mirror of https://github.com/open-mmlab/mmpose
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
# dataset settings
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dataset_type = 'CocoDataset'
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data_root = 'data/'
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# Example to use different file client
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# Method 1: simply set the data root and let the file I/O module
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# automatically infer from prefix (not support LMDB and Memcache yet)
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# data_root = 's3://openmmlab/datasets/detection/coco/'
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# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
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# backend_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# './data/': 's3://openmmlab/datasets/detection/',
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# 'data/': 's3://openmmlab/datasets/detection/'
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# }))
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backend_args = None
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train_pipeline = [
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dict(type='LoadImageFromFile', backend_args=backend_args),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(type='Resize', scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', prob=0.5),
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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=backend_args),
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dict(type='Resize', scale=(1333, 800), keep_ratio=True),
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# If you don't have a gt annotation, delete the pipeline
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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(
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batch_size=2,
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num_workers=2,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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batch_sampler=dict(type='AspectRatioBatchSampler'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='HumanArt/annotations/training_humanart_coco.json',
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data_prefix=dict(img=''),
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filter_cfg=dict(filter_empty_gt=True, min_size=32),
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pipeline=train_pipeline,
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backend_args=backend_args))
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val_dataloader = dict(
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batch_size=1,
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num_workers=2,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='HumanArt/annotations/validation_humanart_coco.json',
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data_prefix=dict(img=''),
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test_mode=True,
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pipeline=test_pipeline,
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backend_args=backend_args))
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test_dataloader = val_dataloader
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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'HumanArt/annotations/validation_humanart_coco.json',
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metric='bbox',
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format_only=False,
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backend_args=backend_args)
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test_evaluator = val_evaluator
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# inference on test dataset and
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# format the output results for submission.
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# test_dataloader = dict(
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# batch_size=1,
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# num_workers=2,
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# persistent_workers=True,
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# drop_last=False,
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# sampler=dict(type='DefaultSampler', shuffle=False),
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# dataset=dict(
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# type=dataset_type,
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# data_root=data_root,
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# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
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# data_prefix=dict(img='test2017/'),
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# test_mode=True,
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# pipeline=test_pipeline))
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# test_evaluator = dict(
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# type='CocoMetric',
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# metric='bbox',
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# format_only=True,
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# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
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# outfile_prefix='./work_dirs/coco_detection/test')
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