423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-08-22 22:02:49 +08:00
Code Issues Packages Projects Releases Wiki Activity

Make cuda tensors contiguous in host function and remove contiguous check

Browse Source 
Summary:
Update the cuda kernels to:
- remove contiguous checks for the grad tensors and for cpu functions which use accessors
- for cuda implementations call `.contiguous()` on all tensors in the host function before invoking the kernel

Reviewed By: gkioxari

Differential Revision: D21598008

fbshipit-source-id: 9b97bda4582fd4269c8a00999874d4552a1aea2d
This commit is contained in:
Nikhila Ravi 2020-05-15 14:58:04 -07:00 committed by Facebook GitHub Bot
parent a8377f1f06
commit 3fef506895
21 changed files with 219 additions and 233 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
pytorch3d/csrc/compositing/alpha_composite.cu
View File

@ -168,6 +168,8 @@ at::Tensor alphaCompositeCudaForward(
// doubles. Currently, support is for floats only.
alphaCompositeCudaForwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// clang-format off
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
result.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
@ -211,6 +213,8 @@ std::tuple<at::Tensor, at::Tensor> alphaCompositeCudaBackward(
// doubles. Currently, support is for floats only.
alphaCompositeCudaBackwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// clang-format off
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
grad_features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
grad_alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
grad_outputs.packed_accessor64<float, 4, at::RestrictPtrTraits>(),

23
pytorch3d/csrc/compositing/alpha_composite.h
View File

@ -60,18 +60,14 @@ torch::Tensor alphaCompositeForward(
if (features.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return alphaCompositeCudaForward(features, alphas, points_idx);
#else
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return alphaCompositeCpuForward(features, alphas, points_idx);
}
}
@ -88,10 +84,10 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeBackward(
if (grad_outputs.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(grad_outputs);
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(grad_outputs);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return alphaCompositeCudaBackward(
grad_outputs, features, alphas, points_idx);
@ -99,11 +95,6 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeBackward(
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(grad_outputs);
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return alphaCompositeCpuBackward(
grad_outputs, features, alphas, points_idx);
}

4
pytorch3d/csrc/compositing/norm_weighted_sum.cu
View File

@ -183,6 +183,8 @@ at::Tensor weightedSumNormCudaForward(
// doubles. Currently, support is for floats only.
// clang-format off
weightedSumNormCudaForwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
result.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
@ -227,6 +229,8 @@ std::tuple<at::Tensor, at::Tensor> weightedSumNormCudaBackward(
// doubles. Currently, support is for floats only.
weightedSumNormCudaBackwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// clang-format off
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
grad_features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
grad_alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
grad_outputs.packed_accessor64<float, 4, at::RestrictPtrTraits>(),

23
pytorch3d/csrc/compositing/norm_weighted_sum.h
View File

@ -58,19 +58,15 @@ torch::Tensor weightedSumNormForward(
if (features.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return weightedSumNormCudaForward(features, alphas, points_idx);
#else
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return weightedSumNormCpuForward(features, alphas, points_idx);
}
}
@ -87,10 +83,10 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumNormBackward(
if (grad_outputs.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(grad_outputs);
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(grad_outputs);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return weightedSumNormCudaBackward(
grad_outputs, features, alphas, points_idx);
@ -98,11 +94,6 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumNormBackward(
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(grad_outputs);
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return weightedSumNormCpuBackward(
grad_outputs, features, alphas, points_idx);
}

4
pytorch3d/csrc/compositing/weighted_sum.cu
View File

@ -142,6 +142,8 @@ at::Tensor weightedSumCudaForward(
// doubles. Currently, support is for floats only.
weightedSumCudaForwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// clang-format off
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
result.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
@ -185,6 +187,8 @@ std::tuple<at::Tensor, at::Tensor> weightedSumCudaBackward(
// doubles. Currently, support is for floats only.
weightedSumCudaBackwardKernel<<<numBlocks, threadsPerBlock, 0, stream>>>(
// clang-format off
// As we are using packed accessors here the tensors
// do not need to be made contiguous.
grad_features.packed_accessor64<float, 2, at::RestrictPtrTraits>(),
grad_alphas.packed_accessor64<float, 4, at::RestrictPtrTraits>(),
grad_outputs.packed_accessor64<float, 4, at::RestrictPtrTraits>(),

