IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add IR reader test for hsigmoid op (#2825)

Browse Source 
* Add IR reader test for hsigmoid op

* update ir

* Fix decomposition

* Update hsigmoid ir reader test

* Update unit test

* fix test
This commit is contained in:
iliya mironov 2020-11-03 14:13:08 +03:00 committed by GitHub
parent 78bf78660f
commit 1e67eaf030
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 199 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
inference-engine/src/transformations/src/transformations/op_conversions/hsigmoid_decomposition.cpp
View File

@ -30,11 +30,12 @@ ngraph::pass::HSigmoidDecomposition::HSigmoidDecomposition() {
auto relu = std::make_shared<ngraph::opset5::Relu>(add);
auto min_constant = ngraph::opset5::Constant::create(input_type, ngraph::Shape{}, {6.0});
auto min = register_new_node<ngraph::opset5::Minimum>(relu, min_constant);
auto mul = std::make_shared<ngraph::opset5::Multiply>(hsigmoid_node->input_value(0), min);
auto mul_constant = ngraph::opset5::Constant::create(input_type, ngraph::Shape{}, {(1.0/6.0)}); // const(1/6)
auto mul = std::make_shared<ngraph::opset5::Multiply>(min, mul_constant);
mul->set_friendly_name(m.get_match_root()->get_friendly_name());
ngraph::copy_runtime_info(hsigmoid_node,
{add_constant, add, relu, min_constant, min, mul});
{add_constant, add, relu, min_constant, min, min_constant, mul});
ngraph::replace_node(m.get_match_root(), mul);
return true;
};

194
inference-engine/tests/functional/inference_engine/ngraph_reader/hsigmoid_test.cpp Normal file
View File

@ -0,0 +1,194 @@
// Copyright (C) 2018-2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <string>
#include "ngraph_reader_tests.hpp"
TEST_F(NGraphReaderTests, ReadHSigmoidNetwork) {
std::string model = R"V0G0N(
<net name="Network" version="10">
<layers>
<layer name="in1" type="Parameter" id="0" version="opset1">
<data element_type="f32" shape="1,3,22,22"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="activation" id="1" type="HSigmoid" version="opset5">
<input>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="output" type="Result" id="2" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
</edges>
</net>
)V0G0N";
std::string modelV5 = R"V0G0N(
<net name="Network" version="5" precision="FP32" batch="1">
<layers>
<layer name="in1" type="Input" precision="FP32" id="0">
<data originalLayersNames="in1" />
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="Add_735" type="Power" precision="FP32" id="1">
<data originalLayersNames="activation" power="1" scale="1" shift="3" />
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="Relu_736" type="ReLU" precision="FP32" id="2">
<data originalLayersNames="activation" />
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="Multiply_742" type="Power" precision="FP32" id="3">
<data originalLayersNames="activation" power="1" scale="-1" shift="0" />
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="Multiply_744" type="Const" precision="FP32" id="4">
<output>
<port id="0" precision="FP32">
<dim>1</dim>
</port>
</output>
<blobs>
<custom offset="0" size="4" precision="FP32" />
</blobs>
</layer>
<layer name="Maximum_745" type="Eltwise" precision="FP32" id="5">
<data auto_broadcast="numpy" operation="max" originalLayersNames="activation" />
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
<port id="1">
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="activation" type="Power" precision="FP32" id="6">
<data originalLayersNames="activation" power="1" scale="-0.166667" shift="0" />
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="1" to-port="0" />
<edge from-layer="1" from-port="1" to-layer="2" to-port="0" />
<edge from-layer="2" from-port="1" to-layer="3" to-port="0" />
<edge from-layer="3" from-port="1" to-layer="5" to-port="0" />
<edge from-layer="4" from-port="0" to-layer="5" to-port="1" />
<edge from-layer="5" from-port="2" to-layer="6" to-port="0" />
</edges>
</net>
)V0G0N";
compareIRs(model, modelV5, 40);
}

3
inference-engine/tests/functional/inference_engine/transformations/hsigmoid_decomposition_test.cpp
View File

@ -40,7 +40,8 @@ TEST(TransformationTests, HSigmoidDecompositionTest) {
auto relu = std::make_shared<ngraph::opset5::Relu>(add);
auto min_constant = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{}, {6.0});
auto min = std::make_shared<ngraph::opset5::Minimum>(relu, min_constant);
auto mul = std::make_shared<ngraph::opset5::Multiply>(input, min);
auto mul_constant = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{}, {(1.0/6.0)}); // const(1/6)
auto mul = std::make_shared<ngraph::opset5::Multiply>(min, mul_constant);
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{input});
}