arm_compute v19.05
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index 9da02c1..c6f79d3 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -23,188 +23,117 @@
*/
#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 <cmath>
#include <memory>
#include <tuple>
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
-CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> 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<IMemoryManager> 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,
+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_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
+
+ switch(CLDeconvolutionLayer::get_deconvolution_method(input->info(), weights->info(), nullptr, output->info(), deconv_info, weights_info))
+ {
+ case DeconvolutionMethod::DIRECT:
+ {
+ auto f = arm_compute::support::cpp14::make_unique<CLDirectDeconvolutionLayer>();
+ f->configure(input, weights, bias, output, deconv_info, weights_info);
+ _function = std::move(f);
+ break;
+ }
+ case DeconvolutionMethod::GEMM:
+ {
+ auto f = arm_compute::support::cpp14::make_unique<CLGEMMDeconvolutionLayer>(_memory_manager);
+ f->configure(input, weights, bias, output, deconv_info);
+ _function = std::move(f);
+ break;
+ }
+ default:
+ ARM_COMPUTE_ERROR("Not supported.");
+ break;
+ }
+}
+
+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_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);
+ ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
+
+ switch(CLDeconvolutionLayer::get_deconvolution_method(input, weights, bias, output, deconv_info, weights_info))
+ {
+ case DeconvolutionMethod::DIRECT:
+ {
+ // Validate direct convolution layer
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
+ break;
+ }
+ case DeconvolutionMethod::GEMM:
+ {
+ // Validate gemm-based convolution layer
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMDeconvolutionLayer::validate(input, weights, bias, output, deconv_info));
+ break;
+ }
+ default:
+ ARM_COMPUTE_ERROR("Not supported.");
+ break;
+ }
+
+ return Status{};
+}
+
+DeconvolutionMethod CLDeconvolutionLayer::get_deconvolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
+ const WeightsInfo &weights_info)
+{
+ ARM_COMPUTE_UNUSED(output, bias, weights_info);
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(weights->dimension(idx_w) != deconv_info.stride().first || weights->dimension(idx_h) != deconv_info.stride().second)
{
- 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);
+ return DeconvolutionMethod::DIRECT;
}
- 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{};
+ return DeconvolutionMethod::GEMM;
}
-void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
- unsigned int inner_border_right, unsigned int inner_border_top, 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(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();
-}
-
-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();
}