434 lines
16 KiB
Plaintext
434 lines
16 KiB
Plaintext
/**********************************
|
|
* Original Author: Haoqiang Fan
|
|
* Modified by: Kaichun Mo
|
|
*********************************/
|
|
|
|
#ifndef _EMD_KERNEL
|
|
#define _EMD_KERNEL
|
|
|
|
#include <cmath>
|
|
#include <vector>
|
|
|
|
#include <ATen/ATen.h>
|
|
#include <ATen/cuda/CUDAApplyUtils.cuh> // at::cuda::getApplyGrid
|
|
|
|
#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
|
|
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
|
|
#define CHECK_INPUT(x) \
|
|
CHECK_CUDA(x); \
|
|
CHECK_CONTIGUOUS(x)
|
|
|
|
/********************************
|
|
* Forward kernel for approxmatch
|
|
*********************************/
|
|
|
|
template <typename scalar_t>
|
|
__global__ void approxmatch(int b, int n, int m, const scalar_t *__restrict__ xyz1, const scalar_t *__restrict__ xyz2, scalar_t *__restrict__ match, scalar_t *temp)
|
|
{
|
|
scalar_t *remainL = temp + blockIdx.x * (n + m) * 2;
|
|
scalar_t *remainR = temp + blockIdx.x * (n + m) * 2 + n;
|
|
scalar_t *ratioL = temp + blockIdx.x * (n + m) * 2 + n + m;
|
|
scalar_t *ratioR = temp + blockIdx.x * (n + m) * 2 + n + m + n;
|
|
scalar_t multiL, multiR;
|
|
|
|
if (n >= m)
|
|
{
|
|
multiL = 1;
|
|
multiR = n / m;
|
|
}
|
|
else
|
|
{
|
|
multiL = m / n;
|
|
multiR = 1;
|
|
}
|
|
const int Block = 1024;
|
|
__shared__ scalar_t buf[Block * 4];
|
|
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
|
{
|
|
for (int j = threadIdx.x; j < n * m; j += blockDim.x)
|
|
match[i * n * m + j] = 0;
|
|
for (int j = threadIdx.x; j < n; j += blockDim.x)
|
|
remainL[j] = multiL;
|
|
for (int j = threadIdx.x; j < m; j += blockDim.x)
|
|
remainR[j] = multiR;
|
|
__syncthreads();
|
|
for (int j = 7; j >= -2; j--)
|
|
{
|
|
scalar_t level = -powf(4.0f, j);
|
|
if (j == -2)
|
|
{
|
|
level = 0;
|
|
}
|
|
for (int k0 = 0; k0 < n; k0 += blockDim.x)
|
|
{
|
|
int k = k0 + threadIdx.x;
|
|
scalar_t x1 = 0, y1 = 0, z1 = 0;
|
|
if (k < n)
|
|
{
|
|
x1 = xyz1[i * n * 3 + k * 3 + 0];
|
|
y1 = xyz1[i * n * 3 + k * 3 + 1];
|
|
z1 = xyz1[i * n * 3 + k * 3 + 2];
|
|
}
|
|
scalar_t suml = 1e-9f;
|
|
for (int l0 = 0; l0 < m; l0 += Block)
|
|
{
|
|
int lend = min(m, l0 + Block) - l0;
|
|
for (int l = threadIdx.x; l < lend; l += blockDim.x)
|
|
{
|
|
scalar_t x2 = xyz2[i * m * 3 + l0 * 3 + l * 3 + 0];
|
|
scalar_t y2 = xyz2[i * m * 3 + l0 * 3 + l * 3 + 1];
|
|
scalar_t z2 = xyz2[i * m * 3 + l0 * 3 + l * 3 + 2];
|
|
buf[l * 4 + 0] = x2;
|
|
buf[l * 4 + 1] = y2;
|
|
buf[l * 4 + 2] = z2;
|
|
buf[l * 4 + 3] = remainR[l0 + l];
|
|
}
|
|
__syncthreads();
|
|
for (int l = 0; l < lend; l++)
|
|
{
|
|
scalar_t x2 = buf[l * 4 + 0];
