tf-distribution-options/code/utilities.py [19:50]:
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def _train_preprocess_fn(image):

    # Resize the image to add four extra pixels on each side.
    image = tf.image.resize_image_with_crop_or_pad(image, HEIGHT + 8, WIDTH + 8)

    # Randomly crop a [HEIGHT, WIDTH] section of the image.
    image = tf.random_crop(image, [HEIGHT, WIDTH, DEPTH])

    # Randomly flip the image horizontally.
    image = tf.image.random_flip_left_right(image)

    return image


def _dataset_parser(value):

    featdef = {
        'image': tf.FixedLenFeature([], tf.string),
        'label': tf.FixedLenFeature([], tf.int64),
    }

    example = tf.parse_single_example(value, featdef)
    image = tf.decode_raw(example['image'], tf.uint8)
    image.set_shape([DEPTH * HEIGHT * WIDTH])

    # Reshape from [depth * height * width] to [depth, height, width].
    image = tf.cast(
        tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),
        tf.float32)
    label = tf.cast(example['label'], tf.int32)
    image = _train_preprocess_fn(image)
    return image, tf.one_hot(label, NUM_CLASSES)
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tf-horovod-inference-pipeline/train.py [158:189]:
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def _train_preprocess_fn(image):
    """Preprocess a single training image of layout [height, width, depth]."""
    # Resize the image to add four extra pixels on each side.
    image = tf.image.resize_image_with_crop_or_pad(image, HEIGHT + 8, WIDTH + 8)

    # Randomly crop a [HEIGHT, WIDTH] section of the image.
    image = tf.random_crop(image, [HEIGHT, WIDTH, DEPTH])

    # Randomly flip the image horizontally.
    image = tf.image.random_flip_left_right(image)

    return image


def _dataset_parser(value):
    """Parse a CIFAR-10 record from value."""
    featdef = {
        'image': tf.FixedLenFeature([], tf.string),
        'label': tf.FixedLenFeature([], tf.int64),
    }

    example = tf.parse_single_example(value, featdef)
    image = tf.decode_raw(example['image'], tf.uint8)
    image.set_shape([DEPTH * HEIGHT * WIDTH])

    # Reshape from [depth * height * width] to [depth, height, width].
    image = tf.cast(
        tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),
        tf.float32)
    label = tf.cast(example['label'], tf.int32)
    image = _train_preprocess_fn(image)
    return image, tf.one_hot(label, NUM_CLASSES)
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