COMPMID-471 Implement Deconvolution on OpenCL
Change-Id: Ie00c6b08a51d30c5ce2637d40ee3d165b8a68686
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110311
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
new file mode 100644
index 0000000..1c55722
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2017, 2018 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/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 <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(),
+ _scaled_output()
+{
+}
+
+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)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
+
+ 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(0), input->dimension(1), weights->dimension(0), weights->dimension(1),
+ info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
+
+ const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias);
+
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
+ }
+
+ 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.");
+
+ TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
+ info)));
+ 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(CLDirectConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info));
+
+ return Status{};
+}
+
+void CLDeconvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+ unsigned int inner_border_right, unsigned int inner_border_top)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+
+ auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
+ info.pad().first, info.pad().second, inner_border_top, inner_border_right, stride_x, stride_y);
+
+ const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
+
+ // Output auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ // 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));
+
+ _memory_group.manage(&_scaled_output);
+
+ // configure scale function
+ // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
+ TensorShape scale_out_shape(input->info()->tensor_shape());
+ const unsigned int out_x = input->info()->dimension(0) + (input->info()->dimension(0) - 1) * (stride_x - 1) + inner_border_right + 2 * info.pad().first;
+ const unsigned int out_y = input->info()->dimension(1) + (input->info()->dimension(1) - 1) * (stride_y - 1) + inner_border_top + 2 * info.pad().second;
+ scale_out_shape.set(0, out_x);
+ scale_out_shape.set(1, out_y);
+ TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ _scaled_output.allocator()->init(scale_out_info);
+
+ _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), 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, bias, output, conv_info);
+ _scaled_output.allocator()->allocate();
+}
+
+void CLDeconvolutionLayer::run()
+{
+ _memory_group.acquire();
+ _scale_f.run();
+ _conv_f.run();
+ _memory_group.release();
+}
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
new file mode 100644
index 0000000..13a24f8
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2017, 2018 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/CLDeconvolutionLayerUpsample.h"
+
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <cmath>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+
+CLDeconvolutionLayerUpsample::CLDeconvolutionLayerUpsample() // NOLINT
+ : _upsample(),
+ _output(nullptr)
+{
+}
+
+Status CLDeconvolutionLayerUpsample::validate(const ITensorInfo *input, const ITensorInfo *output, const BorderSize &inner_border,
+ const PadStrideInfo &info)
+{
+ return CLDeconvolutionLayerUpsampleKernel::validate(input, output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::configure(ICLTensor *input, ICLTensor *output, const BorderSize &inner_border,
+ const PadStrideInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+ _output = output;
+ _upsample.configure(input, _output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::run()
+{
+ _output->map(CLScheduler::get().queue(), true);
+ memset(_output->buffer(), 0, _output->info()->total_size());
+ _output->unmap(CLScheduler::get().queue());
+
+ CLScheduler::get().enqueue(_upsample, false);
+}
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
index 7b4e77b..c4bca11 100644
--- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017, 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,39 +24,42 @@
#include "arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h"
#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
- _scale_f(),
_conv_f(),
- _scaled_output()
+ _scaled_output(),
+ _input(nullptr),
+ _info(),
+ _inner_border()
{
}
void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
- unsigned int ax, unsigned int ay, float upscalex, float upscaley)
+ unsigned int inner_border_right, unsigned int inner_border_top)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) < 1);
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5);
- auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
- info.pad().first, info.pad().second, ax, ay, upscalex, upscaley, info.round());
+ _input = input;
+ _info = info;
+ _inner_border = std::make_pair(inner_border_right, inner_border_top);
+
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+ auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
+ info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
- // Output auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
-
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias);
-
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
@@ -64,51 +67,51 @@
_memory_group.manage(&_scaled_output);
// configure scale function
- //Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
- TensorShape scale_out_shape(input->info()->tensor_shape());
- scale_out_shape.set(0, output->info()->dimension(0));
- scale_out_shape.set(1, output->info()->dimension(1));
- TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
+ const TensorInfo scale_out_info(compute_deconvolution_shape(*input->info(), stride_x, stride_y, inner_border_right, inner_border_top, info), 1, input->info()->data_type(),
+ input->info()->fixed_point_position());
_scaled_output.allocator()->init(scale_out_info);
- const unsigned int kernel_size = weights->info()->dimension(0);
- // Padding for the upsampled image is calculated with the equiation: p' = k - p - 1, where k is kernel size and p is the input padding
- ARM_COMPUTE_ERROR_ON(info.pad().first > (kernel_size - 1));
- const unsigned int tr_px = kernel_size - info.pad().first - 1;
- const unsigned int tr_py = kernel_size - info.pad().second - 1;
- const unsigned int tr_stride = 1;
- const PadStrideInfo transposed_info(tr_stride, tr_stride, tr_px, tr_py);
- _scale_f.configure(input, &_scaled_output, std::make_pair(ax, ay), std::make_pair(info.stride().first - 1u, info.stride().second - 1u), transposed_info);
+
// setup the function to convolve the upscaled output
- switch(kernel_size)
- {
- case 1:
- {
- _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL));
- break;
- }
- case 3:
- {
- _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL));
- break;
- }
- case 5:
- {
- _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 2, 2, DimensionRoundingType::CEIL));
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Not supported");
- break;
- }
- }
+ const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+ _conv_f.configure(&_scaled_output, weights, bias, output, conv_info);
_scaled_output.allocator()->allocate();
}
void NEDeconvolutionLayer::run()
{
_memory_group.acquire();
- _scale_f.run();
+
+ // Initialize _scaled_output buffer
+ const int width_in = _input->info()->dimension(0);
+ const int height_in = _input->info()->dimension(1);
+ const int width_scaled = _scaled_output.info()->dimension(0);
+ const int height_scaled = _scaled_output.info()->dimension(1);
+ const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in);
+ const int stride_x = _info.stride().first;
+ const int stride_y = _info.stride().second;
+
+ std::fill_n(reinterpret_cast<float *>(_scaled_output.buffer()), _scaled_output.info()->tensor_shape().total_size(), 0.f);
+
+ // scaled_output is the input for the forward convolution. We copy the input elements to scaled_output
+ // and insert rows and columns with zeroes depending on the stride values.
