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