224 lines
9.8 KiB
C++
224 lines
9.8 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_SCALE_FIXTURE
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#define ARM_COMPUTE_TEST_SCALE_FIXTURE
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "tests/AssetsLibrary.h"
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#include "tests/Globals.h"
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#include "tests/IAccessor.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/reference/Permute.h"
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#include "tests/validation/reference/Scale.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ScaleValidationGenericFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
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bool align_corners)
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{
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_shape = shape;
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_policy = policy;
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_border_mode = border_mode;
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_sampling_policy = sampling_policy;
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_data_type = data_type;
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_quantization_info = quantization_info;
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_align_corners = align_corners;
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generate_scale(shape);
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std::mt19937 generator(library->seed());
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std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
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_constant_border_value = static_cast<T>(distribution_u8(generator));
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_target = compute_target(shape, data_layout);
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_reference = compute_reference(shape);
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}
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protected:
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void generate_scale(const TensorShape &shape)
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{
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static constexpr float _min_scale{ 0.25f };
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static constexpr float _max_scale{ 3.f };
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constexpr float max_width{ 8192.0f };
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constexpr float max_height{ 6384.0f };
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const float min_width{ 1.f };
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const float min_height{ 1.f };
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std::mt19937 generator(library->seed());
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std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);
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auto generate = [&](size_t input_size, float min_output, float max_output) -> float
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{
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const float generated_scale = distribution_float(generator);
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const float output_size = utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output);
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return output_size / input_size;
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};
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// Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
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const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
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const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);
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_scale_x = generate(shape[idx_width], min_width, max_width);
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_scale_y = generate(shape[idx_height], min_height, max_height);
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}
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template <typename U>
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void fill(U &&tensor)
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{
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if(is_data_type_float(_data_type))
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{
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library->fill_tensor_uniform(tensor, 0);
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}
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else if(is_data_type_quantized(tensor.data_type()))
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{
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std::uniform_int_distribution<> distribution(0, 100);
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library->fill(tensor, distribution, 0);
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}
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else
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{
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// Restrict range for float to avoid any floating point issues
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std::uniform_real_distribution<> distribution(-5.0f, 5.0f);
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library->fill(tensor, distribution, 0);
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}
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}
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TensorType compute_target(TensorShape shape, DataLayout data_layout)
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{
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// Change shape in case of NHWC.
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if(data_layout == DataLayout::NHWC)
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{
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permute(shape, PermutationVector(2U, 0U, 1U));
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}
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// Create tensors
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TensorType src = create_tensor<TensorType>(shape, _data_type, 1, _quantization_info, data_layout);
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const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
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const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
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TensorShape shape_scaled(shape);
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shape_scaled.set(idx_width, shape[idx_width] * _scale_x, /* apply_dim_correction = */ false);
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shape_scaled.set(idx_height, shape[idx_height] * _scale_y, /* apply_dim_correction = */ false);
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TensorType dst = create_tensor<TensorType>(shape_scaled, _data_type, 1, _quantization_info, data_layout);
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// Create and configure function
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FunctionType scale;
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scale.configure(&src, &dst, ScaleKernelInfo{ _policy, _border_mode, _constant_border_value, _sampling_policy, /* use_padding */ false, _align_corners });
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(src));
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// Compute function
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scale.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &shape)
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{
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// Create reference
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SimpleTensor<T> src{ shape, _data_type, 1, _quantization_info };
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// Fill reference
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fill(src);
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return reference::scale<T>(src, _scale_x, _scale_y, _policy, _border_mode, _constant_border_value, _sampling_policy, /* ceil_policy_scale */ false, _align_corners);
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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TensorShape _shape{};
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InterpolationPolicy _policy{};
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BorderMode _border_mode{};
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T _constant_border_value{};
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SamplingPolicy _sampling_policy{};
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DataType _data_type{};
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QuantizationInfo _quantization_info{};
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bool _align_corners{ false };
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float _scale_x{ 1.f };
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float _scale_y{ 1.f };
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ScaleValidationQuantizedFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
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bool align_corners)
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{
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ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
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data_type,
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quantization_info,
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data_layout,
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policy,
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border_mode,
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sampling_policy,
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align_corners);
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}
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class ScaleValidationFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy, bool align_corners)
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{
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ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
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data_type,
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QuantizationInfo(),
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data_layout,
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policy,
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border_mode,
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sampling_policy,
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align_corners);
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}
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};
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */
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