23
pytorch3d/csrc/compositing/weighted_sum.h
View File

@ -58,18 +58,14 @@ torch::Tensor weightedSumForward(
if (features.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return weightedSumCudaForward(features, alphas, points_idx);
#else
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return weightedSumCpuForward(features, alphas, points_idx);
}
}
@ -86,21 +82,16 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumBackward(
if (grad_outputs.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(grad_outputs);
CHECK_CONTIGUOUS_CUDA(features);
CHECK_CONTIGUOUS_CUDA(alphas);
CHECK_CONTIGUOUS_CUDA(points_idx);
CHECK_CUDA(grad_outputs);
CHECK_CUDA(features);
CHECK_CUDA(alphas);
CHECK_CUDA(points_idx);
return weightedSumCudaBackward(grad_outputs, features, alphas, points_idx);
#else
AT_ERROR("Not compiled with GPU support");
#endif
} else {
CHECK_CONTIGUOUS(grad_outputs);
CHECK_CONTIGUOUS(features);
CHECK_CONTIGUOUS(alphas);
CHECK_CONTIGUOUS(points_idx);
return weightedSumCpuBackward(grad_outputs, features, alphas, points_idx);
}
}

12
pytorch3d/csrc/face_areas_normals/face_areas_normals.cu
View File

@ -239,8 +239,8 @@ std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForwardCuda(
AT_DISPATCH_FLOATING_TYPES(
verts.scalar_type(), "face_areas_normals_forward_cuda", ([&] {
FaceAreasNormalsForwardKernel<scalar_t><<<blocks, threads, 0, stream>>>(
verts.data_ptr<scalar_t>(),
faces.data_ptr<int64_t>(),
verts.contiguous().data_ptr<scalar_t>(),
faces.contiguous().data_ptr<int64_t>(),
areas.data_ptr<scalar_t>(),
normals.data_ptr<scalar_t>(),
V,
@ -282,10 +282,10 @@ at::Tensor FaceAreasNormalsBackwardCuda(
// TODO(gkioxari) add AT_DISPATCH_FLOATING_TYPES once atomicAdd supports
// doubles. Currently, support is for floats only.
FaceAreasNormalsBackwardKernel<<<blocks, threads, 0, stream>>>(
grad_areas.data_ptr<float>(),
grad_normals.data_ptr<float>(),
verts.data_ptr<float>(),
faces.data_ptr<int64_t>(),
grad_areas.contiguous().data_ptr<float>(),
grad_normals.contiguous().data_ptr<float>(),
verts.contiguous().data_ptr<float>(),
faces.contiguous().data_ptr<int64_t>(),
grad_verts.data_ptr<float>(),
V,
F);

12
pytorch3d/csrc/face_areas_normals/face_areas_normals.h
View File

@ -47,8 +47,8 @@ std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForward(
const at::Tensor faces) {
if (verts.is_cuda() && faces.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(verts);
CHECK_CONTIGUOUS_CUDA(faces);
CHECK_CUDA(verts);
CHECK_CUDA(faces);
return FaceAreasNormalsForwardCuda(verts, faces);
#else
AT_ERROR("Not compiled with GPU support.");
@ -65,10 +65,10 @@ at::Tensor FaceAreasNormalsBackward(
const at::Tensor faces) {
if (verts.is_cuda() && faces.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(verts);
CHECK_CONTIGUOUS_CUDA(faces);
CHECK_CONTIGUOUS_CUDA(grad_areas);
CHECK_CONTIGUOUS_CUDA(grad_normals);
CHECK_CUDA(verts);
CHECK_CUDA(faces);
CHECK_CUDA(grad_areas);
CHECK_CUDA(grad_normals);
return FaceAreasNormalsBackwardCuda(grad_areas, grad_normals, verts, faces);
#else
AT_ERROR("Not compiled with GPU support.");

4
pytorch3d/csrc/gather_scatter/gather_scatter.cu
View File

@ -72,8 +72,8 @@ at::Tensor GatherScatterCuda(
}
GatherScatterCudaKernel<<<blocks, threads, 0, stream>>>(
input.data_ptr<float>(),
edges.data_ptr<int64_t>(),
input.contiguous().data_ptr<float>(),
edges.contiguous().data_ptr<int64_t>(),
output.data_ptr<float>(),
directed,
backward,

