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[Feature] Add RTMDet Swin / ConvNeXt (#11259)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| _base_ = './rtmdet_l_8xb32-300e_coco.py' | ||
|
|
||
| custom_imports = dict( | ||
| imports=['mmpretrain.models'], allow_failed_imports=False) | ||
|
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| norm_cfg = dict(type='GN', num_groups=32) | ||
| checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_in1k-384px_20221219-4570f792.pth' # noqa | ||
| model = dict( | ||
| type='RTMDet', | ||
| data_preprocessor=dict( | ||
| _delete_=True, | ||
| type='DetDataPreprocessor', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| bgr_to_rgb=True, | ||
| batch_augments=None), | ||
| backbone=dict( | ||
| _delete_=True, | ||
| type='mmpretrain.ConvNeXt', | ||
| arch='base', | ||
| out_indices=[1, 2, 3], | ||
| drop_path_rate=0.7, | ||
| layer_scale_init_value=1.0, | ||
| gap_before_final_norm=False, | ||
| with_cp=True, | ||
| init_cfg=dict( | ||
| type='Pretrained', checkpoint=checkpoint_file, | ||
| prefix='backbone.')), | ||
| neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg), | ||
| bbox_head=dict(norm_cfg=norm_cfg)) | ||
|
|
||
| max_epochs = 100 | ||
| stage2_num_epochs = 10 | ||
| interval = 10 | ||
| base_lr = 0.001 | ||
|
|
||
| train_cfg = dict( | ||
| max_epochs=max_epochs, | ||
| val_interval=interval, | ||
| dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)]) | ||
|
|
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| optim_wrapper = dict( | ||
| constructor='LearningRateDecayOptimizerConstructor', | ||
| paramwise_cfg={ | ||
| 'decay_rate': 0.8, | ||
| 'decay_type': 'layer_wise', | ||
| 'num_layers': 12 | ||
| }, | ||
| optimizer=dict(lr=base_lr)) | ||
|
|
||
| # learning rate | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=1.0e-5, | ||
| by_epoch=False, | ||
| begin=0, | ||
| end=1000), | ||
| dict( | ||
| # use cosine lr from 50 to 100 epoch | ||
| type='CosineAnnealingLR', | ||
| eta_min=base_lr * 0.05, | ||
| begin=max_epochs // 2, | ||
| end=max_epochs, | ||
| T_max=max_epochs // 2, | ||
| by_epoch=True, | ||
| convert_to_iter_based=True), | ||
| ] | ||
|
|
||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0002, | ||
| update_buffers=True, | ||
| priority=49), | ||
| dict( | ||
| type='PipelineSwitchHook', | ||
| switch_epoch=max_epochs - stage2_num_epochs, | ||
| switch_pipeline={{_base_.train_pipeline_stage2}}) | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| _base_ = './rtmdet_l_8xb32-300e_coco.py' | ||
|
|
||
| norm_cfg = dict(type='GN', num_groups=32) | ||
| checkpoint = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa | ||
| model = dict( | ||
| type='RTMDet', | ||
| data_preprocessor=dict( | ||
| _delete_=True, | ||
| type='DetDataPreprocessor', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| bgr_to_rgb=True, | ||
| batch_augments=None), | ||
| backbone=dict( | ||
| _delete_=True, | ||
| type='SwinTransformer', | ||
| pretrain_img_size=384, | ||
| embed_dims=128, | ||
| depths=[2, 2, 18, 2], | ||
| num_heads=[4, 8, 16, 32], | ||
| window_size=12, | ||
| mlp_ratio=4, | ||
| qkv_bias=True, | ||
| qk_scale=None, | ||
| drop_rate=0., | ||
| attn_drop_rate=0., | ||
| drop_path_rate=0.3, | ||
| patch_norm=True, | ||
| out_indices=(1, 2, 3), | ||
| with_cp=True, | ||
| convert_weights=True, | ||
| init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), | ||
| neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg), | ||
| bbox_head=dict(norm_cfg=norm_cfg)) | ||
|
|
||
| max_epochs = 100 | ||
| stage2_num_epochs = 10 | ||
| interval = 10 | ||
| base_lr = 0.001 | ||
|
|
||
| train_cfg = dict( | ||
| max_epochs=max_epochs, | ||
| val_interval=interval, | ||
| dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)]) | ||
|
|
||
| optim_wrapper = dict(optimizer=dict(lr=base_lr)) | ||
|
|
||
| # learning rate | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=1.0e-5, | ||
