mirror of https://github.com/open-mmlab/mmyolo.git
71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
_base_ = './yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py'
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load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth' # noqa
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deepen_factor = 0.33
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widen_factor = 0.375
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scaling_ratio_range = (0.75, 1.0)
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# model settings
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model = dict(
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data_preprocessor=dict(batch_augments=[
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dict(
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type='YOLOXBatchSyncRandomResize',
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random_size_range=(320, 640),
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size_divisor=32,
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interval=1)
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]),
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backbone=dict(
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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),
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neck=dict(
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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),
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bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
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# data settings
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img_scale = _base_.img_scale
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pre_transform = _base_.pre_transform
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train_pipeline_stage1 = [
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*pre_transform,
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dict(
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type='Mosaic',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(
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type='RandomAffine',
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scaling_ratio_range=scaling_ratio_range,
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border=(-img_scale[0] // 2, -img_scale[1] // 2)),
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dict(type='mmdet.YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(
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type='FilterAnnotations',
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by_keypoints=True,
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min_gt_bbox_wh=(1, 1),
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keep_empty=False),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
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]
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test_pipeline = [
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*pre_transform,
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dict(type='Resize', scale=(416, 416), keep_ratio=True),
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dict(
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type='mmdet.Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(
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type='PackDetInputs',
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meta_keys=('id', 'img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor', 'flip_indices'))
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline_stage1))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = val_dataloader
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