|
|
scalar_t y2 = buf[l * 4 + 1];
|
|
scalar_t z2 = buf[l * 4 + 2];
|
|
scalar_t d = level * ((x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1));
|
|
scalar_t w = __expf(d) * buf[l * 4 + 3];
|
|
suml += w;
|
|
}
|
|
__syncthreads();
|
|
}
|
|
if (k < n)
|
|
ratioL[k] = remainL[k] / suml;
|
|
}
|
|
__syncthreads();
|
|
for (int l0 = 0; l0 < m; l0 += blockDim.x)
|
|
{
|
|
int l = l0 + threadIdx.x;
|
|
scalar_t x2 = 0, y2 = 0, z2 = 0;
|
|
if (l < m)
|
|
{
|
|
x2 = xyz2[i * m * 3 + l * 3 + 0];
|
|
y2 = xyz2[i * m * 3 + l * 3 + 1];
|
|
z2 = xyz2[i * m * 3 + l * 3 + 2];
|
|
}
|
|
scalar_t sumr = 0;
|
|
for (int k0 = 0; k0 < n; k0 += Block)
|
|
{
|
|
int kend = min(n, k0 + Block) - k0;
|
|
for (int k = threadIdx.x; k < kend; k += blockDim.x)
|
|
{
|
|
buf[k * 4 + 0] = xyz1[i * n * 3 + k0 * 3 + k * 3 + 0];
|
|
buf[k * 4 + 1] = xyz1[i * n * 3 + k0 * 3 + k * 3 + 1];
|
|
buf[k * 4 + 2] = xyz1[i * n * 3 + k0 * 3 + k * 3 + 2];
|
|
buf[k * 4 + 3] = ratioL[k0 + k];
|
|
}
|
|
__syncthreads();
|
|
for (int k = 0; k < kend; k++)
|
|
{
|
|
scalar_t x1 = buf[k * 4 + 0];
|
|
scalar_t y1 = buf[k * 4 + 1];
|
|
scalar_t z1 = buf[k * 4 + 2];
|
|
scalar_t w = __expf(level * ((x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1))) * buf[k * 4 + 3];
|
|
sumr += w;
|
|
}
|
|
__syncthreads();
|
|
}
|
|
if (l < m)
|
|
{
|
|
sumr *= remainR[l];
|
|
scalar_t consumption = fminf(remainR[l] / (sumr + 1e-9f), 1.0f);
|
|
ratioR[l] = consumption * remainR[l];
|
|
remainR[l] = fmaxf(0.0f, remainR[l] - sumr);
|
|
}
|
|
}
|
|
__syncthreads();
|
|
for (int k0 = 0; k0 < n; k0 += blockDim.x)
|
|
{
|
|
int k = k0 + threadIdx.x;
|
|
scalar_t x1 = 0, y1 = 0, z1 = 0;
|
|
if (k < n)
|
|
{
|
|
x1 = xyz1[i * n * 3 + k * 3 + 0];
|
|
y1 = xyz1[i * n * 3 + k * 3 + 1];
|
|
z1 = xyz1[i * n * 3 + k * 3 + 2];
|
|
}
|
|
scalar_t suml = 0;
|
|
for (int l0 = 0; l0 < m; l0 += Block)
|
|
{
|
|
int lend = min(m, l0 + Block) - l0;
|
|
for (int l = threadIdx.x; l < lend; l += blockDim.x)
|
|
{
|
|
buf[l * 4 + 0] = xyz2[i * m * 3 + l0 * 3 + l * 3 + 0];
|
|
buf[l * 4 + 1] = xyz2[i * m * 3 + l0 * 3 + l * 3 + 1];
|
|
buf[l * 4 + 2] = xyz2[i * m * 3 + l0 * 3 + l * 3 + 2];
|
|
buf[l * 4 + 3] = ratioR[l0 + l];
|
|
}
|
|
__syncthreads();
|
|
scalar_t rl = ratioL[k];
|
|
if (k < n)
|
|
{
|
|
for (int l = 0; l < lend; l++)
|
|
{
|
|
scalar_t x2 = buf[l * 4 + 0];
|
|
scalar_t y2 = buf[l * 4 + 1];
|
|
scalar_t z2 = buf[l * 4 + 2];
|
|
scalar_t w = __expf(level * ((x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1))) * rl * buf[l * 4 + 3];
|
|
match[i * n * m + (l0 + l) * n + k] += w;
|
|
suml += w;
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
