| /* |
| * Copyright (c) 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/CL/functions/CLWinogradConvolutionLayer.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| |
| using namespace arm_compute; |
| |
| CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _is_first_run(true) |
| { |
| } |
| |
| void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) |
| { |
| // TODO(COMPMID-1013): This part will be removed |
| // Get indeces for the width and height |
| const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); |
| |
| // Kernel size |
| const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; |
| const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; |
| |
| // Number of tiles along the X and Y direction |
| const unsigned int num_tiles_x = std::ceil((input->info()->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); |
| const unsigned int num_tiles_y = std::ceil((input->info()->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); |
| |
| // Compute output shape |
| const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info); |
| |
| // Manage intermediate tensors |
| _memory_group.manage(&_input0); |
| _memory_group.manage(&_batched_mm_output); |
| |
| // Do not manage _input1 as it contains the weights |
| |
| // Configure input transform |
| _input_transform.configure(input, &_input0, conv_info, Size2D(kernel_w, kernel_h)); |
| |
| // Configure filter transform |
| _filter_transform.configure(weights, &_input1, Size2D(2U, 2U)); |
| |
| // Configure batched matrix multiply |
| _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); |
| |
| // Configure output transform |
| _output_transform.configure(&_batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], output_convolved_shape[idx_height]), Size2D(num_tiles_x, |
| num_tiles_y)); |
| |
| // Allocate temporary tensors |
| _input0.allocator()->allocate(); |
| _input1.allocator()->allocate(); |
| _batched_mm_output.allocator()->allocate(); |
| } |
| |
| Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| // TODO(COMPMID-1013): This part will be removed |
| // Get indeces for the width and height |
| const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| |
| // Kernel size |
| const unsigned int kernel_w = weights->tensor_shape()[idx_width]; |
| const unsigned int kernel_h = weights->tensor_shape()[idx_height]; |
| |
| // Number of tiles along the X and Y direction |
| const unsigned int num_tiles_x = std::ceil((input->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); |
| const unsigned int num_tiles_y = std::ceil((input->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); |
| |
| // Compute output shape |
| const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); |
| |
| // Validate input transform |
| const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, Size2D(kernel_w, kernel_h)); |
| const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h))); |
| |
| // Validate filter transform |
| const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, Size2D(2U, 2U)); |
| const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, Size2D(2U, 2U))); |
| |
| // Configure batched matrix multiply |
| TensorShape batched_mm_output_shape = input0.tensor_shape(); |
| batched_mm_output_shape[0] = input1.tensor_shape()[0]; |
| const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/))); |
| |
| // Configure output transform |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], |
| output_convolved_shape[idx_height]), |
| Size2D(num_tiles_x, num_tiles_y))); |
| |
| return Status{}; |
| } |
| |
| void CLWinogradConvolutionLayer::run() |
| { |
| if(_is_first_run) |
| { |
| // Run filter transform |
| CLScheduler::get().enqueue(_filter_transform, false); |
| |
| _is_first_run = false; |
| } |
| |
| _memory_group.acquire(); |
| |
| // Run input transform |
| _input_transform.run(); |
| |
| // Run batched matrix multiplication |
| _batched_mm.run(); |
| |
| // Run output transform |
| CLScheduler::get().enqueue(_output_transform); |
| |
| _memory_group.release(); |
| } |