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[Feature] Release dyhead big model. (open-mmlab#7733)
* Release dyhead with big model * Update new config * Update config * Fix lint * Update * Update
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configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| _base_ = '../_base_/default_runtime.py' | ||
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|
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| pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa | ||
| model = dict( | ||
| type='ATSS', | ||
| backbone=dict( | ||
| type='SwinTransformer', | ||
| pretrain_img_size=384, | ||
| embed_dims=192, | ||
| depths=[2, 2, 18, 2], | ||
| num_heads=[6, 12, 24, 48], | ||
| window_size=12, | ||
| mlp_ratio=4, | ||
| qkv_bias=True, | ||
| qk_scale=None, | ||
| drop_rate=0., | ||
| attn_drop_rate=0., | ||
| drop_path_rate=0.2, | ||
| patch_norm=True, | ||
| out_indices=(1, 2, 3), | ||
| # Please only add indices that would be used | ||
| # in FPN, otherwise some parameter will not be used | ||
| with_cp=False, | ||
| convert_weights=True, | ||
| init_cfg=dict(type='Pretrained', checkpoint=pretrained)), | ||
| neck=[ | ||
| dict( | ||
| type='FPN', | ||
| in_channels=[384, 768, 1536], | ||
| out_channels=256, | ||
| start_level=0, | ||
| add_extra_convs='on_output', | ||
| num_outs=5), | ||
| dict( | ||
| type='DyHead', | ||
| in_channels=256, | ||
| out_channels=256, | ||
| num_blocks=6, | ||
| # disable zero_init_offset to follow official implementation | ||
| zero_init_offset=False) | ||
| ], | ||
| bbox_head=dict( | ||
| type='ATSSHead', | ||
| num_classes=80, | ||
| in_channels=256, | ||
| pred_kernel_size=1, # follow DyHead official implementation | ||
| stacked_convs=0, | ||
| feat_channels=256, | ||
| anchor_generator=dict( | ||
| type='AnchorGenerator', | ||
| ratios=[1.0], | ||
| octave_base_scale=8, | ||
| scales_per_octave=1, | ||
| strides=[8, 16, 32, 64, 128], | ||
| center_offset=0.5), # follow DyHead official implementation | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[.0, .0, .0, .0], | ||
| target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
| loss_cls=dict( | ||
| type='FocalLoss', | ||
| use_sigmoid=True, | ||
| gamma=2.0, | ||
| alpha=0.25, | ||
| loss_weight=1.0), | ||
| loss_bbox=dict(type='GIoULoss', loss_weight=2.0), | ||
| loss_centerness=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), | ||
| # training and testing settings | ||
| train_cfg=dict( | ||
| assigner=dict(type='ATSSAssigner', topk=9), | ||
| allowed_border=-1, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| test_cfg=dict( | ||
| nms_pre=1000, | ||
| min_bbox_size=0, | ||
| score_thr=0.05, | ||
| nms=dict(type='nms', iou_threshold=0.6), | ||
| max_per_img=100)) | ||
|
|
||
| # dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
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| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='Resize', | ||
| img_scale=[(2000, 480), (2000, 1200)], | ||
| multiscale_mode='range', | ||
| keep_ratio=True, | ||
| backend='pillow'), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=128), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(2000, 1200), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True, backend='pillow'), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=128), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
|
|
||
| # Use RepeatDataset to speed up training | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=2, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| pipeline=train_pipeline)), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(interval=1, metric='bbox') | ||
|
|
||
| # optimizer | ||
| optimizer_config = dict(grad_clip=None) | ||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.00005, | ||
| 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.) | ||
| })) | ||
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||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=0.001, | ||
| step=[8, 11]) | ||
| runner = dict(type='EpochBasedRunner', max_epochs=12) |
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