From 4a8ec803747780c97a444ca3df4bdeaa8c10190b Mon Sep 17 00:00:00 2001 From: giuros01 Date: Mon, 18 Mar 2019 13:25:05 +0000 Subject: Optimize CL DeconvolutionLayer-Part II: Add CLDirectDeconvolution function to be used by CLDeconvolution. This is only a code refactoring (no optimizations have been added) Change-Id: I78488f4aecfe1cce93c31dba31489dcee4c85c67 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/895 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Gian Marco Iodice --- .../CL/functions/CLDirectDeconvolutionLayer.cpp | 198 +++++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp (limited to 'src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp new file mode 100644 index 0000000000..c01588a164 --- /dev/null +++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp @@ -0,0 +1,198 @@ +/* + * Copyright (c) 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/CL/functions/CLDirectDeconvolutionLayer.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/CL/CLScheduler.h" +#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include "utils/TypePrinter.h" + +#include +#include + +namespace arm_compute +{ +using namespace arm_compute::misc::shape_calculator; + +CLDirectDeconvolutionLayer::CLDirectDeconvolutionLayer(std::shared_ptr memory_manager) // NOLINT + : _memory_group(std::move(memory_manager)), + _scale_f(), + _conv_f(), + _flip_weights(), + _scaled_output(), + _original_weights(nullptr), + _weights_flipped(), + _is_prepared(false) +{ +} + +Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, + const WeightsInfo &weights_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); + + 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 size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 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; + + auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), + info.pad().first, info.pad().second, stride_x, stride_y); + + const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); + + 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); + } + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "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, 0, 0, out_dims, padx, pady); + TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout)); + const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); + + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(), info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); + + return Status{}; +} + +void CLDirectDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, + const WeightsInfo &weights_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + + const unsigned int stride_x = info.stride().first; + const unsigned int stride_y = info.stride().second; + + const DataLayout data_layout = input->info()->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); + + _original_weights = weights; + _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout)); + _flip_weights.configure(weights, &_weights_flipped); + + auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), + 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(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout)); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(CLDirectDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info)); + + _is_prepared = weights_info.retain_internal_weights(); + + _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, 0, 0, out_dims, padx, pady); + + 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); + + // configure scale function + const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2); + _scale_f.configure(input, &_scaled_output, BorderSize(), upsample_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_flipped, bias, output, conv_info, weights_info); + _scaled_output.allocator()->allocate(); +} + +void CLDirectDeconvolutionLayer::run() +{ + prepare(); + + _memory_group.acquire(); + + _scale_f.run(); + _conv_f.run(); + + _memory_group.release(); +} + +void CLDirectDeconvolutionLayer::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(); + _weights_flipped.map(true); + _original_weights->map(CLScheduler::get().queue(), true); + CPPScheduler::get().schedule(&_flip_weights, Window::DimZ); + _weights_flipped.unmap(); + _original_weights->unmap(CLScheduler::get().queue()); + _original_weights->mark_as_unused(); + + // Prepare convolution + _conv_f.prepare(); + + if(!_weights_flipped.is_used()) + { + _weights_flipped.allocator()->free(); + } + + _is_prepared = true; + } +} +} // namespace arm_compute -- cgit v1.2.1