisenetv1_spatial_path():
@@ -65,7 +67,7 @@ def test_bisenetv1_spatial_path():
def test_bisenetv1_context_path():
backbone_cfg = dict(
- type='ResNet',
+ type="ResNet",
in_channels=3,
depth=50,
num_stages=4,
@@ -73,13 +75,13 @@ def test_bisenetv1_context_path():
dilations=(1, 1, 1, 1),
strides=(1, 2, 2, 2),
norm_eval=False,
- style='pytorch',
- contract_dilation=True)
+ style="pytorch",
+ contract_dilation=True,
+ )
with pytest.raises(AssertionError):
# BiSeNetV1 context path constraints.
- ContextPath(
- backbone_cfg=backbone_cfg, context_channels=(16, 32, 64, 128))
+ ContextPath(backbone_cfg=backbone_cfg, context_channels=(16, 32, 64, 128))
def test_bisenetv1_attention_refinement_module():
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_bisenetv2.py b/mmsegmentation/tests/test_models/test_backbones/test_bisenetv2.py
index cf2dfb3..81ead85 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_bisenetv2.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_bisenetv2.py
@@ -3,8 +3,7 @@
from mmcv.cnn import ConvModule
from mmseg.models.backbones import BiSeNetV2
-from mmseg.models.backbones.bisenetv2 import (BGALayer, DetailBranch,
- SemanticBranch)
+from mmseg.models.backbones.bisenetv2 import BGALayer, DetailBranch, SemanticBranch
def test_bisenetv2_backbone():
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_blocks.py b/mmsegmentation/tests/test_models/test_backbones/test_blocks.py
index 77c8564..4fcfcf1 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_blocks.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_blocks.py
@@ -4,8 +4,12 @@
import torch
from mmcv.utils import TORCH_VERSION, digit_version
-from mmseg.models.utils import (InvertedResidual, InvertedResidualV3, SELayer,
- make_divisible)
+from mmseg.models.utils import (
+ InvertedResidual,
+ InvertedResidualV3,
+ SELayer,
+ make_divisible,
+)
def test_make_divisible():
@@ -78,7 +82,7 @@ def test_inv_residualv3():
assert inv_module.with_res_shortcut is True
assert inv_module.with_se is False
assert inv_module.with_expand_conv is False
- assert not hasattr(inv_module, 'expand_conv')
+ assert not hasattr(inv_module, "expand_conv")
assert isinstance(inv_module.depthwise_conv.conv, torch.nn.Conv2d)
assert inv_module.depthwise_conv.conv.kernel_size == (3, 3)
assert inv_module.depthwise_conv.conv.stride == (1, 1)
@@ -98,11 +102,10 @@ def test_inv_residualv3():
se_cfg = dict(
channels=16,
ratio=4,
- act_cfg=(dict(type='ReLU'),
- dict(type='HSigmoid', bias=3.0, divisor=6.0)))
- act_cfg = dict(type='HSwish')
- inv_module = InvertedResidualV3(
- 32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg)
+ act_cfg=(dict(type="ReLU"), dict(type="HSigmoid", bias=3.0, divisor=6.0)),
+ )
+ act_cfg = dict(type="HSwish")
+ inv_module = InvertedResidualV3(32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg)
assert inv_module.with_res_shortcut is False
assert inv_module.with_se is True
assert inv_module.with_expand_conv is True
@@ -110,8 +113,9 @@ def test_inv_residualv3():
assert inv_module.expand_conv.conv.stride == (1, 1)
assert inv_module.expand_conv.conv.padding == (0, 0)
- assert isinstance(inv_module.depthwise_conv.conv,
- mmcv.cnn.bricks.Conv2dAdaptivePadding)
+ assert isinstance(
+ inv_module.depthwise_conv.conv, mmcv.cnn.bricks.Conv2dAdaptivePadding
+ )
assert inv_module.depthwise_conv.conv.kernel_size == (3, 3)
assert inv_module.depthwise_conv.conv.stride == (2, 2)
assert inv_module.depthwise_conv.conv.padding == (0, 0)
@@ -122,8 +126,9 @@ def test_inv_residualv3():
assert inv_module.linear_conv.conv.padding == (0, 0)
assert isinstance(inv_module.linear_conv.bn, torch.nn.BatchNorm2d)
- if (TORCH_VERSION == 'parrots'
- or digit_version(TORCH_VERSION) < digit_version('1.7')):
+ if TORCH_VERSION == "parrots" or digit_version(TORCH_VERSION) < digit_version(
+ "1.7"
+ ):
# Note: Use PyTorch official HSwish
# when torch>=1.7 after MMCV >= 1.4.5.
# Hardswish is not supported when PyTorch version < 1.6.
@@ -134,8 +139,7 @@ def test_inv_residualv3():
assert isinstance(inv_module.depthwise_conv.activate, mmcv.cnn.HSwish)
else:
assert isinstance(inv_module.expand_conv.activate, torch.nn.Hardswish)
- assert isinstance(inv_module.depthwise_conv.activate,
- torch.nn.Hardswish)
+ assert isinstance(inv_module.depthwise_conv.activate, torch.nn.Hardswish)
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
@@ -143,7 +147,8 @@ def test_inv_residualv3():
# test with checkpoint forward
inv_module = InvertedResidualV3(
- 32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg, with_cp=True)
+ 32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg, with_cp=True
+ )
assert inv_module.with_cp
x = torch.randn(2, 32, 64, 64, requires_grad=True)
output = inv_module(x)
@@ -153,7 +158,7 @@ def test_inv_residualv3():
def test_se_layer():
with pytest.raises(AssertionError):
# test act_cfg assertion.
- SELayer(32, act_cfg=(dict(type='ReLU'), ))
+ SELayer(32, act_cfg=(dict(type="ReLU"),))
# test config with channels = 16.
se_layer = SELayer(16)
@@ -171,7 +176,7 @@ def test_se_layer():
assert output.shape == (1, 16, 64, 64)
# test config with channels = 16, act_cfg = dict(type='ReLU').
- se_layer = SELayer(16, act_cfg=dict(type='ReLU'))
+ se_layer = SELayer(16, act_cfg=dict(type="ReLU"))
assert se_layer.conv1.conv.kernel_size == (1, 1)
assert se_layer.conv1.conv.stride == (1, 1)
assert se_layer.conv1.conv.padding == (0, 0)
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_cgnet.py b/mmsegmentation/tests/test_models/test_backbones/test_cgnet.py
index f938525..e63ac24 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_cgnet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_cgnet.py
@@ -3,8 +3,7 @@
import torch
from mmseg.models.backbones import CGNet
-from mmseg.models.backbones.cgnet import (ContextGuidedBlock,
- GlobalContextExtractor)
+from mmseg.models.backbones.cgnet import ContextGuidedBlock, GlobalContextExtractor
def test_cgnet_GlobalContextExtractor():
@@ -21,8 +20,7 @@ def test_cgnet_context_guided_block():
ContextGuidedBlock(8, 8)
# test cgnet ContextGuidedBlock with checkpoint forward
- block = ContextGuidedBlock(
- 16, 16, act_cfg=dict(type='PReLU'), with_cp=True)
+ block = ContextGuidedBlock(16, 16, act_cfg=dict(type="PReLU"), with_cp=True)
assert block.with_cp
x = torch.randn(2, 16, 64, 64, requires_grad=True)
x_out = block(x)
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_erfnet.py b/mmsegmentation/tests/test_models/test_backbones/test_erfnet.py
index 6ae7345..1687aa7 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_erfnet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_erfnet.py
@@ -3,8 +3,11 @@
import torch
from mmseg.models.backbones import ERFNet
-from mmseg.models.backbones.erfnet import (DownsamplerBlock, NonBottleneck1d,
- UpsamplerBlock)
+from mmseg.models.backbones.erfnet import (
+ DownsamplerBlock,
+ NonBottleneck1d,
+ UpsamplerBlock,
+)
def test_erfnet_backbone():
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_fast_scnn.py b/mmsegmentation/tests/test_models/test_backbones/test_fast_scnn.py
index 7ee638b..df294b9 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_fast_scnn.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_fast_scnn.py
@@ -9,11 +9,15 @@ def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(
- 3, (32, 48),
- 64, (64, 96, 128), (2, 2, 1),
+ 3,
+ (32, 48),
+ 64,
+ (64, 96, 128),
+ (2, 2, 1),
global_out_channels=127,
higher_in_channels=64,
- lower_in_channels=128)
+ lower_in_channels=128,
+ )
# Test FastSCNN Standard Forward
model = FastSCNN(
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_hrnet.py b/mmsegmentation/tests/test_models/test_backbones/test_hrnet.py
index 8329c84..feffc5d 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_hrnet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_hrnet.py
@@ -7,7 +7,7 @@
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
-@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
+@pytest.mark.parametrize("block", [BasicBlock, Bottleneck])
def test_hrmodule(block):
# Test multiscale forward
num_channles = (32, 64)
@@ -22,7 +22,7 @@ def test_hrmodule(block):
feats = [
torch.randn(1, in_channels[0], 64, 64),
- torch.randn(1, in_channels[1], 32, 32)
+ torch.randn(1, in_channels[1], 32, 32),
]
feats = hrmodule(feats)
@@ -44,7 +44,7 @@ def test_hrmodule(block):
feats = [
torch.randn(1, in_channels[0], 64, 64),
- torch.randn(1, in_channels[1], 32, 32)
+ torch.randn(1, in_channels[1], 32, 32),
]
feats = hrmodule(feats)
@@ -58,37 +58,42 @@ def test_hrnet_backbone():
stage1=dict(
num_modules=1,
num_branches=1,
- block='BOTTLENECK',
- num_blocks=(4, ),
- num_channels=(64, )),
+ block="BOTTLENECK",
+ num_blocks=(4,),
+ num_channels=(64,),
+ ),
stage2=dict(
num_modules=1,
num_branches=2,
- block='BASIC',
+ block="BASIC",
num_blocks=(4, 4),
- num_channels=(32, 64)),
+ num_channels=(32, 64),
+ ),
stage3=dict(
num_modules=4,
num_branches=3,
- block='BASIC',
+ block="BASIC",
num_blocks=(4, 4, 4),
- num_channels=(32, 64, 128)))
+ num_channels=(32, 64, 128),
+ ),
+ )
with pytest.raises(AssertionError):
# HRNet now only support 4 stages
HRNet(extra=extra)
- extra['stage4'] = dict(
+ extra["stage4"] = dict(
num_modules=3,
num_branches=3, # should be 4
- block='BASIC',
+ block="BASIC",
num_blocks=(4, 4, 4, 4),
- num_channels=(32, 64, 128, 256))
+ num_channels=(32, 64, 128, 256),
+ )
with pytest.raises(AssertionError):
# len(num_blocks) should equal num_branches
HRNet(extra=extra)
- extra['stage4']['num_branches'] = 4
+ extra["stage4"]["num_branches"] = 4
# Test hrnetv2p_w32
model = HRNet(extra=extra)
@@ -123,13 +128,13 @@ def test_hrnet_backbone():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
if i == 1:
- layer = getattr(model, f'layer{i}')
- transition = getattr(model, f'transition{i}')
+ layer = getattr(model, f"layer{i}")
+ transition = getattr(model, f"transition{i}")
elif i == 4:
- layer = getattr(model, f'stage{i}')
+ layer = getattr(model, f"stage{i}")
else:
- layer = getattr(model, f'stage{i}')
- transition = getattr(model, f'transition{i}')
+ layer = getattr(model, f"stage{i}")
+ transition = getattr(model, f"transition{i}")
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_icnet.py b/mmsegmentation/tests/test_models/test_backbones/test_icnet.py
index a96d8d8..10f0450 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_icnet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_icnet.py
@@ -14,26 +14,29 @@ def test_icnet_backbone():
light_branch_middle_channels=8,
psp_out_channels=128,
out_channels=(16, 128, 128),
- backbone_cfg=None)
+ backbone_cfg=None,
+ )
# Test ICNet Standard Forward
model = ICNet(
layer_channels=(128, 512),
backbone_cfg=dict(
- type='ResNetV1c',
+ type="ResNetV1c",
in_channels=3,
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=(1, 2, 1, 1),
- norm_cfg=dict(type='BN', requires_grad=True),
+ norm_cfg=dict(type="BN", requires_grad=True),
norm_eval=False,
- style='pytorch',
- contract_dilation=True),
+ style="pytorch",
+ contract_dilation=True,
+ ),
+ )
+ assert (
+ hasattr(model.backbone, "maxpool") and model.backbone.maxpool.ceil_mode is True
)
- assert hasattr(model.backbone,
- 'maxpool') and model.backbone.maxpool.ceil_mode is True
model.init_weights()
model.train()
batch_size = 2
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_mae.py b/mmsegmentation/tests/test_models/test_backbones/test_mae.py
index 562d067..a5c2e5f 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_mae.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_mae.py
@@ -18,7 +18,7 @@ def test_mae_backbone():
with pytest.raises(TypeError):
# out_indices must be int ,list or tuple
- model = MAE(out_indices=1.)
+ model = MAE(out_indices=1.0)
with pytest.raises(AssertionError):
# The length of img_size tuple must be lower than 3.
@@ -30,7 +30,7 @@ def test_mae_backbone():
# Test img_size isinstance tuple
imgs = torch.randn(1, 3, 224, 224)
- model = MAE(img_size=(224, ))
+ model = MAE(img_size=(224,))
model.init_weights()
model(imgs)
@@ -111,7 +111,7 @@ def test_mae_backbone():
def test_mae_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = MAE(pretrained=None, init_cfg=None)
@@ -120,9 +120,8 @@ def test_mae_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
- model = MAE(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ model = MAE(pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path))
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -131,10 +130,10 @@ def test_mae_init():
value = torch.randn(732, 16)
abs_pos_embed_value = torch.rand(1, 17, 768)
ckpt = {
- 'state_dict': {
- 'layers.0.attn.relative_position_index': 0,
- 'layers.0.attn.relative_position_bias_table': value,
- 'pos_embed': abs_pos_embed_value
+ "state_dict": {
+ "layers.0.attn.relative_position_index": 0,
+ "layers.0.attn.relative_position_bias_table": value,
+ "pos_embed": abs_pos_embed_value,
}
}
model = MAE(img_size=(512, 512))
@@ -142,7 +141,7 @@ def test_mae_init():
model.resize_rel_pos_embed(ckpt)
# test resize abs pos embed
- ckpt = model.resize_abs_pos_embed(ckpt['state_dict'])
+ ckpt = model.resize_abs_pos_embed(ckpt["state_dict"])
# pretrained=None
# init_cfg=123, whose type is unsupported
@@ -153,7 +152,7 @@ def test_mae_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = MAE(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -161,8 +160,7 @@ def test_mae_init():
# pretrained loads pretrain from an non-existent file
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = MAE(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = MAE(pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path))
with pytest.raises(AssertionError):
model = MAE(pretrained=path, init_cfg=123)
@@ -174,8 +172,7 @@ def test_mae_init():
# pretrain=123, whose type is unsupported
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = MAE(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = MAE(pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path))
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_mit.py b/mmsegmentation/tests/test_models/test_backbones/test_mit.py
index 72f74fe..5b00610 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_mit.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_mit.py
@@ -3,8 +3,11 @@
import torch
from mmseg.models.backbones import MixVisionTransformer
-from mmseg.models.backbones.mit import (EfficientMultiheadAttention, MixFFN,
- TransformerEncoderLayer)
+from mmseg.models.backbones.mit import (
+ EfficientMultiheadAttention,
+ MixFFN,
+ TransformerEncoderLayer,
+)
def test_mit():
@@ -16,7 +19,8 @@ def test_mit():
H, W = (224, 224)
temp = torch.randn((1, 3, H, W))
model = MixVisionTransformer(
- embed_dims=32, num_heads=[1, 2, 5, 8], out_indices=(0, 1, 2, 3))
+ embed_dims=32, num_heads=[1, 2, 5, 8], out_indices=(0, 1, 2, 3)
+ )
model.init_weights()
outs = model(temp)
assert outs[0].shape == (1, 32, H // 4, W // 4)
@@ -59,7 +63,8 @@ def test_mit():
# Test TransformerEncoderLayer with checkpoint forward
block = TransformerEncoderLayer(
- embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True)
+ embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True
+ )
assert block.with_cp
x = torch.randn(1, 56 * 56, 64)
x_out = block(x, (56, 56))
@@ -67,7 +72,7 @@ def test_mit():
def test_mit_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = MixVisionTransformer(pretrained=None, init_cfg=None)
@@ -77,8 +82,9 @@ def test_mit_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
model = MixVisionTransformer(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -92,7 +98,7 @@ def test_mit_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = MixVisionTransformer(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -101,7 +107,8 @@ def test_mit_init():
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
MixVisionTransformer(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
with pytest.raises(AssertionError):
MixVisionTransformer(pretrained=path, init_cfg=123)
@@ -114,7 +121,8 @@ def test_mit_init():
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
MixVisionTransformer(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_mobilenet_v3.py b/mmsegmentation/tests/test_models/test_backbones/test_mobilenet_v3.py
index 769ee14..04e574c 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_mobilenet_v3.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_mobilenet_v3.py
@@ -8,7 +8,7 @@
def test_mobilenet_v3():
with pytest.raises(AssertionError):
# check invalid arch
- MobileNetV3('big')
+ MobileNetV3("big")
with pytest.raises(AssertionError):
# check invalid reduction_factor
@@ -40,7 +40,7 @@ def test_mobilenet_v3():
assert feat[2].shape == (2, 576, 7, 7)
# Test MobileNetV3 with arch = 'large'
- model = MobileNetV3(arch='large', out_indices=(1, 3, 16))
+ model = MobileNetV3(arch="large", out_indices=(1, 3, 16))
model.init_weights()
model.train()
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_resnest.py b/mmsegmentation/tests/test_models/test_backbones/test_resnest.py
index 3013f34..07848ec 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_resnest.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_resnest.py
@@ -9,11 +9,10 @@
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
- BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow')
+ BottleneckS(64, 64, radix=2, reduction_factor=4, style="tensorflow")
# Test ResNeSt Bottleneck structure
- block = BottleneckS(
- 64, 256, radix=2, reduction_factor=4, stride=2, style='pytorch')
+ block = BottleneckS(64, 256, radix=2, reduction_factor=4, stride=2, style="pytorch")
assert block.avd_layer.stride == 2
assert block.conv2.channels == 256
@@ -30,8 +29,7 @@ def test_resnest_backbone():
ResNeSt(depth=18)
# Test ResNeSt with radix 2, reduction_factor 4
- model = ResNeSt(
- depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3))
+ model = ResNeSt(depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_resnet.py b/mmsegmentation/tests/test_models/test_backbones/test_resnet.py
index fa632f5..b7525e5 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_resnet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_resnet.py
@@ -14,15 +14,13 @@
def test_resnet_basic_block():
with pytest.raises(AssertionError):
# Not implemented yet.
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
BasicBlock(64, 64, dcn=dcn)
with pytest.raises(AssertionError):
# Not implemented yet.
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3")
]
BasicBlock(64, 64, plugins=plugins)
@@ -31,12 +29,14 @@ def test_resnet_basic_block():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ )
]
BasicBlock(64, 64, plugins=plugins)
@@ -63,32 +63,26 @@ def test_resnet_basic_block():
def test_resnet_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
- Bottleneck(64, 64, style='tensorflow')
+ Bottleneck(64, 64, style="tensorflow")
with pytest.raises(AssertionError):
# Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv4')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv4")
]
Bottleneck(64, 16, plugins=plugins)
with pytest.raises(AssertionError):
# Need to specify different postfix to avoid duplicate plugin name
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3'),
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3"),
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3"),
]
Bottleneck(64, 16, plugins=plugins)
with pytest.raises(KeyError):
# Plugin type is not supported
- plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
+ plugins = [dict(cfg=dict(type="WrongPlugin"), position="after_conv3")]
Bottleneck(64, 16, plugins=plugins)
# Test Bottleneck with checkpoint forward
@@ -99,17 +93,17 @@ def test_resnet_bottleneck():
assert x_out.shape == torch.Size([1, 64, 56, 56])
# Test Bottleneck style
- block = Bottleneck(64, 64, stride=2, style='pytorch')
+ block = Bottleneck(64, 64, stride=2, style="pytorch")
assert block.conv1.stride == (1, 1)
assert block.conv2.stride == (2, 2)
- block = Bottleneck(64, 64, stride=2, style='caffe')
+ block = Bottleneck(64, 64, stride=2, style="caffe")
assert block.conv1.stride == (2, 2)
assert block.conv2.stride == (1, 1)
# Test Bottleneck DCN
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
with pytest.raises(AssertionError):
- Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv'))
+ Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type="Conv"))
block = Bottleneck(64, 64, dcn=dcn)
assert isinstance(block.conv2, DeformConv2dPack)
@@ -121,9 +115,7 @@ def test_resnet_bottleneck():
# Test Bottleneck with 1 ContextBlock after conv3
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3")
]
block = Bottleneck(64, 16, plugins=plugins)
assert block.context_block.in_channels == 64
@@ -135,12 +127,14 @@ def test_resnet_bottleneck():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ )
]
block = Bottleneck(64, 16, plugins=plugins)
assert block.gen_attention_block.in_channels == 16
@@ -153,16 +147,16 @@ def test_resnet_bottleneck():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2'),
- dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ ),
+ dict(cfg=dict(type="NonLocal2d"), position="after_conv2"),
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3"),
]
block = Bottleneck(64, 16, plugins=plugins)
assert block.gen_attention_block.in_channels == 16
@@ -176,14 +170,17 @@ def test_resnet_bottleneck():
# conv3
plugins = [
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
- position='after_conv2'),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=1),
+ position="after_conv2",
+ ),
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
- position='after_conv3'),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=2),
+ position="after_conv3",
+ ),
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
- position='after_conv3')
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=3),
+ position="after_conv3",
+ ),
]
block = Bottleneck(64, 16, plugins=plugins)
assert block.context_block1.in_channels == 16
@@ -278,16 +275,17 @@ def test_resnet_backbone():
with pytest.raises(AssertionError):
# len(stage_with_dcn) == num_stages
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
- ResNet(50, dcn=dcn, stage_with_dcn=(True, ))
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
+ ResNet(50, dcn=dcn, stage_with_dcn=(True,))
with pytest.raises(AssertionError):
# len(stage_with_plugin) == num_stages
plugins = [
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16),
stages=(False, True, True),
- position='after_conv3')
+ position="after_conv3",
+ )
]
ResNet(50, plugins=plugins)
@@ -297,7 +295,7 @@ def test_resnet_backbone():
with pytest.raises(AssertionError):
# len(strides) == len(dilations) == num_stages
- ResNet(18, strides=(1, ), dilations=(1, 1), num_stages=3)
+ ResNet(18, strides=(1,), dilations=(1, 1), num_stages=3)
with pytest.raises(TypeError):
# pretrained must be a string path
@@ -306,7 +304,7 @@ def test_resnet_backbone():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
- ResNet(50, style='tensorflow')
+ ResNet(50, style="tensorflow")
# Test ResNet18 norm_eval=True
model = ResNet(18, norm_eval=True)
@@ -315,8 +313,7 @@ def test_resnet_backbone():
assert check_norm_state(model.modules(), False)
# Test ResNet18 with torchvision pretrained weight
- model = ResNet(
- depth=18, norm_eval=True, pretrained='torchvision://resnet18')
+ model = ResNet(depth=18, norm_eval=True, pretrained="torchvision://resnet18")
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
@@ -331,7 +328,7 @@ def test_resnet_backbone():
for param in layer.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
- layer = getattr(model, 'layer{}'.format(i))
+ layer = getattr(model, f"layer{i}")
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
@@ -347,7 +344,7 @@ def test_resnet_backbone():
for param in model.stem.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
- layer = getattr(model, 'layer{}'.format(i))
+ layer = getattr(model, f"layer{i}")
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
@@ -428,8 +425,7 @@ def test_resnet_backbone():
assert feat[3].shape == torch.Size([1, 512, 7, 7])
# Test ResNet18 with GroupNorm forward
- model = ResNet(
- 18, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))
+ model = ResNet(18, norm_cfg=dict(type="GN", num_groups=32, requires_grad=True))
for m in model.modules():
if is_norm(m):
assert isinstance(m, GroupNorm)
@@ -449,24 +445,27 @@ def test_resnet_backbone():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
+ attention_type="0010",
+ kv_stride=2,
+ ),
stages=(False, True, True, True),
- position='after_conv2'),
- dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
+ position="after_conv2",
+ ),
+ dict(cfg=dict(type="NonLocal2d"), position="after_conv2"),
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16),
stages=(False, True, True, False),
- position='after_conv3')
+ position="after_conv3",
+ ),
]
model = ResNet(50, plugins=plugins)
for m in model.layer1.modules():
if is_block(m):
- assert not hasattr(m, 'context_block')
- assert not hasattr(m, 'gen_attention_block')
+ assert not hasattr(m, "context_block")
+ assert not hasattr(m, "gen_attention_block")
assert m.nonlocal_block.in_channels == 64
for m in model.layer2.modules():
if is_block(m):
@@ -484,7 +483,7 @@ def test_resnet_backbone():
if is_block(m):
assert m.nonlocal_block.in_channels == 512
assert m.gen_attention_block.in_channels == 512
- assert not hasattr(m, 'context_block')
+ assert not hasattr(m, "context_block")
model.init_weights()
model.train()
@@ -500,38 +499,40 @@ def test_resnet_backbone():
# conv3 in layers 2, 3, 4
plugins = [
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=1),
stages=(False, True, True, False),
- position='after_conv3'),
+ position="after_conv3",
+ ),
dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
+ cfg=dict(type="ContextBlock", ratio=1.0 / 16, postfix=2),
stages=(False, True, True, False),
- position='after_conv3')
+ position="after_conv3",
+ ),
]
model = ResNet(50, plugins=plugins)
for m in model.layer1.modules():
if is_block(m):
- assert not hasattr(m, 'context_block')
- assert not hasattr(m, 'context_block1')
- assert not hasattr(m, 'context_block2')
+ assert not hasattr(m, "context_block")
+ assert not hasattr(m, "context_block1")
+ assert not hasattr(m, "context_block2")
for m in model.layer2.modules():
if is_block(m):
- assert not hasattr(m, 'context_block')
+ assert not hasattr(m, "context_block")
assert m.context_block1.in_channels == 512
assert m.context_block2.in_channels == 512
for m in model.layer3.modules():
if is_block(m):
- assert not hasattr(m, 'context_block')
+ assert not hasattr(m, "context_block")
assert m.context_block1.in_channels == 1024
assert m.context_block2.in_channels == 1024
for m in model.layer4.modules():
if is_block(m):
- assert not hasattr(m, 'context_block')
- assert not hasattr(m, 'context_block1')
- assert not hasattr(m, 'context_block2')
+ assert not hasattr(m, "context_block")
+ assert not hasattr(m, "context_block1")
+ assert not hasattr(m, "context_block2")
model.init_weights()
model.train()
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_resnext.py b/mmsegmentation/tests/test_models/test_backbones/test_resnext.py
index 2aecaf0..36493db 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_resnext.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_resnext.py
@@ -10,26 +10,21 @@
def test_renext_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
- BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow')
+ BottleneckX(64, 64, groups=32, base_width=4, style="tensorflow")
# Test ResNeXt Bottleneck structure
- block = BottleneckX(
- 64, 64, groups=32, base_width=4, stride=2, style='pytorch')
+ block = BottleneckX(64, 64, groups=32, base_width=4, stride=2, style="pytorch")
assert block.conv2.stride == (2, 2)
assert block.conv2.groups == 32
assert block.conv2.out_channels == 128
# Test ResNeXt Bottleneck with DCN
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
with pytest.raises(AssertionError):
# conv_cfg must be None if dcn is not None
BottleneckX(
- 64,
- 64,
- groups=32,
- base_width=4,
- dcn=dcn,
- conv_cfg=dict(type='Conv'))
+ 64, 64, groups=32, base_width=4, dcn=dcn, conv_cfg=dict(type="Conv")
+ )
BottleneckX(64, 64, dcn=dcn)
# Test ResNeXt Bottleneck forward
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_stdc.py b/mmsegmentation/tests/test_models/test_backbones/test_stdc.py
index 1e3862b..9a50a14 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_stdc.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_stdc.py
@@ -3,27 +3,32 @@
import torch
from mmseg.models.backbones import STDCContextPathNet
-from mmseg.models.backbones.stdc import (AttentionRefinementModule,
- FeatureFusionModule, STDCModule,
- STDCNet)
+from mmseg.models.backbones.stdc import (
+ AttentionRefinementModule,
+ FeatureFusionModule,
+ STDCModule,
+ STDCNet,
+)
def test_stdc_context_path_net():
# Test STDCContextPathNet Standard Forward
model = STDCContextPathNet(
backbone_cfg=dict(
- type='STDCNet',
- stdc_type='STDCNet1',
+ type="STDCNet",
+ stdc_type="STDCNet1",
in_channels=3,
channels=(32, 64, 256, 512, 1024),
- bottleneck_type='cat',
+ bottleneck_type="cat",
num_convs=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- with_final_conv=True),
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ with_final_conv=True,
+ ),
last_in_channels=(1024, 512),
out_channels=128,
- ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4))
+ ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4),
+ )
model.init_weights()
model.train()
batch_size = 2
@@ -45,18 +50,20 @@ def test_stdc_context_path_net():
imgs = torch.randn(batch_size, 3, 527, 279)
model = STDCContextPathNet(
backbone_cfg=dict(
- type='STDCNet',
- stdc_type='STDCNet1',
+ type="STDCNet",
+ stdc_type="STDCNet1",
in_channels=3,
channels=(32, 64, 256, 512, 1024),
- bottleneck_type='add',
+ bottleneck_type="add",
num_convs=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- with_final_conv=False),
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ with_final_conv=False,
+ ),
last_in_channels=(1024, 512),
out_channels=128,
- ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4))
+ ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4),
+ )
model.init_weights()
model.train()
feat = model(imgs)
@@ -67,38 +74,41 @@ def test_stdcnet():
with pytest.raises(AssertionError):
# STDC backbone constraints.
STDCNet(
- stdc_type='STDCNet3',
+ stdc_type="STDCNet3",
in_channels=3,
channels=(32, 64, 256, 512, 1024),
- bottleneck_type='cat',
+ bottleneck_type="cat",
num_convs=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- with_final_conv=False)
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ with_final_conv=False,
+ )
with pytest.raises(AssertionError):
# STDC bottleneck type constraints.
STDCNet(
- stdc_type='STDCNet1',
+ stdc_type="STDCNet1",
in_channels=3,
channels=(32, 64, 256, 512, 1024),
- bottleneck_type='dog',
+ bottleneck_type="dog",
num_convs=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- with_final_conv=False)
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ with_final_conv=False,
+ )
with pytest.raises(AssertionError):
# STDC channels length constraints.
STDCNet(
- stdc_type='STDCNet1',
+ stdc_type="STDCNet1",
in_channels=3,
channels=(16, 32, 64, 256, 512, 1024),
- bottleneck_type='cat',
+ bottleneck_type="cat",
num_convs=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- act_cfg=dict(type='ReLU'),
- with_final_conv=False)
+ norm_cfg=dict(type="BN", requires_grad=True),
+ act_cfg=dict(type="ReLU"),
+ with_final_conv=False,
+ )
def test_feature_fusion_module():
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_swin.py b/mmsegmentation/tests/test_models/test_backbones/test_swin.py
index 8d14d47..0df6385 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_swin.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_swin.py
@@ -17,7 +17,8 @@ def test_swin_block():
# Test BasicBlock with checkpoint forward
block = SwinBlock(
- embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True)
+ embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True
+ )
assert block.with_cp
x = torch.randn(1, 56 * 56, 64)
x_out = block(x, (56, 56))
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_timm_backbone.py b/mmsegmentation/tests/test_models/test_backbones/test_timm_backbone.py
index 85ef9aa..2072698 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_timm_backbone.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_timm_backbone.py
@@ -15,26 +15,26 @@ def test_timm_backbone():
# Test different norm_layer, can be: 'SyncBN', 'BN2d', 'GN', 'LN', 'IN'
# Test resnet18 from timm, norm_layer='BN2d'
model = TIMMBackbone(
- model_name='resnet18',
+ model_name="resnet18",
features_only=True,
pretrained=False,
output_stride=32,
- norm_layer='BN2d')
+ norm_layer="BN2d",
+ )
# Test resnet18 from timm, norm_layer='SyncBN'
model = TIMMBackbone(
- model_name='resnet18',
+ model_name="resnet18",
features_only=True,
pretrained=False,
output_stride=32,
- norm_layer='SyncBN')
+ norm_layer="SyncBN",
+ )
# Test resnet18 from timm, features_only=True, output_stride=32
model = TIMMBackbone(
- model_name='resnet18',
- features_only=True,
- pretrained=False,
- output_stride=32)
+ model_name="resnet18", features_only=True, pretrained=False, output_stride=32
+ )
model.init_weights()
model.train()
assert check_norm_state(model.modules(), True)
@@ -51,10 +51,8 @@ def test_timm_backbone():
# Test resnet18 from timm, features_only=True, output_stride=16
model = TIMMBackbone(
- model_name='resnet18',
- features_only=True,
- pretrained=False,
- output_stride=16)
+ model_name="resnet18", features_only=True, pretrained=False, output_stride=16
+ )
imgs = torch.randn(1, 3, 224, 224)
feats = model(imgs)
feats = [feat.shape for feat in feats]
@@ -67,10 +65,8 @@ def test_timm_backbone():
# Test resnet18 from timm, features_only=True, output_stride=8
model = TIMMBackbone(
- model_name='resnet18',
- features_only=True,
- pretrained=False,
- output_stride=8)
+ model_name="resnet18", features_only=True, pretrained=False, output_stride=8
+ )
imgs = torch.randn(1, 3, 224, 224)
feats = model(imgs)
feats = [feat.shape for feat in feats]
@@ -82,14 +78,15 @@ def test_timm_backbone():
assert feats[4] == torch.Size((1, 512, 28, 28))
# Test efficientnet_b1 with pretrained weights
- model = TIMMBackbone(model_name='efficientnet_b1', pretrained=True)
+ model = TIMMBackbone(model_name="efficientnet_b1", pretrained=True)
# Test resnetv2_50x1_bitm from timm, features_only=True, output_stride=8
model = TIMMBackbone(
- model_name='resnetv2_50x1_bitm',
+ model_name="resnetv2_50x1_bitm",
features_only=True,
pretrained=False,
- output_stride=8)
+ output_stride=8,
+ )
imgs = torch.randn(1, 3, 8, 8)
feats = model(imgs)
feats = [feat.shape for feat in feats]
@@ -102,10 +99,11 @@ def test_timm_backbone():
# Test resnetv2_50x3_bitm from timm, features_only=True, output_stride=8
model = TIMMBackbone(
- model_name='resnetv2_50x3_bitm',
+ model_name="resnetv2_50x3_bitm",
features_only=True,
pretrained=False,
- output_stride=8)
+ output_stride=8,
+ )
imgs = torch.randn(1, 3, 8, 8)
feats = model(imgs)
feats = [feat.shape for feat in feats]
@@ -118,10 +116,11 @@ def test_timm_backbone():
# Test resnetv2_101x1_bitm from timm, features_only=True, output_stride=8
model = TIMMBackbone(
- model_name='resnetv2_101x1_bitm',
+ model_name="resnetv2_101x1_bitm",
features_only=True,
pretrained=False,
- output_stride=8)
+ output_stride=8,
+ )
imgs = torch.randn(1, 3, 8, 8)
feats = model(imgs)
feats = [feat.shape for feat in feats]
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_twins.py b/mmsegmentation/tests/test_models/test_backbones/test_twins.py
index aa3eaf9..30ce13b 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_twins.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_twins.py
@@ -2,9 +2,12 @@
import pytest
import torch
-from mmseg.models.backbones.twins import (PCPVT, SVT,
- ConditionalPositionEncoding,
- LocallyGroupedSelfAttention)
+from mmseg.models.backbones.twins import (
+ PCPVT,
+ SVT,
+ ConditionalPositionEncoding,
+ LocallyGroupedSelfAttention,
+)
def test_pcpvt():
@@ -18,7 +21,8 @@ def test_pcpvt():
qkv_bias=True,
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
- norm_after_stage=False)
+ norm_after_stage=False,
+ )
model.init_weights()
outs = model(temp)
assert outs[0].shape == (1, 32, H // 4, W // 4)
@@ -38,7 +42,8 @@ def test_svt():
qkv_bias=False,
depths=[4, 4, 4],
windiow_sizes=[7, 7, 7],
- norm_after_stage=True)
+ norm_after_stage=True,
+ )
model.init_weights()
outs = model(temp)
@@ -48,7 +53,7 @@ def test_svt():
def test_svt_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = SVT(pretrained=None, init_cfg=None)
@@ -57,9 +62,8 @@ def test_svt_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
- model = SVT(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ model = SVT(pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path))
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -73,7 +77,7 @@ def test_svt_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = SVT(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -81,8 +85,7 @@ def test_svt_init():
# pretrained loads pretrain from an non-existent file
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = SVT(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = SVT(pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path))
with pytest.raises(AssertionError):
model = SVT(pretrained=path, init_cfg=123)
@@ -94,8 +97,7 @@ def test_svt_init():
# pretrain=123, whose type is unsupported
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = SVT(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = SVT(pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path))
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
@@ -104,7 +106,7 @@ def test_svt_init():
def test_pcpvt_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = PCPVT(pretrained=None, init_cfg=None)
@@ -113,9 +115,8 @@ def test_pcpvt_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
- model = PCPVT(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ model = PCPVT(pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path))
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -129,7 +130,7 @@ def test_pcpvt_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = PCPVT(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -138,7 +139,8 @@ def test_pcpvt_init():
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
model = PCPVT(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
with pytest.raises(AssertionError):
model = PCPVT(pretrained=path, init_cfg=123)
@@ -150,8 +152,7 @@ def test_pcpvt_init():
# pretrain=123, whose type is unsupported
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
- model = PCPVT(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ model = PCPVT(pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path))
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_unet.py b/mmsegmentation/tests/test_models/test_backbones/test_unet.py
index 9beb727..995645c 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_unet.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_unet.py
@@ -3,8 +3,13 @@
import torch
from mmcv.cnn import ConvModule
-from mmseg.models.backbones.unet import (BasicConvBlock, DeconvModule,
- InterpConv, UNet, UpConvBlock)
+from mmseg.models.backbones.unet import (
+ BasicConvBlock,
+ DeconvModule,
+ InterpConv,
+ UNet,
+ UpConvBlock,
+)
from mmseg.ops import Upsample
from .utils import check_norm_state
@@ -12,15 +17,13 @@
def test_unet_basic_conv_block():
with pytest.raises(AssertionError):
# Not implemented yet.
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
BasicConvBlock(64, 64, dcn=dcn)
with pytest.raises(AssertionError):
# Not implemented yet.
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3")
]
BasicConvBlock(64, 64, plugins=plugins)
@@ -29,12 +32,14 @@ def test_unet_basic_conv_block():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ )
]
BasicConvBlock(64, 64, plugins=plugins)
@@ -163,41 +168,37 @@ def test_interp_conv():
64,
32,
conv_first=False,
- upsample_cfg=dict(
- scale_factor=2, mode='bilinear', align_corners=False))
+ upsample_cfg=dict(scale_factor=2, mode="bilinear", align_corners=False),
+ )
x = torch.randn(1, 64, 128, 128)
x_out = block(x)
assert isinstance(block.interp_upsample[0], Upsample)
assert isinstance(block.interp_upsample[1], ConvModule)
assert x_out.shape == torch.Size([1, 32, 256, 256])
- assert block.interp_upsample[0].mode == 'bilinear'
+ assert block.interp_upsample[0].mode == "bilinear"
# test InterpConv with nearest upsample for upsample 2X.
block = InterpConv(
- 64,
- 32,
- conv_first=False,
- upsample_cfg=dict(scale_factor=2, mode='nearest'))
+ 64, 32, conv_first=False, upsample_cfg=dict(scale_factor=2, mode="nearest")
+ )
x = torch.randn(1, 64, 128, 128)
x_out = block(x)
assert isinstance(block.interp_upsample[0], Upsample)
assert isinstance(block.interp_upsample[1], ConvModule)
assert x_out.shape == torch.Size([1, 32, 256, 256])
- assert block.interp_upsample[0].mode == 'nearest'
+ assert block.interp_upsample[0].mode == "nearest"
def test_up_conv_block():
with pytest.raises(AssertionError):
# Not implemented yet.
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
UpConvBlock(BasicConvBlock, 64, 32, 32, dcn=dcn)
with pytest.raises(AssertionError):
# Not implemented yet.
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3")
]
UpConvBlock(BasicConvBlock, 64, 32, 32, plugins=plugins)
@@ -206,12 +207,14 @@ def test_up_conv_block():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ )
]
UpConvBlock(BasicConvBlock, 64, 32, 32, plugins=plugins)
@@ -225,7 +228,8 @@ def test_up_conv_block():
# test UpConvBlock with upsample=True for upsample 2X. The spatial size of
# skip_x is 2X larger than x.
block = UpConvBlock(
- BasicConvBlock, 64, 32, 32, upsample_cfg=dict(type='InterpConv'))
+ BasicConvBlock, 64, 32, 32, upsample_cfg=dict(type="InterpConv")
+ )
skip_x = torch.randn(1, 32, 256, 256)
x = torch.randn(1, 64, 128, 128)
x_out = block(skip_x, x)
@@ -247,9 +251,10 @@ def test_up_conv_block():
32,
32,
upsample_cfg=dict(
- type='InterpConv',
- upsample_cfg=dict(
- scale_factor=2, mode='bilinear', align_corners=False)))
+ type="InterpConv",
+ upsample_cfg=dict(scale_factor=2, mode="bilinear", align_corners=False),
+ ),
+ )
skip_x = torch.randn(1, 32, 256, 256)
x = torch.randn(1, 64, 128, 128)
x_out = block(skip_x, x)
@@ -262,7 +267,8 @@ def test_up_conv_block():
64,
32,
32,
- upsample_cfg=dict(type='DeconvModule', kernel_size=4, scale_factor=2))
+ upsample_cfg=dict(type="DeconvModule", kernel_size=4, scale_factor=2),
+ )
skip_x = torch.randn(1, 32, 256, 256)
x = torch.randn(1, 64, 128, 128)
x_out = block(skip_x, x)
@@ -277,9 +283,10 @@ def test_up_conv_block():
num_convs=3,
dilation=3,
upsample_cfg=dict(
- type='InterpConv',
- upsample_cfg=dict(
- scale_factor=2, mode='bilinear', align_corners=False)))
+ type="InterpConv",
+ upsample_cfg=dict(scale_factor=2, mode="bilinear", align_corners=False),
+ ),
+ )
skip_x = torch.randn(1, 32, 256, 256)
x = torch.randn(1, 64, 128, 128)
x_out = block(skip_x, x)
@@ -313,15 +320,13 @@ def test_up_conv_block():
def test_unet():
with pytest.raises(AssertionError):
# Not implemented yet.
- dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+ dcn = dict(type="DCN", deform_groups=1, fallback_on_stride=False)
UNet(3, 64, 5, dcn=dcn)
with pytest.raises(AssertionError):
# Not implemented yet.
plugins = [
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- position='after_conv3')
+ dict(cfg=dict(type="ContextBlock", ratio=1.0 / 16), position="after_conv3")
]
UNet(3, 64, 5, plugins=plugins)
@@ -330,12 +335,14 @@ def test_unet():
plugins = [
dict(
cfg=dict(
- type='GeneralizedAttention',
+ type="GeneralizedAttention",
spatial_range=-1,
num_heads=8,
- attention_type='0010',
- kv_stride=2),
- position='after_conv2')
+ attention_type="0010",
+ kv_stride=2,
+ ),
+ position="after_conv2",
+ )
]
UNet(3, 64, 5, plugins=plugins)
@@ -352,7 +359,8 @@ def test_unet():
dec_num_convs=(2, 2, 2),
downsamples=(True, True, True),
enc_dilations=(1, 1, 1, 1),
- dec_dilations=(1, 1, 1))
+ dec_dilations=(1, 1, 1),
+ )
x = torch.randn(2, 3, 65, 65)
unet(x)
@@ -369,7 +377,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 65, 65)
unet(x)
@@ -386,7 +395,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 65, 65)
unet(x)
@@ -403,7 +413,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 65, 65)
unet(x)
@@ -420,7 +431,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2, 2),
downsamples=(True, True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 65, 65)
unet(x)
@@ -435,7 +447,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -450,7 +463,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -465,7 +479,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -480,7 +495,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -495,7 +511,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -510,7 +527,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 64, 64)
unet(x)
@@ -525,7 +543,8 @@ def test_unet():
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
dec_dilations=(1, 1, 1, 1),
- norm_eval=True)
+ norm_eval=True,
+ )
unet.train()
assert check_norm_state(unet.modules(), False)
@@ -540,7 +559,8 @@ def test_unet():
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
dec_dilations=(1, 1, 1, 1),
- norm_eval=False)
+ norm_eval=False,
+ )
unet.train()
assert check_norm_state(unet.modules(), True)
@@ -554,7 +574,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -574,7 +595,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -594,7 +616,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -614,7 +637,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -634,7 +658,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -654,7 +679,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -674,7 +700,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -694,7 +721,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, False, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -714,7 +742,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(False, False, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
@@ -734,7 +763,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, True),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
assert x_outs[0].shape == torch.Size([2, 64, 8, 8])
@@ -753,7 +783,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
assert x_outs[0].shape == torch.Size([2, 64, 16, 16])
@@ -772,7 +803,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, True, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
assert x_outs[0].shape == torch.Size([2, 64, 16, 16])
@@ -791,7 +823,8 @@ def test_unet():
dec_num_convs=(2, 2, 2, 2),
downsamples=(True, True, False, False),
enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1))
+ dec_dilations=(1, 1, 1, 1),
+ )
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
assert x_outs[0].shape == torch.Size([2, 64, 32, 32])
@@ -811,7 +844,8 @@ def test_unet():
downsamples=(True, True, False, False),
enc_dilations=(1, 1, 1, 1, 1),
dec_dilations=(1, 1, 1, 1),
- pretrained=None)
+ pretrained=None,
+ )
unet.init_weights()
x = torch.randn(2, 3, 128, 128)
x_outs = unet(x)
diff --git a/mmsegmentation/tests/test_models/test_backbones/test_vit.py b/mmsegmentation/tests/test_models/test_backbones/test_vit.py
index 0d1ba70..4a96e2f 100644
--- a/mmsegmentation/tests/test_models/test_backbones/test_vit.py
+++ b/mmsegmentation/tests/test_models/test_backbones/test_vit.py
@@ -2,8 +2,7 @@
import pytest
import torch
-from mmseg.models.backbones.vit import (TransformerEncoderLayer,
- VisionTransformer)
+from mmseg.models.backbones.vit import TransformerEncoderLayer, VisionTransformer
from .utils import check_norm_state
@@ -19,12 +18,12 @@ def test_vit_backbone():
with pytest.raises(TypeError):
# out_indices must be int ,list or tuple
- model = VisionTransformer(out_indices=1.)
+ model = VisionTransformer(out_indices=1.0)
with pytest.raises(TypeError):
# test upsample_pos_embed function
x = torch.randn(1, 196)
- VisionTransformer.resize_pos_embed(x, 512, 512, 224, 224, 'bilinear')
+ VisionTransformer.resize_pos_embed(x, 512, 512, 224, 224, "bilinear")
with pytest.raises(AssertionError):
# The length of img_size tuple must be lower than 3.
@@ -40,7 +39,7 @@ def test_vit_backbone():
# Test img_size isinstance tuple
imgs = torch.randn(1, 3, 224, 224)
- model = VisionTransformer(img_size=(224, ))
+ model = VisionTransformer(img_size=(224,))
model.init_weights()
model(imgs)
@@ -122,7 +121,8 @@ def test_vit_backbone():
# Test TransformerEncoderLayer with checkpoint forward
block = TransformerEncoderLayer(
- embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True)
+ embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True
+ )
assert block.with_cp
x = torch.randn(1, 56 * 56, 64)
x_out = block(x)
@@ -130,7 +130,7 @@ def test_vit_backbone():
def test_vit_init():
- path = 'PATH_THAT_DO_NOT_EXIST'
+ path = "PATH_THAT_DO_NOT_EXIST"
# Test all combinations of pretrained and init_cfg
# pretrained=None, init_cfg=None
model = VisionTransformer(pretrained=None, init_cfg=None)
@@ -140,8 +140,9 @@ def test_vit_init():
# pretrained=None
# init_cfg loads pretrain from an non-existent file
model = VisionTransformer(
- pretrained=None, init_cfg=dict(type='Pretrained', checkpoint=path))
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ pretrained=None, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -155,7 +156,7 @@ def test_vit_init():
# pretrained loads pretrain from an non-existent file
# init_cfg=None
model = VisionTransformer(pretrained=path, init_cfg=None)
- assert model.init_cfg == dict(type='Pretrained', checkpoint=path)
+ assert model.init_cfg == dict(type="Pretrained", checkpoint=path)
# Test loading a checkpoint from an non-existent file
with pytest.raises(OSError):
model.init_weights()
@@ -164,7 +165,8 @@ def test_vit_init():
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
model = VisionTransformer(
- pretrained=path, init_cfg=dict(type='Pretrained', checkpoint=path))
+ pretrained=path, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
with pytest.raises(AssertionError):
model = VisionTransformer(pretrained=path, init_cfg=123)
@@ -177,7 +179,8 @@ def test_vit_init():
# init_cfg loads pretrain from an non-existent file
with pytest.raises(AssertionError):
model = VisionTransformer(
- pretrained=123, init_cfg=dict(type='Pretrained', checkpoint=path))
+ pretrained=123, init_cfg=dict(type="Pretrained", checkpoint=path)
+ )
# pretrain=123, whose type is unsupported
# init_cfg=123, whose type is unsupported
diff --git a/mmsegmentation/tests/test_models/test_backbones/utils.py b/mmsegmentation/tests/test_models/test_backbones/utils.py
index 54b6404..4ecd7b7 100644
--- a/mmsegmentation/tests/test_models/test_backbones/utils.py
+++ b/mmsegmentation/tests/test_models/test_backbones/utils.py
@@ -23,11 +23,13 @@ def is_norm(modules):
def all_zeros(modules):
"""Check if the weight(and bias) is all zero."""
