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/*
* Copyright (c) 2017-2020 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_SCALE_FIXTURE
#define ARM_COMPUTE_TEST_SCALE_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.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/Permute.h"
#include "tests/validation/reference/Scale.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ScaleValidationGenericFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
bool align_corners)
{
_shape = shape;
_policy = policy;
_border_mode = border_mode;
_sampling_policy = sampling_policy;
_data_type = data_type;
_quantization_info = quantization_info;
_align_corners = align_corners;
generate_scale(shape);
std::mt19937 generator(library->seed());
std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
_constant_border_value = static_cast<T>(distribution_u8(generator));
_target = compute_target(shape, data_layout);
_reference = compute_reference(shape);
}
protected:
void generate_scale(const TensorShape &shape)
{
static constexpr float _min_scale{ 0.25f };
static constexpr float _max_scale{ 3.f };
constexpr float max_width{ 8192.0f };
constexpr float max_height{ 6384.0f };
const float min_width{ 1.f };
const float min_height{ 1.f };
std::mt19937 generator(library->seed());
std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);
auto generate = [&](size_t input_size, float min_output, float max_output) -> float
{
const float generated_scale = distribution_float(generator);
const float output_size = utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output);
return output_size / input_size;
};
// Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);
_scale_x = generate(shape[idx_width], min_width, max_width);
_scale_y = generate(shape[idx_height], min_height, max_height);
}
template <typename U>
void fill(U &&tensor)
{
if(is_data_type_float(_data_type))
{
library->fill_tensor_uniform(tensor, 0);
}
else if(is_data_type_quantized(tensor.data_type()))
{
std::uniform_int_distribution<> distribution(0, 100);
library->fill(tensor, distribution, 0);
}
else
{
// Restrict range for float to avoid any floating point issues
std::uniform_real_distribution<> distribution(-5.0f, 5.0f);
library->fill(tensor, distribution, 0);
}
}
TensorType compute_target(TensorShape shape, DataLayout data_layout)
{
// 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, _quantization_info, data_layout);
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
TensorShape shape_scaled(shape);
shape_scaled.set(idx_width, shape[idx_width] * _scale_x, /* apply_dim_correction = */ false);
shape_scaled.set(idx_height, shape[idx_height] * _scale_y, /* apply_dim_correction = */ false);
TensorType dst = create_tensor<TensorType>(shape_scaled, _data_type, 1, _quantization_info, data_layout);
// Create and configure function
FunctionType scale;
scale.configure(&src, &dst, ScaleKernelInfo{ _policy, _border_mode, _constant_border_value, _sampling_policy, /* use_padding */ true, _align_corners });
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
scale.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape)
{
// Create reference
SimpleTensor<T> src{ shape, _data_type, 1, _quantization_info };
// Fill reference
fill(src);
return reference::scale<T>(src, _scale_x, _scale_y, _policy, _border_mode, _constant_border_value, _sampling_policy, /* ceil_policy_scale */ false, _align_corners);
}
TensorType _target{};
SimpleTensor<T> _reference{};
TensorShape _shape{};
InterpolationPolicy _policy{};
BorderMode _border_mode{};
T _constant_border_value{};
SamplingPolicy _sampling_policy{};
DataType _data_type{};
QuantizationInfo _quantization_info{};
bool _align_corners{ false };
float _scale_x{ 1.f };
float _scale_y{ 1.f };
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ScaleValidationQuantizedFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
bool align_corners)
{
ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
data_type,
quantization_info,
data_layout,
policy,
border_mode,
sampling_policy,
align_corners);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ScaleValidationFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy, bool align_corners)
{
ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
data_type,
QuantizationInfo(),
data_layout,
policy,
border_mode,
sampling_policy,
align_corners);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */
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