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- _base_ = './decoupled-solo_r50_fpn_3x_coco.py'
- # model settings
- model = dict(
- mask_head=dict(
- type='DecoupledSOLOLightHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- strides=[8, 8, 16, 32, 32],
- scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
- pos_scale=0.2,
- num_grids=[40, 36, 24, 16, 12],
- cls_down_index=0,
- loss_mask=dict(
- type='DiceLoss', use_sigmoid=True, activate=False,
- loss_weight=3.0),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='RandomChoiceResize',
- scales=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
- (852, 352)],
- keep_ratio=True),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='Resize', scale=(852, 512), keep_ratio=True),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
- val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
- test_dataloader = val_dataloader
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