old_contents: # Copyright 2018 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.
  # ==============================================================================
  """Script to test TF-TensorRT integration."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.compiler.tensorrt.test import tf_trt_integration_test_base as trt_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import nn
  from tensorflow.python.platform import test
  
  
  class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase):
    """Test for Constant broadcasting in TF-TRT."""
  
    def GraphFn(self, x):
      dtype = x.dtype
      filt1 = constant_op.constant(
          0.3, shape=(3, 3, 2, 1), dtype=dtype, name='filt1')
      y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1')
      z1 = nn.relu(y1, name='z1')
      filt2 = constant_op.constant(
          np.random.randn(9), shape=(3, 3, 1, 1), dtype=dtype, name='filt2')
      y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2')
      z2 = nn.relu(y2, name='z')
      filt3 = constant_op.constant(
          np.random.randn(3, 3, 1, 1),
          shape=(3, 3, 1, 1),
          dtype=dtype,
          name='filt3')
      y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3')
      return nn.relu(y3, name='output_0')
  
    def GetParams(self):
      return self.BuildParams(self.GraphFn, dtypes.float32, [[5, 12, 12, 2]],
                              [[5, 12, 12, 1]])
  
    def ExpectedEnginesToBuild(self, run_params):
      """Return the expected engines to build."""
      return ['TRTEngineOp_0']
  
    def ExpectedAbsoluteTolerance(self, run_params):
      """The absolute tolerance to compare floating point results."""
      return 1.e-04 if run_params.precision_mode == 'FP32' else 1.e-02
  
    def ExpectedRelativeTolerance(self, run_params):
      """The relative tolerance to compare floating point results."""
      return 1.e-04 if run_params.precision_mode == 'FP32' else 1.e-02
  
  
  if __name__ == '__main__':
    test.main()
  
new_contents: # Copyright 2018 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.
  # ==============================================================================
  """Script to test TF-TensorRT integration."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.compiler.tensorrt.test import tf_trt_integration_test_base as trt_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import nn
  from tensorflow.python.platform import test
  
  
  class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase):
    """Test for Constant broadcasting in TF-TRT."""
  
    def GraphFn(self, x):
      dtype = x.dtype
      filt1 = constant_op.constant(
          0.3, shape=(3, 3, 2, 1), dtype=dtype, name='filt1')
      y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1')
      z1 = nn.relu(y1, name='z1')
      filt2 = constant_op.constant(
          0.3, shape=(3, 3, 1, 1), dtype=dtype, name='filt2')
      y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2')
      z2 = nn.relu(y2, name='z')
      filt3 = constant_op.constant(
          0.3, shape=(3, 3, 1, 1), dtype=dtype, name='filt3')
      y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3')
      return nn.relu(y3, name='output_0')
  
    def GetParams(self):
      return self.BuildParams(self.GraphFn, dtypes.float32, [[5, 12, 12, 2]],
                              [[5, 12, 12, 1]])
  
    def ExpectedEnginesToBuild(self, run_params):
      """Return the expected engines to build."""
      return ['TRTEngineOp_0']
  
    def ExpectedAbsoluteTolerance(self, run_params):
      """The absolute tolerance to compare floating point results."""
      return 1.e-04 if run_params.precision_mode == 'FP32' else 1.e-02
  
    def ExpectedRelativeTolerance(self, run_params):
      """The relative tolerance to compare floating point results."""
      return 1.e-04 if run_params.precision_mode == 'FP32' else 1.e-02
  
  
  if __name__ == '__main__':
    test.main()
  
subject: Use constant input test tensors instead of random tensors