f4334277
 Hu Chunming
提交3rdparty
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_PADDING_HPP
#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_PADDING_HPP
#include "../../op_cuda.hpp"
#include "../csl/stream.hpp"
#include "../csl/tensor.hpp"
#include "../kernels/fill_copy.hpp"
#include "../kernels/concat.hpp"
#include "../kernels/padding.hpp"
#include <opencv2/core.hpp>
#include <cstddef>
#include <vector>
#include <algorithm>
#include <utility>
namespace cv { namespace dnn { namespace cuda4dnn {
enum class PaddingType {
CONSTANT,
REFLECTION101
};
template <class T>
class PaddingOp final : public CUDABackendNode {
public:
using wrapper_type = GetCUDABackendWrapperType<T>;
/* `ranges` is indexed by axis and contains the range in the output where the input is copied to */
PaddingOp(csl::Stream stream_, PaddingType type_, T value_, std::vector<cv::Range> ranges)
: stream(std::move(stream_)), type{ type_ }, value{ value_ }, dstRanges(std::move(ranges))
{
}
void forward(
const std::vector<cv::Ptr<BackendWrapper>>& inputs,
const std::vector<cv::Ptr<BackendWrapper>>& outputs,
csl::Workspace& workspace) override
{
CV_Assert(inputs.size() == 1 && outputs.size() == 1);
auto input_wrapper = inputs[0].dynamicCast<wrapper_type>();
auto input = input_wrapper->getView();
auto output_wrapper = outputs[0].dynamicCast<wrapper_type>();
auto output = output_wrapper->getSpan();
/* suppose we require padding for the first spatial axis (H in NCHW or D in NCDHW)
*
* there could be a case where the batch axis, channel axis, and the first spatial axis are all one
* this would result in effective rank being less than the number of axes requiring padding
*/
/* the effective rank of the input may be smaller than the effective rank of the output but the converse is never true
* input: [1, 1, 1, 3]; effective rank = 1
* output: [1, 1, 3, 3]; effective rank = 2
*
* hence, we use the effective rank of the output tensor for the padding operation
*/
auto effective_rank = get_effective_rank(output);
CV_Assert(get_effective_rank(input) <= effective_rank);
effective_rank = std::max(effective_rank, dstRanges.size());
for (int i = effective_rank - dstRanges.size(); i < effective_rank; i++)
{
if (dstRanges[i] == Range::all())
CV_Assert(input.get_axis_size(i) == output.get_axis_size(i));
else
CV_Assert(input.get_axis_size(i) == dstRanges[i].size());
}
if (type == PaddingType::CONSTANT)
{
kernels::fill<T>(stream, output, value);
std::vector<std::size_t> offsets(effective_rank, 0);
for (int i = 0; i < dstRanges.size(); i++)
{
const auto delta = effective_rank - dstRanges.size();
if (dstRanges[i] != Range::all())
offsets[delta + i] = dstRanges[i].start;
}
kernels::concat_with_offsets<T>(stream, output, input, offsets);
}
else if (type == PaddingType::REFLECTION101)
{
std::vector<std::pair<std::size_t, std::size_t>> ranges(effective_rank);
for (int i = 0; i < effective_rank; i++)
{
const auto delta = effective_rank - dstRanges.size();
if (i < delta || dstRanges[i - delta] == Range::all())
ranges[i] = { 0, input.get_axis_size(i) };
else
ranges[i] = { dstRanges[i].start, dstRanges[i].end };
}
kernels::copy_with_reflection101<T>(stream, output, input, ranges);
}
}
private:
csl::Stream stream;
PaddingType type;
T value;
std::vector<cv::Range> dstRanges;
};
}}} /* namespace cv::dnn::cuda4dnn */
#endif /* OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_PADDING_HPP */
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