# 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.contrib.tensorrt.python import trt_convert
from tensorflow.contrib.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.framework import ops
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 _GetParams(add_quantization_nodes, dtype=dtypes.float32):
  input_name = "input"
  input_dims = [8, 8]
  output_name = "output"

  def _Quantize(x, r):
    if add_quantization_nodes:
      x = gen_array_ops.fake_quant_with_min_max_vars(x, -r, r)
    return x

  g = ops.Graph()
  with g.as_default():
    x = array_ops.placeholder(
        dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
    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)
    x = array_ops.identity(x, name=output_name)

  return trt_test.TfTrtIntegrationTestParams(
      gdef=g.as_graph_def(),
      input_names=[input_name],
      input_dims=[[input_dims]],
      output_names=[output_name],
      expected_output_dims=[[[8, 1]]])


class QuantizationMissingAllRangesTest(trt_test.TfTrtIntegrationTestBase):

  def GetParams(self):
    """Create a graph containing single segment with no quantization ranges."""
    return _GetParams(add_quantization_nodes=False)

  def ShouldRunTest(self, run_params):
    if trt_convert.get_linked_tensorrt_version()[0] < 5:
      return False
    # Only test static engine mode, with or without calibration.
    return (trt_test.IsQuantizationMode(run_params.precision_mode) and
            not run_params.use_optimizer and not run_params.dynamic_engine)

  def ExpectedEnginesToBuild(self, run_params):
    """Return the expected engines to build."""
    if run_params.use_calibration:
      # In static engine mode with calibration, it should build a calibration
      # engine.
      return ["TRTEngineOp_0"]
    # In static engine mode without calibration, the engine building will fail
    # since no quantization ranges are set, which results in no TRT nodes.
    return []


class QuantizationWithRangesTest(trt_test.TfTrtIntegrationTestBase):

  def GetParams(self):
    """Create a graph containing single segment with no quantization ranges."""
    return _GetParams(add_quantization_nodes=True)

  def ShouldRunTest(self, run_params):
    if trt_convert.get_linked_tensorrt_version()[0] < 5:
      return False
    # Test static/dynamic engine with/without calibration.
    return (trt_test.IsQuantizationMode(run_params.precision_mode) and
            not run_params.use_optimizer)

  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):

  def GetParams(self):
    """Create a graph containing single segment with no quantization ranges."""
    return _GetParams(add_quantization_nodes=True)

  def ShouldRunTest(self, run_params):
    # Only test FP32/FP16 mode.
    return not trt_test.IsQuantizationMode(run_params.precision_mode)

  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()