- weight_zero = torch.allclose(modules.weight.data,
- torch.zeros_like(modules.weight.data))
- if hasattr(modules, 'bias'):
- bias_zero = torch.allclose(modules.bias.data,
- torch.zeros_like(modules.bias.data))
+ weight_zero = torch.allclose(
+ modules.weight.data, torch.zeros_like(modules.weight.data)
+ )
+ if hasattr(modules, "bias"):
+ bias_zero = torch.allclose(
+ modules.bias.data, torch.zeros_like(modules.bias.data)
+ )
else:
bias_zero = True
diff --git a/mmsegmentation/tests/test_models/test_forward.py b/mmsegmentation/tests/test_models/test_forward.py
index ee707b3..6a7a832 100644
--- a/mmsegmentation/tests/test_models/test_forward.py
+++ b/mmsegmentation/tests/test_models/test_forward.py
@@ -26,23 +26,25 @@ def _demo_mm_inputs(input_shape=(2, 3, 8, 16), num_classes=10):
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
- segs = rng.randint(
- low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
-
- img_metas = [{
- 'img_shape': (H, W, C),
- 'ori_shape': (H, W, C),
- 'pad_shape': (H, W, C),
- 'filename': '.png',
- 'scale_factor': 1.0,
- 'flip': False,
- 'flip_direction': 'horizontal'
- } for _ in range(N)]
+ segs = rng.randint(low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
+
+ img_metas = [
+ {
+ "img_shape": (H, W, C),
+ "ori_shape": (H, W, C),
+ "pad_shape": (H, W, C),
+ "filename": ".png",
+ "scale_factor": 1.0,
+ "flip": False,
+ "flip_direction": "horizontal",
+ }
+ for _ in range(N)
+ ]
mm_inputs = {
- 'imgs': torch.FloatTensor(imgs),
- 'img_metas': img_metas,
- 'gt_semantic_seg': torch.LongTensor(segs)
+ "imgs": torch.FloatTensor(imgs),
+ "img_metas": img_metas,
+ "gt_semantic_seg": torch.LongTensor(segs),
}
return mm_inputs
@@ -55,16 +57,18 @@ def _get_config_directory():
except NameError:
# For IPython development when this __file__ is not defined
import mmseg
+
repo_dpath = dirname(dirname(dirname(mmseg.__file__)))
- config_dpath = join(repo_dpath, 'configs')
+ config_dpath = join(repo_dpath, "configs")
if not exists(config_dpath):
- raise Exception('Cannot find config path')
+ raise Exception("Cannot find config path")
return config_dpath
def _get_config_module(fname):
"""Load a configuration as a python module."""
from mmcv import Config
+
config_dpath = _get_config_directory()
config_fpath = join(config_dpath, fname)
config_mod = Config.fromfile(config_fpath)
@@ -83,101 +87,93 @@ def _get_segmentor_cfg(fname):
def test_pspnet_forward():
- _test_encoder_decoder_forward(
- 'pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py")
def test_fcn_forward():
- _test_encoder_decoder_forward('fcn/fcn_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("fcn/fcn_r50-d8_512x1024_40k_cityscapes.py")
def test_deeplabv3_forward():
_test_encoder_decoder_forward(
- 'deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py')
+ "deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py"
+ )
def test_deeplabv3plus_forward():
_test_encoder_decoder_forward(
- 'deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py')
+ "deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py"
+ )
def test_gcnet_forward():
- _test_encoder_decoder_forward(
- 'gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py")
def test_ann_forward():
- _test_encoder_decoder_forward('ann/ann_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("ann/ann_r50-d8_512x1024_40k_cityscapes.py")
def test_ccnet_forward():
if not torch.cuda.is_available():
- pytest.skip('CCNet requires CUDA')
- _test_encoder_decoder_forward(
- 'ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py')
+ pytest.skip("CCNet requires CUDA")
+ _test_encoder_decoder_forward("ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py")
def test_danet_forward():
- _test_encoder_decoder_forward(
- 'danet/danet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("danet/danet_r50-d8_512x1024_40k_cityscapes.py")
def test_nonlocal_net_forward():
_test_encoder_decoder_forward(
- 'nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py')
+ "nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py"
+ )
def test_upernet_forward():
- _test_encoder_decoder_forward(
- 'upernet/upernet_r50_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("upernet/upernet_r50_512x1024_40k_cityscapes.py")
def test_hrnet_forward():
- _test_encoder_decoder_forward('hrnet/fcn_hr18s_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("hrnet/fcn_hr18s_512x1024_40k_cityscapes.py")
def test_ocrnet_forward():
- _test_encoder_decoder_forward(
- 'ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py")
def test_psanet_forward():
- _test_encoder_decoder_forward(
- 'psanet/psanet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("psanet/psanet_r50-d8_512x1024_40k_cityscapes.py")
def test_encnet_forward():
- _test_encoder_decoder_forward(
- 'encnet/encnet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("encnet/encnet_r50-d8_512x1024_40k_cityscapes.py")
def test_sem_fpn_forward():
- _test_encoder_decoder_forward('sem_fpn/fpn_r50_512x1024_80k_cityscapes.py')
+ _test_encoder_decoder_forward("sem_fpn/fpn_r50_512x1024_80k_cityscapes.py")
def test_point_rend_forward():
- _test_encoder_decoder_forward(
- 'point_rend/pointrend_r50_512x1024_80k_cityscapes.py')
+ _test_encoder_decoder_forward("point_rend/pointrend_r50_512x1024_80k_cityscapes.py")
def test_mobilenet_v2_forward():
_test_encoder_decoder_forward(
- 'mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py')
+ "mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py"
+ )
def test_dnlnet_forward():
- _test_encoder_decoder_forward(
- 'dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py")
def test_emanet_forward():
- _test_encoder_decoder_forward(
- 'emanet/emanet_r50-d8_512x1024_80k_cityscapes.py')
+ _test_encoder_decoder_forward("emanet/emanet_r50-d8_512x1024_80k_cityscapes.py")
def test_isanet_forward():
- _test_encoder_decoder_forward(
- 'isanet/isanet_r50-d8_512x1024_40k_cityscapes.py')
+ _test_encoder_decoder_forward("isanet/isanet_r50-d8_512x1024_40k_cityscapes.py")
def get_world_size(process_group):
@@ -189,15 +185,15 @@ def _check_input_dim(self, inputs):
pass
-@patch('torch.nn.modules.batchnorm._BatchNorm._check_input_dim',
- _check_input_dim)
-@patch('torch.distributed.get_world_size', get_world_size)
+@patch("torch.nn.modules.batchnorm._BatchNorm._check_input_dim", _check_input_dim)
+@patch("torch.distributed.get_world_size", get_world_size)
def _test_encoder_decoder_forward(cfg_file):
model = _get_segmentor_cfg(cfg_file)
- model['pretrained'] = None
- model['test_cfg']['mode'] = 'whole'
+ model["pretrained"] = None
+ model["test_cfg"]["mode"] = "whole"
from mmseg.models import build_segmentor
+
segmentor = build_segmentor(model)
segmentor.init_weights()
@@ -209,9 +205,9 @@ def _test_encoder_decoder_forward(cfg_file):
input_shape = (2, 3, 32, 32)
mm_inputs = _demo_mm_inputs(input_shape, num_classes=num_classes)
- imgs = mm_inputs.pop('imgs')
- img_metas = mm_inputs.pop('img_metas')
- gt_semantic_seg = mm_inputs['gt_semantic_seg']
+ imgs = mm_inputs.pop("imgs")
+ img_metas = mm_inputs.pop("img_metas")
+ gt_semantic_seg = mm_inputs["gt_semantic_seg"]
# convert to cuda Tensor if applicable
if torch.cuda.is_available():
@@ -223,7 +219,8 @@ def _test_encoder_decoder_forward(cfg_file):
# Test forward train
losses = segmentor.forward(
- imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True)
+ imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True
+ )
assert isinstance(losses, dict)
# Test forward test
diff --git a/mmsegmentation/tests/test_models/test_heads/test_ann_head.py b/mmsegmentation/tests/test_models/test_heads/test_ann_head.py
index c1e44bc..6da311f 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_ann_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_ann_head.py
@@ -13,7 +13,8 @@ def test_ann_head():
channels=2,
num_classes=19,
in_index=[-2, -1],
- project_channels=8)
+ project_channels=8,
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_apc_head.py b/mmsegmentation/tests/test_models/test_heads/test_apc_head.py
index dc55ccc..23cbab9 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_apc_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_apc_head.py
@@ -18,20 +18,15 @@ def test_apc_head():
# test with norm_cfg
head = APCHead(
- in_channels=8,
- channels=2,
- num_classes=19,
- norm_cfg=dict(type='SyncBN'))
+ in_channels=8, channels=2, num_classes=19, norm_cfg=dict(type="SyncBN")
+ )
assert _conv_has_norm(head, sync_bn=True)
# fusion=True
inputs = [torch.randn(1, 8, 45, 45)]
head = APCHead(
- in_channels=8,
- channels=2,
- num_classes=19,
- pool_scales=(1, 2, 3),
- fusion=True)
+ in_channels=8, channels=2, num_classes=19, pool_scales=(1, 2, 3), fusion=True
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.fusion is True
@@ -44,11 +39,8 @@ def test_apc_head():
# fusion=False
inputs = [torch.randn(1, 8, 45, 45)]
head = APCHead(
- in_channels=8,
- channels=2,
- num_classes=19,
- pool_scales=(1, 2, 3),
- fusion=False)
+ in_channels=8, channels=2, num_classes=19, pool_scales=(1, 2, 3), fusion=False
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.fusion is False
diff --git a/mmsegmentation/tests/test_models/test_heads/test_aspp_head.py b/mmsegmentation/tests/test_models/test_heads/test_aspp_head.py
index db9e893..3ebf380 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_aspp_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_aspp_head.py
@@ -18,15 +18,12 @@ def test_aspp_head():
# test with norm_cfg
head = ASPPHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- norm_cfg=dict(type='SyncBN'))
+ in_channels=8, channels=4, num_classes=19, norm_cfg=dict(type="SyncBN")
+ )
assert _conv_has_norm(head, sync_bn=True)
inputs = [torch.randn(1, 8, 45, 45)]
- head = ASPPHead(
- in_channels=8, channels=4, num_classes=19, dilations=(1, 12, 24))
+ head = ASPPHead(in_channels=8, channels=4, num_classes=19, dilations=(1, 12, 24))
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.aspp_modules[0].conv.dilation == (1, 1)
@@ -46,7 +43,8 @@ def test_dw_aspp_head():
in_channels=8,
channels=4,
num_classes=19,
- dilations=(1, 12, 24))
+ dilations=(1, 12, 24),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.c1_bottleneck is None
@@ -64,7 +62,8 @@ def test_dw_aspp_head():
in_channels=16,
channels=8,
num_classes=19,
- dilations=(1, 12, 24))
+ dilations=(1, 12, 24),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.c1_bottleneck.in_channels == 4
diff --git a/mmsegmentation/tests/test_models/test_heads/test_cc_head.py b/mmsegmentation/tests/test_models/test_heads/test_cc_head.py
index 0630417..ab4c486 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_cc_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_cc_head.py
@@ -9,9 +9,9 @@
def test_cc_head():
head = CCHead(in_channels=16, channels=8, num_classes=19)
assert len(head.convs) == 2
- assert hasattr(head, 'cca')
+ assert hasattr(head, "cca")
if not torch.cuda.is_available():
- pytest.skip('CCHead requires CUDA')
+ pytest.skip("CCHead requires CUDA")
inputs = [torch.randn(1, 16, 23, 23)]
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_decode_head.py b/mmsegmentation/tests/test_models/test_heads/test_decode_head.py
index 87cadbc..a716de2 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_decode_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_decode_head.py
@@ -21,27 +21,23 @@ def test_decode_head():
with pytest.raises(AssertionError):
# supported mode is resize_concat only
- BaseDecodeHead(32, 16, num_classes=19, input_transform='concat')
+ BaseDecodeHead(32, 16, num_classes=19, input_transform="concat")
with pytest.raises(AssertionError):
# in_channels should be list|tuple
- BaseDecodeHead(32, 16, num_classes=19, input_transform='resize_concat')
+ BaseDecodeHead(32, 16, num_classes=19, input_transform="resize_concat")
with pytest.raises(AssertionError):
# in_index should be list|tuple
- BaseDecodeHead([32],
- 16,
- in_index=-1,
- num_classes=19,
- input_transform='resize_concat')
+ BaseDecodeHead(
+ [32], 16, in_index=-1, num_classes=19, input_transform="resize_concat"
+ )
with pytest.raises(AssertionError):
# len(in_index) should equal len(in_channels)
- BaseDecodeHead([32, 16],
- 16,
- num_classes=19,
- in_index=[-1],
- input_transform='resize_concat')
+ BaseDecodeHead(
+ [32, 16], 16, num_classes=19, in_index=[-1], input_transform="resize_concat"
+ )
with pytest.raises(ValueError):
# out_channels should be equal to num_classes
@@ -57,11 +53,11 @@ def test_decode_head():
# test default dropout
head = BaseDecodeHead(32, 16, num_classes=19)
- assert hasattr(head, 'dropout') and head.dropout.p == 0.1
+ assert hasattr(head, "dropout") and head.dropout.p == 0.1
# test set dropout
head = BaseDecodeHead(32, 16, num_classes=19, dropout_ratio=0.2)
- assert hasattr(head, 'dropout') and head.dropout.p == 0.2
+ assert hasattr(head, "dropout") and head.dropout.p == 0.2
# test no input_transform
inputs = [torch.randn(1, 32, 45, 45)]
@@ -75,22 +71,20 @@ def test_decode_head():
# test input_transform = resize_concat
inputs = [torch.randn(1, 32, 45, 45), torch.randn(1, 16, 21, 21)]
- head = BaseDecodeHead([32, 16],
- 16,
- num_classes=19,
- in_index=[0, 1],
- input_transform='resize_concat')
+ head = BaseDecodeHead(
+ [32, 16], 16, num_classes=19, in_index=[0, 1], input_transform="resize_concat"
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.in_channels == 48
- assert head.input_transform == 'resize_concat'
+ assert head.input_transform == "resize_concat"
transformed_inputs = head._transform_inputs(inputs)
assert transformed_inputs.shape == (1, 48, 45, 45)
# test multi-loss, loss_decode is dict
with pytest.raises(TypeError):
# loss_decode must be a dict or sequence of dict.
- BaseDecodeHead(3, 16, num_classes=19, loss_decode=['CrossEntropyLoss'])
+ BaseDecodeHead(3, 16, num_classes=19, loss_decode=["CrossEntropyLoss"])
inputs = torch.randn(2, 19, 8, 8).float()
target = torch.ones(2, 1, 64, 64).long()
@@ -98,13 +92,13 @@ def test_decode_head():
3,
16,
num_classes=19,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+ loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
head, target = to_cuda(head, target)
loss = head.losses(seg_logit=inputs, seg_label=target)
- assert 'loss_ce' in loss
+ assert "loss_ce" in loss
# test multi-loss, loss_decode is list of dict
inputs = torch.randn(2, 19, 8, 8).float()
@@ -114,19 +108,20 @@ def test_decode_head():
16,
num_classes=19,
loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_1'),
- dict(type='CrossEntropyLoss', loss_name='loss_2')
- ])
+ dict(type="CrossEntropyLoss", loss_name="loss_1"),
+ dict(type="CrossEntropyLoss", loss_name="loss_2"),
+ ],
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
head, target = to_cuda(head, target)
loss = head.losses(seg_logit=inputs, seg_label=target)
- assert 'loss_1' in loss
- assert 'loss_2' in loss
+ assert "loss_1" in loss
+ assert "loss_2" in loss
# 'loss_decode' must be a dict or sequence of dict
with pytest.raises(TypeError):
- BaseDecodeHead(3, 16, num_classes=19, loss_decode=['CrossEntropyLoss'])
+ BaseDecodeHead(3, 16, num_classes=19, loss_decode=["CrossEntropyLoss"])
with pytest.raises(TypeError):
BaseDecodeHead(3, 16, num_classes=19, loss_decode=0)
@@ -137,16 +132,19 @@ def test_decode_head():
3,
16,
num_classes=19,
- loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_1'),
- dict(type='CrossEntropyLoss', loss_name='loss_2'),
- dict(type='CrossEntropyLoss', loss_name='loss_3')))
+ loss_decode=(
+ dict(type="CrossEntropyLoss", loss_name="loss_1"),
+ dict(type="CrossEntropyLoss", loss_name="loss_2"),
+ dict(type="CrossEntropyLoss", loss_name="loss_3"),
+ ),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
head, target = to_cuda(head, target)
loss = head.losses(seg_logit=inputs, seg_label=target)
- assert 'loss_1' in loss
- assert 'loss_2' in loss
- assert 'loss_3' in loss
+ assert "loss_1" in loss
+ assert "loss_2" in loss
+ assert "loss_3" in loss
# test multi-loss, loss_decode is list of dict, names of them are identical
inputs = torch.randn(2, 19, 8, 8).float()
@@ -155,9 +153,12 @@ def test_decode_head():
3,
16,
num_classes=19,
- loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_ce'),
- dict(type='CrossEntropyLoss', loss_name='loss_ce'),
- dict(type='CrossEntropyLoss', loss_name='loss_ce')))
+ loss_decode=(
+ dict(type="CrossEntropyLoss", loss_name="loss_ce"),
+ dict(type="CrossEntropyLoss", loss_name="loss_ce"),
+ dict(type="CrossEntropyLoss", loss_name="loss_ce"),
+ ),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
head, target = to_cuda(head, target)
@@ -167,11 +168,12 @@ def test_decode_head():
3,
16,
num_classes=19,
- loss_decode=(dict(type='CrossEntropyLoss', loss_name='loss_ce')))
+ loss_decode=(dict(type="CrossEntropyLoss", loss_name="loss_ce")),
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
head, target = to_cuda(head, target)
loss = head.losses(seg_logit=inputs, seg_label=target)
- assert 'loss_ce' in loss
- assert 'loss_ce' in loss_3
- assert loss_3['loss_ce'] == 3 * loss['loss_ce']
+ assert "loss_ce" in loss
+ assert "loss_ce" in loss_3
+ assert loss_3["loss_ce"] == 3 * loss["loss_ce"]
diff --git a/mmsegmentation/tests/test_models/test_heads/test_dm_head.py b/mmsegmentation/tests/test_models/test_heads/test_dm_head.py
index a922ff7..5e9575d 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_dm_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_dm_head.py
@@ -18,20 +18,15 @@ def test_dm_head():
# test with norm_cfg
head = DMHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- norm_cfg=dict(type='SyncBN'))
+ in_channels=8, channels=4, num_classes=19, norm_cfg=dict(type="SyncBN")
+ )
assert _conv_has_norm(head, sync_bn=True)
# fusion=True
inputs = [torch.randn(1, 8, 23, 23)]
head = DMHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- filter_sizes=(1, 3, 5),
- fusion=True)
+ in_channels=8, channels=4, num_classes=19, filter_sizes=(1, 3, 5), fusion=True
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.fusion is True
@@ -44,11 +39,8 @@ def test_dm_head():
# fusion=False
inputs = [torch.randn(1, 8, 23, 23)]
head = DMHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- filter_sizes=(1, 3, 5),
- fusion=False)
+ in_channels=8, channels=4, num_classes=19, filter_sizes=(1, 3, 5), fusion=False
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.fusion is False
diff --git a/mmsegmentation/tests/test_models/test_heads/test_dnl_head.py b/mmsegmentation/tests/test_models/test_heads/test_dnl_head.py
index 720cb07..9564f91 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_dnl_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_dnl_head.py
@@ -9,7 +9,7 @@ def test_dnl_head():
# DNL with 'embedded_gaussian' mode
head = DNLHead(in_channels=8, channels=4, num_classes=19)
assert len(head.convs) == 2
- assert hasattr(head, 'dnl_block')
+ assert hasattr(head, "dnl_block")
assert head.dnl_block.temperature == 0.05
inputs = [torch.randn(1, 8, 23, 23)]
if torch.cuda.is_available():
@@ -18,8 +18,7 @@ def test_dnl_head():
assert outputs.shape == (1, head.num_classes, 23, 23)
# NonLocal2d with 'dot_product' mode
- head = DNLHead(
- in_channels=8, channels=4, num_classes=19, mode='dot_product')
+ head = DNLHead(in_channels=8, channels=4, num_classes=19, mode="dot_product")
inputs = [torch.randn(1, 8, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
@@ -27,7 +26,7 @@ def test_dnl_head():
assert outputs.shape == (1, head.num_classes, 23, 23)
# NonLocal2d with 'gaussian' mode
- head = DNLHead(in_channels=8, channels=4, num_classes=19, mode='gaussian')
+ head = DNLHead(in_channels=8, channels=4, num_classes=19, mode="gaussian")
inputs = [torch.randn(1, 8, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
@@ -35,8 +34,7 @@ def test_dnl_head():
assert outputs.shape == (1, head.num_classes, 23, 23)
# NonLocal2d with 'concatenation' mode
- head = DNLHead(
- in_channels=8, channels=4, num_classes=19, mode='concatenation')
+ head = DNLHead(in_channels=8, channels=4, num_classes=19, mode="concatenation")
inputs = [torch.randn(1, 8, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_dpt_head.py b/mmsegmentation/tests/test_models/test_heads/test_dpt_head.py
index 0a6af61..da97dbd 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_dpt_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_dpt_head.py
@@ -13,17 +13,18 @@ def test_dpt_head():
in_channels=[768, 768, 768, 768],
channels=4,
num_classes=19,
- in_index=[0, 1, 2, 3])
+ in_index=[0, 1, 2, 3],
+ )
head = DPTHead(
in_channels=[768, 768, 768, 768],
channels=4,
num_classes=19,
in_index=[0, 1, 2, 3],
- input_transform='multiple_select')
+ input_transform="multiple_select",
+ )
- inputs = [[torch.randn(4, 768, 2, 2),
- torch.randn(4, 768)] for _ in range(4)]
+ inputs = [[torch.randn(4, 768, 2, 2), torch.randn(4, 768)] for _ in range(4)]
output = head(inputs)
assert output.shape == torch.Size((4, 19, 16, 16))
@@ -33,8 +34,9 @@ def test_dpt_head():
channels=4,
num_classes=19,
in_index=[0, 1, 2, 3],
- input_transform='multiple_select',
- readout_type='add')
+ input_transform="multiple_select",
+ readout_type="add",
+ )
output = head(inputs)
assert output.shape == torch.Size((4, 19, 16, 16))
@@ -43,7 +45,8 @@ def test_dpt_head():
channels=4,
num_classes=19,
in_index=[0, 1, 2, 3],
- input_transform='multiple_select',
- readout_type='project')
+ input_transform="multiple_select",
+ readout_type="project",
+ )
output = head(inputs)
assert output.shape == torch.Size((4, 19, 16, 16))
diff --git a/mmsegmentation/tests/test_models/test_heads/test_ema_head.py b/mmsegmentation/tests/test_models/test_heads/test_ema_head.py
index 1811cd2..9e0f6c0 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_ema_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_ema_head.py
@@ -12,10 +12,11 @@ def test_emanet_head():
channels=2,
num_stages=3,
num_bases=2,
- num_classes=19)
+ num_classes=19,
+ )
for param in head.ema_mid_conv.parameters():
assert not param.requires_grad
- assert hasattr(head, 'ema_module')
+ assert hasattr(head, "ema_module")
inputs = [torch.randn(1, 4, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_enc_head.py b/mmsegmentation/tests/test_models/test_heads/test_enc_head.py
index 9c84c75..93b7720 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_enc_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_enc_head.py
@@ -19,11 +19,8 @@ def test_enc_head():
# w.o se_loss, w.o. lateral
inputs = [torch.randn(1, 8, 21, 21)]
head = EncHead(
- in_channels=[8],
- channels=4,
- use_se_loss=False,
- num_classes=19,
- in_index=[-1])
+ in_channels=[8], channels=4, use_se_loss=False, num_classes=19, in_index=[-1]
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -36,7 +33,8 @@ def test_enc_head():
channels=4,
add_lateral=True,
num_classes=19,
- in_index=[-2, -1])
+ in_index=[-2, -1],
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_fcn_head.py b/mmsegmentation/tests/test_models/test_heads/test_fcn_head.py
index 4e633fb..62b6927 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_fcn_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_fcn_head.py
@@ -22,29 +22,25 @@ def test_fcn_head():
# test with norm_cfg
head = FCNHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- norm_cfg=dict(type='SyncBN'))
+ in_channels=8, channels=4, num_classes=19, norm_cfg=dict(type="SyncBN")
+ )
for m in head.modules():
if isinstance(m, ConvModule):
assert m.with_norm and isinstance(m.bn, SyncBatchNorm)
# test concat_input=False
inputs = [torch.randn(1, 8, 23, 23)]
- head = FCNHead(
- in_channels=8, channels=4, num_classes=19, concat_input=False)
+ head = FCNHead(in_channels=8, channels=4, num_classes=19, concat_input=False)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert len(head.convs) == 2
- assert not head.concat_input and not hasattr(head, 'conv_cat')
+ assert not head.concat_input and not hasattr(head, "conv_cat")
outputs = head(inputs)
assert outputs.shape == (1, head.num_classes, 23, 23)
# test concat_input=True
inputs = [torch.randn(1, 8, 23, 23)]
- head = FCNHead(
- in_channels=8, channels=4, num_classes=19, concat_input=True)
+ head = FCNHead(in_channels=8, channels=4, num_classes=19, concat_input=True)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert len(head.convs) == 2
@@ -87,11 +83,8 @@ def test_fcn_head():
# test num_conv = 0
inputs = [torch.randn(1, 8, 23, 23)]
head = FCNHead(
- in_channels=8,
- channels=8,
- num_classes=19,
- num_convs=0,
- concat_input=False)
+ in_channels=8, channels=8, num_classes=19, num_convs=0, concat_input=False
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert isinstance(head.convs, torch.nn.Identity)
@@ -107,7 +100,8 @@ def test_sep_fcn_head():
concat_input=False,
num_classes=19,
in_index=-1,
- norm_cfg=dict(type='BN', requires_grad=True, momentum=0.01))
+ norm_cfg=dict(type="BN", requires_grad=True, momentum=0.01),
+ )
x = [torch.rand(2, 128, 8, 8)]
output = head(x)
assert output.shape == (2, head.num_classes, 8, 8)
@@ -122,7 +116,8 @@ def test_sep_fcn_head():
concat_input=True,
num_classes=19,
in_index=-1,
- norm_cfg=dict(type='BN', requires_grad=True, momentum=0.01))
+ norm_cfg=dict(type="BN", requires_grad=True, momentum=0.01),
+ )
x = [torch.rand(3, 64, 8, 8)]
output = head(x)
assert output.shape == (3, head.num_classes, 8, 8)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_gc_head.py b/mmsegmentation/tests/test_models/test_heads/test_gc_head.py
index c62ac9a..99c382b 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_gc_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_gc_head.py
@@ -8,7 +8,7 @@
def test_gc_head():
head = GCHead(in_channels=4, channels=4, num_classes=19)
assert len(head.convs) == 2
- assert hasattr(head, 'gc_block')
+ assert hasattr(head, "gc_block")
inputs = [torch.randn(1, 4, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_isa_head.py b/mmsegmentation/tests/test_models/test_heads/test_isa_head.py
index b177f6d..f0edb49 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_isa_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_isa_head.py
@@ -9,11 +9,8 @@ def test_isa_head():
inputs = [torch.randn(1, 8, 23, 23)]
isa_head = ISAHead(
- in_channels=8,
- channels=4,
- num_classes=19,
- isa_channels=4,
- down_factor=(8, 8))
+ in_channels=8, channels=4, num_classes=19, isa_channels=4, down_factor=(8, 8)
+ )
if torch.cuda.is_available():
isa_head, inputs = to_cuda(isa_head, inputs)
output = isa_head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_knet_head.py b/mmsegmentation/tests/test_models/test_heads/test_knet_head.py
index e6845a6..4bbc50a 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_knet_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_knet_head.py
@@ -1,22 +1,22 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
-from mmseg.models.decode_heads.knet_head import (IterativeDecodeHead,
- KernelUpdateHead)
+from mmseg.models.decode_heads.knet_head import IterativeDecodeHead, KernelUpdateHead
from .utils import to_cuda
num_stages = 3
conv_kernel_size = 1
kernel_updator_cfg = dict(
- type='KernelUpdator',
+ type="KernelUpdator",
in_channels=16,
feat_channels=16,
out_channels=16,
gate_norm_act=True,
activate_out=True,
- act_cfg=dict(type='ReLU', inplace=True),
- norm_cfg=dict(type='LN'))
+ act_cfg=dict(type="ReLU", inplace=True),
+ norm_cfg=dict(type="LN"),
+)
def test_knet_head():
@@ -31,18 +31,19 @@ def test_knet_head():
out_channels=32,
dropout=0.0,
conv_kernel_size=conv_kernel_size,
- ffn_act_cfg=dict(type='ReLU', inplace=True),
+ ffn_act_cfg=dict(type="ReLU", inplace=True),
with_ffn=True,
- feat_transform_cfg=dict(conv_cfg=dict(type='Conv2d'), act_cfg=None),
+ feat_transform_cfg=dict(conv_cfg=dict(type="Conv2d"), act_cfg=None),
kernel_init=True,
- kernel_updator_cfg=kernel_updator_cfg)
+ kernel_updator_cfg=kernel_updator_cfg,
+ )
kernel_update_head.init_weights()
head = IterativeDecodeHead(
num_stages=num_stages,
kernel_update_head=[
dict(
- type='KernelUpdateHead',
+ type="KernelUpdateHead",
num_classes=150,
num_ffn_fcs=2,
num_heads=8,
@@ -52,16 +53,16 @@ def test_knet_head():
out_channels=32,
dropout=0.0,
conv_kernel_size=conv_kernel_size,
- ffn_act_cfg=dict(type='ReLU', inplace=True),
+ ffn_act_cfg=dict(type="ReLU", inplace=True),
with_ffn=True,
- feat_transform_cfg=dict(
- conv_cfg=dict(type='Conv2d'), act_cfg=None),
+ feat_transform_cfg=dict(conv_cfg=dict(type="Conv2d"), act_cfg=None),
kernel_init=False,
- kernel_updator_cfg=kernel_updator_cfg)
+ kernel_updator_cfg=kernel_updator_cfg,
+ )
for _ in range(num_stages)
],
kernel_generate_head=dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=128,
in_index=3,
channels=32,
@@ -69,13 +70,15 @@ def test_knet_head():
concat_input=True,
dropout_ratio=0.1,
num_classes=150,
- align_corners=False))
+ align_corners=False,
+ ),
+ )
head.init_weights()
inputs = [
torch.randn(1, 16, 27, 32),
torch.randn(1, 32, 27, 16),
torch.randn(1, 64, 27, 16),
- torch.randn(1, 128, 27, 16)
+ torch.randn(1, 128, 27, 16),
]
if torch.cuda.is_available():
@@ -95,7 +98,7 @@ def test_knet_head():
num_stages=num_stages,
kernel_update_head=[
dict(
- type='KernelUpdateHead',
+ type="KernelUpdateHead",
num_classes=150,
num_ffn_fcs=2,
num_heads=8,
@@ -105,14 +108,15 @@ def test_knet_head():
out_channels=32,
dropout=0.0,
conv_kernel_size=conv_kernel_size,
- ffn_act_cfg=dict(type='ReLU', inplace=True),
+ ffn_act_cfg=dict(type="ReLU", inplace=True),
with_ffn=True,
feat_transform_cfg=None,
- kernel_updator_cfg=kernel_updator_cfg)
+ kernel_updator_cfg=kernel_updator_cfg,
+ )
for _ in range(num_stages)
],
kernel_generate_head=dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=128,
in_index=3,
channels=32,
@@ -120,14 +124,16 @@ def test_knet_head():
concat_input=True,
dropout_ratio=0.1,
num_classes=150,
- align_corners=False))
+ align_corners=False,
+ ),
+ )
head.init_weights()
inputs = [
torch.randn(1, 16, 27, 32),
torch.randn(1, 32, 27, 16),
torch.randn(1, 64, 27, 16),
- torch.randn(1, 128, 27, 16)
+ torch.randn(1, 128, 27, 16),
]
if torch.cuda.is_available():
@@ -141,7 +147,7 @@ def test_knet_head():
num_stages=num_stages,
kernel_update_head=[
dict(
- type='KernelUpdateHead',
+ type="KernelUpdateHead",
num_classes=150,
num_ffn_fcs=2,
num_heads=8,
@@ -151,18 +157,18 @@ def test_knet_head():
out_channels=32,
dropout=0.0,
conv_kernel_size=conv_kernel_size,
- ffn_act_cfg=dict(type='ReLU', inplace=True),
+ ffn_act_cfg=dict(type="ReLU", inplace=True),
with_ffn=True,
- feat_transform_cfg=dict(
- conv_cfg=dict(type='Conv2d'), act_cfg=None),
+ feat_transform_cfg=dict(conv_cfg=dict(type="Conv2d"), act_cfg=None),
kernel_init=False,
mask_transform_stride=2,
feat_gather_stride=1,
- kernel_updator_cfg=kernel_updator_cfg)
+ kernel_updator_cfg=kernel_updator_cfg,
+ )
for _ in range(num_stages)
],
kernel_generate_head=dict(
- type='FCNHead',
+ type="FCNHead",
in_channels=128,
in_index=3,
channels=32,
@@ -170,14 +176,16 @@ def test_knet_head():
concat_input=True,
dropout_ratio=0.1,
num_classes=150,
- align_corners=False))
+ align_corners=False,
+ ),
+ )
head.init_weights()
inputs = [
torch.randn(1, 16, 27, 32),
torch.randn(1, 32, 27, 16),
torch.randn(1, 64, 27, 16),
- torch.randn(1, 128, 27, 16)
+ torch.randn(1, 128, 27, 16),
]
if torch.cuda.is_available():
@@ -186,10 +194,9 @@ def test_knet_head():
assert outputs[-1].shape == (1, head.num_classes, 26, 16)
# test loss function in K-Net
- fake_label = torch.ones_like(
- outputs[-1][:, 0:1, :, :], dtype=torch.int16).long()
+ fake_label = torch.ones_like(outputs[-1][:, 0:1, :, :], dtype=torch.int16).long()
loss = head.losses(seg_logit=outputs, seg_label=fake_label)
- assert loss['loss_ce.s0'] != torch.zeros_like(loss['loss_ce.s0'])
- assert loss['loss_ce.s1'] != torch.zeros_like(loss['loss_ce.s1'])
- assert loss['loss_ce.s2'] != torch.zeros_like(loss['loss_ce.s2'])
- assert loss['loss_ce.s3'] != torch.zeros_like(loss['loss_ce.s3'])
+ assert loss["loss_ce.s0"] != torch.zeros_like(loss["loss_ce.s0"])
+ assert loss["loss_ce.s1"] != torch.zeros_like(loss["loss_ce.s1"])
+ assert loss["loss_ce.s2"] != torch.zeros_like(loss["loss_ce.s2"])
+ assert loss["loss_ce.s3"] != torch.zeros_like(loss["loss_ce.s3"])
diff --git a/mmsegmentation/tests/test_models/test_heads/test_lraspp_head.py b/mmsegmentation/tests/test_models/test_heads/test_lraspp_head.py
index a46e6a1..f637e95 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_lraspp_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_lraspp_head.py
@@ -12,14 +12,16 @@ def test_lraspp_head():
in_channels=(4, 4, 123),
in_index=(0, 1, 2),
channels=32,
- input_transform='resize_concat',
+ input_transform="resize_concat",
dropout_ratio=0.1,
num_classes=19,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
+ ),
+ )
with pytest.raises(AssertionError):
# check invalid branch_channels
@@ -28,32 +30,34 @@ def test_lraspp_head():
in_index=(0, 1, 2),
channels=32,
branch_channels=64,
- input_transform='multiple_select',
+ input_transform="multiple_select",
dropout_ratio=0.1,
num_classes=19,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
align_corners=False,
loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+ type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0
+ ),
+ )
# test with default settings
lraspp_head = LRASPPHead(
in_channels=(4, 4, 123),
in_index=(0, 1, 2),
channels=32,
- input_transform='multiple_select',
+ input_transform="multiple_select",
dropout_ratio=0.1,
num_classes=19,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'),
+ norm_cfg=dict(type="BN"),
+ act_cfg=dict(type="ReLU"),
align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+ loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+ )
inputs = [
torch.randn(2, 4, 45, 45),
torch.randn(2, 4, 28, 28),
- torch.randn(2, 123, 14, 14)
+ torch.randn(2, 123, 14, 14),
]
with pytest.raises(RuntimeError):
# check invalid inputs
@@ -62,7 +66,7 @@ def test_lraspp_head():
inputs = [
torch.randn(2, 4, 111, 111),
torch.randn(2, 4, 77, 77),
- torch.randn(2, 123, 55, 55)
+ torch.randn(2, 123, 55, 55),
]
output = lraspp_head(inputs)
assert output.shape == (2, 19, 111, 111)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_nl_head.py b/mmsegmentation/tests/test_models/test_heads/test_nl_head.py
index d4ef0b9..1d631f0 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_nl_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_nl_head.py
@@ -8,7 +8,7 @@
def test_nl_head():
head = NLHead(in_channels=8, channels=4, num_classes=19)
assert len(head.convs) == 2
- assert hasattr(head, 'nl_block')
+ assert hasattr(head, "nl_block")
inputs = [torch.randn(1, 8, 23, 23)]
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_ocr_head.py b/mmsegmentation/tests/test_models/test_heads/test_ocr_head.py
index 5e5d669..77cdd7c 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_ocr_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_ocr_head.py
@@ -8,8 +8,7 @@
def test_ocr_head():
inputs = [torch.randn(1, 8, 23, 23)]
- ocr_head = OCRHead(
- in_channels=8, channels=4, num_classes=19, ocr_channels=8)
+ ocr_head = OCRHead(in_channels=8, channels=4, num_classes=19, ocr_channels=8)
fcn_head = FCNHead(in_channels=8, channels=4, num_classes=19)
if torch.cuda.is_available():
head, inputs = to_cuda(ocr_head, inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_point_head.py b/mmsegmentation/tests/test_models/test_heads/test_point_head.py
index 142ab16..8cd1083 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_point_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_point_head.py
@@ -9,8 +9,7 @@
def test_point_head():
inputs = [torch.randn(1, 32, 45, 45)]
- point_head = PointHead(
- in_channels=[32], in_index=[0], channels=16, num_classes=19)
+ point_head = PointHead(in_channels=[32], in_index=[0], channels=16, num_classes=19)
assert len(point_head.fcs) == 3
fcn_head = FCNHead(in_channels=32, channels=16, num_classes=19)
if torch.cuda.is_available():
@@ -18,7 +17,8 @@ def test_point_head():
head, inputs = to_cuda(fcn_head, inputs)
prev_output = fcn_head(inputs)
test_cfg = ConfigDict(
- subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
+ subdivision_steps=2, subdivision_num_points=8196, scale_factor=2
+ )
output = point_head.forward_test(inputs, prev_output, None, test_cfg)
assert output.shape == (1, point_head.num_classes, 180, 180)
@@ -30,26 +30,30 @@ def test_point_head():
channels=16,
num_classes=19,
loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_1'),
- dict(type='CrossEntropyLoss', loss_name='loss_2')
- ])
+ dict(type="CrossEntropyLoss", loss_name="loss_1"),
+ dict(type="CrossEntropyLoss", loss_name="loss_2"),
+ ],
+ )
assert len(point_head_multiple_losses.fcs) == 3
fcn_head_multiple_losses = FCNHead(
in_channels=32,
channels=16,
num_classes=19,
loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_1'),
- dict(type='CrossEntropyLoss', loss_name='loss_2')
- ])
+ dict(type="CrossEntropyLoss", loss_name="loss_1"),
+ dict(type="CrossEntropyLoss", loss_name="loss_2"),
+ ],
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(point_head_multiple_losses, inputs)
head, inputs = to_cuda(fcn_head_multiple_losses, inputs)
prev_output = fcn_head_multiple_losses(inputs)
test_cfg = ConfigDict(
- subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
- output = point_head_multiple_losses.forward_test(inputs, prev_output, None,
- test_cfg)
+ subdivision_steps=2, subdivision_num_points=8196, scale_factor=2
+ )
+ output = point_head_multiple_losses.forward_test(
+ inputs, prev_output, None, test_cfg
+ )
assert output.shape == (1, point_head.num_classes, 180, 180)
fake_label = torch.ones([1, 180, 180], dtype=torch.long)
@@ -57,5 +61,5 @@ def test_point_head():
if torch.cuda.is_available():
fake_label = fake_label.cuda()
loss = point_head_multiple_losses.losses(output, fake_label)
- assert 'pointloss_1' in loss
- assert 'pointloss_2' in loss
+ assert "pointloss_1" in loss
+ assert "pointloss_2" in loss
diff --git a/mmsegmentation/tests/test_models/test_heads/test_psa_head.py b/mmsegmentation/tests/test_models/test_heads/test_psa_head.py
index 34f592b..5a04763 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_psa_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_psa_head.py
@@ -15,11 +15,11 @@ def test_psa_head():
channels=2,
num_classes=19,
mask_size=(13, 13),
- psa_type='gather')
+ psa_type="gather",
+ )
# test no norm_cfg
- head = PSAHead(
- in_channels=4, channels=2, num_classes=19, mask_size=(13, 13))
+ head = PSAHead(in_channels=4, channels=2, num_classes=19, mask_size=(13, 13))
assert not _conv_has_norm(head, sync_bn=False)
# test with norm_cfg
@@ -28,13 +28,13 @@ def test_psa_head():
channels=2,
num_classes=19,
mask_size=(13, 13),
- norm_cfg=dict(type='SyncBN'))
+ norm_cfg=dict(type="SyncBN"),
+ )
assert _conv_has_norm(head, sync_bn=True)
# test 'bi-direction' psa_type
inputs = [torch.randn(1, 4, 13, 13)]
- head = PSAHead(
- in_channels=4, channels=2, num_classes=19, mask_size=(13, 13))
+ head = PSAHead(in_channels=4, channels=2, num_classes=19, mask_size=(13, 13))
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -43,11 +43,8 @@ def test_psa_head():
# test 'bi-direction' psa_type, shrink_factor=1
inputs = [torch.randn(1, 4, 13, 13)]
head = PSAHead(
- in_channels=4,
- channels=2,
- num_classes=19,
- mask_size=(13, 13),
- shrink_factor=1)
+ in_channels=4, channels=2, num_classes=19, mask_size=(13, 13), shrink_factor=1
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -56,11 +53,8 @@ def test_psa_head():
# test 'bi-direction' psa_type with soft_max
inputs = [torch.randn(1, 4, 13, 13)]
head = PSAHead(
- in_channels=4,
- channels=2,
- num_classes=19,
- mask_size=(13, 13),
- psa_softmax=True)
+ in_channels=4, channels=2, num_classes=19, mask_size=(13, 13), psa_softmax=True
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -73,7 +67,8 @@ def test_psa_head():
channels=2,
num_classes=19,
mask_size=(13, 13),
- psa_type='collect')
+ psa_type="collect",
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -87,7 +82,8 @@ def test_psa_head():
num_classes=19,
mask_size=(13, 13),
shrink_factor=1,
- psa_type='collect')
+ psa_type="collect",
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -100,9 +96,10 @@ def test_psa_head():
channels=2,
num_classes=19,
mask_size=(13, 13),
- psa_type='collect',
+ psa_type="collect",
shrink_factor=1,
- compact=True)
+ compact=True,
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
@@ -115,7 +112,8 @@ def test_psa_head():
channels=2,
num_classes=19,
mask_size=(13, 13),
- psa_type='distribute')
+ psa_type="distribute",
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_psp_head.py b/mmsegmentation/tests/test_models/test_heads/test_psp_head.py
index fde4087..9c63215 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_psp_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_psp_head.py
@@ -18,15 +18,12 @@ def test_psp_head():
# test with norm_cfg
head = PSPHead(
- in_channels=4,
- channels=2,
- num_classes=19,
- norm_cfg=dict(type='SyncBN'))
+ in_channels=4, channels=2, num_classes=19, norm_cfg=dict(type="SyncBN")
+ )
assert _conv_has_norm(head, sync_bn=True)
inputs = [torch.randn(1, 4, 23, 23)]
- head = PSPHead(
- in_channels=4, channels=2, num_classes=19, pool_scales=(1, 2, 3))
+ head = PSPHead(in_channels=4, channels=2, num_classes=19, pool_scales=(1, 2, 3))
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
assert head.psp_modules[0][0].output_size == 1
diff --git a/mmsegmentation/tests/test_models/test_heads/test_segformer_head.py b/mmsegmentation/tests/test_models/test_heads/test_segformer_head.py
index 73afaba..28324dc 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_segformer_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_segformer_head.py
@@ -8,18 +8,14 @@
def test_segformer_head():
with pytest.raises(AssertionError):
# `in_channels` must have same length as `in_index`
- SegformerHead(
- in_channels=(1, 2, 3), in_index=(0, 1), channels=5, num_classes=2)
+ SegformerHead(in_channels=(1, 2, 3), in_index=(0, 1), channels=5, num_classes=2)
H, W = (64, 64)
in_channels = (32, 64, 160, 256)
- shapes = [(H // 2**(i + 2), W // 2**(i + 2))
- for i in range(len(in_channels))]
+ shapes = [(H // 2 ** (i + 2), W // 2 ** (i + 2)) for i in range(len(in_channels))]
model = SegformerHead(
- in_channels=in_channels,
- in_index=[0, 1, 2, 3],
- channels=256,
- num_classes=19)
+ in_channels=in_channels, in_index=[0, 1, 2, 3], channels=256, num_classes=19
+ )
with pytest.raises(IndexError):
# in_index must match the input feature maps.
diff --git a/mmsegmentation/tests/test_models/test_heads/test_segmenter_mask_head.py b/mmsegmentation/tests/test_models/test_heads/test_segmenter_mask_head.py
index 7b681ac..80b6ed0 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_segmenter_mask_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_segmenter_mask_head.py
@@ -13,7 +13,8 @@ def test_segmenter_mask_transformer_head():
num_layers=2,
num_heads=3,
embed_dims=192,
- dropout_ratio=0.0)
+ dropout_ratio=0.0,
+ )
assert _conv_has_norm(head, sync_bn=True)
head.init_weights()
diff --git a/mmsegmentation/tests/test_models/test_heads/test_setr_mla_head.py b/mmsegmentation/tests/test_models/test_heads/test_setr_mla_head.py
index 301bc0b..ae84c24 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_setr_mla_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_setr_mla_head.py
@@ -14,8 +14,7 @@ def test_setr_mla_head(capsys):
with pytest.raises(AssertionError):
# multiple in_indexs requires multiple in_channels.
- SETRMLAHead(
- in_channels=8, channels=4, num_classes=19, in_index=(0, 1, 2, 3))
+ SETRMLAHead(in_channels=8, channels=4, num_classes=19, in_index=(0, 1, 2, 3))
with pytest.raises(AssertionError):
# channels should be len(in_channels) * mla_channels
@@ -24,7 +23,8 @@ def test_setr_mla_head(capsys):
channels=8,
mla_channels=4,
in_index=(0, 1, 2, 3),
- num_classes=19)
+ num_classes=19,
+ )
# test inference of MLA head
img_size = (8, 8)
@@ -35,7 +35,8 @@ def test_setr_mla_head(capsys):
mla_channels=4,
in_index=(0, 1, 2, 3),
num_classes=19,
- norm_cfg=dict(type='BN'))
+ norm_cfg=dict(type="BN"),
+ )
h, w = img_size[0] // patch_size, img_size[1] // patch_size
# Input square NCHW format feature information
@@ -43,7 +44,7 @@ def test_setr_mla_head(capsys):
torch.randn(1, 8, h, w),
torch.randn(1, 8, h, w),
torch.randn(1, 8, h, w),
- torch.randn(1, 8, h, w)
+ torch.randn(1, 8, h, w),
]
if torch.cuda.is_available():
head, x = to_cuda(head, x)
@@ -55,7 +56,7 @@ def test_setr_mla_head(capsys):
torch.randn(1, 8, h, w * 2),
torch.randn(1, 8, h, w * 2),
torch.randn(1, 8, h, w * 2),
- torch.randn(1, 8, h, w * 2)
+ torch.randn(1, 8, h, w * 2),
]
if torch.cuda.is_available():
head, x = to_cuda(head, x)
diff --git a/mmsegmentation/tests/test_models/test_heads/test_setr_up_head.py b/mmsegmentation/tests/test_models/test_heads/test_setr_up_head.py
index a051922..2478d69 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_setr_up_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_setr_up_head.py
@@ -21,9 +21,10 @@ def test_setr_up_head(capsys):
head = SETRUPHead(
in_channels=4,
channels=2,
- norm_cfg=dict(type='SyncBN'),
+ norm_cfg=dict(type="SyncBN"),
num_classes=19,
- init_cfg=dict(type='Kaiming'))
+ init_cfg=dict(type="Kaiming"),
+ )
super(SETRUPHead, head).init_weights()
# test inference of Naive head
@@ -37,7 +38,8 @@ def test_setr_up_head(capsys):
num_convs=1,
up_scale=4,
kernel_size=1,
- norm_cfg=dict(type='BN'))
+ norm_cfg=dict(type="BN"),
+ )
h, w = img_size[0] // patch_size, img_size[1] // patch_size
diff --git a/mmsegmentation/tests/test_models/test_heads/test_stdc_head.py b/mmsegmentation/tests/test_models/test_heads/test_stdc_head.py
index 1628209..328f1ff 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_stdc_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_stdc_head.py
@@ -14,18 +14,17 @@ def test_stdc_head():
num_classes=2,
in_index=-1,
loss_decode=[
- dict(
- type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
- dict(type='DiceLoss', loss_name='loss_dice', loss_weight=1.0)
- ])
+ dict(type="CrossEntropyLoss", loss_name="loss_ce", loss_weight=1.0),
+ dict(type="DiceLoss", loss_name="loss_dice", loss_weight=1.0),
+ ],
+ )
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
assert isinstance(outputs, torch.Tensor) and len(outputs) == 1
assert outputs.shape == torch.Size([1, head.num_classes, 21, 21])
- fake_label = torch.ones_like(
- outputs[:, 0:1, :, :], dtype=torch.int16).long()
+ fake_label = torch.ones_like(outputs[:, 0:1, :, :], dtype=torch.int16).long()
loss = head.losses(seg_logit=outputs, seg_label=fake_label)
- assert loss['loss_ce'] != torch.zeros_like(loss['loss_ce'])
- assert loss['loss_dice'] != torch.zeros_like(loss['loss_dice'])
+ assert loss["loss_ce"] != torch.zeros_like(loss["loss_ce"])
+ assert loss["loss_dice"] != torch.zeros_like(loss["loss_dice"])
diff --git a/mmsegmentation/tests/test_models/test_heads/test_uper_head.py b/mmsegmentation/tests/test_models/test_heads/test_uper_head.py
index 09456a8..0d607cb 100644
--- a/mmsegmentation/tests/test_models/test_heads/test_uper_head.py
+++ b/mmsegmentation/tests/test_models/test_heads/test_uper_head.py
@@ -13,8 +13,7 @@ def test_uper_head():
UPerHead(in_channels=4, channels=2, num_classes=19)
# test no norm_cfg
- head = UPerHead(
- in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1])
+ head = UPerHead(in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1])
assert not _conv_has_norm(head, sync_bn=False)
# test with norm_cfg
@@ -22,13 +21,13 @@ def test_uper_head():
in_channels=[4, 2],
channels=2,
num_classes=19,
- norm_cfg=dict(type='SyncBN'),
- in_index=[-2, -1])
+ norm_cfg=dict(type="SyncBN"),
+ in_index=[-2, -1],
+ )
assert _conv_has_norm(head, sync_bn=True)
inputs = [torch.randn(1, 4, 45, 45), torch.randn(1, 2, 21, 21)]
- head = UPerHead(
- in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1])
+ head = UPerHead(in_channels=[4, 2], channels=2, num_classes=19, in_index=[-2, -1])
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs = head(inputs)
diff --git a/mmsegmentation/tests/test_models/test_losses/test_ce_loss.py b/mmsegmentation/tests/test_models/test_losses/test_ce_loss.py
index afa5706..4400b5c 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_ce_loss.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_ce_loss.py
@@ -5,38 +5,35 @@
from mmseg.models.losses.cross_entropy_loss import _expand_onehot_labels
-@pytest.mark.parametrize('use_sigmoid', [True, False])
-@pytest.mark.parametrize('reduction', ('mean', 'sum', 'none'))
-@pytest.mark.parametrize('avg_non_ignore', [True, False])
-@pytest.mark.parametrize('bce_input_same_dim', [True, False])
+@pytest.mark.parametrize("use_sigmoid", [True, False])
+@pytest.mark.parametrize("reduction", ("mean", "sum", "none"))
+@pytest.mark.parametrize("avg_non_ignore", [True, False])
+@pytest.mark.parametrize("bce_input_same_dim", [True, False])
def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
from mmseg.models import build_loss
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
- type='CrossEntropyLoss',
- use_mask=True,
- use_sigmoid=True,
- loss_weight=1.0)
+ type="CrossEntropyLoss", use_mask=True, use_sigmoid=True, loss_weight=1.0
+ )
build_loss(loss_cfg)
# test loss with simple case for ce/bce
fake_pred = torch.Tensor([[100, -100]])
fake_label = torch.Tensor([1]).long()
loss_cls_cfg = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=use_sigmoid,
loss_weight=1.0,
avg_non_ignore=avg_non_ignore,
- loss_name='loss_ce')
+ loss_name="loss_ce",
+ )
loss_cls = build_loss(loss_cls_cfg)
if use_sigmoid:
- assert torch.allclose(
- loss_cls(fake_pred, fake_label), torch.tensor(100.))
+ assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(100.0))
else:
- assert torch.allclose(
- loss_cls(fake_pred, fake_label), torch.tensor(200.))
+ assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.0))
# test loss with complicated case for ce/bce
# when avg_non_ignore is False, `avg_factor` would not be calculated
@@ -52,24 +49,22 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
fake_label[0, [1, 2, 5, 7]] = 255 # set ignore_index
fake_label[1, [0, 5, 8, 9]] = 255
loss_cls = build_loss(loss_cls_cfg)
- loss = loss_cls(
- fake_pred, fake_label, weight=fake_weight, ignore_index=255)
+ loss = loss_cls(fake_pred, fake_label, weight=fake_weight, ignore_index=255)
if use_sigmoid:
if fake_pred.dim() != fake_label.dim():
fake_label, weight, valid_mask = _expand_onehot_labels(
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
- ignore_index=255)
+ ignore_index=255,
+ )
else:
# should mask out the ignored elements
valid_mask = ((fake_label >= 0) & (fake_label != 255)).float()
weight = valid_mask
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
- fake_pred,
- fake_label.float(),
- reduction='none',
- weight=fake_weight)
+ fake_pred, fake_label.float(), reduction="none", weight=fake_weight
+ )
if avg_non_ignore:
avg_factor = valid_mask.sum().item()
torch_loss = (torch_loss * weight).sum() / avg_factor
@@ -78,11 +73,15 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
else:
if avg_non_ignore:
torch_loss = torch.nn.functional.cross_entropy(
- fake_pred, fake_label, reduction='mean', ignore_index=255)
+ fake_pred, fake_label, reduction="mean", ignore_index=255
+ )
else:
- torch_loss = torch.nn.functional.cross_entropy(
- fake_pred, fake_label, reduction='sum',
- ignore_index=255) / fake_label.numel()
+ torch_loss = (
+ torch.nn.functional.cross_entropy(
+ fake_pred, fake_label, reduction="sum", ignore_index=255
+ )
+ / fake_label.numel()
+ )
assert torch.allclose(loss, torch_loss)
if use_sigmoid:
@@ -94,19 +93,20 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
fake_weight = torch.rand(2, 8, 8)
loss_cls = build_loss(loss_cls_cfg)
- loss = loss_cls(
- fake_pred, fake_label, weight=fake_weight, ignore_index=255)
+ loss = loss_cls(fake_pred, fake_label, weight=fake_weight, ignore_index=255)
if use_sigmoid:
fake_label, weight, valid_mask = _expand_onehot_labels(
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
- ignore_index=255)
+ ignore_index=255,
+ )
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred,
fake_label.float(),
- reduction='none',
- weight=fake_weight.unsqueeze(1).expand(fake_pred.shape))
+ reduction="none",
+ weight=fake_weight.unsqueeze(1).expand(fake_pred.shape),
+ )
if avg_non_ignore:
avg_factor = valid_mask.sum().item()
torch_loss = (torch_loss * weight).sum() / avg_factor
@@ -122,56 +122,61 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
import mmcv
import numpy as np
+
tmp_file = tempfile.NamedTemporaryFile()
- mmcv.dump([0.8, 0.2], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
+ mmcv.dump([0.8, 0.2], f"{tmp_file.name}.pkl", "pkl") # from pkl file
loss_cls_cfg = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=False,
- class_weight=f'{tmp_file.name}.pkl',
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
- loss_name='loss_ce')
+ loss_name="loss_ce",
+ )
loss_cls = build_loss(loss_cls_cfg)
- assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
+ assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.0))
- np.save(f'{tmp_file.name}.npy', np.array([0.8, 0.2])) # from npy file
+ np.save(f"{tmp_file.name}.npy", np.array([0.8, 0.2])) # from npy file
loss_cls_cfg = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=False,
- class_weight=f'{tmp_file.name}.npy',
+ class_weight=f"{tmp_file.name}.npy",
loss_weight=1.0,
- loss_name='loss_ce')
+ loss_name="loss_ce",
+ )
loss_cls = build_loss(loss_cls_cfg)
- assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
+ assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.0))
tmp_file.close()
- os.remove(f'{tmp_file.name}.pkl')
- os.remove(f'{tmp_file.name}.npy')
+ os.remove(f"{tmp_file.name}.pkl")
+ os.remove(f"{tmp_file.name}.npy")
- loss_cls_cfg = dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
+ loss_cls_cfg = dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
- assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.))
+ assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.0))
# test `avg_non_ignore` without ignore index would not affect ce/bce loss
# when reduction='sum'/'none'/'mean'
loss_cls_cfg1 = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=use_sigmoid,
reduction=reduction,
loss_weight=1.0,
- avg_non_ignore=True)
+ avg_non_ignore=True,
+ )
loss_cls1 = build_loss(loss_cls_cfg1)
loss_cls_cfg2 = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=use_sigmoid,
reduction=reduction,
loss_weight=1.0,
- avg_non_ignore=False)
+ avg_non_ignore=False,
+ )
loss_cls2 = build_loss(loss_cls_cfg2)
assert torch.allclose(
loss_cls1(fake_pred, fake_label, ignore_index=255) / fake_pred.numel(),
loss_cls2(fake_pred, fake_label, ignore_index=255) / fake_pred.numel(),
- atol=1e-4)
+ atol=1e-4,
+ )
# test ce/bce loss with ignore index and class weight
# in 5-way classification
@@ -189,33 +194,36 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
- ignore_index=-100)
+ ignore_index=-100,
+ )
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
- fake_pred,
- fake_label.float(),
- reduction='mean',
- pos_weight=class_weight)
+ fake_pred, fake_label.float(), reduction="mean", pos_weight=class_weight
+ )
else:
fake_pred = torch.randn(2, 5, 10).float() # 5-way classification
fake_label = torch.randint(0, 5, (2, 10)).long()
class_weight = torch.rand(5)
class_weight /= class_weight.sum()
- torch_loss = torch.nn.functional.cross_entropy(
- fake_pred, fake_label, reduction='sum',
- weight=class_weight) / fake_label.numel()
+ torch_loss = (
+ torch.nn.functional.cross_entropy(
+ fake_pred, fake_label, reduction="sum", weight=class_weight
+ )
+ / fake_label.numel()
+ )
loss_cls_cfg = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=use_sigmoid,
- reduction='mean',
+ reduction="mean",
class_weight=class_weight,
loss_weight=1.0,
- avg_non_ignore=avg_non_ignore)
+ avg_non_ignore=avg_non_ignore,
+ )
loss_cls = build_loss(loss_cls_cfg)
# test cross entropy loss has name `loss_ce`
- assert loss_cls.loss_name == 'loss_ce'
+ assert loss_cls.loss_name == "loss_ce"
# test avg_non_ignore is in extra_repr
- assert loss_cls.extra_repr() == f'avg_non_ignore={avg_non_ignore}'
+ assert loss_cls.extra_repr() == f"avg_non_ignore={avg_non_ignore}"
loss = loss_cls(fake_pred, fake_label)
assert torch.allclose(loss, torch_loss)
@@ -229,33 +237,46 @@ def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
fake_pred[fake_label != 10],
fake_label[fake_label != 10].float(),
pos_weight=class_weight[fake_label != 10],
- reduction='mean')
+ reduction="mean",
+ )
else:
- torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
- fake_pred[fake_label != 10],
- fake_label[fake_label != 10].float(),
- pos_weight=class_weight[fake_label != 10],
- reduction='sum') / fake_label.numel()
+ torch_loss = (
+ torch.nn.functional.binary_cross_entropy_with_logits(
+ fake_pred[fake_label != 10],
+ fake_label[fake_label != 10].float(),
+ pos_weight=class_weight[fake_label != 10],
+ reduction="sum",
+ )
+ / fake_label.numel()
+ )
else:
if avg_non_ignore:
- torch_loss = torch.nn.functional.cross_entropy(
- fake_pred,
- fake_label,
- ignore_index=10,
- reduction='sum',
- weight=class_weight) / fake_label[fake_label != 10].numel()
+ torch_loss = (
+ torch.nn.functional.cross_entropy(
+ fake_pred,
+ fake_label,
+ ignore_index=10,
+ reduction="sum",
+ weight=class_weight,
+ )
+ / fake_label[fake_label != 10].numel()
+ )
else:
- torch_loss = torch.nn.functional.cross_entropy(
- fake_pred,
- fake_label,
- ignore_index=10,
- reduction='sum',
- weight=class_weight) / fake_label.numel()
+ torch_loss = (
+ torch.nn.functional.cross_entropy(
+ fake_pred,
+ fake_label,
+ ignore_index=10,
+ reduction="sum",
+ weight=class_weight,
+ )
+ / fake_label.numel()
+ )
assert torch.allclose(loss, torch_loss)
-@pytest.mark.parametrize('avg_non_ignore', [True, False])
-@pytest.mark.parametrize('with_weight', [True, False])
+@pytest.mark.parametrize("avg_non_ignore", [True, False])
+@pytest.mark.parametrize("with_weight", [True, False])
def test_binary_class_ce_loss(avg_non_ignore, with_weight):
from mmseg.models import build_loss
@@ -268,27 +289,29 @@ def test_binary_class_ce_loss(avg_non_ignore, with_weight):
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred,
fake_label.unsqueeze(1).float(),
- reduction='none',
- weight=fake_weight.unsqueeze(1).float() if with_weight else None)
+ reduction="none",
+ weight=fake_weight.unsqueeze(1).float() if with_weight else None,
+ )
if avg_non_ignore:
eps = torch.finfo(torch.float32).eps
avg_factor = valid_mask.sum().item()
- torch_loss = (torch_loss * weight.unsqueeze(1)).sum() / (
- avg_factor + eps)
+ torch_loss = (torch_loss * weight.unsqueeze(1)).sum() / (avg_factor + eps)
else:
torch_loss = (torch_loss * weight.unsqueeze(1)).mean()
loss_cls_cfg = dict(
- type='CrossEntropyLoss',
+ type="CrossEntropyLoss",
use_sigmoid=True,
loss_weight=1.0,
avg_non_ignore=avg_non_ignore,
- reduction='mean',
- loss_name='loss_ce')
+ reduction="mean",
+ loss_name="loss_ce",
+ )
loss_cls = build_loss(loss_cls_cfg)
loss = loss_cls(
fake_pred,
fake_label,
weight=fake_weight if with_weight else None,
- ignore_index=255)
+ ignore_index=255,
+ )
assert torch.allclose(loss, torch_loss)
diff --git a/mmsegmentation/tests/test_models/test_losses/test_dice_loss.py b/mmsegmentation/tests/test_models/test_losses/test_dice_loss.py
index 3936f5d..f8322dc 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_dice_loss.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_dice_loss.py
@@ -7,12 +7,13 @@ def test_dice_lose():
# test dice loss with loss_type = 'multi_class'
loss_cfg = dict(
- type='DiceLoss',
- reduction='none',
+ type="DiceLoss",
+ reduction="none",
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_dice')
+ loss_name="loss_dice",
+ )
dice_loss = build_loss(loss_cfg)
logits = torch.rand(8, 3, 4, 4)
labels = (torch.rand(8, 4, 4) * 3).long()
@@ -24,42 +25,46 @@ def test_dice_lose():
import mmcv
import numpy as np
+
tmp_file = tempfile.NamedTemporaryFile()
- mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
+ mmcv.dump([1.0, 2.0, 3.0], f"{tmp_file.name}.pkl", "pkl") # from pkl file
loss_cfg = dict(
- type='DiceLoss',
- reduction='none',
- class_weight=f'{tmp_file.name}.pkl',
+ type="DiceLoss",
+ reduction="none",
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_dice')
+ loss_name="loss_dice",
+ )
dice_loss = build_loss(loss_cfg)
dice_loss(logits, labels, ignore_index=None)
- np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0])) # from npy file
+ np.save(f"{tmp_file.name}.npy", np.array([1.0, 2.0, 3.0])) # from npy file
loss_cfg = dict(
- type='DiceLoss',
- reduction='none',
- class_weight=f'{tmp_file.name}.pkl',
+ type="DiceLoss",
+ reduction="none",
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_dice')
+ loss_name="loss_dice",
+ )
dice_loss = build_loss(loss_cfg)
dice_loss(logits, labels, ignore_index=None)
tmp_file.close()
- os.remove(f'{tmp_file.name}.pkl')
- os.remove(f'{tmp_file.name}.npy')
+ os.remove(f"{tmp_file.name}.pkl")
+ os.remove(f"{tmp_file.name}.npy")
# test dice loss with loss_type = 'binary'
loss_cfg = dict(
- type='DiceLoss',
+ type="DiceLoss",
smooth=2,
exponent=3,
- reduction='sum',
+ reduction="sum",
loss_weight=1.0,
ignore_index=0,
- loss_name='loss_dice')
+ loss_name="loss_dice",
+ )
dice_loss = build_loss(loss_cfg)
logits = torch.rand(8, 2, 4, 4)
labels = (torch.rand(8, 4, 4) * 2).long()
@@ -67,12 +72,13 @@ def test_dice_lose():
# test dice loss has name `loss_dice`
loss_cfg = dict(
- type='DiceLoss',
+ type="DiceLoss",
smooth=2,
exponent=3,
- reduction='sum',
+ reduction="sum",
loss_weight=1.0,
ignore_index=0,
- loss_name='loss_dice')
+ loss_name="loss_dice",
+ )
dice_loss = build_loss(loss_cfg)
- assert dice_loss.loss_name == 'loss_dice'
+ assert dice_loss.loss_name == "loss_dice"
diff --git a/mmsegmentation/tests/test_models/test_losses/test_focal_loss.py b/mmsegmentation/tests/test_models/test_losses/test_focal_loss.py
index 687312b..9e75542 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_focal_loss.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_focal_loss.py
@@ -10,12 +10,12 @@
def test_use_sigmoid():
# can't init with use_sigmoid=True
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', use_sigmoid=False)
+ loss_cfg = dict(type="FocalLoss", use_sigmoid=False)
build_loss(loss_cfg)
# can't forward with use_sigmoid=True
with pytest.raises(NotImplementedError):
- loss_cfg = dict(type='FocalLoss', use_sigmoid=True)
+ loss_cfg = dict(type="FocalLoss", use_sigmoid=True)
focal_loss = build_loss(loss_cfg)
focal_loss.use_sigmoid = False
fake_pred = torch.rand(3, 4, 5, 6)
@@ -27,70 +27,71 @@ def test_use_sigmoid():
def test_wrong_reduction_type():
# can't init with wrong reduction
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', reduction='test')
+ loss_cfg = dict(type="FocalLoss", reduction="test")
build_loss(loss_cfg)
# can't forward with wrong reduction override
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss')
+ loss_cfg = dict(type="FocalLoss")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
- focal_loss(fake_pred, fake_target, reduction_override='test')
+ focal_loss(fake_pred, fake_target, reduction_override="test")
# test focal loss can handle input parameters with
# unacceptable types
def test_unacceptable_parameters():
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', gamma='test')
+ loss_cfg = dict(type="FocalLoss", gamma="test")
build_loss(loss_cfg)
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', alpha='test')
+ loss_cfg = dict(type="FocalLoss", alpha="test")
build_loss(loss_cfg)
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', class_weight='test')
+ loss_cfg = dict(type="FocalLoss", class_weight="test")
build_loss(loss_cfg)
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', loss_weight='test')
+ loss_cfg = dict(type="FocalLoss", loss_weight="test")
build_loss(loss_cfg)
with pytest.raises(AssertionError):
- loss_cfg = dict(type='FocalLoss', loss_name=123)
+ loss_cfg = dict(type="FocalLoss", loss_name=123)
build_loss(loss_cfg)
# test if focal loss can be correctly initialize
def test_init_focal_loss():
loss_cfg = dict(
- type='FocalLoss',
+ type="FocalLoss",
use_sigmoid=True,
gamma=3.0,
alpha=3.0,
class_weight=[1, 2, 3, 4],
- reduction='sum')
+ reduction="sum",
+ )
focal_loss = build_loss(loss_cfg)
assert focal_loss.use_sigmoid is True
assert focal_loss.gamma == 3.0
assert focal_loss.alpha == 3.0
- assert focal_loss.reduction == 'sum'
+ assert focal_loss.reduction == "sum"
assert focal_loss.class_weight == [1, 2, 3, 4]
assert focal_loss.loss_weight == 1.0
- assert focal_loss.loss_name == 'loss_focal'
+ assert focal_loss.loss_name == "loss_focal"
# test reduction override
def test_reduction_override():
- loss_cfg = dict(type='FocalLoss', reduction='mean')
+ loss_cfg = dict(type="FocalLoss", reduction="mean")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
- loss = focal_loss(fake_pred, fake_target, reduction_override='none')
+ loss = focal_loss(fake_pred, fake_target, reduction_override="none")
assert loss.shape == fake_pred.shape
# test wrong pred and target shape
def test_wrong_pred_and_target_shape():
- loss_cfg = dict(type='FocalLoss')
+ loss_cfg = dict(type="FocalLoss")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 2, 2))
@@ -102,7 +103,7 @@ def test_wrong_pred_and_target_shape():
# test forward with different shape of target
def test_forward_with_different_shape_of_target():
- loss_cfg = dict(type='FocalLoss')
+ loss_cfg = dict(type="FocalLoss")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
@@ -117,7 +118,7 @@ def test_forward_with_different_shape_of_target():
# test forward with weight
def test_forward_with_weight():
- loss_cfg = dict(type='FocalLoss')
+ loss_cfg = dict(type="FocalLoss")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
@@ -134,7 +135,7 @@ def test_forward_with_weight():
# test none reduction type
def test_none_reduction_type():
- loss_cfg = dict(type='FocalLoss', reduction='none')
+ loss_cfg = dict(type="FocalLoss", reduction="none")
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
@@ -145,8 +146,9 @@ def test_none_reduction_type():
# test the usage of class weight
def test_class_weight():
loss_cfg_cw = dict(
- type='FocalLoss', reduction='none', class_weight=[1.0, 2.0, 3.0, 4.0])
- loss_cfg = dict(type='FocalLoss', reduction='none')
+ type="FocalLoss", reduction="none", class_weight=[1.0, 2.0, 3.0, 4.0]
+ )
+ loss_cfg = dict(type="FocalLoss", reduction="none")
focal_loss_cw = build_loss(loss_cfg_cw)
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 4, 5, 6)
@@ -159,14 +161,14 @@ def test_class_weight():
# test ignore index
def test_ignore_index():
- loss_cfg = dict(type='FocalLoss', reduction='none')
+ loss_cfg = dict(type="FocalLoss", reduction="none")
# ignore_index within C classes
focal_loss = build_loss(loss_cfg)
fake_pred = torch.rand(3, 5, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
- dim1 = torch.randint(0, 3, (4, ))
- dim2 = torch.randint(0, 5, (4, ))
- dim3 = torch.randint(0, 6, (4, ))
+ dim1 = torch.randint(0, 3, (4,))
+ dim2 = torch.randint(0, 5, (4,))
+ dim3 = torch.randint(0, 6, (4,))
fake_target[dim1, dim2, dim3] = 4
loss1 = focal_loss(fake_pred, fake_target, ignore_index=4)
one_hot_target = F.one_hot(fake_target, num_classes=5)
@@ -190,9 +192,9 @@ def test_ignore_index():
# ignore index is not in prediction's classes
fake_pred = torch.rand(3, 4, 5, 6)
fake_target = torch.randint(0, 4, (3, 5, 6))
- dim1 = torch.randint(0, 3, (4, ))
- dim2 = torch.randint(0, 5, (4, ))
- dim3 = torch.randint(0, 6, (4, ))
+ dim1 = torch.randint(0, 3, (4,))
+ dim2 = torch.randint(0, 5, (4,))
+ dim3 = torch.randint(0, 6, (4,))
fake_target[dim1, dim2, dim3] = 255
loss1 = focal_loss(fake_pred, fake_target, ignore_index=255)
assert (loss1[dim1, :, dim2, dim3] == 0).all()
@@ -200,7 +202,7 @@ def test_ignore_index():
# test list alpha
def test_alpha():
- loss_cfg = dict(type='FocalLoss')
+ loss_cfg = dict(type="FocalLoss")
focal_loss = build_loss(loss_cfg)
alpha_float = 0.4
alpha = [0.4, 0.4, 0.4, 0.4]
diff --git a/mmsegmentation/tests/test_models/test_losses/test_lovasz_loss.py b/mmsegmentation/tests/test_models/test_losses/test_lovasz_loss.py
index bea3f4b..ac78bfc 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_lovasz_loss.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_lovasz_loss.py
@@ -9,27 +9,25 @@ def test_lovasz_loss():
# loss_type should be 'binary' or 'multi_class'
with pytest.raises(AssertionError):
loss_cfg = dict(
- type='LovaszLoss',
- loss_type='Binary',
- reduction='none',
+ type="LovaszLoss",
+ loss_type="Binary",
+ reduction="none",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
build_loss(loss_cfg)
# reduction should be 'none' when per_image is False.
with pytest.raises(AssertionError):
loss_cfg = dict(
- type='LovaszLoss',
- loss_type='multi_class',
- loss_name='loss_lovasz')
+ type="LovaszLoss", loss_type="multi_class", loss_name="loss_lovasz"
+ )
build_loss(loss_cfg)
# test lovasz loss with loss_type = 'multi_class' and per_image = False
loss_cfg = dict(
- type='LovaszLoss',
- reduction='none',
- loss_weight=1.0,
- loss_name='loss_lovasz')
+ type="LovaszLoss", reduction="none", loss_weight=1.0, loss_name="loss_lovasz"
+ )
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(1, 3, 4, 4)
labels = (torch.rand(1, 4, 4) * 2).long()
@@ -37,12 +35,13 @@ def test_lovasz_loss():
# test lovasz loss with loss_type = 'multi_class' and per_image = True
loss_cfg = dict(
- type='LovaszLoss',
+ type="LovaszLoss",
per_image=True,
- reduction='mean',
+ reduction="mean",
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(1, 3, 4, 4)
labels = (torch.rand(1, 4, 4) * 2).long()
@@ -54,40 +53,44 @@ def test_lovasz_loss():
import mmcv
import numpy as np
+
tmp_file = tempfile.NamedTemporaryFile()
- mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
+ mmcv.dump([1.0, 2.0, 3.0], f"{tmp_file.name}.pkl", "pkl") # from pkl file
loss_cfg = dict(
- type='LovaszLoss',
+ type="LovaszLoss",
per_image=True,
- reduction='mean',
- class_weight=f'{tmp_file.name}.pkl',
+ reduction="mean",
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
lovasz_loss(logits, labels, ignore_index=None)
- np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0])) # from npy file
+ np.save(f"{tmp_file.name}.npy", np.array([1.0, 2.0, 3.0])) # from npy file
loss_cfg = dict(
- type='LovaszLoss',
+ type="LovaszLoss",
per_image=True,
- reduction='mean',
- class_weight=f'{tmp_file.name}.npy',
+ reduction="mean",
+ class_weight=f"{tmp_file.name}.npy",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
lovasz_loss(logits, labels, ignore_index=None)
tmp_file.close()
- os.remove(f'{tmp_file.name}.pkl')
- os.remove(f'{tmp_file.name}.npy')
+ os.remove(f"{tmp_file.name}.pkl")
+ os.remove(f"{tmp_file.name}.npy")
# test lovasz loss with loss_type = 'binary' and per_image = False
loss_cfg = dict(
- type='LovaszLoss',
- loss_type='binary',
- reduction='none',
+ type="LovaszLoss",
+ loss_type="binary",
+ reduction="none",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(2, 4, 4)
labels = (torch.rand(2, 4, 4)).long()
@@ -95,12 +98,13 @@ def test_lovasz_loss():
# test lovasz loss with loss_type = 'binary' and per_image = True
loss_cfg = dict(
- type='LovaszLoss',
- loss_type='binary',
+ type="LovaszLoss",
+ loss_type="binary",
per_image=True,
- reduction='mean',
+ reduction="mean",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(2, 4, 4)
labels = (torch.rand(2, 4, 4)).long()
@@ -108,11 +112,12 @@ def test_lovasz_loss():
# test lovasz loss has name `loss_lovasz`
loss_cfg = dict(
- type='LovaszLoss',
- loss_type='binary',
+ type="LovaszLoss",
+ loss_type="binary",
per_image=True,
- reduction='mean',
+ reduction="mean",
loss_weight=1.0,
- loss_name='loss_lovasz')
+ loss_name="loss_lovasz",
+ )
lovasz_loss = build_loss(loss_cfg)
- assert lovasz_loss.loss_name == 'loss_lovasz'
+ assert lovasz_loss.loss_name == "loss_lovasz"
diff --git a/mmsegmentation/tests/test_models/test_losses/test_tversky_loss.py b/mmsegmentation/tests/test_models/test_losses/test_tversky_loss.py
index 24a4b57..7fb6c8b 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_tversky_loss.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_tversky_loss.py
@@ -9,12 +9,13 @@ def test_tversky_lose():
# test alpha + beta != 1
with pytest.raises(AssertionError):
loss_cfg = dict(
- type='TverskyLoss',
+ type="TverskyLoss",
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
alpha=0.4,
beta=0.7,
- loss_name='loss_tversky')
+ loss_name="loss_tversky",
+ )
tversky_loss = build_loss(loss_cfg)
logits = torch.rand(8, 3, 4, 4)
labels = (torch.rand(8, 4, 4) * 3).long()
@@ -22,11 +23,12 @@ def test_tversky_lose():
# test tversky loss
loss_cfg = dict(
- type='TverskyLoss',
+ type="TverskyLoss",
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_tversky')
+ loss_name="loss_tversky",
+ )
tversky_loss = build_loss(loss_cfg)
logits = torch.rand(8, 3, 4, 4)
labels = (torch.rand(8, 4, 4) * 3).long()
@@ -38,39 +40,43 @@ def test_tversky_lose():
import mmcv
import numpy as np
+
tmp_file = tempfile.NamedTemporaryFile()
- mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
+ mmcv.dump([1.0, 2.0, 3.0], f"{tmp_file.name}.pkl", "pkl") # from pkl file
loss_cfg = dict(
- type='TverskyLoss',
- class_weight=f'{tmp_file.name}.pkl',
+ type="TverskyLoss",
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_tversky')
+ loss_name="loss_tversky",
+ )
tversky_loss = build_loss(loss_cfg)
tversky_loss(logits, labels)
- np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0])) # from npy file
+ np.save(f"{tmp_file.name}.npy", np.array([1.0, 2.0, 3.0])) # from npy file
loss_cfg = dict(
- type='TverskyLoss',
- class_weight=f'{tmp_file.name}.pkl',
+ type="TverskyLoss",
+ class_weight=f"{tmp_file.name}.pkl",
loss_weight=1.0,
ignore_index=1,
- loss_name='loss_tversky')
+ loss_name="loss_tversky",
+ )
tversky_loss = build_loss(loss_cfg)
tversky_loss(logits, labels)
tmp_file.close()
- os.remove(f'{tmp_file.name}.pkl')
- os.remove(f'{tmp_file.name}.npy')
+ os.remove(f"{tmp_file.name}.pkl")
+ os.remove(f"{tmp_file.name}.npy")
# test tversky loss has name `loss_tversky`
loss_cfg = dict(
- type='TverskyLoss',
+ type="TverskyLoss",
smooth=2,
loss_weight=1.0,
ignore_index=1,
alpha=0.3,
beta=0.7,
- loss_name='loss_tversky')
+ loss_name="loss_tversky",
+ )
tversky_loss = build_loss(loss_cfg)
- assert tversky_loss.loss_name == 'loss_tversky'
+ assert tversky_loss.loss_name == "loss_tversky"
diff --git a/mmsegmentation/tests/test_models/test_losses/test_utils.py b/mmsegmentation/tests/test_models/test_losses/test_utils.py
index ab9927f..1bae2ac 100644
--- a/mmsegmentation/tests/test_models/test_losses/test_utils.py
+++ b/mmsegmentation/tests/test_models/test_losses/test_utils.py
@@ -12,33 +12,33 @@ def test_weight_reduce_loss():
weight[:, :, :2, :2] = 1
# test reduce_loss()
- reduced = reduce_loss(loss, 'none')
+ reduced = reduce_loss(loss, "none")
assert reduced is loss
- reduced = reduce_loss(loss, 'mean')
+ reduced = reduce_loss(loss, "mean")
np.testing.assert_almost_equal(reduced.numpy(), loss.mean())
- reduced = reduce_loss(loss, 'sum')
+ reduced = reduce_loss(loss, "sum")
np.testing.assert_almost_equal(reduced.numpy(), loss.sum())
# test weight_reduce_loss()
- reduced = weight_reduce_loss(loss, weight=None, reduction='none')
+ reduced = weight_reduce_loss(loss, weight=None, reduction="none")
assert reduced is loss
- reduced = weight_reduce_loss(loss, weight=weight, reduction='mean')
+ reduced = weight_reduce_loss(loss, weight=weight, reduction="mean")
target = (loss * weight).mean()
np.testing.assert_almost_equal(reduced.numpy(), target)
- reduced = weight_reduce_loss(loss, weight=weight, reduction='sum')
+ reduced = weight_reduce_loss(loss, weight=weight, reduction="sum")
np.testing.assert_almost_equal(reduced.numpy(), (loss * weight).sum())
with pytest.raises(AssertionError):
weight_wrong = weight[0, 0, ...]
- weight_reduce_loss(loss, weight=weight_wrong, reduction='mean')
+ weight_reduce_loss(loss, weight=weight_wrong, reduction="mean")
with pytest.raises(AssertionError):
weight_wrong = weight[:, 0:2, ...]
- weight_reduce_loss(loss, weight=weight_wrong, reduction='mean')
+ weight_reduce_loss(loss, weight=weight_wrong, reduction="mean")
def test_accuracy():
@@ -49,9 +49,15 @@ def test_accuracy():
acc = accuracy(pred, label)
assert acc.item() == 0
- pred = torch.Tensor([[0.2, 0.3, 0.6, 0.5], [0.1, 0.1, 0.2, 0.6],
- [0.9, 0.0, 0.0, 0.1], [0.4, 0.7, 0.1, 0.1],
- [0.0, 0.0, 0.99, 0]])
+ pred = torch.Tensor(
+ [
+ [0.2, 0.3, 0.6, 0.5],
+ [0.1, 0.1, 0.2, 0.6],
+ [0.9, 0.0, 0.0, 0.1],
+ [0.4, 0.7, 0.1, 0.1],
+ [0.0, 0.0, 0.99, 0],
+ ]
+ )
# test for ignore_index
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=None)
@@ -114,7 +120,7 @@ def test_accuracy():
# wrong topk type
with pytest.raises(AssertionError):
- accuracy = Accuracy(topk='wrong type')
+ accuracy = Accuracy(topk="wrong type")
accuracy(pred, true_label)
# label size is larger than required
diff --git a/mmsegmentation/tests/test_models/test_necks/test_feature2pyramid.py b/mmsegmentation/tests/test_models/test_necks/test_feature2pyramid.py
index 44fd02c..5cb17e0 100644
--- a/mmsegmentation/tests/test_models/test_necks/test_feature2pyramid.py
+++ b/mmsegmentation/tests/test_models/test_necks/test_feature2pyramid.py
@@ -12,7 +12,8 @@ def test_feature2pyramid():
inputs = [torch.randn(1, embed_dim, 32, 32) for i in range(len(rescales))]
fpn = Feature2Pyramid(
- embed_dim, rescales, norm_cfg=dict(type='BN', requires_grad=True))
+ embed_dim, rescales, norm_cfg=dict(type="BN", requires_grad=True)
+ )
outputs = fpn(inputs)
assert outputs[0].shape == torch.Size([1, 64, 128, 128])
assert outputs[1].shape == torch.Size([1, 64, 64, 64])
@@ -24,7 +25,8 @@ def test_feature2pyramid():
inputs = [torch.randn(1, embed_dim, 32, 32) for i in range(len(rescales))]
fpn = Feature2Pyramid(
- embed_dim, rescales, norm_cfg=dict(type='BN', requires_grad=True))
+ embed_dim, rescales, norm_cfg=dict(type="BN", requires_grad=True)
+ )
outputs = fpn(inputs)
assert outputs[0].shape == torch.Size([1, 64, 64, 64])
assert outputs[1].shape == torch.Size([1, 64, 32, 32])
@@ -35,4 +37,5 @@ def test_feature2pyramid():
rescales = [4, 2, 0.25, 0]
with pytest.raises(KeyError):
fpn = Feature2Pyramid(
- embed_dim, rescales, norm_cfg=dict(type='BN', requires_grad=True))
+ embed_dim, rescales, norm_cfg=dict(type="BN", requires_grad=True)
+ )
diff --git a/mmsegmentation/tests/test_models/test_necks/test_fpn.py b/mmsegmentation/tests/test_models/test_necks/test_fpn.py
index c294006..d2cf409 100644
--- a/mmsegmentation/tests/test_models/test_necks/test_fpn.py
+++ b/mmsegmentation/tests/test_models/test_necks/test_fpn.py
@@ -7,8 +7,7 @@
def test_fpn():
in_channels = [64, 128, 256, 512]
inputs = [
- torch.randn(1, c, 56 // 2**i, 56 // 2**i)
- for i, c in enumerate(in_channels)
+ torch.randn(1, c, 56 // 2**i, 56 // 2**i) for i, c in enumerate(in_channels)
]
fpn = FPN(in_channels, 64, len(in_channels))
@@ -22,7 +21,8 @@ def test_fpn():
in_channels,
64,
len(in_channels),
- upsample_cfg=dict(mode='nearest', scale_factor=2.0))
+ upsample_cfg=dict(mode="nearest", scale_factor=2.0),
+ )
outputs = fpn(inputs)
assert outputs[0].shape == torch.Size([1, 64, 56, 56])
assert outputs[1].shape == torch.Size([1, 64, 28, 28])
diff --git a/mmsegmentation/tests/test_models/test_necks/test_ic_neck.py b/mmsegmentation/tests/test_models/test_necks/test_ic_neck.py
index 3d13008..683c871 100644
--- a/mmsegmentation/tests/test_models/test_necks/test_ic_neck.py
+++ b/mmsegmentation/tests/test_models/test_necks/test_ic_neck.py
@@ -12,20 +12,22 @@ def test_ic_neck():
neck = ICNeck(
in_channels=(4, 16, 16),
out_channels=8,
- norm_cfg=dict(type='SyncBN'),
- align_corners=False)
+ norm_cfg=dict(type="SyncBN"),
+ align_corners=False,
+ )
assert _conv_has_norm(neck, sync_bn=True)
inputs = [
torch.randn(1, 4, 32, 64),
torch.randn(1, 16, 16, 32),
- torch.randn(1, 16, 8, 16)
+ torch.randn(1, 16, 8, 16),
]
neck = ICNeck(
in_channels=(4, 16, 16),
out_channels=4,
- norm_cfg=dict(type='BN', requires_grad=True),
- align_corners=False)
+ norm_cfg=dict(type="BN", requires_grad=True),
+ align_corners=False,
+ )
if torch.cuda.is_available():
neck, inputs = to_cuda(neck, inputs)
@@ -49,5 +51,6 @@ def test_ic_neck_input_channels():
ICNeck(
in_channels=(16, 64, 64, 64),
out_channels=32,
- norm_cfg=dict(type='BN', requires_grad=True),
- align_corners=False)
+ norm_cfg=dict(type="BN", requires_grad=True),
+ align_corners=False,
+ )
diff --git a/mmsegmentation/tests/test_models/test_necks/test_jpu.py b/mmsegmentation/tests/test_models/test_necks/test_jpu.py
index 4c3fa9f..aefa9d6 100644
--- a/mmsegmentation/tests/test_models/test_necks/test_jpu.py
+++ b/mmsegmentation/tests/test_models/test_necks/test_jpu.py
@@ -20,7 +20,7 @@ def test_fastfcn_neck():
input = [
torch.randn(batch_size, 64, 64, 128),
torch.randn(batch_size, 128, 32, 64),
- torch.randn(batch_size, 256, 16, 32)
+ torch.randn(batch_size, 256, 16, 32),
]
feat = model(input)
@@ -38,7 +38,7 @@ def test_fastfcn_neck():
input = [
torch.randn(batch_size, 64, 64, 128),
torch.randn(batch_size, 128, 32, 64),
- torch.randn(batch_size, 256, 16, 32)
+ torch.randn(batch_size, 256, 16, 32),
]
feat = model(input)
assert len(feat) == 2
diff --git a/mmsegmentation/tests/test_models/test_segmentors/test_cascade_encoder_decoder.py b/mmsegmentation/tests/test_models/test_segmentors/test_cascade_encoder_decoder.py
index 07ad5c3..13f88b8 100644
--- a/mmsegmentation/tests/test_models/test_segmentors/test_cascade_encoder_decoder.py
+++ b/mmsegmentation/tests/test_models/test_segmentors/test_cascade_encoder_decoder.py
@@ -9,49 +9,49 @@ def test_cascade_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = ConfigDict(
- type='CascadeEncoderDecoder',
+ type="CascadeEncoderDecoder",
num_stages=2,
- backbone=dict(type='ExampleBackbone'),
+ backbone=dict(type="ExampleBackbone"),
decode_head=[
- dict(type='ExampleDecodeHead'),
- dict(type='ExampleCascadeDecodeHead')
- ])
- cfg.test_cfg = ConfigDict(mode='whole')
+ dict(type="ExampleDecodeHead"),
+ dict(type="ExampleCascadeDecodeHead"),
+ ],
+ )
+ cfg.test_cfg = ConfigDict(mode="whole")
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test slide mode
- cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
+ cfg.test_cfg = ConfigDict(mode="slide", crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = ConfigDict(
- type='CascadeEncoderDecoder',
+ type="CascadeEncoderDecoder",
num_stages=2,
- backbone=dict(type='ExampleBackbone'),
+ backbone=dict(type="ExampleBackbone"),
decode_head=[
- dict(type='ExampleDecodeHead'),
- dict(type='ExampleCascadeDecodeHead')
+ dict(type="ExampleDecodeHead"),
+ dict(type="ExampleCascadeDecodeHead"),
],
- auxiliary_head=dict(type='ExampleDecodeHead'))
- cfg.test_cfg = ConfigDict(mode='whole')
+ auxiliary_head=dict(type="ExampleDecodeHead"),
+ )
+ cfg.test_cfg = ConfigDict(mode="whole")
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = ConfigDict(
- type='CascadeEncoderDecoder',
+ type="CascadeEncoderDecoder",
num_stages=2,
- backbone=dict(type='ExampleBackbone'),
+ backbone=dict(type="ExampleBackbone"),
decode_head=[
- dict(type='ExampleDecodeHead'),
- dict(type='ExampleCascadeDecodeHead')
+ dict(type="ExampleDecodeHead"),
+ dict(type="ExampleCascadeDecodeHead"),
],
- auxiliary_head=[
- dict(type='ExampleDecodeHead'),
- dict(type='ExampleDecodeHead')
- ])
- cfg.test_cfg = ConfigDict(mode='whole')
+ auxiliary_head=[dict(type="ExampleDecodeHead"), dict(type="ExampleDecodeHead")],
+ )
+ cfg.test_cfg = ConfigDict(mode="whole")
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
diff --git a/mmsegmentation/tests/test_models/test_segmentors/test_encoder_decoder.py b/mmsegmentation/tests/test_models/test_segmentors/test_encoder_decoder.py
index 2739b58..276d053 100644
--- a/mmsegmentation/tests/test_models/test_segmentors/test_encoder_decoder.py
+++ b/mmsegmentation/tests/test_models/test_segmentors/test_encoder_decoder.py
@@ -10,11 +10,12 @@ def test_encoder_decoder():
# test 1 decode head, w.o. aux head
cfg = ConfigDict(
- type='EncoderDecoder',
- backbone=dict(type='ExampleBackbone'),
- decode_head=dict(type='ExampleDecodeHead'),
+ type="EncoderDecoder",
+ backbone=dict(type="ExampleBackbone"),
+ decode_head=dict(type="ExampleDecodeHead"),
train_cfg=None,
- test_cfg=dict(mode='whole'))
+ test_cfg=dict(mode="whole"),
+ )
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
@@ -25,29 +26,28 @@ def test_encoder_decoder():
_segmentor_forward_train_test(segmentor)
# test slide mode
- cfg.test_cfg = ConfigDict(mode='slide', crop_size=(3, 3), stride=(2, 2))
+ cfg.test_cfg = ConfigDict(mode="slide", crop_size=(3, 3), stride=(2, 2))
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 1 aux head
cfg = ConfigDict(
- type='EncoderDecoder',
- backbone=dict(type='ExampleBackbone'),
- decode_head=dict(type='ExampleDecodeHead'),
- auxiliary_head=dict(type='ExampleDecodeHead'))
- cfg.test_cfg = ConfigDict(mode='whole')
+ type="EncoderDecoder",
+ backbone=dict(type="ExampleBackbone"),
+ decode_head=dict(type="ExampleDecodeHead"),
+ auxiliary_head=dict(type="ExampleDecodeHead"),
+ )
+ cfg.test_cfg = ConfigDict(mode="whole")
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
# test 1 decode head, 2 aux head
cfg = ConfigDict(
- type='EncoderDecoder',
- backbone=dict(type='ExampleBackbone'),
- decode_head=dict(type='ExampleDecodeHead'),
- auxiliary_head=[
- dict(type='ExampleDecodeHead'),
- dict(type='ExampleDecodeHead')
- ])
- cfg.test_cfg = ConfigDict(mode='whole')
+ type="EncoderDecoder",
+ backbone=dict(type="ExampleBackbone"),
+ decode_head=dict(type="ExampleDecodeHead"),
+ auxiliary_head=[dict(type="ExampleDecodeHead"), dict(type="ExampleDecodeHead")],
+ )
+ cfg.test_cfg = ConfigDict(mode="whole")
segmentor = build_segmentor(cfg)
_segmentor_forward_train_test(segmentor)
diff --git a/mmsegmentation/tests/test_models/test_segmentors/utils.py b/mmsegmentation/tests/test_models/test_segmentors/utils.py
index 1826dbf..0f02f08 100644
--- a/mmsegmentation/tests/test_models/test_segmentors/utils.py
+++ b/mmsegmentation/tests/test_models/test_segmentors/utils.py
@@ -23,23 +23,25 @@ def _demo_mm_inputs(input_shape=(1, 3, 8, 16), num_classes=10):
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
- segs = rng.randint(
- low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
-
- img_metas = [{
- 'img_shape': (H, W, C),
- 'ori_shape': (H, W, C),
- 'pad_shape': (H, W, C),
- 'filename': '.png',
- 'scale_factor': 1.0,
- 'flip': False,
- 'flip_direction': 'horizontal'
- } for _ in range(N)]
+ segs = rng.randint(low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
+
+ img_metas = [
+ {
+ "img_shape": (H, W, C),
+ "ori_shape": (H, W, C),
+ "pad_shape": (H, W, C),
+ "filename": ".png",
+ "scale_factor": 1.0,
+ "flip": False,
+ "flip_direction": "horizontal",
+ }
+ for _ in range(N)
+ ]
mm_inputs = {
- 'imgs': torch.FloatTensor(imgs),
- 'img_metas': img_metas,
- 'gt_semantic_seg': torch.LongTensor(segs)
+ "imgs": torch.FloatTensor(imgs),
+ "img_metas": img_metas,
+ "gt_semantic_seg": torch.LongTensor(segs),
}
return mm_inputs
@@ -48,7 +50,7 @@ def _demo_mm_inputs(input_shape=(1, 3, 8, 16), num_classes=10):
class ExampleBackbone(nn.Module):
def __init__(self):
- super(ExampleBackbone, self).__init__()
+ super().__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
@@ -62,7 +64,7 @@ def forward(self, x):
class ExampleDecodeHead(BaseDecodeHead):
def __init__(self):
- super(ExampleDecodeHead, self).__init__(3, 3, num_classes=19)
+ super().__init__(3, 3, num_classes=19)
def forward(self, inputs):
return self.cls_seg(inputs[0])
@@ -72,7 +74,7 @@ def forward(self, inputs):
class ExampleCascadeDecodeHead(BaseCascadeDecodeHead):
def __init__(self):
- super(ExampleCascadeDecodeHead, self).__init__(3, 3, num_classes=19)
+ super().__init__(3, 3, num_classes=19)
def forward(self, inputs, prev_out):
return self.cls_seg(inputs[0])
@@ -86,9 +88,9 @@ def _segmentor_forward_train_test(segmentor):
# batch_size=2 for BatchNorm
mm_inputs = _demo_mm_inputs(num_classes=num_classes)
- imgs = mm_inputs.pop('imgs')
- img_metas = mm_inputs.pop('img_metas')
- gt_semantic_seg = mm_inputs['gt_semantic_seg']
+ imgs = mm_inputs.pop("imgs")
+ img_metas = mm_inputs.pop("img_metas")
+ gt_semantic_seg = mm_inputs["gt_semantic_seg"]
# convert to cuda Tensor if applicable
if torch.cuda.is_available():
@@ -98,28 +100,29 @@ def _segmentor_forward_train_test(segmentor):
# Test forward train
losses = segmentor.forward(
- imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True)
+ imgs, img_metas, gt_semantic_seg=gt_semantic_seg, return_loss=True
+ )
assert isinstance(losses, dict)
# Test train_step
- data_batch = dict(
- img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg)
+ data_batch = dict(img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg)
outputs = segmentor.train_step(data_batch, None)
assert isinstance(outputs, dict)
- assert 'loss' in outputs
- assert 'log_vars' in outputs
- assert 'num_samples' in outputs
+ assert "loss" in outputs
+ assert "log_vars" in outputs
+ assert "num_samples" in outputs
# Test val_step
with torch.no_grad():
segmentor.eval()
data_batch = dict(
- img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg)
+ img=imgs, img_metas=img_metas, gt_semantic_seg=gt_semantic_seg
+ )
outputs = segmentor.val_step(data_batch, None)
assert isinstance(outputs, dict)
- assert 'loss' in outputs
- assert 'log_vars' in outputs
- assert 'num_samples' in outputs
+ assert "loss" in outputs
+ assert "log_vars" in outputs
+ assert "num_samples" in outputs
# Test forward simple test
with torch.no_grad():
diff --git a/mmsegmentation/tests/test_models/test_utils/test_embed.py b/mmsegmentation/tests/test_models/test_utils/test_embed.py
index be20c97..dfffc9f 100644
--- a/mmsegmentation/tests/test_models/test_utils/test_embed.py
+++ b/mmsegmentation/tests/test_models/test_utils/test_embed.py
@@ -7,16 +7,14 @@
def test_adaptive_padding():
- for padding in ('same', 'corner'):
+ for padding in ("same", "corner"):
kernel_size = 16
stride = 16
dilation = 1
input = torch.rand(1, 1, 15, 17)
adap_pool = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
out = adap_pool(input)
# padding to divisible by 16
assert (out.shape[2], out.shape[3]) == (16, 32)
@@ -30,10 +28,8 @@ def test_adaptive_padding():
dilation = (1, 1)
adap_pad = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
input = torch.rand(1, 1, 11, 13)
out = adap_pad(input)
# padding to divisible by 2
@@ -44,10 +40,8 @@ def test_adaptive_padding():
dilation = (1, 1)
adap_pad = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
input = torch.rand(1, 1, 10, 13)
out = adap_pad(input)
# no padding
@@ -55,10 +49,8 @@ def test_adaptive_padding():
kernel_size = (11, 11)
adap_pad = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
input = torch.rand(1, 1, 11, 13)
out = adap_pad(input)
# all padding
@@ -71,19 +63,15 @@ def test_adaptive_padding():
dilation = (2, 2)
# actually (7, 9)
adap_pad = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
dilation_out = adap_pad(input)
assert (dilation_out.shape[2], dilation_out.shape[3]) == (16, 21)
kernel_size = (7, 9)
dilation = (1, 1)
adap_pad = AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=padding)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding
+ )
kernel79_out = adap_pad(input)
assert (kernel79_out.shape[2], kernel79_out.shape[3]) == (16, 21)
assert kernel79_out.shape == dilation_out.shape
@@ -91,10 +79,8 @@ def test_adaptive_padding():
# assert only support "same" "corner"
with pytest.raises(AssertionError):
AdaptivePadding(
- kernel_size=kernel_size,
- stride=stride,
- dilation=dilation,
- padding=1)
+ kernel_size=kernel_size, stride=stride, dilation=dilation, padding=1
+ )
def test_patch_embed():
@@ -113,7 +99,8 @@ def test_patch_embed():
stride=stride,
padding=0,
dilation=1,
- norm_cfg=None)
+ norm_cfg=None,
+ )
x1, shape = patch_merge_1(dummy_input)
# test out shape
@@ -162,8 +149,9 @@ def test_patch_embed():
stride=stride,
padding=0,
dilation=2,
- norm_cfg=dict(type='LN'),
- input_size=input_size)
+ norm_cfg=dict(type="LN"),
+ input_size=input_size,
+ )
x3, shape = patch_merge_3(dummy_input)
# test out shape
@@ -174,10 +162,8 @@ def test_patch_embed():
assert shape[0] * shape[1] == x3.shape[1]
# test the init_out_size with nn.Unfold
- assert patch_merge_3.init_out_size[1] == (input_size[0] - 2 * 4 -
- 1) // 2 + 1
- assert patch_merge_3.init_out_size[0] == (input_size[0] - 2 * 4 -
- 1) // 2 + 1
+ assert patch_merge_3.init_out_size[1] == (input_size[0] - 2 * 4 - 1) // 2 + 1
+ assert patch_merge_3.init_out_size[0] == (input_size[0] - 2 * 4 - 1) // 2 + 1
H = 11
W = 12
input_size = (H, W)
@@ -190,8 +176,9 @@ def test_patch_embed():
stride=stride,
padding=0,
dilation=2,
- norm_cfg=dict(type='LN'),
- input_size=input_size)
+ norm_cfg=dict(type="LN"),
+ input_size=input_size,
+ )
_, shape = patch_merge_3(dummy_input)
# when input_size equal to real input
@@ -208,8 +195,9 @@ def test_patch_embed():
stride=stride,
padding=0,
dilation=2,
- norm_cfg=dict(type='LN'),
- input_size=input_size)
+ norm_cfg=dict(type="LN"),
+ input_size=input_size,
+ )
_, shape = patch_merge_3(dummy_input)
# when input_size equal to real input
@@ -217,7 +205,7 @@ def test_patch_embed():
assert shape == patch_merge_3.init_out_size
# test adap padding
- for padding in ('same', 'corner'):
+ for padding in ("same", "corner"):
in_c = 2
embed_dims = 3
B = 2
@@ -237,7 +225,8 @@ def test_patch_embed():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_embed(x)
assert x_out.size() == (B, 25, 3)
@@ -259,7 +248,8 @@ def test_patch_embed():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_embed(x)
assert x_out.size() == (B, 1, 3)
@@ -281,7 +271,8 @@ def test_patch_embed():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_embed(x)
assert x_out.size() == (B, 2, 3)
@@ -303,7 +294,8 @@ def test_patch_embed():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_embed(x)
assert x_out.size() == (B, 3, 3)
@@ -329,7 +321,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
B, L, C = 1, 100, 3
input_size = (10, 10)
x = torch.rand(B, L, C)
@@ -352,7 +345,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
B, L, C = 1, 100, 4
input_size = (10, 10)
x = torch.rand(B, L, C)
@@ -363,7 +357,7 @@ def test_patch_merging():
assert x_out.size(1) == out_size[0] * out_size[1]
# Test with adaptive padding
- for padding in ('same', 'corner'):
+ for padding in ("same", "corner"):
in_c = 2
out_c = 3
B = 2
@@ -384,7 +378,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_merge(x, input_size)
assert x_out.size() == (B, 25, 3)
@@ -407,7 +402,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_merge(x, input_size)
assert x_out.size() == (B, 1, 3)
@@ -430,7 +426,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_merge(x, input_size)
assert x_out.size() == (B, 2, 3)
@@ -453,7 +450,8 @@ def test_patch_merging():
stride=stride,
padding=padding,
dilation=dilation,
- bias=bias)
+ bias=bias,
+ )
x_out, out_size = patch_merge(x, input_size)
assert x_out.size() == (B, 3, 3)
diff --git a/mmsegmentation/tests/test_models/test_utils/test_shape_convert.py b/mmsegmentation/tests/test_models/test_utils/test_shape_convert.py
index 60e87f3..dead1ef 100644
--- a/mmsegmentation/tests/test_models/test_utils/test_shape_convert.py
+++ b/mmsegmentation/tests/test_models/test_utils/test_shape_convert.py
@@ -1,8 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
-from mmseg.models.utils import (nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc,
- nlc_to_nchw)
+from mmseg.models.utils import nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc, nlc_to_nchw
def test_nchw2nlc2nchw():
diff --git a/mmsegmentation/tests/test_sampler.py b/mmsegmentation/tests/test_sampler.py
index 1409224..a50fb3e 100644
--- a/mmsegmentation/tests/test_sampler.py
+++ b/mmsegmentation/tests/test_sampler.py
@@ -16,9 +16,10 @@ def _context_for_ohem_multiple_loss():
channels=16,
num_classes=19,
loss_decode=[
- dict(type='CrossEntropyLoss', loss_name='loss_1'),
- dict(type='CrossEntropyLoss', loss_name='loss_2')
- ])
+ dict(type="CrossEntropyLoss", loss_name="loss_1"),
+ dict(type="CrossEntropyLoss", loss_name="loss_2"),
+ ],
+ )
def test_ohem_sampler():
@@ -31,8 +32,7 @@ def test_ohem_sampler():
sampler.sample(seg_logit, seg_label)
# test with thresh
- sampler = OHEMPixelSampler(
- context=_context_for_ohem(), thresh=0.7, min_kept=200)
+ sampler = OHEMPixelSampler(context=_context_for_ohem(), thresh=0.7, min_kept=200)
seg_logit = torch.randn(1, 19, 45, 45)
seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
seg_weight = sampler.sample(seg_logit, seg_label)
@@ -59,7 +59,8 @@ def test_ohem_sampler():
# test with thresh in multiple losses case
sampler = OHEMPixelSampler(
- context=_context_for_ohem_multiple_loss(), thresh=0.7, min_kept=200)
+ context=_context_for_ohem_multiple_loss(), thresh=0.7, min_kept=200
+ )
seg_logit = torch.randn(1, 19, 45, 45)
seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
seg_weight = sampler.sample(seg_logit, seg_label)
@@ -68,8 +69,7 @@ def test_ohem_sampler():
assert seg_weight.sum() > 200
# test w.o thresh in multiple losses case
- sampler = OHEMPixelSampler(
- context=_context_for_ohem_multiple_loss(), min_kept=200)
+ sampler = OHEMPixelSampler(context=_context_for_ohem_multiple_loss(), min_kept=200)
seg_logit = torch.randn(1, 19, 45, 45)
seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
seg_weight = sampler.sample(seg_logit, seg_label)
diff --git a/mmsegmentation/tests/test_utils/test_misc.py b/mmsegmentation/tests/test_utils/test_misc.py
index 7ce1fa6..29dfa24 100644
--- a/mmsegmentation/tests/test_utils/test_misc.py
+++ b/mmsegmentation/tests/test_utils/test_misc.py
@@ -13,28 +13,28 @@ def test_find_latest_checkpoint():
assert latest is None
# The path doesn't exist
- path = osp.join(tempdir, 'none')
+ path = osp.join(tempdir, "none")
latest = find_latest_checkpoint(path)
assert latest is None
# test when latest.pth exists
with tempfile.TemporaryDirectory() as tempdir:
- with open(osp.join(tempdir, 'latest.pth'), 'w') as f:
- f.write('latest')
+ with open(osp.join(tempdir, "latest.pth"), "w") as f:
+ f.write("latest")
path = tempdir
latest = find_latest_checkpoint(path)
- assert latest == osp.join(tempdir, 'latest.pth')
+ assert latest == osp.join(tempdir, "latest.pth")
with tempfile.TemporaryDirectory() as tempdir:
for iter in range(1600, 160001, 1600):
- with open(osp.join(tempdir, f'iter_{iter}.pth'), 'w') as f:
- f.write(f'iter_{iter}.pth')
+ with open(osp.join(tempdir, f"iter_{iter}.pth"), "w") as f:
+ f.write(f"iter_{iter}.pth")
latest = find_latest_checkpoint(tempdir)
- assert latest == osp.join(tempdir, 'iter_160000.pth')
+ assert latest == osp.join(tempdir, "iter_160000.pth")
with tempfile.TemporaryDirectory() as tempdir:
for epoch in range(1, 21):
- with open(osp.join(tempdir, f'epoch_{epoch}.pth'), 'w') as f:
- f.write(f'epoch_{epoch}.pth')
+ with open(osp.join(tempdir, f"epoch_{epoch}.pth"), "w") as f:
+ f.write(f"epoch_{epoch}.pth")
latest = find_latest_checkpoint(tempdir)
- assert latest == osp.join(tempdir, 'epoch_20.pth')
+ assert latest == osp.join(tempdir, "epoch_20.pth")
diff --git a/mmsegmentation/tests/test_utils/test_set_env.py b/mmsegmentation/tests/test_utils/test_set_env.py
index 0af4424..4ecb789 100644
--- a/mmsegmentation/tests/test_utils/test_set_env.py
+++ b/mmsegmentation/tests/test_utils/test_set_env.py
@@ -10,26 +10,37 @@
from mmseg.utils import setup_multi_processes
-@pytest.mark.parametrize('workers_per_gpu', (0, 2))
-@pytest.mark.parametrize(('valid', 'env_cfg'), [(True,
- dict(
- mp_start_method='fork',
- opencv_num_threads=0,
- omp_num_threads=1,
- mkl_num_threads=1)),
- (False,
- dict(
- mp_start_method=1,
- opencv_num_threads=0.1,
- omp_num_threads='s',
- mkl_num_threads='1'))])
+@pytest.mark.parametrize("workers_per_gpu", (0, 2))
+@pytest.mark.parametrize(
+ ("valid", "env_cfg"),
+ [
+ (
+ True,
+ dict(
+ mp_start_method="fork",
+ opencv_num_threads=0,
+ omp_num_threads=1,
+ mkl_num_threads=1,
+ ),
+ ),
+ (
+ False,
+ dict(
+ mp_start_method=1,
+ opencv_num_threads=0.1,
+ omp_num_threads="s",
+ mkl_num_threads="1",
+ ),
+ ),
+ ],
+)
def test_setup_multi_processes(workers_per_gpu, valid, env_cfg):
# temp save system setting
sys_start_mehod = mp.get_start_method(allow_none=True)
sys_cv_threads = cv2.getNumThreads()
# pop and temp save system env vars
- sys_omp_threads = os.environ.pop('OMP_NUM_THREADS', default=None)
- sys_mkl_threads = os.environ.pop('MKL_NUM_THREADS', default=None)
