| /* |
| * Copyright (c) 2017-2019 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. |
| */ |
| #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/DeconvolutionLayer.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> |
| class DeconvolutionLayerFixtureBase : 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, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, |
| DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias) |
| { |
| _data_type = data_type; |
| _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| _data_layout = data_layout; |
| _input_quantization_info = input_quantization_info; |
| _output_quantization_info = output_quantization_info; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, add_bias); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, add_bias); |
| } |
| |
| protected: |
| 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::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(-100, 100); |
| 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; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| template <typename U> |
| void fill_zeros(U &&tensor) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::S32: |
| { |
| const int32_t value = static_cast<int32_t>(tensor.quantization_info().uniform().offset); |
| library->fill_tensor_value(tensor, value); |
| break; |
| } |
| case DataType::F16: |
| library->fill_tensor_value(tensor, static_cast<half>(0.0f)); |
| break; |
| case DataType::F32: |
| library->fill_tensor_value(tensor, static_cast<float>(0.0f)); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| |
| TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape, |
| const PadStrideInfo &info, bool add_bias) |
| { |
| 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, _input_quantization_info, _data_layout); |
| TensorType weights = create_tensor<TensorType>(weights_shape, _data_type, 1, _input_quantization_info, _data_layout); |
| TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _input_quantization_info, _data_layout); |
| TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _output_quantization_info, _data_layout); |
| |
| // Create and configure function |
| FunctionType conv; |
| conv.configure(&src, &weights, add_bias ? &bias : nullptr, &dst, info); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| if(add_bias) |
| { |
| 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(); |
| if(add_bias) |
| { |
| 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); |
| if(add_bias) |
| { |
| 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); |
| if(add_bias) |
| { |
| fill(AccessorType(bias), 2); |
| } |
| |
| // Compute DeconvolutionLayer 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, bool add_bias) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, _data_type, 1, _input_quantization_info }; |
| SimpleTensor<T> weights{ weights_shape, _data_type, 1, _input_quantization_info }; |
| SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _input_quantization_info }; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| |
| if(add_bias) |
| { |
| fill(bias, 2); |
| } |
| else |
| { |
| fill_zeros(bias); |
| } |
| |
| return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, _output_quantization_info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataType _bias_data_type{}; |
| DataLayout _data_layout{}; |
| QuantizationInfo _input_quantization_info{}; |
| QuantizationInfo _output_quantization_info{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y> |
| class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady, |
| unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported"); |
| const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL); |
| auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info); |
| TensorInfo input_info(input_shape, 1, data_type); |
| TensorInfo weights_info(weights_shape, 1, data_type); |
| TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info); |
| DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo(), |
| QuantizationInfo(), add_bias); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y> |
| class DeconvolutionValidationAsymmFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int pad_left, unsigned int pad_right, unsigned int pad_top, |
| unsigned int pad_bottom, unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported"); |
| const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(sx, sy, pad_left, pad_right, pad_top, pad_bottom, DimensionRoundingType::CEIL); |
| auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info); |
| TensorInfo input_info(input_shape, 1, data_type); |
| TensorInfo weights_info(weights_shape, 1, data_type); |
| TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info); |
| DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, QuantizationInfo(), |
| QuantizationInfo(), add_bias); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y> |
| class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady, |
| unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported"); |
| const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL); |
| auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info); |
| TensorInfo input_info(input_shape, 1, data_type, input_quantization_info); |
| TensorInfo weights_info(weights_shape, 1, data_type, input_quantization_info); |
| TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info); |
| DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_layout, input_quantization_info, |
| output_quantization_info, add_bias); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |