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/* Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
*
* 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.
*
* Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
* Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
* of the code.
*/
#ifndef INTERPOLATION_KERNEL
#define INTERPOLATION_KERNEL
#include "math_functions.hpp"
#include <limits>
namespace minkowski {
/**
* CPU pooling function. The p_out_feat must be initialized and set to 0.
* p_num_nonzero is set to 0 inside this function.
*
* TODO consistent memset
*/
template <typename Dtype, typename Wtype, typename Itype>
void InterpolationForwardKernelCPU(Dtype const *const p_in_feat,
Dtype *p_out_feat, //
uint32_t const nchannel, //
Itype const *const in_maps, //
Itype const *const out_maps, //
Wtype const *const weights, //
uint32_t const nnz) {
const Dtype *p_curr_in;
Dtype *p_curr_out;
// Set all values to - Dtype min
// std::fill(p_out_feat, p_out_feat + nnz * nchannel, 0);
// Iterate through each spatial kernel out of filter_volume spatial kernels
for (uint32_t i = 0; i < nnz; ++i) {
// Define current pointers
p_curr_in = p_in_feat + in_maps[i] * nchannel;
p_curr_out = p_out_feat + out_maps[i] * nchannel;
cpu_axpy<Dtype>(nchannel, (Dtype)weights[i], p_curr_in, p_curr_out);
}
}
template <typename Dtype, typename Wtype, typename Itype>
void InterpolationBackwardKernelCPU(Dtype *p_grad_in_feat,
uint32_t const in_nrows,
uint32_t const nchannel, //
Dtype const *const p_grad_out_feat,
Itype const *const in_maps, //
Itype const *const out_maps,
Wtype const *const weights,
uint32_t const nnz) {
Dtype *p_curr_grad_in;
Dtype const *p_curr_grad_out;
// cleanup gradients
// std::fill(p_grad_in_feat, p_grad_in_feat + in_nrows * nchannel, 0);
for (uint32_t i = 0; i < nnz; ++i) {
// Define current pointers
p_curr_grad_in = p_grad_in_feat + in_maps[i] * nchannel;
p_curr_grad_out = p_grad_out_feat + out_maps[i] * nchannel;
cpu_axpy<Dtype>(nchannel, (Dtype)weights[i], p_curr_grad_out,
p_curr_grad_in);
}
}
template void
InterpolationForwardKernelCPU<float, float, int>(float const *const p_in_feat,
float *p_out_feat, //
uint32_t const nchannel, //
int const *const in_maps, //
int const *const out_maps, //
float const *const weights, //
uint32_t const nnz);
template void
InterpolationForwardKernelCPU<double, float, int>(double const *const p_in_feat,
double *p_out_feat, //
uint32_t const nchannel, //
int const *const in_maps, //
int const *const out_maps, //
float const *const weights, //
uint32_t const nnz);
template void InterpolationBackwardKernelCPU<float, float, int>(
float *p_grad_in_feat, //
uint32_t const in_nrows, //
uint32_t const nchannel, //
float const *const p_grad_out_feat,
int const *const in_maps, //
int const *const out_maps, //
float const *const weights, //
uint32_t const nnz);
template void InterpolationBackwardKernelCPU<double, float, int>(
double *p_grad_in_feat, //
uint32_t const in_nrows, //
uint32_t const nchannel, //
double const *const p_grad_out_feat,
int const *const in_maps, //
int const *const out_maps, //
float const *const weights, //
uint32_t const nnz);
} // namespace minkowski
#endif // end INTERPOLATION_KERNEL
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