4
pytorch3d/csrc/gather_scatter/gather_scatter.h
View File

@ -35,8 +35,8 @@ at::Tensor GatherScatter(
bool backward) {
if (input.is_cuda() && edges.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(input);
CHECK_CONTIGUOUS_CUDA(edges);
CHECK_CUDA(input);
CHECK_CUDA(edges);
return GatherScatterCuda(input, edges, directed, backward);
#else
AT_ERROR("Not compiled with GPU support.");

66
pytorch3d/csrc/knn/knn.cu
View File

@ -347,21 +347,21 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
const size_t threads = 256;
const size_t blocks = 256;
if (version == 0) {
AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
KNearestNeighborKernelV0<scalar_t>
<<<blocks, threads, 0, stream>>>(
p1.data_ptr<scalar_t>(),
p2.data_ptr<scalar_t>(),
lengths1.data_ptr<int64_t>(),
lengths2.data_ptr<int64_t>(),
dists.data_ptr<scalar_t>(),
idxs.data_ptr<int64_t>(),
N,
P1,
P2,
D,
K);
}));
AT_DISPATCH_FLOATING_TYPES(
p1.scalar_type(), "knn_kernel_cuda", ([&] {
KNearestNeighborKernelV0<scalar_t><<<blocks, threads, 0, stream>>>(
p1.contiguous().data_ptr<scalar_t>(),
p2.contiguous().data_ptr<scalar_t>(),
lengths1.contiguous().data_ptr<int64_t>(),
lengths2.contiguous().data_ptr<int64_t>(),
dists.data_ptr<scalar_t>(),
idxs.data_ptr<int64_t>(),
N,
P1,
P2,
D,
K);
}));
} else if (version == 1) {
AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
DispatchKernel1D<
@ -372,10 +372,10 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
D,
blocks,
threads,
p1.data_ptr<scalar_t>(),
p2.data_ptr<scalar_t>(),
lengths1.data_ptr<int64_t>(),
lengths2.data_ptr<int64_t>(),
p1.contiguous().data_ptr<scalar_t>(),
p2.contiguous().data_ptr<scalar_t>(),
lengths1.contiguous().data_ptr<int64_t>(),
lengths2.contiguous().data_ptr<int64_t>(),
dists.data_ptr<scalar_t>(),
idxs.data_ptr<int64_t>(),
N,
@ -396,10 +396,10 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
K_64,
blocks,
threads,
p1.data_ptr<scalar_t>(),
p2.data_ptr<scalar_t>(),
lengths1.data_ptr<int64_t>(),
lengths2.data_ptr<int64_t>(),
p1.contiguous().data_ptr<scalar_t>(),
p2.contiguous().data_ptr<scalar_t>(),
lengths1.contiguous().data_ptr<int64_t>(),
lengths2.contiguous().data_ptr<int64_t>(),
dists.data_ptr<scalar_t>(),
idxs.data_ptr<int64_t>(),
N,
@ -419,10 +419,10 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
K_64,
blocks,
threads,
p1.data_ptr<scalar_t>(),
p2.data_ptr<scalar_t>(),
lengths1.data_ptr<int64_t>(),
lengths2.data_ptr<int64_t>(),
p1.contiguous().data_ptr<scalar_t>(),
p2.contiguous().data_ptr<scalar_t>(),
lengths1.contiguous().data_ptr<int64_t>(),
lengths2.contiguous().data_ptr<int64_t>(),
dists.data_ptr<scalar_t>(),
idxs.data_ptr<int64_t>(),
N,
@ -525,12 +525,12 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborBackwardCuda(
const int threads = 512;
KNearestNeighborBackwardKernel<<<blocks, threads, 0, stream>>>(
p1.data_ptr<float>(),
p2.data_ptr<float>(),
lengths1.data_ptr<int64_t>(),
lengths2.data_ptr<int64_t>(),
idxs.data_ptr<int64_t>(),
grad_dists.data_ptr<float>(),
p1.contiguous().data_ptr<float>(),
p2.contiguous().data_ptr<float>(),
lengths1.contiguous().data_ptr<int64_t>(),
lengths2.contiguous().data_ptr<int64_t>(),
idxs.contiguous().data_ptr<int64_t>(),
grad_dists.contiguous().data_ptr<float>(),
grad_p1.data_ptr<float>(),
grad_p2.data_ptr<float>(),
N,

