update cwt training

configs/_base_/datasets/cwt_group8.py

@@ -0,0 +1,64 @@
+# dataset settings
+img_size = (1920, 1080)
+crop_size = (375, 600)
+
+dataset_type = 'CWT_Dataset'
+data_root = 'data/CWT/'
+# img_norm_cfg = dict(
+#     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_norm_cfg = dict(
+    mean=[127.38, 127.96, 128.21], std=[53.941, 54.258, 54.389], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_size, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size),
+    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='MultiScaleFlipAug',
+        img_scale=crop_size,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        # 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='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=6,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img',
+        ann_dir='annotation/grey_mask',
+        split='train.txt',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img',
+        ann_dir='annotation/grey_mask',
+        split='test.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='img',
+        ann_dir='annotation/grey_mask',
+        split='test.txt',
+        pipeline=test_pipeline))

configs/_base_/models/ours_class_att_group8.py

@@ -0,0 +1,75 @@
+# model settings
+img_size = (1920, 1080)
+crop_size = (375, 600)
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        type='MixVisionTransformer',
+        in_channels=3,
+        embed_dims=32,
+        num_stages=4,
+        num_layers=[2, 2, 2, 2],
+        num_heads=[1, 2, 5, 8],
+        patch_sizes=[7, 3, 3, 3],
+        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=0.1),
+    decode_head=dict(
+        type='OursHeadClassAttNew',
+        in_channels=[32, 64, 160, 256],
+        in_index=[0, 1, 2, 3],
+        channels=384,
+        mask_size=(97, 97),
+        psa_type='bi-direction',
+        compact=False,
+        shrink_factor=2,
+        normalization_factor=1.0,
+        psa_softmax=True,
+        dropout_ratio=0.1,
+        num_classes=8,
+        input_transform='multiple_select',
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        attn_split=1,
+        strides=(2,1),
+        size_index=1,
+        img_size=crop_size,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, static_weight=False)),
+    auxiliary_head=[
+        dict(
+            type='FCNHead',
+            in_channels=160,
+            channels=32,
+            num_convs=1,
+            num_classes=8,
+            in_index=-2,
+            norm_cfg=norm_cfg,
+            concat_input=False,
+            align_corners=False,
+            img_size=crop_size,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+        dict(
+            type='FCNHead',
+            in_channels=64,
+            channels=32,
+            num_convs=1,
+            num_classes=8,
+            in_index=-3,
+            norm_cfg=norm_cfg,
+            concat_input=False,
+            align_corners=False,
+            img_size=crop_size,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    ],
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))

configs/ours/ganav_group8_cwt.py

@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/ours_class_att_group8.py', '../_base_/datasets/cwt_group8.py',
+    '../_base_/default_runtime.py'
+]
+
+img_size = (1920, 1080)
+crop_size = (375, 600)
+
+optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=4e-5)
+optimizer_config = dict()
+# learning policy
+# runtime settings
+runner = dict(type='IterBasedRunner', max_iters=240000)
+total_iters = 240000
+checkpoint_config = dict(by_epoch=False, interval=16000)
+evaluation = dict(interval=240000, metric='mIoU')
+
+# optimizer
+lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, warmup='linear',
+    warmup_iters=1500,
+    warmup_ratio=1e-6,
+    by_epoch=False)
+
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=6)
+
+
+

mmseg/datasets/__init__.py

@@ -5,8 +5,9 @@
 from .rugd_group6 import RUGDDataset_Group6
 from .rugd_group4 import RUGDDataset_Group4
 from .rellis_group4 import RELLISDataset_Group4
+from .cwt import CWT_Dataset
 
 __all__ = [
     'CustomDataset', 'RUGDDataset', 'RELLISDataset', 'RELLISDataset_Group6', 
-    'RUGDDataset_Group6', 'RUGDDataset_Group4', 'RELLISDataset_Group4'
+    'RUGDDataset_Group6', 'RUGDDataset_Group4', 'RELLISDataset_Group4', 'CWT_Dataset'
 ]

mmseg/datasets/cwt.py

@@ -0,0 +1,25 @@
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class CWT_Dataset(CustomDataset):
+    """cwt dataset.
+
+    """
+
+
+
+    CLASSES = ("flat", "bumpy", "water", "rock", "mixed", "excavator", "obstacle")
+
+    PALETTE = [[0, 255, 0], [255, 255, 0], [255, 0, 0], [128, 0, 0], [100, 65, 0], [0, 255, 255], [0, 0, 255]]
+
+    def __init__(self, **kwargs):
+        super(CWT_Dataset, self).__init__(
+            img_suffix='.jpg',
+            seg_map_suffix='.png',
+            **kwargs)
+        self.CLASSES = ("flat", "bumpy", "water", "rock", "mixed", "excavator", "obstacle")
+        self.PALETTE =[[0, 255, 0], [255, 255, 0], [255, 0, 0], [128, 0, 0], [100, 65, 0], [0, 255, 255], [0, 0, 255]]
+
+        # assert osp.exists(self.img_dir)