/* * 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/runtime/NEON/functions/NELocallyConnectedLayer.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include #include using namespace arm_compute; NELocallyConnectedLayer::NELocallyConnectedLayer(std::shared_ptr memory_manager) : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(), _is_first_run(false) { } void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(!conv_info.padding_is_symmetric()); if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 2); } bool _has_bias = (biases != nullptr); _is_first_run = true; // Get parameters for conv_info unsigned int stride_x = 0; unsigned int stride_y = 0; unsigned int pad_x = 0; unsigned int pad_y = 0; std::tie(stride_x, stride_y) = conv_info.stride(); std::tie(pad_x, pad_y) = conv_info.pad(); const unsigned int kernel_width = weights->info()->dimension(0); const unsigned int kernel_height = weights->info()->dimension(1); // Get convolved dimensions unsigned int conv_w = 0; unsigned int conv_h = 0; std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, conv_info); ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one"); // Create tensor to store the reshaped weights const size_t mat_weights_cols = weights->info()->dimension(3); const size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0); const size_t mat_weights_num = weights->info()->dimension(4); const TensorShape shape_wr(mat_weights_cols, mat_weights_rows, mat_weights_num); _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type())); // Create tensor to store im2col reshaped inputs const size_t mat_input_cols = mat_weights_rows; const size_t mat_input_rows = conv_w * conv_h; TensorShape shape_im2col = input->info()->tensor_shape(); shape_im2col.set(0, mat_input_cols); shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type())); // Create locally connected layer output tensor TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type())); // Manage intermediate buffers _memory_group.manage(&_input_im2col_reshaped); _memory_group.manage(&_gemm_output); // Configure kernels _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); _mm_kernel.configure(&_input_im2col_reshaped, &_weights_reshaped, &_gemm_output); _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h)); // Allocate intermediate tensors _weights_reshaped.allocator()->allocate(); _input_im2col_reshaped.allocator()->allocate(); _gemm_output.allocator()->allocate(); } void NELocallyConnectedLayer::run() { // Run weights reshaping (Runs once for every configure) if(_is_first_run) { _is_first_run = false; NEScheduler::get().schedule(&_weights_reshape_kernel, 3); } _memory_group.acquire(); // Run input reshaping NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY); // Runs GEMM on reshaped matrices NEScheduler::get().schedule(&_mm_kernel, Window::DimX); // Reshape output matrix NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); _memory_group.release(); }