+ sys_omp_threads = os.environ.pop("OMP_NUM_THREADS", default=None)
+ sys_mkl_threads = os.environ.pop("MKL_NUM_THREADS", default=None)
config = dict(data=dict(workers_per_gpu=workers_per_gpu))
config.update(env_cfg)
@@ -41,45 +52,51 @@ def test_setup_multi_processes(workers_per_gpu, valid, env_cfg):
if valid and workers_per_gpu > 0:
# test config without setting env
- assert os.getenv('OMP_NUM_THREADS') == str(env_cfg['omp_num_threads'])
- assert os.getenv('MKL_NUM_THREADS') == str(env_cfg['mkl_num_threads'])
+ assert os.getenv("OMP_NUM_THREADS") == str(env_cfg["omp_num_threads"])
+ assert os.getenv("MKL_NUM_THREADS") == str(env_cfg["mkl_num_threads"])
# when set to 0, the num threads will be 1
- assert cv2.getNumThreads() == env_cfg[
- 'opencv_num_threads'] if env_cfg['opencv_num_threads'] > 0 else 1
- if platform.system() != 'Windows':
- assert mp.get_start_method() == env_cfg['mp_start_method']
+ assert (
+ cv2.getNumThreads() == env_cfg["opencv_num_threads"]
+ if env_cfg["opencv_num_threads"] > 0
+ else 1
+ )
+ if platform.system() != "Windows":
+ assert mp.get_start_method() == env_cfg["mp_start_method"]
# revert setting to avoid affecting other programs
if sys_start_mehod:
mp.set_start_method(sys_start_mehod, force=True)
cv2.setNumThreads(sys_cv_threads)
if sys_omp_threads:
- os.environ['OMP_NUM_THREADS'] = sys_omp_threads
+ os.environ["OMP_NUM_THREADS"] = sys_omp_threads
else:
- os.environ.pop('OMP_NUM_THREADS')
+ os.environ.pop("OMP_NUM_THREADS")
if sys_mkl_threads:
- os.environ['MKL_NUM_THREADS'] = sys_mkl_threads
+ os.environ["MKL_NUM_THREADS"] = sys_mkl_threads
else:
- os.environ.pop('MKL_NUM_THREADS')
+ os.environ.pop("MKL_NUM_THREADS")
elif valid and workers_per_gpu == 0:
- if platform.system() != 'Windows':
- assert mp.get_start_method() == env_cfg['mp_start_method']
- assert cv2.getNumThreads() == env_cfg[
- 'opencv_num_threads'] if env_cfg['opencv_num_threads'] > 0 else 1
- assert 'OMP_NUM_THREADS' not in os.environ
- assert 'MKL_NUM_THREADS' not in os.environ
+ if platform.system() != "Windows":
+ assert mp.get_start_method() == env_cfg["mp_start_method"]
+ assert (
+ cv2.getNumThreads() == env_cfg["opencv_num_threads"]
+ if env_cfg["opencv_num_threads"] > 0
+ else 1
+ )
+ assert "OMP_NUM_THREADS" not in os.environ
+ assert "MKL_NUM_THREADS" not in os.environ
if sys_start_mehod:
mp.set_start_method(sys_start_mehod, force=True)
cv2.setNumThreads(sys_cv_threads)
if sys_omp_threads:
- os.environ['OMP_NUM_THREADS'] = sys_omp_threads
+ os.environ["OMP_NUM_THREADS"] = sys_omp_threads
if sys_mkl_threads:
- os.environ['MKL_NUM_THREADS'] = sys_mkl_threads
+ os.environ["MKL_NUM_THREADS"] = sys_mkl_threads
else:
assert mp.get_start_method() == sys_start_mehod
assert cv2.getNumThreads() == sys_cv_threads
- assert 'OMP_NUM_THREADS' not in os.environ
- assert 'MKL_NUM_THREADS' not in os.environ
+ assert "OMP_NUM_THREADS" not in os.environ
+ assert "MKL_NUM_THREADS" not in os.environ
diff --git a/mmsegmentation/tests/test_utils/test_util_distribution.py b/mmsegmentation/tests/test_utils/test_util_distribution.py
index 5523879..1c39aef 100644
--- a/mmsegmentation/tests/test_utils/test_util_distribution.py
+++ b/mmsegmentation/tests/test_utils/test_util_distribution.py
@@ -4,8 +4,7 @@
import mmcv
import torch
import torch.nn as nn
-from mmcv.parallel import (MMDataParallel, MMDistributedDataParallel,
- is_module_wrapper)
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel, is_module_wrapper
from mmseg import digit_version
from mmseg.utils import build_ddp, build_dp
@@ -25,44 +24,44 @@ def forward(self, x):
return self.conv(x)
-@patch('torch.distributed._broadcast_coalesced', mock)
-@patch('torch.distributed.broadcast', mock)
-@patch('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
+@patch("torch.distributed._broadcast_coalesced", mock)
+@patch("torch.distributed.broadcast", mock)
+@patch("torch.nn.parallel.DistributedDataParallel._ddp_init_helper", mock)
def test_build_dp():
model = Model()
assert not is_module_wrapper(model)
- mmdp = build_dp(model, 'cpu')
+ mmdp = build_dp(model, "cpu")
assert isinstance(mmdp, MMDataParallel)
if torch.cuda.is_available():
- mmdp = build_dp(model, 'cuda')
+ mmdp = build_dp(model, "cuda")
assert isinstance(mmdp, MMDataParallel)
- if digit_version(mmcv.__version__) >= digit_version('1.5.0'):
+ if digit_version(mmcv.__version__) >= digit_version("1.5.0"):
from mmcv.device.mlu import MLUDataParallel
from mmcv.utils import IS_MLU_AVAILABLE
+
if IS_MLU_AVAILABLE:
- mludp = build_dp(model, 'mlu')
+ mludp = build_dp(model, "mlu")
assert isinstance(mludp, MLUDataParallel)
-@patch('torch.distributed._broadcast_coalesced', mock)
-@patch('torch.distributed.broadcast', mock)
-@patch('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
+@patch("torch.distributed._broadcast_coalesced", mock)
+@patch("torch.distributed.broadcast", mock)
+@patch("torch.nn.parallel.DistributedDataParallel._ddp_init_helper", mock)
def test_build_ddp():
model = Model()
assert not is_module_wrapper(model)
if torch.cuda.is_available():
- mmddp = build_ddp(
- model, 'cuda', device_ids=[0], process_group=MagicMock())
+ mmddp = build_ddp(model, "cuda", device_ids=[0], process_group=MagicMock())
assert isinstance(mmddp, MMDistributedDataParallel)
- if digit_version(mmcv.__version__) >= digit_version('1.5.0'):
+ if digit_version(mmcv.__version__) >= digit_version("1.5.0"):
from mmcv.device.mlu import MLUDistributedDataParallel
from mmcv.utils import IS_MLU_AVAILABLE
+
if IS_MLU_AVAILABLE:
- mluddp = build_ddp(
- model, 'mlu', device_ids=[0], process_group=MagicMock())
+ mluddp = build_ddp(model, "mlu", device_ids=[0], process_group=MagicMock())
assert isinstance(mluddp, MLUDistributedDataParallel)
diff --git a/mmsegmentation/tools/analyze_logs.py b/mmsegmentation/tools/analyze_logs.py
index e2127d4..85f217a 100644
--- a/mmsegmentation/tools/analyze_logs.py
+++ b/mmsegmentation/tools/analyze_logs.py
@@ -19,7 +19,7 @@ def plot_curve(log_dicts, args):
legend = []
for json_log in args.json_logs:
for metric in args.keys:
- legend.append(f'{json_log}_{metric}')
+ legend.append(f"{json_log}_{metric}")
assert len(legend) == (len(args.json_logs) * len(args.keys))
metrics = args.keys
@@ -27,7 +27,7 @@ def plot_curve(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
epochs = list(log_dict.keys())
for j, metric in enumerate(metrics):
- print(f'plot curve of {args.json_logs[i]}, metric is {metric}')
+ print(f"plot curve of {args.json_logs[i]}, metric is {metric}")
plot_epochs = []
plot_iters = []
plot_values = []
@@ -38,22 +38,22 @@ def plot_curve(log_dicts, args):
epoch_logs = log_dict[epoch]
if metric not in epoch_logs.keys():
continue
- if metric in ['mIoU', 'mAcc', 'aAcc']:
+ if metric in ["mIoU", "mAcc", "aAcc"]:
plot_epochs.append(epoch)
plot_values.append(epoch_logs[metric][0])
else:
for idx in range(len(epoch_logs[metric])):
- if epoch_logs['mode'][idx] == 'train':
- plot_iters.append(epoch_logs['iter'][idx])
+ if epoch_logs["mode"][idx] == "train":
+ plot_iters.append(epoch_logs["iter"][idx])
plot_values.append(epoch_logs[metric][idx])
ax = plt.gca()
label = legend[i * num_metrics + j]
- if metric in ['mIoU', 'mAcc', 'aAcc']:
+ if metric in ["mIoU", "mAcc", "aAcc"]:
ax.set_xticks(plot_epochs)
- plt.xlabel('epoch')
- plt.plot(plot_epochs, plot_values, label=label, marker='o')
+ plt.xlabel("epoch")
+ plt.plot(plot_epochs, plot_values, label=label, marker="o")
else:
- plt.xlabel('iter')
+ plt.xlabel("iter")
plt.plot(plot_iters, plot_values, label=label, linewidth=0.5)
plt.legend()
if args.title is not None:
@@ -61,36 +61,30 @@ def plot_curve(log_dicts, args):
if args.out is None:
plt.show()
else:
- print(f'save curve to: {args.out}')
+ print(f"save curve to: {args.out}")
plt.savefig(args.out)
plt.cla()
def parse_args():
- parser = argparse.ArgumentParser(description='Analyze Json Log')
+ parser = argparse.ArgumentParser(description="Analyze Json Log")
parser.add_argument(
- 'json_logs',
- type=str,
- nargs='+',
- help='path of train log in json format')
- parser.add_argument(
- '--keys',
- type=str,
- nargs='+',
- default=['mIoU'],
- help='the metric that you want to plot')
- parser.add_argument('--title', type=str, help='title of figure')
+ "json_logs", type=str, nargs="+", help="path of train log in json format"
+ )
parser.add_argument(
- '--legend',
+ "--keys",
type=str,
- nargs='+',
- default=None,
- help='legend of each plot')
- parser.add_argument(
- '--backend', type=str, default=None, help='backend of plt')
+ nargs="+",
+ default=["mIoU"],
+ help="the metric that you want to plot",
+ )
+ parser.add_argument("--title", type=str, help="title of figure")
parser.add_argument(
- '--style', type=str, default='dark', help='style of plt')
- parser.add_argument('--out', type=str, default=None)
+ "--legend", type=str, nargs="+", default=None, help="legend of each plot"
+ )
+ parser.add_argument("--backend", type=str, default=None, help="backend of plt")
+ parser.add_argument("--style", type=str, default="dark", help="style of plt")
+ parser.add_argument("--out", type=str, default=None)
args = parser.parse_args()
return args
@@ -101,13 +95,13 @@ def load_json_logs(json_logs):
# value of sub dict is a list of corresponding values of all iterations
log_dicts = [dict() for _ in json_logs]
for json_log, log_dict in zip(json_logs, log_dicts):
- with open(json_log, 'r') as log_file:
+ with open(json_log) as log_file:
for line in log_file:
log = json.loads(line.strip())
# skip lines without `epoch` field
- if 'epoch' not in log:
+ if "epoch" not in log:
continue
- epoch = log.pop('epoch')
+ epoch = log.pop("epoch")
if epoch not in log_dict:
log_dict[epoch] = defaultdict(list)
for k, v in log.items():
@@ -119,10 +113,10 @@ def main():
args = parse_args()
json_logs = args.json_logs
for json_log in json_logs:
- assert json_log.endswith('.json')
+ assert json_log.endswith(".json")
log_dicts = load_json_logs(json_logs)
plot_curve(log_dicts, args)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/benchmark.py b/mmsegmentation/tools/benchmark.py
index f6d6888..f5b76f6 100644
--- a/mmsegmentation/tools/benchmark.py
+++ b/mmsegmentation/tools/benchmark.py
@@ -15,16 +15,17 @@
def parse_args():
- parser = argparse.ArgumentParser(description='MMSeg benchmark a model')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint', help='checkpoint file')
+ parser = argparse.ArgumentParser(description="MMSeg benchmark a model")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("checkpoint", help="checkpoint file")
parser.add_argument(
- '--log-interval', type=int, default=50, help='interval of logging')
+ "--log-interval", type=int, default=50, help="interval of logging"
+ )
parser.add_argument(
- '--work-dir',
- help=('if specified, the results will be dumped '
- 'into the directory as json'))
- parser.add_argument('--repeat-times', type=int, default=1)
+ "--work-dir",
+ help=("if specified, the results will be dumped " "into the directory as json"),
+ )
+ parser.add_argument("--repeat-times", type=int, default=1)
args = parser.parse_args()
return args
@@ -33,16 +34,15 @@ def main():
args = parse_args()
cfg = Config.fromfile(args.config)
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if args.work_dir is not None:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
- json_file = osp.join(args.work_dir, f'fps_{timestamp}.json')
+ json_file = osp.join(args.work_dir, f"fps_{timestamp}.json")
else:
# use config filename as default work_dir if cfg.work_dir is None
- work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
+ work_dir = osp.join("./work_dirs", osp.splitext(osp.basename(args.config))[0])
mmcv.mkdir_or_exist(osp.abspath(work_dir))
- json_file = osp.join(work_dir, f'fps_{timestamp}.json')
+ json_file = osp.join(work_dir, f"fps_{timestamp}.json")
repeat_times = args.repeat_times
# set cudnn_benchmark
@@ -50,10 +50,10 @@ def main():
cfg.model.pretrained = None
cfg.data.test.test_mode = True
- benchmark_dict = dict(config=args.config, unit='img / s')
+ benchmark_dict = dict(config=args.config, unit="img / s")
overall_fps_list = []
for time_index in range(repeat_times):
- print(f'Run {time_index + 1}:')
+ print(f"Run {time_index + 1}:")
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
@@ -62,16 +62,17 @@ def main():
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
- shuffle=False)
+ shuffle=False,
+ )
# build the model and load checkpoint
cfg.model.train_cfg = None
- model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
- fp16_cfg = cfg.get('fp16', None)
+ model = build_segmentor(cfg.model, test_cfg=cfg.get("test_cfg"))
+ fp16_cfg = cfg.get("fp16", None)
if fp16_cfg is not None:
wrap_fp16_model(model)
- if 'checkpoint' in args and osp.exists(args.checkpoint):
- load_checkpoint(model, args.checkpoint, map_location='cpu')
+ if "checkpoint" in args and osp.exists(args.checkpoint):
+ load_checkpoint(model, args.checkpoint, map_location="cpu")
model = MMDataParallel(model, device_ids=[0])
@@ -98,23 +99,29 @@ def main():
pure_inf_time += elapsed
if (i + 1) % args.log_interval == 0:
fps = (i + 1 - num_warmup) / pure_inf_time
- print(f'Done image [{i + 1:<3}/ {total_iters}], '
- f'fps: {fps:.2f} img / s')
+ print(
+ f"Done image [{i + 1:<3}/ {total_iters}], "
+ f"fps: {fps:.2f} img / s"
+ )
if (i + 1) == total_iters:
fps = (i + 1 - num_warmup) / pure_inf_time
- print(f'Overall fps: {fps:.2f} img / s\n')
- benchmark_dict[f'overall_fps_{time_index + 1}'] = round(fps, 2)
+ print(f"Overall fps: {fps:.2f} img / s\n")
+ benchmark_dict[f"overall_fps_{time_index + 1}"] = round(fps, 2)
overall_fps_list.append(fps)
break
- benchmark_dict['average_fps'] = round(np.mean(overall_fps_list), 2)
- benchmark_dict['fps_variance'] = round(np.var(overall_fps_list), 4)
- print(f'Average fps of {repeat_times} evaluations: '
- f'{benchmark_dict["average_fps"]}')
- print(f'The variance of {repeat_times} evaluations: '
- f'{benchmark_dict["fps_variance"]}')
+ benchmark_dict["average_fps"] = round(np.mean(overall_fps_list), 2)
+ benchmark_dict["fps_variance"] = round(np.var(overall_fps_list), 4)
+ print(
+ f"Average fps of {repeat_times} evaluations: "
+ f'{benchmark_dict["average_fps"]}'
+ )
+ print(
+ f"The variance of {repeat_times} evaluations: "
+ f'{benchmark_dict["fps_variance"]}'
+ )
mmcv.dump(benchmark_dict, json_file, indent=4)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/browse_dataset.py b/mmsegmentation/tools/browse_dataset.py
index 0aa9430..d568025 100644
--- a/mmsegmentation/tools/browse_dataset.py
+++ b/mmsegmentation/tools/browse_dataset.py
@@ -12,60 +12,62 @@
def parse_args():
- parser = argparse.ArgumentParser(description='Browse a dataset')
- parser.add_argument('config', help='train config file path')
+ parser = argparse.ArgumentParser(description="Browse a dataset")
+ parser.add_argument("config", help="train config file path")
parser.add_argument(
- '--show-origin',
+ "--show-origin",
default=False,
- action='store_true',
- help='if True, omit all augmentation in pipeline,'
- ' show origin image and seg map')
+ action="store_true",
+ help="if True, omit all augmentation in pipeline,"
+ " show origin image and seg map",
+ )
parser.add_argument(
- '--skip-type',
+ "--skip-type",
type=str,
- nargs='+',
- default=['DefaultFormatBundle', 'Normalize', 'Collect'],
- help='skip some useless pipeline，if `show-origin` is true, '
- 'all pipeline except `Load` will be skipped')
+ nargs="+",
+ default=["DefaultFormatBundle", "Normalize", "Collect"],
+ help="skip some useless pipeline，if `show-origin` is true, "
+ "all pipeline except `Load` will be skipped",
+ )
parser.add_argument(
- '--output-dir',
- default='./output',
+ "--output-dir",
+ default="./output",
type=str,
- help='If there is no display interface, you can save it')
- parser.add_argument('--show', default=False, action='store_true')
+ help="If there is no display interface, you can save it",
+ )
+ parser.add_argument("--show", default=False, action="store_true")
parser.add_argument(
- '--show-interval',
- type=int,
- default=999,
- help='the interval of show (ms)')
+ "--show-interval", type=int, default=999, help="the interval of show (ms)"
+ )
parser.add_argument(
- '--opacity',
- type=float,
- default=0.5,
- help='the opacity of semantic map')
+ "--opacity", type=float, default=0.5, help="the opacity of semantic map"
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
args = parser.parse_args()
return args
-def imshow_semantic(img,
- seg,
- class_names,
- palette=None,
- win_name='',
- show=False,
- wait_time=0,
- out_file=None,
- opacity=0.5):
+def imshow_semantic(
+ img,
+ seg,
+ class_names,
+ palette=None,
+ win_name="",
+ show=False,
+ wait_time=0,
+ out_file=None,
+ opacity=0.5,
+):
"""Draw `result` over `img`.
Args:
@@ -116,20 +118,20 @@ def imshow_semantic(img,
mmcv.imwrite(img, out_file)
if not (show or out_file):
- warnings.warn('show==False and out_file is not specified, only '
- 'result image will be returned')
+ warnings.warn(
+ "show==False and out_file is not specified, only "
+ "result image will be returned"
+ )
return img
def _retrieve_data_cfg(_data_cfg, skip_type, show_origin):
if show_origin is True:
# only keep pipeline of Loading data and ann
- _data_cfg['pipeline'] = [
- x for x in _data_cfg.pipeline if 'Load' in x['type']
- ]
+ _data_cfg["pipeline"] = [x for x in _data_cfg.pipeline if "Load" in x["type"]]
else:
- _data_cfg['pipeline'] = [
- x for x in _data_cfg.pipeline if x['type'] not in skip_type
+ _data_cfg["pipeline"] = [
+ x for x in _data_cfg.pipeline if x["type"] not in skip_type
]
@@ -140,34 +142,40 @@ def retrieve_data_cfg(config_path, skip_type, cfg_options, show_origin=False):
train_data_cfg = cfg.data.train
if isinstance(train_data_cfg, list):
for _data_cfg in train_data_cfg:
- while 'dataset' in _data_cfg and _data_cfg[
- 'type'] != 'MultiImageMixDataset':
- _data_cfg = _data_cfg['dataset']
- if 'pipeline' in _data_cfg:
+ while (
+ "dataset" in _data_cfg and _data_cfg["type"] != "MultiImageMixDataset"
+ ):
+ _data_cfg = _data_cfg["dataset"]
+ if "pipeline" in _data_cfg:
_retrieve_data_cfg(_data_cfg, skip_type, show_origin)
else:
raise ValueError
else:
- while 'dataset' in train_data_cfg and train_data_cfg[
- 'type'] != 'MultiImageMixDataset':
- train_data_cfg = train_data_cfg['dataset']
+ while (
+ "dataset" in train_data_cfg
+ and train_data_cfg["type"] != "MultiImageMixDataset"
+ ):
+ train_data_cfg = train_data_cfg["dataset"]
_retrieve_data_cfg(train_data_cfg, skip_type, show_origin)
return cfg
def main():
args = parse_args()
- cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options,
- args.show_origin)
+ cfg = retrieve_data_cfg(
+ args.config, args.skip_type, args.cfg_options, args.show_origin
+ )
dataset = build_dataset(cfg.data.train)
progress_bar = mmcv.ProgressBar(len(dataset))
for item in dataset:
- filename = os.path.join(args.output_dir,
- Path(item['filename']).name
- ) if args.output_dir is not None else None
+ filename = (
+ os.path.join(args.output_dir, Path(item["filename"]).name)
+ if args.output_dir is not None
+ else None
+ )
imshow_semantic(
- item['img'],
- item['gt_semantic_seg'],
+ item["img"],
+ item["gt_semantic_seg"],
dataset.CLASSES,
dataset.PALETTE,
show=args.show,
@@ -178,5 +186,5 @@ def main():
progress_bar.update()
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/confusion_matrix.py b/mmsegmentation/tools/confusion_matrix.py
index 4166722..670c3ce 100644
--- a/mmsegmentation/tools/confusion_matrix.py
+++ b/mmsegmentation/tools/confusion_matrix.py
@@ -13,32 +13,35 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Generate confusion matrix from segmentation results')
- parser.add_argument('config', help='test config file path')
+ description="Generate confusion matrix from segmentation results"
+ )
+ parser.add_argument("config", help="test config file path")
parser.add_argument(
- 'prediction_path', help='prediction path where test .pkl result')
+ "prediction_path", help="prediction path where test .pkl result"
+ )
parser.add_argument(
- 'save_dir', help='directory where confusion matrix will be saved')
+ "save_dir", help="directory where confusion matrix will be saved"
+ )
+ parser.add_argument("--show", action="store_true", help="show confusion matrix")
parser.add_argument(
- '--show', action='store_true', help='show confusion matrix')
+ "--color-theme", default="winter", help="theme of the matrix color map"
+ )
parser.add_argument(
- '--color-theme',
- default='winter',
- help='theme of the matrix color map')
+ "--title",
+ default="Normalized Confusion Matrix",
+ help="title of the matrix color map",
+ )
parser.add_argument(
- '--title',
- default='Normalized Confusion Matrix',
- help='title of the matrix color map')
- parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
args = parser.parse_args()
return args
@@ -68,12 +71,14 @@ def calculate_confusion_matrix(dataset, results):
return confusion_matrix
-def plot_confusion_matrix(confusion_matrix,
- labels,
- save_dir=None,
- show=True,
- title='Normalized Confusion Matrix',
- color_theme='winter'):
+def plot_confusion_matrix(
+ confusion_matrix,
+ labels,
+ save_dir=None,
+ show=True,
+ title="Normalized Confusion Matrix",
+ color_theme="winter",
+):
"""Draw confusion matrix with matplotlib.
Args:
@@ -87,21 +92,19 @@ def plot_confusion_matrix(confusion_matrix,
"""
# normalize the confusion matrix
per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis]
- confusion_matrix = \
- confusion_matrix.astype(np.float32) / per_label_sums * 100
+ confusion_matrix = confusion_matrix.astype(np.float32) / per_label_sums * 100
num_classes = len(labels)
- fig, ax = plt.subplots(
- figsize=(2 * num_classes, 2 * num_classes * 0.8), dpi=180)
+ fig, ax = plt.subplots(figsize=(2 * num_classes, 2 * num_classes * 0.8), dpi=180)
cmap = plt.get_cmap(color_theme)
im = ax.imshow(confusion_matrix, cmap=cmap)
plt.colorbar(mappable=im, ax=ax)
- title_font = {'weight': 'bold', 'size': 12}
+ title_font = {"weight": "bold", "size": 12}
ax.set_title(title, fontdict=title_font)
- label_font = {'size': 10}
- plt.ylabel('Ground Truth Label', fontdict=label_font)
- plt.xlabel('Prediction Label', fontdict=label_font)
+ label_font = {"size": 10}
+ plt.ylabel("Ground Truth Label", fontdict=label_font)
+ plt.xlabel("Prediction Label", fontdict=label_font)
# draw locator
xmajor_locator = MultipleLocator(1)
@@ -114,7 +117,7 @@ def plot_confusion_matrix(confusion_matrix,
ax.yaxis.set_minor_locator(yminor_locator)
# draw grid
- ax.grid(True, which='minor', linestyle='-')
+ ax.grid(True, which="minor", linestyle="-")
# draw label
ax.set_xticks(np.arange(num_classes))
@@ -122,10 +125,8 @@ def plot_confusion_matrix(confusion_matrix,
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)
- ax.tick_params(
- axis='x', bottom=False, top=True, labelbottom=False, labeltop=True)
- plt.setp(
- ax.get_xticklabels(), rotation=45, ha='left', rotation_mode='anchor')
+ ax.tick_params(axis="x", bottom=False, top=True, labelbottom=False, labeltop=True)
+ plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")
# draw confusion matrix value
for i in range(num_classes):
@@ -133,20 +134,22 @@ def plot_confusion_matrix(confusion_matrix,
ax.text(
j,
i,
- '{}%'.format(
- round(confusion_matrix[i, j], 2
- ) if not np.isnan(confusion_matrix[i, j]) else -1),
- ha='center',
- va='center',
- color='w',
- size=7)
+ "{}%".format(
+ round(confusion_matrix[i, j], 2)
+ if not np.isnan(confusion_matrix[i, j])
+ else -1
+ ),
+ ha="center",
+ va="center",
+ color="w",
+ size=7,
+ )
ax.set_ylim(len(confusion_matrix) - 0.5, -0.5) # matplotlib>3.1.1
fig.tight_layout()
if save_dir is not None:
- plt.savefig(
- os.path.join(save_dir, 'confusion_matrix.png'), format='png')
+ plt.savefig(os.path.join(save_dir, "confusion_matrix.png"), format="png")
if show:
plt.show()
@@ -164,7 +167,7 @@ def main():
if isinstance(results[0], np.ndarray):
pass
else:
- raise TypeError('invalid type of prediction results')
+ raise TypeError("invalid type of prediction results")
if isinstance(cfg.data.test, dict):
cfg.data.test.test_mode = True
@@ -180,8 +183,9 @@ def main():
save_dir=args.save_dir,
show=args.show,
title=args.title,
- color_theme=args.color_theme)
+ color_theme=args.color_theme,
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/chase_db1.py b/mmsegmentation/tools/convert_datasets/chase_db1.py
index 580e6e7..9c79c7f 100644
--- a/mmsegmentation/tools/convert_datasets/chase_db1.py
+++ b/mmsegmentation/tools/convert_datasets/chase_db1.py
@@ -13,10 +13,11 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert CHASE_DB1 dataset to mmsegmentation format')
- parser.add_argument('dataset_path', help='path of CHASEDB1.zip')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert CHASE_DB1 dataset to mmsegmentation format"
+ )
+ parser.add_argument("dataset_path", help="path of CHASEDB1.zip")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -25,36 +26,42 @@ def main():
args = parse_args()
dataset_path = args.dataset_path
if args.out_dir is None:
- out_dir = osp.join('data', 'CHASE_DB1')
+ out_dir = osp.join("data", "CHASE_DB1")
else:
out_dir = args.out_dir
- print('Making directories...')
+ print("Making directories...")
mmcv.mkdir_or_exist(out_dir)
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "validation"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "validation"))
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- print('Extracting CHASEDB1.zip...')
+ print("Extracting CHASEDB1.zip...")
zip_file = zipfile.ZipFile(dataset_path)
zip_file.extractall(tmp_dir)
- print('Generating training dataset...')
+ print("Generating training dataset...")
- assert len(os.listdir(tmp_dir)) == CHASE_DB1_LEN, \
- 'len(os.listdir(tmp_dir)) != {}'.format(CHASE_DB1_LEN)
+ assert (
+ len(os.listdir(tmp_dir)) == CHASE_DB1_LEN
+ ), f"len(os.listdir(tmp_dir)) != {CHASE_DB1_LEN}"
for img_name in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, img_name))
- if osp.splitext(img_name)[1] == '.jpg':
+ if osp.splitext(img_name)[1] == ".jpg":
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'training',
- osp.splitext(img_name)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "images",
+ "training",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
else:
# The annotation img should be divided by 128, because some of
# the annotation imgs are not standard. We should set a
@@ -63,26 +70,41 @@ def main():
# else 0'
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'training',
- osp.splitext(img_name)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "training",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
for img_name in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(tmp_dir, img_name))
- if osp.splitext(img_name)[1] == '.jpg':
+ if osp.splitext(img_name)[1] == ".jpg":
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'validation',
- osp.splitext(img_name)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "images",
+ "validation",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
else:
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(img_name)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/cityscapes.py b/mmsegmentation/tools/convert_datasets/cityscapes.py
index 17b6168..9644130 100644
--- a/mmsegmentation/tools/convert_datasets/cityscapes.py
+++ b/mmsegmentation/tools/convert_datasets/cityscapes.py
@@ -7,18 +7,18 @@
def convert_json_to_label(json_file):
- label_file = json_file.replace('_polygons.json', '_labelTrainIds.png')
- json2labelImg(json_file, label_file, 'trainIds')
+ label_file = json_file.replace("_polygons.json", "_labelTrainIds.png")
+ json2labelImg(json_file, label_file, "trainIds")
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert Cityscapes annotations to TrainIds')
- parser.add_argument('cityscapes_path', help='cityscapes data path')
- parser.add_argument('--gt-dir', default='gtFine', type=str)
- parser.add_argument('-o', '--out-dir', help='output path')
- parser.add_argument(
- '--nproc', default=1, type=int, help='number of process')
+ description="Convert Cityscapes annotations to TrainIds"
+ )
+ parser.add_argument("cityscapes_path", help="cityscapes data path")
+ parser.add_argument("--gt-dir", default="gtFine", type=str)
+ parser.add_argument("-o", "--out-dir", help="output path")
+ parser.add_argument("--nproc", default=1, type=int, help="number of process")
args = parser.parse_args()
return args
@@ -32,25 +32,25 @@ def main():
gt_dir = osp.join(cityscapes_path, args.gt_dir)
poly_files = []
- for poly in mmcv.scandir(gt_dir, '_polygons.json', recursive=True):
+ for poly in mmcv.scandir(gt_dir, "_polygons.json", recursive=True):
poly_file = osp.join(gt_dir, poly)
poly_files.append(poly_file)
if args.nproc > 1:
- mmcv.track_parallel_progress(convert_json_to_label, poly_files,
- args.nproc)
+ mmcv.track_parallel_progress(convert_json_to_label, poly_files, args.nproc)
else:
mmcv.track_progress(convert_json_to_label, poly_files)
- split_names = ['train', 'val', 'test']
+ split_names = ["train", "val", "test"]
for split in split_names:
filenames = []
for poly in mmcv.scandir(
- osp.join(gt_dir, split), '_polygons.json', recursive=True):
- filenames.append(poly.replace('_gtFine_polygons.json', ''))
- with open(osp.join(out_dir, f'{split}.txt'), 'w') as f:
- f.writelines(f + '\n' for f in filenames)
+ osp.join(gt_dir, split), "_polygons.json", recursive=True
+ ):
+ filenames.append(poly.replace("_gtFine_polygons.json", ""))
+ with open(osp.join(out_dir, f"{split}.txt"), "w") as f:
+ f.writelines(f + "\n" for f in filenames)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/coco_stuff10k.py b/mmsegmentation/tools/convert_datasets/coco_stuff10k.py
index 374f819..412a03e 100644
--- a/mmsegmentation/tools/convert_datasets/coco_stuff10k.py
+++ b/mmsegmentation/tools/convert_datasets/coco_stuff10k.py
@@ -183,48 +183,57 @@
179: 168,
180: 169,
181: 170,
- 182: 171
+ 182: 171,
}
-def convert_to_trainID(tuple_path, in_img_dir, in_ann_dir, out_img_dir,
- out_mask_dir, is_train):
+def convert_to_trainID(
+ tuple_path, in_img_dir, in_ann_dir, out_img_dir, out_mask_dir, is_train
+):
imgpath, maskpath = tuple_path
shutil.copyfile(
osp.join(in_img_dir, imgpath),
- osp.join(out_img_dir, 'train2014', imgpath) if is_train else osp.join(
- out_img_dir, 'test2014', imgpath))
+ (
+ osp.join(out_img_dir, "train2014", imgpath)
+ if is_train
+ else osp.join(out_img_dir, "test2014", imgpath)
+ ),
+ )
annotate = loadmat(osp.join(in_ann_dir, maskpath))
- mask = annotate['S'].astype(np.uint8)
+ mask = annotate["S"].astype(np.uint8)
mask_copy = mask.copy()
for clsID, trID in clsID_to_trID.items():
mask_copy[mask == clsID] = trID
- seg_filename = osp.join(out_mask_dir, 'train2014',
- maskpath.split('.')[0] +
- '_labelTrainIds.png') if is_train else osp.join(
- out_mask_dir, 'test2014',
- maskpath.split('.')[0] + '_labelTrainIds.png')
- Image.fromarray(mask_copy).save(seg_filename, 'PNG')
+ seg_filename = (
+ osp.join(
+ out_mask_dir, "train2014", maskpath.split(".")[0] + "_labelTrainIds.png"
+ )
+ if is_train
+ else osp.join(
+ out_mask_dir, "test2014", maskpath.split(".")[0] + "_labelTrainIds.png"
+ )
+ )
+ Image.fromarray(mask_copy).save(seg_filename, "PNG")
def generate_coco_list(folder):
- train_list = osp.join(folder, 'imageLists', 'train.txt')
- test_list = osp.join(folder, 'imageLists', 'test.txt')
+ train_list = osp.join(folder, "imageLists", "train.txt")
+ test_list = osp.join(folder, "imageLists", "test.txt")
train_paths = []
test_paths = []
with open(train_list) as f:
for filename in f:
basename = filename.strip()
- imgpath = basename + '.jpg'
- maskpath = basename + '.mat'
+ imgpath = basename + ".jpg"
+ maskpath = basename + ".mat"
train_paths.append((imgpath, maskpath))
with open(test_list) as f:
for filename in f:
basename = filename.strip()
- imgpath = basename + '.jpg'
- maskpath = basename + '.mat'
+ imgpath = basename + ".jpg"
+ maskpath = basename + ".mat"
test_paths.append((imgpath, maskpath))
return train_paths, test_paths
@@ -232,12 +241,11 @@ def generate_coco_list(folder):
def parse_args():
parser = argparse.ArgumentParser(
- description=\
- 'Convert COCO Stuff 10k annotations to mmsegmentation format') # noqa
- parser.add_argument('coco_path', help='coco stuff path')
- parser.add_argument('-o', '--out_dir', help='output path')
- parser.add_argument(
- '--nproc', default=16, type=int, help='number of process')
+ description="Convert COCO Stuff 10k annotations to mmsegmentation format"
+ ) # noqa
+ parser.add_argument("coco_path", help="coco stuff path")
+ parser.add_argument("-o", "--out_dir", help="output path")
+ parser.add_argument("--nproc", default=16, type=int, help="number of process")
args = parser.parse_args()
return args
@@ -248,60 +256,72 @@ def main():
nproc = args.nproc
out_dir = args.out_dir or coco_path
- out_img_dir = osp.join(out_dir, 'images')
- out_mask_dir = osp.join(out_dir, 'annotations')
+ out_img_dir = osp.join(out_dir, "images")
+ out_mask_dir = osp.join(out_dir, "annotations")
- mmcv.mkdir_or_exist(osp.join(out_img_dir, 'train2014'))
- mmcv.mkdir_or_exist(osp.join(out_img_dir, 'test2014'))
- mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'train2014'))
- mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'test2014'))
+ mmcv.mkdir_or_exist(osp.join(out_img_dir, "train2014"))
+ mmcv.mkdir_or_exist(osp.join(out_img_dir, "test2014"))
+ mmcv.mkdir_or_exist(osp.join(out_mask_dir, "train2014"))
+ mmcv.mkdir_or_exist(osp.join(out_mask_dir, "test2014"))
train_list, test_list = generate_coco_list(coco_path)
- assert (len(train_list) +
- len(test_list)) == COCO_LEN, 'Wrong length of list {} & {}'.format(
- len(train_list), len(test_list))
+ assert (
+ len(train_list) + len(test_list)
+ ) == COCO_LEN, "Wrong length of list {} & {}".format(
+ len(train_list), len(test_list)
+ )
if args.nproc > 1:
mmcv.track_parallel_progress(
partial(
convert_to_trainID,
- in_img_dir=osp.join(coco_path, 'images'),
- in_ann_dir=osp.join(coco_path, 'annotations'),
+ in_img_dir=osp.join(coco_path, "images"),
+ in_ann_dir=osp.join(coco_path, "annotations"),
out_img_dir=out_img_dir,
out_mask_dir=out_mask_dir,
- is_train=True),
+ is_train=True,
+ ),
train_list,
- nproc=nproc)
+ nproc=nproc,
+ )
mmcv.track_parallel_progress(
partial(
convert_to_trainID,
- in_img_dir=osp.join(coco_path, 'images'),
- in_ann_dir=osp.join(coco_path, 'annotations'),
+ in_img_dir=osp.join(coco_path, "images"),
+ in_ann_dir=osp.join(coco_path, "annotations"),
out_img_dir=out_img_dir,
out_mask_dir=out_mask_dir,
- is_train=False),
+ is_train=False,
+ ),
test_list,
- nproc=nproc)
+ nproc=nproc,
+ )
else:
mmcv.track_progress(
partial(
convert_to_trainID,
- in_img_dir=osp.join(coco_path, 'images'),
- in_ann_dir=osp.join(coco_path, 'annotations'),
+ in_img_dir=osp.join(coco_path, "images"),
+ in_ann_dir=osp.join(coco_path, "annotations"),
out_img_dir=out_img_dir,
out_mask_dir=out_mask_dir,
- is_train=True), train_list)
+ is_train=True,
+ ),
+ train_list,
+ )
mmcv.track_progress(
partial(
convert_to_trainID,
- in_img_dir=osp.join(coco_path, 'images'),
- in_ann_dir=osp.join(coco_path, 'annotations'),
+ in_img_dir=osp.join(coco_path, "images"),
+ in_ann_dir=osp.join(coco_path, "annotations"),
out_img_dir=out_img_dir,
out_mask_dir=out_mask_dir,
- is_train=False), test_list)
+ is_train=False,
+ ),
+ test_list,
+ )
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/coco_stuff164k.py b/mmsegmentation/tools/convert_datasets/coco_stuff164k.py
index 6d8e2f2..a1eff6a 100644
--- a/mmsegmentation/tools/convert_datasets/coco_stuff164k.py
+++ b/mmsegmentation/tools/convert_datasets/coco_stuff164k.py
@@ -183,7 +183,7 @@
179: 168,
180: 169,
181: 170,
- 255: 255
+ 255: 255,
}
@@ -192,23 +192,29 @@ def convert_to_trainID(maskpath, out_mask_dir, is_train):
mask_copy = mask.copy()
for clsID, trID in clsID_to_trID.items():
mask_copy[mask == clsID] = trID
- seg_filename = osp.join(
- out_mask_dir, 'train2017',
- osp.basename(maskpath).split('.')[0] +
- '_labelTrainIds.png') if is_train else osp.join(
- out_mask_dir, 'val2017',
- osp.basename(maskpath).split('.')[0] + '_labelTrainIds.png')
- Image.fromarray(mask_copy).save(seg_filename, 'PNG')
+ seg_filename = (
+ osp.join(
+ out_mask_dir,
+ "train2017",
+ osp.basename(maskpath).split(".")[0] + "_labelTrainIds.png",
+ )
+ if is_train
+ else osp.join(
+ out_mask_dir,
+ "val2017",
+ osp.basename(maskpath).split(".")[0] + "_labelTrainIds.png",
+ )
+ )
+ Image.fromarray(mask_copy).save(seg_filename, "PNG")
def parse_args():
parser = argparse.ArgumentParser(
- description=\
- 'Convert COCO Stuff 164k annotations to mmsegmentation format') # noqa
- parser.add_argument('coco_path', help='coco stuff path')
- parser.add_argument('-o', '--out_dir', help='output path')
- parser.add_argument(
- '--nproc', default=16, type=int, help='number of process')
+ description="Convert COCO Stuff 164k annotations to mmsegmentation format"
+ ) # noqa
+ parser.add_argument("coco_path", help="coco stuff path")
+ parser.add_argument("-o", "--out_dir", help="output path")
+ parser.add_argument("--nproc", default=16, type=int, help="number of process")
args = parser.parse_args()
return args
@@ -219,46 +225,48 @@ def main():
nproc = args.nproc
out_dir = args.out_dir or coco_path
- out_img_dir = osp.join(out_dir, 'images')
- out_mask_dir = osp.join(out_dir, 'annotations')
+ out_img_dir = osp.join(out_dir, "images")
+ out_mask_dir = osp.join(out_dir, "annotations")
- mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'train2017'))
- mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'val2017'))
+ mmcv.mkdir_or_exist(osp.join(out_mask_dir, "train2017"))
+ mmcv.mkdir_or_exist(osp.join(out_mask_dir, "val2017"))
if out_dir != coco_path:
- shutil.copytree(osp.join(coco_path, 'images'), out_img_dir)
+ shutil.copytree(osp.join(coco_path, "images"), out_img_dir)
- train_list = glob(osp.join(coco_path, 'annotations', 'train2017', '*.png'))
- train_list = [file for file in train_list if '_labelTrainIds' not in file]
- test_list = glob(osp.join(coco_path, 'annotations', 'val2017', '*.png'))
- test_list = [file for file in test_list if '_labelTrainIds' not in file]
- assert (len(train_list) +
- len(test_list)) == COCO_LEN, 'Wrong length of list {} & {}'.format(
- len(train_list), len(test_list))
+ train_list = glob(osp.join(coco_path, "annotations", "train2017", "*.png"))
+ train_list = [file for file in train_list if "_labelTrainIds" not in file]
+ test_list = glob(osp.join(coco_path, "annotations", "val2017", "*.png"))
+ test_list = [file for file in test_list if "_labelTrainIds" not in file]
+ assert (
+ len(train_list) + len(test_list)
+ ) == COCO_LEN, "Wrong length of list {} & {}".format(
+ len(train_list), len(test_list)
+ )
if args.nproc > 1:
mmcv.track_parallel_progress(
- partial(
- convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
+ partial(convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
train_list,
- nproc=nproc)
+ nproc=nproc,
+ )
mmcv.track_parallel_progress(
- partial(
- convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
+ partial(convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
test_list,
- nproc=nproc)
+ nproc=nproc,
+ )
else:
mmcv.track_progress(
- partial(
- convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
- train_list)
+ partial(convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
+ train_list,
+ )
mmcv.track_progress(
- partial(
- convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
- test_list)
+ partial(convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
+ test_list,
+ )
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/drive.py b/mmsegmentation/tools/convert_datasets/drive.py
index f547579..dfa4d70 100644
--- a/mmsegmentation/tools/convert_datasets/drive.py
+++ b/mmsegmentation/tools/convert_datasets/drive.py
@@ -11,13 +11,12 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert DRIVE dataset to mmsegmentation format')
- parser.add_argument(
- 'training_path', help='the training part of DRIVE dataset')
- parser.add_argument(
- 'testing_path', help='the testing part of DRIVE dataset')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert DRIVE dataset to mmsegmentation format"
+ )
+ parser.add_argument("training_path", help="the training part of DRIVE dataset")
+ parser.add_argument("testing_path", help="the testing part of DRIVE dataset")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -27,59 +26,71 @@ def main():
training_path = args.training_path
testing_path = args.testing_path
if args.out_dir is None:
- out_dir = osp.join('data', 'DRIVE')
+ out_dir = osp.join("data", "DRIVE")
else:
out_dir = args.out_dir
- print('Making directories...')
+ print("Making directories...")
mmcv.mkdir_or_exist(out_dir)
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "validation"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "validation"))
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- print('Extracting training.zip...')
+ print("Extracting training.zip...")
zip_file = zipfile.ZipFile(training_path)
zip_file.extractall(tmp_dir)
- print('Generating training dataset...')
- now_dir = osp.join(tmp_dir, 'training', 'images')
+ print("Generating training dataset...")
+ now_dir = osp.join(tmp_dir, "training", "images")
for img_name in os.listdir(now_dir):
img = mmcv.imread(osp.join(now_dir, img_name))
mmcv.imwrite(
img,
osp.join(
- out_dir, 'images', 'training',
- osp.splitext(img_name)[0].replace('_training', '') +
- '.png'))
-
- now_dir = osp.join(tmp_dir, 'training', '1st_manual')
+ out_dir,
+ "images",
+ "training",
+ osp.splitext(img_name)[0].replace("_training", "") + ".png",
+ ),
+ )
+
+ now_dir = osp.join(tmp_dir, "training", "1st_manual")
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
ret, img = cap.read()
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'training',
- osp.splitext(img_name)[0] + '.png'))
-
- print('Extracting test.zip...')
+ osp.join(
+ out_dir,
+ "annotations",
+ "training",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
+
+ print("Extracting test.zip...")
zip_file = zipfile.ZipFile(testing_path)
zip_file.extractall(tmp_dir)
- print('Generating validation dataset...')
- now_dir = osp.join(tmp_dir, 'test', 'images')
+ print("Generating validation dataset...")
+ now_dir = osp.join(tmp_dir, "test", "images")
for img_name in os.listdir(now_dir):
img = mmcv.imread(osp.join(now_dir, img_name))
mmcv.imwrite(
img,
osp.join(
- out_dir, 'images', 'validation',
- osp.splitext(img_name)[0].replace('_test', '') + '.png'))
-
- now_dir = osp.join(tmp_dir, 'test', '1st_manual')
+ out_dir,
+ "images",
+ "validation",
+ osp.splitext(img_name)[0].replace("_test", "") + ".png",
+ ),
+ )
+
+ now_dir = osp.join(tmp_dir, "test", "1st_manual")
if osp.exists(now_dir):
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
@@ -91,23 +102,33 @@ def main():
# else 0'
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(img_name)[0] + '.png'))
-
- now_dir = osp.join(tmp_dir, 'test', '2nd_manual')
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
+
+ now_dir = osp.join(tmp_dir, "test", "2nd_manual")
if osp.exists(now_dir):
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
ret, img = cap.read()
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(img_name)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(img_name)[0] + ".png",
+ ),
+ )
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/hrf.py b/mmsegmentation/tools/convert_datasets/hrf.py
index 5e016e3..930f8bc 100644
--- a/mmsegmentation/tools/convert_datasets/hrf.py
+++ b/mmsegmentation/tools/convert_datasets/hrf.py
@@ -13,21 +13,25 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert HRF dataset to mmsegmentation format')
- parser.add_argument('healthy_path', help='the path of healthy.zip')
+ description="Convert HRF dataset to mmsegmentation format"
+ )
+ parser.add_argument("healthy_path", help="the path of healthy.zip")
parser.add_argument(
- 'healthy_manualsegm_path', help='the path of healthy_manualsegm.zip')
- parser.add_argument('glaucoma_path', help='the path of glaucoma.zip')
+ "healthy_manualsegm_path", help="the path of healthy_manualsegm.zip"
+ )
+ parser.add_argument("glaucoma_path", help="the path of glaucoma.zip")
parser.add_argument(
- 'glaucoma_manualsegm_path', help='the path of glaucoma_manualsegm.zip')
+ "glaucoma_manualsegm_path", help="the path of glaucoma_manualsegm.zip"
+ )
parser.add_argument(
- 'diabetic_retinopathy_path',
- help='the path of diabetic_retinopathy.zip')
+ "diabetic_retinopathy_path", help="the path of diabetic_retinopathy.zip"
+ )
parser.add_argument(
- 'diabetic_retinopathy_manualsegm_path',
- help='the path of diabetic_retinopathy_manualsegm.zip')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ "diabetic_retinopathy_manualsegm_path",
+ help="the path of diabetic_retinopathy_manualsegm.zip",
+ )
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -35,56 +39,71 @@ def parse_args():
def main():
args = parse_args()
images_path = [
- args.healthy_path, args.glaucoma_path, args.diabetic_retinopathy_path
+ args.healthy_path,
+ args.glaucoma_path,
+ args.diabetic_retinopathy_path,
]
annotations_path = [
- args.healthy_manualsegm_path, args.glaucoma_manualsegm_path,
- args.diabetic_retinopathy_manualsegm_path
+ args.healthy_manualsegm_path,
+ args.glaucoma_manualsegm_path,
+ args.diabetic_retinopathy_manualsegm_path,
]
if args.out_dir is None:
- out_dir = osp.join('data', 'HRF')
+ out_dir = osp.join("data", "HRF")
else:
out_dir = args.out_dir
- print('Making directories...')
+ print("Making directories...")
mmcv.mkdir_or_exist(out_dir)
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
-
- print('Generating images...')
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "validation"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "validation"))
+
+ print("Generating images...")
for now_path in images_path:
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
zip_file = zipfile.ZipFile(now_path)
zip_file.extractall(tmp_dir)
- assert len(os.listdir(tmp_dir)) == HRF_LEN, \
- 'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
+ assert (
+ len(os.listdir(tmp_dir)) == HRF_LEN
+ ), f"len(os.listdir(tmp_dir)) != {HRF_LEN}"
for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, filename))
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'training',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "images",
+ "training",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(tmp_dir, filename))
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'validation',
- osp.splitext(filename)[0] + '.png'))
-
- print('Generating annotations...')
+ osp.join(
+ out_dir,
+ "images",
+ "validation",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
+
+ print("Generating annotations...")
for now_path in annotations_path:
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
zip_file = zipfile.ZipFile(now_path)
zip_file.extractall(tmp_dir)
- assert len(os.listdir(tmp_dir)) == HRF_LEN, \
- 'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
+ assert (
+ len(os.listdir(tmp_dir)) == HRF_LEN
+ ), f"len(os.listdir(tmp_dir)) != {HRF_LEN}"
for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, filename))
@@ -95,17 +114,27 @@ def main():
# else 0'
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'training',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "training",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(tmp_dir, filename))
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/isaid.py b/mmsegmentation/tools/convert_datasets/isaid.py
index 314fb89..76ebaba 100644
--- a/mmsegmentation/tools/convert_datasets/isaid.py
+++ b/mmsegmentation/tools/convert_datasets/isaid.py
@@ -11,25 +11,24 @@
import numpy as np
from PIL import Image
-iSAID_palette = \
- {
- 0: (0, 0, 0),
- 1: (0, 0, 63),
- 2: (0, 63, 63),
- 3: (0, 63, 0),
- 4: (0, 63, 127),
- 5: (0, 63, 191),
- 6: (0, 63, 255),
- 7: (0, 127, 63),
- 8: (0, 127, 127),
- 9: (0, 0, 127),
- 10: (0, 0, 191),
- 11: (0, 0, 255),
- 12: (0, 191, 127),
- 13: (0, 127, 191),
- 14: (0, 127, 255),
- 15: (0, 100, 155)
- }
+iSAID_palette = {
+ 0: (0, 0, 0),
+ 1: (0, 0, 63),
+ 2: (0, 63, 63),
+ 3: (0, 63, 0),
+ 4: (0, 63, 127),
+ 5: (0, 63, 191),
+ 6: (0, 63, 255),
+ 7: (0, 127, 63),
+ 8: (0, 127, 127),
+ 9: (0, 0, 127),
+ 10: (0, 0, 191),
+ 11: (0, 0, 255),
+ 12: (0, 191, 127),
+ 13: (0, 127, 191),
+ 14: (0, 127, 255),
+ 15: (0, 100, 155),
+}
iSAID_invert_palette = {v: k for k, v in iSAID_palette.items()}
@@ -46,7 +45,7 @@ def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
- img = np.asarray(Image.open(src_path).convert('RGB'))
+ img = np.asarray(Image.open(src_path).convert("RGB"))
img_H, img_W, _ = img.shape
@@ -85,16 +84,25 @@ def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
img_patch = img[y_str:y_end, x_str:x_end, :]
img_patch = Image.fromarray(img_patch.astype(np.uint8))
- image = osp.basename(src_path).split('.')[0] + '_' + str(
- y_str) + '_' + str(y_end) + '_' + str(x_str) + '_' + str(
- x_end) + '.png'
+ image = (
+ osp.basename(src_path).split(".")[0]
+ + "_"
+ + str(y_str)
+ + "_"
+ + str(y_end)
+ + "_"
+ + str(x_str)
+ + "_"
+ + str(x_end)
+ + ".png"
+ )
# print(image)
- save_path_image = osp.join(out_dir, 'img_dir', mode, str(image))
+ save_path_image = osp.join(out_dir, "img_dir", mode, str(image))
img_patch.save(save_path_image)
def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
- label = mmcv.imread(src_path, channel_order='rgb')
+ label = mmcv.imread(src_path, channel_order="rgb")
label = iSAID_convert_from_color(label)
img_H, img_W = label.shape
@@ -133,33 +141,42 @@ def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
y_end = img_H
lab_patch = label[y_str:y_end, x_str:x_end]
- lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode='P')
-
- image = osp.basename(src_path).split('.')[0].split(
- '_')[0] + '_' + str(y_str) + '_' + str(y_end) + '_' + str(
- x_str) + '_' + str(x_end) + '_instance_color_RGB' + '.png'
- lab_patch.save(osp.join(out_dir, 'ann_dir', mode, str(image)))
+ lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode="P")
+
+ image = (
+ osp.basename(src_path).split(".")[0].split("_")[0]
+ + "_"
+ + str(y_str)
+ + "_"
+ + str(y_end)
+ + "_"
+ + str(x_str)
+ + "_"
+ + str(x_end)
+ + "_instance_color_RGB"
+ + ".png"
+ )
+ lab_patch.save(osp.join(out_dir, "ann_dir", mode, str(image)))
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert iSAID dataset to mmsegmentation format')
- parser.add_argument('dataset_path', help='iSAID folder path')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert iSAID dataset to mmsegmentation format"
+ )
+ parser.add_argument("dataset_path", help="iSAID folder path")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
parser.add_argument(
- '--patch_width',
- default=896,
- type=int,
- help='Width of the cropped image patch')
+ "--patch_width", default=896, type=int, help="Width of the cropped image patch"
+ )
parser.add_argument(
- '--patch_height',
+ "--patch_height",
default=896,
type=int,
- help='Height of the cropped image patch')
- parser.add_argument(
- '--overlap_area', default=384, type=int, help='Overlap area')
+ help="Height of the cropped image patch",
+ )
+ parser.add_argument("--overlap_area", default=384, type=int, help="Overlap area")
args = parser.parse_args()
return args
@@ -173,73 +190,80 @@ def main():
overlap = args.overlap_area # overlap area
if args.out_dir is None:
- out_dir = osp.join('data', 'iSAID')
+ out_dir = osp.join("data", "iSAID")
else:
out_dir = args.out_dir
- print('Making directories...')
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
-
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
-
- assert os.path.exists(os.path.join(dataset_path, 'train')), \
- 'train is not in {}'.format(dataset_path)
- assert os.path.exists(os.path.join(dataset_path, 'val')), \
- 'val is not in {}'.format(dataset_path)
- assert os.path.exists(os.path.join(dataset_path, 'test')), \
- 'test is not in {}'.format(dataset_path)
+ print("Making directories...")
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "val"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "test"))
+
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "val"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "test"))
+
+ assert os.path.exists(
+ os.path.join(dataset_path, "train")
+ ), f"train is not in {dataset_path}"
+ assert os.path.exists(
+ os.path.join(dataset_path, "val")
+ ), f"val is not in {dataset_path}"
+ assert os.path.exists(
+ os.path.join(dataset_path, "test")
+ ), f"test is not in {dataset_path}"
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- for dataset_mode in ['train', 'val', 'test']:
+ for dataset_mode in ["train", "val", "test"]:
# for dataset_mode in [ 'test']:
- print('Extracting {}ing.zip...'.format(dataset_mode))
+ print(f"Extracting {dataset_mode}ing.zip...")
img_zipp_list = glob.glob(
- os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
- print('Find the data', img_zipp_list)
+ os.path.join(dataset_path, dataset_mode, "images", "*.zip")
+ )
+ print("Find the data", img_zipp_list)
for img_zipp in img_zipp_list:
zip_file = zipfile.ZipFile(img_zipp)
- zip_file.extractall(os.path.join(tmp_dir, dataset_mode, 'img'))
+ zip_file.extractall(os.path.join(tmp_dir, dataset_mode, "img"))
src_path_list = glob.glob(
- os.path.join(tmp_dir, dataset_mode, 'img', 'images', '*.png'))
+ os.path.join(tmp_dir, dataset_mode, "img", "images", "*.png")
+ )
src_prog_bar = mmcv.ProgressBar(len(src_path_list))
for i, img_path in enumerate(src_path_list):
- if dataset_mode != 'test':
- slide_crop_image(img_path, out_dir, dataset_mode, patch_H,
- patch_W, overlap)
+ if dataset_mode != "test":
+ slide_crop_image(
+ img_path, out_dir, dataset_mode, patch_H, patch_W, overlap
+ )
else:
- shutil.move(img_path,
- os.path.join(out_dir, 'img_dir', dataset_mode))
+ shutil.move(
+ img_path, os.path.join(out_dir, "img_dir", dataset_mode)
+ )
src_prog_bar.update()
- if dataset_mode != 'test':
+ if dataset_mode != "test":
label_zipp_list = glob.glob(
- os.path.join(dataset_path, dataset_mode, 'Semantic_masks',
- '*.zip'))
+ os.path.join(dataset_path, dataset_mode, "Semantic_masks", "*.zip")
+ )
for label_zipp in label_zipp_list:
zip_file = zipfile.ZipFile(label_zipp)
- zip_file.extractall(
- os.path.join(tmp_dir, dataset_mode, 'lab'))
+ zip_file.extractall(os.path.join(tmp_dir, dataset_mode, "lab"))
lab_path_list = glob.glob(
- os.path.join(tmp_dir, dataset_mode, 'lab', 'images',
- '*.png'))
+ os.path.join(tmp_dir, dataset_mode, "lab", "images", "*.png")
+ )
lab_prog_bar = mmcv.ProgressBar(len(lab_path_list))
for i, lab_path in enumerate(lab_path_list):
- slide_crop_label(lab_path, out_dir, dataset_mode, patch_H,
- patch_W, overlap)
+ slide_crop_label(
+ lab_path, out_dir, dataset_mode, patch_H, patch_W, overlap
+ )
lab_prog_bar.update()
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/loveda.py b/mmsegmentation/tools/convert_datasets/loveda.py
index 3a06268..59760eb 100644
--- a/mmsegmentation/tools/convert_datasets/loveda.py
+++ b/mmsegmentation/tools/convert_datasets/loveda.py
@@ -11,10 +11,11 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert LoveDA dataset to mmsegmentation format')
- parser.add_argument('dataset_path', help='LoveDA folder path')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert LoveDA dataset to mmsegmentation format"
+ )
+ parser.add_argument("dataset_path", help="LoveDA folder path")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -23,51 +24,48 @@ def main():
args = parse_args()
dataset_path = args.dataset_path
if args.out_dir is None:
- out_dir = osp.join('data', 'loveDA')
+ out_dir = osp.join("data", "loveDA")
else:
out_dir = args.out_dir
- print('Making directories...')
+ print("Making directories...")
mmcv.mkdir_or_exist(out_dir)
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "val"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "test"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "val"))
- assert 'Train.zip' in os.listdir(dataset_path), \
- 'Train.zip is not in {}'.format(dataset_path)
- assert 'Val.zip' in os.listdir(dataset_path), \
- 'Val.zip is not in {}'.format(dataset_path)
- assert 'Test.zip' in os.listdir(dataset_path), \
- 'Test.zip is not in {}'.format(dataset_path)
+ assert "Train.zip" in os.listdir(
+ dataset_path
+ ), f"Train.zip is not in {dataset_path}"
+ assert "Val.zip" in os.listdir(dataset_path), f"Val.zip is not in {dataset_path}"
+ assert "Test.zip" in os.listdir(dataset_path), f"Test.zip is not in {dataset_path}"
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- for dataset in ['Train', 'Val', 'Test']:
- zip_file = zipfile.ZipFile(
- os.path.join(dataset_path, dataset + '.zip'))
+ for dataset in ["Train", "Val", "Test"]:
+ zip_file = zipfile.ZipFile(os.path.join(dataset_path, dataset + ".zip"))
zip_file.extractall(tmp_dir)
data_type = dataset.lower()
- for location in ['Rural', 'Urban']:
- for image_type in ['images_png', 'masks_png']:
- if image_type == 'images_png':
- dst = osp.join(out_dir, 'img_dir', data_type)
+ for location in ["Rural", "Urban"]:
+ for image_type in ["images_png", "masks_png"]:
+ if image_type == "images_png":
+ dst = osp.join(out_dir, "img_dir", data_type)
else:
- dst = osp.join(out_dir, 'ann_dir', data_type)
- if dataset == 'Test' and image_type == 'masks_png':
+ dst = osp.join(out_dir, "ann_dir", data_type)
+ if dataset == "Test" and image_type == "masks_png":
continue
else:
- src_dir = osp.join(tmp_dir, dataset, location,
- image_type)
+ src_dir = osp.join(tmp_dir, dataset, location, image_type)
src_lst = os.listdir(src_dir)
for file in src_lst:
shutil.move(osp.join(src_dir, file), dst)
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/pascal_context.py b/mmsegmentation/tools/convert_datasets/pascal_context.py
index 03b79d5..6dd9674 100644
--- a/mmsegmentation/tools/convert_datasets/pascal_context.py
+++ b/mmsegmentation/tools/convert_datasets/pascal_context.py
@@ -9,13 +9,72 @@
from PIL import Image
_mapping = np.sort(
- np.array([
- 0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25, 284,
- 158, 159, 416, 33, 162, 420, 454, 295, 296, 427, 44, 45, 46, 308, 59,
- 440, 445, 31, 232, 65, 354, 424, 68, 326, 72, 458, 34, 207, 80, 355,
- 85, 347, 220, 349, 360, 98, 187, 104, 105, 366, 189, 368, 113, 115
- ]))
-_key = np.array(range(len(_mapping))).astype('uint8')
+ np.array(
+ [
+ 0,
+ 2,
+ 259,
+ 260,
+ 415,
+ 324,
+ 9,
+ 258,
+ 144,
+ 18,
+ 19,
+ 22,
+ 23,
+ 397,
+ 25,
+ 284,
+ 158,
+ 159,
+ 416,
+ 33,
+ 162,
+ 420,
+ 454,
+ 295,
+ 296,
+ 427,
+ 44,
+ 45,
+ 46,
+ 308,
+ 59,
+ 440,
+ 445,
+ 31,
+ 232,
+ 65,
+ 354,
+ 424,
+ 68,
+ 326,
+ 72,
+ 458,
+ 34,
+ 207,
+ 80,
+ 355,
+ 85,
+ 347,
+ 220,
+ 349,
+ 360,
+ 98,
+ 187,
+ 104,
+ 105,
+ 366,
+ 189,
+ 368,
+ 113,
+ 115,
+ ]
+ )
+)
+_key = np.array(range(len(_mapping))).astype("uint8")
def generate_labels(img_id, detail, out_dir):
@@ -24,23 +83,25 @@ def _class_to_index(mask, _mapping, _key):
# assert the values
values = np.unique(mask)
for i in range(len(values)):
- assert (values[i] in _mapping)
+ assert values[i] in _mapping
index = np.digitize(mask.ravel(), _mapping, right=True)
return _key[index].reshape(mask.shape)
mask = Image.fromarray(
- _class_to_index(detail.getMask(img_id), _mapping=_mapping, _key=_key))
- filename = img_id['file_name']
- mask.save(osp.join(out_dir, filename.replace('jpg', 'png')))
+ _class_to_index(detail.getMask(img_id), _mapping=_mapping, _key=_key)
+ )
+ filename = img_id["file_name"]
+ mask.save(osp.join(out_dir, filename.replace("jpg", "png")))
return osp.splitext(osp.basename(filename))[0]
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert PASCAL VOC annotations to mmsegmentation format')
- parser.add_argument('devkit_path', help='pascal voc devkit path')
- parser.add_argument('json_path', help='annoation json filepath')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert PASCAL VOC annotations to mmsegmentation format"
+ )
+ parser.add_argument("devkit_path", help="pascal voc devkit path")
+ parser.add_argument("json_path", help="annoation json filepath")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -49,39 +110,39 @@ def main():
args = parse_args()
devkit_path = args.devkit_path
if args.out_dir is None:
- out_dir = osp.join(devkit_path, 'VOC2010', 'SegmentationClassContext')
+ out_dir = osp.join(devkit_path, "VOC2010", "SegmentationClassContext")
else:
out_dir = args.out_dir
json_path = args.json_path
mmcv.mkdir_or_exist(out_dir)
- img_dir = osp.join(devkit_path, 'VOC2010', 'JPEGImages')
+ img_dir = osp.join(devkit_path, "VOC2010", "JPEGImages")
- train_detail = Detail(json_path, img_dir, 'train')
+ train_detail = Detail(json_path, img_dir, "train")
train_ids = train_detail.getImgs()
- val_detail = Detail(json_path, img_dir, 'val')
+ val_detail = Detail(json_path, img_dir, "val")
val_ids = val_detail.getImgs()
- mmcv.mkdir_or_exist(
- osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext'))
+ mmcv.mkdir_or_exist(osp.join(devkit_path, "VOC2010/ImageSets/SegmentationContext"))
train_list = mmcv.track_progress(
- partial(generate_labels, detail=train_detail, out_dir=out_dir),
- train_ids)
+ partial(generate_labels, detail=train_detail, out_dir=out_dir), train_ids
+ )
with open(
- osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext',
- 'train.txt'), 'w') as f:
- f.writelines(line + '\n' for line in sorted(train_list))
+ osp.join(devkit_path, "VOC2010/ImageSets/SegmentationContext", "train.txt"), "w"
+ ) as f:
+ f.writelines(line + "\n" for line in sorted(train_list))
val_list = mmcv.track_progress(
- partial(generate_labels, detail=val_detail, out_dir=out_dir), val_ids)
+ partial(generate_labels, detail=val_detail, out_dir=out_dir), val_ids
+ )
with open(
- osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext',
- 'val.txt'), 'w') as f:
- f.writelines(line + '\n' for line in sorted(val_list))
+ osp.join(devkit_path, "VOC2010/ImageSets/SegmentationContext", "val.txt"), "w"
+ ) as f:
+ f.writelines(line + "\n" for line in sorted(val_list))
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/potsdam.py b/mmsegmentation/tools/convert_datasets/potsdam.py
index 87e67d5..14804ec 100644
--- a/mmsegmentation/tools/convert_datasets/potsdam.py
+++ b/mmsegmentation/tools/convert_datasets/potsdam.py
@@ -13,20 +13,23 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert potsdam dataset to mmsegmentation format')
- parser.add_argument('dataset_path', help='potsdam folder path')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert potsdam dataset to mmsegmentation format"
+ )
+ parser.add_argument("dataset_path", help="potsdam folder path")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
parser.add_argument(
- '--clip_size',
+ "--clip_size",
type=int,
- help='clipped size of image after preparation',
- default=512)
+ help="clipped size of image after preparation",
+ default=512,
+ )
parser.add_argument(
- '--stride_size',
+ "--stride_size",
type=int,
- help='stride of clipping original images',
- default=256)
+ help="stride of clipping original images",
+ default=256,
+ )
args = parser.parse_args()
return args
@@ -44,14 +47,16 @@ def clip_big_image(image_path, clip_save_dir, args, to_label=False):
clip_size = args.clip_size
stride_size = args.stride_size
- num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
- (h - clip_size) /
- stride_size) * stride_size + clip_size >= h else math.ceil(
- (h - clip_size) / stride_size) + 1
- num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
- (w - clip_size) /
- stride_size) * stride_size + clip_size >= w else math.ceil(
- (w - clip_size) / stride_size) + 1
+ num_rows = (
+ math.ceil((h - clip_size) / stride_size)
+ if math.ceil((h - clip_size) / stride_size) * stride_size + clip_size >= h
+ else math.ceil((h - clip_size) / stride_size) + 1
+ )
+ num_cols = (
+ math.ceil((w - clip_size) / stride_size)
+ if math.ceil((w - clip_size) / stride_size) * stride_size + clip_size >= w
+ else math.ceil((w - clip_size) / stride_size) + 1
+ )
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
xmin = x * clip_size
@@ -59,99 +64,145 @@ def clip_big_image(image_path, clip_save_dir, args, to_label=False):
xmin = xmin.ravel()
ymin = ymin.ravel()
- xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
- np.zeros_like(xmin))
- ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
- np.zeros_like(ymin))
- boxes = np.stack([
- xmin + xmin_offset, ymin + ymin_offset,
- np.minimum(xmin + clip_size, w),
- np.minimum(ymin + clip_size, h)
- ],
- axis=1)
+ xmin_offset = np.where(
+ xmin + clip_size > w, w - xmin - clip_size, np.zeros_like(xmin)
+ )
+ ymin_offset = np.where(
+ ymin + clip_size > h, h - ymin - clip_size, np.zeros_like(ymin)
+ )
+ boxes = np.stack(
+ [
+ xmin + xmin_offset,
+ ymin + ymin_offset,
+ np.minimum(xmin + clip_size, w),
+ np.minimum(ymin + clip_size, h),
+ ],
+ axis=1,
+ )
if to_label:
- color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
- [255, 255, 0], [0, 255, 0], [0, 255, 255],
- [0, 0, 255]])
- flatten_v = np.matmul(
- image.reshape(-1, c),
- np.array([2, 3, 4]).reshape(3, 1))
+ color_map = np.array(
+ [
+ [0, 0, 0],
+ [255, 255, 255],
+ [255, 0, 0],
+ [255, 255, 0],
+ [0, 255, 0],
+ [0, 255, 255],
+ [0, 0, 255],
+ ]
+ )
+ flatten_v = np.matmul(image.reshape(-1, c), np.array([2, 3, 4]).reshape(3, 1))
out = np.zeros_like(flatten_v)
for idx, class_color in enumerate(color_map):
- value_idx = np.matmul(class_color,
- np.array([2, 3, 4]).reshape(3, 1))
+ value_idx = np.matmul(class_color, np.array([2, 3, 4]).reshape(3, 1))
out[flatten_v == value_idx] = idx
image = out.reshape(h, w)
for box in boxes:
start_x, start_y, end_x, end_y = box
- clipped_image = image[start_y:end_y,
- start_x:end_x] if to_label else image[
- start_y:end_y, start_x:end_x, :]
- idx_i, idx_j = osp.basename(image_path).split('_')[2:4]
+ clipped_image = (
+ image[start_y:end_y, start_x:end_x]
+ if to_label
+ else image[start_y:end_y, start_x:end_x, :]
+ )
+ idx_i, idx_j = osp.basename(image_path).split("_")[2:4]
mmcv.imwrite(
clipped_image.astype(np.uint8),
osp.join(
clip_save_dir,
- f'{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
+ f"{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png",
+ ),
+ )
def main():
args = parse_args()
splits = {
- 'train': [
- '2_10', '2_11', '2_12', '3_10', '3_11', '3_12', '4_10', '4_11',
- '4_12', '5_10', '5_11', '5_12', '6_10', '6_11', '6_12', '6_7',
- '6_8', '6_9', '7_10', '7_11', '7_12', '7_7', '7_8', '7_9'
+ "train": [
+ "2_10",
+ "2_11",
+ "2_12",
+ "3_10",
+ "3_11",
+ "3_12",
+ "4_10",
+ "4_11",
+ "4_12",
+ "5_10",
+ "5_11",
+ "5_12",
+ "6_10",
+ "6_11",
+ "6_12",
+ "6_7",
+ "6_8",
+ "6_9",
+ "7_10",
+ "7_11",
+ "7_12",
+ "7_7",
+ "7_8",
+ "7_9",
+ ],
+ "val": [
+ "5_15",
+ "6_15",
+ "6_13",
+ "3_13",
+ "4_14",
+ "6_14",
+ "5_14",
+ "2_13",
+ "4_15",
+ "2_14",
+ "5_13",
+ "4_13",
+ "3_14",
+ "7_13",
],
- 'val': [
- '5_15', '6_15', '6_13', '3_13', '4_14', '6_14', '5_14', '2_13',
- '4_15', '2_14', '5_13', '4_13', '3_14', '7_13'
- ]
}
dataset_path = args.dataset_path
if args.out_dir is None:
- out_dir = osp.join('data', 'potsdam')
+ out_dir = osp.join("data", "potsdam")
else:
out_dir = args.out_dir
- print('Making directories...')
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
+ print("Making directories...")
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "val"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "val"))
- zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
- print('Find the data', zipp_list)
+ zipp_list = glob.glob(os.path.join(dataset_path, "*.zip"))
+ print("Find the data", zipp_list)
for zipp in zipp_list:
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
zip_file = zipfile.ZipFile(zipp)
zip_file.extractall(tmp_dir)
- src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
+ src_path_list = glob.glob(os.path.join(tmp_dir, "*.tif"))
if not len(src_path_list):
sub_tmp_dir = os.path.join(tmp_dir, os.listdir(tmp_dir)[0])
- src_path_list = glob.glob(os.path.join(sub_tmp_dir, '*.tif'))
+ src_path_list = glob.glob(os.path.join(sub_tmp_dir, "*.tif"))
prog_bar = mmcv.ProgressBar(len(src_path_list))
for i, src_path in enumerate(src_path_list):
- idx_i, idx_j = osp.basename(src_path).split('_')[2:4]
- data_type = 'train' if f'{idx_i}_{idx_j}' in splits[
- 'train'] else 'val'
- if 'label' in src_path:
- dst_dir = osp.join(out_dir, 'ann_dir', data_type)
+ idx_i, idx_j = osp.basename(src_path).split("_")[2:4]
+ data_type = "train" if f"{idx_i}_{idx_j}" in splits["train"] else "val"
+ if "label" in src_path:
+ dst_dir = osp.join(out_dir, "ann_dir", data_type)
clip_big_image(src_path, dst_dir, args, to_label=True)
else:
- dst_dir = osp.join(out_dir, 'img_dir', data_type)
+ dst_dir = osp.join(out_dir, "img_dir", data_type)
clip_big_image(src_path, dst_dir, args, to_label=False)
prog_bar.update()
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/stare.py b/mmsegmentation/tools/convert_datasets/stare.py
index 29b78c0..d6b3ba2 100644
--- a/mmsegmentation/tools/convert_datasets/stare.py
+++ b/mmsegmentation/tools/convert_datasets/stare.py
@@ -14,19 +14,20 @@
def un_gz(src, dst):
g_file = gzip.GzipFile(src)
- with open(dst, 'wb+') as f:
+ with open(dst, "wb+") as f:
f.write(g_file.read())
g_file.close()
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert STARE dataset to mmsegmentation format')
- parser.add_argument('image_path', help='the path of stare-images.tar')
- parser.add_argument('labels_ah', help='the path of labels-ah.tar')
- parser.add_argument('labels_vk', help='the path of labels-vk.tar')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert STARE dataset to mmsegmentation format"
+ )
+ parser.add_argument("image_path", help="the path of stare-images.tar")
+ parser.add_argument("labels_ah", help="the path of labels-ah.tar")
+ parser.add_argument("labels_vk", help="the path of labels-vk.tar")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
args = parser.parse_args()
return args
@@ -37,72 +38,78 @@ def main():
labels_ah = args.labels_ah
labels_vk = args.labels_vk
if args.out_dir is None:
- out_dir = osp.join('data', 'STARE')
+ out_dir = osp.join("data", "STARE")
else:
out_dir = args.out_dir
- print('Making directories...')
+ print("Making directories...")
mmcv.mkdir_or_exist(out_dir)
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "images", "validation"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "training"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "annotations", "validation"))
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'gz'))
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'files'))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "gz"))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "files"))
- print('Extracting stare-images.tar...')
+ print("Extracting stare-images.tar...")
with tarfile.open(image_path) as f:
- f.extractall(osp.join(tmp_dir, 'gz'))
+ f.extractall(osp.join(tmp_dir, "gz"))
- for filename in os.listdir(osp.join(tmp_dir, 'gz')):
+ for filename in os.listdir(osp.join(tmp_dir, "gz")):
un_gz(
- osp.join(tmp_dir, 'gz', filename),
- osp.join(tmp_dir, 'files',
- osp.splitext(filename)[0]))
+ osp.join(tmp_dir, "gz", filename),
+ osp.join(tmp_dir, "files", osp.splitext(filename)[0]),
+ )
- now_dir = osp.join(tmp_dir, 'files')
+ now_dir = osp.join(tmp_dir, "files")
- assert len(os.listdir(now_dir)) == STARE_LEN, \
- 'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
+ assert (
+ len(os.listdir(now_dir)) == STARE_LEN
+ ), f"len(os.listdir(now_dir)) != {STARE_LEN}"
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'training',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir, "images", "training", osp.splitext(filename)[0] + ".png"
+ ),
+ )
for filename in sorted(os.listdir(now_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(now_dir, filename))
mmcv.imwrite(
img,
- osp.join(out_dir, 'images', 'validation',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir, "images", "validation", osp.splitext(filename)[0] + ".png"
+ ),
+ )
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'gz'))
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'files'))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "gz"))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "files"))
- print('Extracting labels-ah.tar...')
+ print("Extracting labels-ah.tar...")
with tarfile.open(labels_ah) as f:
- f.extractall(osp.join(tmp_dir, 'gz'))
+ f.extractall(osp.join(tmp_dir, "gz"))
- for filename in os.listdir(osp.join(tmp_dir, 'gz')):
+ for filename in os.listdir(osp.join(tmp_dir, "gz")):
un_gz(
- osp.join(tmp_dir, 'gz', filename),
- osp.join(tmp_dir, 'files',
- osp.splitext(filename)[0]))
+ osp.join(tmp_dir, "gz", filename),
+ osp.join(tmp_dir, "files", osp.splitext(filename)[0]),
+ )
- now_dir = osp.join(tmp_dir, 'files')
+ now_dir = osp.join(tmp_dir, "files")
- assert len(os.listdir(now_dir)) == STARE_LEN, \
- 'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
+ assert (
+ len(os.listdir(now_dir)) == STARE_LEN
+ ), f"len(os.listdir(now_dir)) != {STARE_LEN}"
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))
@@ -112,55 +119,76 @@ def main():
# 128 equivalent to '1 if value >= 128 else 0'
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'training',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "training",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
for filename in sorted(os.listdir(now_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(now_dir, filename))
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'gz'))
- mmcv.mkdir_or_exist(osp.join(tmp_dir, 'files'))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "gz"))
+ mmcv.mkdir_or_exist(osp.join(tmp_dir, "files"))
- print('Extracting labels-vk.tar...')
+ print("Extracting labels-vk.tar...")
with tarfile.open(labels_vk) as f:
- f.extractall(osp.join(tmp_dir, 'gz'))
+ f.extractall(osp.join(tmp_dir, "gz"))
- for filename in os.listdir(osp.join(tmp_dir, 'gz')):
+ for filename in os.listdir(osp.join(tmp_dir, "gz")):
un_gz(
- osp.join(tmp_dir, 'gz', filename),
- osp.join(tmp_dir, 'files',
- osp.splitext(filename)[0]))
+ osp.join(tmp_dir, "gz", filename),
+ osp.join(tmp_dir, "files", osp.splitext(filename)[0]),
+ )
- now_dir = osp.join(tmp_dir, 'files')
+ now_dir = osp.join(tmp_dir, "files")
- assert len(os.listdir(now_dir)) == STARE_LEN, \
- 'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
+ assert (
+ len(os.listdir(now_dir)) == STARE_LEN
+ ), f"len(os.listdir(now_dir)) != {STARE_LEN}"
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'training',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "training",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
for filename in sorted(os.listdir(now_dir))[TRAINING_LEN:]:
img = mmcv.imread(osp.join(now_dir, filename))
mmcv.imwrite(
img[:, :, 0] // 128,
- osp.join(out_dir, 'annotations', 'validation',
- osp.splitext(filename)[0] + '.png'))
+ osp.join(
+ out_dir,
+ "annotations",
+ "validation",
+ osp.splitext(filename)[0] + ".png",
+ ),
+ )
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/convert_datasets/vaihingen.py b/mmsegmentation/tools/convert_datasets/vaihingen.py
index b025ae5..83ea17d 100644
--- a/mmsegmentation/tools/convert_datasets/vaihingen.py
+++ b/mmsegmentation/tools/convert_datasets/vaihingen.py
@@ -13,20 +13,23 @@
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert vaihingen dataset to mmsegmentation format')
- parser.add_argument('dataset_path', help='vaihingen folder path')
- parser.add_argument('--tmp_dir', help='path of the temporary directory')
- parser.add_argument('-o', '--out_dir', help='output path')
+ description="Convert vaihingen dataset to mmsegmentation format"
+ )
+ parser.add_argument("dataset_path", help="vaihingen folder path")
+ parser.add_argument("--tmp_dir", help="path of the temporary directory")
+ parser.add_argument("-o", "--out_dir", help="output path")
parser.add_argument(
- '--clip_size',
+ "--clip_size",
type=int,
- help='clipped size of image after preparation',
- default=512)
+ help="clipped size of image after preparation",
+ default=512,
+ )
parser.add_argument(
- '--stride_size',
+ "--stride_size",
type=int,
- help='stride of clipping original images',
- default=256)
+ help="stride of clipping original images",
+ default=256,
+ )
args = parser.parse_args()
return args
@@ -44,10 +47,16 @@ def clip_big_image(image_path, clip_save_dir, to_label=False):
cs = args.clip_size
ss = args.stride_size
- num_rows = math.ceil((h - cs) / ss) if math.ceil(
- (h - cs) / ss) * ss + cs >= h else math.ceil((h - cs) / ss) + 1
- num_cols = math.ceil((w - cs) / ss) if math.ceil(
- (w - cs) / ss) * ss + cs >= w else math.ceil((w - cs) / ss) + 1
+ num_rows = (
+ math.ceil((h - cs) / ss)
+ if math.ceil((h - cs) / ss) * ss + cs >= h
+ else math.ceil((h - cs) / ss) + 1
+ )
+ num_cols = (
+ math.ceil((w - cs) / ss)
+ if math.ceil((w - cs) / ss) * ss + cs >= w
+ else math.ceil((w - cs) / ss) + 1
+ )
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
xmin = x * cs
@@ -57,99 +66,141 @@ def clip_big_image(image_path, clip_save_dir, to_label=False):
ymin = ymin.ravel()
xmin_offset = np.where(xmin + cs > w, w - xmin - cs, np.zeros_like(xmin))
ymin_offset = np.where(ymin + cs > h, h - ymin - cs, np.zeros_like(ymin))
- boxes = np.stack([
- xmin + xmin_offset, ymin + ymin_offset,
- np.minimum(xmin + cs, w),
- np.minimum(ymin + cs, h)
- ],
- axis=1)
+ boxes = np.stack(
+ [
+ xmin + xmin_offset,
+ ymin + ymin_offset,
+ np.minimum(xmin + cs, w),
+ np.minimum(ymin + cs, h),
+ ],
+ axis=1,
+ )
if to_label:
- color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
- [255, 255, 0], [0, 255, 0], [0, 255, 255],
- [0, 0, 255]])
- flatten_v = np.matmul(
- image.reshape(-1, c),
- np.array([2, 3, 4]).reshape(3, 1))
+ color_map = np.array(
+ [
+ [0, 0, 0],
+ [255, 255, 255],
+ [255, 0, 0],
+ [255, 255, 0],
+ [0, 255, 0],
+ [0, 255, 255],
+ [0, 0, 255],
+ ]
+ )
+ flatten_v = np.matmul(image.reshape(-1, c), np.array([2, 3, 4]).reshape(3, 1))
out = np.zeros_like(flatten_v)
for idx, class_color in enumerate(color_map):
- value_idx = np.matmul(class_color,
- np.array([2, 3, 4]).reshape(3, 1))
+ value_idx = np.matmul(class_color, np.array([2, 3, 4]).reshape(3, 1))
out[flatten_v == value_idx] = idx
image = out.reshape(h, w)
for box in boxes:
start_x, start_y, end_x, end_y = box
- clipped_image = image[start_y:end_y,
- start_x:end_x] if to_label else image[
- start_y:end_y, start_x:end_x, :]
- area_idx = osp.basename(image_path).split('_')[3].strip('.tif')
+ clipped_image = (
+ image[start_y:end_y, start_x:end_x]
+ if to_label
+ else image[start_y:end_y, start_x:end_x, :]
+ )
+ area_idx = osp.basename(image_path).split("_")[3].strip(".tif")
mmcv.imwrite(
clipped_image.astype(np.uint8),
- osp.join(clip_save_dir,
- f'{area_idx}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
+ osp.join(
+ clip_save_dir, f"{area_idx}_{start_x}_{start_y}_{end_x}_{end_y}.png"
+ ),
+ )
def main():
splits = {
- 'train': [
- 'area1', 'area11', 'area13', 'area15', 'area17', 'area21',
- 'area23', 'area26', 'area28', 'area3', 'area30', 'area32',
- 'area34', 'area37', 'area5', 'area7'
+ "train": [
+ "area1",
+ "area11",
+ "area13",
+ "area15",
+ "area17",
+ "area21",
+ "area23",
+ "area26",
+ "area28",
+ "area3",
+ "area30",
+ "area32",
+ "area34",
+ "area37",
+ "area5",
+ "area7",
],
- 'val': [
- 'area6', 'area24', 'area35', 'area16', 'area14', 'area22',
- 'area10', 'area4', 'area2', 'area20', 'area8', 'area31', 'area33',
- 'area27', 'area38', 'area12', 'area29'
+ "val": [
+ "area6",
+ "area24",
+ "area35",
+ "area16",
+ "area14",
+ "area22",
+ "area10",
+ "area4",
+ "area2",
+ "area20",
+ "area8",
+ "area31",
+ "area33",
+ "area27",
+ "area38",
+ "area12",
+ "area29",
],
}
dataset_path = args.dataset_path
if args.out_dir is None:
- out_dir = osp.join('data', 'vaihingen')
+ out_dir = osp.join("data", "vaihingen")
else:
out_dir = args.out_dir
- print('Making directories...')
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
- mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
+ print("Making directories...")
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "img_dir", "val"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "train"))
+ mmcv.mkdir_or_exist(osp.join(out_dir, "ann_dir", "val"))
- zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
- print('Find the data', zipp_list)
+ zipp_list = glob.glob(os.path.join(dataset_path, "*.zip"))
+ print("Find the data", zipp_list)
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
for zipp in zipp_list:
zip_file = zipfile.ZipFile(zipp)
zip_file.extractall(tmp_dir)
- src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
- if 'ISPRS_semantic_labeling_Vaihingen' in zipp:
+ src_path_list = glob.glob(os.path.join(tmp_dir, "*.tif"))
+ if "ISPRS_semantic_labeling_Vaihingen" in zipp:
src_path_list = glob.glob(
- os.path.join(os.path.join(tmp_dir, 'top'), '*.tif'))
- if 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE' in zipp: # noqa
- src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
+ os.path.join(os.path.join(tmp_dir, "top"), "*.tif")
+ )
+ if (
+ "ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE" in zipp
+ ): # noqa
+ src_path_list = glob.glob(os.path.join(tmp_dir, "*.tif"))
# delete unused area9 ground truth
for area_ann in src_path_list:
- if 'area9' in area_ann:
+ if "area9" in area_ann:
src_path_list.remove(area_ann)
prog_bar = mmcv.ProgressBar(len(src_path_list))
for i, src_path in enumerate(src_path_list):
- area_idx = osp.basename(src_path).split('_')[3].strip('.tif')
- data_type = 'train' if area_idx in splits['train'] else 'val'
- if 'noBoundary' in src_path:
- dst_dir = osp.join(out_dir, 'ann_dir', data_type)
+ area_idx = osp.basename(src_path).split("_")[3].strip(".tif")
+ data_type = "train" if area_idx in splits["train"] else "val"
+ if "noBoundary" in src_path:
+ dst_dir = osp.join(out_dir, "ann_dir", data_type)
clip_big_image(src_path, dst_dir, to_label=True)
else:
- dst_dir = osp.join(out_dir, 'img_dir', data_type)
+ dst_dir = osp.join(out_dir, "img_dir", data_type)
clip_big_image(src_path, dst_dir, to_label=False)
prog_bar.update()
- print('Removing the temporary files...')
+ print("Removing the temporary files...")
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
main()
diff --git a/mmsegmentation/tools/convert_datasets/voc_aug.py b/mmsegmentation/tools/convert_datasets/voc_aug.py
index 1d42c27..05342e2 100644
--- a/mmsegmentation/tools/convert_datasets/voc_aug.py
+++ b/mmsegmentation/tools/convert_datasets/voc_aug.py
@@ -13,9 +13,9 @@
def convert_mat(mat_file, in_dir, out_dir):
data = loadmat(osp.join(in_dir, mat_file))
- mask = data['GTcls'][0]['Segmentation'][0].astype(np.uint8)
- seg_filename = osp.join(out_dir, mat_file.replace('.mat', '.png'))
- Image.fromarray(mask).save(seg_filename, 'PNG')
+ mask = data["GTcls"][0]["Segmentation"][0].astype(np.uint8)
+ seg_filename = osp.join(out_dir, mat_file.replace(".mat", ".png"))
+ Image.fromarray(mask).save(seg_filename, "PNG")
def generate_aug_list(merged_list, excluded_list):
@@ -24,12 +24,12 @@ def generate_aug_list(merged_list, excluded_list):
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert PASCAL VOC annotations to mmsegmentation format')
- parser.add_argument('devkit_path', help='pascal voc devkit path')
- parser.add_argument('aug_path', help='pascal voc aug path')
- parser.add_argument('-o', '--out_dir', help='output path')
- parser.add_argument(
- '--nproc', default=1, type=int, help='number of process')
+ description="Convert PASCAL VOC annotations to mmsegmentation format"
+ )
+ parser.add_argument("devkit_path", help="pascal voc devkit path")
+ parser.add_argument("aug_path", help="pascal voc aug path")
+ parser.add_argument("-o", "--out_dir", help="output path")
+ parser.add_argument("--nproc", default=1, type=int, help="number of process")
args = parser.parse_args()
return args
@@ -40,53 +40,50 @@ def main():
aug_path = args.aug_path
nproc = args.nproc
if args.out_dir is None:
- out_dir = osp.join(devkit_path, 'VOC2012', 'SegmentationClassAug')
+ out_dir = osp.join(devkit_path, "VOC2012", "SegmentationClassAug")
else:
out_dir = args.out_dir
mmcv.mkdir_or_exist(out_dir)
- in_dir = osp.join(aug_path, 'dataset', 'cls')
+ in_dir = osp.join(aug_path, "dataset", "cls")
mmcv.track_parallel_progress(
partial(convert_mat, in_dir=in_dir, out_dir=out_dir),
- list(mmcv.scandir(in_dir, suffix='.mat')),
- nproc=nproc)
+ list(mmcv.scandir(in_dir, suffix=".mat")),
+ nproc=nproc,
+ )
full_aug_list = []
- with open(osp.join(aug_path, 'dataset', 'train.txt')) as f:
+ with open(osp.join(aug_path, "dataset", "train.txt")) as f:
full_aug_list += [line.strip() for line in f]
- with open(osp.join(aug_path, 'dataset', 'val.txt')) as f:
+ with open(osp.join(aug_path, "dataset", "val.txt")) as f:
full_aug_list += [line.strip() for line in f]
with open(
- osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
- 'train.txt')) as f:
+ osp.join(devkit_path, "VOC2012/ImageSets/Segmentation", "train.txt")
+ ) as f:
ori_train_list = [line.strip() for line in f]
- with open(
- osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
- 'val.txt')) as f:
+ with open(osp.join(devkit_path, "VOC2012/ImageSets/Segmentation", "val.txt")) as f:
val_list = [line.strip() for line in f]
- aug_train_list = generate_aug_list(ori_train_list + full_aug_list,
- val_list)
- assert len(aug_train_list) == AUG_LEN, 'len(aug_train_list) != {}'.format(
- AUG_LEN)
+ aug_train_list = generate_aug_list(ori_train_list + full_aug_list, val_list)
+ assert len(aug_train_list) == AUG_LEN, "len(aug_train_list) != {}".format(AUG_LEN)
with open(
- osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
- 'trainaug.txt'), 'w') as f:
- f.writelines(line + '\n' for line in aug_train_list)
+ osp.join(devkit_path, "VOC2012/ImageSets/Segmentation", "trainaug.txt"), "w"
+ ) as f:
+ f.writelines(line + "\n" for line in aug_train_list)
aug_list = generate_aug_list(full_aug_list, ori_train_list + val_list)
- assert len(aug_list) == AUG_LEN - len(
- ori_train_list), 'len(aug_list) != {}'.format(AUG_LEN -
- len(ori_train_list))
+ assert len(aug_list) == AUG_LEN - len(ori_train_list), "len(aug_list) != {}".format(
+ AUG_LEN - len(ori_train_list)
+ )
with open(
- osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation', 'aug.txt'),
- 'w') as f:
- f.writelines(line + '\n' for line in aug_list)
+ osp.join(devkit_path, "VOC2012/ImageSets/Segmentation", "aug.txt"), "w"
+ ) as f:
+ f.writelines(line + "\n" for line in aug_list)
- print('Done!')
+ print("Done!")
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/deploy_test.py b/mmsegmentation/tools/deploy_test.py
index eca5430..bc212e2 100644
--- a/mmsegmentation/tools/deploy_test.py
+++ b/mmsegmentation/tools/deploy_test.py
@@ -22,28 +22,31 @@
class ONNXRuntimeSegmentor(BaseSegmentor):
def __init__(self, onnx_file: str, cfg: Any, device_id: int):
- super(ONNXRuntimeSegmentor, self).__init__()
+ super().__init__()
import onnxruntime as ort
# get the custom op path
- ort_custom_op_path = ''
+ ort_custom_op_path = ""
try:
from mmcv.ops import get_onnxruntime_op_path
+
ort_custom_op_path = get_onnxruntime_op_path()
except (ImportError, ModuleNotFoundError):
- warnings.warn('If input model has custom op from mmcv, \
- you may have to build mmcv with ONNXRuntime from source.')
+ warnings.warn(
+ "If input model has custom op from mmcv, \
+ you may have to build mmcv with ONNXRuntime from source."
+ )
session_options = ort.SessionOptions()
# register custom op for onnxruntime
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
- providers = ['CPUExecutionProvider']
+ providers = ["CPUExecutionProvider"]
options = [{}]
- is_cuda_available = ort.get_device() == 'GPU'
+ is_cuda_available = ort.get_device() == "GPU"
if is_cuda_available:
- providers.insert(0, 'CUDAExecutionProvider')
- options.insert(0, {'device_id': device_id})
+ providers.insert(0, "CUDAExecutionProvider")
+ options.insert(0, {"device_id": device_id})
sess.set_providers(providers, options)
@@ -58,58 +61,60 @@ def __init__(self, onnx_file: str, cfg: Any, device_id: int):
self.is_cuda_available = is_cuda_available
def extract_feat(self, imgs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
def encode_decode(self, img, img_metas):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
def forward_train(self, imgs, img_metas, **kwargs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
- def simple_test(self, img: torch.Tensor, img_meta: Iterable,
- **kwargs) -> list:
+ def simple_test(self, img: torch.Tensor, img_meta: Iterable, **kwargs) -> list:
if not self.is_cuda_available:
img = img.detach().cpu()
elif self.device_id >= 0:
img = img.cuda(self.device_id)
device_type = img.device.type
self.io_binding.bind_input(
- name='input',
+ name="input",
device_type=device_type,
device_id=self.device_id,
element_type=np.float32,
shape=img.shape,
- buffer_ptr=img.data_ptr())
+ buffer_ptr=img.data_ptr(),
+ )
self.sess.run_with_iobinding(self.io_binding)
seg_pred = self.io_binding.copy_outputs_to_cpu()[0]
# whole might support dynamic reshape
- ori_shape = img_meta[0]['ori_shape']
- if not (ori_shape[0] == seg_pred.shape[-2]
- and ori_shape[1] == seg_pred.shape[-1]):
+ ori_shape = img_meta[0]["ori_shape"]
+ if not (
+ ori_shape[0] == seg_pred.shape[-2] and ori_shape[1] == seg_pred.shape[-1]
+ ):
seg_pred = torch.from_numpy(seg_pred).float()
- seg_pred = resize(
- seg_pred, size=tuple(ori_shape[:2]), mode='nearest')
+ seg_pred = resize(seg_pred, size=tuple(ori_shape[:2]), mode="nearest")
seg_pred = seg_pred.long().detach().cpu().numpy()
seg_pred = seg_pred[0]
seg_pred = list(seg_pred)
return seg_pred
def aug_test(self, imgs, img_metas, **kwargs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
class TensorRTSegmentor(BaseSegmentor):
def __init__(self, trt_file: str, cfg: Any, device_id: int):
- super(TensorRTSegmentor, self).__init__()
+ super().__init__()
from mmcv.tensorrt import TRTWraper, load_tensorrt_plugin
+
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
- warnings.warn('If input model has custom op from mmcv, \
- you may have to build mmcv with TensorRT from source.')
- model = TRTWraper(
- trt_file, input_names=['input'], output_names=['output'])
+ warnings.warn(
+ "If input model has custom op from mmcv, \
+ you may have to build mmcv with TensorRT from source."
+ )
+ model = TRTWraper(trt_file, input_names=["input"], output_names=["output"])
self.model = model
self.device_id = device_id
@@ -117,104 +122,111 @@ def __init__(self, trt_file: str, cfg: Any, device_id: int):
self.test_mode = cfg.model.test_cfg.mode
def extract_feat(self, imgs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
def encode_decode(self, img, img_metas):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
def forward_train(self, imgs, img_metas, **kwargs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
- def simple_test(self, img: torch.Tensor, img_meta: Iterable,
- **kwargs) -> list:
+ def simple_test(self, img: torch.Tensor, img_meta: Iterable, **kwargs) -> list:
with torch.cuda.device(self.device_id), torch.no_grad():
- seg_pred = self.model({'input': img})['output']
+ seg_pred = self.model({"input": img})["output"]
seg_pred = seg_pred.detach().cpu().numpy()
# whole might support dynamic reshape
- ori_shape = img_meta[0]['ori_shape']
- if not (ori_shape[0] == seg_pred.shape[-2]
- and ori_shape[1] == seg_pred.shape[-1]):
+ ori_shape = img_meta[0]["ori_shape"]
+ if not (
+ ori_shape[0] == seg_pred.shape[-2] and ori_shape[1] == seg_pred.shape[-1]
+ ):
seg_pred = torch.from_numpy(seg_pred).float()
- seg_pred = resize(
- seg_pred, size=tuple(ori_shape[:2]), mode='nearest')
+ seg_pred = resize(seg_pred, size=tuple(ori_shape[:2]), mode="nearest")
seg_pred = seg_pred.long().detach().cpu().numpy()
seg_pred = seg_pred[0]
seg_pred = list(seg_pred)
return seg_pred
def aug_test(self, imgs, img_metas, **kwargs):
- raise NotImplementedError('This method is not implemented.')
+ raise NotImplementedError("This method is not implemented.")
def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser(
- description='mmseg backend test (and eval)')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('model', help='Input model file')
+ parser = argparse.ArgumentParser(description="mmseg backend test (and eval)")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("model", help="Input model file")
parser.add_argument(
- '--backend',
- help='Backend of the model.',
- choices=['onnxruntime', 'tensorrt'])
- parser.add_argument('--out', help='output result file in pickle format')
+ "--backend", help="Backend of the model.", choices=["onnxruntime", "tensorrt"]
+ )
+ parser.add_argument("--out", help="output result file in pickle format")
parser.add_argument(
- '--format-only',
- action='store_true',
- help='Format the output results without perform evaluation. It is'
- 'useful when you want to format the result to a specific format and '
- 'submit it to the test server')
+ "--format-only",
+ action="store_true",
+ help="Format the output results without perform evaluation. It is"
+ "useful when you want to format the result to a specific format and "
+ "submit it to the test server",
+ )
parser.add_argument(
- '--eval',
+ "--eval",
type=str,
- nargs='+',
+ nargs="+",
help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
- ' for generic datasets, and "cityscapes" for Cityscapes')
- parser.add_argument('--show', action='store_true', help='show results')
+ ' for generic datasets, and "cityscapes" for Cityscapes',
+ )
+ parser.add_argument("--show", action="store_true", help="show results")
parser.add_argument(
- '--show-dir', help='directory where painted images will be saved')
+ "--show-dir", help="directory where painted images will be saved"
+ )
parser.add_argument(
- '--options',
- nargs='+',
+ "--options",
+ nargs="+",
action=DictAction,
help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. Override some settings in the '
- 'used config, the key-value pair in xxx=yyy format will be merged '
- 'into config file. If the value to be overwritten is a list, it '
+ "not be supported in version v0.22.0. Override some settings in the "
+ "used config, the key-value pair in xxx=yyy format will be merged "
+ "into config file. If the value to be overwritten is a list, it "
'should be like key="[a,b]" or key=a,b It also allows nested '
'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
- 'marks are necessary and that no white space is allowed.')
+ "marks are necessary and that no white space is allowed.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
parser.add_argument(
- '--eval-options',
- nargs='+',
+ "--eval-options",
+ nargs="+",
action=DictAction,
- help='custom options for evaluation')
+ help="custom options for evaluation",
+ )
parser.add_argument(
- '--opacity',
+ "--opacity",
type=float,
default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
- parser.add_argument('--local_rank', type=int, default=0)
+ help="Opacity of painted segmentation map. In (0, 1] range.",
+ )
+ parser.add_argument("--local_rank", type=int, default=0)
args = parser.parse_args()
- if 'LOCAL_RANK' not in os.environ:
- os.environ['LOCAL_RANK'] = str(args.local_rank)
+ if "LOCAL_RANK" not in os.environ:
+ os.environ["LOCAL_RANK"] = str(args.local_rank)
if args.options and args.cfg_options:
raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ "--options and --cfg-options cannot be both "
+ "specified, --options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ warnings.warn(
+ "--options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
args.cfg_options = args.options
return args
@@ -223,17 +235,17 @@ def parse_args() -> argparse.Namespace:
def main():
args = parse_args()
- assert args.out or args.eval or args.format_only or args.show \
- or args.show_dir, \
- ('Please specify at least one operation (save/eval/format/show the '
- 'results / save the results) with the argument "--out", "--eval"'
- ', "--format-only", "--show" or "--show-dir"')
+ assert args.out or args.eval or args.format_only or args.show or args.show_dir, (
+ "Please specify at least one operation (save/eval/format/show the "
+ 'results / save the results) with the argument "--out", "--eval"'
+ ', "--format-only", "--show" or "--show-dir"'
+ )
if args.eval and args.format_only:
- raise ValueError('--eval and --format_only cannot be both specified')
+ raise ValueError("--eval and --format_only cannot be both specified")
- if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
- raise ValueError('The output file must be a pkl file.')
