From 96b16b65dd96351b8af1b2a785856ce13cc8ba84 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 1 Dec 2020 17:41:34 +0000 Subject: Remove support for (NE/CL)LocallyConnectedLayer Remove out-of-date and unmaintained LocallyConnectedLayer for both NEON and OpenCL. Resolves: COMPMID-3924 Signed-off-by: Georgios Pinitas Change-Id: Ia61398ed8cfa3876f41c1b342c4a80d1cca0ca83 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4634 Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins --- .../NEON/functions/NELocallyConnectedLayer.cpp | 203 --------------------- 1 file changed, 203 deletions(-) delete mode 100644 src/runtime/NEON/functions/NELocallyConnectedLayer.cpp (limited to 'src/runtime/NEON/functions/NELocallyConnectedLayer.cpp') diff --git a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp deleted file mode 100644 index c1164c3bee..0000000000 --- a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp +++ /dev/null @@ -1,203 +0,0 @@ -/* - * Copyright (c) 2017-2020 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 "src/core/NEON/kernels/NEIm2ColKernel.h" -#include "src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h" -#include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" - -#include -#include - -namespace arm_compute -{ -namespace -{ -void calculate_shapes(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - TensorShape &shape_wr, TensorShape &shape_im2col, TensorShape &shape_gemm) -{ - ARM_COMPUTE_UNUSED(output); - - const unsigned int kernel_width = weights->dimension(0); - const unsigned int kernel_height = weights->dimension(1); - - bool has_bias = (biases != nullptr); - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, - conv_info); - - const size_t mat_weights_cols = weights->dimension(3); - const size_t mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + ((has_bias) ? 1 : 0); - const size_t mat_weights_num = weights->dimension(4); - - shape_wr = TensorShape(mat_weights_cols, mat_weights_rows, mat_weights_num); - - const size_t mat_input_cols = mat_weights_rows; - const size_t mat_input_rows = conv_w * conv_h; - - shape_im2col = input->tensor_shape(); - shape_im2col.set(0, mat_input_cols); - shape_im2col.set(1, mat_input_rows); - shape_im2col.set(2, 1); - - shape_gemm = shape_im2col; - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, mat_input_rows); -} -} // namespace -NELocallyConnectedLayer::~NELocallyConnectedLayer() = default; - -NELocallyConnectedLayer::NELocallyConnectedLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _input_im2col(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(), - _is_prepared(false), _original_weights(nullptr) -{ -} - -Status NELocallyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2)); - ARM_COMPUTE_RETURN_ERROR_ON(!conv_info.padding_is_symmetric()); - - bool has_bias = (biases != nullptr); - - if(has_bias) - { - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 2); - } - - const unsigned int kernel_width = weights->dimension(0); - const unsigned int kernel_height = weights->dimension(1); - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, - conv_info); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one"); - - // Calculate intermediate buffer shapes - TensorShape shape_wr; - TensorShape shape_im2col; - TensorShape shape_gemm; - calculate_shapes(input, weights, biases, output, conv_info, shape_wr, shape_im2col, shape_gemm); - - TensorInfo weights_reshaped_info(shape_wr, 1, weights->data_type()); - TensorInfo input_im2col_reshaped_info(shape_im2col, 1, input->data_type()); - TensorInfo gemm_output_info(shape_gemm, 1, input->data_type()); - - ARM_COMPUTE_RETURN_ON_ERROR(NEIm2Col::validate(input, &input_im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, has_bias)); - ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped_info)); - ARM_COMPUTE_RETURN_ON_ERROR(NELocallyConnectedMatrixMultiplyKernel::validate(&input_im2col_reshaped_info, &weights_reshaped_info, &gemm_output_info)); - ARM_COMPUTE_RETURN_ON_ERROR(NECol2Im::validate(&gemm_output_info, output, Size2D(conv_w, conv_h))); - - return Status{}; -} - -void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(NELocallyConnectedLayer::validate(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info)); - - bool _has_bias = (biases != nullptr); - _is_prepared = false; - _original_weights = weights; - - 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); - - // Calculate intermediate buffer shapes - TensorShape shape_wr; - TensorShape shape_im2col; - TensorShape shape_gemm; - calculate_shapes(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info, shape_wr, shape_im2col, shape_gemm); - - _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type())); - _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type())); - _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.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); - _weights_reshape_kernel = std::make_unique(); - _weights_reshape_kernel->configure(weights, biases, &_weights_reshaped); - _mm_kernel = std::make_unique(); - _mm_kernel->configure(&_input_im2col_reshaped, &_weights_reshaped, &_gemm_output); - _output_col2im.configure(&_gemm_output, output, Size2D(conv_w, conv_h)); - - // Allocate intermediate tensors - _input_im2col_reshaped.allocator()->allocate(); - _gemm_output.allocator()->allocate(); -} - -void NELocallyConnectedLayer::run() -{ - prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Run input reshaping - _input_im2col.run(); - - // Runs GEMM on reshaped matrices - NEScheduler::get().schedule(_mm_kernel.get(), Window::DimX); - - // Reshape output matrix - _output_col2im.run(); -} - -void NELocallyConnectedLayer::prepare() -{ - if(!_is_prepared) - { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - - // Run weights reshaping and mark original weights tensor as unused - _weights_reshaped.allocator()->allocate(); - NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3); - _original_weights->mark_as_unused(); - - _is_prepared = true; - } -} -} // namespace arm_compute -- cgit v1.2.1