text: # 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.
  # ==============================================================================
  """Model 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 array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
  
  
  def _GraphFn(x, add_quantization_nodes):
  
    def _Quantize(x, r):
      if add_quantization_nodes:
        x = gen_array_ops.fake_quant_with_min_max_vars(x, -r, r)
      return x
  
    x = _Quantize(x, 10.0)
    x = x + 5
    x = _Quantize(x, 15.0)
    x = x - 5
    x = _Quantize(x, 10.0)
    x = x * 0.1
    x = _Quantize(x, 1.0)
    w = constant_op.constant(np.ones((8, 1)), dtype=dtypes.float32)
    x = math_ops.matmul(x, w)
    x = _Quantize(x, 10.0)
    return array_ops.identity(x, name="output_0")
  
  
  def _GetParams(self):
    return self.BuildParams(self.GraphFn, dtypes.float32, [[8, 8]], [[8, 1]])
  
  
  class QuantizationMissingAllRangesTest(trt_test.TfTrtIntegrationTestBase):
    """Create a graph containing single segment with no quantization ranges."""
  
    def GraphFn(self, x):
      return _GraphFn(x, add_quantization_nodes=False)
  
    def GetParams(self):
      return _GetParams(self)
  
    def ShouldRunTest(self, run_params):
      # Only test static engine mode, with or without calibration.
      return (trt_test.IsQuantizationMode(run_params.precision_mode) and
              not run_params.convert_online and not run_params.dynamic_engine
             ), "test static engine, offline conversion and INT8"
  
    def ExpectedEnginesToBuild(self, run_params):
      """Return the expected engines to build."""
      # In static engine mode with calibration, it should build a calibration
      # engine.
      # In static engine mode without calibration, the engine building will
      # succeed but fall back to non-quantized ops.
      return ["TRTEngineOp_0"]
  
  
  class QuantizationWithRangesTest(trt_test.TfTrtIntegrationTestBase):
    """Create a graph containing single segment with no quantization ranges."""
  
    def GraphFn(self, x):
      return _GraphFn(x, add_quantization_nodes=True)
  
    def GetParams(self):
      return _GetParams(self)
  
    def ShouldRunTest(self, run_params):
      # Test static/dynamic engine with/without calibration.
      return (trt_test.IsQuantizationMode(run_params.precision_mode) and
              not run_params.convert_online), "test offline conversion and INT8"
  
    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-05 if run_params.precision_mode == "FP32" else 1.e-01
  
    def ExpectedRelativeTolerance(self, run_params):
      """The relative tolerance to compare floating point results."""
      return 1.e-05 if run_params.precision_mode == "FP32" else 1.e-01
  
  
  class NonQuantizedPrecisionsWithRangesTest(trt_test.TfTrtIntegrationTestBase):
    """Create a graph containing single segment with no quantization ranges."""
  
    def GraphFn(self, x):
      return _GraphFn(x, add_quantization_nodes=True)
  
    def GetParams(self):
      return _GetParams(self)
  
    def ShouldRunTest(self, run_params):
      # Only test FP32/FP16 mode.
      return not trt_test.IsQuantizationMode(
          run_params.precision_mode), "test non-INT8"
  
    def ExpectedEnginesToBuild(self, run_params):
      """Return the expected engines to build."""
      # The fake quant ops are not supported in FP32/FP16 mode, and will split the
      # graph into three TRT segments.
      return ["TRTEngineOp_0", "TRTEngineOp_1", "TRTEngineOp_2", "TRTEngineOp_3"]
  
    def ExpectedAbsoluteTolerance(self, run_params):
      """The absolute tolerance to compare floating point results."""
      return 1.e-05 if run_params.precision_mode == "FP32" else 1.e-01
  
    def ExpectedRelativeTolerance(self, run_params):
      """The relative tolerance to compare floating point results."""
      return 1.e-05 if run_params.precision_mode == "FP32" else 1.e-01
  
  
  if __name__ == "__main__":
    test.main()
  