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/*
* Copyright (c) 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_NON_MAX_SUPPRESSION_FIXTURE
#define ARM_COMPUTE_TEST_NON_MAX_SUPPRESSION_FIXTURE
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/Tensor.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/reference/NonMaxSuppression.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType>
class NMSValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape input_shape, unsigned int max_output_size, float score_threshold, float nms_threshold)
{
ARM_COMPUTE_ERROR_ON(max_output_size == 0);
ARM_COMPUTE_ERROR_ON(input_shape.num_dimensions() != 2);
const TensorShape output_shape(max_output_size);
const TensorShape scores_shape(input_shape[1]);
_target = compute_target(input_shape, scores_shape, output_shape, max_output_size, score_threshold, nms_threshold);
_reference = compute_reference(input_shape, scores_shape, output_shape, max_output_size, score_threshold, nms_threshold);
}
protected:
template <typename U>
void fill(U &&tensor, int i, int lo, int hi)
{
std::uniform_real_distribution<> distribution(lo, hi);
library->fill_boxes(tensor, distribution, i);
}
TensorType compute_target(const TensorShape input_shape, const TensorShape scores_shape, const TensorShape output_shape,
unsigned int max_output_size, float score_threshold, float nms_threshold)
{
// Create tensors
TensorType bboxes = create_tensor<TensorType>(input_shape, DataType::F32);
TensorType scores = create_tensor<TensorType>(scores_shape, DataType::F32);
TensorType indices = create_tensor<TensorType>(output_shape, DataType::S32);
// Create and configure function
FunctionType nms_func;
nms_func.configure(&bboxes, &scores, &indices, max_output_size, score_threshold, nms_threshold);
ARM_COMPUTE_EXPECT(bboxes.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(indices.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(scores.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
bboxes.allocator()->allocate();
indices.allocator()->allocate();
scores.allocator()->allocate();
ARM_COMPUTE_EXPECT(!bboxes.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!indices.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!scores.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(bboxes), 0, 0.f, 1.f);
fill(AccessorType(scores), 1, 0.f, 1.f);
// Compute function
nms_func.run();
return indices;
}
SimpleTensor<int> compute_reference(const TensorShape input_shape, const TensorShape scores_shape, const TensorShape output_shape,
unsigned int max_output_size, float score_threshold, float nms_threshold)
{
// Create reference
SimpleTensor<float> bboxes{ input_shape, DataType::F32 };
SimpleTensor<float> scores{ scores_shape, DataType::F32 };
SimpleTensor<int> indices{ output_shape, DataType::S32 };
// Fill reference
fill(bboxes, 0, 0.f, 1.f);
fill(scores, 1, 0.f, 1.f);
return reference::non_max_suppression(bboxes, scores, indices, max_output_size, score_threshold, nms_threshold);
}
TensorType _target{};
SimpleTensor<int> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_NON_MAX_SUPPRESSION_FIXTURE */
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