| /* |
| * Copyright (c) 2017-2018 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. |
| */ |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.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/fixtures/ConvolutionLayerFixture.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationGenericTensorShiftFixture : public framework::Fixture |
| { |
| public: |
| using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type; |
| |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, |
| DataType data_type, QuantizationInfo quantization_info) |
| { |
| _quantization_info = quantization_info; |
| _data_type = data_type; |
| |
| const TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); |
| const TensorShape output_shape = get_output_shape(input_shape, weights_shape, info); |
| const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info); |
| } |
| |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y, |
| DataType data_type, QuantizationInfo quantization_info) |
| { |
| ARM_COMPUTE_UNUSED(dilation_x, dilation_y); |
| |
| _quantization_info = quantization_info; |
| _data_type = data_type; |
| |
| const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| std::uniform_int_distribution<uint8_t> distribution(0, 50); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::F16: |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(-5, 5); |
| 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, |
| DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info) |
| { |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, quantization_info); |
| TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, quantization_info); |
| TensorType bias = create_tensor<TensorType>(bias_shape, bias_data_type, 1, quantization_info); |
| TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info); |
| |
| TensorShape output_shape1 = get_output_shape(output_shape, weights_shape, info); |
| TensorType dst1 = create_tensor<TensorType>(output_shape1, data_type, 1, quantization_info); |
| |
| // Create and configure function |
| FunctionType conv; |
| conv.configure(&src, &weights, &bias, &dst, info); |
| FunctionType conv1; |
| conv1.configure(&dst, &weights, &bias, &dst1, 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); |
| ARM_COMPUTE_EXPECT(dst1.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| weights.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| dst1.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); |
| ARM_COMPUTE_EXPECT(!dst1.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src), 0); |
| fill(AccessorType(weights), 1); |
| fill(AccessorType(bias), 2); |
| |
| // Compute NEConvolutionLayer function |
| GCScheduler::get().memory_barrier(); |
| conv.run(); |
| GCScheduler::get().memory_barrier(); |
| conv1.run(); |
| |
| return dst1; |
| } |
| |
| 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, DataType bias_data_type, QuantizationInfo quantization_info) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, data_type, 1, quantization_info }; |
| SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info }; |
| SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info }; |
| |
| SimpleTensor<T> dst{ output_shape, data_type, 1, quantization_info }; |
| TensorShape output_shape1 = get_output_shape(output_shape, weights_shape, info); |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| return reference::convolution_layer<T>(dst, weights, bias, output_shape1, info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| QuantizationInfo _quantization_info{}; |
| DataType _data_type{}; |
| |
| private: |
| TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info) |
| { |
| TensorShape out_shape(in_shape); |
| const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(), |
| in_shape.y(), |
| kernel_shape.x(), |
| kernel_shape.y(), |
| info); |
| out_shape.set(0, scaled_dims.first); |
| out_shape.set(1, scaled_dims.second); |
| out_shape.set(2, kernel_shape[3]); |
| return out_shape; |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type) |
| { |
| DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, |
| QuantizationInfo()); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationQuantizedTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info) |
| { |
| DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, |
| quantization_info); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationWithTensorShapesQuantizedTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y, |
| DataType data_type, QuantizationInfo quantization_info) |
| { |
| DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation_x, dilation_y, data_type, |
| quantization_info); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationWithTensorShapesTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y, |
| DataType data_type) |
| { |
| DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation_x, dilation_y, data_type, |
| QuantizationInfo()); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |