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Properly reading parameters with whitespaces from IR (#12650)

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* Add overrided method to generating vector of strings

* Trim the value from the the left and right

* Add test to verify that output names are correctly read from IR

* Use spaces instead of tabs

* Add C++ tests for read model contains outputs with whitespaces

* Fix test for add output

* Remove python test
This commit is contained in:
Artur Kulikowski 2022-08-23 22:19:15 +02:00 committed by GitHub
parent 5b3cc5514a
commit caf547b5eb
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 183 additions and 1 deletions
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11
src/frontends/ir/src/utils.cpp
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@ -113,4 +113,15 @@ void str_to_set_of_strings(const std::string& value, std::set<std::string>& res)
}
}
void str_to_container(const std::string& value, std::vector<std::string>& res) {
std::stringstream ss(value);
std::string field;
while (getline(ss, field, ',')) {
field = ov::util::trim(field);
if (!field.empty()) {
res.emplace_back(field);
}
}
}
} // namespace ov

3
src/frontends/ir/src/utils.hpp
View File

@ -19,6 +19,8 @@ PartialShape str_to_partial_shape(const std::string& value);
bool get_dimension_from_attribute(const pugi::xml_node& node, const std::string& name, Dimension& value);
bool get_partial_shape_from_attribute(const pugi::xml_node& node, const std::string& name, PartialShape& value);
void str_to_container(const std::string& value, std::vector<std::string>& res);
template <class T>
void str_to_container(const std::string& value, T& res) {
std::stringstream ss(value);
@ -32,6 +34,7 @@ void str_to_container(const std::string& value, T& res) {
res.insert(res.end(), val);
}
}
// separated function for set<string> to keep whitespaces in values
// because stringstream splits its values with whitespace delimiter
void str_to_set_of_strings(const std::string& value, std::set<std::string>& res);

170
src/tests/functional/inference_engine/ngraph_reader/tensor_names.cpp
View File

@ -2,10 +2,11 @@
// SPDX-License-Identifier: Apache-2.0
//
#include <string>
#include <ngraph/function.hpp>
#include <string>
#include "ngraph_reader_tests.hpp"
#include "openvino/openvino.hpp"
TEST_F(NGraphReaderTests, ReadNetworkWithTensorNames) {
std::string model = R"V0G0N(
@ -88,3 +89,170 @@ TEST_F(NGraphReaderTests, ReadNetworkWithTensorNames) {
}
ASSERT_NO_THROW(network.getOVNameForTensor("relu,t"));
}
TEST_F(NGraphReaderTests, ReadModelWithTensorNamesWithSpaces) {
std::string model_str = R"V0G0N(
<net name="graph" version="11">
<layers>
<layer id="1" name="input1" type="Parameter" version="opset1">
<data shape="1,4,512" element_type="f32"/>
<output>
<port id="0" precision="FP32" names="input1">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="0" name="input2" type="Parameter" version="opset1">
<data shape="1,4,512" element_type="f32"/>
<output>
<port id="0" precision="FP32" names="input2">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="2" name="output 0([1 4 512])" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="output 0([1 4 512])">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="3" name="output 0([1 4 512])/sink_port_0" type="Result" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="2" to-port="1"/>
<edge from-layer="1" from-port="0" to-layer="2" to-port="0"/>
<edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
</edges>
</net>
)V0G0N";
ov::Core core;
auto model = core.read_model(model_str, ov::Tensor{});
auto outputs = model->outputs();
EXPECT_EQ(outputs.size(), 1);
auto names = outputs.at(0).get_names();
EXPECT_EQ(names.size(), 1);
auto it = names.find("output 0([1 4 512])");
EXPECT_NE(it, names.end());
}
TEST_F(NGraphReaderTests, ReadModelWithTensorNamesAddOutput) {
std::string model_str = R"V0G0N(
<net name="graph" version="11">
<layers>
<layer id="1" name="input1" type="Parameter" version="opset1">
<data shape="1,4,512" element_type="f32"/>
<output>
<port id="0" precision="FP32" names="input1">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="0" name="input2" type="Parameter" version="opset1">
<data shape="1,4,512" element_type="f32"/>
<output>
<port id="0" precision="FP32" names="input2">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="2" name="Add 221" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="output add">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="3" name="output 0([1 4 512])" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="output 0([1 4 512])">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="4" name="output 0([1 4 512])/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="output 0([1 4 512])/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4</dim>
<dim>512</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="2" to-port="1"/>
<edge from-layer="1" from-port="0" to-layer="2" to-port="0"/>
<edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
<edge from-layer="3" from-port="1" to-layer="4" to-port="0"/>
</edges>
</net>)V0G0N";
ov::Core core;
std::string tensor_name = "output add";
auto model = core.read_model(model_str, ov::Tensor{});
model->add_output(tensor_name);
auto outputs = model->outputs();
EXPECT_EQ(outputs.size(), 2);
auto names = outputs.at(1).get_names();
EXPECT_EQ(names.size(), 1);
auto it = names.find(tensor_name);
EXPECT_NE(it, names.end());
}