mirror of https://github.com/open-mmlab/mmpose
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
_base_ = './yolox_s_8xb8-300e_humanart.py'
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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='BatchSyncRandomResize',
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random_size_range=(320, 640),
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size_divisor=32,
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interval=10)
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]),
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backbone=dict(deepen_factor=0.33, widen_factor=0.375),
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neck=dict(in_channels=[96, 192, 384], out_channels=96),
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bbox_head=dict(in_channels=96, feat_channels=96))
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img_scale = (640, 640) # width, height
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train_pipeline = [
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dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
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dict(
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type='RandomAffine',
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scaling_ratio_range=(0.5, 1.5),
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# img_scale is (width, height)
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border=(-img_scale[0] // 2, -img_scale[1] // 2)),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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# Resize and Pad are for the last 15 epochs when Mosaic and
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# RandomAffine are closed by YOLOXModeSwitchHook.
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dict(type='Resize', scale=img_scale, keep_ratio=True),
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dict(
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type='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(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
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dict(type='PackDetInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
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dict(type='Resize', scale=(416, 416), keep_ratio=True),
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dict(
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type='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(type='LoadAnnotations', with_bbox=True),
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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(dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = val_dataloader
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