/* * Copyright (c) 2017-2018 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/NEDepthwiseConvolutionLayer3x3Kernel.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" #include "arm_compute/core/utils/misc/ShapeCalculator.h" using namespace arm_compute; using namespace arm_compute::detail; using namespace arm_compute::misc::shape_calculator; namespace { 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_offset = -input->info()->quantization_info().offset; const int weights_offset = -weights->info()->quantization_info().offset; 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 = conv_info.pad_left(); const unsigned int conv_pad_y = conv_info.pad_top(); // 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) { int ih = 0; int oh = 0; const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y; 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, weights_offset); const auto vw_r1 = load_matrix_row(ptr_weights_r1, weights_offset); const auto vw_r2 = load_matrix_row(ptr_weights_r2, weights_offset); 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, input_offset); store_results(p_out, vres); } } }, in, out); } }; template inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration, const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) { const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); switch(conv_stride_x) { case 1: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); break; case 2: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); break; case 3: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); break; default: ARM_COMPUTE_ERROR("Not implemented"); } } } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0) { } BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const { return _border_size; } void NEDepthwiseConvolutionLayer3x3Kernel::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::QASYMM8, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); // Get convolved dimensions const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info); const DataType output_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type(); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); _input = input; _output = output; _weights = weights; _conv_info = conv_info; const unsigned int conv_stride_x = conv_info.stride().first; const unsigned int conv_stride_y = conv_info.stride().second; const unsigned int conv_pad_left = conv_info.pad_left(); const unsigned int conv_pad_top = conv_info.pad_top(); ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3); unsigned int num_elems_read_per_iteration = 0; switch(input->info()->data_type()) { case DataType::QASYMM8: num_elems_read_per_iteration = 16; _num_elems_written_per_iteration = 16 >> conv_stride_x; break; case DataType::F32: num_elems_read_per_iteration = 12; _num_elems_written_per_iteration = 16 >> conv_stride_x; break; default: ARM_COMPUTE_ERROR("Data type not supported."); } _border_size = BorderSize(conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), conv_pad_left); // Configure kernel window Window win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration)); const unsigned int num_x_steps = (output_shape.x() + _num_elems_written_per_iteration - 1) / _num_elems_written_per_iteration; const int input_num_elems_processed = get_input_num_elems_processed(_num_elems_written_per_iteration, conv_stride_x); AccessWindowStatic input_access(input->info(), -conv_pad_left, -conv_pad_top, (num_x_steps - 1) * input_num_elems_processed + num_elems_read_per_iteration, conv_stride_y * (output_shape.y() - 1) + 2); AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1)); AccessWindowStatic output_access(output->info(), 0, 0, num_x_steps * _num_elems_written_per_iteration, output_shape.y()); 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); } void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_UNUSED(info); switch(_input->info()->data_type()) { case DataType::F32: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); break; case DataType::QASYMM8: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); break; default: ARM_COMPUTE_ERROR("Not implemented"); } }