+ if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
+ raise ValueError("The output file must be a pkl file.")
cfg = mmcv.Config.fromfile(args.config)
if args.cfg_options is not None:
@@ -252,14 +264,15 @@ def main():
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
- shuffle=False)
+ shuffle=False,
+ )
# load onnx config and meta
cfg.model.train_cfg = None
- if args.backend == 'onnxruntime':
+ if args.backend == "onnxruntime":
model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0)
- elif args.backend == 'tensorrt':
+ elif args.backend == "tensorrt":
model = TensorRTSegmentor(args.model, cfg=cfg, device_id=0)
model.CLASSES = dataset.CLASSES
@@ -270,25 +283,27 @@ def main():
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
- efficient_test = eval_kwargs.get('efficient_test', False)
+ efficient_test = eval_kwargs.get("efficient_test", False)
if efficient_test:
warnings.warn(
- '``efficient_test=True`` does not have effect in tools/test.py, '
- 'the evaluation and format results are CPU memory efficient by '
- 'default')
+ "``efficient_test=True`` does not have effect in tools/test.py, "
+ "the evaluation and format results are CPU memory efficient by "
+ "default"
+ )
- eval_on_format_results = (
- args.eval is not None and 'cityscapes' in args.eval)
+ eval_on_format_results = args.eval is not None and "cityscapes" in args.eval
if eval_on_format_results:
- assert len(args.eval) == 1, 'eval on format results is not ' \
- 'applicable for metrics other than ' \
- 'cityscapes'
+ assert len(args.eval) == 1, (
+ "eval on format results is not "
+ "applicable for metrics other than "
+ "cityscapes"
+ )
if args.format_only or eval_on_format_results:
- if 'imgfile_prefix' in eval_kwargs:
- tmpdir = eval_kwargs['imgfile_prefix']
+ if "imgfile_prefix" in eval_kwargs:
+ tmpdir = eval_kwargs["imgfile_prefix"]
else:
- tmpdir = '.format_cityscapes'
- eval_kwargs.setdefault('imgfile_prefix', tmpdir)
+ tmpdir = ".format_cityscapes"
+ eval_kwargs.setdefault("imgfile_prefix", tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
@@ -303,17 +318,19 @@ def main():
args.opacity,
pre_eval=args.eval is not None and not eval_on_format_results,
format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
+ format_args=eval_kwargs,
+ )
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
- 'The behavior of ``args.out`` has been changed since MMSeg '
- 'v0.16, the pickled outputs could be seg map as type of '
- 'np.array, pre-eval results or file paths for '
- '``dataset.format_results()``.')
- print(f'\nwriting results to {args.out}')
+ "The behavior of ``args.out`` has been changed since MMSeg "
+ "v0.16, the pickled outputs could be seg map as type of "
+ "np.array, pre-eval results or file paths for "
+ "``dataset.format_results()``."
+ )
+ print(f"\nwriting results to {args.out}")
mmcv.dump(results, args.out)
if args.eval:
dataset.evaluate(results, args.eval, **eval_kwargs)
@@ -322,17 +339,17 @@ def main():
shutil.rmtree(tmpdir)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
# Following strings of text style are from colorama package
- bright_style, reset_style = '\x1b[1m', '\x1b[0m'
- red_text, blue_text = '\x1b[31m', '\x1b[34m'
- white_background = '\x1b[107m'
+ bright_style, reset_style = "\x1b[1m", "\x1b[0m"
+ red_text, blue_text = "\x1b[31m", "\x1b[34m"
+ white_background = "\x1b[107m"
msg = white_background + bright_style + red_text
- msg += 'DeprecationWarning: This tool will be deprecated in future. '
- msg += blue_text + 'Welcome to use the unified model deployment toolbox '
- msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+ msg += "DeprecationWarning: This tool will be deprecated in future. "
+ msg += blue_text + "Welcome to use the unified model deployment toolbox "
+ msg += "MMDeploy: https://github.com/open-mmlab/mmdeploy"
msg += reset_style
warnings.warn(msg)
diff --git a/mmsegmentation/tools/get_flops.py b/mmsegmentation/tools/get_flops.py
index e30c36f..e9ac47d 100644
--- a/mmsegmentation/tools/get_flops.py
+++ b/mmsegmentation/tools/get_flops.py
@@ -8,15 +8,11 @@
def parse_args():
- parser = argparse.ArgumentParser(
- description='Get the FLOPs of a segmentor')
- parser.add_argument('config', help='train config file path')
+ parser = argparse.ArgumentParser(description="Get the FLOPs of a segmentor")
+ parser.add_argument("config", help="train config file path")
parser.add_argument(
- '--shape',
- type=int,
- nargs='+',
- default=[2048, 1024],
- help='input image size')
+ "--shape", type=int, nargs="+", default=[2048, 1024], help="input image size"
+ )
args = parser.parse_args()
return args
@@ -28,33 +24,39 @@ def main():
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
- input_shape = (3, ) + tuple(args.shape)
+ input_shape = (3,) + tuple(args.shape)
else:
- raise ValueError('invalid input shape')
+ raise ValueError("invalid input shape")
cfg = Config.fromfile(args.config)
cfg.model.pretrained = None
model = build_segmentor(
- cfg.model,
- train_cfg=cfg.get('train_cfg'),
- test_cfg=cfg.get('test_cfg')).cuda()
+ cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg")
+ ).cuda()
model.eval()
- if hasattr(model, 'forward_dummy'):
+ if hasattr(model, "forward_dummy"):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
- 'FLOPs counter is currently not currently supported with {}'.
- format(model.__class__.__name__))
+ "FLOPs counter is currently not currently supported with {}".format(
+ model.__class__.__name__
+ )
+ )
flops, params = get_model_complexity_info(model, input_shape)
- split_line = '=' * 30
- print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
- split_line, input_shape, flops, params))
- print('!!!Please be cautious if you use the results in papers. '
- 'You may need to check if all ops are supported and verify that the '
- 'flops computation is correct.')
-
-
-if __name__ == '__main__':
+ split_line = "=" * 30
+ print(
+ "{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}".format(
+ split_line, input_shape, flops, params
+ )
+ )
+ print(
+ "!!!Please be cautious if you use the results in papers. "
+ "You may need to check if all ops are supported and verify that the "
+ "flops computation is correct."
+ )
+
+
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/inference.py b/mmsegmentation/tools/inference.py
index 53c55fd..93ab0d2 100644
--- a/mmsegmentation/tools/inference.py
+++ b/mmsegmentation/tools/inference.py
@@ -7,7 +7,7 @@
import pandas as pd
from mmcv import Config
from mmcv.parallel import MMDataParallel
-from mmcv.runner import load_checkpoint
+
from mmseg.apis import single_gpu_test
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.models import build_segmentor
@@ -36,7 +36,7 @@ def get_latest(work_dir: Path) -> Union[str, None]:
if not latest_file.exists():
return None
- with open(latest_file, "r", encoding="utf8") as f:
+ with open(latest_file, encoding="utf8") as f:
path = f.read()
return path
@@ -47,7 +47,7 @@ def get_last_checkpoint(work_dir: Path) -> Union[str, None]:
if not latest_checkpoint_file.exists():
return None
- with open(latest_checkpoint_file, "r", encoding="utf8") as f:
+ with open(latest_checkpoint_file, encoding="utf8") as f:
checkpoint_path = f.read()
return checkpoint_path
@@ -107,7 +107,7 @@ def main():
submission = pd.read_csv(SAMPLE_PATH, index_col=None)
- with open(TEST_JSON_PATH, "r", encoding="utf8") as outfile:
+ with open(TEST_JSON_PATH, encoding="utf8") as outfile:
datas = json.load(outfile)
# PredictionString 대입
diff --git a/mmsegmentation/tools/model_converters/beit2mmseg.py b/mmsegmentation/tools/model_converters/beit2mmseg.py
index 91b91fa..681f07e 100644
--- a/mmsegmentation/tools/model_converters/beit2mmseg.py
+++ b/mmsegmentation/tools/model_converters/beit2mmseg.py
@@ -12,17 +12,17 @@ def convert_beit(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
- if k.startswith('blocks'):
- new_key = k.replace('blocks', 'layers')
- if 'norm' in new_key:
- new_key = new_key.replace('norm', 'ln')
- elif 'mlp.fc1' in new_key:
- new_key = new_key.replace('mlp.fc1', 'ffn.layers.0.0')
- elif 'mlp.fc2' in new_key:
- new_key = new_key.replace('mlp.fc2', 'ffn.layers.1')
+ if k.startswith("blocks"):
+ new_key = k.replace("blocks", "layers")
+ if "norm" in new_key:
+ new_key = new_key.replace("norm", "ln")
+ elif "mlp.fc1" in new_key:
+ new_key = new_key.replace("mlp.fc1", "ffn.layers.0.0")
+ elif "mlp.fc2" in new_key:
+ new_key = new_key.replace("mlp.fc2", "ffn.layers.1")
new_ckpt[new_key] = v
- elif k.startswith('patch_embed'):
- new_key = k.replace('patch_embed.proj', 'patch_embed.projection')
+ elif k.startswith("patch_embed"):
+ new_key = k.replace("patch_embed.proj", "patch_embed.projection")
new_ckpt[new_key] = v
else:
new_key = k
@@ -33,18 +33,19 @@ def convert_beit(ckpt):
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in official pretrained beit models to'
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys in official pretrained beit models to"
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
+ parser.add_argument("dst", help="save path")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
- elif 'model' in checkpoint:
- state_dict = checkpoint['model']
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ elif "model" in checkpoint:
+ state_dict = checkpoint["model"]
else:
state_dict = checkpoint
weight = convert_beit(state_dict)
@@ -52,5 +53,5 @@ def main():
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/mit2mmseg.py b/mmsegmentation/tools/model_converters/mit2mmseg.py
index 2eff1f7..ec8ac4e 100644
--- a/mmsegmentation/tools/model_converters/mit2mmseg.py
+++ b/mmsegmentation/tools/model_converters/mit2mmseg.py
@@ -12,43 +12,43 @@ def convert_mit(ckpt):
new_ckpt = OrderedDict()
# Process the concat between q linear weights and kv linear weights
for k, v in ckpt.items():
- if k.startswith('head'):
+ if k.startswith("head"):
continue
# patch embedding conversion
- elif k.startswith('patch_embed'):
- stage_i = int(k.split('.')[0].replace('patch_embed', ''))
- new_k = k.replace(f'patch_embed{stage_i}', f'layers.{stage_i-1}.0')
+ elif k.startswith("patch_embed"):
+ stage_i = int(k.split(".")[0].replace("patch_embed", ""))
+ new_k = k.replace(f"patch_embed{stage_i}", f"layers.{stage_i-1}.0")
new_v = v
- if 'proj.' in new_k:
- new_k = new_k.replace('proj.', 'projection.')
+ if "proj." in new_k:
+ new_k = new_k.replace("proj.", "projection.")
# transformer encoder layer conversion
- elif k.startswith('block'):
- stage_i = int(k.split('.')[0].replace('block', ''))
- new_k = k.replace(f'block{stage_i}', f'layers.{stage_i-1}.1')
+ elif k.startswith("block"):
+ stage_i = int(k.split(".")[0].replace("block", ""))
+ new_k = k.replace(f"block{stage_i}", f"layers.{stage_i-1}.1")
new_v = v
- if 'attn.q.' in new_k:
- sub_item_k = k.replace('q.', 'kv.')
- new_k = new_k.replace('q.', 'attn.in_proj_')
+ if "attn.q." in new_k:
+ sub_item_k = k.replace("q.", "kv.")
+ new_k = new_k.replace("q.", "attn.in_proj_")
new_v = torch.cat([v, ckpt[sub_item_k]], dim=0)
- elif 'attn.kv.' in new_k:
+ elif "attn.kv." in new_k:
continue
- elif 'attn.proj.' in new_k:
- new_k = new_k.replace('proj.', 'attn.out_proj.')
- elif 'attn.sr.' in new_k:
- new_k = new_k.replace('sr.', 'sr.')
- elif 'mlp.' in new_k:
- string = f'{new_k}-'
- new_k = new_k.replace('mlp.', 'ffn.layers.')
- if 'fc1.weight' in new_k or 'fc2.weight' in new_k:
+ elif "attn.proj." in new_k:
+ new_k = new_k.replace("proj.", "attn.out_proj.")
+ elif "attn.sr." in new_k:
+ new_k = new_k.replace("sr.", "sr.")
+ elif "mlp." in new_k:
+ string = f"{new_k}-"
+ new_k = new_k.replace("mlp.", "ffn.layers.")
+ if "fc1.weight" in new_k or "fc2.weight" in new_k:
new_v = v.reshape((*v.shape, 1, 1))
- new_k = new_k.replace('fc1.', '0.')
- new_k = new_k.replace('dwconv.dwconv.', '1.')
- new_k = new_k.replace('fc2.', '4.')
- string += f'{new_k} {v.shape}-{new_v.shape}'
+ new_k = new_k.replace("fc1.", "0.")
+ new_k = new_k.replace("dwconv.dwconv.", "1.")
+ new_k = new_k.replace("fc2.", "4.")
+ string += f"{new_k} {v.shape}-{new_v.shape}"
# norm layer conversion
- elif k.startswith('norm'):
- stage_i = int(k.split('.')[0].replace('norm', ''))
- new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i-1}.2')
+ elif k.startswith("norm"):
+ stage_i = int(k.split(".")[0].replace("norm", ""))
+ new_k = k.replace(f"norm{stage_i}", f"layers.{stage_i-1}.2")
new_v = v
else:
new_k = k
@@ -59,18 +59,19 @@ def convert_mit(ckpt):
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in official pretrained segformer to '
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys in official pretrained segformer to "
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
+ parser.add_argument("dst", help="save path")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
- elif 'model' in checkpoint:
- state_dict = checkpoint['model']
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ elif "model" in checkpoint:
+ state_dict = checkpoint["model"]
else:
state_dict = checkpoint
weight = convert_mit(state_dict)
@@ -78,5 +79,5 @@ def main():
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/stdc2mmseg.py b/mmsegmentation/tools/model_converters/stdc2mmseg.py
index 9241f86..c2daa07 100644
--- a/mmsegmentation/tools/model_converters/stdc2mmseg.py
+++ b/mmsegmentation/tools/model_converters/stdc2mmseg.py
@@ -9,32 +9,44 @@
def convert_stdc(ckpt, stdc_type):
new_state_dict = {}
- if stdc_type == 'STDC1':
- stage_lst = ['0', '1', '2.0', '2.1', '3.0', '3.1', '4.0', '4.1']
+ if stdc_type == "STDC1":
+ stage_lst = ["0", "1", "2.0", "2.1", "3.0", "3.1", "4.0", "4.1"]
else:
stage_lst = [
- '0', '1', '2.0', '2.1', '2.2', '2.3', '3.0', '3.1', '3.2', '3.3',
- '3.4', '4.0', '4.1', '4.2'
+ "0",
+ "1",
+ "2.0",
+ "2.1",
+ "2.2",
+ "2.3",
+ "3.0",
+ "3.1",
+ "3.2",
+ "3.3",
+ "3.4",
+ "4.0",
+ "4.1",
+ "4.2",
]
for k, v in ckpt.items():
ori_k = k
flag = False
- if 'cp.' in k:
- k = k.replace('cp.', '')
- if 'features.' in k:
- num_layer = int(k.split('.')[1])
- feature_key_lst = 'features.' + str(num_layer) + '.'
- stages_key_lst = 'stages.' + stage_lst[num_layer] + '.'
+ if "cp." in k:
+ k = k.replace("cp.", "")
+ if "features." in k:
+ num_layer = int(k.split(".")[1])
+ feature_key_lst = "features." + str(num_layer) + "."
+ stages_key_lst = "stages." + stage_lst[num_layer] + "."
k = k.replace(feature_key_lst, stages_key_lst)
flag = True
- if 'conv_list' in k:
- k = k.replace('conv_list', 'layers')
+ if "conv_list" in k:
+ k = k.replace("conv_list", "layers")
flag = True
- if 'avd_layer.' in k:
- if 'avd_layer.0' in k:
- k = k.replace('avd_layer.0', 'downsample.conv')
- elif 'avd_layer.1' in k:
- k = k.replace('avd_layer.1', 'downsample.bn')
+ if "avd_layer." in k:
+ if "avd_layer.0" in k:
+ k = k.replace("avd_layer.0", "downsample.conv")
+ elif "avd_layer.1" in k:
+ k = k.replace("avd_layer.1", "downsample.bn")
flag = True
if flag:
new_state_dict[k] = ckpt[ori_k]
@@ -44,28 +56,28 @@ def convert_stdc(ckpt, stdc_type):
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in official pretrained STDC1/2 to '
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path')
+ description="Convert keys in official pretrained STDC1/2 to "
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
- parser.add_argument('type', help='model type: STDC1 or STDC2')
+ parser.add_argument("dst", help="save path")
+ parser.add_argument("type", help="model type: STDC1 or STDC2")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
- elif 'model' in checkpoint:
- state_dict = checkpoint['model']
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ elif "model" in checkpoint:
+ state_dict = checkpoint["model"]
else:
state_dict = checkpoint
- assert args.type in ['STDC1',
- 'STDC2'], 'STD type should be STDC1 or STDC2!'
+ assert args.type in ["STDC1", "STDC2"], "STD type should be STDC1 or STDC2!"
weight = convert_stdc(state_dict, args.type)
mmcv.mkdir_or_exist(osp.dirname(args.dst))
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/swin2mmseg.py b/mmsegmentation/tools/model_converters/swin2mmseg.py
index 03b24ce..ebe98c2 100644
--- a/mmsegmentation/tools/model_converters/swin2mmseg.py
+++ b/mmsegmentation/tools/model_converters/swin2mmseg.py
@@ -14,8 +14,7 @@ def convert_swin(ckpt):
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
- x = x[:, [0, 2, 1, 3], :].transpose(1,
- 2).reshape(out_channel, in_channel)
+ x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel)
return x
def correct_unfold_norm_order(x):
@@ -25,32 +24,32 @@ def correct_unfold_norm_order(x):
return x
for k, v in ckpt.items():
- if k.startswith('head'):
+ if k.startswith("head"):
continue
- elif k.startswith('layers'):
+ elif k.startswith("layers"):
new_v = v
- if 'attn.' in k:
- new_k = k.replace('attn.', 'attn.w_msa.')
- elif 'mlp.' in k:
- if 'mlp.fc1.' in k:
- new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.')
- elif 'mlp.fc2.' in k:
- new_k = k.replace('mlp.fc2.', 'ffn.layers.1.')
+ if "attn." in k:
+ new_k = k.replace("attn.", "attn.w_msa.")
+ elif "mlp." in k:
+ if "mlp.fc1." in k:
+ new_k = k.replace("mlp.fc1.", "ffn.layers.0.0.")
+ elif "mlp.fc2." in k:
+ new_k = k.replace("mlp.fc2.", "ffn.layers.1.")
else:
- new_k = k.replace('mlp.', 'ffn.')
- elif 'downsample' in k:
+ new_k = k.replace("mlp.", "ffn.")
+ elif "downsample" in k:
new_k = k
- if 'reduction.' in k:
+ if "reduction." in k:
new_v = correct_unfold_reduction_order(v)
- elif 'norm.' in k:
+ elif "norm." in k:
new_v = correct_unfold_norm_order(v)
else:
new_k = k
- new_k = new_k.replace('layers', 'stages', 1)
- elif k.startswith('patch_embed'):
+ new_k = new_k.replace("layers", "stages", 1)
+ elif k.startswith("patch_embed"):
new_v = v
- if 'proj' in k:
- new_k = k.replace('proj', 'projection')
+ if "proj" in k:
+ new_k = k.replace("proj", "projection")
else:
new_k = k
else:
@@ -64,18 +63,19 @@ def correct_unfold_norm_order(x):
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in official pretrained swin models to'
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys in official pretrained swin models to"
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
+ parser.add_argument("dst", help="save path")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
- if 'state_dict' in checkpoint:
- state_dict = checkpoint['state_dict']
- elif 'model' in checkpoint:
- state_dict = checkpoint['model']
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
+ if "state_dict" in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ elif "model" in checkpoint:
+ state_dict = checkpoint["model"]
else:
state_dict = checkpoint
weight = convert_swin(state_dict)
@@ -83,5 +83,5 @@ def main():
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/twins2mmseg.py b/mmsegmentation/tools/model_converters/twins2mmseg.py
index ab64aa5..a87d441 100644
--- a/mmsegmentation/tools/model_converters/twins2mmseg.py
+++ b/mmsegmentation/tools/model_converters/twins2mmseg.py
@@ -14,67 +14,67 @@ def convert_twins(args, ckpt):
for k, v in list(ckpt.items()):
new_v = v
- if k.startswith('head'):
+ if k.startswith("head"):
continue
- elif k.startswith('patch_embeds'):
- if 'proj.' in k:
- new_k = k.replace('proj.', 'projection.')
+ elif k.startswith("patch_embeds"):
+ if "proj." in k:
+ new_k = k.replace("proj.", "projection.")
else:
new_k = k
- elif k.startswith('blocks'):
+ elif k.startswith("blocks"):
# Union
- if 'attn.q.' in k:
- new_k = k.replace('q.', 'attn.in_proj_')
- new_v = torch.cat([v, ckpt[k.replace('attn.q.', 'attn.kv.')]],
- dim=0)
- elif 'mlp.fc1' in k:
- new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
- elif 'mlp.fc2' in k:
- new_k = k.replace('mlp.fc2', 'ffn.layers.1')
+ if "attn.q." in k:
+ new_k = k.replace("q.", "attn.in_proj_")
+ new_v = torch.cat([v, ckpt[k.replace("attn.q.", "attn.kv.")]], dim=0)
+ elif "mlp.fc1" in k:
+ new_k = k.replace("mlp.fc1", "ffn.layers.0.0")
+ elif "mlp.fc2" in k:
+ new_k = k.replace("mlp.fc2", "ffn.layers.1")
# Only pcpvt
- elif args.model == 'pcpvt':
- if 'attn.proj.' in k:
- new_k = k.replace('proj.', 'attn.out_proj.')
+ elif args.model == "pcpvt":
+ if "attn.proj." in k:
+ new_k = k.replace("proj.", "attn.out_proj.")
else:
new_k = k
# Only svt
else:
- if 'attn.proj.' in k:
- k_lst = k.split('.')
+ if "attn.proj." in k:
+ k_lst = k.split(".")
if int(k_lst[2]) % 2 == 1:
- new_k = k.replace('proj.', 'attn.out_proj.')
+ new_k = k.replace("proj.", "attn.out_proj.")
else:
new_k = k
else:
new_k = k
- new_k = new_k.replace('blocks.', 'layers.')
- elif k.startswith('pos_block'):
- new_k = k.replace('pos_block', 'position_encodings')
- if 'proj.0.' in new_k:
- new_k = new_k.replace('proj.0.', 'proj.')
+ new_k = new_k.replace("blocks.", "layers.")
+ elif k.startswith("pos_block"):
+ new_k = k.replace("pos_block", "position_encodings")
+ if "proj.0." in new_k:
+ new_k = new_k.replace("proj.0.", "proj.")
else:
new_k = k
- if 'attn.kv.' not in k:
+ if "attn.kv." not in k:
new_ckpt[new_k] = new_v
return new_ckpt
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in timm pretrained vit models to '
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys in timm pretrained vit models to "
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
- parser.add_argument('model', help='model: pcpvt or svt')
+ parser.add_argument("dst", help="save path")
+ parser.add_argument("model", help="model: pcpvt or svt")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
- if 'state_dict' in checkpoint:
+ if "state_dict" in checkpoint:
# timm checkpoint
- state_dict = checkpoint['state_dict']
+ state_dict = checkpoint["state_dict"]
else:
state_dict = checkpoint
@@ -83,5 +83,5 @@ def main():
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/vit2mmseg.py b/mmsegmentation/tools/model_converters/vit2mmseg.py
index bc18ebe..1ad665f 100644
--- a/mmsegmentation/tools/model_converters/vit2mmseg.py
+++ b/mmsegmentation/tools/model_converters/vit2mmseg.py
@@ -13,29 +13,29 @@ def convert_vit(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
- if k.startswith('head'):
+ if k.startswith("head"):
continue
- if k.startswith('norm'):
- new_k = k.replace('norm.', 'ln1.')
- elif k.startswith('patch_embed'):
- if 'proj' in k:
- new_k = k.replace('proj', 'projection')
+ if k.startswith("norm"):
+ new_k = k.replace("norm.", "ln1.")
+ elif k.startswith("patch_embed"):
+ if "proj" in k:
+ new_k = k.replace("proj", "projection")
else:
new_k = k
- elif k.startswith('blocks'):
- if 'norm' in k:
- new_k = k.replace('norm', 'ln')
- elif 'mlp.fc1' in k:
- new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
- elif 'mlp.fc2' in k:
- new_k = k.replace('mlp.fc2', 'ffn.layers.1')
- elif 'attn.qkv' in k:
- new_k = k.replace('attn.qkv.', 'attn.attn.in_proj_')
- elif 'attn.proj' in k:
- new_k = k.replace('attn.proj', 'attn.attn.out_proj')
+ elif k.startswith("blocks"):
+ if "norm" in k:
+ new_k = k.replace("norm", "ln")
+ elif "mlp.fc1" in k:
+ new_k = k.replace("mlp.fc1", "ffn.layers.0.0")
+ elif "mlp.fc2" in k:
+ new_k = k.replace("mlp.fc2", "ffn.layers.1")
+ elif "attn.qkv" in k:
+ new_k = k.replace("attn.qkv.", "attn.attn.in_proj_")
+ elif "attn.proj" in k:
+ new_k = k.replace("attn.proj", "attn.attn.out_proj")
else:
new_k = k
- new_k = new_k.replace('blocks.', 'layers.')
+ new_k = new_k.replace("blocks.", "layers.")
else:
new_k = k
new_ckpt[new_k] = v
@@ -45,20 +45,21 @@ def convert_vit(ckpt):
def main():
parser = argparse.ArgumentParser(
- description='Convert keys in timm pretrained vit models to '
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys in timm pretrained vit models to "
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
+ parser.add_argument("dst", help="save path")
args = parser.parse_args()
- checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
- if 'state_dict' in checkpoint:
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location="cpu")
+ if "state_dict" in checkpoint:
# timm checkpoint
- state_dict = checkpoint['state_dict']
- elif 'model' in checkpoint:
+ state_dict = checkpoint["state_dict"]
+ elif "model" in checkpoint:
# deit checkpoint
- state_dict = checkpoint['model']
+ state_dict = checkpoint["model"]
else:
state_dict = checkpoint
weight = convert_vit(state_dict)
@@ -66,5 +67,5 @@ def main():
torch.save(weight, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_converters/vitjax2mmseg.py b/mmsegmentation/tools/model_converters/vitjax2mmseg.py
index 585f408..58c075b 100644
--- a/mmsegmentation/tools/model_converters/vitjax2mmseg.py
+++ b/mmsegmentation/tools/model_converters/vitjax2mmseg.py
@@ -11,84 +11,95 @@ def vit_jax_to_torch(jax_weights, num_layer=12):
torch_weights = dict()
# patch embedding
- conv_filters = jax_weights['embedding/kernel']
+ conv_filters = jax_weights["embedding/kernel"]
conv_filters = conv_filters.permute(3, 2, 0, 1)
- torch_weights['patch_embed.projection.weight'] = conv_filters
- torch_weights['patch_embed.projection.bias'] = jax_weights[
- 'embedding/bias']
+ torch_weights["patch_embed.projection.weight"] = conv_filters
+ torch_weights["patch_embed.projection.bias"] = jax_weights["embedding/bias"]
# pos embedding
- torch_weights['pos_embed'] = jax_weights[
- 'Transformer/posembed_input/pos_embedding']
+ torch_weights["pos_embed"] = jax_weights["Transformer/posembed_input/pos_embedding"]
# cls token
- torch_weights['cls_token'] = jax_weights['cls']
+ torch_weights["cls_token"] = jax_weights["cls"]
# head
- torch_weights['ln1.weight'] = jax_weights['Transformer/encoder_norm/scale']
- torch_weights['ln1.bias'] = jax_weights['Transformer/encoder_norm/bias']
+ torch_weights["ln1.weight"] = jax_weights["Transformer/encoder_norm/scale"]
+ torch_weights["ln1.bias"] = jax_weights["Transformer/encoder_norm/bias"]
# transformer blocks
for i in range(num_layer):
- jax_block = f'Transformer/encoderblock_{i}'
- torch_block = f'layers.{i}'
+ jax_block = f"Transformer/encoderblock_{i}"
+ torch_block = f"layers.{i}"
# attention norm
- torch_weights[f'{torch_block}.ln1.weight'] = jax_weights[
- f'{jax_block}/LayerNorm_0/scale']
- torch_weights[f'{torch_block}.ln1.bias'] = jax_weights[
- f'{jax_block}/LayerNorm_0/bias']
+ torch_weights[f"{torch_block}.ln1.weight"] = jax_weights[
+ f"{jax_block}/LayerNorm_0/scale"
+ ]
+ torch_weights[f"{torch_block}.ln1.bias"] = jax_weights[
+ f"{jax_block}/LayerNorm_0/bias"
+ ]
# attention
query_weight = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/query/kernel']
+ f"{jax_block}/MultiHeadDotProductAttention_1/query/kernel"
+ ]
query_bias = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/query/bias']
+ f"{jax_block}/MultiHeadDotProductAttention_1/query/bias"
+ ]
key_weight = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/key/kernel']
- key_bias = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/key/bias']
+ f"{jax_block}/MultiHeadDotProductAttention_1/key/kernel"
+ ]
+ key_bias = jax_weights[f"{jax_block}/MultiHeadDotProductAttention_1/key/bias"]
value_weight = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/value/kernel']
+ f"{jax_block}/MultiHeadDotProductAttention_1/value/kernel"
+ ]
value_bias = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/value/bias']
+ f"{jax_block}/MultiHeadDotProductAttention_1/value/bias"
+ ]
qkv_weight = torch.from_numpy(
- np.stack((query_weight, key_weight, value_weight), 1))
+ np.stack((query_weight, key_weight, value_weight), 1)
+ )
qkv_weight = torch.flatten(qkv_weight, start_dim=1)
- qkv_bias = torch.from_numpy(
- np.stack((query_bias, key_bias, value_bias), 0))
+ qkv_bias = torch.from_numpy(np.stack((query_bias, key_bias, value_bias), 0))
qkv_bias = torch.flatten(qkv_bias, start_dim=0)
- torch_weights[f'{torch_block}.attn.attn.in_proj_weight'] = qkv_weight
- torch_weights[f'{torch_block}.attn.attn.in_proj_bias'] = qkv_bias
+ torch_weights[f"{torch_block}.attn.attn.in_proj_weight"] = qkv_weight
+ torch_weights[f"{torch_block}.attn.attn.in_proj_bias"] = qkv_bias
to_out_weight = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/out/kernel']
+ f"{jax_block}/MultiHeadDotProductAttention_1/out/kernel"
+ ]
to_out_weight = torch.flatten(to_out_weight, start_dim=0, end_dim=1)
- torch_weights[
- f'{torch_block}.attn.attn.out_proj.weight'] = to_out_weight
- torch_weights[f'{torch_block}.attn.attn.out_proj.bias'] = jax_weights[
- f'{jax_block}/MultiHeadDotProductAttention_1/out/bias']
+ torch_weights[f"{torch_block}.attn.attn.out_proj.weight"] = to_out_weight
+ torch_weights[f"{torch_block}.attn.attn.out_proj.bias"] = jax_weights[
+ f"{jax_block}/MultiHeadDotProductAttention_1/out/bias"
+ ]
# mlp norm
- torch_weights[f'{torch_block}.ln2.weight'] = jax_weights[
- f'{jax_block}/LayerNorm_2/scale']
- torch_weights[f'{torch_block}.ln2.bias'] = jax_weights[
- f'{jax_block}/LayerNorm_2/bias']
+ torch_weights[f"{torch_block}.ln2.weight"] = jax_weights[
+ f"{jax_block}/LayerNorm_2/scale"
+ ]
+ torch_weights[f"{torch_block}.ln2.bias"] = jax_weights[
+ f"{jax_block}/LayerNorm_2/bias"
+ ]
# mlp
- torch_weights[f'{torch_block}.ffn.layers.0.0.weight'] = jax_weights[
- f'{jax_block}/MlpBlock_3/Dense_0/kernel']
- torch_weights[f'{torch_block}.ffn.layers.0.0.bias'] = jax_weights[
- f'{jax_block}/MlpBlock_3/Dense_0/bias']
- torch_weights[f'{torch_block}.ffn.layers.1.weight'] = jax_weights[
- f'{jax_block}/MlpBlock_3/Dense_1/kernel']
- torch_weights[f'{torch_block}.ffn.layers.1.bias'] = jax_weights[
- f'{jax_block}/MlpBlock_3/Dense_1/bias']
+ torch_weights[f"{torch_block}.ffn.layers.0.0.weight"] = jax_weights[
+ f"{jax_block}/MlpBlock_3/Dense_0/kernel"
+ ]
+ torch_weights[f"{torch_block}.ffn.layers.0.0.bias"] = jax_weights[
+ f"{jax_block}/MlpBlock_3/Dense_0/bias"
+ ]
+ torch_weights[f"{torch_block}.ffn.layers.1.weight"] = jax_weights[
+ f"{jax_block}/MlpBlock_3/Dense_1/kernel"
+ ]
+ torch_weights[f"{torch_block}.ffn.layers.1.bias"] = jax_weights[
+ f"{jax_block}/MlpBlock_3/Dense_1/bias"
+ ]
# transpose weights
for k, v in torch_weights.items():
- if 'weight' in k and 'patch_embed' not in k and 'ln' not in k:
+ if "weight" in k and "patch_embed" not in k and "ln" not in k:
v = v.permute(1, 0)
torch_weights[k] = v
@@ -98,11 +109,12 @@ def vit_jax_to_torch(jax_weights, num_layer=12):
def main():
# stole refactoring code from Robin Strudel, thanks
parser = argparse.ArgumentParser(
- description='Convert keys from jax official pretrained vit models to '
- 'MMSegmentation style.')
- parser.add_argument('src', help='src model path or url')
+ description="Convert keys from jax official pretrained vit models to "
+ "MMSegmentation style."
+ )
+ parser.add_argument("src", help="src model path or url")
# The dst path must be a full path of the new checkpoint.
- parser.add_argument('dst', help='save path')
+ parser.add_argument("dst", help="save path")
args = parser.parse_args()
jax_weights = np.load(args.src)
@@ -110,7 +122,7 @@ def main():
for key in jax_weights.files:
value = torch.from_numpy(jax_weights[key])
jax_weights_tensor[key] = value
- if 'L_16-i21k' in args.src:
+ if "L_16-i21k" in args.src:
num_layer = 24
else:
num_layer = 12
@@ -119,5 +131,5 @@ def main():
torch.save(torch_weights, args.dst)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/model_ensemble.py b/mmsegmentation/tools/model_ensemble.py
index b526650..9422d0f 100644
--- a/mmsegmentation/tools/model_ensemble.py
+++ b/mmsegmentation/tools/model_ensemble.py
@@ -25,9 +25,7 @@ def main(args):
cfg = mmcv.Config.fromfile(configs[0])
if args.aug_test:
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0
- ]
+ cfg.data.test.pipeline[1].img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
cfg.data.test.pipeline[1].flip = True
else:
cfg.data.test.pipeline[1].img_ratios = [1.0]
@@ -50,10 +48,10 @@ def main(args):
cfg.model.pretrained = None
cfg.data.test.test_mode = True
- model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
- if cfg.get('fp16', None):
+ model = build_segmentor(cfg.model, test_cfg=cfg.get("test_cfg"))
+ if cfg.get("fp16", None):
wrap_fp16_model(model)
- load_checkpoint(model, ckpt, map_location='cpu')
+ load_checkpoint(model, ckpt, map_location="cpu")
torch.cuda.empty_cache()
tmpdir = args.out
mmcv.mkdir_or_exist(tmpdir)
@@ -69,7 +67,8 @@ def main(args):
for model in models:
x, _ = scatter_kwargs(
- inputs=data, kwargs=None, target_gpus=model.device_ids)
+ inputs=data, kwargs=None, target_gpus=model.device_ids
+ )
if args.aug_test:
logits = model.module.aug_test_logits(**x[0])
else:
@@ -83,39 +82,42 @@ def main(args):
pred = result_logits.argmax(axis=1).squeeze()
img_info = dataset.img_infos[batch_indices[0]]
file_name = os.path.join(
- tmpdir, img_info['ann']['seg_map'].split(os.path.sep)[-1])
+ tmpdir, img_info["ann"]["seg_map"].split(os.path.sep)[-1]
+ )
Image.fromarray(pred.astype(np.uint8)).save(file_name)
prog_bar.update()
def parse_args():
- parser = argparse.ArgumentParser(
- description='Model Ensemble with logits result')
+ parser = argparse.ArgumentParser(description="Model Ensemble with logits result")
parser.add_argument(
- '--config', type=str, nargs='+', help='ensemble config files path')
+ "--config", type=str, nargs="+", help="ensemble config files path"
+ )
parser.add_argument(
- '--checkpoint',
- type=str,
- nargs='+',
- help='ensemble checkpoint files path')
+ "--checkpoint", type=str, nargs="+", help="ensemble checkpoint files path"
+ )
parser.add_argument(
- '--aug-test',
- action='store_true',
- help='control ensemble aug-result or single-result (default)')
+ "--aug-test",
+ action="store_true",
+ help="control ensemble aug-result or single-result (default)",
+ )
parser.add_argument(
- '--out', type=str, default='results', help='the dir to save result')
+ "--out", type=str, default="results", help="the dir to save result"
+ )
parser.add_argument(
- '--gpus', type=int, nargs='+', default=[0], help='id of gpu to use')
+ "--gpus", type=int, nargs="+", default=[0], help="id of gpu to use"
+ )
args = parser.parse_args()
- assert len(args.config) == len(args.checkpoint), \
- f'len(config) must equal len(checkpoint), ' \
- f'but len(config) = {len(args.config)} and' \
- f'len(checkpoint) = {len(args.checkpoint)}'
+ assert len(args.config) == len(args.checkpoint), (
+ f"len(config) must equal len(checkpoint), "
+ f"but len(config) = {len(args.config)} and"
+ f"len(checkpoint) = {len(args.checkpoint)}"
+ )
assert args.out, "ensemble result out-dir can't be None"
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
main(args)
diff --git a/mmsegmentation/tools/onnx2tensorrt.py b/mmsegmentation/tools/onnx2tensorrt.py
index 0f60dce..55762fe 100644
--- a/mmsegmentation/tools/onnx2tensorrt.py
+++ b/mmsegmentation/tools/onnx2tensorrt.py
@@ -11,8 +11,12 @@
import onnxruntime as ort
import torch
from mmcv.ops import get_onnxruntime_op_path
-from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt,
- save_trt_engine)
+from mmcv.tensorrt import (
+ TRTWraper,
+ is_tensorrt_plugin_loaded,
+ onnx2trt,
+ save_trt_engine,
+)
from mmseg.apis.inference import LoadImage
from mmseg.datasets import DATASETS
@@ -24,27 +28,29 @@ def get_GiB(x: int):
return x * (1 << 30)
-def _prepare_input_img(img_path: str,
- test_pipeline: Iterable[dict],
- shape: Optional[Iterable] = None,
- rescale_shape: Optional[Iterable] = None) -> dict:
+def _prepare_input_img(
+ img_path: str,
+ test_pipeline: Iterable[dict],
+ shape: Optional[Iterable] = None,
+ rescale_shape: Optional[Iterable] = None,
+) -> dict:
# build the data pipeline
if shape is not None:
- test_pipeline[1]['img_scale'] = (shape[1], shape[0])
- test_pipeline[1]['transforms'][0]['keep_ratio'] = False
+ test_pipeline[1]["img_scale"] = (shape[1], shape[0])
+ test_pipeline[1]["transforms"][0]["keep_ratio"] = False
test_pipeline = [LoadImage()] + test_pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img_path)
data = test_pipeline(data)
- imgs = data['img']
- img_metas = [i.data for i in data['img_metas']]
+ imgs = data["img"]
+ img_metas = [i.data for i in data["img_metas"]]
if rescale_shape is not None:
for img_meta in img_metas:
- img_meta['ori_shape'] = tuple(rescale_shape) + (3, )
+ img_meta["ori_shape"] = tuple(rescale_shape) + (3,)
- mm_inputs = {'imgs': imgs, 'img_metas': img_metas}
+ mm_inputs = {"imgs": imgs, "img_metas": img_metas}
return mm_inputs
@@ -53,34 +59,39 @@ def _update_input_img(img_list: Iterable, img_meta_list: Iterable):
# update img and its meta list
N = img_list[0].size(0)
img_meta = img_meta_list[0][0]
- img_shape = img_meta['img_shape']
- ori_shape = img_meta['ori_shape']
- pad_shape = img_meta['pad_shape']
- new_img_meta_list = [[{
- 'img_shape':
- img_shape,
- 'ori_shape':
- ori_shape,
- 'pad_shape':
- pad_shape,
- 'filename':
- img_meta['filename'],
- 'scale_factor':
- (img_shape[1] / ori_shape[1], img_shape[0] / ori_shape[0]) * 2,
- 'flip':
- False,
- } for _ in range(N)]]
+ img_shape = img_meta["img_shape"]
+ ori_shape = img_meta["ori_shape"]
+ pad_shape = img_meta["pad_shape"]
+ new_img_meta_list = [
+ [
+ {
+ "img_shape": img_shape,
+ "ori_shape": ori_shape,
+ "pad_shape": pad_shape,
+ "filename": img_meta["filename"],
+ "scale_factor": (
+ img_shape[1] / ori_shape[1],
+ img_shape[0] / ori_shape[0],
+ )
+ * 2,
+ "flip": False,
+ }
+ for _ in range(N)
+ ]
+ ]
return img_list, new_img_meta_list
-def show_result_pyplot(img: Union[str, np.ndarray],
- result: np.ndarray,
- palette: Optional[Iterable] = None,
- fig_size: Iterable[int] = (15, 10),
- opacity: float = 0.5,
- title: str = '',
- block: bool = True):
+def show_result_pyplot(
+ img: Union[str, np.ndarray],
+ result: np.ndarray,
+ palette: Optional[Iterable] = None,
+ fig_size: Iterable[int] = (15, 10),
+ opacity: float = 0.5,
+ title: str = "",
+ block: bool = True,
+):
img = mmcv.imread(img)
img = img.copy()
seg = result[0]
@@ -105,42 +116,45 @@ def show_result_pyplot(img: Union[str, np.ndarray],
plt.show(block=block)
-def onnx2tensorrt(onnx_file: str,
- trt_file: str,
- config: dict,
- input_config: dict,
- fp16: bool = False,
- verify: bool = False,
- show: bool = False,
- dataset: str = 'CityscapesDataset',
- workspace_size: int = 1,
- verbose: bool = False):
+def onnx2tensorrt(
+ onnx_file: str,
+ trt_file: str,
+ config: dict,
+ input_config: dict,
+ fp16: bool = False,
+ verify: bool = False,
+ show: bool = False,
+ dataset: str = "CityscapesDataset",
+ workspace_size: int = 1,
+ verbose: bool = False,
+):
import tensorrt as trt
- min_shape = input_config['min_shape']
- max_shape = input_config['max_shape']
+
+ min_shape = input_config["min_shape"]
+ max_shape = input_config["max_shape"]
# create trt engine and wrapper
- opt_shape_dict = {'input': [min_shape, min_shape, max_shape]}
+ opt_shape_dict = {"input": [min_shape, min_shape, max_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_file,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=fp16,
- max_workspace_size=max_workspace_size)
+ max_workspace_size=max_workspace_size,
+ )
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
- print(f'Successfully created TensorRT engine: {trt_file}')
+ print(f"Successfully created TensorRT engine: {trt_file}")
if verify:
inputs = _prepare_input_img(
- input_config['input_path'],
- config.data.test.pipeline,
- shape=min_shape[2:])
+ input_config["input_path"], config.data.test.pipeline, shape=min_shape[2:]
+ )
- imgs = inputs['imgs']
- img_metas = inputs['img_metas']
+ imgs = inputs["imgs"]
+ img_metas = inputs["img_metas"]
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
# update img_meta
@@ -160,15 +174,16 @@ def onnx2tensorrt(onnx_file: str,
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
- sess.set_providers(['CPUExecutionProvider'], [{}]) # use cpu mode
- onnx_output = sess.run(['output'],
- {'input': img_list[0].detach().numpy()})[0][0]
+ sess.set_providers(["CPUExecutionProvider"], [{}]) # use cpu mode
+ onnx_output = sess.run(["output"], {"input": img_list[0].detach().numpy()})[0][
+ 0
+ ]
# Get results from TensorRT
- trt_model = TRTWraper(trt_file, ['input'], ['output'])
+ trt_model = TRTWraper(trt_file, ["input"], ["output"])
with torch.no_grad():
- trt_outputs = trt_model({'input': img_list[0].contiguous().cuda()})
- trt_output = trt_outputs['output'][0].cpu().detach().numpy()
+ trt_outputs = trt_model({"input": img_list[0].contiguous().cuda()})
+ trt_output = trt_outputs["output"][0].cpu().detach().numpy()
if show:
dataset = DATASETS.get(dataset)
@@ -176,91 +191,94 @@ def onnx2tensorrt(onnx_file: str,
palette = dataset.PALETTE
show_result_pyplot(
- input_config['input_path'],
- (onnx_output[0].astype(np.uint8), ),
+ input_config["input_path"],
+ (onnx_output[0].astype(np.uint8),),
palette=palette,
- title='ONNXRuntime',
- block=False)
+ title="ONNXRuntime",
+ block=False,
+ )
show_result_pyplot(
- input_config['input_path'], (trt_output[0].astype(np.uint8), ),
+ input_config["input_path"],
+ (trt_output[0].astype(np.uint8),),
palette=palette,
- title='TensorRT')
+ title="TensorRT",
+ )
- np.testing.assert_allclose(
- onnx_output, trt_output, rtol=1e-03, atol=1e-05)
- print('TensorRT and ONNXRuntime output all close.')
+ np.testing.assert_allclose(onnx_output, trt_output, rtol=1e-03, atol=1e-05)
+ print("TensorRT and ONNXRuntime output all close.")
def parse_args():
parser = argparse.ArgumentParser(
- description='Convert MMSegmentation models from ONNX to TensorRT')
- parser.add_argument('config', help='Config file of the model')
- parser.add_argument('model', help='Path to the input ONNX model')
+ description="Convert MMSegmentation models from ONNX to TensorRT"
+ )
+ parser.add_argument("config", help="Config file of the model")
+ parser.add_argument("model", help="Path to the input ONNX model")
parser.add_argument(
- '--trt-file', type=str, help='Path to the output TensorRT engine')
+ "--trt-file", type=str, help="Path to the output TensorRT engine"
+ )
parser.add_argument(
- '--max-shape',
+ "--max-shape",
type=int,
nargs=4,
default=[1, 3, 400, 600],
- help='Maximum shape of model input.')
+ help="Maximum shape of model input.",
+ )
parser.add_argument(
- '--min-shape',
+ "--min-shape",
type=int,
nargs=4,
default=[1, 3, 400, 600],
- help='Minimum shape of model input.')
- parser.add_argument('--fp16', action='store_true', help='Enable fp16 mode')
- parser.add_argument(
- '--workspace-size',
- type=int,
- default=1,
- help='Max workspace size in GiB')
+ help="Minimum shape of model input.",
+ )
+ parser.add_argument("--fp16", action="store_true", help="Enable fp16 mode")
parser.add_argument(
- '--input-img', type=str, default='', help='Image for test')
+ "--workspace-size", type=int, default=1, help="Max workspace size in GiB"
+ )
+ parser.add_argument("--input-img", type=str, default="", help="Image for test")
parser.add_argument(
- '--show', action='store_true', help='Whether to show output results')
+ "--show", action="store_true", help="Whether to show output results"
+ )
parser.add_argument(
- '--dataset',
- type=str,
- default='CityscapesDataset',
- help='Dataset name')
+ "--dataset", type=str, default="CityscapesDataset", help="Dataset name"
+ )
parser.add_argument(
- '--verify',
- action='store_true',
- help='Verify the outputs of ONNXRuntime and TensorRT')
+ "--verify",
+ action="store_true",
+ help="Verify the outputs of ONNXRuntime and TensorRT",
+ )
parser.add_argument(
- '--verbose',
- action='store_true',
- help='Whether to verbose logging messages while creating \
- TensorRT engine.')
+ "--verbose",
+ action="store_true",
+ help="Whether to verbose logging messages while creating \
+ TensorRT engine.",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
- assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
+ assert is_tensorrt_plugin_loaded(), "TensorRT plugin should be compiled."
args = parse_args()
if not args.input_img:
- args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.png')
+ args.input_img = osp.join(osp.dirname(__file__), "../demo/demo.png")
# check arguments
- assert osp.exists(args.config), 'Config {} not found.'.format(args.config)
- assert osp.exists(args.model), \
- 'ONNX model {} not found.'.format(args.model)
- assert args.workspace_size >= 0, 'Workspace size less than 0.'
- assert DATASETS.get(args.dataset) is not None, \
- 'Dataset {} does not found.'.format(args.dataset)
+ assert osp.exists(args.config), f"Config {args.config} not found."
+ assert osp.exists(args.model), f"ONNX model {args.model} not found."
+ assert args.workspace_size >= 0, "Workspace size less than 0."
+ assert (
+ DATASETS.get(args.dataset) is not None
+ ), f"Dataset {args.dataset} does not found."
for max_value, min_value in zip(args.max_shape, args.min_shape):
- assert max_value >= min_value, \
- 'max_shape should be larger than min shape'
+ assert max_value >= min_value, "max_shape should be larger than min shape"
input_config = {
- 'min_shape': args.min_shape,
- 'max_shape': args.max_shape,
- 'input_path': args.input_img
+ "min_shape": args.min_shape,
+ "max_shape": args.max_shape,
+ "input_path": args.input_img,
}
cfg = mmcv.Config.fromfile(args.config)
@@ -274,16 +292,17 @@ def parse_args():
show=args.show,
dataset=args.dataset,
workspace_size=args.workspace_size,
- verbose=args.verbose)
+ verbose=args.verbose,
+ )
# Following strings of text style are from colorama package
- bright_style, reset_style = '\x1b[1m', '\x1b[0m'
- red_text, blue_text = '\x1b[31m', '\x1b[34m'
- white_background = '\x1b[107m'
+ bright_style, reset_style = "\x1b[1m", "\x1b[0m"
+ red_text, blue_text = "\x1b[31m", "\x1b[34m"
+ white_background = "\x1b[107m"
msg = white_background + bright_style + red_text
- msg += 'DeprecationWarning: This tool will be deprecated in future. '
- msg += blue_text + 'Welcome to use the unified model deployment toolbox '
- msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+ msg += "DeprecationWarning: This tool will be deprecated in future. "
+ msg += blue_text + "Welcome to use the unified model deployment toolbox "
+ msg += "MMDeploy: https://github.com/open-mmlab/mmdeploy"
msg += reset_style
warnings.warn(msg)
diff --git a/mmsegmentation/tools/print_config.py b/mmsegmentation/tools/print_config.py
index 3f9c08d..77bb514 100644
--- a/mmsegmentation/tools/print_config.py
+++ b/mmsegmentation/tools/print_config.py
@@ -8,41 +8,45 @@
def parse_args():
- parser = argparse.ArgumentParser(description='Print the whole config')
- parser.add_argument('config', help='config file path')
+ parser = argparse.ArgumentParser(description="Print the whole config")
+ parser.add_argument("config", help="config file path")
+ parser.add_argument("--graph", action="store_true", help="print the models graph")
parser.add_argument(
- '--graph', action='store_true', help='print the models graph')
- parser.add_argument(
- '--options',
- nargs='+',
+ "--options",
+ nargs="+",
action=DictAction,
help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. Override some settings in the '
- 'used config, the key-value pair in xxx=yyy format will be merged '
- 'into config file. If the value to be overwritten is a list, it '
+ "not be supported in version v0.22.0. Override some settings in the "
+ "used config, the key-value pair in xxx=yyy format will be merged "
+ "into config file. If the value to be overwritten is a list, it "
'should be like key="[a,b]" or key=a,b It also allows nested '
'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
- 'marks are necessary and that no white space is allowed.')
+ "marks are necessary and that no white space is allowed.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
args = parser.parse_args()
if args.options and args.cfg_options:
raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ "--options and --cfg-options cannot be both "
+ "specified, --options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options, '
- '--options will not be supported in version v0.22.0.')
+ warnings.warn(
+ "--options is deprecated in favor of --cfg-options, "
+ "--options will not be supported in version v0.22.0."
