mirror of https://github.com/open-mmlab/mmyolo.git
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
_base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py'
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deepen_factor = 0.6
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widen_factor = 0.75
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affine_scale = 0.9
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model = dict(
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backbone=dict(
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type='YOLOv6CSPBep',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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hidden_ratio=2. / 3,
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block_cfg=dict(type='RepVGGBlock'),
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act_cfg=dict(type='ReLU', inplace=True)),
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neck=dict(
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type='YOLOv6CSPRepPAFPN',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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block_cfg=dict(type='RepVGGBlock'),
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hidden_ratio=2. / 3,
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block_act_cfg=dict(type='ReLU', inplace=True)),
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bbox_head=dict(
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type='YOLOv6Head', head_module=dict(widen_factor=widen_factor)))
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mosaic_affine_pipeline = [
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dict(
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type='Mosaic',
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img_scale=_base_.img_scale,
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pad_val=114.0,
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pre_transform=_base_.pre_transform),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
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# img_scale is (width, height)
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border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
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border_val=(114, 114, 114))
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]
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train_pipeline = [
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*_base_.pre_transform, *mosaic_affine_pipeline,
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dict(
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type='YOLOv5MixUp',
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prob=0.1,
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pre_transform=[*_base_.pre_transform, *mosaic_affine_pipeline]),
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dict(type='YOLOv5HSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
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'flip_direction'))
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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