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Merge pull request SwinTransformer#32 from impiga/master
MoBY with Swin Transformer
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.../cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,140 @@ | ||
| _base_ = [ | ||
| '../_base_/models/cascade_mask_rcnn_swin_fpn.py', | ||
| '../_base_/datasets/coco_instance.py', | ||
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| model = dict( | ||
| backbone=dict( | ||
| embed_dim=96, | ||
| depths=[2, 2, 6, 2], | ||
| num_heads=[3, 6, 12, 24], | ||
| window_size=7, | ||
| ape=False, | ||
| drop_path_rate=0.0, | ||
| patch_norm=True, | ||
| use_checkpoint=False | ||
| ), | ||
| neck=dict(in_channels=[96, 192, 384, 768]), | ||
| roi_head=dict( | ||
| bbox_head=[ | ||
| dict( | ||
| type='ConvFCBBoxHead', | ||
| num_shared_convs=4, | ||
| num_shared_fcs=1, | ||
| in_channels=256, | ||
| conv_out_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=80, | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
| reg_class_agnostic=False, | ||
| reg_decoded_bbox=True, | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), | ||
| dict( | ||
| type='ConvFCBBoxHead', | ||
| num_shared_convs=4, | ||
| num_shared_fcs=1, | ||
| in_channels=256, | ||
| conv_out_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=80, | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.05, 0.05, 0.1, 0.1]), | ||
| reg_class_agnostic=False, | ||
| reg_decoded_bbox=True, | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), | ||
| dict( | ||
| type='ConvFCBBoxHead', | ||
| num_shared_convs=4, | ||
| num_shared_fcs=1, | ||
| in_channels=256, | ||
| conv_out_channels=256, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=80, | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.033, 0.033, 0.067, 0.067]), | ||
| reg_class_agnostic=False, | ||
| reg_decoded_bbox=True, | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) | ||
| ])) | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
|
||
| # augmentation strategy originates from DETR / Sparse RCNN | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='AutoAugment', | ||
| policies=[ | ||
| [ | ||
| dict(type='Resize', | ||
| img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), | ||
| (608, 1333), (640, 1333), (672, 1333), (704, 1333), | ||
| (736, 1333), (768, 1333), (800, 1333)], | ||
| multiscale_mode='value', | ||
| keep_ratio=True) | ||
| ], | ||
| [ | ||
| dict(type='Resize', | ||
| img_scale=[(400, 1333), (500, 1333), (600, 1333)], | ||
| multiscale_mode='value', | ||
| keep_ratio=True), | ||
| dict(type='RandomCrop', | ||
| crop_type='absolute_range', | ||
| crop_size=(384, 600), | ||
| allow_negative_crop=True), | ||
| dict(type='Resize', | ||
| img_scale=[(480, 1333), (512, 1333), (544, 1333), | ||
| (576, 1333), (608, 1333), (640, 1333), | ||
| (672, 1333), (704, 1333), (736, 1333), | ||
| (768, 1333), (800, 1333)], | ||
| multiscale_mode='value', | ||
| override=True, | ||
| keep_ratio=True) | ||
| ] | ||
| ]), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
| ] | ||
| data = dict(train=dict(pipeline=train_pipeline)) | ||
|
|
||
| optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05, | ||
| 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.)})) | ||
| lr_config = dict(step=[8, 11]) | ||
| runner = dict(type='EpochBasedRunnerAmp', max_epochs=12) | ||
|
|
||
| # do not use mmdet version fp16 | ||
| fp16 = None | ||
| optimizer_config = dict( | ||
| type="DistOptimizerHook", | ||
| update_interval=1, | ||
| grad_clip=None, | ||
| coalesce=True, | ||
| bucket_size_mb=-1, | ||
| use_fp16=True, | ||
| ) |
80 changes: 80 additions & 0 deletions
80
configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| _base_ = [ | ||
| '../_base_/models/mask_rcnn_swin_fpn.py', | ||
| '../_base_/datasets/coco_instance.py', | ||
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| model = dict( | ||
| backbone=dict( | ||
| embed_dim=96, | ||
| depths=[2, 2, 6, 2], | ||
| num_heads=[3, 6, 12, 24], | ||
| window_size=7, | ||
| ape=False, | ||
| drop_path_rate=0.1, | ||
| patch_norm=True, | ||
| use_checkpoint=False | ||
| ), | ||
| neck=dict(in_channels=[96, 192, 384, 768])) | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
|
||
| # augmentation strategy originates from DETR / Sparse RCNN | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='AutoAugment', | ||
| policies=[ | ||
| [ | ||
| dict(type='Resize', | ||
| img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), | ||
| (608, 1333), (640, 1333), (672, 1333), (704, 1333), | ||
| (736, 1333), (768, 1333), (800, 1333)], | ||
| multiscale_mode='value', | ||
| keep_ratio=True) | ||
| ], | ||
| [ | ||
| dict(type='Resize', | ||
| img_scale=[(400, 1333), (500, 1333), (600, 1333)], | ||
| multiscale_mode='value', | ||
| keep_ratio=True), | ||
| dict(type='RandomCrop', | ||
| crop_type='absolute_range', | ||
| crop_size=(384, 600), | ||
| allow_negative_crop=True), | ||
| dict(type='Resize', | ||
| img_scale=[(480, 1333), (512, 1333), (544, 1333), | ||
| (576, 1333), (608, 1333), (640, 1333), | ||
| (672, 1333), (704, 1333), (736, 1333), | ||
| (768, 1333), (800, 1333)], | ||
| multiscale_mode='value', | ||
| override=True, | ||
| keep_ratio=True) | ||
| ] | ||
| ]), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
| ] | ||
| data = dict(train=dict(pipeline=train_pipeline)) | ||
|
|
||
| optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05, | ||
| 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.)})) | ||
| lr_config = dict(step=[8, 11]) | ||
| runner = dict(type='EpochBasedRunnerAmp', max_epochs=12) | ||
|
|
||
| # do not use mmdet version fp16 | ||
| fp16 = None | ||
| optimizer_config = dict( | ||
| type="DistOptimizerHook", | ||
| update_interval=1, | ||
| grad_clip=None, | ||
| coalesce=True, | ||
| bucket_size_mb=-1, | ||
| use_fp16=True, | ||
| ) |
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