+ )
args.cfg_options = args.options
return args
@@ -54,16 +58,16 @@ def main():
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
- print(f'Config:\n{cfg.pretty_text}')
+ print(f"Config:\n{cfg.pretty_text}")
# dump config
- cfg.dump('example.py')
+ cfg.dump("example.py")
# dump models graph
if args.graph:
- model = init_segmentor(args.config, device='cpu')
- print(f'Model graph:\n{str(model)}')
- with open('example-graph.txt', 'w') as f:
+ model = init_segmentor(args.config, device="cpu")
+ print(f"Model graph:\n{str(model)}")
+ with open("example-graph.txt", "w") as f:
f.writelines(str(model))
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/publish_model.py b/mmsegmentation/tools/publish_model.py
index e266057..dbfc972 100644
--- a/mmsegmentation/tools/publish_model.py
+++ b/mmsegmentation/tools/publish_model.py
@@ -6,25 +6,24 @@
def parse_args():
- parser = argparse.ArgumentParser(
- description='Process a checkpoint to be published')
- parser.add_argument('in_file', help='input checkpoint filename')
- parser.add_argument('out_file', help='output checkpoint filename')
+ parser = argparse.ArgumentParser(description="Process a checkpoint to be published")
+ parser.add_argument("in_file", help="input checkpoint filename")
+ parser.add_argument("out_file", help="output checkpoint filename")
args = parser.parse_args()
return args
def process_checkpoint(in_file, out_file):
- checkpoint = torch.load(in_file, map_location='cpu')
+ checkpoint = torch.load(in_file, map_location="cpu")
# remove optimizer for smaller file size
- if 'optimizer' in checkpoint:
- del checkpoint['optimizer']
+ if "optimizer" in checkpoint:
+ del checkpoint["optimizer"]
# if it is necessary to remove some sensitive data in checkpoint['meta'],
# add the code here.
torch.save(checkpoint, out_file)
- sha = subprocess.check_output(['sha256sum', out_file]).decode()
- final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
- subprocess.Popen(['mv', out_file, final_file])
+ sha = subprocess.check_output(["sha256sum", out_file]).decode()
+ final_file = out_file.rstrip(".pth") + f"-{sha[:8]}.pth"
+ subprocess.Popen(["mv", out_file, final_file])
def main():
@@ -32,5 +31,5 @@ def main():
process_checkpoint(args.in_file, args.out_file)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/pytorch2onnx.py b/mmsegmentation/tools/pytorch2onnx.py
index 060d187..31e4410 100644
--- a/mmsegmentation/tools/pytorch2onnx.py
+++ b/mmsegmentation/tools/pytorch2onnx.py
@@ -26,9 +26,13 @@
def _convert_batchnorm(module):
module_output = module
if isinstance(module, torch.nn.SyncBatchNorm):
- module_output = torch.nn.BatchNorm2d(module.num_features, module.eps,
- module.momentum, module.affine,
- module.track_running_stats)
+ module_output = torch.nn.BatchNorm2d(
+ module.num_features,
+ module.eps,
+ module.momentum,
+ module.affine,
+ module.track_running_stats,
+ )
if module.affine:
module_output.weight.data = module.weight.data.clone().detach()
module_output.bias.data = module.bias.data.clone().detach()
@@ -56,45 +60,44 @@ def _demo_mm_inputs(input_shape, num_classes):
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
- segs = rng.randint(
- low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
- img_metas = [{
- 'img_shape': (H, W, C),
- 'ori_shape': (H, W, C),
- 'pad_shape': (H, W, C),
- 'filename': '.png',
- 'scale_factor': 1.0,
- 'flip': False,
- } for _ in range(N)]
+ segs = rng.randint(low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
+ img_metas = [
+ {
+ "img_shape": (H, W, C),
+ "ori_shape": (H, W, C),
+ "pad_shape": (H, W, C),
+ "filename": ".png",
+ "scale_factor": 1.0,
+ "flip": False,
+ }
+ for _ in range(N)
+ ]
mm_inputs = {
- 'imgs': torch.FloatTensor(imgs).requires_grad_(True),
- 'img_metas': img_metas,
- 'gt_semantic_seg': torch.LongTensor(segs)
+ "imgs": torch.FloatTensor(imgs).requires_grad_(True),
+ "img_metas": img_metas,
+ "gt_semantic_seg": torch.LongTensor(segs),
}
return mm_inputs
-def _prepare_input_img(img_path,
- test_pipeline,
- shape=None,
- rescale_shape=None):
+def _prepare_input_img(img_path, test_pipeline, shape=None, rescale_shape=None):
# build the data pipeline
if shape is not None:
- test_pipeline[1]['img_scale'] = (shape[1], shape[0])
- test_pipeline[1]['transforms'][0]['keep_ratio'] = False
+ test_pipeline[1]["img_scale"] = (shape[1], shape[0])
+ test_pipeline[1]["transforms"][0]["keep_ratio"] = False
test_pipeline = [LoadImage()] + test_pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = dict(img=img_path)
data = test_pipeline(data)
- imgs = data['img']
- img_metas = [i.data for i in data['img_metas']]
+ imgs = data["img"]
+ img_metas = [i.data for i in data["img_metas"]]
if rescale_shape is not None:
for img_meta in img_metas:
- img_meta['ori_shape'] = tuple(rescale_shape) + (3, )
+ img_meta["ori_shape"] = tuple(rescale_shape) + (3,)
- mm_inputs = {'imgs': imgs, 'img_metas': img_metas}
+ mm_inputs = {"imgs": imgs, "img_metas": img_metas}
return mm_inputs
@@ -107,33 +110,38 @@ def _update_input_img(img_list, img_meta_list, update_ori_shape=False):
if update_ori_shape:
ori_shape = img_shape
else:
- ori_shape = img_meta['ori_shape']
+ ori_shape = img_meta["ori_shape"]
pad_shape = img_shape
- new_img_meta_list = [[{
- 'img_shape':
- img_shape,
- 'ori_shape':
- ori_shape,
- 'pad_shape':
- pad_shape,
- 'filename':
- img_meta['filename'],
- 'scale_factor':
- (img_shape[1] / ori_shape[1], img_shape[0] / ori_shape[0]) * 2,
- 'flip':
- False,
- } for _ in range(N)]]
+ new_img_meta_list = [
+ [
+ {
+ "img_shape": img_shape,
+ "ori_shape": ori_shape,
+ "pad_shape": pad_shape,
+ "filename": img_meta["filename"],
+ "scale_factor": (
+ img_shape[1] / ori_shape[1],
+ img_shape[0] / ori_shape[0],
+ )
+ * 2,
+ "flip": False,
+ }
+ for _ in range(N)
+ ]
+ ]
return img_list, new_img_meta_list
-def pytorch2onnx(model,
- mm_inputs,
- opset_version=11,
- show=False,
- output_file='tmp.onnx',
- verify=False,
- dynamic_export=False):
+def pytorch2onnx(
+ model,
+ mm_inputs,
+ opset_version=11,
+ show=False,
+ output_file="tmp.onnx",
+ verify=False,
+ dynamic_export=False,
+):
"""Export Pytorch model to ONNX model and verify the outputs are same
between Pytorch and ONNX.
@@ -157,8 +165,8 @@ def pytorch2onnx(model,
else:
num_classes = model.decode_head.num_classes
- imgs = mm_inputs.pop('imgs')
- img_metas = mm_inputs.pop('img_metas')
+ imgs = mm_inputs.pop("imgs")
+ img_metas = mm_inputs.pop("img_metas")
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
@@ -168,50 +176,43 @@ def pytorch2onnx(model,
# replace original forward function
origin_forward = model.forward
model.forward = partial(
- model.forward,
- img_metas=img_meta_list,
- return_loss=False,
- rescale=True)
+ model.forward, img_metas=img_meta_list, return_loss=False, rescale=True
+ )
dynamic_axes = None
if dynamic_export:
- if test_mode == 'slide':
- dynamic_axes = {'input': {0: 'batch'}, 'output': {1: 'batch'}}
+ if test_mode == "slide":
+ dynamic_axes = {"input": {0: "batch"}, "output": {1: "batch"}}
else:
dynamic_axes = {
- 'input': {
- 0: 'batch',
- 2: 'height',
- 3: 'width'
- },
- 'output': {
- 1: 'batch',
- 2: 'height',
- 3: 'width'
- }
+ "input": {0: "batch", 2: "height", 3: "width"},
+ "output": {1: "batch", 2: "height", 3: "width"},
}
register_extra_symbolics(opset_version)
with torch.no_grad():
torch.onnx.export(
- model, (img_list, ),
+ model,
+ (img_list,),
output_file,
- input_names=['input'],
- output_names=['output'],
+ input_names=["input"],
+ output_names=["output"],
export_params=True,
keep_initializers_as_inputs=False,
verbose=show,
opset_version=opset_version,
- dynamic_axes=dynamic_axes)
- print(f'Successfully exported ONNX model: {output_file}')
+ dynamic_axes=dynamic_axes,
+ )
+ print(f"Successfully exported ONNX model: {output_file}")
model.forward = origin_forward
if verify:
# check by onnx
import onnx
+
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
- if dynamic_export and test_mode == 'whole':
+ if dynamic_export and test_mode == "whole":
# scale image for dynamic shape test
img_list = [resize(_, scale_factor=1.5) for _ in img_list]
# concate flip image for batch test
@@ -223,7 +224,8 @@ def pytorch2onnx(model,
# update img_meta
img_list, img_meta_list = _update_input_img(
- img_list, img_meta_list, test_mode == 'whole')
+ img_list, img_meta_list, test_mode == "whole"
+ )
# check the numerical value
# get pytorch output
@@ -233,102 +235,110 @@ def pytorch2onnx(model,
# get onnx output
input_all = [node.name for node in onnx_model.graph.input]
- input_initializer = [
- node.name for node in onnx_model.graph.initializer
- ]
+ input_initializer = [node.name for node in onnx_model.graph.initializer]
net_feed_input = list(set(input_all) - set(input_initializer))
- assert (len(net_feed_input) == 1)
+ assert len(net_feed_input) == 1
sess = rt.InferenceSession(output_file)
- onnx_result = sess.run(
- None, {net_feed_input[0]: img_list[0].detach().numpy()})[0][0]
+ onnx_result = sess.run(None, {net_feed_input[0]: img_list[0].detach().numpy()})[
+ 0
+ ][0]
# show segmentation results
if show:
import os.path as osp
import cv2
- img = img_meta_list[0][0]['filename']
+
+ img = img_meta_list[0][0]["filename"]
if not osp.exists(img):
img = imgs[0][:3, ...].permute(1, 2, 0) * 255
img = img.detach().numpy().astype(np.uint8)
ori_shape = img.shape[:2]
else:
- ori_shape = LoadImage()({'img': img})['ori_shape']
+ ori_shape = LoadImage()({"img": img})["ori_shape"]
# resize onnx_result to ori_shape
- onnx_result_ = cv2.resize(onnx_result[0].astype(np.uint8),
- (ori_shape[1], ori_shape[0]))
+ onnx_result_ = cv2.resize(
+ onnx_result[0].astype(np.uint8), (ori_shape[1], ori_shape[0])
+ )
show_result_pyplot(
model,
- img, (onnx_result_, ),
+ img,
+ (onnx_result_,),
palette=model.PALETTE,
block=False,
- title='ONNXRuntime',
- opacity=0.5)
+ title="ONNXRuntime",
+ opacity=0.5,
+ )
# resize pytorch_result to ori_shape
- pytorch_result_ = cv2.resize(pytorch_result[0].astype(np.uint8),
- (ori_shape[1], ori_shape[0]))
+ pytorch_result_ = cv2.resize(
+ pytorch_result[0].astype(np.uint8), (ori_shape[1], ori_shape[0])
+ )
show_result_pyplot(
model,
- img, (pytorch_result_, ),
- title='PyTorch',
+ img,
+ (pytorch_result_,),
+ title="PyTorch",
palette=model.PALETTE,
- opacity=0.5)
+ opacity=0.5,
+ )
# compare results
np.testing.assert_allclose(
pytorch_result.astype(np.float32) / num_classes,
onnx_result.astype(np.float32) / num_classes,
rtol=1e-5,
atol=1e-5,
- err_msg='The outputs are different between Pytorch and ONNX')
- print('The outputs are same between Pytorch and ONNX')
+ err_msg="The outputs are different between Pytorch and ONNX",
+ )
+ print("The outputs are same between Pytorch and ONNX")
def parse_args():
- parser = argparse.ArgumentParser(description='Convert MMSeg to ONNX')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('--checkpoint', help='checkpoint file', default=None)
- parser.add_argument(
- '--input-img', type=str, help='Images for input', default=None)
- parser.add_argument(
- '--show',
- action='store_true',
- help='show onnx graph and segmentation results')
+ parser = argparse.ArgumentParser(description="Convert MMSeg to ONNX")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("--checkpoint", help="checkpoint file", default=None)
+ parser.add_argument("--input-img", type=str, help="Images for input", default=None)
parser.add_argument(
- '--verify', action='store_true', help='verify the onnx model')
- parser.add_argument('--output-file', type=str, default='tmp.onnx')
- parser.add_argument('--opset-version', type=int, default=11)
+ "--show", action="store_true", help="show onnx graph and segmentation results"
+ )
+ parser.add_argument("--verify", action="store_true", help="verify the onnx model")
+ parser.add_argument("--output-file", type=str, default="tmp.onnx")
+ parser.add_argument("--opset-version", type=int, default=11)
parser.add_argument(
- '--shape',
+ "--shape",
type=int,
- nargs='+',
+ nargs="+",
default=None,
- help='input image height and width.')
+ help="input image height and width.",
+ )
parser.add_argument(
- '--rescale_shape',
+ "--rescale_shape",
type=int,
- nargs='+',
+ nargs="+",
default=None,
- help='output image rescale height and width, work for slide mode.')
+ help="output image rescale height and width, work for slide mode.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='Override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="Override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
parser.add_argument(
- '--dynamic-export',
- action='store_true',
- help='Whether to export onnx with dynamic axis.')
+ "--dynamic-export",
+ action="store_true",
+ help="Whether to export onnx with dynamic axis.",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
@@ -337,7 +347,7 @@ def parse_args():
cfg.model.pretrained = None
if args.shape is None:
- img_scale = cfg.test_pipeline[1]['img_scale']
+ img_scale = cfg.test_pipeline[1]["img_scale"]
input_shape = (1, 3, img_scale[1], img_scale[0])
elif len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
@@ -347,22 +357,20 @@ def parse_args():
3,
) + tuple(args.shape)
else:
- raise ValueError('invalid input shape')
+ raise ValueError("invalid input shape")
test_mode = cfg.model.test_cfg.mode
# build the model and load checkpoint
cfg.model.train_cfg = None
- segmentor = build_segmentor(
- cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
+ segmentor = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.get("test_cfg"))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
- checkpoint = load_checkpoint(
- segmentor, args.checkpoint, map_location='cpu')
- segmentor.CLASSES = checkpoint['meta']['CLASSES']
- segmentor.PALETTE = checkpoint['meta']['PALETTE']
+ checkpoint = load_checkpoint(segmentor, args.checkpoint, map_location="cpu")
+ segmentor.CLASSES = checkpoint["meta"]["CLASSES"]
+ segmentor.PALETTE = checkpoint["meta"]["PALETTE"]
# read input or create dummpy input
if args.input_img is not None:
@@ -374,7 +382,8 @@ def parse_args():
args.input_img,
cfg.data.test.pipeline,
shape=preprocess_shape,
- rescale_shape=rescale_shape)
+ rescale_shape=rescale_shape,
+ )
else:
if isinstance(segmentor.decode_head, nn.ModuleList):
num_classes = segmentor.decode_head[-1].num_classes
@@ -390,16 +399,17 @@ def parse_args():
show=args.show,
output_file=args.output_file,
verify=args.verify,
- dynamic_export=args.dynamic_export)
+ dynamic_export=args.dynamic_export,
+ )
# Following strings of text style are from colorama package
- bright_style, reset_style = '\x1b[1m', '\x1b[0m'
- red_text, blue_text = '\x1b[31m', '\x1b[34m'
- white_background = '\x1b[107m'
+ bright_style, reset_style = "\x1b[1m", "\x1b[0m"
+ red_text, blue_text = "\x1b[31m", "\x1b[34m"
+ white_background = "\x1b[107m"
msg = white_background + bright_style + red_text
- msg += 'DeprecationWarning: This tool will be deprecated in future. '
- msg += blue_text + 'Welcome to use the unified model deployment toolbox '
- msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+ msg += "DeprecationWarning: This tool will be deprecated in future. "
+ msg += blue_text + "Welcome to use the unified model deployment toolbox "
+ msg += "MMDeploy: https://github.com/open-mmlab/mmdeploy"
msg += reset_style
warnings.warn(msg)
diff --git a/mmsegmentation/tools/pytorch2torchscript.py b/mmsegmentation/tools/pytorch2torchscript.py
index d76f5ec..5d40089 100644
--- a/mmsegmentation/tools/pytorch2torchscript.py
+++ b/mmsegmentation/tools/pytorch2torchscript.py
@@ -16,31 +16,36 @@
def digit_version(version_str):
digit_version = []
- for x in version_str.split('.'):
+ for x in version_str.split("."):
if x.isdigit():
digit_version.append(int(x))
- elif x.find('rc') != -1:
- patch_version = x.split('rc')
+ elif x.find("rc") != -1:
+ patch_version = x.split("rc")
digit_version.append(int(patch_version[0]) - 1)
digit_version.append(int(patch_version[1]))
return digit_version
def check_torch_version():
- torch_minimum_version = '1.8.0'
+ torch_minimum_version = "1.8.0"
torch_version = digit_version(torch.__version__)
- assert (torch_version >= digit_version(torch_minimum_version)), \
- f'Torch=={torch.__version__} is not support for converting to ' \
- f'torchscript. Please install pytorch>={torch_minimum_version}.'
+ assert torch_version >= digit_version(torch_minimum_version), (
+ f"Torch=={torch.__version__} is not support for converting to "
+ f"torchscript. Please install pytorch>={torch_minimum_version}."
+ )
def _convert_batchnorm(module):
module_output = module
if isinstance(module, torch.nn.SyncBatchNorm):
- module_output = torch.nn.BatchNorm2d(module.num_features, module.eps,
- module.momentum, module.affine,
- module.track_running_stats)
+ module_output = torch.nn.BatchNorm2d(
+ module.num_features,
+ module.eps,
+ module.momentum,
+ module.affine,
+ module.track_running_stats,
+ )
if module.affine:
module_output.weight.data = module.weight.data.clone().detach()
module_output.bias.data = module.bias.data.clone().detach()
@@ -68,29 +73,29 @@ def _demo_mm_inputs(input_shape, num_classes):
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
- segs = rng.randint(
- low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
- img_metas = [{
- 'img_shape': (H, W, C),
- 'ori_shape': (H, W, C),
- 'pad_shape': (H, W, C),
- 'filename': '.png',
- 'scale_factor': 1.0,
- 'flip': False,
- } for _ in range(N)]
+ segs = rng.randint(low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
+ img_metas = [
+ {
+ "img_shape": (H, W, C),
+ "ori_shape": (H, W, C),
+ "pad_shape": (H, W, C),
+ "filename": ".png",
+ "scale_factor": 1.0,
+ "flip": False,
+ }
+ for _ in range(N)
+ ]
mm_inputs = {
- 'imgs': torch.FloatTensor(imgs).requires_grad_(True),
- 'img_metas': img_metas,
- 'gt_semantic_seg': torch.LongTensor(segs)
+ "imgs": torch.FloatTensor(imgs).requires_grad_(True),
+ "img_metas": img_metas,
+ "gt_semantic_seg": torch.LongTensor(segs),
}
return mm_inputs
-def pytorch2libtorch(model,
- input_shape,
- show=False,
- output_file='tmp.pt',
- verify=False):
+def pytorch2libtorch(
+ model, input_shape, show=False, output_file="tmp.pt", verify=False
+):
"""Export Pytorch model to TorchScript model and verify the outputs are
same between Pytorch and TorchScript.
@@ -111,7 +116,7 @@ def pytorch2libtorch(model,
mm_inputs = _demo_mm_inputs(input_shape, num_classes)
- imgs = mm_inputs.pop('imgs')
+ imgs = mm_inputs.pop("imgs")
# replace the original forword with forward_dummy
model.forward = model.forward_dummy
@@ -126,30 +131,30 @@ def pytorch2libtorch(model,
print(traced_model.graph)
traced_model.save(output_file)
- print('Successfully exported TorchScript model: {}'.format(output_file))
+ print(f"Successfully exported TorchScript model: {output_file}")
def parse_args():
- parser = argparse.ArgumentParser(
- description='Convert MMSeg to TorchScript')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('--checkpoint', help='checkpoint file', default=None)
- parser.add_argument(
- '--show', action='store_true', help='show TorchScript graph')
+ parser = argparse.ArgumentParser(description="Convert MMSeg to TorchScript")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("--checkpoint", help="checkpoint file", default=None)
+ parser.add_argument("--show", action="store_true", help="show TorchScript graph")
parser.add_argument(
- '--verify', action='store_true', help='verify the TorchScript model')
- parser.add_argument('--output-file', type=str, default='tmp.pt')
+ "--verify", action="store_true", help="verify the TorchScript model"
+ )
+ parser.add_argument("--output-file", type=str, default="tmp.pt")
parser.add_argument(
- '--shape',
+ "--shape",
type=int,
- nargs='+',
+ nargs="+",
default=[512, 512],
- help='input image size (height, width)')
+ help="input image size (height, width)",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
check_torch_version()
@@ -161,20 +166,19 @@ def parse_args():
3,
) + tuple(args.shape)
else:
- raise ValueError('invalid input shape')
+ raise ValueError("invalid input shape")
cfg = mmcv.Config.fromfile(args.config)
cfg.model.pretrained = None
# build the model and load checkpoint
cfg.model.train_cfg = None
- segmentor = build_segmentor(
- cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
+ segmentor = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.get("test_cfg"))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
- load_checkpoint(segmentor, args.checkpoint, map_location='cpu')
+ load_checkpoint(segmentor, args.checkpoint, map_location="cpu")
# convert the PyTorch model to LibTorch model
pytorch2libtorch(
@@ -182,4 +186,5 @@ def parse_args():
input_shape,
show=args.show,
output_file=args.output_file,
- verify=args.verify)
+ verify=args.verify,
+ )
diff --git a/mmsegmentation/tools/test.py b/mmsegmentation/tools/test.py
index a643b08..ded55cb 100644
--- a/mmsegmentation/tools/test.py
+++ b/mmsegmentation/tools/test.py
@@ -9,8 +9,7 @@
import mmcv
import torch
from mmcv.cnn.utils import revert_sync_batchnorm
-from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
- wrap_fp16_model)
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint, wrap_fp16_model
from mmcv.utils import DictAction
from mmseg import digit_version
@@ -21,95 +20,111 @@
def parse_args():
- parser = argparse.ArgumentParser(
- description='mmseg test (and eval) a model')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint', help='checkpoint file')
+ parser = argparse.ArgumentParser(description="mmseg test (and eval) a model")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("checkpoint", help="checkpoint file")
parser.add_argument(
- '--work-dir',
- help=('if specified, the evaluation metric results will be dumped'
- 'into the directory as json'))
+ "--work-dir",
+ help=(
+ "if specified, the evaluation metric results will be dumped"
+ "into the directory as json"
+ ),
+ )
parser.add_argument(
- '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
- parser.add_argument('--out', help='output result file in pickle format')
+ "--aug-test", action="store_true", help="Use Flip and Multi scale aug"
+ )
+ parser.add_argument("--out", help="output result file in pickle format")
parser.add_argument(
- '--format-only',
- action='store_true',
- help='Format the output results without perform evaluation. It is'
- 'useful when you want to format the result to a specific format and '
- 'submit it to the test server')
+ "--format-only",
+ action="store_true",
+ help="Format the output results without perform evaluation. It is"
+ "useful when you want to format the result to a specific format and "
+ "submit it to the test server",
+ )
parser.add_argument(
- '--eval',
+ "--eval",
type=str,
- nargs='+',
+ nargs="+",
help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
- ' for generic datasets, and "cityscapes" for Cityscapes')
- parser.add_argument('--show', action='store_true', help='show results')
+ ' for generic datasets, and "cityscapes" for Cityscapes',
+ )
+ parser.add_argument("--show", action="store_true", help="show results")
parser.add_argument(
- '--show-dir', help='directory where painted images will be saved')
+ "--show-dir", help="directory where painted images will be saved"
+ )
parser.add_argument(
- '--gpu-collect',
- action='store_true',
- help='whether to use gpu to collect results.')
+ "--gpu-collect",
+ action="store_true",
+ help="whether to use gpu to collect results.",
+ )
parser.add_argument(
- '--gpu-id',
+ "--gpu-id",
type=int,
default=0,
- help='id of gpu to use '
- '(only applicable to non-distributed testing)')
+ help="id of gpu to use " "(only applicable to non-distributed testing)",
+ )
parser.add_argument(
- '--tmpdir',
- help='tmp directory used for collecting results from multiple '
- 'workers, available when gpu_collect is not specified')
+ "--tmpdir",
+ help="tmp directory used for collecting results from multiple "
+ "workers, available when gpu_collect is not specified",
+ )
parser.add_argument(
- '--options',
- nargs='+',
+ "--options",
+ nargs="+",
action=DictAction,
help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. Override some settings in the '
- 'used config, the key-value pair in xxx=yyy format will be merged '
- 'into config file. If the value to be overwritten is a list, it '
+ "not be supported in version v0.22.0. Override some settings in the "
+ "used config, the key-value pair in xxx=yyy format will be merged "
+ "into config file. If the value to be overwritten is a list, it "
'should be like key="[a,b]" or key=a,b It also allows nested '
'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
- 'marks are necessary and that no white space is allowed.')
+ "marks are necessary and that no white space is allowed.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
parser.add_argument(
- '--eval-options',
- nargs='+',
+ "--eval-options",
+ nargs="+",
action=DictAction,
- help='custom options for evaluation')
+ help="custom options for evaluation",
+ )
parser.add_argument(
- '--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
+ "--launcher",
+ choices=["none", "pytorch", "slurm", "mpi"],
+ default="none",
+ help="job launcher",
+ )
parser.add_argument(
- '--opacity',
+ "--opacity",
type=float,
default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
- parser.add_argument('--local_rank', type=int, default=0)
+ help="Opacity of painted segmentation map. In (0, 1] range.",
+ )
+ parser.add_argument("--local_rank", type=int, default=0)
args = parser.parse_args()
- if 'LOCAL_RANK' not in os.environ:
- os.environ['LOCAL_RANK'] = str(args.local_rank)
+ if "LOCAL_RANK" not in os.environ:
+ os.environ["LOCAL_RANK"] = str(args.local_rank)
if args.options and args.cfg_options:
raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ "--options and --cfg-options cannot be both "
+ "specified, --options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ warnings.warn(
+ "--options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
args.cfg_options = args.options
return args
@@ -117,17 +132,17 @@ def parse_args():
def main():
args = parse_args()
- assert args.out or args.eval or args.format_only or args.show \
- or args.show_dir, \
- ('Please specify at least one operation (save/eval/format/show the '
- 'results / save the results) with the argument "--out", "--eval"'
- ', "--format-only", "--show" or "--show-dir"')
+ assert args.out or args.eval or args.format_only or args.show or args.show_dir, (
+ "Please specify at least one operation (save/eval/format/show the "
+ 'results / save the results) with the argument "--out", "--eval"'
+ ', "--format-only", "--show" or "--show-dir"'
+ )
if args.eval and args.format_only:
- raise ValueError('--eval and --format_only cannot be both specified')
+ raise ValueError("--eval and --format_only cannot be both specified")
- if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
- raise ValueError('The output file must be a pkl file.')
+ if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
+ raise ValueError("The output file must be a pkl file.")
cfg = mmcv.Config.fromfile(args.config)
if args.cfg_options is not None:
@@ -137,13 +152,11 @@ def main():
setup_multi_processes(cfg)
# set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
+ if cfg.get("cudnn_benchmark", False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
- ]
+ cfg.data.test.pipeline[1].img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
cfg.data.test.pipeline[1].flip = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
@@ -152,13 +165,15 @@ def main():
cfg.gpu_ids = [args.gpu_id]
# init distributed env first, since logger depends on the dist info.
- if args.launcher == 'none':
+ if args.launcher == "none":
cfg.gpu_ids = [args.gpu_id]
distributed = False
if len(cfg.gpu_ids) > 1:
- warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
- f'{cfg.gpu_ids[0:1]} to avoid potential error in '
- 'non-distribute testing time.')
+ warnings.warn(
+ f"The gpu-ids is reset from {cfg.gpu_ids} to "
+ f"{cfg.gpu_ids[0:1]} to avoid potential error in "
+ "non-distribute testing time."
+ )
cfg.gpu_ids = cfg.gpu_ids[0:1]
else:
distributed = True
@@ -168,24 +183,19 @@ def main():
# allows not to create
if args.work_dir is not None and rank == 0:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if args.aug_test:
- json_file = osp.join(args.work_dir,
- f'eval_multi_scale_{timestamp}.json')
+ json_file = osp.join(args.work_dir, f"eval_multi_scale_{timestamp}.json")
else:
- json_file = osp.join(args.work_dir,
- f'eval_single_scale_{timestamp}.json')
+ json_file = osp.join(args.work_dir, f"eval_single_scale_{timestamp}.json")
elif rank == 0:
- work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
+ work_dir = osp.join("./work_dirs", osp.splitext(osp.basename(args.config))[0])
mmcv.mkdir_or_exist(osp.abspath(work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if args.aug_test:
- json_file = osp.join(work_dir,
- f'eval_multi_scale_{timestamp}.json')
+ json_file = osp.join(work_dir, f"eval_multi_scale_{timestamp}.json")
else:
- json_file = osp.join(work_dir,
- f'eval_single_scale_{timestamp}.json')
+ json_file = osp.join(work_dir, f"eval_single_scale_{timestamp}.json")
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
@@ -195,38 +205,47 @@ def main():
# cfg.gpus will be ignored if distributed
num_gpus=len(cfg.gpu_ids),
dist=distributed,
- shuffle=False)
+ shuffle=False,
+ )
# The overall dataloader settings
- loader_cfg.update({
- k: v
- for k, v in cfg.data.items() if k not in [
- 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
- 'test_dataloader'
- ]
- })
+ loader_cfg.update(
+ {
+ k: v
+ for k, v in cfg.data.items()
+ if k
+ not in [
+ "train",
+ "val",
+ "test",
+ "train_dataloader",
+ "val_dataloader",
+ "test_dataloader",
+ ]
+ }
+ )
test_loader_cfg = {
**loader_cfg,
- 'samples_per_gpu': 1,
- 'shuffle': False, # Not shuffle by default
- **cfg.data.get('test_dataloader', {})
+ "samples_per_gpu": 1,
+ "shuffle": False, # Not shuffle by default
+ **cfg.data.get("test_dataloader", {}),
}
# build the dataloader
data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the model and load checkpoint
cfg.model.train_cfg = None
- model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
- fp16_cfg = cfg.get('fp16', None)
+ model = build_segmentor(cfg.model, test_cfg=cfg.get("test_cfg"))
+ fp16_cfg = cfg.get("fp16", None)
if fp16_cfg is not None:
wrap_fp16_model(model)
- checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
- if 'CLASSES' in checkpoint.get('meta', {}):
- model.CLASSES = checkpoint['meta']['CLASSES']
+ checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
+ if "CLASSES" in checkpoint.get("meta", {}):
+ model.CLASSES = checkpoint["meta"]["CLASSES"]
else:
print('"CLASSES" not found in meta, use dataset.CLASSES instead')
model.CLASSES = dataset.CLASSES
- if 'PALETTE' in checkpoint.get('meta', {}):
- model.PALETTE = checkpoint['meta']['PALETTE']
+ if "PALETTE" in checkpoint.get("meta", {}):
+ model.PALETTE = checkpoint["meta"]["PALETTE"]
else:
print('"PALETTE" not found in meta, use dataset.PALETTE instead')
model.PALETTE = dataset.PALETTE
@@ -236,25 +255,27 @@ def main():
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
- efficient_test = eval_kwargs.get('efficient_test', False)
+ efficient_test = eval_kwargs.get("efficient_test", False)
if efficient_test:
warnings.warn(
- '``efficient_test=True`` does not have effect in tools/test.py, '
- 'the evaluation and format results are CPU memory efficient by '
- 'default')
+ "``efficient_test=True`` does not have effect in tools/test.py, "
+ "the evaluation and format results are CPU memory efficient by "
+ "default"
+ )
- eval_on_format_results = (
- args.eval is not None and 'cityscapes' in args.eval)
+ eval_on_format_results = args.eval is not None and "cityscapes" in args.eval
if eval_on_format_results:
- assert len(args.eval) == 1, 'eval on format results is not ' \
- 'applicable for metrics other than ' \
- 'cityscapes'
+ assert len(args.eval) == 1, (
+ "eval on format results is not "
+ "applicable for metrics other than "
+ "cityscapes"
+ )
if args.format_only or eval_on_format_results:
- if 'imgfile_prefix' in eval_kwargs:
- tmpdir = eval_kwargs['imgfile_prefix']
+ if "imgfile_prefix" in eval_kwargs:
+ tmpdir = eval_kwargs["imgfile_prefix"]
else:
- tmpdir = '.format_cityscapes'
- eval_kwargs.setdefault('imgfile_prefix', tmpdir)
+ tmpdir = ".format_cityscapes"
+ eval_kwargs.setdefault("imgfile_prefix", tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
@@ -262,12 +283,14 @@ def main():
cfg.device = get_device()
if not distributed:
warnings.warn(
- 'SyncBN is only supported with DDP. To be compatible with DP, '
- 'we convert SyncBN to BN. Please use dist_train.sh which can '
- 'avoid this error.')
+ "SyncBN is only supported with DDP. To be compatible with DP, "
+ "we convert SyncBN to BN. Please use dist_train.sh which can "
+ "avoid this error."
+ )
if not torch.cuda.is_available():
- assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
- 'Please use MMCV >= 1.4.4 for CPU training!'
+ assert digit_version(mmcv.__version__) >= digit_version(
+ "1.4.4"
+ ), "Please use MMCV >= 1.4.4 for CPU training!"
model = revert_sync_batchnorm(model)
model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
results = single_gpu_test(
@@ -279,13 +302,15 @@ def main():
args.opacity,
pre_eval=args.eval is not None and not eval_on_format_results,
format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
+ format_args=eval_kwargs,
+ )
else:
model = build_ddp(
model,
cfg.device,
- device_ids=[int(os.environ['LOCAL_RANK'])],
- broadcast_buffers=False)
+ device_ids=[int(os.environ["LOCAL_RANK"])],
+ broadcast_buffers=False,
+ )
results = multi_gpu_test(
model,
data_loader,
@@ -294,17 +319,19 @@ def main():
False,
pre_eval=args.eval is not None and not eval_on_format_results,
format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
+ format_args=eval_kwargs,
+ )
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
- 'The behavior of ``args.out`` has been changed since MMSeg '
- 'v0.16, the pickled outputs could be seg map as type of '
- 'np.array, pre-eval results or file paths for '
- '``dataset.format_results()``.')
- print(f'\nwriting results to {args.out}')
+ "The behavior of ``args.out`` has been changed since MMSeg "
+ "v0.16, the pickled outputs could be seg map as type of "
+ "np.array, pre-eval results or file paths for "
+ "``dataset.format_results()``."
+ )
+ print(f"\nwriting results to {args.out}")
mmcv.dump(results, args.out)
if args.eval:
eval_kwargs.update(metric=args.eval)
@@ -316,5 +343,5 @@ def main():
shutil.rmtree(tmpdir)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/test_jwseo.py b/mmsegmentation/tools/test_jwseo.py
index a643b08..ded55cb 100644
--- a/mmsegmentation/tools/test_jwseo.py
+++ b/mmsegmentation/tools/test_jwseo.py
@@ -9,8 +9,7 @@
import mmcv
import torch
from mmcv.cnn.utils import revert_sync_batchnorm
-from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
- wrap_fp16_model)
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint, wrap_fp16_model
from mmcv.utils import DictAction
from mmseg import digit_version
@@ -21,95 +20,111 @@
def parse_args():
- parser = argparse.ArgumentParser(
- description='mmseg test (and eval) a model')
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint', help='checkpoint file')
+ parser = argparse.ArgumentParser(description="mmseg test (and eval) a model")
+ parser.add_argument("config", help="test config file path")
+ parser.add_argument("checkpoint", help="checkpoint file")
parser.add_argument(
- '--work-dir',
- help=('if specified, the evaluation metric results will be dumped'
- 'into the directory as json'))
+ "--work-dir",
+ help=(
+ "if specified, the evaluation metric results will be dumped"
+ "into the directory as json"
+ ),
+ )
parser.add_argument(
- '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
- parser.add_argument('--out', help='output result file in pickle format')
+ "--aug-test", action="store_true", help="Use Flip and Multi scale aug"
+ )
+ parser.add_argument("--out", help="output result file in pickle format")
parser.add_argument(
- '--format-only',
- action='store_true',
- help='Format the output results without perform evaluation. It is'
- 'useful when you want to format the result to a specific format and '
- 'submit it to the test server')
+ "--format-only",
+ action="store_true",
+ help="Format the output results without perform evaluation. It is"
+ "useful when you want to format the result to a specific format and "
+ "submit it to the test server",
+ )
parser.add_argument(
- '--eval',
+ "--eval",
type=str,
- nargs='+',
+ nargs="+",
help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
- ' for generic datasets, and "cityscapes" for Cityscapes')
- parser.add_argument('--show', action='store_true', help='show results')
+ ' for generic datasets, and "cityscapes" for Cityscapes',
+ )
+ parser.add_argument("--show", action="store_true", help="show results")
parser.add_argument(
- '--show-dir', help='directory where painted images will be saved')
+ "--show-dir", help="directory where painted images will be saved"
+ )
parser.add_argument(
- '--gpu-collect',
- action='store_true',
- help='whether to use gpu to collect results.')
+ "--gpu-collect",
+ action="store_true",
+ help="whether to use gpu to collect results.",
+ )
parser.add_argument(
- '--gpu-id',
+ "--gpu-id",
type=int,
default=0,
- help='id of gpu to use '
- '(only applicable to non-distributed testing)')
+ help="id of gpu to use " "(only applicable to non-distributed testing)",
+ )
parser.add_argument(
- '--tmpdir',
- help='tmp directory used for collecting results from multiple '
- 'workers, available when gpu_collect is not specified')
+ "--tmpdir",
+ help="tmp directory used for collecting results from multiple "
+ "workers, available when gpu_collect is not specified",
+ )
parser.add_argument(
- '--options',
- nargs='+',
+ "--options",
+ nargs="+",
action=DictAction,
help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. Override some settings in the '
- 'used config, the key-value pair in xxx=yyy format will be merged '
- 'into config file. If the value to be overwritten is a list, it '
+ "not be supported in version v0.22.0. Override some settings in the "
+ "used config, the key-value pair in xxx=yyy format will be merged "
+ "into config file. If the value to be overwritten is a list, it "
'should be like key="[a,b]" or key=a,b It also allows nested '
'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
- 'marks are necessary and that no white space is allowed.')
+ "marks are necessary and that no white space is allowed.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
parser.add_argument(
- '--eval-options',
- nargs='+',
+ "--eval-options",
+ nargs="+",
action=DictAction,
- help='custom options for evaluation')
+ help="custom options for evaluation",
+ )
parser.add_argument(
- '--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
+ "--launcher",
+ choices=["none", "pytorch", "slurm", "mpi"],
+ default="none",
+ help="job launcher",
+ )
parser.add_argument(
- '--opacity',
+ "--opacity",
type=float,
default=0.5,
- help='Opacity of painted segmentation map. In (0, 1] range.')
- parser.add_argument('--local_rank', type=int, default=0)
+ help="Opacity of painted segmentation map. In (0, 1] range.",
+ )
+ parser.add_argument("--local_rank", type=int, default=0)
args = parser.parse_args()
- if 'LOCAL_RANK' not in os.environ:
- os.environ['LOCAL_RANK'] = str(args.local_rank)
+ if "LOCAL_RANK" not in os.environ:
+ os.environ["LOCAL_RANK"] = str(args.local_rank)
if args.options and args.cfg_options:
raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ "--options and --cfg-options cannot be both "
+ "specified, --options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ warnings.warn(
+ "--options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
args.cfg_options = args.options
return args
@@ -117,17 +132,17 @@ def parse_args():
def main():
args = parse_args()
- assert args.out or args.eval or args.format_only or args.show \
- or args.show_dir, \
- ('Please specify at least one operation (save/eval/format/show the '
- 'results / save the results) with the argument "--out", "--eval"'
- ', "--format-only", "--show" or "--show-dir"')
+ assert args.out or args.eval or args.format_only or args.show or args.show_dir, (
+ "Please specify at least one operation (save/eval/format/show the "
+ 'results / save the results) with the argument "--out", "--eval"'
+ ', "--format-only", "--show" or "--show-dir"'
+ )
if args.eval and args.format_only:
- raise ValueError('--eval and --format_only cannot be both specified')
+ raise ValueError("--eval and --format_only cannot be both specified")
- if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
- raise ValueError('The output file must be a pkl file.')
+ if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
+ raise ValueError("The output file must be a pkl file.")
cfg = mmcv.Config.fromfile(args.config)
if args.cfg_options is not None:
@@ -137,13 +152,11 @@ def main():
setup_multi_processes(cfg)
# set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
+ if cfg.get("cudnn_benchmark", False):
torch.backends.cudnn.benchmark = True
if args.aug_test:
# hard code index
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
- ]
+ cfg.data.test.pipeline[1].img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
cfg.data.test.pipeline[1].flip = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
@@ -152,13 +165,15 @@ def main():
cfg.gpu_ids = [args.gpu_id]
# init distributed env first, since logger depends on the dist info.
- if args.launcher == 'none':
+ if args.launcher == "none":
cfg.gpu_ids = [args.gpu_id]
distributed = False
if len(cfg.gpu_ids) > 1:
- warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
- f'{cfg.gpu_ids[0:1]} to avoid potential error in '
- 'non-distribute testing time.')
+ warnings.warn(
+ f"The gpu-ids is reset from {cfg.gpu_ids} to "
+ f"{cfg.gpu_ids[0:1]} to avoid potential error in "
+ "non-distribute testing time."
+ )
cfg.gpu_ids = cfg.gpu_ids[0:1]
else:
distributed = True
@@ -168,24 +183,19 @@ def main():
# allows not to create
if args.work_dir is not None and rank == 0:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if args.aug_test:
- json_file = osp.join(args.work_dir,
- f'eval_multi_scale_{timestamp}.json')
+ json_file = osp.join(args.work_dir, f"eval_multi_scale_{timestamp}.json")
else:
- json_file = osp.join(args.work_dir,
- f'eval_single_scale_{timestamp}.json')
+ json_file = osp.join(args.work_dir, f"eval_single_scale_{timestamp}.json")
elif rank == 0:
- work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
+ work_dir = osp.join("./work_dirs", osp.splitext(osp.basename(args.config))[0])
mmcv.mkdir_or_exist(osp.abspath(work_dir))
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if args.aug_test:
- json_file = osp.join(work_dir,
- f'eval_multi_scale_{timestamp}.json')
+ json_file = osp.join(work_dir, f"eval_multi_scale_{timestamp}.json")
else:
- json_file = osp.join(work_dir,
- f'eval_single_scale_{timestamp}.json')
+ json_file = osp.join(work_dir, f"eval_single_scale_{timestamp}.json")
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
@@ -195,38 +205,47 @@ def main():
# cfg.gpus will be ignored if distributed
num_gpus=len(cfg.gpu_ids),
dist=distributed,
- shuffle=False)
+ shuffle=False,
+ )
# The overall dataloader settings
- loader_cfg.update({
- k: v
- for k, v in cfg.data.items() if k not in [
- 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
- 'test_dataloader'
- ]
- })
+ loader_cfg.update(
+ {
+ k: v
+ for k, v in cfg.data.items()
+ if k
+ not in [
+ "train",
+ "val",
+ "test",
+ "train_dataloader",
+ "val_dataloader",
+ "test_dataloader",
+ ]
+ }
+ )
test_loader_cfg = {
**loader_cfg,
- 'samples_per_gpu': 1,
- 'shuffle': False, # Not shuffle by default
- **cfg.data.get('test_dataloader', {})
+ "samples_per_gpu": 1,
+ "shuffle": False, # Not shuffle by default
+ **cfg.data.get("test_dataloader", {}),
}
# build the dataloader
data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the model and load checkpoint
cfg.model.train_cfg = None
- model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
- fp16_cfg = cfg.get('fp16', None)
+ model = build_segmentor(cfg.model, test_cfg=cfg.get("test_cfg"))
+ fp16_cfg = cfg.get("fp16", None)
if fp16_cfg is not None:
wrap_fp16_model(model)
- checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
- if 'CLASSES' in checkpoint.get('meta', {}):
- model.CLASSES = checkpoint['meta']['CLASSES']
+ checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
+ if "CLASSES" in checkpoint.get("meta", {}):
+ model.CLASSES = checkpoint["meta"]["CLASSES"]
else:
print('"CLASSES" not found in meta, use dataset.CLASSES instead')
model.CLASSES = dataset.CLASSES
- if 'PALETTE' in checkpoint.get('meta', {}):
- model.PALETTE = checkpoint['meta']['PALETTE']
+ if "PALETTE" in checkpoint.get("meta", {}):
+ model.PALETTE = checkpoint["meta"]["PALETTE"]
else:
print('"PALETTE" not found in meta, use dataset.PALETTE instead')
model.PALETTE = dataset.PALETTE
@@ -236,25 +255,27 @@ def main():
eval_kwargs = {} if args.eval_options is None else args.eval_options
# Deprecated
- efficient_test = eval_kwargs.get('efficient_test', False)
+ efficient_test = eval_kwargs.get("efficient_test", False)
if efficient_test:
warnings.warn(
- '``efficient_test=True`` does not have effect in tools/test.py, '
- 'the evaluation and format results are CPU memory efficient by '
- 'default')
+ "``efficient_test=True`` does not have effect in tools/test.py, "
+ "the evaluation and format results are CPU memory efficient by "
+ "default"
+ )
- eval_on_format_results = (
- args.eval is not None and 'cityscapes' in args.eval)
+ eval_on_format_results = args.eval is not None and "cityscapes" in args.eval
if eval_on_format_results:
- assert len(args.eval) == 1, 'eval on format results is not ' \
- 'applicable for metrics other than ' \
- 'cityscapes'
+ assert len(args.eval) == 1, (
+ "eval on format results is not "
+ "applicable for metrics other than "
+ "cityscapes"
+ )
if args.format_only or eval_on_format_results:
- if 'imgfile_prefix' in eval_kwargs:
- tmpdir = eval_kwargs['imgfile_prefix']
+ if "imgfile_prefix" in eval_kwargs:
+ tmpdir = eval_kwargs["imgfile_prefix"]
else:
- tmpdir = '.format_cityscapes'
- eval_kwargs.setdefault('imgfile_prefix', tmpdir)
+ tmpdir = ".format_cityscapes"
+ eval_kwargs.setdefault("imgfile_prefix", tmpdir)
mmcv.mkdir_or_exist(tmpdir)
else:
tmpdir = None
@@ -262,12 +283,14 @@ def main():
cfg.device = get_device()
if not distributed:
warnings.warn(
- 'SyncBN is only supported with DDP. To be compatible with DP, '
- 'we convert SyncBN to BN. Please use dist_train.sh which can '
- 'avoid this error.')
+ "SyncBN is only supported with DDP. To be compatible with DP, "
+ "we convert SyncBN to BN. Please use dist_train.sh which can "
+ "avoid this error."
+ )
if not torch.cuda.is_available():
- assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
- 'Please use MMCV >= 1.4.4 for CPU training!'
+ assert digit_version(mmcv.__version__) >= digit_version(
+ "1.4.4"
+ ), "Please use MMCV >= 1.4.4 for CPU training!"
model = revert_sync_batchnorm(model)
model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
results = single_gpu_test(
@@ -279,13 +302,15 @@ def main():
args.opacity,
pre_eval=args.eval is not None and not eval_on_format_results,
format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
+ format_args=eval_kwargs,
+ )
else:
model = build_ddp(
model,
cfg.device,
- device_ids=[int(os.environ['LOCAL_RANK'])],
- broadcast_buffers=False)
+ device_ids=[int(os.environ["LOCAL_RANK"])],
+ broadcast_buffers=False,
+ )
results = multi_gpu_test(
model,
data_loader,
@@ -294,17 +319,19 @@ def main():
False,
pre_eval=args.eval is not None and not eval_on_format_results,
format_only=args.format_only or eval_on_format_results,
- format_args=eval_kwargs)
+ format_args=eval_kwargs,
+ )
rank, _ = get_dist_info()
if rank == 0:
if args.out:
warnings.warn(
- 'The behavior of ``args.out`` has been changed since MMSeg '
- 'v0.16, the pickled outputs could be seg map as type of '
- 'np.array, pre-eval results or file paths for '
- '``dataset.format_results()``.')
- print(f'\nwriting results to {args.out}')
+ "The behavior of ``args.out`` has been changed since MMSeg "
+ "v0.16, the pickled outputs could be seg map as type of "
+ "np.array, pre-eval results or file paths for "
+ "``dataset.format_results()``."
+ )
+ print(f"\nwriting results to {args.out}")
mmcv.dump(results, args.out)
if args.eval:
eval_kwargs.update(metric=args.eval)
@@ -316,5 +343,5 @@ def main():
shutil.rmtree(tmpdir)
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/torchserve/mmseg2torchserve.py b/mmsegmentation/tools/torchserve/mmseg2torchserve.py
index 9063634..8d9eb02 100644
--- a/mmsegmentation/tools/torchserve/mmseg2torchserve.py
+++ b/mmsegmentation/tools/torchserve/mmseg2torchserve.py
@@ -17,7 +17,7 @@ def mmseg2torchserve(
checkpoint_file: str,
output_folder: str,
model_name: str,
- model_version: str = '1.0',
+ model_version: str = "1.0",
force: bool = False,
):
"""Converts mmsegmentation model (config + checkpoint) to TorchServe
@@ -48,64 +48,75 @@ def mmseg2torchserve(
config = mmcv.Config.fromfile(config_file)
with TemporaryDirectory() as tmpdir:
- config.dump(f'{tmpdir}/config.py')
+ config.dump(f"{tmpdir}/config.py")
args = Namespace(
**{
- 'model_file': f'{tmpdir}/config.py',
- 'serialized_file': checkpoint_file,
- 'handler': f'{Path(__file__).parent}/mmseg_handler.py',
- 'model_name': model_name or Path(checkpoint_file).stem,
- 'version': model_version,
- 'export_path': output_folder,
- 'force': force,
- 'requirements_file': None,
- 'extra_files': None,
- 'runtime': 'python',
- 'archive_format': 'default'
- })
+ "model_file": f"{tmpdir}/config.py",
+ "serialized_file": checkpoint_file,
+ "handler": f"{Path(__file__).parent}/mmseg_handler.py",
+ "model_name": model_name or Path(checkpoint_file).stem,
+ "version": model_version,
+ "export_path": output_folder,
+ "force": force,
+ "requirements_file": None,
+ "extra_files": None,
+ "runtime": "python",
+ "archive_format": "default",
+ }
+ )
manifest = ModelExportUtils.generate_manifest_json(args)
package_model(args, manifest)
def parse_args():
parser = ArgumentParser(
- description='Convert mmseg models to TorchServe `.mar` format.')
- parser.add_argument('config', type=str, help='config file path')
- parser.add_argument('checkpoint', type=str, help='checkpoint file path')
+ description="Convert mmseg models to TorchServe `.mar` format."
+ )
+ parser.add_argument("config", type=str, help="config file path")
+ parser.add_argument("checkpoint", type=str, help="checkpoint file path")
parser.add_argument(
- '--output-folder',
+ "--output-folder",
type=str,
required=True,
- help='Folder where `{model_name}.mar` will be created.')
+ help="Folder where `{model_name}.mar` will be created.",
+ )
parser.add_argument(
- '--model-name',
+ "--model-name",
type=str,
default=None,
- help='If not None, used for naming the `{model_name}.mar`'
- 'file that will be created under `output_folder`.'
- 'If None, `{Path(checkpoint_file).stem}` will be used.')
+ help="If not None, used for naming the `{model_name}.mar`"
+ "file that will be created under `output_folder`."
+ "If None, `{Path(checkpoint_file).stem}` will be used.",
+ )
parser.add_argument(
- '--model-version',
- type=str,
- default='1.0',
- help='Number used for versioning.')
+ "--model-version", type=str, default="1.0", help="Number used for versioning."
+ )
parser.add_argument(
- '-f',
- '--force',
- action='store_true',
- help='overwrite the existing `{model_name}.mar`')
+ "-f",
+ "--force",
+ action="store_true",
+ help="overwrite the existing `{model_name}.mar`",
+ )
args = parser.parse_args()
return args
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
if package_model is None:
- raise ImportError('`torch-model-archiver` is required.'
- 'Try: pip install torch-model-archiver')
+ raise ImportError(
+ "`torch-model-archiver` is required."
+ "Try: pip install torch-model-archiver"
+ )
- mmseg2torchserve(args.config, args.checkpoint, args.output_folder,
- args.model_name, args.model_version, args.force)
+ mmseg2torchserve(
+ args.config,
+ args.checkpoint,
+ args.output_folder,
+ args.model_name,
+ args.model_version,
+ args.force,
+ )
diff --git a/mmsegmentation/tools/torchserve/mmseg_handler.py b/mmsegmentation/tools/torchserve/mmseg_handler.py
index 28fe501..76bbe31 100644
--- a/mmsegmentation/tools/torchserve/mmseg_handler.py
+++ b/mmsegmentation/tools/torchserve/mmseg_handler.py
@@ -15,16 +15,18 @@ class MMsegHandler(BaseHandler):
def initialize(self, context):
properties = context.system_properties
- self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
- self.device = torch.device(self.map_location + ':' +
- str(properties.get('gpu_id')) if torch.cuda.
