code: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Test configs for unroll_batch_matmul."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v1 as tf
  from tensorflow.lite.testing.zip_test_utils import create_tensor_data
  from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
  from tensorflow.lite.testing.zip_test_utils import register_make_test_function
  
  
  @register_make_test_function()
  def make_unroll_batch_matmul_tests(options):
    """Make a set of tests to test unroll_batch_matmul."""
  
    # The test cases below requires broadcasting support (BatchMatMulV2 semantic),
    # whis isn't supported as of this change.
    broadcast_shape_params = [
        # Simple broadcast.
        [(1, 2, 3), (3, 5), False, False],
        # Empty batch broadcast.
        [(2, 5, 3), (3, 7), False, False],
        # Single batch with non-empty batch broadcast.
        [(1, 5, 3), (4, 3, 7), False, False],
        # Broadcast both operands
        [(3, 1, 5, 3), (1, 4, 3, 7), False, False],
    ]
  
    test_parameters = [{
        "dtype": [tf.float32],
        "shape": [[(2, 2, 3),
                   (2, 3, 2), False, False], [(2, 2, 3), (2, 3, 2), True, True],
                  [(2, 2, 3),
                   (2, 2, 3), False, True], [(2, 2, 3), (2, 2, 3), True, False],
                  [(4, 2, 2, 3), (4, 2, 3, 2), False, False],
                  [(4, 2, 2, 3), (4, 2, 3, 2), True, True],
                  [(4, 2, 2, 3), (4, 2, 2, 3), False, True],
                  [(4, 2, 2, 3),
                   (4, 2, 2, 3), True, False]] + broadcast_shape_params,
        # TODO(b/130887442): Improve the forward compatibility tests for every
        # ops.
        "forward_compatibility_test": [False, True],
    }]
  
    def build_graph(parameters):
      """Build the batch_matmul op testing graph."""
  
      def _build_graph():
        """Build the graph."""
        input_tensor1 = tf.compat.v1.placeholder(
            dtype=parameters["dtype"], shape=parameters["shape"][0])
        input_tensor2 = tf.compat.v1.placeholder(
            dtype=parameters["dtype"], shape=parameters["shape"][1])
        # Should be unrolled and replaced with fully_connected ops in the end.
        out = tf.matmul(
            input_tensor1,
            input_tensor2,
            transpose_a=parameters["shape"][2],
            transpose_b=parameters["shape"][3])
        return [input_tensor1, input_tensor2], [out]
  
      if parameters["forward_compatibility_test"]:
        # This is hardcoded to the date after MatMulV2 is activated.
        # TODO(b/130887442): Improve the forward compatibility tests for every
        # ops, and remove the hardcoded date.
        with tf.compat.forward_compatibility_horizon(2019, 4, 26):
          return _build_graph()
      else:
        return _build_graph()
  
    def build_inputs(parameters, sess, inputs, outputs):
      input_value1 = create_tensor_data(
          parameters["dtype"], shape=parameters["shape"][0])
      input_value2 = create_tensor_data(
          parameters["dtype"], shape=parameters["shape"][1])
      return [input_value1, input_value2], sess.run(
          outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
  
    make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
   # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Keras image preprocessing layers."""
  # pylint: disable=g-classes-have-attributes
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.engine.input_spec import InputSpec
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import image_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateful_random_ops
  from tensorflow.python.ops import stateless_random_ops
  
  ResizeMethod = image_ops.ResizeMethod
  
  _RESIZE_METHODS = {
      'bilinear': ResizeMethod.BILINEAR,
      'nearest': ResizeMethod.NEAREST_NEIGHBOR,
      'bicubic': ResizeMethod.BICUBIC,
      'area': ResizeMethod.AREA,
      'lanczos3': ResizeMethod.LANCZOS3,
      'lanczos5': ResizeMethod.LANCZOS5,
      'gaussian': ResizeMethod.GAUSSIAN,
      'mitchellcubic': ResizeMethod.MITCHELLCUBIC
  }
  
  
  class Resizing(Layer):
    """Image resizing layer.
  
    Resize the batched image input to target height and width. The input should
    be a 4-D tensor in the format of NHWC.
  
    Arguments:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      interpolation: String, the interpolation method. Defaults to `bilinear`.
        Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
        `gaussian`, `mitchellcubic`
    """
  
    def __init__(self, height, width, interpolation='bilinear', **kwargs):
      self.target_height = height
      self.target_width = width
      self.interpolation = interpolation
      self._interpolation_method = get_interpolation(interpolation)
      self.input_spec = InputSpec(ndim=4)
      super(Resizing, self).__init__(**kwargs)
  
    def build(self, input_shape):
      channel_axis = 3
      channel_dim = int(input_shape[channel_axis])
      self.input_spec = InputSpec(ndim=4, axes={channel_axis: channel_dim})
      self.built = True
  
    def call(self, inputs):
      outputs = image_ops.resize_images_v2(
          images=inputs,
          size=[self.target_height, self.target_width],
          method=self._interpolation_method)
      return outputs
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      return tensor_shape.TensorShape(
          [input_shape[0], self.target_height, self.target_width, input_shape[3]])
  
    def get_config(self):
      config = {
          'height': self.target_height,
          'width': self.target_width,
          'interpolation': self.interpolation,
      }
      base_config = super(Resizing, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class CenterCrop(Layer):
    """Crop the central portion of the images to target height and width.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, target_height, target_width, channels)`.
  
