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Code Block

# Only use the blending dataset class in training
 
    # ###
    # def collate_fn(batch):
    #     batch = [item for item in batch if item is not None]  # 过滤无效样本
    #     if len(batch) == 0:
    #         return [], [], []
    #     images, labels, img_names = zip(*batch)
    #     return torch.stack(images), torch.tensor(labels), list(img_names)
    # ###
 
    if 'dataset_type' in config and config['dataset_type'] == 'blend':
        if config['model_name'] == 'facexray':
            train_set = FFBlendDataset(config)
        elif config['model_name'] == 'fwa':
            train_set = FWABlendDataset(config)
        elif config['model_name'] == 'sbi':
            train_set = SBIDataset(config, mode='train')
        elif config['model_name'] == 'lsda':
            train_set = LSDADataset(config, mode='train')
        else:
            raise NotImplementedError(
                'Only facexray, fwa, sbi, and lsda are currently supported for blending dataset'
            )
    elif 'dataset_type' in config and config['dataset_type'] == 'pair':
        train_set = pairDataset(config, mode='train')  # Only use the pair dataset class in training
    elif 'dataset_type' in config and config['dataset_type'] == 'iid':
        train_set = IIDDataset(config, mode='train')
    elif 'dataset_type' in config and config['dataset_type'] == 'I2G':
        train_set = I2GDataset(config, mode='train')
    elif 'dataset_type' in config and config['dataset_type'] == 'lrl':
        train_set = LRLDataset(config, mode='train')
    else:
        train_set = DeepfakeAbstractBaseDataset(
                    config=config,
                    mode='train',
                )
    if config['model_name'] == 'lsda':
        from dataset.lsda_dataset import CustomSampler
        custom_sampler = CustomSampler(num_groups=2*360, n_frame_per_vid=config['frame_num']['train'], batch_size=config['train_batchSize'], videos_per_group=5)
        train_data_loader = \
            torch.utils.data.DataLoader(
                dataset=train_set,
                batch_size=config['train_batchSize'],
                num_workers=int(config['workers']),
                sampler=custom_sampler,
                collate_fn=train_set.collate_fn,
            )
    elif config['ddp']:
        sampler = DistributedSampler(train_set)
        train_data_loader = \
            torch.utils.data.DataLoader(
                dataset=train_set,
                batch_size=config['train_batchSize'],
                num_workers=int(config['workers']),
                collate_fn=train_set.collate_fn,
                sampler=sampler
            )
    else:
        train_data_loader = \
            torch.utils.data.DataLoader(
                dataset=train_set,
                batch_size=config['train_batchSize'],
                shuffle=True,
                num_workers=int(config['workers']),
                collate_fn=train_set.collate_fn,
                )
    return train_data_loader

Excalidraw Data

Text Elements

def prepare_training_data(config)

会进入LSDA_dataset

train_data_loader

如果探测器不为需要特殊处理数据的探测器,则会进入abstract_dataset,里的DeepfakeAbstractBaseDataset

def prepare_testing_data(config)

test_data_loader

prepare the model (detector)

lsda_detector.py

DS

Element Links

MSYrD2Fm: abstract_dataset