f4334277
 Hu Chunming
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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_CUDA4DNN_CSL_CUDNN_POOLING_HPP
#define OPENCV_DNN_CUDA4DNN_CSL_CUDNN_POOLING_HPP
#include "cudnn.hpp"
#include "../pointer.hpp"
#include <opencv2/core.hpp>
#include <cudnn.h>
#include <cstddef>
#include <array>
#include <algorithm>
#include <vector>
#include <type_traits>
#include <iterator>
namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cudnn {
class PoolingDescriptor {
public:
enum class PoolingType {
MAX,
MAX_DETERMINISTIC,
AVERAGE_EXCLUDE_PADDING,
AVERAGE_INCLUDE_PADDING
};
PoolingDescriptor() noexcept : descriptor{ nullptr } { }
PoolingDescriptor(const PoolingDescriptor&) = delete;
PoolingDescriptor(PoolingDescriptor&& other) noexcept
: descriptor{ other.descriptor } {
other.descriptor = nullptr;
}
/** constructs a pooling descriptor
*
* Pre-conditions:
* - \p window_size, \p padding and \p stride must have the same size
*
* The length of the containers is interpreted as the order of the pooling operation.
*
* Exception Guarantee: Basic
*/
template <class SequenceContainer, typename = decltype(std::begin(std::declval<SequenceContainer>()))>
PoolingDescriptor(
const SequenceContainer& window_size,
const SequenceContainer& padding,
const SequenceContainer& stride,
PoolingType type)
{
constructor(window_size, padding, stride, type);
}
~PoolingDescriptor() noexcept {
if (descriptor != nullptr) {
/* cudnnDestroyPoolingDescriptor will not fail for a valid descriptor */
CUDA4DNN_CHECK_CUDNN(cudnnDestroyPoolingDescriptor(descriptor));
}
}
PoolingDescriptor& operator=(const PoolingDescriptor&) = delete;
PoolingDescriptor& operator=(PoolingDescriptor&& other) noexcept {
descriptor = other.descriptor;
other.descriptor = nullptr;
return *this;
};
cudnnPoolingDescriptor_t get() const noexcept { return descriptor; }
private:
template <class SequenceContainer>
void constructor(
const SequenceContainer& window_size,
const SequenceContainer& padding,
const SequenceContainer& stride,
PoolingType type)
{
CV_Assert(window_size.size() == padding.size());
CV_Assert(window_size.size() == stride.size());
auto get_pooling_type = [] (PoolingType type) {
switch (type) {
case PoolingType::MAX:
return CUDNN_POOLING_MAX;
case PoolingType::MAX_DETERMINISTIC:
return CUDNN_POOLING_MAX_DETERMINISTIC;
case PoolingType::AVERAGE_EXCLUDE_PADDING:
return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
case PoolingType::AVERAGE_INCLUDE_PADDING:
return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
}
CV_Error(Error::StsBadArg, "unknown pooling type");
};
CUDA4DNN_CHECK_CUDNN(cudnnCreatePoolingDescriptor(&descriptor));
try {
const auto rank = window_size.size();
if (rank == 2) {
CUDA4DNN_CHECK_CUDNN(
cudnnSetPooling2dDescriptor(
descriptor,
get_pooling_type(type), CUDNN_PROPAGATE_NAN,
window_size[0], window_size[1],
padding[0], padding[1],
stride[0], stride[1]
)
);
} else {
std::vector<int> iwindow_size(std::begin(window_size), std::end(window_size));
std::vector<int> ipadding(std::begin(padding), std::end(padding));
std::vector<int> istride(std::begin(stride), std::end(stride));
CUDA4DNN_CHECK_CUDNN(
cudnnSetPoolingNdDescriptor(
descriptor,
get_pooling_type(type), CUDNN_PROPAGATE_NAN,
rank, iwindow_size.data(), ipadding.data(), istride.data()
)
);
}
} catch (...) {
/* cudnnDestroyPoolingDescriptor will not fail for a valid descriptor */
CUDA4DNN_CHECK_CUDNN(cudnnDestroyPoolingDescriptor(descriptor));
throw;
}
}
cudnnPoolingDescriptor_t descriptor;
};
/** gives the shape of the output tensor after pooling
*
* @note it's not required to enforce the this shape in the output tensor; slightly different shapes will work
*
* Exception Guarantee: Basic
*/
template <class T> inline
void getPoolingForwardOutputDim(
const PoolingDescriptor& poolingDesc,
const TensorDescriptor<T>& inputDesc,
std::vector<int>& output_dim)
{
output_dim.clear();
output_dim.resize(CUDNN_DIM_MAX); /* we use `output_dim` to hold temporaries */
std::vector<int> temp(CUDNN_DIM_MAX);
cudnnDataType_t tempDataType;
CUDA4DNN_CHECK_CUDNN(
cudnnGetTensorNdDescriptor(
inputDesc.get(),
CUDNN_DIM_MAX + 1, /* according to docs, this is what we do to get the rank */
&tempDataType,
output_dim.data(),
temp.data(),
temp.data()
)
);
const auto rank = output_dim[0];
output_dim.resize(rank);
CUDA4DNN_CHECK_CUDNN(
cudnnGetPoolingNdForwardOutputDim(poolingDesc.get(), inputDesc.get(), rank, output_dim.data())
);
}
/** @brief performs pooling operation
*
* dstValue = alpha * result + beta * priorDstValue
*
* @tparam T pooling element type (must be `half` or `float`)
*
* @param handle valid cuDNN Handle
* @param poolingDesc pooling description
* @param inputDesc tensor descriptor describing the input
* @param[in] inputPtr pointer to input tensor in device memory
* @param alpha result scale factor
* @param beta previous value scale factor
* @param outputDesc tensor descriptor describing the output
* @param[out] outputPtr pointer to output tensor in device memory
*
* Exception Guarantee: Basic
*/
template <class T>
void pool(
const Handle& handle,
const PoolingDescriptor& poolingDesc,
const TensorDescriptor<T>& inputDesc,
const DevicePtr<const T> inputPtr,
T alpha, T beta,
const TensorDescriptor<T>& outputDesc,
DevicePtr<T> outputPtr)
{
CV_Assert(handle);
CUDA4DNN_CHECK_CUDNN(
cudnnPoolingForward(
handle.get(),
poolingDesc.get(),
&alpha, inputDesc.get(), inputPtr.get(),
&beta, outputDesc.get(), outputPtr.get()
)
);
}
template <> inline
void pool(
const Handle& handle,
const PoolingDescriptor& poolingDesc,
const TensorDescriptor<half>& inputDesc,
const DevicePtr<const half> inputPtr,
half alpha, half beta,
const TensorDescriptor<half>& outputDesc,
DevicePtr<half> outputPtr)
{
CV_Assert(handle);
/* we specalize for fp16 as the scaling factors must be provided as `float` */
float alpha_ = alpha, beta_ = beta;
CUDA4DNN_CHECK_CUDNN(
cudnnPoolingForward(
handle.get(),
poolingDesc.get(),
&alpha_, inputDesc.get(), inputPtr.get(),
&beta_, outputDesc.get(), outputPtr.get()
)
);
}
}}}}} /* namespace cv::dnn::cuda4dnn::csl::cudnn */
#endif /* OPENCV_DNN_CUDA4DNN_CSL_CUDNN_POOLING_HPP */
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