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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 gen_array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.platform import test
class ConcatenationTest(trt_test.TfTrtIntegrationTestBase):
def GetParams(self):
"""Testing Concatenation in TF-TRT conversion."""
dtype = dtypes.float32
input_name = "input"
input_dims = [2, 3, 3, 1]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
# scale
a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype)
r1 = x / a
a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype)
r2 = a / x
a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype)
r3 = a + x
a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype)
r4 = x * a
a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype)
r5 = x - a
a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype)
r6 = a - x
a = constant_op.constant(np.random.randn(3, 1), dtype=dtype)
r7 = x - a
a = constant_op.constant(np.random.randn(3, 1), dtype=dtype)
r8 = a - x
a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype)
r9 = gen_math_ops.maximum(x, a)
a = constant_op.constant(np.random.randn(3, 1), dtype=dtype)
r10 = gen_math_ops.minimum(a, x)
a = constant_op.constant(np.random.randn(3), dtype=dtype)
r11 = x * a
a = constant_op.constant(np.random.randn(1), dtype=dtype)
r12 = a * x
concat1 = array_ops.concat([r1, r2, r3, r4, r5, r6], axis=-1)
concat2 = array_ops.concat([r7, r8, r9, r10, r11, r12], axis=3)
x = array_ops.concat([concat1, concat2], axis=-1)
gen_array_ops.reshape(x, [2, -1], name=self.output_name)
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[input_dims],
num_expected_engines=1,
expected_output_dims=(2, 126),
allclose_atol=1.e-03,
allclose_rtol=1.e-03)
if __name__ == "__main__":
test.main()
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