if (k < n)
|
|
remainL[k] = fmaxf(0.0f, remainL[k] - suml);
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
}
|
|
|
|
/* ApproxMatch forward interface
|
|
Input:
|
|
xyz1: (B, N1, 3) # dataset_points
|
|
xyz2: (B, N2, 3) # query_points
|
|
Output:
|
|
match: (B, N2, N1)
|
|
*/
|
|
at::Tensor ApproxMatchForward(
|
|
const at::Tensor xyz1,
|
|
const at::Tensor xyz2)
|
|
{
|
|
const auto b = xyz1.size(0);
|
|
const auto n = xyz1.size(1);
|
|
const auto m = xyz2.size(1);
|
|
|
|
TORCH_CHECK_EQ(xyz2.size(0), b);
|
|
TORCH_CHECK_EQ(xyz1.size(2), 3);
|
|
TORCH_CHECK_EQ(xyz2.size(2), 3);
|
|
CHECK_INPUT(xyz1);
|
|
CHECK_INPUT(xyz2);
|
|
|
|
auto match = at::zeros({b, m, n}, xyz1.type());
|
|
auto temp = at::zeros({b, (n + m) * 2}, xyz1.type());
|
|
|
|
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "ApproxMatchForward", ([&]
|
|
{ approxmatch<scalar_t><<<32, 512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), temp.data<scalar_t>()); }));
|
|
C10_CUDA_CHECK(cudaGetLastError());
|
|
|
|
return match;
|
|
}
|
|
|
|
/********************************
|
|
* Forward kernel for matchcost
|
|
*********************************/
|
|
|
|
template <typename scalar_t>
|
|
__global__ void matchcost(int b, int n, int m, const scalar_t *__restrict__ xyz1, const scalar_t *__restrict__ xyz2, const scalar_t *__restrict__ match, scalar_t *__restrict__ out)
|
|
{
|
|
__shared__ scalar_t allsum[512];
|
|
const int Block = 1024;
|
|
__shared__ scalar_t buf[Block * 3];
|
|
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
|
{
|
|
scalar_t subsum = 0;
|
|
for (int k0 = 0; k0 < n; k0 += blockDim.x)
|
|
{
|
|
int k = k0 + threadIdx.x;
|
|
scalar_t x1 = 0, y1 = 0, z1 = 0;
|
|
if (k < n)
|
|
{
|
|
x1 = xyz1[i * n * 3 + k * 3 + 0];
|
|
y1 = xyz1[i * n * 3 + k * 3 + 1];
|
|
z1 = xyz1[i * n * 3 + k * 3 + 2];
|
|
}
|
|
for (int l0 = 0; l0 < m; l0 += Block)
|
|
{
|
|
int lend = min(m, l0 + Block) - l0;
|
|
for (int l = threadIdx.x; l < lend * 3; l += blockDim.x)
|
|
buf[l] = xyz2[i * m * 3 + l0 * 3 + l];
|
|
__syncthreads();
|
|
if (k < n)
|
|
{
|
|
for (int l = 0; l < lend; l++)
|
|
{
|
|
scalar_t x2 = buf[l * 3 + 0];
|
|
scalar_t y2 = buf[l * 3 + 1];
|
|
scalar_t z2 = buf[l * 3 + 2];
|
|
scalar_t d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
|
|
subsum += d * match[i * n * m + (l0 + l) * n + k];
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
allsum[threadIdx.x] = subsum;
|
|
for (int j = 1; j < blockDim.x; j <<= 1)
|
|
{
|
|
__syncthreads();
|
|
if ((threadIdx.x & j) == 0 && threadIdx.x + j < blockDim.x)
|
|
{
|
|
allsum[threadIdx.x] += allsum[threadIdx.x + j];
|
|
}
|
|
}
|
|
if (threadIdx.x == 0)
|
|
out[i] = allsum[0];
