| /* |
| * Copyright (c) 2017 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/CPP/ConvolutionLayer.h" |
| #include "tests/validation/CPP/Utils.h" |
| #include "tests/validation/Helpers.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| class NEConvolutionLayer; |
| |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ConvolutionValidationFixedPointFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits) |
| { |
| _fractional_bits = fractional_bits; |
| _data_type = data_type; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| 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(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, |
| bool reshape_weights, DataType data_type, int fixed_point_position) |
| { |
| WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]); |
| TensorShape reshaped_weights_shape(weights_shape); |
| |
| if(!reshape_weights) |
| { |
| // Check if its a "fully connected" convolution |
| const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); |
| bool is_optimised = false; |
| #if defined(__arm__) |
| is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32; |
| #elif defined(__aarch64__) |
| is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32; |
| #endif /* defined(__arm__) || defined(__aarch64__) */ |
| |
| reshaped_weights_shape.collapse(3); |
| |
| if(bias_shape.total_size() > 0) |
| { |
| reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1); |
| } |
| |
| if(is_fully_connected_convolution || is_optimised) |
| { |
| const size_t shape_x = reshaped_weights_shape.x(); |
| reshaped_weights_shape.set(0, reshaped_weights_shape.y()); |
| reshaped_weights_shape.set(1, shape_x); |
| } |
| else |
| { |
| const int interleave_width = 16 / data_size_from_type(data_type); |
| reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width); |
| reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width)))); |
| } |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); |
| TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position); |
| TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); |
| TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); |
| |
| // Create and configure function |
| FunctionType conv; |
| conv.configure(&src, &weights, &bias, &dst, info, weights_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(); |
| 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); |
| |
| if(!reshape_weights) |
| { |
| const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); |
| bool is_optimised = false; |
| #if defined(__arm__) |
| is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32; |
| #elif defined(__aarch64__) |
| is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32; |
| #endif /* defined(__arm__) || defined(__aarch64__) */ |
| |
| TensorShape tmp_weights_shape(weights_shape); |
| SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position); |
| SimpleTensor<T> tmp_bias(bias_shape, data_type, 1, fixed_point_position); |
| |
| // Fill with original shape |
| fill(tmp_weights, 1); |
| fill(tmp_bias, 2); |
| |
| tmp_weights = linearise_weights(tmp_weights, &tmp_bias); |
| |
| if(!is_fully_connected_convolution && !is_optimised) |
| { |
| // Transpose with interleave |
| const int interleave_size = 16 / tmp_weights.element_size(); |
| tmp_weights = transpose(std::move(tmp_weights), interleave_size); |
| } |
| |
| AccessorType weights_accessor(weights); |
| |
| for(int i = 0; i < tmp_weights.num_elements(); ++i) |
| { |
| Coordinates coord = index2coord(tmp_weights.shape(), i); |
| std::copy_n(static_cast<const T *>(tmp_weights(coord)), 1, static_cast<T *>(weights_accessor(coord))); |
| } |
| } |
| else |
| { |
| 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, |
| DataType data_type, int fixed_point_position) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position }; |
| SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position }; |
| SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position }; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| return reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| int _fractional_bits{}; |
| DataType _data_type{}; |
| |
| private: |
| template <typename U> |
| SimpleTensor<U> linearise_weights(const SimpleTensor<U> &weights, const SimpleTensor<U> *biases = nullptr) |
| { |
| TensorShape dst_shape(weights.shape()); |
| dst_shape.collapse(3); |
| |
| if(biases != nullptr) |
| { |
| dst_shape.set(0, dst_shape.x() + 1); |
| } |
| |
| const size_t shape_x = dst_shape.x(); |
| dst_shape.set(0, dst_shape.y()); |
| dst_shape.set(1, shape_x); |
| |
| SimpleTensor<U> dst(dst_shape, weights.data_type()); |
| |
| // Don't iterate over biases yet |
| for(int weights_idx = 0; weights_idx < weights.num_elements(); ++weights_idx) |
| { |
| Coordinates weights_coord = index2coord(weights.shape(), weights_idx); |
| const int dst_row = weights_idx % weights.shape().total_size_lower(3); |
| Coordinates dst_coord{ weights_coord[3], dst_row, weights_coord[4] }; |
| const int dst_idx = coord2index(dst.shape(), dst_coord); |
| |
| dst[dst_idx] = weights[weights_idx]; |
| } |
| |
| if(biases != nullptr) |
| { |
| // Fill last row with biases |
| for(int bias_idx = 0; bias_idx < biases->num_elements(); ++bias_idx) |
| { |
| Coordinates bias_coord = index2coord(biases->shape(), bias_idx); |
| Coordinates dst_coord{ bias_coord.x(), static_cast<int>(dst.shape().y()) - 1, bias_coord.y() }; |
| int dst_idx = coord2index(dst.shape(), dst_coord); |
| |
| dst[dst_idx] = (*biases)[bias_idx]; |
| } |
| } |
| |
| return dst; |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type) |
| { |
| ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0); |
| } |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE */ |