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convolution_kernel.hpp
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convolution_kernel.hpp
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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 CPU_CONVOLUTION
#define CPU_CONVOLUTION
#include "math_functions.hpp"
#include "types.hpp"
namespace minkowski {
template <typename Dtype, typename Itype>
void ConvolutionForwardKernelCPU(const Dtype *p_in_feat, int in_nchannel,
Dtype *p_out_feat, int out_nchannel,
const Dtype *p_kernel,
const cpu_in_maps &in_maps,
const cpu_out_maps &out_maps) {
int kernel_volume, n_active_in_volume, row;
std::vector<Dtype> input_buffer, output_buffer;
// Number of weights
kernel_volume = in_maps.size();
// Iterate through each spatial kernel out of filter_volume spatial kernels
// for (auto ¤t_in2out : in2out) {
for (int k = 0; k < kernel_volume; k++) {
n_active_in_volume = in_maps[k].size();
if (n_active_in_volume == 0)
continue;
input_buffer.resize(n_active_in_volume * in_nchannel);
output_buffer.resize(n_active_in_volume * out_nchannel);
// Gather all features (im2col)
for (row = 0; row < n_active_in_volume; row++)
std::memcpy(&input_buffer[row * in_nchannel],
p_in_feat + in_maps[k][row] * in_nchannel,
sizeof(Dtype) * in_nchannel);
// C := alpha*op(A)*op(B) + beta*C
cpu_gemm<Dtype>(CblasColMajor, CblasNoTrans, CblasNoTrans,
out_nchannel, // M
n_active_in_volume, // N
in_nchannel, // K
1, // alpha
&p_kernel[k * in_nchannel * out_nchannel], // A
&input_buffer[0], // B
0, // beta
&output_buffer[0]); // C
// Put it back to the correct index
for (row = 0; row < n_active_in_volume; row++) {
Dtype *dst = &p_out_feat[out_maps[k][row] * out_nchannel];
Dtype *src = &output_buffer[row * out_nchannel];
cpu_add<Dtype>(out_nchannel, src, dst, dst);
}
}
}
template <typename Dtype, typename Itype>
void ConvolutionBackwardKernelCPU(const Dtype *p_in_feat, Dtype *p_grad_in_feat,
int in_nchannel, const Dtype *p_grad_out_feat,
int out_nchannel, const Dtype *p_kernel,
Dtype *p_grad_kernel,
const cpu_in_maps &in_maps,
const cpu_out_maps &out_maps) {
int kernel_volume, n_active_in_volume, row;
std::vector<Dtype> input_buffer, output_buffer;
// Number of weights
kernel_volume = in_maps.size();
// for (auto ¤t_in2out : in2out) {
for (int k = 0; k < kernel_volume; k++) {
n_active_in_volume = in_maps[k].size();
if (n_active_in_volume == 0)
continue;
input_buffer.resize(n_active_in_volume * in_nchannel);
output_buffer.resize(n_active_in_volume * out_nchannel);
// Gather all features for a matrix multiplication (im2col)
for (row = 0; row < n_active_in_volume; row++)
std::memcpy(&output_buffer[row * out_nchannel],
&p_grad_out_feat[out_maps[k][row] * out_nchannel],
sizeof(Dtype) * out_nchannel);
cpu_gemm<Dtype>(CblasColMajor, CblasTrans, CblasNoTrans,
in_nchannel, // M
n_active_in_volume, // N
out_nchannel, // K
1, // alpha
&p_kernel[k * in_nchannel * out_nchannel], // A
&output_buffer[0], // B
0, // beta
&input_buffer[0] // C
);
// Accumulate gradients back to the input grad feat
for (row = 0; row < n_active_in_volume; row++) {
Dtype *src = &input_buffer[row * in_nchannel];
Dtype *dst = &p_grad_in_feat[in_maps[k][row] * in_nchannel];
cpu_add<Dtype>(in_nchannel, src, dst, dst);
}
// Compute gradient for kernel
for (row = 0; row < n_active_in_volume; row++)
std::memcpy(&input_buffer[row * in_nchannel],
p_in_feat + in_maps[k][row] * in_nchannel,
sizeof(Dtype) * in_nchannel);
cpu_gemm<Dtype>(CblasColMajor, CblasNoTrans, CblasTrans,
out_nchannel, // M
in_nchannel, // N
n_active_in_volume, // K
1, // alpha
&output_buffer[0], // A
&input_buffer[0], // B
1, // beta
&p_grad_kernel[k * in_nchannel * out_nchannel] // C
);
}
}
} // end namespace minkowski
#endif // CPU_CONVOLUTION