    If the input height/width is even and the target height/width is odd (or
    inversely), the input image is left-padded by 1 pixel.
  
    Arguments:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
    """
  
    def __init__(self, height, width, **kwargs):
      self.target_height = height
      self.target_width = width
      self.input_spec = InputSpec(ndim=4)
      super(CenterCrop, self).__init__(**kwargs)
  
    def build(self, input_shape):
      channel_axis = 3
      channel_dim = int(input_shape[channel_axis])
      self.input_spec = InputSpec(ndim=4, axes={channel_axis: channel_dim})
      self.built = True
  
    def call(self, inputs):
      inputs_shape = array_ops.shape(inputs)
      h_axis, w_axis = 1, 2
      img_hd = inputs_shape[h_axis]
      img_wd = inputs_shape[w_axis]
      img_hd_diff = img_hd - self.target_height
      img_wd_diff = img_wd - self.target_width
      checks = []
      checks.append(
          check_ops.assert_non_negative(
              img_hd_diff,
              message='The crop height {} should not be greater than input '
              'height.'.format(self.target_height)))
      checks.append(
          check_ops.assert_non_negative(
              img_wd_diff,
              message='The crop width {} should not be greater than input '
              'width.'.format(self.target_width)))
      with ops.control_dependencies(checks):
        bbox_h_start = math_ops.cast(img_hd_diff / 2, dtypes.int32)
        bbox_w_start = math_ops.cast(img_wd_diff / 2, dtypes.int32)
        bbox_begin = array_ops.stack([0, bbox_h_start, bbox_w_start, 0])
        bbox_size = array_ops.stack(
            [-1, self.target_height, self.target_width, -1])
        outputs = array_ops.slice(inputs, bbox_begin, bbox_size)
        return outputs
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      return tensor_shape.TensorShape(
          [input_shape[0], self.target_height, self.target_width, input_shape[3]])
  
    def get_config(self):
      config = {
          'height': self.target_height,
          'width': self.target_width,
      }
      base_config = super(CenterCrop, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomCrop(Layer):
    """Randomly crop the images to target height and width.
  
    This layer will crop all the images in the same batch to the same cropping
    location.
    By default, random cropping is only applied during training. At inference
    time, the images will be first rescaled to preserve the shorter side, and
    center cropped. If you need to apply random cropping at inference time,
    set `training` to True when calling the layer.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, target_height, target_width, channels)`.
  
    Arguments:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      seed: Integer. Used to create a random seed.
    """
  
    def __init__(self, height, width, seed=None, **kwargs):
      self.height = height
      self.width = width
      self.seed = seed
      self._rng = make_generator(self.seed)
      self.input_spec = InputSpec(ndim=4)
      super(RandomCrop, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_cropped_inputs():
        """Cropped inputs with stateless random ops."""
        input_shape = array_ops.shape(inputs)
        crop_size = array_ops.stack(
            [input_shape[0], self.height, self.width, input_shape[3]])
        check = control_flow_ops.Assert(
            math_ops.reduce_all(input_shape >= crop_size),
            [self.height, self.width])
        input_shape = control_flow_ops.with_dependencies([check], input_shape)
        limit = input_shape - crop_size + 1
        offset = stateless_random_ops.stateless_random_uniform(
            array_ops.shape(input_shape),
            dtype=crop_size.dtype,
            maxval=crop_size.dtype.max,
            seed=self._rng.make_seeds()[:, 0]) % limit
        return array_ops.slice(inputs, offset, crop_size)
  
      # TODO(b/143885775): Share logic with Resize and CenterCrop.
      def resize_and_center_cropped_inputs():
        """Deterministically resize to shorter side and center crop."""
        input_shape = array_ops.shape(inputs)
        input_height_t = input_shape[1]
        input_width_t = input_shape[2]
        ratio_cond = (input_height_t / input_width_t > 1.)
        # pylint: disable=g-long-lambda
        resized_height = tf_utils.smart_cond(
            ratio_cond,
            lambda: math_ops.cast(self.width * input_height_t / input_width_t,
                                  input_height_t.dtype), lambda: self.height)
        resized_width = tf_utils.smart_cond(
            ratio_cond, lambda: self.width,
            lambda: math_ops.cast(self.height * input_width_t / input_height_t,
                                  input_width_t.dtype))
        # pylint: enable=g-long-lambda
        resized_inputs = image_ops.resize_images_v2(
            images=inputs, size=array_ops.stack([resized_height, resized_width]))
  
        img_hd_diff = resized_height - self.height
        img_wd_diff = resized_width - self.width
        bbox_h_start = math_ops.cast(img_hd_diff / 2, dtypes.int32)
        bbox_w_start = math_ops.cast(img_wd_diff / 2, dtypes.int32)
        bbox_begin = array_ops.stack([0, bbox_h_start, bbox_w_start, 0])
        bbox_size = array_ops.stack([-1, self.height, self.width, -1])
        outputs = array_ops.slice(resized_inputs, bbox_begin, bbox_size)
        return outputs
  
