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- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
- model = dict(
- type='ATSS',
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=128),
- 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
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='RandomResize',
- scale=[(2000, 480), (2000, 1200)],
- keep_ratio=True,
- backend='pillow'),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='Resize', scale=(2000, 1200), keep_ratio=True, backend='pillow'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(
- dataset=dict(
- _delete_=True,
- type='RepeatDataset',
- times=2,
- dataset=dict(
- type={{_base_.dataset_type}},
- data_root={{_base_.data_root}},
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
- filter_cfg=dict(filter_empty_gt=True, min_size=32),
- pipeline=train_pipeline,
- backend_args={{_base_.backend_args}})))
- val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
- test_dataloader = val_dataloader
- # optimizer
- optim_wrapper = dict(
- _delete_=True,
- type='OptimWrapper',
- 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.)
- }),
- clip_grad=None)
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