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_base_ = [
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'../../_base_/models/tsm_mobilenet_v2.py',
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'../../_base_/default_runtime.py'
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]
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dataset_type = 'VideoDataset'
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data_root = 'data'
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data_root_val = 'data'
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ann_file_train = 'data/GenVidBench/label/fake_real_label/train.txt'
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ann_file_val = 'data/GenVidBench/label/fake_real_label/test.txt'
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ann_file_test = 'data/GenVidBench/label/fake_real_label/test.txt'
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model = dict(cls_head=dict(num_classes=2))
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file_client_args = dict(io_backend='disk')
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train_pipeline = [
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dict(type='DecordInit', **file_client_args),
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dict(type='SampleFrames', clip_len=8, frame_interval=2, num_clips=1),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(
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type='MultiScaleCrop',
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input_size=224,
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scales=(1, 0.875, 0.75, 0.66),
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random_crop=False,
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max_wh_scale_gap=1,
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num_fixed_crops=13),
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dict(type='Resize', scale=(224, 224), keep_ratio=False),
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dict(type='Flip', flip_ratio=0.5),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]
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val_pipeline = [
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dict(type='DecordInit', **file_client_args),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=8,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='CenterCrop', crop_size=224),
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dict(type='FormatShape', input_format='NCHW'),
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dict(type='PackActionInputs')
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]
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test_pipeline = [
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dict(type='DecordInit', **file_client_args),
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dict(
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type='SampleFrames',
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clip_len=1,
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frame_interval=1,
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num_clips=8,
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test_mode=True),
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dict(type='DecordDecode'),
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dict(type='Resize', scale=(-1, 256)),
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dict(type='ThreeCrop', crop_size=256),
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dict(type='FormatShape', input_format='NCHW'),
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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=8,
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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=dataset_type,
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ann_file=ann_file_train,
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data_prefix=dict(video=data_root),
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=16,
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num_workers=8,
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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_val,
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data_prefix=dict(video=data_root_val),
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pipeline=val_pipeline,
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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=8,
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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_val,
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data_prefix=dict(video=data_root_val),
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pipeline=test_pipeline,
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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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default_hooks = dict(checkpoint=dict(interval=3, max_keep_ckpts=3))
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train_cfg = dict(
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type='EpochBasedTrainLoop', max_epochs=100, 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='MultiStepLR',
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begin=0,
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end=100,
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by_epoch=True,
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milestones=[40, 80],
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gamma=0.1)
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]
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optim_wrapper = dict(
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constructor='TSMOptimWrapperConstructor',
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paramwise_cfg=dict(fc_lr5=True),
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optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00002),
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clip_grad=dict(max_norm=20, norm_type=2))
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auto_scale_lr = dict(enable=True, base_batch_size=128)
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