mirror of https://github.com/open-mmlab/mmpose
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
_base_ = ['./yolox-pose_s_8xb32-300e_coco.py']
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# model settings
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model = dict(
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init_cfg=dict(checkpoint='https://download.openmmlab.com/mmyolo/v0/yolox/'
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'yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_'
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'8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth'),
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data_preprocessor=dict(batch_augments=[
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dict(
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type='PoseBatchSyncRandomResize',
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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=0.33,
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widen_factor=0.375,
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),
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neck=dict(
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deepen_factor=0.33,
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widen_factor=0.375,
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),
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bbox_head=dict(head_module=dict(widen_factor=0.375)))
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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='mmdet.RandomAffine',
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scaling_ratio_range=(0.75, 1.0),
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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='mmdet.RandomFlip', prob=0.5),
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dict(
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type='FilterDetPoseAnnotations',
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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='PackDetPoseInputs',
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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='mmdet.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='PackDetPoseInputs',
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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(
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batch_size=64, 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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