Source code for sparseml.tensorflow_v1.datasets.helpers

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"""
General utilities for dataset implementations for TensorFlow
"""

from typing import Tuple

from sparseml.tensorflow_v1.utils import tf_compat


__all__ = [
    "resize",
    "random_scaling_crop",
    "center_square_crop",
]


[docs]def resize(image_size: Tuple[int, int], name: str = "resize"): """ Resize an image tensor to the desired size :param image_size: a tuple containing the height, width to resize to :param name: name for the scope to put the ops under :return: the callable function for resize op, takes in the image and outputs the resized image """ def res(img: tf_compat.Tensor): with tf_compat.name_scope(name): try: img = tf_compat.image.resize(img, image_size) except Exception: img = tf_compat.image.resize_images(img, image_size) return img return res
[docs]def random_scaling_crop( scale_range: Tuple[int, int] = (0.08, 1.0), ratio_range: Tuple[int, int] = (3.0 / 4.0, 4.0 / 3.0), name: str = "random_scaling_crop", ): """ Random crop implementation which also randomly scales the crop taken as well as the aspect ratio of the crop. :param scale_range: the (min, max) of the crop scales to take from the orig image :param ratio_range: the (min, max) of the aspect ratios to take from the orig image :param name: name for the scope to put the ops under :return: the callable function for random scaling crop op, takes in the image and outputs randomly cropped image """ def rand_crop(img: tf_compat.Tensor): with tf_compat.name_scope(name): orig_shape = tf_compat.shape(img) scale = tf_compat.random_uniform( shape=[1], minval=scale_range[0], maxval=scale_range[1] )[0] ratio = tf_compat.random_uniform( shape=[1], minval=ratio_range[0], maxval=ratio_range[1] )[0] height = tf_compat.minimum( tf_compat.cast( tf_compat.round( tf_compat.cast(orig_shape[0], dtype=tf_compat.float32) * scale / ratio ), tf_compat.int32, ), orig_shape[0], ) width = tf_compat.minimum( tf_compat.cast( tf_compat.round( tf_compat.cast(orig_shape[1], dtype=tf_compat.float32) * scale ), tf_compat.int32, ), orig_shape[1], ) img = tf_compat.random_crop(img, [height, width, orig_shape[2]]) return img return rand_crop
[docs]def center_square_crop(padding: int = 0, name: str = "center_square_crop"): """ Take a square crop centered in the a image :param padding: additional padding to apply to all sides of the image to crop away :param name: name for the scope to put the ops under :return: the callable function for square crop op, takes in the image and outputs the cropped image """ def cent_crop(img: tf_compat.Tensor): with tf_compat.name_scope(name): orig_shape = tf_compat.shape(img) min_size = tf_compat.cond( tf_compat.greater_equal(orig_shape[0], orig_shape[1]), lambda: orig_shape[1], lambda: orig_shape[0], ) if padding > 0: orig_shape_list = img.get_shape().as_list() resize( (orig_shape_list[0] + 2 * padding, orig_shape_list[1] + 2 * padding) ) padding_height = tf_compat.add( tf_compat.cast( tf_compat.round( tf_compat.div( tf_compat.cast( tf_compat.subtract(orig_shape[0], min_size), tf_compat.float32, ), 2.0, ) ), tf_compat.int32, ), padding, ) padding_width = tf_compat.add( tf_compat.cast( tf_compat.round( tf_compat.div( tf_compat.cast( tf_compat.subtract(orig_shape[1], min_size), tf_compat.float32, ), 2.0, ) ), tf_compat.int32, ), padding, ) img = tf_compat.image.crop_to_bounding_box( img, padding_height, padding_width, min_size, min_size ) return img return cent_crop