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 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/895
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
new file mode 100644
index 0000000..c01588a
--- /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 <memory>
+#include <tuple>
+
+namespace arm_compute
+{
+using namespace arm_compute::misc::shape_calculator;
+
+CLDirectDeconvolutionLayer::CLDirectDeconvolutionLayer(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)
+{
+}
+
+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