- is_available() else self.map_location)
+ self.map_location = "cuda" if torch.cuda.is_available() else "cpu"
+ self.device = torch.device(
+ self.map_location + ":" + str(properties.get("gpu_id"))
+ if torch.cuda.is_available()
+ else self.map_location
+ )
self.manifest = context.manifest
- model_dir = properties.get('model_dir')
- serialized_file = self.manifest['model']['serializedFile']
+ model_dir = properties.get("model_dir")
+ serialized_file = self.manifest["model"]["serializedFile"]
checkpoint = os.path.join(model_dir, serialized_file)
- self.config_file = os.path.join(model_dir, 'config.py')
+ self.config_file = os.path.join(model_dir, "config.py")
self.model = init_segmentor(self.config_file, checkpoint, self.device)
self.model = revert_sync_batchnorm(self.model)
@@ -34,7 +36,7 @@ def preprocess(self, data):
images = []
for row in data:
- image = row.get('data') or row.get('body')
+ image = row.get("data") or row.get("body")
if isinstance(image, str):
image = base64.b64decode(image)
image = mmcv.imfrombytes(image)
@@ -50,7 +52,7 @@ def postprocess(self, data):
output = []
for image_result in data:
- _, buffer = cv2.imencode('.png', image_result[0].astype('uint8'))
+ _, buffer = cv2.imencode(".png", image_result[0].astype("uint8"))
content = buffer.tobytes()
output.append(content)
return output
diff --git a/mmsegmentation/tools/torchserve/test_torchserve.py b/mmsegmentation/tools/torchserve/test_torchserve.py
index 432834a..dd92756 100644
--- a/mmsegmentation/tools/torchserve/test_torchserve.py
+++ b/mmsegmentation/tools/torchserve/test_torchserve.py
@@ -11,37 +11,38 @@
def parse_args():
parser = ArgumentParser(
- description='Compare result of torchserve and pytorch,'
- 'and visualize them.')
- parser.add_argument('img', help='Image file')
- parser.add_argument('config', help='Config file')
- parser.add_argument('checkpoint', help='Checkpoint file')
- parser.add_argument('model_name', help='The model name in the server')
+ description="Compare result of torchserve and pytorch," "and visualize them."
+ )
+ parser.add_argument("img", help="Image file")
+ parser.add_argument("config", help="Config file")
+ parser.add_argument("checkpoint", help="Checkpoint file")
+ parser.add_argument("model_name", help="The model name in the server")
parser.add_argument(
- '--inference-addr',
- default='127.0.0.1:8080',
- help='Address and port of the inference server')
+ "--inference-addr",
+ default="127.0.0.1:8080",
+ help="Address and port of the inference server",
+ )
parser.add_argument(
- '--result-image',
+ "--result-image",
type=str,
default=None,
- help='save server output in result-image')
- parser.add_argument(
- '--device', default='cuda:0', help='Device used for inference')
+ help="save server output in result-image",
+ )
+ parser.add_argument("--device", default="cuda:0", help="Device used for inference")
args = parser.parse_args()
return args
def main(args):
- url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
- with open(args.img, 'rb') as image:
+ url = "http://" + args.inference_addr + "/predictions/" + args.model_name
+ with open(args.img, "rb") as image:
tmp_res = requests.post(url, image)
content = tmp_res.content
if args.result_image:
- with open(args.result_image, 'wb') as out_image:
+ with open(args.result_image, "wb") as out_image:
out_image.write(content)
- plt.imshow(mmcv.imread(args.result_image, 'grayscale'))
+ plt.imshow(mmcv.imread(args.result_image, "grayscale"))
plt.show()
else:
plt.imshow(plt.imread(BytesIO(content)))
@@ -53,6 +54,6 @@ def main(args):
plt.show()
-if __name__ == '__main__':
+if __name__ == "__main__":
args = parse_args()
main(args)
diff --git a/mmsegmentation/tools/train.py b/mmsegmentation/tools/train.py
index c4219b0..cefc482 100644
--- a/mmsegmentation/tools/train.py
+++ b/mmsegmentation/tools/train.py
@@ -17,92 +17,101 @@
from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
-from mmseg.utils import (collect_env, get_device, get_root_logger,
- setup_multi_processes)
+from mmseg.utils import collect_env, get_device, get_root_logger, setup_multi_processes
def parse_args():
- parser = argparse.ArgumentParser(description='Train a segmentor')
- parser.add_argument('config', help='train config file path')
- parser.add_argument('--work-dir', help='the dir to save logs and models')
+ parser = argparse.ArgumentParser(description="Train a segmentor")
+ parser.add_argument("config", help="train config file path")
+ parser.add_argument("--work-dir", help="the dir to save logs and models")
+ parser.add_argument("--load-from", help="the checkpoint file to load weights from")
+ parser.add_argument("--resume-from", help="the checkpoint file to resume from")
parser.add_argument(
- '--load-from', help='the checkpoint file to load weights from')
- parser.add_argument(
- '--resume-from', help='the checkpoint file to resume from')
- parser.add_argument(
- '--no-validate',
- action='store_true',
- help='whether not to evaluate the checkpoint during training')
+ "--no-validate",
+ action="store_true",
+ help="whether not to evaluate the checkpoint during training",
+ )
group_gpus = parser.add_mutually_exclusive_group()
group_gpus.add_argument(
- '--gpus',
+ "--gpus",
type=int,
- help='(Deprecated, please use --gpu-id) number of gpus to use '
- '(only applicable to non-distributed training)')
+ help="(Deprecated, please use --gpu-id) number of gpus to use "
+ "(only applicable to non-distributed training)",
+ )
group_gpus.add_argument(
- '--gpu-ids',
+ "--gpu-ids",
type=int,
- nargs='+',
- help='(Deprecated, please use --gpu-id) ids of gpus to use '
- '(only applicable to non-distributed training)')
+ nargs="+",
+ help="(Deprecated, please use --gpu-id) ids of gpus to use "
+ "(only applicable to non-distributed training)",
+ )
group_gpus.add_argument(
- '--gpu-id',
+ "--gpu-id",
type=int,
default=0,
- help='id of gpu to use '
- '(only applicable to non-distributed training)')
- parser.add_argument('--seed', type=int, default=None, help='random seed')
+ help="id of gpu to use " "(only applicable to non-distributed training)",
+ )
+ parser.add_argument("--seed", type=int, default=None, help="random seed")
parser.add_argument(
- '--diff_seed',
- action='store_true',
- help='Whether or not set different seeds for different ranks')
+ "--diff_seed",
+ action="store_true",
+ help="Whether or not set different seeds for different ranks",
+ )
parser.add_argument(
- '--deterministic',
- action='store_true',
- help='whether to set deterministic options for CUDNN backend.')
+ "--deterministic",
+ action="store_true",
+ help="whether to set deterministic options for CUDNN backend.",
+ )
parser.add_argument(
- '--options',
- nargs='+',
+ "--options",
+ nargs="+",
action=DictAction,
help="--options is deprecated in favor of --cfg_options' and it will "
- 'not be supported in version v0.22.0. Override some settings in the '
- 'used config, the key-value pair in xxx=yyy format will be merged '
- 'into config file. If the value to be overwritten is a list, it '
+ "not be supported in version v0.22.0. Override some settings in the "
+ "used config, the key-value pair in xxx=yyy format will be merged "
+ "into config file. If the value to be overwritten is a list, it "
'should be like key="[a,b]" or key=a,b It also allows nested '
'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
- 'marks are necessary and that no white space is allowed.')
+ "marks are necessary and that no white space is allowed.",
+ )
parser.add_argument(
- '--cfg-options',
- nargs='+',
+ "--cfg-options",
+ nargs="+",
action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
+ help="override some settings in the used config, the key-value pair "
+ "in xxx=yyy format will be merged into config file. If the value to "
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
+ "Note that the quotation marks are necessary and that no white space "
+ "is allowed.",
+ )
parser.add_argument(
- '--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
- parser.add_argument('--local_rank', type=int, default=0)
+ "--launcher",
+ choices=["none", "pytorch", "slurm", "mpi"],
+ default="none",
+ help="job launcher",
+ )
+ parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
- '--auto-resume',
- action='store_true',
- help='resume from the latest checkpoint automatically.')
+ "--auto-resume",
+ action="store_true",
+ help="resume from the latest checkpoint automatically.",
+ )
args = parser.parse_args()
- if 'LOCAL_RANK' not in os.environ:
- os.environ['LOCAL_RANK'] = str(args.local_rank)
+ if "LOCAL_RANK" not in os.environ:
+ os.environ["LOCAL_RANK"] = str(args.local_rank)
if args.options and args.cfg_options:
raise ValueError(
- '--options and --cfg-options cannot be both '
- 'specified, --options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ "--options and --cfg-options cannot be both "
+ "specified, --options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options. '
- '--options will not be supported in version v0.22.0.')
+ warnings.warn(
+ "--options is deprecated in favor of --cfg-options. "
+ "--options will not be supported in version v0.22.0."
+ )
args.cfg_options = args.options
return args
@@ -116,39 +125,44 @@ def main():
cfg.merge_from_dict(args.cfg_options)
# set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
+ if cfg.get("cudnn_benchmark", False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
- elif cfg.get('work_dir', None) is None:
+ elif cfg.get("work_dir", None) is None:
# use config filename as default work_dir if cfg.work_dir is None
- cfg.work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
+ cfg.work_dir = osp.join(
+ "./work_dirs", osp.splitext(osp.basename(args.config))[0]
+ )
if args.load_from is not None:
cfg.load_from = args.load_from
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpus is not None:
cfg.gpu_ids = range(1)
- warnings.warn('`--gpus` is deprecated because we only support '
- 'single GPU mode in non-distributed training. '
- 'Use `gpus=1` now.')
+ warnings.warn(
+ "`--gpus` is deprecated because we only support "
+ "single GPU mode in non-distributed training. "
+ "Use `gpus=1` now."
+ )
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids[0:1]
- warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
- 'Because we only support single GPU mode in '
- 'non-distributed training. Use the first GPU '
- 'in `gpu_ids` now.')
+ warnings.warn(
+ "`--gpu-ids` is deprecated, please use `--gpu-id`. "
+ "Because we only support single GPU mode in "
+ "non-distributed training. Use the first GPU "
+ "in `gpu_ids` now."
+ )
if args.gpus is None and args.gpu_ids is None:
cfg.gpu_ids = [args.gpu_id]
cfg.auto_resume = args.auto_resume
# init distributed env first, since logger depends on the dist info.
- if args.launcher == 'none':
+ if args.launcher == "none":
distributed = False
else:
distributed = True
@@ -162,8 +176,8 @@ def main():
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
- log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
+ log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# set multi-process settings
@@ -174,39 +188,37 @@ def main():
meta = dict()
# log env info
env_info_dict = collect_env()
- env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
- dash_line = '-' * 60 + '\n'
- logger.info('Environment info:\n' + dash_line + env_info + '\n' +
- dash_line)
- meta['env_info'] = env_info
+ env_info = "\n".join([f"{k}: {v}" for k, v in env_info_dict.items()])
+ dash_line = "-" * 60 + "\n"
+ logger.info("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
+ meta["env_info"] = env_info
# log some basic info
- logger.info(f'Distributed training: {distributed}')
- logger.info(f'Config:\n{cfg.pretty_text}')
+ logger.info(f"Distributed training: {distributed}")
+ logger.info(f"Config:\n{cfg.pretty_text}")
# set random seeds
cfg.device = get_device()
seed = init_random_seed(args.seed, device=cfg.device)
seed = seed + dist.get_rank() if args.diff_seed else seed
- logger.info(f'Set random seed to {seed}, '
- f'deterministic: {args.deterministic}')
+ logger.info(f"Set random seed to {seed}, " f"deterministic: {args.deterministic}")
set_random_seed(seed, deterministic=args.deterministic)
cfg.seed = seed
- meta['seed'] = seed
- meta['exp_name'] = osp.basename(args.config)
+ meta["seed"] = seed
+ meta["exp_name"] = osp.basename(args.config)
model = build_segmentor(
- cfg.model,
- train_cfg=cfg.get('train_cfg'),
- test_cfg=cfg.get('test_cfg'))
+ cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg")
+ )
model.init_weights()
# SyncBN is not support for DP
if not distributed:
warnings.warn(
- 'SyncBN is only supported with DDP. To be compatible with DP, '
- 'we convert SyncBN to BN. Please use dist_train.sh which can '
- 'avoid this error.')
+ "SyncBN is only supported with DDP. To be compatible with DP, "
+ "we convert SyncBN to BN. Please use dist_train.sh which can "
+ "avoid this error."
+ )
model = revert_sync_batchnorm(model)
logger.info(model)
@@ -220,10 +232,11 @@ def main():
# save mmseg version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
- mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
+ mmseg_version=f"{__version__}+{get_git_hash()[:7]}",
config=cfg.pretty_text,
CLASSES=datasets[0].CLASSES,
- PALETTE=datasets[0].PALETTE)
+ PALETTE=datasets[0].PALETTE,
+ )
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
# passing checkpoint meta for saving best checkpoint
@@ -235,8 +248,9 @@ def main():
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
- meta=meta)
+ meta=meta,
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
main()
diff --git a/mmsegmentation/tools/train_jwseo.py b/mmsegmentation/tools/train_jwseo.py
index 5264b3c..8f538c7 100644
--- a/mmsegmentation/tools/train_jwseo.py
+++ b/mmsegmentation/tools/train_jwseo.py
@@ -18,12 +18,13 @@
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmcv.utils.config import ConfigDict
+from rich.console import Console
+
from mmseg import __version__
from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
from mmseg.datasets import build_dataset
from mmseg.models import build_segmentor
from mmseg.utils import collect_env, get_device, get_root_logger, setup_multi_processes
-from rich.console import Console
KST_TZ = pytz.timezone("Asia/Seoul")
GPU_ID = 0
@@ -48,7 +49,7 @@ def get_latest_checkpoint(work_dir: Path) -> Union[str, None]:
if not latest_checkpoint_file.exists():
return None
- with open(latest_checkpoint_file, "r", encoding="utf8") as f:
+ with open(latest_checkpoint_file, encoding="utf8") as f:
checkpoint_path = f.read()
return checkpoint_path
diff --git a/src/baseline_fcn_resnet50.ipynb b/src/baseline_fcn_resnet50.ipynb
index 5f4378d..767758f 100644
--- a/src/baseline_fcn_resnet50.ipynb
+++ b/src/baseline_fcn_resnet50.ipynb
@@ -1,1736 +1,1736 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "toc": true
- },
- "source": [
- "Table of Contents
\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:38:29.366172Z",
- "start_time": "2022-12-08T19:38:29.351646Z"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc": true
+ },
+ "source": [
+ "Table of Contents
\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:38:29.366172Z",
+ "start_time": "2022-12-08T19:38:29.351646Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pytorch version: 1.7.1\n",
+ "GPU 사용 가능 여부: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import random\n",
+ "import time\n",
+ "import json\n",
+ "import warnings\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from utils import label_accuracy_score, add_hist\n",
+ "import cv2\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "# 전처리를 위한 라이브러리\n",
+ "from pycocotools.coco import COCO\n",
+ "import torchvision\n",
+ "import torchvision.transforms as transforms\n",
+ "\n",
+ "#!pip install albumentations==0.4.6\n",
+ "import albumentations as A\n",
+ "from albumentations.pytorch import ToTensorV2\n",
+ "\n",
+ "# 시각화를 위한 라이브러리\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "sns.set()\n",
+ "from matplotlib.patches import Patch\n",
+ "\n",
+ "#!pip install webcolors\n",
+ "import webcolors\n",
+ "\n",
+ "plt.rcParams[\"axes.grid\"] = False\n",
+ "\n",
+ "print(\"pytorch version: {}\".format(torch.__version__))\n",
+ "print(\"GPU 사용 가능 여부: {}\".format(torch.cuda.is_available()))\n",
+ "\n",
+ "# print(torch.cuda.get_device_name(0))\n",
+ "# print(torch.cuda.device_count())\n",
+ "\n",
+ "# GPU 사용 가능 여부에 따라 device 정보 저장\n",
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 하이퍼파라미터 세팅 및 seed 고정"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:38:46.873164Z",
+ "start_time": "2022-12-08T19:38:46.855505Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 4 # Mini-batch size\n",
+ "num_epochs = 2\n",
+ "learning_rate = 0.0001"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:38:48.088642Z",
+ "start_time": "2022-12-08T19:38:48.074145Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# seed 고정\n",
+ "random_seed = 21\n",
+ "torch.manual_seed(random_seed)\n",
+ "torch.cuda.manual_seed(random_seed)\n",
+ "torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n",
+ "torch.backends.cudnn.deterministic = True\n",
+ "torch.backends.cudnn.benchmark = False\n",
+ "np.random.seed(random_seed)\n",
+ "random.seed(random_seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 학습 데이터 EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:38:56.320707Z",
+ "start_time": "2022-12-08T19:38:52.314207Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of super categories: 10\n",
+ "Number of categories: 10\n",
+ "Number of annotations: 26240\n",
+ "Number of images: 3272\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "dataset_path = \"/opt/ml/input/data\"\n",
+ "anns_file_path = dataset_path + \"/\" + \"train_all.json\"\n",
+ "\n",
+ "# Read annotations\n",
+ "with open(anns_file_path, \"r\") as f:\n",
+ " dataset = json.loads(f.read())\n",
+ "\n",
+ "categories = dataset[\"categories\"]\n",
+ "anns = dataset[\"annotations\"]\n",
+ "imgs = dataset[\"images\"]\n",
+ "\n",
+ "nr_cats = len(categories)\n",
+ "nr_annotations = len(anns)\n",
+ "nr_images = len(imgs)\n",
+ "\n",
+ "# Load categories and super categories\n",
+ "cat_names = []\n",
+ "super_cat_names = []\n",
+ "super_cat_ids = {}\n",
+ "super_cat_last_name = \"\"\n",
+ "nr_super_cats = 0\n",
+ "for cat_it in categories:\n",
+ " cat_names.append(cat_it[\"name\"])\n",
+ " super_cat_name = cat_it[\"supercategory\"]\n",
+ " # Adding new supercat\n",
+ " if super_cat_name != super_cat_last_name:\n",
+ " super_cat_names.append(super_cat_name)\n",
+ " super_cat_ids[super_cat_name] = nr_super_cats\n",
+ " super_cat_last_name = super_cat_name\n",
+ " nr_super_cats += 1\n",
+ "\n",
+ "print(\"Number of super categories:\", nr_super_cats)\n",
+ "print(\"Number of categories:\", nr_cats)\n",
+ "print(\"Number of annotations:\", nr_annotations)\n",
+ "print(\"Number of images:\", nr_images)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:38:57.879486Z",
+ "start_time": "2022-12-08T19:38:57.682460Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Count annotations\n",
+ "cat_histogram = np.zeros(nr_cats, dtype=int)\n",
+ "for ann in anns:\n",
+ " cat_histogram[ann[\"category_id\"] - 1] += 1\n",
+ "\n",
+ "# Initialize the matplotlib figure\n",
+ "f, ax = plt.subplots(figsize=(5, 5))\n",
+ "\n",
+ "# Convert to DataFrame\n",
+ "df = pd.DataFrame({\"Categories\": cat_names, \"Number of annotations\": cat_histogram})\n",
+ "df = df.sort_values(\"Number of annotations\", 0, False)\n",
+ "\n",
+ "# Plot the histogram\n",
+ "plt.title(\"category distribution of train_all set \")\n",
+ "plot_1 = sns.barplot(\n",
+ " x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:39:00.016714Z",
+ "start_time": "2022-12-08T19:39:00.011712Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# category labeling\n",
+ "sorted_temp_df = df.sort_index()\n",
+ "\n",
+ "# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n",
+ "sorted_df = pd.DataFrame([\"Backgroud\"], columns=[\"Categories\"])\n",
+ "sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:39:01.972676Z",
+ "start_time": "2022-12-08T19:39:01.957173Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
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+ " | \n",
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+ " \n",
+ " | 5 | \n",
+ " Glass | \n",
+ " 64 | \n",
+ " 0 | \n",
+ " 128 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " Plastic | \n",
+ " 64 | \n",
+ " 0 | \n",
+ " 192 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " Styrofoam | \n",
+ " 192 | \n",
+ " 128 | \n",
+ " 64 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " Plastic bag | \n",
+ " 192 | \n",
+ " 192 | \n",
+ " 128 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " Battery | \n",
+ " 64 | \n",
+ " 64 | \n",
+ " 128 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " Clothing | \n",
+ " 128 | \n",
+ " 0 | \n",
+ " 192 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# train_loader의 output 결과(image 및 mask) 확인\n",
+ "for imgs, masks, image_infos in train_loader:\n",
+ " image_infos = image_infos[0]\n",
+ " temp_images = imgs\n",
+ " temp_masks = masks\n",
+ " break\n",
+ "\n",
+ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n",
+ "\n",
+ "print(\"image shape:\", list(temp_images[0].shape))\n",
+ "print(\"mask shape: \", list(temp_masks[0].shape))\n",
+ "print(\n",
+ " \"Unique values, category of transformed mask : \\n\",\n",
+ " [{int(i), category_names[int(i)]} for i in list(np.unique(temp_masks[0]))],\n",
+ ")\n",
+ "\n",
+ "ax1.imshow(temp_images[0].permute([1, 2, 0]))\n",
+ "ax1.grid(False)\n",
+ "ax1.set_title(\"input image : {}\".format(image_infos[\"file_name\"]), fontsize=15)\n",
+ "\n",
+ "ax2.imshow(temp_masks[0])\n",
+ "ax2.grid(False)\n",
+ "ax2.set_title(\"masks : {}\".format(image_infos[\"file_name\"]), fontsize=15)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:40:50.136843Z",
+ "start_time": "2022-12-08T19:40:49.810841Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "image shape: [3, 512, 512]\n",
+ "mask shape: [512, 512]\n",
+ "Unique values, category of transformed mask : \n",
+ " [{0, 'Backgroud'}, {'Glass', 5}, {'Plastic', 6}, {8, 'Plastic bag'}]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# val_loader의 output 결과(image 및 mask) 확인\n",
+ "for imgs, masks, image_infos in val_loader:\n",
+ " image_infos = image_infos[0]\n",
+ " temp_images = imgs\n",
+ " temp_masks = masks\n",
+ "\n",
+ " break\n",
+ "\n",
+ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 12))\n",
+ "\n",
+ "print(\"image shape:\", list(temp_images[0].shape))\n",
+ "print(\"mask shape: \", list(temp_masks[0].shape))\n",
+ "\n",
+ "print(\n",
+ " \"Unique values, category of transformed mask : \\n\",\n",
+ " [{int(i), category_names[int(i)]} for i in list(np.unique(temp_masks[0]))],\n",
+ ")\n",
+ "\n",
+ "ax1.imshow(temp_images[0].permute([1, 2, 0]))\n",
+ "ax1.grid(False)\n",
+ "ax1.set_title(\"input image : {}\".format(image_infos[\"file_name\"]), fontsize=15)\n",
+ "\n",
+ "ax2.imshow(temp_masks[0])\n",
+ "ax2.grid(False)\n",
+ "ax2.set_title(\"masks : {}\".format(image_infos[\"file_name\"]), fontsize=15)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:40:52.849364Z",
+ "start_time": "2022-12-08T19:40:52.641857Z"
+ },
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "image shape: [3, 512, 512]\n"
+ ]
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# test_loader의 output 결과(image) 확인\n",
+ "for imgs, image_infos in test_loader:\n",
+ " image_infos = image_infos[0]\n",
+ " temp_images = imgs\n",
+ "\n",
+ " break\n",
+ "\n",
+ "fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
+ "\n",
+ "print(\"image shape:\", list(temp_images[0].shape))\n",
+ "\n",
+ "ax1.imshow(temp_images[0].permute([1, 2, 0]))\n",
+ "ax1.grid(False)\n",
+ "ax1.set_title(\"input image : {}\".format(image_infos[\"file_name\"]), fontsize=15)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## baseline model\n",
+ "\n",
+ "### models.segmentation.fcn_resnet50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:40:58.105295Z",
+ "start_time": "2022-12-08T19:40:57.699576Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /opt/ml/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n",
+ "100%|██████████| 97.8M/97.8M [00:00<00:00, 183MB/s] \n",
+ "Downloading: \"https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth\" to /opt/ml/.cache/torch/hub/checkpoints/fcn_resnet50_coco-1167a1af.pth\n",
+ "100%|██████████| 135M/135M [00:00<00:00, 217MB/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "from torchvision import models\n",
+ "\n",
+ "model = models.segmentation.fcn_resnet50(pretrained=True)\n",
+ "\n",
+ "# output class를 data set에 맞도록 수정\n",
+ "model.classifier[4] = nn.Conv2d(512, 11, kernel_size=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:41:01.106908Z",
+ "start_time": "2022-12-08T19:40:59.147941Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "input shape : torch.Size([2, 3, 512, 512])\n",
+ "output shape : torch.Size([2, 11, 512, 512])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test\n",
+ "x = torch.randn([2, 3, 512, 512])\n",
+ "print(f\"input shape : {x.shape}\")\n",
+ "out = model(x)[\"out\"]\n",
+ "print(f\"output shape : {out.size()}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## train, validation, test 함수 정의"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:41:06.927954Z",
+ "start_time": "2022-12-08T19:41:06.909453Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train(\n",
+ " num_epochs,\n",
+ " model,\n",
+ " data_loader,\n",
+ " val_loader,\n",
+ " criterion,\n",
+ " optimizer,\n",
+ " saved_dir,\n",
+ " val_every,\n",
+ " device,\n",
+ "):\n",
+ " print(f\"Start training..\")\n",
+ " n_class = 11\n",
+ " best_loss = 9999999\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " model.train()\n",
+ "\n",
+ " hist = np.zeros((n_class, n_class))\n",
+ " for step, (images, masks, _) in enumerate(data_loader):\n",
+ " images = torch.stack(images)\n",
+ " masks = torch.stack(masks).long()\n",
+ "\n",
+ " # gpu 연산을 위해 device 할당\n",
+ " images, masks = images.to(device), masks.to(device)\n",
+ "\n",
+ " # device 할당\n",
+ " model = model.to(device)\n",
+ "\n",
+ " # inference\n",
+ " outputs = model(images)[\"out\"]\n",
+ "\n",
+ " # loss 계산 (cross entropy loss)\n",
+ " loss = criterion(outputs, masks)\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " outputs = torch.argmax(outputs, dim=1).detach().cpu().numpy()\n",
+ " masks = masks.detach().cpu().numpy()\n",
+ "\n",
+ " hist = add_hist(hist, masks, outputs, n_class=n_class)\n",
+ " acc, acc_cls, mIoU, fwavacc, IoU = label_accuracy_score(hist)\n",
+ "\n",
+ " # step 주기에 따른 loss 출력\n",
+ " if (step + 1) % 25 == 0:\n",
+ " print(\n",
+ " f\"Epoch [{epoch+1}/{num_epochs}], Step [{step+1}/{len(train_loader)}], \\\n",
+ " Loss: {round(loss.item(),4)}, mIoU: {round(mIoU,4)}\"\n",
+ " )\n",
+ "\n",
+ " # validation 주기에 따른 loss 출력 및 best model 저장\n",
+ " if (epoch + 1) % val_every == 0:\n",
+ " avrg_loss = validation(epoch + 1, model, val_loader, criterion, device)\n",
+ " if avrg_loss < best_loss:\n",
+ " print(f\"Best performance at epoch: {epoch + 1}\")\n",
+ " print(f\"Save model in {saved_dir}\")\n",
+ " best_loss = avrg_loss\n",
+ " save_model(model, saved_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:41:08.328518Z",
+ "start_time": "2022-12-08T19:41:08.310516Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def validation(epoch, model, data_loader, criterion, device):\n",
+ " print(f\"Start validation #{epoch}\")\n",
+ " model.eval()\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " n_class = 11\n",
+ " total_loss = 0\n",
+ " cnt = 0\n",
+ "\n",
+ " hist = np.zeros((n_class, n_class))\n",
+ " for step, (images, masks, _) in enumerate(data_loader):\n",
+ "\n",
+ " images = torch.stack(images)\n",
+ " masks = torch.stack(masks).long()\n",
+ "\n",
+ " images, masks = images.to(device), masks.to(device)\n",
+ "\n",
+ " # device 할당\n",
+ " model = model.to(device)\n",
+ "\n",
+ " outputs = model(images)[\"out\"]\n",
+ " loss = criterion(outputs, masks)\n",
+ " total_loss += loss\n",
+ " cnt += 1\n",
+ "\n",
+ " outputs = torch.argmax(outputs, dim=1).detach().cpu().numpy()\n",
+ " masks = masks.detach().cpu().numpy()\n",
+ "\n",
+ " hist = add_hist(hist, masks, outputs, n_class=n_class)\n",
+ "\n",
+ " acc, acc_cls, mIoU, fwavacc, IoU = label_accuracy_score(hist)\n",
+ " IoU_by_class = [\n",
+ " {classes: round(IoU, 4)}\n",
+ " for IoU, classes in zip(IoU, sorted_df[\"Categories\"])\n",
+ " ]\n",
+ "\n",
+ " avrg_loss = total_loss / cnt\n",
+ " print(\n",
+ " f\"Validation #{epoch} Average Loss: {round(avrg_loss.item(), 4)}, Accuracy : {round(acc, 4)}, \\\n",
+ " mIoU: {round(mIoU, 4)}\"\n",
+ " )\n",
+ " print(f\"IoU by class : {IoU_by_class}\")\n",
+ "\n",
+ " return avrg_loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 모델 저장 함수 정의"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:41:10.330323Z",
+ "start_time": "2022-12-08T19:41:10.314300Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 모델 저장 함수 정의\n",
+ "val_every = 1\n",
+ "\n",
+ "saved_dir = \"./saved\"\n",
+ "if not os.path.isdir(saved_dir):\n",
+ " os.mkdir(saved_dir)\n",
+ "\n",
+ "\n",
+ "def save_model(model, saved_dir, file_name=\"fcn_resnet50_best_model(pretrained).pt\"):\n",
+ " check_point = {\"net\": model.state_dict()}\n",
+ " output_path = os.path.join(saved_dir, file_name)\n",
+ " torch.save(model, output_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 모델 생성 및 Loss function, Optimizer 정의"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-12-08T19:41:11.473944Z",
+ "start_time": "2022-12-08T19:41:11.457829Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Loss function 정의\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ "# Optimizer 정의\n",
+ "optimizer = torch.optim.Adam(\n",
+ " params=model.parameters(), lr=learning_rate, weight_decay=1e-6\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2022-12-08T19:41:13.372Z"
+ },
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Start training..\n",
+ "Epoch [1/2], Step [25/655], Loss: 1.3341, mIoU: 0.1307\n",
+ "Epoch [1/2], Step [50/655], Loss: 1.0182, mIoU: 0.1497\n",
+ "Epoch [1/2], Step [75/655], Loss: 0.9583, mIoU: 0.1557\n",
+ "Epoch [1/2], Step [100/655], Loss: 1.1138, mIoU: 0.1601\n",
+ "Epoch [1/2], Step [125/655], Loss: 0.5944, mIoU: 0.1655\n",
+ "Epoch [1/2], Step [150/655], Loss: 0.9268, mIoU: 0.1696\n",
+ "Epoch [1/2], Step [175/655], Loss: 0.8897, mIoU: 0.174\n",
+ "Epoch [1/2], Step [200/655], Loss: 0.773, mIoU: 0.1801\n",
+ "Epoch [1/2], Step [225/655], Loss: 0.4943, mIoU: 0.1799\n",
+ "Epoch [1/2], Step [250/655], Loss: 0.8204, mIoU: 0.1822\n",
+ "Epoch [1/2], Step [275/655], Loss: 0.7642, mIoU: 0.1863\n",
+ "Epoch [1/2], Step [300/655], Loss: 0.5602, mIoU: 0.1882\n",
+ "Epoch [1/2], Step [325/655], Loss: 0.3391, mIoU: 0.1899\n",
+ "Epoch [1/2], Step [350/655], Loss: 0.5187, mIoU: 0.1923\n",
+ "Epoch [1/2], Step [375/655], Loss: 0.2389, mIoU: 0.194\n",
+ "Epoch [1/2], Step [400/655], Loss: 0.5855, mIoU: 0.197\n",
+ "Epoch [1/2], Step [425/655], Loss: 0.375, mIoU: 0.1999\n",
+ "Epoch [1/2], Step [450/655], Loss: 0.4523, mIoU: 0.2015\n",
+ "Epoch [1/2], Step [475/655], Loss: 0.3413, mIoU: 0.203\n",
+ "Epoch [1/2], Step [500/655], Loss: 0.3566, mIoU: 0.2036\n",
+ "Epoch [1/2], Step [525/655], Loss: 0.3664, mIoU: 0.2061\n",
+ "Epoch [1/2], Step [550/655], Loss: 1.2213, mIoU: 0.2078\n",
+ "Epoch [1/2], Step [575/655], Loss: 0.7944, mIoU: 0.211\n",
+ "Epoch [1/2], Step [600/655], Loss: 0.793, mIoU: 0.2123\n",
+ "Epoch [1/2], Step [625/655], Loss: 0.7086, mIoU: 0.2145\n",
+ "Epoch [1/2], Step [650/655], Loss: 0.6148, mIoU: 0.2155\n",
+ "Start validation #1\n",
+ "Validation #1 Average Loss: 0.4162, Accuracy : 0.8746, mIoU: 0.2653\n",
+ "IoU by class : [{'Backgroud': 0.9229}, {'General trash': 0.0585}, {'Paper': 0.6031}, {'Paper pack': 0.0324}, {'Metal': 0.0}, {'Glass': 0.0238}, {'Plastic': 0.1444}, {'Styrofoam': 0.475}, {'Plastic bag': 0.6577}, {'Battery': 0.0}, {'Clothing': 0.0}]\n",
+ "Best performance at epoch: 1\n",
+ "Save model in ./saved\n",
+ "Epoch [2/2], Step [25/655], Loss: 0.7474, mIoU: 0.2642\n",
+ "Epoch [2/2], Step [50/655], Loss: 0.3197, mIoU: 0.2614\n",
+ "Epoch [2/2], Step [75/655], Loss: 0.4744, mIoU: 0.2816\n",
+ "Epoch [2/2], Step [100/655], Loss: 0.199, mIoU: 0.2888\n",
+ "Epoch [2/2], Step [125/655], Loss: 0.2723, mIoU: 0.2909\n",
+ "Epoch [2/2], Step [150/655], Loss: 0.3306, mIoU: 0.2963\n",
+ "Epoch [2/2], Step [175/655], Loss: 0.6264, mIoU: 0.2958\n",
+ "Epoch [2/2], Step [200/655], Loss: 0.7068, mIoU: 0.2894\n",
+ "Epoch [2/2], Step [225/655], Loss: 0.2424, mIoU: 0.287\n",
+ "Epoch [2/2], Step [250/655], Loss: 0.5533, mIoU: 0.286\n",
+ "Epoch [2/2], Step [275/655], Loss: 0.3617, mIoU: 0.2863\n",
+ "Epoch [2/2], Step [300/655], Loss: 0.1784, mIoU: 0.2876\n",
+ "Epoch [2/2], Step [325/655], Loss: 0.3911, mIoU: 0.288\n",
+ "Epoch [2/2], Step [350/655], Loss: 0.2949, mIoU: 0.2896\n",
+ "Epoch [2/2], Step [375/655], Loss: 0.5096, mIoU: 0.289\n",
+ "Epoch [2/2], Step [400/655], Loss: 0.8113, mIoU: 0.2881\n",
+ "Epoch [2/2], Step [425/655], Loss: 0.2619, mIoU: 0.2878\n",
+ "Epoch [2/2], Step [450/655], Loss: 0.3197, mIoU: 0.2853\n",
+ "Epoch [2/2], Step [475/655], Loss: 0.3614, mIoU: 0.2863\n",
+ "Epoch [2/2], Step [500/655], Loss: 0.6408, mIoU: 0.2866\n",
+ "Epoch [2/2], Step [525/655], Loss: 0.6666, mIoU: 0.2864\n",
+ "Epoch [2/2], Step [550/655], Loss: 0.5818, mIoU: 0.2881\n",
+ "Epoch [2/2], Step [575/655], Loss: 0.1217, mIoU: 0.2879\n",
+ "Epoch [2/2], Step [600/655], Loss: 0.2846, mIoU: 0.2877\n",
+ "Epoch [2/2], Step [625/655], Loss: 0.2802, mIoU: 0.289\n",
+ "Epoch [2/2], Step [650/655], Loss: 0.5739, mIoU: 0.289\n",
+ "Start validation #2\n",
+ "Validation #2 Average Loss: 0.3588, Accuracy : 0.8912, mIoU: 0.326\n",
+ "IoU by class : [{'Backgroud': 0.9341}, {'General trash': 0.2686}, {'Paper': 0.6469}, {'Paper pack': 0.0211}, {'Metal': 0.0467}, {'Glass': 0.0598}, {'Plastic': 0.294}, {'Styrofoam': 0.5259}, {'Plastic bag': 0.7764}, {'Battery': 0.0}, {'Clothing': 0.0123}]\n",
+ "Best performance at epoch: 2\n",
+ "Save model in ./saved\n"
+ ]
+ }
+ ],
+ "source": [
+ "train(\n",
+ " num_epochs,\n",
+ " model,\n",
+ " train_loader,\n",
+ " val_loader,\n",
+ " criterion,\n",
+ " optimizer,\n",
+ " saved_dir,\n",
+ " val_every,\n",
+ " device,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 저장된 model 불러오기 (학습된 이후) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T05:17:34.490690Z",
+ "start_time": "2021-10-04T05:17:34.339161Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# best model 저장된 경로\n",
+ "model_path = \"./saved/fcn_resnet50_best_model(pretrained).pt\"\n",
+ "\n",
+ "# best model 불러오기\n",
+ "checkpoint = torch.load(model_path, map_location=device)\n",
+ "state_dict = checkpoint.state_dict()\n",
+ "model.load_state_dict(state_dict)\n",
+ "\n",
+ "model = model.to(device)\n",
+ "# 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정)\n",
+ "# model.eval()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### `plot_examples()` 시각화 함수 정의"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T05:13:53.321160Z",
+ "start_time": "2021-10-04T05:13:53.304161Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_examples(\n",
+ " mode=\"train\", batch_id=0, num_examples=batch_size, dataloaer=train_loader\n",
+ "):\n",
+ " \"\"\"Visualization of images and masks according to batch size\n",
+ " Args:\n",
+ " mode: train/val/test (str)\n",
+ " batch_id : 0 (int)\n",
+ " num_examples : 1 ~ batch_size(e.g. 8) (int)\n",
+ " dataloaer : data_loader (dataloader)\n",
+ " Returns:\n",
+ " None\n",
+ " \"\"\"\n",
+ " # variable for legend\n",
+ " category_and_rgb = [\n",
+ " [category, (r, g, b)]\n",
+ " for idx, (category, r, g, b) in enumerate(class_colormap.values)\n",
+ " ]\n",
+ " legend_elements = [\n",
+ " Patch(\n",
+ " facecolor=webcolors.rgb_to_hex(rgb),\n",
+ " edgecolor=webcolors.rgb_to_hex(rgb),\n",
+ " label=category,\n",
+ " )\n",
+ " for category, rgb in category_and_rgb\n",
+ " ]\n",
+ "\n",
+ " # test / validation set에 대한 시각화\n",
+ " if mode in (\"train\", \"val\"):\n",
+ " with torch.no_grad():\n",
+ " for index, (imgs, masks, image_infos) in enumerate(dataloaer):\n",
+ " if index == batch_id:\n",
+ " image_infos = image_infos\n",
+ " temp_images = imgs\n",
+ " temp_masks = masks\n",
+ "\n",
+ " model.eval()\n",
+ " # inference\n",
+ " outs = model(torch.stack(temp_images).to(device))[\"out\"]\n",
+ " oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n",
+ "\n",
+ " break\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ " fig, ax = plt.subplots(\n",
+ " nrows=num_examples,\n",
+ " ncols=3,\n",
+ " figsize=(12, 4 * num_examples),\n",
+ " constrained_layout=True,\n",
+ " )\n",
+ " fig.tight_layout()\n",
+ " for row_num in range(num_examples):\n",
+ " # Original Image\n",
+ " ax[row_num][0].imshow(temp_images[row_num].permute([1, 2, 0]))\n",
+ " ax[row_num][0].set_title(\n",
+ " f\"Orignal Image : {image_infos[row_num]['file_name']}\"\n",
+ " )\n",
+ " # Groud Truth\n",
+ " ax[row_num][1].imshow(\n",
+ " label_to_color_image(masks[row_num].detach().cpu().numpy())\n",
+ " )\n",
+ " ax[row_num][1].set_title(\n",
+ " f\"Groud Truth : {image_infos[row_num]['file_name']}\"\n",
+ " )\n",
+ " # Pred Mask\n",
+ " ax[row_num][2].imshow(label_to_color_image(oms[row_num]))\n",
+ " ax[row_num][2].set_title(f\"Pred Mask : {image_infos[row_num]['file_name']}\")\n",
+ " ax[row_num][2].legend(\n",
+ " handles=legend_elements,\n",
+ " bbox_to_anchor=(1.05, 1),\n",
+ " loc=2,\n",
+ " borderaxespad=0,\n",
+ " )\n",
+ " plt.show()\n",
+ "\n",
+ " # test set에 대한 시각화\n",
+ " else:\n",
+ " with torch.no_grad():\n",
+ " for index, (imgs, image_infos) in enumerate(dataloaer):\n",
+ " if index == batch_id:\n",
+ " image_infos = image_infos\n",
+ " temp_images = imgs\n",
+ "\n",
+ " model.eval()\n",
+ "\n",
+ " # inference\n",
+ " outs = model(torch.stack(temp_images).to(device))[\"out\"]\n",
+ " oms = torch.argmax(outs, dim=1).detach().cpu().numpy()\n",
+ " break\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ " fig, ax = plt.subplots(\n",
+ " nrows=num_examples,\n",
+ " ncols=2,\n",
+ " figsize=(10, 4 * num_examples),\n",
+ " constrained_layout=True,\n",
+ " )\n",
+ "\n",
+ " for row_num in range(num_examples):\n",
+ " # Original Image\n",
+ " ax[row_num][0].imshow(temp_images[row_num].permute([1, 2, 0]))\n",
+ " ax[row_num][0].set_title(\n",
+ " f\"Orignal Image : {image_infos[row_num]['file_name']}\"\n",
+ " )\n",
+ " # Pred Mask\n",
+ " ax[row_num][1].imshow(label_to_color_image(oms[row_num]))\n",
+ " ax[row_num][1].set_title(f\"Pred Mask : {image_infos[row_num]['file_name']}\")\n",
+ " ax[row_num][1].legend(\n",
+ " handles=legend_elements,\n",
+ " bbox_to_anchor=(1.05, 1),\n",
+ " loc=2,\n",
+ " borderaxespad=0,\n",
+ " )\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### train set 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T05:13:54.065182Z",
+ "start_time": "2021-10-04T05:13:54.051662Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_examples(mode=\"train\", batch_id=7, num_examples=batch_size, dataloaer=train_loader)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### validation set 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T05:14:19.516160Z",
+ "start_time": "2021-10-04T05:14:18.709160Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_examples(mode=\"val\", batch_id=0, num_examples=batch_size, dataloaer=val_loader)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### test set 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T05:55:27.950201Z",
+ "start_time": "2021-10-04T05:55:21.585687Z"
+ },
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_examples(mode=\"test\", batch_id=0, num_examples=batch_size, dataloaer=test_loader)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## submission을 위한 test 함수 정의"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T06:16:19.666705Z",
+ "start_time": "2021-10-04T06:16:19.657706Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def test(model, data_loader, device):\n",
+ " size = 256\n",
+ " transform = A.Compose([A.Resize(size, size)])\n",
+ " print(\"Start prediction.\")\n",
+ "\n",
+ " model.eval()\n",
+ "\n",
+ " file_name_list = []\n",
+ " preds_array = np.empty((0, size * size), dtype=np.long)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " for step, (imgs, image_infos) in enumerate(tqdm(test_loader)):\n",
+ "\n",
+ " # inference (512 x 512)\n",
+ " outs = model(torch.stack(imgs).to(device))[\"out\"]\n",
+ " oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy()\n",
+ "\n",
+ " # resize (256 x 256)\n",
+ " temp_mask = []\n",
+ " for img, mask in zip(np.stack(imgs), oms):\n",
+ " transformed = transform(image=img, mask=mask)\n",
+ " mask = transformed[\"mask\"]\n",
+ " temp_mask.append(mask)\n",
+ "\n",
+ " oms = np.array(temp_mask)\n",
+ "\n",
+ " oms = oms.reshape([oms.shape[0], size * size]).astype(int)\n",
+ " preds_array = np.vstack((preds_array, oms))\n",
+ "\n",
+ " file_name_list.append([i[\"file_name\"] for i in image_infos])\n",
+ " print(\"End prediction.\")\n",
+ " file_names = [y for x in file_name_list for y in x]\n",
+ "\n",
+ " return file_names, preds_array"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## submission.csv 생성"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-10-04T06:19:10.926207Z",
+ "start_time": "2021-10-04T06:16:20.313208Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Start prediction.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 205/205 [07:22<00:00, 2.16s/it]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End prediction.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# sample_submisson.csv 열기\n",
+ "submission = pd.read_csv(\"./submission/sample_submission.csv\", index_col=None)\n",
+ "\n",
+ "# test set에 대한 prediction\n",
+ "file_names, preds = test(model, test_loader, device)\n",
+ "\n",
+ "# PredictionString 대입\n",
+ "for file_name, string in zip(file_names, preds):\n",
+ " submission = submission.append(\n",
+ " {\n",
+ " \"image_id\": file_name,\n",
+ " \"PredictionString\": \" \".join(str(e) for e in string.tolist()),\n",
+ " },\n",
+ " ignore_index=True,\n",
+ " )\n",
+ "\n",
+ "# submission.csv로 저장\n",
+ "submission.to_csv(\"./submission/fcn_resnet50_best_model(pretrained).csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **콘텐츠 라이선스**\n",
+ "\n",
+ "**WARNING** : **본 교육 콘텐츠의 지식재산권은 재단법인 네이버커넥트에 귀속됩니다. 본 콘텐츠를 어떠한 경로로든 외부로 유출 및 수정하는 행위를 엄격히 금합니다.** 다만, 비영리적 교육 및 연구활동에 한정되어 사용할 수 있으나 재단의 허락을 받아야 합니다. 이를 위반하는 경우, 관련 법률에 따라 책임을 질 수 있습니다.\n",
+ "\n",
+ "## **데이터셋 저작권**\n",
+ "\n",
+ "**WARNING** : CC BY 2.0"
+ ]
+ }
+ ],
+ "metadata": {
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3.8.15 ('segmentation')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "394.25px"
+ },
+ "toc_section_display": true,
+ "toc_window_display": true
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "d36e052b391be8c28b05838ade06426769a29575d5fe21a7bc69c7dec0c04c06"
+ }
+ }
},
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pytorch version: 1.7.1\n",
- "GPU 사용 가능 여부: True\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import random\n",
- "import time\n",
- "import json\n",
- "import warnings\n",
- "\n",
- "warnings.filterwarnings(\"ignore\")\n",
- "\n",
- "import torch\n",
- "import torch.nn as nn\n",
- "from torch.utils.data import Dataset, DataLoader\n",
- "from utils import label_accuracy_score, add_hist\n",
- "import cv2\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "from tqdm import tqdm\n",
- "\n",
- "# 전처리를 위한 라이브러리\n",
- "from pycocotools.coco import COCO\n",
- "import torchvision\n",
- "import torchvision.transforms as transforms\n",
- "\n",
- "#!pip install albumentations==0.4.6\n",
- "import albumentations as A\n",
- "from albumentations.pytorch import ToTensorV2\n",
- "\n",
- "# 시각화를 위한 라이브러리\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "\n",
- "sns.set()\n",
- "from matplotlib.patches import Patch\n",
- "\n",
- "#!pip install webcolors\n",
- "import webcolors\n",
- "\n",
- "plt.rcParams[\"axes.grid\"] = False\n",
- "\n",
- "print(\"pytorch version: {}\".format(torch.__version__))\n",
- "print(\"GPU 사용 가능 여부: {}\".format(torch.cuda.is_available()))\n",
- "\n",
- "# print(torch.cuda.get_device_name(0))\n",
- "# print(torch.cuda.device_count())\n",
- "\n",
- "# GPU 사용 가능 여부에 따라 device 정보 저장\n",
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 하이퍼파라미터 세팅 및 seed 고정"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:38:46.873164Z",
- "start_time": "2022-12-08T19:38:46.855505Z"
- }
- },
- "outputs": [],
- "source": [
- "batch_size = 4 # Mini-batch size\n",
- "num_epochs = 2\n",
- "learning_rate = 0.0001"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:38:48.088642Z",
- "start_time": "2022-12-08T19:38:48.074145Z"
- }
- },
- "outputs": [],
- "source": [
- "# seed 고정\n",
- "random_seed = 21\n",
- "torch.manual_seed(random_seed)\n",
- "torch.cuda.manual_seed(random_seed)\n",
- "torch.cuda.manual_seed_all(random_seed) # if use multi-GPU\n",
- "torch.backends.cudnn.deterministic = True\n",
- "torch.backends.cudnn.benchmark = False\n",
- "np.random.seed(random_seed)\n",
- "random.seed(random_seed)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 학습 데이터 EDA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:38:56.320707Z",
- "start_time": "2022-12-08T19:38:52.314207Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of super categories: 10\n",
- "Number of categories: 10\n",
- "Number of annotations: 26240\n",
- "Number of images: 3272\n"
- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "dataset_path = \"/opt/ml/input/data\"\n",
- "anns_file_path = dataset_path + \"/\" + \"train_all.json\"\n",
- "\n",
- "# Read annotations\n",
- "with open(anns_file_path, \"r\") as f:\n",
- " dataset = json.loads(f.read())\n",
- "\n",
- "categories = dataset[\"categories\"]\n",
- "anns = dataset[\"annotations\"]\n",
- "imgs = dataset[\"images\"]\n",
- "\n",
- "nr_cats = len(categories)\n",
- "nr_annotations = len(anns)\n",
- "nr_images = len(imgs)\n",
- "\n",
- "# Load categories and super categories\n",
- "cat_names = []\n",
- "super_cat_names = []\n",
- "super_cat_ids = {}\n",
- "super_cat_last_name = \"\"\n",
- "nr_super_cats = 0\n",
- "for cat_it in categories:\n",
- " cat_names.append(cat_it[\"name\"])\n",
- " super_cat_name = cat_it[\"supercategory\"]\n",
- " # Adding new supercat\n",
- " if super_cat_name != super_cat_last_name:\n",
- " super_cat_names.append(super_cat_name)\n",
- " super_cat_ids[super_cat_name] = nr_super_cats\n",
- " super_cat_last_name = super_cat_name\n",
- " nr_super_cats += 1\n",
- "\n",
- "print(\"Number of super categories:\", nr_super_cats)\n",
- "print(\"Number of categories:\", nr_cats)\n",
- "print(\"Number of annotations:\", nr_annotations)\n",
- "print(\"Number of images:\", nr_images)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:38:57.879486Z",
- "start_time": "2022-12-08T19:38:57.682460Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Count annotations\n",
- "cat_histogram = np.zeros(nr_cats, dtype=int)\n",
- "for ann in anns:\n",
- " cat_histogram[ann[\"category_id\"] - 1] += 1\n",
- "\n",
- "# Initialize the matplotlib figure\n",
- "f, ax = plt.subplots(figsize=(5, 5))\n",
- "\n",
- "# Convert to DataFrame\n",
- "df = pd.DataFrame({\"Categories\": cat_names, \"Number of annotations\": cat_histogram})\n",
- "df = df.sort_values(\"Number of annotations\", 0, False)\n",
- "\n",
- "# Plot the histogram\n",
- "plt.title(\"category distribution of train_all set \")\n",
- "plot_1 = sns.barplot(\n",
- " x=\"Number of annotations\", y=\"Categories\", data=df, label=\"Total\", color=\"b\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:39:00.016714Z",
- "start_time": "2022-12-08T19:39:00.011712Z"
- }
- },
- "outputs": [],
- "source": [
- "# category labeling\n",
- "sorted_temp_df = df.sort_index()\n",
- "\n",
- "# background = 0 에 해당되는 label 추가 후 기존들을 모두 label + 1 로 설정\n",
- "sorted_df = pd.DataFrame([\"Backgroud\"], columns=[\"Categories\"])\n",
- "sorted_df = sorted_df.append(sorted_temp_df, ignore_index=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2022-12-08T19:39:01.972676Z",
- "start_time": "2022-12-08T19:39:01.957173Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
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- " \n",
- " | 5 | \n",
- " Glass | \n",
- " 64 | \n",
- " 0 | \n",
- " 128 | \n",
- "
\n",
- " \n",
- " | 6 | \n",
- " Plastic | \n",
- " 64 | \n",
- " 0 | \n",
- " 192 | \n",
- "
\n",
- " \n",
- " | 7 | \n",
- " Styrofoam | \n",
- " 192 | \n",
- " 128 | \n",
- " 64 | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " Plastic bag | \n",
- " 192 | \n",
- " 192 | \n",
- " 128 | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " Battery | \n",
- " 64 | \n",
- " 64 | \n",
- " 128 | \n",
- "
\n",
- " \n",
- " | 10 | \n",
- " Clothing | \n",
- " 128 | \n",
- " 0 | \n",
- " 192 | \n",
- "
\n",
- " \n",
- "
\n",
- "