def get_data_rec()

in examples/mxnet_imagenet_resnet50.py [0:0]

• 53 lines of code
• 1 McCabe index (conditional complexity)

def get_data_rec(rec_train, rec_train_idx, rec_val, rec_val_idx, batch_size,
                 data_nthreads):
    rec_train = os.path.expanduser(rec_train)
    rec_train_idx = os.path.expanduser(rec_train_idx)
    rec_val = os.path.expanduser(rec_val)
    rec_val_idx = os.path.expanduser(rec_val_idx)
    jitter_param = 0.4
    lighting_param = 0.1
    mean_rgb = [123.68, 116.779, 103.939]

    train_iter = mx.io.ImageRecordIter(
        path_imgrec=rec_train,
        path_imgidx=rec_train_idx,
        preprocess_threads=data_nthreads,
        shuffle=True,
        batch_size=batch_size,
        label_width=1,
        data_shape=(3, 224, 224),
        mean_r=mean_rgb[0],
        mean_g=mean_rgb[1],
        mean_b=mean_rgb[2],
        rand_mirror=True,
        rand_crop=False,
        random_resized_crop=True,
        max_aspect_ratio=4. / 3.,
        min_aspect_ratio=3. / 4.,
        max_random_area=1,
        min_random_area=0.08,
        verbose=False,
        brightness=jitter_param,
        saturation=jitter_param,
        contrast=jitter_param,
        pca_noise=lighting_param,
        num_parts=num_workers,
        part_index=rank,
        device_id=local_rank
    )
    # Kept each node to use full val data to make it easy to monitor results
    val_iter = mx.io.ImageRecordIter(
        path_imgrec=rec_val,
        path_imgidx=rec_val_idx,
        preprocess_threads=data_nthreads,
        shuffle=False,
        batch_size=batch_size,
        resize=256,
        label_width=1,
        rand_crop=False,
        rand_mirror=False,
        data_shape=(3, 224, 224),
        mean_r=mean_rgb[0],
        mean_g=mean_rgb[1],
        mean_b=mean_rgb[2],
        device_id=local_rank
    )

    return train_iter, val_iter