# 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-TRT INT8 conversion without calibration on Mnist model."""

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.framework import tensor_spec
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn
from tensorflow.python.platform import test


class DynamicInputShapesTest(trt_test.TfTrtIntegrationTestBase):

  def GraphFn(self, x):
    conv_filter1 = constant_op.constant(
        np.ones([3, 3, 1, 8]), name="weights1", dtype=dtypes.float32)
    bias1 = constant_op.constant(np.random.randn(8), dtype=dtypes.float32)
    x = nn.conv2d(
        input=x,
        filter=conv_filter1,
        strides=[1, 1, 1, 1],
        padding="SAME",
        name="conv")
    x = nn.bias_add(x, bias1)
    x = nn.relu(x)
    conv_filter2 = constant_op.constant(
        np.ones([3, 3, 8, 1]), name="weights2", dtype=dtypes.float32)
    bias2 = constant_op.constant(np.random.randn(1), dtype=dtypes.float32)
    x = nn.conv2d(
        input=x,
        filter=conv_filter2,
        strides=[1, 1, 1, 1],
        padding="SAME",
        name="conv")
    x = nn.bias_add(x, bias2)
    return array_ops.identity(x, name="output")

  def GetParams(self):
    # TODO(laigd): we should test the following cases:
    # - batch size is not changed, other dims are changing
    # - batch size is decreasing, other dims are identical
    # - batch size is decreasing, other dims are changing
    # - batch size is increasing, other dims are identical
    # - batch size is increasing, other dims are changing
    input_dims = [[[1, 5, 5, 1]], [[10, 5, 5, 1]], [[3, 5, 5, 1]],
                  [[1, 5, 5, 1]], [[1, 3, 1, 1]], [[2, 9, 9, 1]],
                  [[1, 224, 224, 1]], [[1, 128, 224, 1]]]
    expected_output_dims = input_dims

    return trt_test.TfTrtIntegrationTestParams(
        graph_fn=self.GraphFn,
        input_specs=[
            tensor_spec.TensorSpec([None, None, None, 1], dtypes.float32,
                                   "input")
        ],
        output_specs=[
            tensor_spec.TensorSpec([None, None, None, 1], dtypes.float32,
                                   "output")
        ],
        input_dims=input_dims,
        expected_output_dims=expected_output_dims)

  def GetConversionParams(self, run_params):
    """Return a ConversionParams for test."""
    conversion_params = super(DynamicInputShapesTest,
                              self).GetConversionParams(run_params)
    return conversion_params._replace(
        maximum_cached_engines=10,
        # Disable layout optimizer, since it will convert BiasAdd with NHWC
        # format to NCHW format under four dimentional input.
        rewriter_config_template=trt_test.OptimizerDisabledRewriterConfig())

  def ExpectedEnginesToBuild(self, run_params):
    return ["TRTEngineOp_0"]

  def ShouldRunTest(self, run_params):
    return (run_params.dynamic_engine and
            not trt_test.IsQuantizationMode(run_params.precision_mode))

  def ExpectedAbsoluteTolerance(self, run_params):
    """The absolute tolerance to compare floating point results."""
    return 1.e-03 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-03 if run_params.precision_mode == "FP32" else 1.e-01


if __name__ == "__main__":
  test.main()
