| /* |
| * 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/NEDeconvolutionLayer.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT |
| : _memory_group(std::move(memory_manager)), |
| _conv_f(), |
| _scaled_output(), |
| _input(nullptr), |
| _info(), |
| _inner_border() |
| { |
| } |
| |
| void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info, |
| unsigned int inner_border_right, unsigned int inner_border_top) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5); |
| |
| _input = input; |
| _info = info; |
| _inner_border = std::make_pair(inner_border_right, inner_border_top); |
| |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1), |
| info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y); |
| |
| const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape()); |
| |
| ARM_COMPUTE_UNUSED(output_shape); |
| ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid."); |
| ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid."); |
| ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid."); |
| |
| _memory_group.manage(&_scaled_output); |
| |
| // configure scale function |
| // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor |
| const TensorInfo scale_out_info(compute_deconvolution_shape(*input->info(), stride_x, stride_y, inner_border_right, inner_border_top, info), 1, input->info()->data_type(), |
| input->info()->fixed_point_position()); |
| _scaled_output.allocator()->init(scale_out_info); |
| |
| // setup the function to convolve the upscaled output |
| const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); |
| _conv_f.configure(&_scaled_output, weights, bias, output, conv_info); |
| _scaled_output.allocator()->allocate(); |
| } |
| |
| void NEDeconvolutionLayer::run() |
| { |
| _memory_group.acquire(); |
| |
| // Initialize _scaled_output buffer |
| const int width_in = _input->info()->dimension(0); |
| const int height_in = _input->info()->dimension(1); |
| const int width_scaled = _scaled_output.info()->dimension(0); |
| const int height_scaled = _scaled_output.info()->dimension(1); |
| const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in); |
| const int stride_x = _info.stride().first; |
| const int stride_y = _info.stride().second; |
| |
| std::fill_n(reinterpret_cast<float *>(_scaled_output.buffer()), _scaled_output.info()->tensor_shape().total_size(), 0.f); |
| |
| // scaled_output is the input for the forward convolution. We copy the input elements to scaled_output |
| // and insert rows and columns with zeroes depending on the stride values. |
| for(int slice = 0; slice < num_2d_slices; ++slice) |
| { |
| const int start_x = _info.pad().first; |
| const int start_y = _inner_border.second + _info.pad().second; |
| const int end_y = height_scaled - _info.pad().second; |
| const int end_x = width_scaled - _inner_border.first - _info.pad().first; |
| |
| for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++) |
| { |
| for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++) |
| { |
| const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice)))); |
| *(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in; |
| } |
| } |
| } |
| |
| _conv_f.run(); |
| _memory_group.release(); |
| } |