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/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();
}