mirror of https://github.com/open-mmlab/mmpose
105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
_base_ = 'mmdet::rtmdet/rtmdet_l_8xb32-300e_coco.py'
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input_shape = 320
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model = dict(
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backbone=dict(
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deepen_factor=0.33,
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widen_factor=0.25,
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use_depthwise=True,
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),
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neck=dict(
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in_channels=[64, 128, 256],
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out_channels=64,
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num_csp_blocks=1,
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use_depthwise=True,
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),
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bbox_head=dict(
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in_channels=64,
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feat_channels=64,
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share_conv=False,
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exp_on_reg=False,
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use_depthwise=True,
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num_classes=1),
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test_cfg=dict(
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nms_pre=1000,
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min_bbox_size=0,
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.6),
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max_per_img=100))
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='CachedMosaic',
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img_scale=(input_shape, input_shape),
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pad_val=114.0,
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max_cached_images=20,
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random_pop=False),
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dict(
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type='RandomResize',
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scale=(input_shape * 2, input_shape * 2),
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ratio_range=(0.5, 1.5),
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keep_ratio=True),
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dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(
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type='Pad',
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size=(input_shape, input_shape),
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pad_val=dict(img=(114, 114, 114))),
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dict(type='PackDetInputs')
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]
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train_pipeline_stage2 = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='RandomResize',
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scale=(input_shape, input_shape),
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ratio_range=(0.5, 1.5),
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keep_ratio=True),
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dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(
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type='Pad',
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size=(input_shape, input_shape),
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pad_val=dict(img=(114, 114, 114))),
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dict(type='PackDetInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=(input_shape, input_shape), keep_ratio=True),
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dict(
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type='Pad',
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size=(input_shape, input_shape),
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pad_val=dict(img=(114, 114, 114))),
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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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'scale_factor'))
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]
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train_dataloader = dict(
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dataset=dict(pipeline=train_pipeline, metainfo=dict(classes=('person', ))))
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val_dataloader = dict(
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dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=('person', ))))
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test_dataloader = val_dataloader
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custom_hooks = [
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dict(
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type='EMAHook',
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ema_type='ExpMomentumEMA',
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momentum=0.0002,
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update_buffers=True,
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priority=49),
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dict(
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type='PipelineSwitchHook',
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switch_epoch=280,
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switch_pipeline=train_pipeline_stage2)
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]
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