| by_epoch=False, | ||
| begin=0, | ||
| end=1000), | ||
| dict( | ||
| # use cosine lr from 50 to 100 epoch | ||
| type='CosineAnnealingLR', | ||
| eta_min=base_lr * 0.05, | ||
| begin=max_epochs // 2, | ||
| end=max_epochs, | ||
| T_max=max_epochs // 2, | ||
| by_epoch=True, | ||
| convert_to_iter_based=True), | ||
| ] | ||
|
|
||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0002, | ||
| update_buffers=True, | ||
| priority=49), | ||
| dict( | ||
| type='PipelineSwitchHook', | ||
| switch_epoch=max_epochs - stage2_num_epochs, | ||
| switch_pipeline={{_base_.train_pipeline_stage2}}) | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| _base_ = './rtmdet_l_swin_b_4xb32-100e_coco.py' | ||
|
|
||
| model = dict( | ||
| backbone=dict( | ||
| depths=[2, 2, 18, 2, 1], | ||
| num_heads=[4, 8, 16, 32, 64], | ||
| strides=(4, 2, 2, 2, 2), | ||
| out_indices=(1, 2, 3, 4)), | ||
| neck=dict(in_channels=[256, 512, 1024, 2048]), | ||
| bbox_head=dict( | ||
| anchor_generator=dict( | ||
| type='MlvlPointGenerator', offset=0, strides=[8, 16, 32, 64]))) | ||
|
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||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='CachedMosaic', img_scale=(1280, 1280), pad_val=114.0), | ||
| dict( | ||
| type='RandomResize', | ||
| scale=(2560, 2560), | ||
| ratio_range=(0.1, 2.0), | ||
| keep_ratio=True), | ||
| dict(type='RandomCrop', crop_size=(1280, 1280)), | ||
| dict(type='YOLOXHSVRandomAug'), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), | ||
| dict( | ||
| type='CachedMixUp', | ||
| img_scale=(1280, 1280), | ||
| ratio_range=(1.0, 1.0), | ||
| max_cached_images=20, | ||
| pad_val=(114, 114, 114)), | ||
| dict(type='PackDetInputs') | ||
| ] | ||
|
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||
| train_pipeline_stage2 = [ | ||
| dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='RandomResize', | ||
| scale=(1280, 1280), | ||
| ratio_range=(0.1, 2.0), | ||
| keep_ratio=True), | ||
| dict(type='RandomCrop', crop_size=(1280, 1280)), | ||
| dict(type='YOLOXHSVRandomAug'), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), | ||
| dict(type='PackDetInputs') | ||
| ] | ||
|
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||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), | ||
| dict(type='Resize', scale=(1280, 1280), keep_ratio=True), | ||
| dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor')) | ||
| ] | ||
|
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||
| train_dataloader = dict( | ||
| batch_size=16, num_workers=20, dataset=dict(pipeline=train_pipeline)) | ||
| val_dataloader = dict(num_workers=20, dataset=dict(pipeline=test_pipeline)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| max_epochs = 100 | ||
| stage2_num_epochs = 10 | ||
|
|
||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0002, | ||
| update_buffers=True, | ||
| priority=49), | ||
| dict( | ||
| type='PipelineSwitchHook', | ||
| switch_epoch=max_epochs - stage2_num_epochs, | ||
| switch_pipeline=train_pipeline_stage2) | ||
| ] | ||
|
|
||
| img_scales = [(1280, 1280), (640, 640), (1920, 1920)] | ||
| tta_pipeline = [ | ||
| dict(type='LoadImageFromFile', backend_args=None), | ||
| dict( | ||
| type='TestTimeAug', | ||
| transforms=[ | ||
| [ | ||
| dict(type='Resize', scale=s, keep_ratio=True) | ||
| for s in img_scales | ||
| ], | ||
| [ | ||
| # ``RandomFlip`` must be placed before ``Pad``, otherwise | ||
| # bounding box coordinates after flipping cannot be | ||
| # recovered correctly. | ||
| dict(type='RandomFlip', prob=1.), | ||
| dict(type='RandomFlip', prob=0.) | ||
| ], | ||
| [ | ||
| dict( | ||
| type='Pad', | ||
| size=(1920, 1920), | ||
| pad_val=dict(img=(114, 114, 114))), | ||
| ], | ||
| [dict(type='LoadAnnotations', with_bbox=True)], | ||
| [ | ||
| dict( | ||
| type='PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor', 'flip', 'flip_direction')) | ||
| ] | ||
| ]) | ||
| ] |