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/*******************************************************************************
* Copyright 2017-2018 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/pass/liveness.hpp"
#include "ngraph/pass/manager.hpp"
#include "util/test_tools.hpp"
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::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, op::ParameterVector{arg0});
pass_manager.run_passes(f0);
auto& outputs = arg0->get_outputs();
ASSERT_EQ(1, outputs.size());
descriptor::Tensor& output = outputs[0].get_tensor();
EXPECT_EQ(2 * 3 * 4, output.size());
}
{
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{});
auto add = make_shared<op::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, op::ParameterVector{arg0});
pass_manager.run_passes(f0);
auto& outputs = arg0->get_outputs();
ASSERT_EQ(1, outputs.size());
descriptor::Tensor& output = outputs[0].get_tensor();
EXPECT_EQ(1 * 4, output.size());
}
{
auto arg0 = make_shared<op::Parameter>(element::f32, Shape{1});
auto add = make_shared<op::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, op::ParameterVector{arg0});
pass_manager.run_passes(f0);
auto& outputs = arg0->get_outputs();
ASSERT_EQ(1, outputs.size());
descriptor::Tensor& output = outputs[0].get_tensor();
EXPECT_EQ(1 * 4, output.size());
}
}
template <typename T>
void test_read_write(const vector<T>& x)
{
auto backend = runtime::Backend::create("INTERPRETER");
auto a = backend->create_tensor(element::from<T>(), Shape{2, x.size()});
vector<T> result(2 * x.size());
a->write(&x[0], 0, x.size() * sizeof(T));
copy(x.begin(), x.end(), result.begin());
a->write(&x[0], x.size() * sizeof(T), x.size() * sizeof(T));
copy(x.begin(), x.end(), result.begin() + x.size());
vector<T> af_vector(2 * x.size());
a->read(af_vector.data(), 0, af_vector.size() * sizeof(T));
ASSERT_EQ(af_vector, result);
vector<T> result1(x.size());
vector<T> result2(x.size());
copy(result.begin() + 1, result.begin() + 1 + x.size(), result1.begin());
a->read(&result2[0], sizeof(T), sizeof(T) * x.size());
ASSERT_EQ(result1, result2);
}
#if defined(NGRAPH_INTERPRETER_ENABLE)
TEST(tensor, read_write)
{
test_read_write<float>({1.0, 3.0, 5.0});
test_read_write<int64_t>({-1, 2, 4});
}
#endif
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::Add>(arg0, arg0);
auto f0 = make_shared<Function>(add, op::ParameterVector{arg0});
pass_manager.run_passes(f0);
for (size_t i = 0; i < f0->get_output_size(); ++i)
{
EXPECT_TRUE(f0->get_output_op(i)->is_output());
}
}