575 lines
23 KiB
C++
575 lines
23 KiB
C++
/*
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* Copyright (c) 2018-2019 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_WINOGRAD_LAYER_FIXTURE
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#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_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 "arm_compute/core/utils/misc/ShapeCalculator.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/Helpers.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include "tests/validation/reference/ConvolutionLayer.h"
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#include "tests/validation/reference/GEMM.h"
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#include "tests/validation/reference/Permute.h"
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#include "tests/validation/reference/Utils.h"
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#include "tests/validation/reference/Winograd.h"
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#include "utils/Utils.h"
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#include <random>
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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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using namespace arm_compute::misc::shape_calculator;
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool use_bias = true>
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class WinogradConvolutionLayerValidationFixture : 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 input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
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DataType data_type, ActivationLayerInfo act_info)
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{
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ARM_COMPUTE_UNUSED(dilation);
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_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
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_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i, float min, float max)
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{
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switch(tensor.data_type())
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{
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case DataType::F16:
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(min, max);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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{
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ARM_COMPUTE_ERROR("Not supported");
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}
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}
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}
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TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info,
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DataType data_type, ActivationLayerInfo act_info)
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{
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// Create tensors
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TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
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TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1);
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TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
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TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);
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// Create and configure function
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FunctionType conv;
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ARM_COMPUTE_EXPECT(static_cast<bool>(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info)), framework::LogLevel::ERRORS);
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conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(bias.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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weights.allocator()->allocate();
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dst.allocator()->allocate();
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bias.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!bias.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), 0, -1.f, 1.f);
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fill(AccessorType(weights), 1, -1.f, 1.f);
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fill(AccessorType(bias), 2, -1.f, 1.f);
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// Compute Winograd Convolution function
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conv.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
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DataType data_type, ActivationLayerInfo act_info)
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{
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// Create reference
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SimpleTensor<T> src{ input_shape, data_type, 1 };
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SimpleTensor<T> weights{ weights_shape, data_type, 1 };
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SimpleTensor<T> bias{ bias_shape, data_type, 1 };
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// Fill reference
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fill(src, 0, -1.f, 1.f);
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fill(weights, 1, -1.f, 1.f);
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if(use_bias)
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{
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fill(bias, 2, -1.f, 1.f);
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}
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else
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{
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fill(bias, 2, 0.f, 0.f);
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}
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SimpleTensor<T> conv_out = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
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return (act_info.enabled()) ? reference::activation_layer<T>(conv_out, act_info) : conv_out;
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename T1 = T, bool use_bias = true>
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class WinogradConvolutionLayerFastMathValidationFixture : 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 input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
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DataType data_type, ActivationLayerInfo act_info, const DataLayout &data_layout)
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{
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ARM_COMPUTE_UNUSED(dilation);
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_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info, data_layout);
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_reference = compute_reference(input_shape, weights_shape, bias_shape, info, data_type, act_info);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i, float min, float max)
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{
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switch(tensor.data_type())
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{
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case DataType::F16:
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{
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arm_compute::utils::uniform_real_distribution_fp16 distribution((half)min, (half)max);
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library->fill(tensor, distribution, i);
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break;
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}
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(min, max);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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{
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ARM_COMPUTE_ERROR("Not supported");
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}
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}
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}
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TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info,
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DataType data_type, ActivationLayerInfo act_info, const DataLayout data_layout)
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{
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if(data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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permute(weights_shape, PermutationVector(2U, 0U, 1U));
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permute(output_shape, PermutationVector(2U, 0U, 1U));
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}
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// Create tensors
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TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
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TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, QuantizationInfo(), data_layout);
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TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, QuantizationInfo(), data_layout);
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TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo(), data_layout);
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// Create and configure function
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FunctionType conv;
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ARM_COMPUTE_EXPECT(static_cast<bool>(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info, true /* Enable fast math */)),
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framework::LogLevel::ERRORS);
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conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info, true /* Enable fast math */);
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ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(bias.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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weights.allocator()->allocate();
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dst.allocator()->allocate();
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bias.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!bias.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), 0, -0.5f, 0.5f);
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fill(AccessorType(weights), 1, -0.5f, 0.5f);
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fill(AccessorType(bias), 2, -0.5f, 0.5f);
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// Compute Winograd Convolution function
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conv.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const PadStrideInfo &info,
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DataType data_type, ActivationLayerInfo act_info)
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{
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// Create reference
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SimpleTensor<T> src_t{ input_shape, data_type, 1 };
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SimpleTensor<T> weights_t{ weights_shape, data_type, 1 };
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SimpleTensor<T> bias_t{ bias_shape, data_type, 1 };
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// Fill reference
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fill(src_t, 0, -0.5f, 0.5f);
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SimpleTensor<T1> src_t1(copy_tensor<T1, T>(src_t));
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fill(weights_t, 1, -0.5f, 0.5f);
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SimpleTensor<T1> weights_t1(copy_tensor<T1, T>(weights_t));
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if(use_bias)
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{
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fill(bias_t, 2, -0.5f, 0.5f);
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}
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else
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{
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fill(bias_t, 2, 0.f, 0.f);
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}
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SimpleTensor<T1> bias_t1(copy_tensor<T1, T>(bias_t));
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// Set output tile
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Size2D output_tile(4U, 4U);
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if(weights_shape[0] == 7 && weights_shape[1] == 1)
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{
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output_tile.width = 2;
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output_tile.height = 1;
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}
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else if(weights_shape[0] == 1 && weights_shape[1] == 7)
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{
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output_tile.width = 1;
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output_tile.height = 2;
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}
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else if(weights_shape[0] == 1)
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{
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output_tile.width = 1;
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}
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else if(weights_shape[1] == 1)
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{
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output_tile.height = 1;
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}
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WinogradInfo winograd_info(output_tile,
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Size2D(weights_shape[0], weights_shape[1]),
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Size2D(input_shape[0], input_shape[1]),
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info,
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src_t1.data_layout());
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// Compute tensor shapes for input, filter and output transforms
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TensorShape input_transform_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
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TensorShape filter_transform_shape = compute_winograd_filter_transform_shape(TensorInfo(weights_shape, 1, data_type), winograd_info);
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TensorShape batched_gemm_shape = input_transform_shape;
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batched_gemm_shape[0] = filter_transform_shape[0];
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TensorShape output_transform_shape = compute_winograd_output_transform_shape(TensorInfo(batched_gemm_shape, 1, data_type), winograd_info);
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// Dummy matrix C to perform matrix multiplication
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SimpleTensor<T1> dummy_c{ batched_gemm_shape, data_type, 1 };
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// Compute Winograd-based convolution
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SimpleTensor<T1> input_transform_out = reference::winograd_input_transform<T1>(src_t1, input_transform_shape, winograd_info);
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SimpleTensor<T1> filter_transform_out = reference::winograd_filter_transform<T1>(weights_t1, filter_transform_shape, winograd_info);
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SimpleTensor<T1> batched_gemm = reference::gemm<T1>(input_transform_out, filter_transform_out, dummy_c, 1.0f, 0.0f);
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SimpleTensor<T1> conv_out = reference::winograd_output_transform<T1>(batched_gemm, bias_t1, output_transform_shape, winograd_info);
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SimpleTensor<T> conv_out_t(std::move(copy_tensor<T, T1>(conv_out)));
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return (act_info.enabled()) ? reference::activation_layer<T>(conv_out_t, act_info) : conv_out_t;
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class WinogradInputTransformValidationFixture : 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 input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type)
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{
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TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
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_target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
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_reference = compute_reference(input_shape, output_shape, winograd_info, data_type);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i, float min, float max)
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{
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switch(tensor.data_type())
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{
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case DataType::F16:
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(min, max);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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{
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ARM_COMPUTE_ERROR("Not supported");
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}
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}
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}
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TensorType compute_target(TensorShape input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
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{
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if(data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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}
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TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
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TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo());
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// Create and configure function
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FunctionType transf;
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transf.configure(&src, &dst, winograd_info);
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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), 0, -1.f, 1.f);
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// Compute Winograd input transform function
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transf.run();
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return dst;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type)
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{
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// Create reference
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SimpleTensor<T> src{ input_shape, data_type, 1, QuantizationInfo() };
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// Fill reference
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fill(src, 0, -1.f, 1.f);
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return reference::winograd_input_transform<T>(src, output_shape, winograd_info);
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}
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TensorType _target{};
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SimpleTensor<T> _reference{};
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};
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template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
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class WinogradFilterTransformValidationFixture : 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 input_shape, Size2D output_tile, DataLayout data_layout, DataType data_type)
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{
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WinogradInfo winograd_info(output_tile, Size2D(input_shape[0], input_shape[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */);
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TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
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_target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
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_reference = compute_reference(input_shape, output_shape, winograd_info, data_type);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i, float min, float max)
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{
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switch(tensor.data_type())
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{
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case DataType::F16:
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case DataType::F32:
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{
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std::uniform_real_distribution<> distribution(min, max);
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library->fill(tensor, distribution, i);
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break;
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}
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default:
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{
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ARM_COMPUTE_ERROR("Not supported");
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}
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}
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}
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TensorType compute_target(TensorShape input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
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{
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if(data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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}
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// Create tensors
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TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
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TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo());
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// Create and configure function
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FunctionType filter_transform;
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|
filter_transform.configure(&src, &dst, winograd_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), 0, -1.f, 1.f);
|
|
|
|
filter_transform.run();
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type)
|
|
{
|
|
// Create reference
|
|
SimpleTensor<T> src{ input_shape, data_type, 1, QuantizationInfo() };
|
|
|
|
// Fill reference
|
|
fill(src, 0, -1.f, 1.f);
|
|
|
|
return reference::winograd_filter_transform<T>(src, output_shape, winograd_info);
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<T> _reference{};
|
|
};
|
|
|
|
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
|
|
class WinogradOutputTransformValidationFixture : public framework::Fixture
|
|
{
|
|
public:
|
|
template <typename...>
|
|
void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info = ActivationLayerInfo())
|
|
{
|
|
_target = compute_target(input_shape, winograd_info, data_type, act_info);
|
|
_reference = compute_reference(input_shape, winograd_info, data_type, act_info);
|
|
}
|
|
|
|
protected:
|
|
template <typename U>
|
|
void fill(U &&tensor, int i, float min, float max)
|
|
{
|
|
switch(tensor.data_type())
|
|
{
|
|
case DataType::F16:
|
|
case DataType::F32:
|
|
{
|
|
std::uniform_real_distribution<> distribution(min, max);
|
|
library->fill(tensor, distribution, i);
|
|
break;
|
|
}
|
|
default:
|
|
{
|
|
ARM_COMPUTE_ERROR("Not supported");
|
|
}
|
|
}
|
|
}
|
|
|
|
TensorType compute_target(const TensorShape &input_shape, const WinogradInfo &winograd_info, DataType data_type, ActivationLayerInfo act_info)
|
|
{
|
|
TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
|
|
|
|
// Create tensors
|
|
TensorType src = create_tensor<TensorType>(input_shape, data_type);
|
|
TensorType bias = create_tensor<TensorType>(output_shape[get_data_layout_dimension_index(winograd_info.output_data_layout, DataLayoutDimension::CHANNEL)], data_type);
|
|
TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, QuantizationInfo(), winograd_info.output_data_layout);
|
|
|
|
// Create and configure function
|
|
FunctionType output_transform;
|
|
output_transform.configure(&src, &bias, &dst, winograd_info, act_info);
|
|
|
|
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Allocate tensors
|
|
src.allocator()->allocate();
|
|
bias.allocator()->allocate();
|
|
dst.allocator()->allocate();
|
|
|
|
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
|
|
|
|
// Fill tensors
|
|
fill(AccessorType(src), 0, -1.f, 1.f);
|
|
fill(AccessorType(bias), 1, -1.f, 1.f);
|
|
|
|
output_transform.run();
|
|
|
|
return dst;
|
|
}
|
|
|
|
SimpleTensor<T> compute_reference(const TensorShape &input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info)
|
|
{
|
|
winograd_info.output_data_layout = DataLayout::NCHW;
|
|
TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
|
|
|
|
// Create reference
|
|
SimpleTensor<T> src{ input_shape, data_type };
|
|
SimpleTensor<T> bias{ TensorShape(input_shape[0]), data_type };
|
|
|
|
// Fill reference
|
|
fill(src, 0, -1.f, 1.f);
|
|
fill(bias, 1, -1.f, 1.f);
|
|
|
|
const SimpleTensor<T> winograd_output = reference::winograd_output_transform<T>(src, bias, output_shape, winograd_info);
|
|
|
|
return (act_info.enabled()) ? reference::activation_layer<T>(winograd_output, act_info) : winograd_output;
|
|
}
|
|
|
|
TensorType _target{};
|
|
SimpleTensor<T> _reference{};
|
|
};
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|
|
#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */
|