| /* |
| * Copyright (c) 2017-2019 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" |
| #include "arm_compute/runtime/NEON/NEScheduler.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(), |
| _upsample_f(), |
| _flip_weights(), |
| _scaled_output(), |
| _weights_flipped(), |
| _original_weights(nullptr), |
| _input(nullptr), |
| _info(), |
| _inner_border(), |
| _is_prepared(false) |
| { |
| } |
| |
| Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info, |
| unsigned int inner_border_right, unsigned int inner_border_top) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1)); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric()); |
| |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y"); |
| |
| auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->dimension(1), |
| info.pad().first, info.pad().second, stride_x, stride_y); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| |
| if(bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); |
| } |
| |
| if(output->tensor_shape().total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| |
| const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid."); |
| } |
| |
| unsigned int padx = 0; |
| unsigned int pady = 0; |
| const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady); |
| TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape)); |
| const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); |
| |
| for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(i) != scale_out_info.dimension(i)); |
| } |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, WeightsInfo())); |
| |
| return Status{}; |
| } |
| |
| 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(input, weights, output); |
| |
| _input = input; |
| _original_weights = weights; |
| _info = info; |
| _inner_border = std::make_pair(inner_border_right, inner_border_top); |
| _is_prepared = false; |
| |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| |
| _weights_flipped.allocator()->init(TensorInfo(weights->info()->tensor_shape(), 1, weights->info()->data_type())); |
| _flip_weights.configure(weights, &_weights_flipped); |
| |
| 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, stride_x, stride_y); |
| |
| const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info()); |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info()); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(NEDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top)); |
| |
| _memory_group.manage(&_scaled_output); |
| |
| // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape |
| unsigned int padx = 0; |
| unsigned int pady = 0; |
| const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady); |
| |
| TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info()); |
| _scaled_output.allocator()->init(scale_out_info); |
| |
| const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2); |
| _upsample_f.configure(input, &_scaled_output, upsample_info, inner_border_right, inner_border_top); |
| |
| // 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_flipped, bias, output, conv_info); |
| _scaled_output.allocator()->allocate(); |
| } |
| Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info) |
| { |
| return NEDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0); |
| } |
| |
| void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info) |
| { |
| configure(input, weights, bias, output, info, 0, 0); |
| } |
| |
| void NEDeconvolutionLayer::run() |
| { |
| prepare(); |
| |
| _memory_group.acquire(); |
| |
| _upsample_f.run(); |
| _conv_f.run(); |
| |
| _memory_group.release(); |
| } |
| |
| void NEDeconvolutionLayer::prepare() |
| { |
| if(!_is_prepared) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| // Run weights flipping and mark original weights tensor as unused |
| _weights_flipped.allocator()->allocate(); |
| NEScheduler::get().schedule(&_flip_weights, Window::DimZ); |
| _original_weights->mark_as_unused(); |
| |
| // Prepare convolution |
| _conv_f.prepare(); |
| |
| if(!_weights_flipped.is_used()) |
| { |
| _weights_flipped.allocator()->free(); |
| } |
| |
| _is_prepared = true; |
| } |
| } |