# 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 nn
from tensorflow.python.ops import nn_impl
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import test


class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase):
  """Single vgg layer in NCHW unit tests in TF-TRT."""

  def GraphFn(self, x):
    dtype = x.dtype
    x, _, _ = nn_impl.fused_batch_norm(
        x, [1.0, 1.0], [0.0, 0.0],
        mean=[0.5, 0.5],
        variance=[1.0, 1.0],
        data_format="NCHW",
        is_training=False)
    e = constant_op.constant(
        np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype)
    conv = nn.conv2d(
        input=x,
        filter=e,
        data_format="NCHW",
        strides=[1, 1, 2, 2],
        padding="SAME",
        name="conv")
    b = constant_op.constant(np.random.randn(6), name="bias", dtype=dtype)
    t = nn.bias_add(conv, b, data_format="NCHW", name="biasAdd")
    relu = nn.relu(t, "relu")
    idty = array_ops.identity(relu, "ID")
    v = nn_ops.max_pool(
        idty, [1, 1, 2, 2], [1, 1, 2, 2],
        "VALID",
        data_format="NCHW",
        name="max_pool")
    return array_ops.squeeze(v, name="output_0")

  def GetParams(self):
    return self.BuildParams(self.GraphFn, dtypes.float32, [[5, 2, 8, 8]],
                            [[5, 6, 2, 2]])

  def ExpectedEnginesToBuild(self, run_params):
    """Return the expected engines to build."""
    return ["TRTEngineOp_0"]


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