|
|
__syncthreads();
|
|
}
|
|
}
|
|
|
|
/* MatchCost forward interface
|
|
Input:
|
|
xyz1: (B, N1, 3) # dataset_points
|
|
xyz2: (B, N2, 3) # query_points
|
|
match: (B, N2, N1)
|
|
Output:
|
|
cost: (B)
|
|
*/
|
|
at::Tensor MatchCostForward(
|
|
const at::Tensor xyz1,
|
|
const at::Tensor xyz2,
|
|
const at::Tensor match)
|
|
{
|
|
const auto b = xyz1.size(0);
|
|
const auto n = xyz1.size(1);
|
|
const auto m = xyz2.size(1);
|
|
|
|
TORCH_CHECK_EQ(xyz2.size(0), b);
|
|
TORCH_CHECK_EQ(xyz1.size(2), 3);
|
|
TORCH_CHECK_EQ(xyz2.size(2), 3);
|
|
CHECK_INPUT(xyz1);
|
|
CHECK_INPUT(xyz2);
|
|
|
|
auto cost = at::zeros({b}, xyz1.type());
|
|
|
|
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostForward", ([&]
|
|
{ matchcost<scalar_t><<<32, 512>>>(b, n, m, xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), cost.data<scalar_t>()); }));
|
|
C10_CUDA_CHECK(cudaGetLastError());
|
|
|
|
return cost;
|
|
}
|
|
|
|
/********************************
|
|
* matchcostgrad2 kernel
|
|
*********************************/
|
|
|
|
template <typename scalar_t>
|
|
__global__ void matchcostgrad2(int b, int n, int m, const scalar_t *__restrict__ grad_cost, const scalar_t *__restrict__ xyz1, const scalar_t *__restrict__ xyz2, const scalar_t *__restrict__ match, scalar_t *__restrict__ grad2)
|
|
{
|
|
__shared__ scalar_t sum_grad[256 * 3];
|
|
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
|
{
|
|
int kbeg = m * blockIdx.y / gridDim.y;
|
|
int kend = m * (blockIdx.y + 1) / gridDim.y;
|
|
for (int k = kbeg; k < kend; k++)
|
|
{
|
|
scalar_t x2 = xyz2[(i * m + k) * 3 + 0];
|
|
scalar_t y2 = xyz2[(i * m + k) * 3 + 1];
|
|
scalar_t z2 = xyz2[(i * m + k) * 3 + 2];
|
|
scalar_t subsumx = 0, subsumy = 0, subsumz = 0;
|
|
for (int j = threadIdx.x; j < n; j += blockDim.x)
|
|
{
|
|
scalar_t x1 = x2 - xyz1[(i * n + j) * 3 + 0];
|
|
scalar_t y1 = y2 - xyz1[(i * n + j) * 3 + 1];
|
|
scalar_t z1 = z2 - xyz1[(i * n + j) * 3 + 2];
|
|
scalar_t d = match[i * n * m + k * n + j] * 2;
|
|
subsumx += x1 * d;
|
|
subsumy += y1 * d;
|
|
subsumz += z1 * d;
|
|
}
|
|
sum_grad[threadIdx.x * 3 + 0] = subsumx;
|
|
sum_grad[threadIdx.x * 3 + 1] = subsumy;
|
|
sum_grad[threadIdx.x * 3 + 2] = subsumz;
|
|
for (int j = 1; j < blockDim.x; j <<= 1)
|
|
{
|
|
__syncthreads();
|
|
int j1 = threadIdx.x;
|
|
int j2 = threadIdx.x + j;
|
|
if ((j1 & j) == 0 && j2 < blockDim.x)
|
|
{
|
|
sum_grad[j1 * 3 + 0] += sum_grad[j2 * 3 + 0];
|
|
sum_grad[j1 * 3 + 1] += sum_grad[j2 * 3 + 1];
|
|
sum_grad[j1 * 3 + 2] += sum_grad[j2 * 3 + 2];
|
|
}
|
|
}
|
|
if (threadIdx.x == 0)
|
|
{
|
|
grad2[(i * m + k) * 3 + 0] = sum_grad[0] * grad_cost[i];
|
|
