/* * 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/NEON/kernels/NEDepthwiseVectorToTensorKernel.h" #include "arm_compute/core/CPP/Validate.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/INEKernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32); if(output->total_size() != 0) { TensorShape output_shape = compute_vector_to_tensor_output_shape(input->tensor_shape(), conv_w, conv_h, output->data_layout()); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } return Status{}; } } // namespace template void NEDepthwiseVectorToTensorKernel::vector_to_tensor(const Window &window) { // const int input_w = _input->info()->dimension(0); const int output_stride_x = _output->info()->strides_in_bytes().x(); const int output_stride_y = _output->info()->strides_in_bytes().y(); const int output_stride_z = _output->info()->strides_in_bytes().z(); // Setup output window Window window_out(window); window_out.set(Window::DimX, Window::Dimension(0, 0, 0)); window_out.set(Window::DimY, Window::Dimension(0, 0, 0)); window_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); Iterator in(_input, window); Iterator out(_output, window_out); const int patch_size = _conv_dims.first * _conv_dims.second; execute_window_loop(window, [&](const Coordinates & id) { const int z = id.x() / patch_size; const int index2D = id.x() - z * patch_size; auto input_ptr = reinterpret_cast(in.ptr()); auto output_ptr = reinterpret_cast(out.ptr() + index2D % _conv_dims.first * output_stride_x + index2D / _conv_dims.first * output_stride_y + z * output_stride_z); *output_ptr = *input_ptr; }, in, out); } NEDepthwiseVectorToTensorKernel::NEDepthwiseVectorToTensorKernel() : _func(nullptr), _input(nullptr), _output(nullptr), _conv_dims() { } void NEDepthwiseVectorToTensorKernel::configure(const ITensor *input, ITensor *output, size_t conv_w, size_t conv_h) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized TensorShape output_shape = compute_vector_to_tensor_output_shape(input->info()->tensor_shape(), conv_w, conv_h, output->info()->data_layout()); auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), conv_w, conv_h)); _input = input; _output = output; _conv_dims = std::pair(conv_w, conv_h); // Set appropriate function to run switch(input->info()->data_type()) { case DataType::QASYMM8: _func = &NEDepthwiseVectorToTensorKernel::vector_to_tensor; break; case DataType::S32: _func = &NEDepthwiseVectorToTensorKernel::vector_to_tensor; break; case DataType::F16: _func = &NEDepthwiseVectorToTensorKernel::vector_to_tensor; break; case DataType::F32: _func = &NEDepthwiseVectorToTensorKernel::vector_to_tensor; break; default: ARM_COMPUTE_ERROR("Unsupported data type"); } // Configure kernel window Window win = calculate_max_window(*input->info(), Steps()); // The NEDepthwisevectorToTensorKernel doesn't need padding so update_window_and_padding() can be skipped output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); INEKernel::configure(win); } Status NEDepthwiseVectorToTensorKernel::validate(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, conv_w, conv_h)); return Status{}; } void NEDepthwiseVectorToTensorKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); if(_func != nullptr) { (this->*_func)(window); } }