text: # Lint as: python3
  # Copyright 2019 Google LLC
  #
  # 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
  #
  #      https://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.
  
  from absl import app
  import numpy as np
  from pyiree.tf.support import tf_test_utils
  from pyiree.tf.support import tf_utils
  import tensorflow.compat.v2 as tf
  
  
  class Conv2dModule(tf.Module):
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 1], tf.float32),
        tf.TensorSpec([1, 1, 1, 1], tf.float32),
    ])
    def conv2d_1451x1111_valid(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "VALID", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([2, 4, 5, 1], tf.float32),
        tf.TensorSpec([1, 1, 1, 1], tf.float32),
    ])
    def conv2d_2451x1111_valid(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "VALID", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 1], tf.float32),
        tf.TensorSpec([2, 3, 1, 1], tf.float32),
    ])
    def conv2d_1451x2311_valid(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "VALID", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 1], tf.float32),
        tf.TensorSpec([2, 3, 1, 1], tf.float32),
    ])
    def conv2d_1451x2311_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([2, 4, 5, 1], tf.float32),
        tf.TensorSpec([2, 3, 1, 1], tf.float32),
    ])
    def conv2d_2451x2311_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 2], tf.float32),
        tf.TensorSpec([3, 2, 2, 1], tf.float32),
    ])
    def conv2d_1452x3221_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 1], tf.float32),
        tf.TensorSpec([1, 1, 1, 2], tf.float32),
    ])
    def conv2d_1451x1112_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 2], tf.float32),
        tf.TensorSpec([1, 1, 2, 2], tf.float32),
    ])
    def conv2d_1452x1122_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 2], tf.float32),
        tf.TensorSpec([2, 2, 2, 3], tf.float32),
    ])
    def conv2d_1452x2223_same(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "SAME", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([1, 4, 5, 2], tf.float32),
        tf.TensorSpec([2, 2, 2, 3], tf.float32),
    ])
    def conv2d_1452x2223_valid(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "VALID", name="result")
  
    @tf.function(input_signature=[
        tf.TensorSpec([2, 4, 5, 2], tf.float32),
        tf.TensorSpec([2, 2, 2, 3], tf.float32),
    ])
    def conv2d_2452x2223_valid(self, img, kernel):
      return tf.nn.conv2d(img, kernel, [1, 1, 1, 1], "VALID", name="result")
  
  
  class ConvTest(tf_test_utils.TracedModuleTestCase):
  
    def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self._modules = tf_test_utils.compile_tf_module(Conv2dModule)
  
    # yapf: disable
    def test_id_batch_size_1(self):
      def id_batch_size_1(module):
        i = tf_utils.ndarange([1, 4, 5, 1])
        k = np.ones([1, 1, 1, 1], dtype=np.float32)
        module.conv2d_1451x1111_valid(i, k)
      self.compare_backends(id_batch_size_1, self._modules)
  
    def test_id_batch_size_2(self):
      def id_batch_size_2(module):
        i = tf_utils.ndarange([2, 4, 5, 1])
        k = np.ones([1, 1, 1, 1], dtype=np.float32)
        module.conv2d_2451x1111_valid(i, k)
      self.compare_backends(id_batch_size_2, self._modules)
  
    def test_asymmetric_kernel(self):
      def asymmetric_kernel(module):
        i = tf_utils.ndarange([1, 4, 5, 1])
        k = np.array([[1, 4, 2], [-2, 0, 1]],
                     dtype=np.float32).reshape(2, 3, 1, 1)
        module.conv2d_1451x2311_valid(i, k)
      self.compare_backends(asymmetric_kernel, self._modules)
  
    def test_padding(self):
      def padding(module):
        i = tf_utils.ndarange([1, 4, 5, 1])
        k = np.array([[1, 4, 2], [-2, 0, 1]],
                     dtype=np.float32).reshape(2, 3, 1, 1)
        module.conv2d_1451x2311_same(i, k)
      self.compare_backends(padding, self._modules)
  
    def test_batched_padding(self):
      def batched_padding(module):
        i = tf_utils.ndarange([2, 4, 5, 1])
        k = np.array([[1, 4, 2], [-2, 0, 1]],
                     dtype=np.float32).reshape(2, 3, 1, 1)
        module.conv2d_2451x2311_same(i, k)
      self.compare_backends(batched_padding, self._modules)
  
    def test_feature_reduce(self):
      def feature_reduce(module):
        i = tf_utils.ndarange([1, 4, 5, 2])
        k = np.ones([3, 2, 2, 1], dtype=np.float32)
        module.conv2d_1452x3221_same(i, k)
      self.compare_backends(feature_reduce, self._modules)
  
    def test_feature_inflate(self):
      def feature_inflate(module):
        i = tf_utils.ndarange([1, 4, 5, 1])
        k = tf_utils.ndarange([1, 1, 1, 2])
        module.conv2d_1451x1112_same(i, k)
      self.compare_backends(feature_inflate, self._modules)
  
    def test_feature_mix(self):
      def feature_mix(module):
        i = tf_utils.ndarange([1, 4, 5, 2])
        k = tf_utils.ndarange([1, 1, 2, 2])
        module.conv2d_1452x1122_same(i, k)
      self.compare_backends(feature_mix, self._modules)
  
    def test_feature_padded(self):
      def feature_padded(module):
        i = tf_utils.ndarange([1, 4, 5, 2])
        k = tf_utils.ndarange([2, 2, 2, 3])
        module.conv2d_1452x2223_same(i, k)
      self.compare_backends(feature_padded, self._modules)
  
    def test_feature_unpadded(self):
      def feature_unpadded(module):
        i = tf_utils.ndarange([1, 4, 5, 2])
        k = tf_utils.ndarange([2, 2, 2, 3])
        module.conv2d_1452x2223_valid(i, k)
      self.compare_backends(feature_unpadded, self._modules)
  
    def test_batched_feature_unpadded(self):
      def batched_feature_unpadded(module):
        i = tf_utils.ndarange([2, 4, 5, 2])
        k = tf_utils.ndarange([2, 2, 2, 3])
        module.conv2d_2452x2223_valid(i, k)
      self.compare_backends(batched_feature_unpadded, self._modules)
    # yapf: enable
  
  
  def main(argv):
    del argv  # Unused
    if hasattr(tf, 'enable_v2_behavior'):
      tf.enable_v2_behavior()
    tf.test.main()
  
  
  if __name__ == '__main__':
    app.run(main)
  