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_base_ = [
'../../_base_/models/tpn_tsm_r50.py', '../../_base_/default_runtime.py'
]
dataset_type = 'VideoDataset'
data_root = 'data'
data_root_val = 'data'
ann_file_train = 'data/GenVidBench/label/fake_real_label/train.txt'
ann_file_val = 'data/GenVidBench/label/fake_real_label/test.txt'
ann_file_test = 'data/GenVidBench/label/fake_real_label/test.txt'
model = dict(cls_head=dict(num_classes=2))
sthv1_flip_label_map = {2: 4, 4: 2, 30: 41, 41: 30, 52: 66, 66: 52}
train_pipeline = [
dict(type='SampleFrames', clip_len=8, frame_interval=1, num_clips=1),
dict(type='RawFrameDecode'),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5, flip_label_map=sthv1_flip_label_map),
dict(type='ColorJitter'),
dict(type='FormatShape', input_format='NCHW'),
dict(type='PackActionInputs')
]
val_pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=8,
test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='FormatShape', input_format='NCHW'),
dict(type='PackActionInputs')
]
test_pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=8,
twice_sample=True,
test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='ThreeCrop', crop_size=256),
dict(type='FormatShape', input_format='NCHW'),
dict(type='PackActionInputs')
]
train_dataloader = dict(
batch_size=8,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
ann_file=ann_file_train,
data_prefix=dict(img=data_root),
filename_tmpl='{:05}.jpg',
pipeline=train_pipeline))
val_dataloader = dict(
batch_size=8,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
ann_file=ann_file_val,
data_prefix=dict(img=data_root_val),
filename_tmpl='{:05}.jpg',
pipeline=val_pipeline,
test_mode=True))
test_dataloader = dict(
batch_size=1,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
ann_file=ann_file_test,
data_prefix=dict(img=data_root_val),
filename_tmpl='{:05}.jpg',
pipeline=test_pipeline,
test_mode=True))
val_evaluator = dict(type='AccMetric')
test_evaluator = val_evaluator
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=150, val_begin=1, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=150,
by_epoch=True,
milestones=[75, 125],
gamma=0.1)
]
optim_wrapper = dict(
optimizer=dict(
type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True),
clip_grad=dict(max_norm=20, norm_type=2))
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