8
pytorch3d/csrc/knn/knn.h
View File

@ -56,8 +56,8 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdx(
int version) {
if (p1.is_cuda() || p2.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(p1);
CHECK_CONTIGUOUS_CUDA(p2);
CHECK_CUDA(p1);
CHECK_CUDA(p2);
return KNearestNeighborIdxCuda(p1, p2, lengths1, lengths2, K, version);
#else
AT_ERROR("Not compiled with GPU support.");
@ -117,8 +117,8 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborBackward(
const at::Tensor& grad_dists) {
if (p1.is_cuda() || p2.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(p1);
CHECK_CONTIGUOUS_CUDA(p2);
CHECK_CUDA(p1);
CHECK_CUDA(p2);
return KNearestNeighborBackwardCuda(
p1, p2, lengths1, lengths2, idxs, grad_dists);
#else

16
pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor.cu
View File

@ -146,8 +146,8 @@ at::Tensor PackedToPaddedCuda(
AT_DISPATCH_FLOATING_TYPES(
inputs_packed.scalar_type(), "packed_to_padded_d1_kernel", ([&] {
PackedToPaddedKernelD1<scalar_t><<<blocks, threads, 0, stream>>>(
inputs_packed.data_ptr<scalar_t>(),
first_idxs.data_ptr<int64_t>(),
inputs_packed.contiguous().data_ptr<scalar_t>(),
first_idxs.contiguous().data_ptr<int64_t>(),
inputs_padded.data_ptr<scalar_t>(),
batch_size,
max_size,
@ -157,8 +157,8 @@ at::Tensor PackedToPaddedCuda(
AT_DISPATCH_FLOATING_TYPES(
inputs_packed.scalar_type(), "packed_to_padded_kernel", ([&] {
PackedToPaddedKernel<scalar_t><<<blocks, threads, 0, stream>>>(
inputs_packed.data_ptr<scalar_t>(),
first_idxs.data_ptr<int64_t>(),
inputs_packed.contiguous().data_ptr<scalar_t>(),
first_idxs.contiguous().data_ptr<int64_t>(),
inputs_padded.data_ptr<scalar_t>(),
batch_size,
max_size,
@ -209,8 +209,8 @@ at::Tensor PaddedToPackedCuda(
AT_DISPATCH_FLOATING_TYPES(
inputs_padded.scalar_type(), "padded_to_packed_d1_kernel", ([&] {
PaddedToPackedKernelD1<scalar_t><<<blocks, threads, 0, stream>>>(
inputs_padded.data_ptr<scalar_t>(),
first_idxs.data_ptr<int64_t>(),
inputs_padded.contiguous().data_ptr<scalar_t>(),
first_idxs.contiguous().data_ptr<int64_t>(),
inputs_packed.data_ptr<scalar_t>(),
batch_size,
max_size,
@ -220,8 +220,8 @@ at::Tensor PaddedToPackedCuda(
AT_DISPATCH_FLOATING_TYPES(
inputs_padded.scalar_type(), "padded_to_packed_kernel", ([&] {
PaddedToPackedKernel<scalar_t><<<blocks, threads, 0, stream>>>(
inputs_padded.data_ptr<scalar_t>(),
first_idxs.data_ptr<int64_t>(),
inputs_padded.contiguous().data_ptr<scalar_t>(),
first_idxs.contiguous().data_ptr<int64_t>(),
inputs_packed.data_ptr<scalar_t>(),
batch_size,
max_size,

8
pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor.h
View File

@ -75,8 +75,8 @@ at::Tensor PackedToPadded(
const int64_t max_size) {
if (inputs_packed.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(inputs_packed);
CHECK_CONTIGUOUS_CUDA(first_idxs);
CHECK_CUDA(inputs_packed);
CHECK_CUDA(first_idxs);
return PackedToPaddedCuda(inputs_packed, first_idxs, max_size);
#else
AT_ERROR("Not compiled with GPU support.");
@ -92,8 +92,8 @@ at::Tensor PaddedToPacked(
const int64_t num_inputs) {
if (inputs_padded.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(inputs_padded);
CHECK_CONTIGUOUS_CUDA(first_idxs);
CHECK_CUDA(inputs_padded);
CHECK_CUDA(first_idxs);
return PaddedToPackedCuda(inputs_padded, first_idxs, num_inputs);
#else
AT_ERROR("Not compiled with GPU support.");

