| /* |
| * 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. |
| */ |
| #include "arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h" |
| |
| #include "arm_compute/core/AccessWindowTranspose.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" |
| |
| using namespace arm_compute; |
| |
| NEDepthwiseIm2ColKernel::NEDepthwiseIm2ColKernel() |
| : _input(nullptr), _output(nullptr), _kernel_dims(), _conv_info(), _has_bias() |
| { |
| } |
| |
| void NEDepthwiseIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(2)); |
| ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0))); |
| |
| _input = input; |
| _output = output; |
| _kernel_dims = kernel_dims; |
| _conv_info = conv_info; |
| _has_bias = has_bias; |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| |
| // The NEDepthwiseIm2ColKernel 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); |
| } |
| |
| void NEDepthwiseIm2ColKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| |
| //const int kernel_depth = _input->info()->dimension(2); |
| const int input_w = _input->info()->dimension(0); |
| const int input_h = _input->info()->dimension(1); |
| const int input_stride_x = _input->info()->strides_in_bytes().x(); |
| const int input_stride_y = _input->info()->strides_in_bytes().y(); |
| const int input_stride_z = _input->info()->strides_in_bytes().z(); |
| const int stride_x = _conv_info.stride().first; |
| const int stride_y = _conv_info.stride().second; |
| |
| const int pad_left = _conv_info.pad_left(); |
| const int pad_right = _conv_info.pad_right(); |
| const int pad_top = _conv_info.pad_top(); |
| |
| Window window_in(window); |
| // The first three dimensions of the input are increased by the inner loops |
| window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| // Setup output window |
| Window window_out(window); |
| window_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _output->info()->dimension(0))); |
| window_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1)); |
| window_out.set(Window::DimZ, Window::Dimension(0, _output->info()->dimension(2), 1)); |
| |
| Iterator in(_input, window_in); |
| Iterator out(_output, window_out); |
| |
| const int full_length = input_w + pad_left + pad_right; |
| const int max_initial_x = stride_x * (((full_length - _kernel_dims.width) / stride_x) + 1); |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| const int src_pixel_linear = id.y() * stride_x; |
| |
| const int src_x = -pad_left + src_pixel_linear % max_initial_x; |
| const int src_y = -pad_top + src_pixel_linear / max_initial_x * stride_y; |
| |
| // Get pointers |
| const uint8_t *const input_ptr = in.ptr() + id.z() * input_stride_z; |
| auto output_ptr = reinterpret_cast<float *>(out.ptr()); |
| const int height = src_y + _kernel_dims.height; |
| const int width = src_x + _kernel_dims.width; |
| |
| for(int y = src_y; y < height; ++y) |
| { |
| for(int x = src_x; x < width; ++x, ++output_ptr) |
| { |
| if(x < 0 || x >= input_w || y < 0 || y >= input_h) |
| { |
| *output_ptr = 0; |
| } |
| else |
| { |
| *output_ptr = *(reinterpret_cast<const float *>(input_ptr + x * input_stride_x + y * input_stride_y)); |
| } |
| } |
| } |
| |
| if(_has_bias) |
| { |
| *output_ptr = static_cast<float>(1); |
| } |
| }, |
| in, out); |
| } |