| /* |
| * 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/NEDepthwiseConvolution3x3Kernel.h" |
| #include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.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; |
| using namespace arm_compute::detail; |
| |
| NEDepthwiseConvolution3x3Kernel::NEDepthwiseConvolution3x3Kernel() |
| : _border_size(0), _input(), _output(), _weights(), _conv_info() |
| { |
| } |
| |
| BorderSize NEDepthwiseConvolution3x3Kernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void NEDepthwiseConvolution3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); |
| |
| std::pair<unsigned int, unsigned int> expected_output = scaled_dimensions(input->info()->tensor_shape().x(), input->info()->tensor_shape().y(), |
| weights->info()->tensor_shape().x(), weights->info()->tensor_shape().y(), |
| conv_info); |
| |
| ARM_COMPUTE_UNUSED(expected_output); |
| ARM_COMPUTE_ERROR_ON(expected_output.first != output->info()->tensor_shape().x()); |
| ARM_COMPUTE_ERROR_ON(expected_output.second != output->info()->tensor_shape().y()); |
| |
| _input = input; |
| _output = output; |
| _weights = weights; |
| _conv_info = conv_info; |
| const unsigned int conv_stride_x = conv_info.stride().first; |
| const unsigned int conv_pad_x = conv_info.pad().first; |
| const unsigned int conv_pad_y = conv_info.pad().second; |
| |
| ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3); |
| |
| const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; |
| _border_size = BorderSize(conv_pad_y, conv_pad_x); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration)); |
| |
| AccessWindowStatic input_access(input->info(), -conv_pad_x, -conv_pad_y, input->info()->dimension(0) + _border_size.right, input->info()->dimension(1) + _border_size.bottom); |
| AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1)); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| INEKernel::configure(win); |
| } |
| |
| template <unsigned int stridex> |
| class convolver_3x3 |
| { |
| public: |
| static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, |
| const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) |
| { |
| const int input_stride_x = input->info()->strides_in_bytes().x(); |
| const int input_stride_y = input->info()->strides_in_bytes().y(); |
| const int output_stride_y = output->info()->strides_in_bytes().y(); |
| const int kernel_stride_y = weights->info()->strides_in_bytes().y(); |
| const int kernel_stride_z = weights->info()->strides_in_bytes().z(); |
| const int output_w = output->info()->dimension(0); |
| const int output_h = output->info()->dimension(1); |
| const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); |
| const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); |
| const unsigned int conv_pad_x = std::get<0>(conv_info.pad()); |
| const unsigned int conv_pad_y = std::get<1>(conv_info.pad()); |
| |
| // setup output window for the iterator |
| Window window_out = window; |
| window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); |
| window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY))); |
| |
| // setup input window for the iterator |
| Window window_in = window; |
| // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0 |
| window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| |
| Window window_k = calculate_max_window(*weights->info(), Steps(1u)); |
| |
| Iterator in(input, window_in); |
| Iterator out(output, window_out); |
| Iterator w(weights, window_k); |
| |
| const uint8_t *weights_ptr = w.ptr(); |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y; |
| int ih = 0; |
| int oh = 0; |
| |
| const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; |
| const auto ptr_weights_r0 = reinterpret_cast<const float *>(ptr_weights_base); |
| const auto ptr_weights_r1 = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y); |
| const auto ptr_weights_r2 = reinterpret_cast<const float *>(ptr_weights_base + kernel_stride_y * 2); |
| const auto vw_r0 = load_matrix_row(ptr_weights_r0); |
| const auto vw_r1 = load_matrix_row(ptr_weights_r1); |
| const auto vw_r2 = load_matrix_row(ptr_weights_r2); |
| |
| for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) |
| { |
| auto in_top = reinterpret_cast<const float *>(input_ptr + (ih + 0) * input_stride_y); |
| auto in_mid = reinterpret_cast<const float *>(input_ptr + (ih + 1) * input_stride_y); |
| auto in_low = reinterpret_cast<const float *>(input_ptr + (ih + 2) * input_stride_y); |
| auto p_out = reinterpret_cast<float *>(out.ptr() + oh * output_stride_y); |
| |
| for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, |
| in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) |
| { |
| auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0); |
| store_results<stridex>(p_out, vres); |
| } |
| } |
| }, |
| in, out); |
| } |
| }; |
| |
| void NEDepthwiseConvolution3x3Kernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_UNUSED(info); |
| |
| const unsigned int conv_stride_x = _conv_info.stride().first; |
| const unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; |
| |
| switch(conv_stride_x) |
| { |
| case 1: |
| convolver_3x3<1>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| case 2: |
| convolver_3x3<2>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| case 3: |
| convolver_3x3<3>::convolve(window, num_elems_written_per_iteration, _input, _weights, _output, _conv_info); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| } |