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- _base_ = [
- '../_base_/models/mask-rcnn_r50_fpn.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
- model = dict(
- type='MaskRCNN',
- backbone=dict(
- _delete_=True,
- type='SwinTransformer',
- embed_dims=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- 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=(0, 1, 2, 3),
- with_cp=False,
- convert_weights=True,
- init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
- neck=dict(in_channels=[96, 192, 384, 768]))
- # augmentation strategy originates from DETR / Sparse RCNN
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='RandomFlip', prob=0.5),
- dict(
- type='RandomChoice',
- transforms=[[
- dict(
- type='RandomChoiceResize',
- scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
- (608, 1333), (640, 1333), (672, 1333), (704, 1333),
- (736, 1333), (768, 1333), (800, 1333)],
- keep_ratio=True)
- ],
- [
- dict(
- type='RandomChoiceResize',
- scales=[(400, 1333), (500, 1333), (600, 1333)],
- keep_ratio=True),
- dict(
- type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=True),
- dict(
- type='RandomChoiceResize',
- scales=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- keep_ratio=True)
- ]]),
- dict(type='PackDetInputs')
- ]
- train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
- max_epochs = 36
- train_cfg = dict(max_epochs=max_epochs)
- # learning rate
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
- end=1000),
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_epochs,
- by_epoch=True,
- milestones=[27, 33],
- gamma=0.1)
- ]
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- 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.)
- }),
- optimizer=dict(
- _delete_=True,
- type='AdamW',
- lr=0.0001,
- betas=(0.9, 0.999),
- weight_decay=0.05))
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