43
pytorch3d/csrc/point_mesh/point_mesh_edge.cu
View File

@ -144,15 +144,16 @@ std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceForwardCuda(
size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
PointEdgeForwardKernel<<<blocks, threads, shared_size, stream>>>(
points.data_ptr<float>(),
points_first_idx.data_ptr<int64_t>(),
segms.data_ptr<float>(),
segms_first_idx.data_ptr<int64_t>(),
points.contiguous().data_ptr<float>(),
points_first_idx.contiguous().data_ptr<int64_t>(),
segms.contiguous().data_ptr<float>(),
segms_first_idx.contiguous().data_ptr<int64_t>(),
dists.data_ptr<float>(),
idxs.data_ptr<int64_t>(),
B,
P,
S);
AT_CUDA_CHECK(cudaGetLastError());
return std::make_tuple(dists, idxs);
}
@ -240,10 +241,10 @@ std::tuple<at::Tensor, at::Tensor> PointEdgeDistanceBackwardCuda(
const int threads = 512;
PointEdgeBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
segms.data_ptr<float>(),
idx_points.data_ptr<int64_t>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
segms.contiguous().data_ptr<float>(),
idx_points.contiguous().data_ptr<int64_t>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_segms.data_ptr<float>(),
P);
@ -386,10 +387,10 @@ std::tuple<at::Tensor, at::Tensor> EdgePointDistanceForwardCuda(
size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
EdgePointForwardKernel<<<blocks, threads, shared_size, stream>>>(
points.data_ptr<float>(),
points_first_idx.data_ptr<int64_t>(),
segms.data_ptr<float>(),
segms_first_idx.data_ptr<int64_t>(),
points.contiguous().data_ptr<float>(),
points_first_idx.contiguous().data_ptr<int64_t>(),
segms.contiguous().data_ptr<float>(),
segms_first_idx.contiguous().data_ptr<int64_t>(),
dists.data_ptr<float>(),
idxs.data_ptr<int64_t>(),
B,
@ -478,10 +479,10 @@ std::tuple<at::Tensor, at::Tensor> EdgePointDistanceBackwardCuda(
const int threads = 512;
EdgePointBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
segms.data_ptr<float>(),
idx_segms.data_ptr<int64_t>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
segms.contiguous().data_ptr<float>(),
idx_segms.contiguous().data_ptr<int64_t>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_segms.data_ptr<float>(),
S);
@ -550,8 +551,8 @@ at::Tensor PointEdgeArrayDistanceForwardCuda(
const size_t threads = 64;
PointEdgeArrayForwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
segms.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
segms.contiguous().data_ptr<float>(),
dists.data_ptr<float>(),
P,
S);
@ -638,9 +639,9 @@ std::tuple<at::Tensor, at::Tensor> PointEdgeArrayDistanceBackwardCuda(
const size_t threads = 64;
PointEdgeArrayBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
segms.data_ptr<float>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
segms.contiguous().data_ptr<float>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_segms.data_ptr<float>(),
P,

42
pytorch3d/csrc/point_mesh/point_mesh_edge.h
View File

@ -54,10 +54,10 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForward(
const int64_t max_points) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(points_first_idx);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CONTIGUOUS_CUDA(segms_first_idx);
CHECK_CUDA(points);
CHECK_CUDA(points_first_idx);
CHECK_CUDA(segms);
CHECK_CUDA(segms_first_idx);
return PointEdgeDistanceForwardCuda(
points, points_first_idx, segms, segms_first_idx, max_points);
#else
@ -98,10 +98,10 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CONTIGUOUS_CUDA(idx_points);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(segms);
CHECK_CUDA(idx_points);
CHECK_CUDA(grad_dists);
return PointEdgeDistanceBackwardCuda(points, segms, idx_points, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");
@ -158,10 +158,10 @@ std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForward(
const int64_t max_segms) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(points_first_idx);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CONTIGUOUS_CUDA(segms_first_idx);
CHECK_CUDA(points);
CHECK_CUDA(points_first_idx);
CHECK_CUDA(segms);
CHECK_CUDA(segms_first_idx);
return EdgePointDistanceForwardCuda(
points, points_first_idx, segms, segms_first_idx, max_segms);
#else
@ -202,10 +202,10 @@ std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CONTIGUOUS_CUDA(idx_segms);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(segms);
CHECK_CUDA(idx_segms);
CHECK_CUDA(grad_dists);
return EdgePointDistanceBackwardCuda(points, segms, idx_segms, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");
@ -247,8 +247,8 @@ torch::Tensor PointEdgeArrayDistanceForward(
const torch::Tensor& segms) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CUDA(points);
CHECK_CUDA(segms);
return PointEdgeArrayDistanceForwardCuda(points, segms);
#else
AT_ERROR("Not compiled with GPU support.");
@ -283,9 +283,9 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(segms);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(segms);
CHECK_CUDA(grad_dists);
return PointEdgeArrayDistanceBackwardCuda(points, segms, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");

