# 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.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_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.platform import test


class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase):

  def GetParams(self):
    """Test for multi connection neighboring nodes wiring tests in TF-TRT."""
    dtype = dtypes.float32
    input_name = "input"
    input_dims = [2, 3, 7, 5]
    output_name = "output"
    g = ops.Graph()
    with g.as_default():
      x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
      e = constant_op.constant(
          np.random.normal(.05, .005, [3, 2, 3, 4]),
          name="weights",
          dtype=dtype)
      conv = nn.conv2d(
          input=x,
          filter=e,
          data_format="NCHW",
          strides=[1, 1, 1, 1],
          padding="VALID",
          name="conv")
      b = constant_op.constant(
          np.random.normal(2.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype)
      t = conv + b

      b = constant_op.constant(
          np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype)
      q = conv - b
      edge = math_ops.sigmoid(q)

      b = constant_op.constant(
          np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype)
      d = b + conv
      edge3 = math_ops.sigmoid(d)

      edge1 = gen_math_ops.tan(conv)
      t = t - edge1
      q = q + edge
      t = t + q
      t = t + d
      t = t - edge3
      array_ops.squeeze(t, 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=[(2, 4, 5, 4)])

  def ExpectedEnginesToBuild(self, run_params):
    """Return the expected engines to build."""
    return ["my_trt_op_0", "my_trt_op_1"]


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