[Feature] Official implementation of SETR #531
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| _base_ = './cityscapes.py' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| crop_size = (768, 768) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations'), | ||
| dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)), | ||
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| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | ||
| dict(type='RandomFlip', flip_ratio=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=(2049, 1025), | ||
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| # 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']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| train=dict(pipeline=train_pipeline), | ||
| val=dict(pipeline=test_pipeline), | ||
| test=dict(pipeline=test_pipeline)) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| # model settings | ||
| backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=\ | ||
| 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', # noqa | ||
| backbone=dict( | ||
| type='VisionTransformer', | ||
| img_size=(768, 768), | ||
| patch_size=16, | ||
| in_channels=3, | ||
| embed_dim=1024, | ||
| depth=24, | ||
| num_heads=16, | ||
| out_indices=(5, 11, 17, 23), | ||
| drop_rate=0.1, | ||
| norm_cfg=backbone_norm_cfg, | ||
| out_shape='NCHW', | ||
| with_cls_token=False, | ||
| interpolate_mode='bilinear', | ||
| ), | ||
| neck=dict( | ||
| type='MLA', | ||
| in_channels=[1024, 1024, 1024, 1024], | ||
| out_channels=256, | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=dict(type='ReLU'), | ||
| ), | ||
| decode_head=dict( | ||
| type='SETRMLAHead', | ||
| in_channels=(1024, 1024, 1024, 1024), | ||
| channels=512, | ||
| in_index=(0, 1, 2, 3), | ||
| img_size=(768, 768), | ||
| mla_channels=256, | ||
| mlahead_channels=128, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=[ | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=0, | ||
| img_size=(768, 768), | ||
| mla_channels=256, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=1, | ||
| img_size=(768, 768), | ||
| mla_channels=256, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=2, | ||
| img_size=(768, 768), | ||
| mla_channels=256, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=3, | ||
| img_size=(768, 768), | ||
| mla_channels=256, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) | ||
| ], | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| # model settings | ||
| backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=\ | ||
| 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', # noqa | ||
| backbone=dict( | ||
| type='VisionTransformer', | ||
| img_size=(768, 768), | ||
| patch_size=16, | ||
| in_channels=3, | ||
| embed_dim=1024, | ||
| depth=24, | ||
| num_heads=16, | ||
| out_indices=(9, 14, 19, 23), | ||
| drop_rate=0.1, | ||
| norm_cfg=backbone_norm_cfg, | ||
| out_shape='NCHW', | ||
| with_cls_token=True, | ||
| interpolate_mode='bilinear', | ||
| ), | ||
| decode_head=dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=3, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=1, | ||
| conv3x3_conv1x1=False, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=[ | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=0, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=1, | ||
| conv3x3_conv1x1=False, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=1, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=1, | ||
| conv3x3_conv1x1=False, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=2, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=1, | ||
| conv3x3_conv1x1=False, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) | ||
| ], | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| # model settings | ||
| backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=\ | ||
| 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', # noqa | ||
| backbone=dict( | ||
| type='VisionTransformer', | ||
| img_size=(768, 768), | ||
| patch_size=16, | ||
| in_channels=3, | ||
| embed_dim=1024, | ||
| depth=24, | ||
| num_heads=16, | ||
| out_indices=(9, 14, 19, 23), | ||
| drop_rate=0.1, | ||
| norm_cfg=backbone_norm_cfg, | ||
| out_shape='NCHW', | ||
| with_cls_token=True, | ||
| interpolate_mode='bilinear', | ||
| ), | ||
| decode_head=dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=3, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=4, | ||
| up_mode='bilinear', | ||
| num_up_layer=4, | ||
| conv3x3_conv1x1=True, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=[ | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=0, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=2, | ||
| conv3x3_conv1x1=True, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=1, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=2, | ||
| conv3x3_conv1x1=True, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=2, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=2, | ||
| conv3x3_conv1x1=True, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRUPHead', | ||
| in_channels=1024, | ||
| channels=512, | ||
| in_index=3, | ||
| img_size=(768, 768), | ||
| embed_dim=1024, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| num_convs=2, | ||
| up_mode='bilinear', | ||
| num_up_layer=2, | ||
| conv3x3_conv1x1=True, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) | ||
| ], | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| _base_ = ['./setr_mla_480x480_80k_pascal_context_bs_8.py'] | ||
|
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|
|
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| data = dict(samples_per_gpu=2) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,62 @@ | ||
| _base_ = [ | ||
|
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||
| '../_base_/models/setr_mla.py', '../_base_/datasets/pascal_context.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' | ||
| ] | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| backbone=dict(img_size=(480, 480), drop_rate=0), | ||
| decode_head=dict(img_size=(480, 480), num_classes=60), | ||
| auxiliary_head=[ | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=0, | ||
| img_size=(480, 480), | ||
| mla_channels=256, | ||
| num_classes=60, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=1, | ||
| img_size=(480, 480), | ||
| mla_channels=256, | ||
| num_classes=60, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=2, | ||
| img_size=(480, 480), | ||
| mla_channels=256, | ||
| num_classes=60, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| dict( | ||
| type='SETRMLAAUXHead', | ||
| in_channels=256, | ||
| channels=512, | ||
| in_index=3, | ||
| img_size=(480, 480), | ||
| mla_channels=256, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) | ||
| ], | ||
| test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320))) | ||
|
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| optimizer = dict( | ||
| lr=0.001, | ||
| weight_decay=0.0, | ||
| paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})) | ||
|
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| data = dict(samples_per_gpu=1) | ||
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We may also benchmark the default
cityscapes.pyconfig.There was a problem hiding this comment.
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We may first benchmark 2-4 configs.
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Already add