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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_POOLING_LAYER_FIXTURE
#define ARM_COMPUTE_TEST_POOLING_LAYER_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.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/PoolingLayer.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class PoolingLayerValidationGenericFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape, PoolingLayerInfo pool_info, DataType data_type, DataLayout data_layout)
{
std::mt19937 gen(library->seed());
std::uniform_int_distribution<> offset_dis(0, 20);
const QuantizationInfo input_qinfo(1.f / 255.f, offset_dis(gen));
const QuantizationInfo output_qinfo(1.f / 255.f, offset_dis(gen));
_pool_info = pool_info;
_target = compute_target(shape, pool_info, data_type, data_layout, input_qinfo, output_qinfo);
_reference = compute_reference(shape, pool_info, data_type, input_qinfo, output_qinfo);
}
protected:
template <typename U>
void fill(U &&tensor)
{
if(!is_data_type_quantized(tensor.data_type()))
{
std::uniform_real_distribution<> distribution(-1.f, 1.f);
library->fill(tensor, distribution, 0);
}
else // data type is quantized_asymmetric
{
library->fill_tensor_uniform(tensor, 0);
}
}
TensorType compute_target(TensorShape shape, PoolingLayerInfo info,
DataType data_type, DataLayout data_layout, QuantizationInfo input_qinfo, QuantizationInfo output_qinfo)
{
// Change shape in case of NHWC.
if(data_layout == DataLayout::NHWC)
{
permute(shape, PermutationVector(2U, 0U, 1U));
}
// Create tensors
TensorType src = create_tensor<TensorType>(shape, data_type, 1, input_qinfo, data_layout);
const TensorShape dst_shape = misc::shape_calculator::compute_pool_shape(*(src.info()), info);
TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout);
// Create and configure function
FunctionType pool_layer;
pool_layer.configure(&src, &dst, info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src));
// Compute function
pool_layer.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape, PoolingLayerInfo info, DataType data_type, QuantizationInfo input_qinfo, QuantizationInfo output_qinfo)
{
// Create reference
SimpleTensor<T> src{ shape, data_type, 1, input_qinfo };
// Fill reference
fill(src);
return reference::pooling_layer<T>(src, info, output_qinfo);
}
TensorType _target{};
SimpleTensor<T> _reference{};
PoolingLayerInfo _pool_info{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class PoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding),
data_type, data_layout);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class PoolingLayerValidationMixedPrecisionFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout, bool fp_mixed_precision = false)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding, fp_mixed_precision),
data_type, data_layout);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class PoolingLayerValidationQuantizedFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout = DataLayout::NCHW)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding),
data_type, data_layout);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class SpecialPoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape src_shape, PoolingLayerInfo pool_info, DataType data_type)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, pool_info, data_type, DataLayout::NCHW);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class GlobalPoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, PoolingType pool_type, DataType data_type, DataLayout data_layout = DataLayout::NCHW)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type), data_type, DataLayout::NCHW);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_POOLING_LAYER_FIXTURE */
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