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VolumetricUpSamplingNearest.cu
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VolumetricUpSamplingNearest.cu
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#include <THCUNN/THCUNN.h>
#include <THCUNN/common.h>
#include <THC/THCTensor.hpp>
#include <THCUNN/upsampling.h>
#include <THC/THCDeviceTensor.cuh>
#include <THC/THCDeviceTensorUtils.cuh>
#include <THC/THCDeviceUtils.cuh>
#include <TH/THHalf.h>
#include <THCUNN/THCHalfAutoNumerics.cuh>
#include <THC/THCAtomics.cuh>
template<typename Dtype, typename Acctype>
__global__ void nearest_neighbor_5d_kernel(
const int n,
const THCDeviceTensor<Dtype, 5> data1,
THCDeviceTensor<Dtype, 5> data2) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
const int batchsize = data1.getSize(0);
const int channels = data1.getSize(1);
const int depth1 = data1.getSize(2);
const int height1 = data1.getSize(3);
const int width1 = data1.getSize(4);
const int depth2 = data2.getSize(2);
const int height2 = data2.getSize(3);
const int width2 = data2.getSize(4);
const float depth_scale = (float) depth1 / (float) depth2;
const float height_scale = (float) height1 / (float) height2;
const float width_scale = (float) width1 / (float) width2;
if (index < n) {
const int w2 = (index % (height2*width2)) % width2; // 0:width2-1
const int h2 = (index % (height2*width2)) / width2; // 0:height2-1
const int d2 = index / (height2*width2); // 0:depth2-1
// special case: just copy
if (depth1 == depth2 && height1 == height2 && width1 == width2) {
const int d1 = d2;
const int h1 = h2;
const int w1 = w2;
for (int n = 0; n < batchsize ; n++){
for (int c = 0; c < channels; ++c) {
const Dtype val = data1[n][c][d1][h1][w1];
data2[n][c][d2][h2][w2] = val;
}
}
return;
}
//
const int h1 = nearest_neighbor_compute_source_index(height_scale, h2, height1);
const int w1 = nearest_neighbor_compute_source_index(width_scale, w2, width1);
const int d1 = nearest_neighbor_compute_source_index(depth_scale, d2, depth1);
for (int n = 0; n < batchsize; n++) {
for (int c = 0; c < channels; ++c) {
const Dtype val = data1[n][c][d1][h1][w1];
data2[n][c][d2][h2][w2] = val;
}
}
}
}
// Backward operation
template <typename Dtype, typename Acctype>
__global__ void nearest_neighbor_5d_kernel_backward(
const int n,
THCDeviceTensor<Dtype, 5> data1,
const THCDeviceTensor<Dtype, 5> data2) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
const int batchsize = data1.getSize(0);
const int channels = data1.getSize(1);
const int depth1 = data1.getSize(2);
const int height1 = data1.getSize(3);
const int width1 = data1.getSize(4);
const int depth2 = data2.getSize(2);
const int height2 = data2.getSize(3);
const int width2 = data2.getSize(4);
const float depth_scale = (float) depth1 / (float) depth2;
const float height_scale = (float) height1 / (float) height2;
const float width_scale = (float) width1 / (float) width2;
if (index < n) {
const int w2 = (index % (height2*width2)) % width2; // 0:width2-1
const int h2 = (index % (height2*width2)) / width2; // 0:height2-1
const int d2 = index / (height2*width2); // 0:depth2-1
// special case: just copy
if (depth1 == depth2 && height1 == height2 && width1 == width2) {
const int d1 = d2;
const int h1 = h2;
const int w1 = w2;
for (int n = 0; n < batchsize ; n++){
for (int c = 0; c < channels; ++c) {
const Dtype val = data2[n][c][d1][h1][w1];
data1[n][c][d2][h2][w2] = val;
}
}
return;
}
//
const int h1 = nearest_neighbor_compute_source_index(height_scale, h2, height1);
const int w1 = nearest_neighbor_compute_source_index(width_scale, w2, width1);
const int d1 = nearest_neighbor_compute_source_index(depth_scale, d2, depth1);
for (int n = 0; n < batchsize; n++) {
for (int c = 0; c < channels; ++c) {
const Dtype val = data2[n][c][d2][h2][w2];
atomicAdd(data1[n][c][d1][h1][w1].data(), val);
}
}
}
}
#include <THCUNN/generic/VolumetricUpSamplingNearest.cu>
#include <THC/THCGenerateFloatTypes.h>