      output = tf_utils.smart_cond(training, random_cropped_inputs,
                                   resize_and_center_cropped_inputs)
      original_shape = inputs.shape.as_list()
      batch_size, num_channels = original_shape[0], original_shape[3]
      output_shape = [batch_size] + [self.height, self.width] + [num_channels]
      output.set_shape(output_shape)
      return output
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      return tensor_shape.TensorShape(
          [input_shape[0], self.height, self.width, input_shape[3]])
  
    def get_config(self):
      config = {
          'height': self.height,
          'width': self.width,
          'seed': self.seed,
      }
      base_config = super(RandomCrop, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Rescaling(Layer):
    """Multiply inputs by `scale`.
  
    For instance, to rescale an input in the `[0, 255]` range
    to be in the `[0, 1]` range, you would pass `scale=1./255`.
  
    The rescaling is applied both during training and inference.
  
    Input shape:
      Arbitrary.
  
    Output shape:
      Same as input.
  
    Arguments:
      scale: Float, the scale to apply to the inputs.
    """
  
    def __init__(self, scale, **kwargs):
      self.scale = scale
      super(Rescaling, self).__init__(**kwargs)
  
    def call(self, inputs):
      dtype = self._compute_dtype
      return math_ops.cast(inputs, dtype) * math_ops.cast(self.scale, dtype)
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'scale': self.scale,
      }
      base_config = super(Rescaling, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomFlip(Layer):
    """Randomly flip each image horizontally and vertically.
  
    This layer will by default flip the images horizontally and then vertically
    during training time.
    `RandomFlip(horizontal=True)` will only flip the input horizontally.
    `RandomFlip(vertical=True)` will only flip the input vertically.
    During inference time, the output will be identical to input. Call the layer
    with `training=True` to flip the input.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Attributes:
      horizontal: Bool, whether to randomly flip horizontally.
      width: Bool, whether to randomly flip vertically.
      seed: Integer. Used to create a random seed.
    """
  
    def __init__(self, horizontal=None, vertical=None, seed=None, **kwargs):
      # If both arguments are None, set both to True.
      if horizontal is None and vertical is None:
        self.horizontal = True
        self.vertical = True
      else:
        self.horizontal = horizontal or False
        self.vertical = vertical or False
      self.seed = seed
      self._rng = make_generator(self.seed)
      self.input_spec = InputSpec(ndim=4)
      super(RandomFlip, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_flipped_inputs():
        flipped_outputs = inputs
        if self.horizontal:
          flipped_outputs = image_ops.random_flip_up_down(flipped_outputs,
                                                          self.seed)
        if self.vertical:
          flipped_outputs = image_ops.random_flip_left_right(
              flipped_outputs, self.seed)
        return flipped_outputs
  
      output = tf_utils.smart_cond(training, random_flipped_inputs,
                                   lambda: inputs)
      output.set_shape(inputs.shape)
      return output
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'horizontal': self.horizontal,
          'vertical': self.vertical,
          'seed': self.seed,
      }
      base_config = super(RandomFlip, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomTranslation(Layer):
    """Randomly translate each image during training.
  
    Arguments:
      height_factor: a positive float represented as fraction of value, or a tuple
        of size 2 representing lower and upper bound for shifting vertically. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `height_factor=(0.2, 0.3)` results in an output
        height varying in the range `[original - 20%, original + 30%]`.
        `height_factor=0.2` results in an output height varying in the range
        `[original - 20%, original + 20%]`.
      width_factor: a positive float represented as fraction of value, or a tuple
        of size 2 representing lower and upper bound for shifting horizontally.
        When represented as a single float, this value is used for both the upper
        and lower bound.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{'nearest', 'bilinear'}`).
      fill_value: Value used for points outside the boundaries of the input if
        `mode='constant'`.
      seed: Integer. Used to create a random seed.
    Input shape:
      4D tensor with shape: `(samples, height, width, channels)`,
        data_format='channels_last'.
    Output shape:
      4D tensor with shape: `(samples, height, width, channels)`,
        data_format='channels_last'.
    Raise:
      ValueError: if lower bound is not between [0, 1], or upper bound is
        negative.
    """
  
    def __init__(self,
                 height_factor,
                 width_factor,
                 fill_mode='nearest',
                 fill_value=0.,
                 seed=None,
                 **kwargs):
      self.height_factor = height_factor
      if isinstance(height_factor, (tuple, list)):
        self.height_lower = abs(height_factor[0])
        self.height_upper = height_factor[1]
      else:
        self.height_lower = self.height_upper = height_factor
      if self.height_upper < 0.:
        raise ValueError('`height_factor` cannot have negative values as upper '
                         'bound, got {}'.format(height_factor))
      if abs(self.height_lower) > 1. or abs(self.height_upper) > 1.:
        raise ValueError('`height_factor` must have values between [-1, 1], '
                         'got {}'.format(height_factor))
  
      self.width_factor = width_factor
      if isinstance(width_factor, (tuple, list)):
        self.width_lower = abs(width_factor[0])
        self.width_upper = width_factor[1]
      else:
        self.width_lower = self.width_upper = width_factor
      if self.width_upper < 0.:
        raise ValueError('`width_factor` cannot have negative values as upper '
                         'bound, got {}'.format(width_factor))
      if abs(self.width_lower) > 1. or abs(self.width_upper) > 1.:
        raise ValueError('`width_factor` must have values between [-1, 1], '
                         'got {}'.format(width_factor))
  
      if fill_mode not in {'nearest', 'bilinear'}:
        raise NotImplementedError(
            '`fill_mode` {} is not supported yet.'.format(fill_mode))
      self.fill_mode = fill_mode
      self.fill_value = fill_value
      self.seed = seed
      self._rng = make_generator(self.seed)
      self.input_spec = InputSpec(ndim=4)
      super(RandomTranslation, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_translated_inputs():
        """Translated inputs with random ops."""
        inputs_shape = array_ops.shape(inputs)
        batch_size = inputs_shape[0]
        h_axis, w_axis = 1, 2
        img_hd = math_ops.cast(inputs_shape[h_axis], dtypes.float32)
        img_wd = math_ops.cast(inputs_shape[w_axis], dtypes.float32)
        height_translate = self._rng.uniform(
            shape=[batch_size, 1],
            minval=-self.height_lower,
            maxval=self.height_upper)
        height_translate = height_translate * img_hd
        width_translate = self._rng.uniform(
            shape=[batch_size, 1],
            minval=-self.width_lower,
            maxval=self.width_upper)
        width_translate = width_translate * img_wd
        translations = math_ops.cast(
            array_ops.concat([height_translate, width_translate], axis=1),
            dtype=inputs.dtype)
        return transform(
            inputs,
            get_translation_matrix(translations),
            interpolation=self.fill_mode)
  
      output = tf_utils.smart_cond(training, random_translated_inputs,
                                   lambda: inputs)
      output.set_shape(inputs.shape)
      return output
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'height_factor': self.height_factor,
          'width_factor': self.width_factor,
          'fill_mode': self.fill_mode,
          'fill_value': self.fill_value,
          'seed': self.seed,
      }
      base_config = super(RandomTranslation, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  def get_translation_matrix(translations, name=None):
    """Returns projective transform(s) for the given translation(s).
  
    Args:
      translations: A matrix of 2-element lists representing [dx, dy] to translate
        for each image (for a batch of images).
      name: The name of the op.
  
    Returns:
      A tensor of shape (num_images, 8) projective transforms which can be given
        to `transform`.
    """
    with ops.name_scope(name, 'translation_matrix'):
      num_translations = array_ops.shape(translations)[0]
      # The translation matrix looks like:
      #     [[1 0 -dx]
      #      [0 1 -dy]
      #      [0 0 1]]
      # where the last entry is implicit.
      # Translation matrices are always float32.
      return array_ops.concat(
          values=[
              array_ops.ones((num_translations, 1), dtypes.float32),
              array_ops.zeros((num_translations, 1), dtypes.float32),
              -translations[:, 0, None],
              array_ops.zeros((num_translations, 1), dtypes.float32),
              array_ops.ones((num_translations, 1), dtypes.float32),
              -translations[:, 1, None],
              array_ops.zeros((num_translations, 2), dtypes.float32),
          ],
          axis=1)
  
  
  def transform(images,
                transforms,
                interpolation='nearest',
                output_shape=None,
                name=None):
    """Applies the given transform(s) to the image(s).
  
    Args:
      images: A tensor of shape (num_images, num_rows, num_columns, num_channels)
        (NHWC), (num_rows, num_columns, num_channels) (HWC), or (num_rows,
        num_columns) (HW). The rank must be statically known (the shape is not
        `TensorShape(None)`.
      transforms: Projective transform matrix/matrices. A vector of length 8 or
        tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1, b2,
        c0, c1], then it maps the *output* point `(x, y)` to a transformed *input*
        point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
        `k = c0 x + c1 y + 1`. The transforms are *inverted* compared to the
        transform mapping input points to output points. Note that gradients are
        not backpropagated into transformation parameters.
      interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
      output_shape: Output dimesion after the transform, [height, width]. If None,
        output is the same size as input image.
      name: The name of the op.
  
    Returns:
      Image(s) with the same type and shape as `images`, with the given
      transform(s) applied. Transformed coordinates outside of the input image
      will be filled with zeros.
  
    Raises:
      TypeError: If `image` is an invalid type.
      ValueError: If output shape is not 1-D int32 Tensor.
    """
    with ops.name_scope(name, 'transform'):
      if output_shape is None:
        output_shape = array_ops.shape(images)[1:3]
        if not context.executing_eagerly():
          output_shape_value = tensor_util.constant_value(output_shape)
          if output_shape_value is not None:
            output_shape = output_shape_value
  
      output_shape = ops.convert_to_tensor_v2(
          output_shape, dtypes.int32, name='output_shape')
  
      if not output_shape.get_shape().is_compatible_with([2]):
        raise ValueError('output_shape must be a 1-D Tensor of 2 elements: '
                         'new_height, new_width, instead got '
                         '{}'.format(output_shape))
  
      return image_ops.image_projective_transform_v2(
          images,
          output_shape=output_shape,
          transforms=transforms,
          interpolation=interpolation.upper())
  
  
  def get_rotation_matrix(angles, image_height, image_width, name=None):
    """Returns projective transform(s) for the given angle(s).
  
    Args:
      angles: A scalar angle to rotate all images by, or (for batches of images) a
        vector with an angle to rotate each image in the batch. The rank must be
        statically known (the shape is not `TensorShape(None)`).
      image_height: Height of the image(s) to be transformed.
      image_width: Width of the image(s) to be transformed.
      name: The name of the op.
  
    Returns:
      A tensor of shape (num_images, 8). Projective transforms which can be given
        to operation `image_projective_transform_v2`. If one row of transforms is
         [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point
         `(x, y)` to a transformed *input* point
         `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
         where `k = c0 x + c1 y + 1`.
    """
    with ops.name_scope(name, 'rotation_matrix'):
      x_offset = ((image_width - 1) - (math_ops.cos(angles) *
                                       (image_width - 1) - math_ops.sin(angles) *
                                       (image_height - 1))) / 2.0
      y_offset = ((image_height - 1) - (math_ops.sin(angles) *
                                        (image_width - 1) + math_ops.cos(angles) *
                                        (image_height - 1))) / 2.0
      num_angles = array_ops.shape(angles)[0]
      return array_ops.concat(
          values=[
              math_ops.cos(angles)[:, None],
              -math_ops.sin(angles)[:, None],
              x_offset[:, None],
              math_ops.sin(angles)[:, None],
              math_ops.cos(angles)[:, None],
              y_offset[:, None],
              array_ops.zeros((num_angles, 2), dtypes.float32),
          ],
          axis=1)
  
  
  class RandomRotation(Layer):
    """Randomly rotate each image.
  
    By default, random rotations are only applied during training.
    At inference time, the layer does nothing. If you need to apply random
    rotations at inference time, set `training` to True when calling the layer.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Attributes:
      factor: a positive float represented as fraction of 2pi, or a tuple of size
        2 representing lower and upper bound for rotating clockwise and
        counter-clockwise. When represented as a single float, lower = upper.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{'constant', 'nearest', 'bilinear', 'reflect',
        'wrap'}`).
      seed: Integer. Used to create a random seed.
    Raise:
      ValueError: if lower bound is not between [0, 1], or upper bound is
        negative.
    """
  
    def __init__(self,
                 factor,
                 fill_mode='nearest',
                 seed=None,
                 **kwargs):
      self.factor = factor
      if isinstance(factor, (tuple, list)):
        self.lower = factor[0]
        self.upper = factor[1]
      else:
        self.lower = self.upper = factor
      if self.lower < 0. or self.upper < 0.:
        raise ValueError('Factor cannot have negative values, '
                         'got {}'.format(factor))
      if fill_mode not in {'nearest', 'bilinear'}:
        raise NotImplementedError(
            '`fill_mode` {} is not supported yet.'.format(fill_mode))
      self.fill_mode = fill_mode
      self.seed = seed
      self._rng = make_generator(self.seed)
      self.input_spec = InputSpec(ndim=4)
      super(RandomRotation, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_rotated_inputs():
        """Rotated inputs with random ops."""
        inputs_shape = array_ops.shape(inputs)
        batch_size = inputs_shape[0]
        h_axis, w_axis = 1, 2
        img_hd = math_ops.cast(inputs_shape[h_axis], dtypes.float32)
        img_wd = math_ops.cast(inputs_shape[w_axis], dtypes.float32)
        min_angle = self.lower * 2. * np.pi
        max_angle = self.upper * 2. * np.pi
        angles = self._rng.uniform(
            shape=[batch_size], minval=-min_angle, maxval=max_angle)
        return transform(
            inputs,
            get_rotation_matrix(angles, img_hd, img_wd),
            interpolation=self.fill_mode)
  
      output = tf_utils.smart_cond(training, random_rotated_inputs,
                                   lambda: inputs)
      output.set_shape(inputs.shape)
      return output
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'factor': self.factor,
          'fill_mode': self.fill_mode,
          'seed': self.seed,
      }
      base_config = super(RandomRotation, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomZoom(Layer):
    """Randomly zoom each image during training.
  
    Arguments:
      height_factor: a positive float represented as fraction of value, or a tuple
        of size 2 representing lower and upper bound for zooming horizontally.
        When represented as a single float, this value is used for both the
        upper and lower bound. For instance, `height_factor=(0.2, 0.3)` result in
        an output zoom varying in the range `[original * 20%, original * 30%]`.
      width_factor: a positive float represented as fraction of value, or a tuple
        of size 2 representing lower and upper bound for zooming vertically.
        When represented as a single float, this value is used for both the
        upper and lower bound. For instance, `width_factor=(0.2, 0.3)` result in
        an output zoom varying in the range `[original * 20%, original * 30%]`.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{'nearest', 'bilinear'}`).
      fill_value: Value used for points outside the boundaries of the input if
        `mode='constant'`.
      seed: Integer. Used to create a random seed.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Raise:
      ValueError: if lower bound is not between [0, 1], or upper bound is
        negative.
    """
  
    def __init__(self,
                 height_factor,
                 width_factor,
                 fill_mode='nearest',
                 fill_value=0.,
                 seed=None,
                 **kwargs):
      self.height_factor = height_factor
      if isinstance(height_factor, (tuple, list)):
        self.height_lower = height_factor[0]
        self.height_upper = height_factor[1]
      else:
        self.height_lower = self.height_upper = height_factor
      if self.height_lower < 0. or self.height_upper < 0.:
        raise ValueError('`height_factor` cannot have negative values, '
                         'got {}'.format(height_factor))
      if self.height_lower > self.height_upper:
        raise ValueError('`height_factor` cannot have lower bound larger than '
                         'upper bound, got {}.'.format(height_factor))
  
      self.width_factor = width_factor
      if isinstance(width_factor, (tuple, list)):
        self.width_lower = width_factor[0]
        self.width_upper = width_factor[1]
      else:
        self.width_lower = self.width_upper = width_factor
      if self.width_lower < 0. or self.width_upper < 0.:
        raise ValueError('`width_factor` cannot have negative values, '
                         'got {}'.format(width_factor))
      if self.width_lower > self.width_upper:
        raise ValueError('`width_factor` cannot have lower bound larger than '
                         'upper bound, got {}.'.format(width_factor))
  
      if fill_mode not in {'nearest', 'bilinear'}:
        raise NotImplementedError(
            '`fill_mode` {} is not supported yet.'.format(fill_mode))
      self.fill_mode = fill_mode
      self.fill_value = fill_value
      self.seed = seed
      self._rng = make_generator(self.seed)
      self.input_spec = InputSpec(ndim=4)
      super(RandomZoom, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_zoomed_inputs():
        """Zoomed inputs with random ops."""
        inputs_shape = array_ops.shape(inputs)
        batch_size = inputs_shape[0]
        h_axis, w_axis = 1, 2
        img_hd = math_ops.cast(inputs_shape[h_axis], dtypes.float32)
        img_wd = math_ops.cast(inputs_shape[w_axis], dtypes.float32)
        height_zoom = self._rng.uniform(
            shape=[batch_size, 1],
            minval=-self.height_lower,
            maxval=self.height_upper)
        height_zoom = height_zoom * img_hd
        width_zoom = self._rng.uniform(
            shape=[batch_size, 1],
            minval=-self.width_lower,
            maxval=self.width_upper)
        width_zoom = width_zoom * img_wd
        zooms = math_ops.cast(
            array_ops.concat([height_zoom, width_zoom], axis=1),
            dtype=inputs.dtype)
        return transform(
            inputs, get_zoom_matrix(zooms, img_hd, img_wd),
            interpolation=self.fill_mode)
  
      output = tf_utils.smart_cond(training, random_zoomed_inputs,
                                   lambda: inputs)
      output.set_shape(inputs.shape)
      return output
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'height_factor': self.height_factor,
          'width_factor': self.width_factor,
          'fill_mode': self.fill_mode,
          'fill_value': self.fill_value,
          'seed': self.seed,
      }
      base_config = super(RandomZoom, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  def get_zoom_matrix(zooms, image_height, image_width, name=None):
    """Returns projective transform(s) for the given zoom(s).
  
    Args:
      zooms: A matrix of 2-element lists representing [zx, zy] to zoom
        for each image (for a batch of images).
      image_height: Height of the image(s) to be transformed.
      image_width: Width of the image(s) to be transformed.
      name: The name of the op.
  
    Returns:
      A tensor of shape (num_images, 8). Projective transforms which can be given
        to operation `image_projective_transform_v2`. If one row of transforms is
         [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point
         `(x, y)` to a transformed *input* point
         `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
         where `k = c0 x + c1 y + 1`.
    """
    with ops.name_scope(name, 'zoom_matrix'):
      num_zooms = array_ops.shape(zooms)[0]
      # The zoom matrix looks like:
      #     [[zx 0 0]
      #      [0 zy 0]
      #      [0 0 1]]
      # where the last entry is implicit.
      # Zoom matrices are always float32.
      x_offset = ((image_height + 1.) / 2.0) * (zooms[:, 0, None] - 1.)
      y_offset = ((image_width + 1.) / 2.0) * (zooms[:, 1, None] - 1.)
      return array_ops.concat(
          values=[
              zooms[:, 0, None],
              array_ops.zeros((num_zooms, 1), dtypes.float32),
              x_offset,
              array_ops.zeros((num_zooms, 1), dtypes.float32),
              zooms[:, 1, None],
              y_offset,
              array_ops.zeros((num_zooms, 2), dtypes.float32),
          ],
          axis=1)
  
  
  class RandomContrast(Layer):
    """Adjust the contrast of an image or images by a random factor.
  
