From 7362f0de41305eccb4b2b9b606647ffe318d32b7 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 18 Oct 2017 17:58:22 +0100 Subject: COMPMID-464 Implement Depthwise convolution 3x3 on NEON Change-Id: Ie4e1803a52afac6b6c597c6e551729dad2347cd1 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92607 Tested-by: Kaizen Reviewed-by: Pablo Tello --- .../kernels/NEDepthwiseConvolution3x3Kernel.cpp | 186 +++++++++++++++++++++ 1 file changed, 186 insertions(+) create mode 100644 src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp (limited to 'src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp new file mode 100644 index 0000000000..62aa934f26 --- /dev/null +++ b/src/core/NEON/kernels/NEDepthwiseConvolution3x3Kernel.cpp @@ -0,0 +1,186 @@ +/* + * 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, ITensor *output, const ITensor *weights, 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 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 +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(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(ptr_weights_base); + const auto ptr_weights_r1 = reinterpret_cast(ptr_weights_base + kernel_stride_y); + const auto ptr_weights_r2 = reinterpret_cast(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(input_ptr + (ih + 0) * input_stride_y); + auto in_mid = reinterpret_cast(input_ptr + (ih + 1) * input_stride_y); + auto in_low = reinterpret_cast(input_ptr + (ih + 2) * input_stride_y); + auto p_out = reinterpret_cast(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(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0); + store_results(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"); + } +} -- cgit v1.2.1