Files
openvino/ngraph/test/tensor.cpp
T
Ilya Churaev 2ebae7cf30 Introduce the Broker API to map original framework names to OV (#3800)
* Added tests

* Fixed tests

* Added tests to check addOutput method

* Added support of port names in the IR

* Update copyrights

* Deprecate tensor name

* Fixed comments

* Enabled functional tests for GPU, GNA and Myriad

* Fixed get_tensor().get_names()

* Added unit test to check tensor names

* Fixed code style

* Skip add output test for GNA

* Added serialization support

* Added PythonAPI

* Fixed tests

* Fixed tests

* Fixed typo

* Try to disable GNA test

* Fixed tests

* Removed unused variables

* Fixed tests

* Update documentation

* Fixed comment
2021-01-29 06:28:31 +03:00

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3.7 KiB
C++

//*****************************************************************************
// Copyright 2017-2021 Intel Corporation
//
// 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.
//*****************************************************************************
#include <algorithm>
#include <memory>
#include <sstream>
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/function.hpp"
#include "ngraph/ngraph.hpp"
#include "ngraph/opsets/opset6.hpp"
#include "ngraph/pass/manager.hpp"
#include "pass/liveness.hpp"
#include "util/test_tools.hpp"
NGRAPH_SUPPRESS_DEPRECATED_START
using namespace std;
using namespace ngraph;
TEST(tensor, size)
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::Liveness>();
{
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{2, 3});
auto add = make_shared<op::v1::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, ParameterVector{arg0});
pass_manager.run_passes(f0);
ASSERT_EQ(1, arg0->get_output_size());
descriptor::Tensor& output = arg0->get_output_tensor(0);
EXPECT_EQ(2 * 3 * 4, output.size());
}
{
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{});
auto add = make_shared<op::v1::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, ParameterVector{arg0});
pass_manager.run_passes(f0);
ASSERT_EQ(1, arg0->get_output_size());
descriptor::Tensor& output = arg0->get_output_tensor(0);
EXPECT_EQ(1 * 4, output.size());
}
{
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{1});
auto add = make_shared<op::v1::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, ParameterVector{arg0});
pass_manager.run_passes(f0);
ASSERT_EQ(1, arg0->get_output_size());
descriptor::Tensor& output = arg0->get_output_tensor(0);
EXPECT_EQ(1 * 4, output.size());
}
}
TEST(tensor, output_flag)
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::Liveness>();
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{1});
auto add = make_shared<op::v1::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, ParameterVector{arg0});
pass_manager.run_passes(f0);
for (size_t i = 0; i < f0->get_output_size(); ++i)
{
EXPECT_TRUE(op::is_output(f0->get_output_op(i)));
}
}
TEST(tensor, tensor_names)
{
auto arg0 = make_shared<opset6::Parameter>(element::f32, Shape{1});
arg0->set_friendly_name("data");
arg0->get_output_tensor(0).set_names({"input"});
auto relu = make_shared<opset6::Relu>(arg0);
relu->set_friendly_name("relu");
relu->get_output_tensor(0).set_names({"relu_t", "identity"});
auto f0 = make_shared<Function>(relu, ParameterVector{arg0});
ASSERT_EQ(arg0->get_output_tensor(0).get_names(), relu->get_input_tensor(0).get_names());
ASSERT_EQ(arg0->get_output_tensor(0).get_names(),
relu->input_value(0).get_tensor().get_names());
ASSERT_EQ(f0->get_result()->get_input_tensor(0).get_names(),
relu->get_output_tensor(0).get_names());
ASSERT_EQ(f0->get_result()->input_value(0).get_tensor().get_names(),
relu->get_output_tensor(0).get_names());
}