    Contrast is adjusted independently for each channel of each image during
    training.
  
    For each channel, this layer computes the mean of the image pixels in the
    channel and then adjusts each component `x` of each pixel to
    `(x - mean) * contrast_factor + mean`.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Output shape:
      4D tensor with shape:
      `(samples, height, width, channels)`, data_format='channels_last'.
  
    Attributes:
      factor: a positive float represented as fraction of value, or a tuple
        of size 2 representing lower and upper bound. When represented as a
        single float, lower = upper. The contrast factor will be randomly picked
        between [1.0 - lower, 1.0 + upper].
      seed: Integer. Used to create a random seed.
    Raise:
      ValueError: if lower bound is not between [0, 1], or upper bound is
        negative.
    """
  
    def __init__(self, factor, seed=None, **kwargs):
      self.factor = factor
      if isinstance(factor, (tuple, list)):
        self.lower = factor[0]
        self.upper = factor[1]
      else:
        self.lower = self.upper = factor
      if self.lower < 0. or self.upper < 0. or self.lower > 1.:
        raise ValueError('Factor cannot have negative values, '
                         'got {}'.format(factor))
      self.seed = seed
      self.input_spec = InputSpec(ndim=4)
      super(RandomContrast, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_contrasted_inputs():
        return image_ops.random_contrast(inputs, 1. - self.lower, 1. + self.upper,
                                         self.seed)
  
      output = tf_utils.smart_cond(training, random_contrasted_inputs,
                                   lambda: inputs)
      output.set_shape(inputs.shape)
      return output
  
    def compute_output_shape(self, input_shape):
      return input_shape
  
    def get_config(self):
      config = {
          'factor': self.factor,
          'seed': self.seed,
      }
      base_config = super(RandomContrast, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomHeight(Layer):
    """Randomly vary the height of a batch of images during training.
  
    Adjusts the height of a batch of images by a random factor. The input
    should be a 4-D tensor in the "channels_last" image data format.
  
    By default, this layer is inactive during inference.
  
    Arguments:
      factor: A positive float (fraction of original height), or a tuple of
        size 2 representing lower and upper bound for resizing vertically. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `factor=(0.2, 0.3)` results in an output height
        varying in the range `[original + 20%, original + 30%]`. `factor=(-0.2,
        0.3)` results in an output height varying in the range `[original - 20%,
        original + 30%]`. `factor=0.2` results in an output height varying in the
        range `[original - 20%, original + 20%]`.
      interpolation: String, the interpolation method. Defaults to `bilinear`.
        Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
        `gaussian`, `mitchellcubic`
      seed: Integer. Used to create a random seed.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)` (data_format='channels_last').
  
    Output shape:
      4D tensor with shape:
      `(samples, random_height, width, channels)`.
    """
  
    def __init__(self, factor, interpolation='bilinear', seed=None, **kwargs):
      self.factor = factor
      if isinstance(factor, (tuple, list)):
        self.height_lower = -factor[0]
        self.height_upper = factor[1]
      else:
        self.height_lower = self.height_upper = factor
      if self.height_lower > 1.:
        raise ValueError('`factor` cannot have abs lower bound larger than 1.0, '
                         'got {}'.format(factor))
      self.interpolation = interpolation
      self._interpolation_method = get_interpolation(interpolation)
      self.input_spec = InputSpec(ndim=4)
      self.seed = seed
      self._rng = make_generator(self.seed)
      super(RandomHeight, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_height_inputs():
        """Inputs height-adjusted with random ops."""
        inputs_shape = array_ops.shape(inputs)
        h_axis, w_axis = 1, 2
        img_hd = math_ops.cast(inputs_shape[h_axis], dtypes.float32)
        img_wd = inputs_shape[w_axis]
        height_factor = self._rng.uniform(
            shape=[],
            minval=(1.0 - self.height_lower),
            maxval=(1.0 + self.height_upper))
        adjusted_height = math_ops.cast(height_factor * img_hd, dtypes.int32)
        adjusted_size = array_ops.stack([adjusted_height, img_wd])
        output = image_ops.resize_images_v2(
            images=inputs, size=adjusted_size, method=self._interpolation_method)
        original_shape = inputs.shape.as_list()
        output_shape = [original_shape[0]] + [None] + original_shape[2:4]
        output.set_shape(output_shape)
        return output
  
      return tf_utils.smart_cond(training, random_height_inputs, lambda: inputs)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      return tensor_shape.TensorShape(
          [input_shape[0], None, input_shape[2], input_shape[3]])
  
    def get_config(self):
      config = {
          'factor': self.factor,
          'interpolation': self.interpolation,
          'seed': self.seed,
      }
      base_config = super(RandomHeight, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class RandomWidth(Layer):
    """Randomly vary the width of a batch of images during training.
  
    Adjusts the width of a batch of images by a random factor. The input
    should be a 4-D tensor in the "channels_last" image data format.
  
    By default, this layer is inactive during inference.
  
