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/*
* Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_TEST_DEPTHCONCATENATELAYERFIXTURE
#define ARM_COMPUTE_TEST_DEPTHCONCATENATELAYERFIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Fixture.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace benchmark
{
/** Fixture that can be used for NE/CL/GC */
template <typename TensorType, typename ITensorType, typename Function, typename AccessorType>
class DepthConcatenateLayerFixture : public framework::Fixture
{
public:
inline std::vector<TensorShape> generate_input_shapes(TensorShape shape)
{
// Create input shapes
std::mt19937 gen(library->seed());
std::uniform_int_distribution<> num_dis(2, 6);
const int num_tensors = num_dis(gen);
std::vector<TensorShape> shapes(num_tensors, shape);
std::uniform_int_distribution<> depth_dis(1, 7);
std::bernoulli_distribution mutate_dis(0.25f);
std::uniform_real_distribution<> change_dis(-0.25f, 0.f);
// Generate more shapes based on the input
for(auto &s : shapes)
{
// Set the depth of the tensor
s.set(2, depth_dis(gen));
// Randomly change the first dimension
if(mutate_dis(gen))
{
// Decrease the dimension by a small percentage. Don't increase
// as that could make tensor too large. Also the change must be
// an even number. Otherwise out depth concatenate fails.
s.set(0, s[0] + 2 * static_cast<int>(s[0] * change_dis(gen)));
}
// Repeat the same as above for the second dimension
if(mutate_dis(gen))
{
s.set(1, s[1] + 2 * static_cast<int>(s[1] * change_dis(gen)));
}
}
return shapes;
}
template <typename...>
void setup(TensorShape shape, DataType data_type)
{
// Generate input shapes
std::vector<TensorShape> src_shapes = generate_input_shapes(shape);
// Create tensors
_srcs.reserve(src_shapes.size());
std::vector<ITensorType *> src_ptrs;
for(const auto &shape : src_shapes)
{
_srcs.emplace_back(create_tensor<TensorType>(shape, data_type, 1));
src_ptrs.emplace_back(&_srcs.back());
}
TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, Window::DimZ);
_dst = create_tensor<TensorType>(dst_shape, data_type, 1);
_depth_concat.configure(src_ptrs, &_dst);
for(auto &src : _srcs)
{
src.allocator()->allocate();
}
_dst.allocator()->allocate();
}
void run()
{
_depth_concat.run();
}
void sync()
{
sync_if_necessary<TensorType>();
sync_tensor_if_necessary<TensorType>(_dst);
}
void teardown()
{
for(auto &src : _srcs)
{
src.allocator()->free();
}
_srcs.clear();
_dst.allocator()->free();
}
private:
std::vector<TensorType> _srcs{};
TensorType _dst{};
Function _depth_concat{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_DEPTHCONCATENATELAYERFIXTURE */
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