mirror of https://github.com/open-mmlab/mmpose
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
_base_ = './rtmdet_s_8xb32-300e_humanart.py'
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checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
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model = dict(
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backbone=dict(
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deepen_factor=0.167,
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widen_factor=0.375,
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init_cfg=dict(
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type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
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neck=dict(in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1),
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bbox_head=dict(in_channels=96, feat_channels=96, exp_on_reg=False))
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train_pipeline = [
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dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
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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=(640, 640),
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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=(1280, 1280),
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ratio_range=(0.5, 2.0),
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keep_ratio=True),
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dict(type='RandomCrop', crop_size=(640, 640)),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
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dict(
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type='CachedMixUp',
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img_scale=(640, 640),
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ratio_range=(1.0, 1.0),
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max_cached_images=10,
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random_pop=False,
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pad_val=(114, 114, 114),
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prob=0.5),
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dict(type='PackDetInputs')
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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