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_base_ = 'mmaction::_base_/default_runtime.py'
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custom_imports = dict(imports='models')
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model = dict(
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type='RecognizerGCN',
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backbone=dict(
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type='CTRGCN', graph_cfg=dict(layout='coco', mode='spatial')),
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cls_head=dict(type='GCNHead', num_classes=60, in_channels=256))
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dataset_type = 'PoseDataset'
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ann_file = 'data/skeleton/ntu60_2d.pkl'
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train_pipeline = [
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dict(type='PreNormalize2D'),
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dict(type='GenSkeFeat', dataset='coco', feats=['j']),
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dict(type='UniformSampleFrames', clip_len=100),
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dict(type='PoseDecode'),
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dict(type='FormatGCNInput', num_person=2),
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dict(type='PackActionInputs')
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]
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val_pipeline = [
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dict(type='PreNormalize2D'),
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dict(type='GenSkeFeat', dataset='coco', feats=['j']),
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dict(
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type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True),
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dict(type='PoseDecode'),
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dict(type='FormatGCNInput', num_person=2),
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dict(type='PackActionInputs')
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]
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test_pipeline = [
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dict(type='PreNormalize2D'),
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dict(type='GenSkeFeat', dataset='coco', feats=['j']),
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dict(
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type='UniformSampleFrames', clip_len=100, num_clips=10,
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test_mode=True),
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dict(type='PoseDecode'),
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dict(type='FormatGCNInput', num_person=2),
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dict(type='PackActionInputs')
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]
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train_dataloader = dict(
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batch_size=16,
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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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dataset=dict(
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type='RepeatDataset',
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times=5,
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dataset=dict(
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type=dataset_type,
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ann_file=ann_file,
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pipeline=train_pipeline,
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split='xsub_train')))
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val_dataloader = dict(
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batch_size=16,
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num_workers=2,
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persistent_workers=True,
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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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ann_file=ann_file,
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pipeline=val_pipeline,
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split='xsub_val',
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test_mode=True))
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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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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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ann_file=ann_file,
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pipeline=test_pipeline,
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split='xsub_val',
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test_mode=True))
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val_evaluator = [dict(type='AccMetric')]
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test_evaluator = val_evaluator
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train_cfg = dict(
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type='EpochBasedTrainLoop', max_epochs=16, val_begin=1, val_interval=1)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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param_scheduler = [
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dict(
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type='CosineAnnealingLR',
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eta_min=0,
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T_max=16,
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by_epoch=True,
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convert_to_iter_based=True)
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]
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optim_wrapper = dict(
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optimizer=dict(
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type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True))
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default_hooks = dict(checkpoint=dict(interval=1), logger=dict(interval=100))
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auto_scale_lr = dict(enable=False, base_batch_size=128)
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