    Arguments:
      factor: A positive float (fraction of original width), or a tuple of
        size 2 representing lower and upper bound for resizing horizontally. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `factor=(0.2, 0.3)` results in an output width
        varying in the range `[original + 20%, original + 30%]`. `factor=(-0.2,
        0.3)` results in an output width varying in the range `[original - 20%,
        original + 30%]`. `factor=0.2` results in an output width varying in the
        range `[original - 20%, original + 20%]`.
      interpolation: String, the interpolation method. Defaults to `bilinear`.
        Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
        `gaussian`, `mitchellcubic`
      seed: Integer. Used to create a random seed.
  
    Input shape:
      4D tensor with shape:
      `(samples, height, width, channels)` (data_format='channels_last').
  
    Output shape:
      4D tensor with shape:
      `(samples, random_height, width, channels)`.
    """
  
    def __init__(self, factor, interpolation='bilinear', seed=None, **kwargs):
      self.factor = factor
      if isinstance(factor, (tuple, list)):
        self.width_lower = -factor[0]
        self.width_upper = factor[1]
      else:
        self.width_lower = self.width_upper = factor
      if self.width_lower > 1.:
        raise ValueError('`factor` cannot have abs lower bound larger than 1.0, '
                         'got {}'.format(factor))
      self.interpolation = interpolation
      self._interpolation_method = get_interpolation(interpolation)
      self.input_spec = InputSpec(ndim=4)
      self.seed = seed
      self._rng = make_generator(self.seed)
      super(RandomWidth, self).__init__(**kwargs)
  
    def call(self, inputs, training=None):
      if training is None:
        training = K.learning_phase()
  
      def random_width_inputs():
        """Inputs width-adjusted with random ops."""
        inputs_shape = array_ops.shape(inputs)
        h_axis, w_axis = 1, 2
        img_hd = inputs_shape[h_axis]
        img_wd = math_ops.cast(inputs_shape[w_axis], dtypes.float32)
        width_factor = self._rng.uniform(
            shape=[],
            minval=(1.0 - self.width_lower),
            maxval=(1.0 + self.width_upper))
        adjusted_width = math_ops.cast(width_factor * img_wd, dtypes.int32)
        adjusted_size = array_ops.stack([img_hd, adjusted_width])
        output = image_ops.resize_images_v2(
            images=inputs, size=adjusted_size, method=self._interpolation_method)
        original_shape = inputs.shape.as_list()
        output_shape = original_shape[0:2] + [None] + [original_shape[3]]
        output.set_shape(output_shape)
        return output
  
      return tf_utils.smart_cond(training, random_width_inputs, lambda: inputs)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      return tensor_shape.TensorShape(
          [input_shape[0], input_shape[1], None, input_shape[3]])
  
    def get_config(self):
      config = {
          'factor': self.factor,
          'interpolation': self.interpolation,
          'seed': self.seed,
      }
      base_config = super(RandomWidth, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  def make_generator(seed=None):
    if seed:
      return stateful_random_ops.Generator.from_seed(seed)
    else:
      return stateful_random_ops.Generator.from_non_deterministic_state()
  
  
  def get_interpolation(interpolation):
    interpolation = interpolation.lower()
    if interpolation not in _RESIZE_METHODS:
      raise NotImplementedError(
          'Value not recognized for `interpolation`: {}. Supported values '
          'are: {}'.format(interpolation, _RESIZE_METHODS.keys()))
    return _RESIZE_METHODS[interpolation]
  
apis: tensorflow.lite.testing.zip_test_utils.create_tensor_data tensorflow.lite.testing.zip_test_utils.register_make_test_function tensorflow.compat.v1.matmul tensorflow.compat.v1.compat.v1.placeholder tensorflow.lite.testing.zip_test_utils.make_zip_of_tests tensorflow.compat.v1.compat.forward_compatibility_horizon
apis: tensorflow.python.ops.image_ops.random_flip_up_down tensorflow.python.ops.stateful_random_ops.Generator.from_seed tensorflow.python.ops.stateful_random_ops.Generator.from_non_deterministic_state tensorflow.python.framework.tensor_shape.TensorShape tensorflow.python.ops.image_ops.resize_images_v2 tensorflow.python.ops.image_ops.random_flip_left_right tensorflow.python.keras.backend.learning_phase tensorflow.python.keras.utils.tf_utils.smart_cond tensorflow.python.ops.math_ops.cos tensorflow.python.ops.array_ops.slice tensorflow.python.ops.math_ops.reduce_all tensorflow.python.framework.tensor_util.constant_value tensorflow.python.framework.ops.convert_to_tensor_v2 tensorflow.python.ops.array_ops.shape tensorflow.python.ops.array_ops.zeros tensorflow.python.ops.math_ops.sin tensorflow.python.framework.ops.control_dependencies tensorflow.python.ops.math_ops.cast tensorflow.python.ops.control_flow_ops.with_dependencies tensorflow.python.ops.array_ops.ones tensorflow.python.framework.ops.name_scope tensorflow.python.ops.array_ops.stack tensorflow.python.ops.array_ops.concat tensorflow.python.keras.engine.input_spec.InputSpec tensorflow.python.eager.context.executing_eagerly tensorflow.python.ops.image_ops.random_contrast