42
pytorch3d/csrc/point_mesh/point_mesh_face.cu
View File

@ -145,10 +145,10 @@ std::tuple<at::Tensor, at::Tensor> PointFaceDistanceForwardCuda(
size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
PointFaceForwardKernel<<<blocks, threads, shared_size, stream>>>(
points.data_ptr<float>(),
points_first_idx.data_ptr<int64_t>(),
tris.data_ptr<float>(),
tris_first_idx.data_ptr<int64_t>(),
points.contiguous().data_ptr<float>(),
points_first_idx.contiguous().data_ptr<int64_t>(),
tris.contiguous().data_ptr<float>(),
tris_first_idx.contiguous().data_ptr<int64_t>(),
dists.data_ptr<float>(),
idxs.data_ptr<int64_t>(),
B,
@ -249,10 +249,10 @@ std::tuple<at::Tensor, at::Tensor> PointFaceDistanceBackwardCuda(
const int threads = 512;
PointFaceBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
tris.data_ptr<float>(),
idx_points.data_ptr<int64_t>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
tris.contiguous().data_ptr<float>(),
idx_points.contiguous().data_ptr<int64_t>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_tris.data_ptr<float>(),
P);
@ -396,10 +396,10 @@ std::tuple<at::Tensor, at::Tensor> FacePointDistanceForwardCuda(
size_t shared_size = threads * sizeof(size_t) + threads * sizeof(int64_t);
FacePointForwardKernel<<<blocks, threads, shared_size, stream>>>(
points.data_ptr<float>(),
points_first_idx.data_ptr<int64_t>(),
tris.data_ptr<float>(),
tris_first_idx.data_ptr<int64_t>(),
points.contiguous().data_ptr<float>(),
points_first_idx.contiguous().data_ptr<int64_t>(),
tris.contiguous().data_ptr<float>(),
tris_first_idx.contiguous().data_ptr<int64_t>(),
dists.data_ptr<float>(),
idxs.data_ptr<int64_t>(),
B,
@ -501,10 +501,10 @@ std::tuple<at::Tensor, at::Tensor> FacePointDistanceBackwardCuda(
const int threads = 512;
FacePointBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
tris.data_ptr<float>(),
idx_tris.data_ptr<int64_t>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
tris.contiguous().data_ptr<float>(),
idx_tris.contiguous().data_ptr<int64_t>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_tris.data_ptr<float>(),
T);
@ -575,8 +575,8 @@ at::Tensor PointFaceArrayDistanceForwardCuda(
const size_t threads = 64;
PointFaceArrayForwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
tris.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
tris.contiguous().data_ptr<float>(),
dists.data_ptr<float>(),
P,
T);
@ -672,9 +672,9 @@ std::tuple<at::Tensor, at::Tensor> PointFaceArrayDistanceBackwardCuda(
const size_t threads = 64;
PointFaceArrayBackwardKernel<<<blocks, threads, 0, stream>>>(
points.data_ptr<float>(),
tris.data_ptr<float>(),
grad_dists.data_ptr<float>(),
points.contiguous().data_ptr<float>(),
tris.contiguous().data_ptr<float>(),
grad_dists.contiguous().data_ptr<float>(),
grad_points.data_ptr<float>(),
grad_tris.data_ptr<float>(),
P,

