| /* |
| * Copyright (c) 2017-2021 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" |
| #include "src/common/utils/Log.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| PadStrideInfo compute_upsample_info(const PadStrideInfo &info, uint32_t deconv_pad_x, uint32_t deconv_pad_y) |
| { |
| const unsigned int pad_left = info.pad_left(); |
| const unsigned int pad_right = info.pad_right(); |
| const unsigned int pad_top = info.pad_top(); |
| const unsigned int pad_bottom = info.pad_bottom(); |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| |
| // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape |
| unsigned int deconv_pad_left = pad_right > pad_left ? pad_right - pad_left : 0; |
| unsigned int deconv_pad_right = pad_left > pad_right ? pad_left - pad_right : 0; |
| deconv_pad_x -= deconv_pad_left + deconv_pad_right; |
| ARM_COMPUTE_ERROR_ON((deconv_pad_x % 2) != 0); |
| deconv_pad_left += deconv_pad_x / 2; |
| deconv_pad_right += deconv_pad_x / 2; |
| |
| unsigned int deconv_pad_top = pad_bottom > pad_top ? pad_bottom - pad_top : 0; |
| unsigned int deconv_pad_bottom = pad_top > pad_bottom ? pad_top - pad_bottom : 0; |
| deconv_pad_y -= deconv_pad_top + deconv_pad_bottom; |
| ARM_COMPUTE_ERROR_ON((deconv_pad_y % 2) != 0); |
| deconv_pad_top += deconv_pad_y / 2; |
| deconv_pad_bottom += deconv_pad_y / 2; |
| |
| return PadStrideInfo(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, deconv_pad_bottom, DimensionRoundingType::FLOOR); |
| } |
| |
| } // namespace |
| |
| 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(), |
| _flip_axis(), |
| _original_weights(nullptr), |
| _input(nullptr), |
| _info(), |
| _is_prepared(false) |
| { |
| } |
| |
| Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| const unsigned int width_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != weights->dimension(height_idx)); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) < 1); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(weights, input); |
| if(is_data_type_quantized_per_channel(weights->data_type()) && is_data_type_quantized(input->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| } |
| |
| auto out_dims = deconvolution_output_dimensions(input->dimension(width_idx), input->dimension(height_idx), weights->dimension(width_idx), weights->dimension(height_idx), info); |
| |
| if(bias != nullptr) |
| { |
| if(is_data_type_quantized_asymmetric(input->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); |
| } |
| else |
| { |
| 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."); |
| } |
| |
| uint32_t deconv_pad_x = 0; |
| uint32_t deconv_pad_y = 0; |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| // Guard against overflows in compute_deconvolution_upsampled_shape() |
| const DataLayout data_layout = input->data_layout(); |
| const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int out_x = (input->dimension(idx_w) - 1) * stride_x + 1; |
| const unsigned int out_y = (input->dimension(idx_h) - 1) * stride_y + 1; |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) > out_x); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) > out_y); |
| ARM_COMPUTE_RETURN_ERROR_ON((out_x - weights->dimension(idx_w) + 1) > out_dims.first); |
| ARM_COMPUTE_RETURN_ERROR_ON((out_y - weights->dimension(idx_h) + 1) > out_dims.second); |
| |
| const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, out_dims, deconv_pad_x, deconv_pad_y); |
| 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); |
| |
| const unsigned int batches_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES); |
| const unsigned int channel_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(batches_idx) != scale_out_info.dimension(batches_idx)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(channel_idx) != scale_out_info.dimension(channel_idx)); |
| |
| 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) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(NEDeconvolutionLayer::validate(input->info(), weights->info(), (bias == nullptr) ? nullptr : bias->info(), output->info(), info)); |
| ARM_COMPUTE_LOG_PARAMS(input, weights, bias, output, info); |
| |
| const DataLayout data_layout = input->info()->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| auto out_dims = deconvolution_output_dimensions(input->info()->dimension(width_idx), input->info()->dimension(height_idx), |
| weights->info()->dimension(width_idx), weights->info()->dimension(height_idx), info); |
| |
| const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info()); |
| |
| _input = input; |
| _original_weights = weights; |
| _info = info; |
| _is_prepared = false; |
| |
| const unsigned int stride_x = info.stride().first; |
| const unsigned int stride_y = info.stride().second; |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info()); |
| |
| _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32)); |
| _memory_group.manage(&_scaled_output); |
| |
| _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout)); |
| _flip_weights.configure(weights, &_weights_flipped, &_flip_axis); |
| |
| // setup the function to convolve the upscaled output |
| const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); |
| uint32_t deconv_pad_x = 0; |
| uint32_t deconv_pad_y = 0; |
| |
| const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), |
| stride_x, stride_y, |
| out_dims, deconv_pad_x, deconv_pad_y); |
| |
| const PadStrideInfo upsample_info = compute_upsample_info(info, deconv_pad_x, deconv_pad_y); |
| |
| TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info()); |
| scale_out_info.set_data_layout(data_layout); |
| _scaled_output.allocator()->init(scale_out_info); |
| |
| _upsample_f.configure(input, &_scaled_output, upsample_info); |
| |
| _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info); |
| |
| // Setup flip axis data |
| _flip_axis.allocator()->allocate(); |
| auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer()); |
| axis_data[0] = static_cast<uint32_t>(width_idx); |
| axis_data[1] = static_cast<uint32_t>(height_idx); |
| |
| _scaled_output.allocator()->allocate(); |
| } |
| |
| void NEDeconvolutionLayer::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| _upsample_f.run(); |
| _conv_f.run(); |
| } |
| |
| 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(); |
| _flip_weights.run(); |
| _original_weights->mark_as_unused(); |
| |
| // Prepare convolution |
| _conv_f.prepare(); |
| |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |
