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// Adapted from interp.cpp from Caffe util by Pauline Luc
// Originally developed by George Papandreou
#include "THCUNN.h"
#include "common.h"
#include "THCDeviceTensor.cuh"
#include "THCDeviceTensorUtils.cuh"
#include "THCDeviceUtils.cuh"
#include "THCHalf.h"
#include "THCHalfAutoNumerics.cuh"
#include "THCAtomics.cuh"
template<typename Dtype, typename Acctype>
__global__ void caffe_gpu_interp2_kernel(const int n,
const Acctype rdepth, const Acctype rheight, const Acctype rwidth,
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);
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 t2 = index / (height2*width2); // 0:depth2-1
// special case: just copy
if (depth1 == depth2 && height1 == height2 && width1 == width2) {
const int t1 = t2;
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][t1][h1][w1];
data2[n][c][t2][h2][w2] = val;
}
}
return;
}
//
const Acctype t1r = rdepth * t2;
const int t1 = t1r;
const int t1p = (t1 < depth1 - 1) ? 1 : 0;
const Acctype t1lambda = t1r - t1;
const Acctype t0lambda = Acctype(1) - t1lambda;
//
const Acctype h1r = rheight * h2;
const int h1 = h1r;
const int h1p = (h1 < height1 - 1) ? 1 : 0;
const Acctype h1lambda = h1r - h1;
const Acctype h0lambda = Acctype(1) - h1lambda;
//
const Acctype w1r = rwidth * w2;
const int w1 = w1r;
const int w1p = (w1 < width1 - 1) ? 1 : 0;
const Acctype w1lambda = w1r - w1;
const Acctype w0lambda = Acctype(1) - w1lambda;
//
for (int n = 0; n < batchsize ; n++){
for (int c = 0; c < channels; ++c) {
const Acctype val = t0lambda * (h0lambda * (w0lambda * data1[n][c][t1][h1][w1]
+ w1lambda * data1[n][c][t1][h1][w1+w1p])
+ h1lambda * (w0lambda * data1[n][c][t1][h1+h1p][w1]
+ w1lambda * data1[n][c][t1][h1+h1p][w1+w1p]))
+ t1lambda * (h0lambda * (w0lambda * data1[n][c][t1+t1p][h1][w1]
+ w1lambda * data1[n][c][t1+t1p][h1][w1+w1p])
+ h1lambda * (w0lambda * data1[n][c][t1+t1p][h1+h1p][w1]
+ w1lambda * data1[n][c][t1+t1p][h1+h1p][w1+w1p]));
data2[n][c][t2][h2][w2] = ScalarConvert<Acctype, Dtype>::to(val);
}
}
}
}
// Backward (adjoint) operation 1 <- 2 (accumulates)
template <typename Dtype, typename Acctype>
__global__ void caffe_gpu_interp2_kernel_backward(const int n,
const Acctype rdepth, const Acctype rheight, const Acctype rwidth,
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);
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 t2 = index / (height2*width2); // 0:depth2-1
// special case: just copy
if (depth1 == depth2 && height1 == height2 && width1 == width2) {
const int t1 = t2;
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][t1][h1][w1];
data1[n][c][t2][h2][w2] += val;
}
}
return;
}
//
const Acctype t1r = rdepth * t2;
const int t1 = t1r;
const int t1p = (t1 < depth1 - 1) ? 1 : 0;
const Acctype t1lambda = t1r - t1;
const Acctype t0lambda = Acctype(1) - t1lambda;
//
const Acctype h1r = rheight * h2;
const int h1 = h1r;
const int h1p = (h1 < height1 - 1) ? 1 : 0;
const Acctype h1lambda = h1r - h1;
const Acctype h0lambda = Acctype(1) - h1lambda;
//
const Acctype w1r = rwidth * w2;
const int w1 = w1r;
const int w1p = (w1 < width1 - 1) ? 1 : 0;
const Acctype w1lambda = w1r - w1;
const Acctype w0lambda = Acctype(1) - w1lambda;
//
for (int n = 0; n < batchsize ; n++){
for (int c = 0; c < channels; ++c) {
const Dtype d2val = data2[n][c][t2][h2][w2];
atomicAdd(data1[n][c][t1][h1][w1].data(),
ScalarConvert<Acctype, Dtype>::to(t0lambda * h0lambda * w0lambda * d2val));
atomicAdd(data1[n][c][t1][h1][w1+w1p].data(),
ScalarConvert<Acctype, Dtype>::to(t0lambda * h0lambda * w1lambda * d2val));
atomicAdd(data1[n][c][t1][h1+h1p][w1].data(),
ScalarConvert<Acctype, Dtype>::to(t0lambda * h1lambda * w0lambda * d2val));
atomicAdd(data1[n][c][t1][h1+h1p][w1+w1p].data(),
ScalarConvert<Acctype, Dtype>::to(t0lambda * h1lambda * w1lambda * d2val));
atomicAdd(data1[n][c][t1+t1p][h1][w1].data(),
ScalarConvert<Acctype, Dtype>::to(t1lambda * h0lambda * w0lambda * d2val));
atomicAdd(data1[n][c][t1+t1p][h1][w1+w1p].data(),
ScalarConvert<Acctype, Dtype>::to(t1lambda * h0lambda * w1lambda * d2val));
atomicAdd(data1[n][c][t1+t1p][h1+h1p][w1].data(),
ScalarConvert<Acctype, Dtype>::to(t1lambda * h1lambda * w0lambda * d2val));
atomicAdd(data1[n][c][t1+t1p][h1+h1p][w1+w1p].data(),
ScalarConvert<Acctype, Dtype>::to(t1lambda * h1lambda * w1lambda * d2val));
}
}
}
/////////////////////////////////////////////////////////
}
#include "generic/VolumetricUpSamplingTrilinear.cu"
#include "THCGenerateFloatTypes.h"
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