42
pytorch3d/csrc/point_mesh/point_mesh_face.h
View File

@ -56,10 +56,10 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceForward(
const int64_t max_points) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(points_first_idx);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CONTIGUOUS_CUDA(tris_first_idx);
CHECK_CUDA(points);
CHECK_CUDA(points_first_idx);
CHECK_CUDA(tris);
CHECK_CUDA(tris_first_idx);
return PointFaceDistanceForwardCuda(
points, points_first_idx, tris, tris_first_idx, max_points);
#else
@ -100,10 +100,10 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CONTIGUOUS_CUDA(idx_points);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(tris);
CHECK_CUDA(idx_points);
CHECK_CUDA(grad_dists);
return PointFaceDistanceBackwardCuda(points, tris, idx_points, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");
@ -160,10 +160,10 @@ std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceForward(
const int64_t max_tris) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(points_first_idx);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CONTIGUOUS_CUDA(tris_first_idx);
CHECK_CUDA(points);
CHECK_CUDA(points_first_idx);
CHECK_CUDA(tris);
CHECK_CUDA(tris_first_idx);
return FacePointDistanceForwardCuda(
points, points_first_idx, tris, tris_first_idx, max_tris);
#else
@ -204,10 +204,10 @@ std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CONTIGUOUS_CUDA(idx_tris);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(tris);
CHECK_CUDA(idx_tris);
CHECK_CUDA(grad_dists);
return FacePointDistanceBackwardCuda(points, tris, idx_tris, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");
@ -250,8 +250,8 @@ torch::Tensor PointFaceArrayDistanceForward(
const torch::Tensor& tris) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CUDA(points);
CHECK_CUDA(tris);
return PointFaceArrayDistanceForwardCuda(points, tris);
#else
AT_ERROR("Not compiled with GPU support.");
@ -285,9 +285,9 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceArrayDistanceBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(tris);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(tris);
CHECK_CUDA(grad_dists);
return PointFaceArrayDistanceBackwardCuda(points, tris, grad_dists);
#else
AT_ERROR("Not compiled with GPU support.");

22
pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu
View File

@ -348,10 +348,10 @@ RasterizeMeshesNaiveCuda(
H,
W,
K,
face_idxs.contiguous().data_ptr<int64_t>(),
zbuf.contiguous().data_ptr<float>(),
pix_dists.contiguous().data_ptr<float>(),
bary.contiguous().data_ptr<float>());
face_idxs.data_ptr<int64_t>(),
zbuf.data_ptr<float>(),
pix_dists.data_ptr<float>(),
bary.data_ptr<float>());
AT_CUDA_CHECK(cudaGetLastError());
return std::make_tuple(face_idxs, zbuf, bary, pix_dists);
@ -530,7 +530,7 @@ at::Tensor RasterizeMeshesBackwardCuda(
grad_zbuf.contiguous().data_ptr<float>(),
grad_bary.contiguous().data_ptr<float>(),
grad_dists.contiguous().data_ptr<float>(),
grad_face_verts.contiguous().data_ptr<float>());
grad_face_verts.data_ptr<float>());
AT_CUDA_CHECK(cudaGetLastError());
return grad_face_verts;
@ -727,8 +727,8 @@ at::Tensor RasterizeMeshesCoarseCuda(
bin_size,
chunk_size,
M,
faces_per_bin.contiguous().data_ptr<int32_t>(),
bin_faces.contiguous().data_ptr<int32_t>());
faces_per_bin.data_ptr<int32_t>(),
bin_faces.data_ptr<int32_t>());
AT_CUDA_CHECK(cudaGetLastError());
return bin_faces;
@ -897,10 +897,10 @@ RasterizeMeshesFineCuda(
H,
W,
K,
face_idxs.contiguous().data_ptr<int64_t>(),
zbuf.contiguous().data_ptr<float>(),
pix_dists.contiguous().data_ptr<float>(),
bary.contiguous().data_ptr<float>());
face_idxs.data_ptr<int64_t>(),
zbuf.data_ptr<float>(),
pix_dists.data_ptr<float>(),
bary.data_ptr<float>());
return std::make_tuple(face_idxs, zbuf, bary, pix_dists);
}