+ for(int slice = 0; slice < num_2d_slices; ++slice)
+ {
+ const int start_x = _info.pad().first;
+ const int start_y = _inner_border.second + _info.pad().second;
+ const int end_y = height_scaled - _info.pad().second;
+ const int end_x = width_scaled - _inner_border.first - _info.pad().first;
+
+ for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
+ {
+ for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++)
+ {
+ const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice))));
+ *(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in;
+ }
+ }
+ }
+
_conv_f.run();
_memory_group.release();
}
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp
deleted file mode 100644
index 63f17bc..0000000
--- a/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (c) 2016, 2017 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/NEDeconvolutionLayerUpsample.h"
-
-#include "arm_compute/core/Coordinates.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.h"
-#include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "arm_compute/runtime/TensorAllocator.h"
-#include "support/ToolchainSupport.h"
-
-#include <cmath>
-#include <cstddef>
-#include <utility>
-
-using namespace arm_compute;
-
-namespace
-{
-inline void precompute_offsets(ITensor *offsets, float wr, size_t input_element_size, const std::pair<unsigned int, unsigned int> &a,
- const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
-{
- ARM_COMPUTE_ERROR_ON(nullptr == offsets);
- Window win;
- const int padx = info.pad().first;
- const int pady = info.pad().second;
- const int ax = a.first;
- const int ay = a.second;
- const int offset_width = offsets->info()->dimension(0);
- const int offset_height = offsets->info()->dimension(1);
- // The values of ax and ay denote the number of ZEROS to be added on the top and right inner border of the image.
- // Step value along the XY axis will depend on the number of zeros to be inserted between samples (number of zeros + 1).
- // Pre-compute the X offset, Y's stride is unknown at this point so we can't precompute Y's offsets
- for(int yi = ay; yi < (offset_height - pady); yi += (1 + iz.second))
- {
- for(int xi = padx; xi < (offset_width - ax); xi += (1 + iz.first))
- {
- int *ptr = reinterpret_cast<int *>(offsets->ptr_to_element(Coordinates(xi, yi)));
- const size_t in_xi = (xi + 0.5f) * wr;
- *reinterpret_cast<int32_t *>(ptr) = in_xi * input_element_size;
- }
- }
-}
-} // namespace
-
-NEDeconvolutionLayerUpsample::NEDeconvolutionLayerUpsample(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
- : _memory_group(std::move(memory_manager)),
- _offsets(),
- _border_handler(),
- _upsample()
-{
-}
-
-void NEDeconvolutionLayerUpsample::configure(ITensor *input, ITensor *output, const std::pair<unsigned int, unsigned int> &a,
- const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
-{
- ARM_COMPUTE_ERROR_ON(nullptr == input);
- ARM_COMPUTE_ERROR_ON(nullptr == output);
-
- for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
- {
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
- }
-
- // Get the tensor shape
- const TensorShape shape(output->info()->dimension(0), output->info()->dimension(1));
-
- // Compute the ratio between source width/height and destination width/height
- const auto wr = static_cast<float>(input->info()->dimension(0)) / static_cast<float>(output->info()->dimension(0));
- const auto hr = static_cast<float>(input->info()->dimension(1)) / static_cast<float>(output->info()->dimension(1));
- ARM_COMPUTE_UNUSED(hr);
- // Get the element size of the input image
- const size_t input_element_size = input->info()->element_size();
-
- TensorInfo tensor_info_offsets(shape, Format::S32);
- _offsets.allocator()->init(tensor_info_offsets);
-
- _upsample.configure(input, &_offsets, output);
-
- // Allocate once the configure methods have been called
- _offsets.allocator()->allocate();
- // Pre-compute offsets for nearest interpolation
- std::fill_n(reinterpret_cast<int32_t *>(_offsets.buffer()), _offsets.info()->total_size() / sizeof(int32_t), -1 * input_element_size);
- precompute_offsets(&_offsets, wr, input_element_size, a, iz, info);
-
- _border_handler.configure(input, _upsample.border_size(), BorderMode::CONSTANT, PixelValue(0));
-}
-
-void NEDeconvolutionLayerUpsample::run()
-{
- NEScheduler::get().schedule(&_border_handler, Window::DimZ);
- _memory_group.acquire();
- NEScheduler::get().schedule(&_upsample, Window::DimY);
- _memory_group.release();
-}