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# 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 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):
def GetParams(self):
"""Single vgg layer in NCHW unit tests in TF-TRT."""
dtype = dtypes.float32
input_name = "input"
input_dims = [5, 2, 8, 8]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
x, _, _ = nn_impl.fused_batch_norm(
x,
np.random.randn(2).astype(np.float32),
np.random.randn(2).astype(np.float32),
mean=np.random.randn(2).astype(np.float32),
variance=np.random.randn(2).astype(np.float32),
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")
array_ops.squeeze(v, name="output")
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[input_dims],
num_expected_engines=1,
expected_output_dims=(5, 6, 2, 2),
allclose_atol=1.e-03,
allclose_rtol=1.e-03)
if __name__ == "__main__":
test.main()
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