grad2[(i * m + k) * 3 + 1] = sum_grad[1] * grad_cost[i];
|
|
grad2[(i * m + k) * 3 + 2] = sum_grad[2] * grad_cost[i];
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
}
|
|
|
|
/********************************
|
|
* matchcostgrad1 kernel
|
|
*********************************/
|
|
|
|
template <typename scalar_t>
|
|
__global__ void matchcostgrad1(int b, int n, int m, const scalar_t *__restrict__ grad_cost, const scalar_t *__restrict__ xyz1, const scalar_t *__restrict__ xyz2, const scalar_t *__restrict__ match, scalar_t *__restrict__ grad1)
|
|
{
|
|
for (int i = blockIdx.x; i < b; i += gridDim.x)
|
|
{
|
|
for (int l = threadIdx.x; l < n; l += blockDim.x)
|
|
{
|
|
scalar_t x1 = xyz1[i * n * 3 + l * 3 + 0];
|
|
scalar_t y1 = xyz1[i * n * 3 + l * 3 + 1];
|
|
scalar_t z1 = xyz1[i * n * 3 + l * 3 + 2];
|
|
scalar_t dx = 0, dy = 0, dz = 0;
|
|
for (int k = 0; k < m; k++)
|
|
{
|
|
scalar_t x2 = xyz2[i * m * 3 + k * 3 + 0];
|
|
scalar_t y2 = xyz2[i * m * 3 + k * 3 + 1];
|
|
scalar_t z2 = xyz2[i * m * 3 + k * 3 + 2];
|
|
scalar_t d = match[i * n * m + k * n + l] * 2;
|
|
dx += (x1 - x2) * d;
|
|
dy += (y1 - y2) * d;
|
|
dz += (z1 - z2) * d;
|
|
}
|
|
grad1[i * n * 3 + l * 3 + 0] = dx * grad_cost[i];
|
|
grad1[i * n * 3 + l * 3 + 1] = dy * grad_cost[i];
|
|
grad1[i * n * 3 + l * 3 + 2] = dz * grad_cost[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
/* MatchCost backward interface
|
|
Input:
|
|
grad_cost: (B) # gradients on cost
|
|
xyz1: (B, N1, 3) # dataset_points
|
|
xyz2: (B, N2, 3) # query_points
|
|
match: (B, N2, N1)
|
|
Output:
|
|
grad1: (B, N1, 3)
|
|
grad2: (B, N2, 3)
|
|
*/
|
|
std::vector<at::Tensor> MatchCostBackward(
|
|
const at::Tensor grad_cost,
|
|
const at::Tensor xyz1,
|
|
const at::Tensor xyz2,
|
|
const at::Tensor match)
|
|
{
|
|
const auto b = xyz1.size(0);
|
|
const auto n = xyz1.size(1);
|
|
const auto m = xyz2.size(1);
|
|
|
|
TORCH_CHECK_EQ(xyz2.size(0), b);
|
|
TORCH_CHECK_EQ(xyz1.size(2), 3);
|
|
TORCH_CHECK_EQ(xyz2.size(2), 3);
|
|
CHECK_INPUT(xyz1);
|
|
CHECK_INPUT(xyz2);
|
|
|
|
auto grad1 = at::zeros({b, n, 3}, xyz1.type());
|
|
auto grad2 = at::zeros({b, m, 3}, xyz1.type());
|
|
|
|
AT_DISPATCH_FLOATING_TYPES(xyz1.scalar_type(), "MatchCostBackward", ([&]
|
|
{
|
|
matchcostgrad1<scalar_t><<<32,512>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad1.data<scalar_t>());
|
|
matchcostgrad2<scalar_t><<<dim3(32,32),256>>>(b, n, m, grad_cost.data<scalar_t>(), xyz1.data<scalar_t>(), xyz2.data<scalar_t>(), match.data<scalar_t>(), grad2.data<scalar_t>()); }));
|
|
C10_CUDA_CHECK(cudaGetLastError());
|
|
|
|
return std::vector<at::Tensor>({grad1, grad2});
|
|
}
|
|
|
|
#endif
|