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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 math_ops
from tensorflow.python.platform import test
class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase):
def GetParams(self):
"""Testing conversion of BatchMatMul in TF-TRT conversion."""
dtype = dtypes.float32
input_name = "input"
input_dims = [12, 5, 8, 12]
w1_name = "matmul_w1"
w1_dims = [12, 5, 12, 7]
w2_name = "matmul_w2"
w2_dims = [12, 12, 7]
g = ops.Graph()
with g.as_default():
inp = array_ops.placeholder(
dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
w1 = array_ops.placeholder(dtype=dtype, shape=w1_dims, name=w1_name)
w2 = array_ops.placeholder(dtype=dtype, shape=w2_dims, name=w2_name)
with g.device("/GPU:0"):
b = constant_op.constant(np.random.randn(12, 5, 12, 7), dtype=dtype)
c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
x1 = math_ops.matmul(inp, b)
x1 = x1 + c
x2 = math_ops.matmul(inp, w1)
x2 = x2 * d
e = gen_array_ops.reshape(inp, [12, 40, 12])
x3 = math_ops.matmul(e, w2)
f = constant_op.constant(np.random.randn(40, 1), dtype=dtype)
x3 = x3 + f
x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7])
out = x1 + x2 + x3
array_ops.squeeze(out, name=self.output_name)
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name, w1_name, w2_name],
input_dims=[input_dims, w1_dims, w2_dims],
num_expected_engines=1,
expected_output_dims=(12, 5, 8, 7),
allclose_atol=1.e-03,
allclose_rtol=1.e-03)
if __name__ == "__main__":
test.main()
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