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 --- src/runtime/CL/functions/CLDeconvolutionLayer.cpp | 172 +++------------------- 1 file changed, 23 insertions(+), 149 deletions(-) (limited to 'src/runtime/CL/functions/CLDeconvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp index 9da02c10ad..2c17473fc7 100644 --- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp @@ -23,188 +23,62 @@ */ #include "arm_compute/runtime/CL/functions/CLDeconvolutionLayer.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/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include #include #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; -CLDeconvolutionLayer::CLDeconvolutionLayer(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) +CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr memory_manager) + : _memory_manager(std::move(memory_manager)), _function() { } -Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, - unsigned int inner_border_right, unsigned int inner_border_top, 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; - - 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(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, 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).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(inner_border_right, inner_border_top), info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); - - return Status{}; -} - -void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, +void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info) { + ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top); ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + auto f = arm_compute::support::cpp14::make_unique(); + f->configure(input, weights, bias, output, deconv_info, weights_info); + _function = std::move(f); +} - 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(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top)); - - _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, 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()); - 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(inner_border_top, inner_border_right), 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(); +Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, + unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top); + ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info)); + return Status{}; } -void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, +void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, const WeightsInfo &weights_info) { - configure(input, weights, bias, output, info, 0, 0, weights_info); + configure(input, weights, bias, output, deconv_info, 0, 0, weights_info); } -Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, +Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, const WeightsInfo &weights_info) { - return CLDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0, weights_info); + return CLDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, 0, 0, weights_info); } void CLDeconvolutionLayer::run() { prepare(); - - _memory_group.acquire(); - - _scale_f.run(); - _conv_f.run(); - - _memory_group.release(); + _function->run(); } void CLDeconvolutionLayer::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; - } + _function->prepare(); } -- cgit v1.2.1