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/*
* Copyright (c) 2018-2020 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_WIDTHCONCATENATE_LAYER_FIXTURE
#define ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/ConcatenateLayer.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename ITensorType, typename AccessorType, typename FunctionType, typename T>
class ConcatenateLayerValidationFixture : public framework::Fixture
{
private:
using SrcITensorType = typename std::conditional<std::is_same<ITensorType, ITensor>::value, const ITensorType, ITensorType>::type;
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, unsigned int axis)
{
// Create input shapes
std::mt19937 gen(library->seed());
std::uniform_int_distribution<> num_dis(2, 8);
std::uniform_int_distribution<> offset_dis(0, 20);
const int num_tensors = num_dis(gen);
std::vector<TensorShape> shapes(num_tensors, shape);
// vector holding the quantization info:
// the last element is the output quantization info
// all other elements are the quantization info for the input tensors
std::vector<QuantizationInfo> qinfo(num_tensors + 1, QuantizationInfo());
for(auto &qi : qinfo)
{
qi = QuantizationInfo(1.f / 255.f, offset_dis(gen));
}
std::bernoulli_distribution mutate_dis(0.5f);
std::uniform_real_distribution<> change_dis(-0.25f, 0.f);
// Generate more shapes based on the input
for(auto &s : shapes)
{
// Randomly change the dimension
if(mutate_dis(gen))
{
// Decrease the dimension by a small percentage. Don't increase
// as that could make tensor too large.
s.set(axis, s[axis] + 2 * static_cast<int>(s[axis] * change_dis(gen)));
}
}
_target = compute_target(shapes, qinfo, data_type, axis);
_reference = compute_reference(shapes, qinfo, data_type, axis);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
library->fill_tensor_uniform(tensor, i);
}
TensorType compute_target(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
{
std::vector<TensorType> srcs;
std::vector<SrcITensorType *> src_ptrs;
// Create tensors
srcs.reserve(shapes.size());
for(size_t j = 0; j < shapes.size(); ++j)
{
srcs.emplace_back(create_tensor<TensorType>(shapes[j], data_type, 1, qinfo[j]));
src_ptrs.emplace_back(&srcs.back());
}
const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis);
TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, qinfo[shapes.size()]);
// Create and configure function
FunctionType concat;
concat.configure(src_ptrs, &dst, axis);
for(auto &src : srcs)
{
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
}
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
for(auto &src : srcs)
{
src.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
}
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
int i = 0;
for(auto &src : srcs)
{
fill(AccessorType(src), i++);
}
// Compute function
concat.run();
return dst;
}
SimpleTensor<T> compute_reference(std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
{
std::vector<SimpleTensor<T>> srcs;
std::vector<TensorShape *> src_ptrs;
// Create and fill tensors
for(size_t j = 0; j < shapes.size(); ++j)
{
srcs.emplace_back(shapes[j], data_type, 1, qinfo[j]);
fill(srcs.back(), j);
src_ptrs.emplace_back(&shapes[j]);
}
const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis);
SimpleTensor<T> dst{ dst_shape, data_type, 1, qinfo[shapes.size()] };
return reference::concatenate_layer<T>(srcs, dst, axis);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE */
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