26
pytorch3d/csrc/rasterize_meshes/rasterize_meshes.h
View File

@ -96,9 +96,9 @@ RasterizeMeshesNaive(
// TODO: Better type checking.
if (face_verts.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(face_verts);
CHECK_CONTIGUOUS_CUDA(mesh_to_face_first_idx);
CHECK_CONTIGUOUS_CUDA(num_faces_per_mesh);
CHECK_CUDA(face_verts);
CHECK_CUDA(mesh_to_face_first_idx);
CHECK_CUDA(num_faces_per_mesh);
return RasterizeMeshesNaiveCuda(
face_verts,
mesh_to_face_first_idx,
@ -179,11 +179,11 @@ torch::Tensor RasterizeMeshesBackward(
const bool perspective_correct) {
if (face_verts.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(face_verts);
CHECK_CONTIGUOUS_CUDA(pix_to_face);
CHECK_CONTIGUOUS_CUDA(grad_zbuf);
CHECK_CONTIGUOUS_CUDA(grad_bary);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(face_verts);
CHECK_CUDA(pix_to_face);
CHECK_CUDA(grad_zbuf);
CHECK_CUDA(grad_bary);
CHECK_CUDA(grad_dists);
return RasterizeMeshesBackwardCuda(
face_verts,
pix_to_face,
@ -260,9 +260,9 @@ torch::Tensor RasterizeMeshesCoarse(
const int max_faces_per_bin) {
if (face_verts.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(face_verts);
CHECK_CONTIGUOUS_CUDA(mesh_to_face_first_idx);
CHECK_CONTIGUOUS_CUDA(num_faces_per_mesh);
CHECK_CUDA(face_verts);
CHECK_CUDA(mesh_to_face_first_idx);
CHECK_CUDA(num_faces_per_mesh);
return RasterizeMeshesCoarseCuda(
face_verts,
mesh_to_face_first_idx,
@ -359,8 +359,8 @@ RasterizeMeshesFine(
const bool cull_backfaces) {
if (face_verts.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(face_verts);
CHECK_CONTIGUOUS_CUDA(bin_faces);
CHECK_CUDA(face_verts);
CHECK_CUDA(bin_faces);
return RasterizeMeshesFineCuda(
face_verts,
bin_faces,

24
pytorch3d/csrc/rasterize_points/rasterize_points.h
View File

@ -67,9 +67,9 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsNaive(
if (points.is_cuda() && cloud_to_packed_first_idx.is_cuda() &&
num_points_per_cloud.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(cloud_to_packed_first_idx);
CHECK_CONTIGUOUS_CUDA(num_points_per_cloud);
CHECK_CUDA(points);
CHECK_CUDA(cloud_to_packed_first_idx);
CHECK_CUDA(num_points_per_cloud);
return RasterizePointsNaiveCuda(
points,
cloud_to_packed_first_idx,
@ -144,9 +144,9 @@ torch::Tensor RasterizePointsCoarse(
if (points.is_cuda() && cloud_to_packed_first_idx.is_cuda() &&
num_points_per_cloud.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(cloud_to_packed_first_idx);
CHECK_CONTIGUOUS_CUDA(num_points_per_cloud);
CHECK_CUDA(points);
CHECK_CUDA(cloud_to_packed_first_idx);
CHECK_CUDA(num_points_per_cloud);
return RasterizePointsCoarseCuda(
points,
cloud_to_packed_first_idx,
@ -215,8 +215,8 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsFine(
const int points_per_pixel) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(bin_points);
CHECK_CUDA(points);
CHECK_CUDA(bin_points);
return RasterizePointsFineCuda(
points, bin_points, image_size, radius, bin_size, points_per_pixel);
#else
@ -266,10 +266,10 @@ torch::Tensor RasterizePointsBackward(
const torch::Tensor& grad_dists) {
if (points.is_cuda()) {
#ifdef WITH_CUDA
CHECK_CONTIGUOUS_CUDA(points);
CHECK_CONTIGUOUS_CUDA(idxs);
CHECK_CONTIGUOUS_CUDA(grad_zbuf);
CHECK_CONTIGUOUS_CUDA(grad_dists);
CHECK_CUDA(points);
CHECK_CUDA(idxs);
CHECK_CUDA(grad_zbuf);
CHECK_CUDA(grad_dists);
return RasterizePointsBackwardCuda(points, idxs, grad_zbuf, grad_dists);
#else
AT_ERROR("Not compiled with GPU support");