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_base_ = [
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'../../_base_/models/tin_r50.py', '../../_base_/schedules/sgd_50e.py',
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'../../_base_/default_runtime.py'
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]
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model = dict(cls_head=dict(is_shift=True))
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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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train_pipeline = [
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dict(type='DecordInit'),
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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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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'),
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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'),
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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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train_dataloader = dict(
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batch_size=6,
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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=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=6,
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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_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=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_test,
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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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train_cfg = dict(val_interval=5)
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optim_wrapper = dict(
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constructor='TSMOptimWrapperConstructor', paramwise_cfg=dict(fc_lr5=True))
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load_from = 'https://download.openmmlab.com/mmaction/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb/tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth'
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