sample_info/scripts/data_summarization.py [106:132]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    if args.cache_dataset:
        train_data = CacheDatasetWrapper(train_data)
        val_data = CacheDatasetWrapper(val_data)
        test_data = CacheDatasetWrapper(test_data)

    shuffle_train = (args.batch_size * args.num_accumulation_steps < len(train_data))
    train_loader, val_loader, test_loader = get_loaders_from_datasets(train_data, val_data, test_data,
                                                                      batch_size=args.batch_size,
                                                                      num_workers=args.num_workers,
                                                                      shuffle_train=shuffle_train)

    # Options
    optimization_args = {
        'optimizer': {
            'name': args.optimizer,
            'lr': args.lr,
        }
    }

    with open(args.config, 'r') as f:
        architecture_args = json.load(f)

    model_class = getattr(methods, args.model_class)

    model = model_class(input_shape=train_loader.dataset[0][0].shape,
                        architecture_args=architecture_args,
                        l2_reg_coef=args.l2_reg_coef,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



sample_info/scripts/ground_truth_effects.py [81:107]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    if args.cache_dataset:
        train_data = CacheDatasetWrapper(train_data)
        val_data = CacheDatasetWrapper(val_data)
        test_data = CacheDatasetWrapper(test_data)

    shuffle_train = (args.batch_size * args.num_accumulation_steps < len(train_data))
    train_loader, val_loader, test_loader = get_loaders_from_datasets(train_data, val_data, test_data,
                                                                      batch_size=args.batch_size,
                                                                      num_workers=args.num_workers,
                                                                      shuffle_train=shuffle_train)

    # Options
    optimization_args = {
        'optimizer': {
            'name': args.optimizer,
            'lr': args.lr,
        }
    }

    with open(args.config, 'r') as f:
        architecture_args = json.load(f)

    model_class = getattr(methods, args.model_class)

    model = model_class(input_shape=train_loader.dataset[0][0].shape,
                        architecture_args=architecture_args,
                        l2_reg_coef=args.l2_reg_coef,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -