| /* |
| * 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_CONVOLUTION_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.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/Permute.h" |
| #include "tests/validation/reference/Utils.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| class NEConvolutionLayer; |
| |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW> |
| class ConvolutionValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value |
| || std::is_same<typename std::decay<T>::type, int8_t>::value, |
| int32_t, T >::type; |
| |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, |
| DataType data_type, DataType weights_data_type, DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info, ActivationLayerInfo act_info) |
| { |
| _data_type = data_type; |
| _weights_data_type = weights_data_type; |
| _is_quantized = is_data_type_quantized_asymmetric(data_type); |
| _is_bfloat16 = data_type == DataType::BFLOAT16; |
| _bias_data_type = _is_quantized ? DataType::S32 : (_is_bfloat16 ? DataType::F32 : data_type); |
| _output_data_type = _is_bfloat16 ? DataType::F32 : data_type; |
| _quantization_info = quantization_info; |
| _weight_quantization_info = weight_quantization_info; |
| _data_layout = data_layout; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info); |
| } |
| |
| protected: |
| void regularize_values(void *values, size_t size) |
| { |
| float *fvalues = static_cast<float *>(values); |
| for(size_t i = 0; i < size; ++i) |
| { |
| fvalues[i] = float(bfloat16(fvalues[i])); |
| } |
| } |
| |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f); |
| std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QASYMM8_SIGNED: |
| { |
| std::pair<int, int> bounds = get_quantized_qasymm8_signed_bounds(tensor.quantization_info(), -1.0f, 1.0f); |
| std::uniform_int_distribution<int8_t> distribution(bounds.first, bounds.second); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QSYMM8_PER_CHANNEL: |
| { |
| int min_bound = 128; |
| int max_bound = -127; |
| for(size_t i = 0; i < _weight_quantization_info.scale().size(); i++) |
| { |
| std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f, i); |
| if(bounds.first < min_bound) |
| { |
| min_bound = bounds.first; |
| } |
| if(bounds.second > max_bound) |
| { |
| max_bound = bounds.second; |
| } |
| } |
| std::uniform_int_distribution<int8_t> distribution(min_bound, max_bound); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(-100, 100); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::BFLOAT16: |
| case DataType::F16: |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info, |
| bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0); |
| |
| const unsigned int num_groups = input_shape[2] / weights_shape[2]; |
| |
| 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)); |
| } |
| |
| 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); |
| |
| WeightsInfo weights_info(!reshape_weights, weights_shape[idx_width], weights_shape[idx_height], weights_shape[3]); |
| TensorShape reshaped_weights_shape(weights_shape); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout); |
| TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _weights_data_type, 1, _weight_quantization_info, _data_layout); |
| TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout); |
| TensorType dst = create_tensor<TensorType>(output_shape, _output_data_type, 1, _quantization_info, _data_layout); |
| |
| // Create and configure function |
| FunctionType conv; |
| conv.configure(&src, &weights, &bias, &dst, info, weights_info, dilation, act_info, num_groups); |
| |
| 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(); |
| bias.allocator()->allocate(); |
| dst.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); |
| fill(AccessorType(weights), 1); |
| fill(AccessorType(bias), 2); |
| |
| // Compute NEConvolutionLayer 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, |
| const Size2D &dilation, const ActivationLayerInfo act_info) |
| { |
| ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0); |
| |
| const unsigned int num_groups = input_shape[2] / weights_shape[2]; |
| |
| // Setup reference data types |
| const DataType src_dt = _is_bfloat16 ? DataType::F32 : _data_type; |
| const DataType weights_dt = _is_bfloat16 ? DataType::F32 : _weights_data_type; |
| const DataType bias_dt = _is_bfloat16 ? DataType::F32 : _bias_data_type; |
| |
| // Create reference |
| SimpleTensor<T> src{ input_shape, src_dt, 1, _quantization_info }; |
| SimpleTensor<TW> weights{ weights_shape, weights_dt, 1, _weight_quantization_info }; |
| SimpleTensor<TBias> bias{ bias_shape, bias_dt, 1, _quantization_info }; |
| |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| // Fill with bfloat16 to perform the conversion and reduce the mismatches in the output |
| if(_is_bfloat16) |
| { |
| regularize_values(static_cast<void *>(src.data()), src.num_elements()); |
| regularize_values(static_cast<void *>(weights.data()), weights.num_elements()); |
| } |
| |
| return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups), |
| act_info) : |
| reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataType _weights_data_type{}; |
| DataType _bias_data_type{}; |
| DataType _output_data_type{}; |
| DataLayout _data_layout{}; |
| QuantizationInfo _quantization_info{}; |
| QuantizationInfo _weight_quantization_info{}; |
| bool _is_quantized = false; |
| bool _is_bfloat16 = false; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ConvolutionValidationFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, ActivationLayerInfo act_info) |
| { |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, |
| data_type, data_type, data_layout, |
| QuantizationInfo(), QuantizationInfo(), act_info); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ConvolutionValidationQuantizedFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, QuantizationInfo quantization_info, ActivationLayerInfo act_info) |
| { |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, |
| data_type, data_type, data_layout, quantization_info, quantization_info, act_info); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW> |
| class ConvolutionValidationQuantizedPerChannelFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataType weights_data_type) |
| { |
| std::vector<float> weights_scales{}; |
| std::mt19937 gen(library->seed()); |
| std::uniform_real_distribution<> dis(0.01f, 1); |
| for(size_t i = 0; i < output_shape[2]; ++i) |
| { |
| weights_scales.push_back(dis(gen)); |
| } |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, |
| reshape_weights, data_type, weights_data_type, data_layout, |
| quantization_info, QuantizationInfo(weights_scales), act_info); |
| } |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE */ |