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Doesn't compile for me unless I change "long" to "int64_t" #195

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42 changes: 21 additions & 21 deletions hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -394,8 +394,8 @@ std::vector<std::vector<torch::Tensor>> build_hierarchy(std::vector<torch::Tenso
std::vector<torch::Tensor> grid_evencorners(grids.size());
std::vector<torch::Tensor> grid_oddcorners(grids.size());

texture_positions[0] = torch::zeros({seq2pos.size() / 3, 3}, float_options);
texture_positions[1] = torch::zeros({seq2pos.size() / 3}, float_options);
texture_positions[0] = torch::zeros({(int64_t)seq2pos.size() / 3, 3}, float_options);
texture_positions[1] = torch::zeros({(int64_t)seq2pos.size() / 3}, float_options);
float* positions_out_ptr = texture_positions[0].data_ptr<float>();
memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size());
positions_out_ptr = texture_positions[1].data_ptr<float>();
Expand All @@ -404,25 +404,25 @@ std::vector<std::vector<torch::Tensor>> build_hierarchy(std::vector<torch::Tenso
}

for (int i = 0; i < grids.size(); ++i) {
grid_neighbors[i] = torch::zeros({grids[i].seq2grid.size(), 9}, int64_options);
long* nptr = grid_neighbors[i].data_ptr<long>();
grid_neighbors[i] = torch::zeros({(int64_t)grids[i].seq2grid.size(), 9}, int64_options);
int64_t* nptr = grid_neighbors[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) {
nptr[j] = grids[i].seq2neighbor[j];
}

grid_evencorners[i] = torch::zeros({grids[i].seq2evencorner.size()}, int64_options);
grid_oddcorners[i] = torch::zeros({grids[i].seq2oddcorner.size()}, int64_options);
long* dptr = grid_evencorners[i].data_ptr<long>();
grid_evencorners[i] = torch::zeros({(int64_t)grids[i].seq2evencorner.size()}, int64_options);
grid_oddcorners[i] = torch::zeros({(int64_t)grids[i].seq2oddcorner.size()}, int64_options);
int64_t* dptr = grid_evencorners[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) {
dptr[j] = grids[i].seq2evencorner[j];
}
dptr = grid_oddcorners[i].data_ptr<long>();
dptr = grid_oddcorners[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) {
dptr[j] = grids[i].seq2oddcorner[j];
}
if (i + 1 < grids.size()) {
grid_downsamples[i] = torch::zeros({grids[i].downsample_seq.size()}, int64_options);
long* dptr = grid_downsamples[i].data_ptr<long>();
grid_downsamples[i] = torch::zeros({(int64_t)grids[i].downsample_seq.size()}, int64_options);
int64_t* dptr = grid_downsamples[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].downsample_seq.size(); ++j) {
dptr[j] = grids[i].downsample_seq[j];
}
Expand Down Expand Up @@ -534,9 +534,9 @@ std::vector<std::vector<torch::Tensor>> build_hierarchy_with_feat(
std::vector<torch::Tensor> grid_evencorners(grids.size());
std::vector<torch::Tensor> grid_oddcorners(grids.size());

texture_positions[0] = torch::zeros({seq2pos.size() / 3, 3}, float_options);
texture_positions[1] = torch::zeros({seq2pos.size() / 3}, float_options);
texture_feats[0] = torch::zeros({seq2feat.size() / feat_channel, feat_channel}, float_options);
texture_positions[0] = torch::zeros({(int64_t)seq2pos.size() / 3, 3}, float_options);
texture_positions[1] = torch::zeros({(int64_t)seq2pos.size() / 3}, float_options);
texture_feats[0] = torch::zeros({(int64_t)seq2feat.size() / feat_channel, feat_channel}, float_options);
float* positions_out_ptr = texture_positions[0].data_ptr<float>();
memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size());
positions_out_ptr = texture_positions[1].data_ptr<float>();
Expand All @@ -547,24 +547,24 @@ std::vector<std::vector<torch::Tensor>> build_hierarchy_with_feat(
memcpy(feats_out_ptr, seq2feat.data(), sizeof(float) * seq2feat.size());

for (int i = 0; i < grids.size(); ++i) {
grid_neighbors[i] = torch::zeros({grids[i].seq2grid.size(), 9}, int64_options);
long* nptr = grid_neighbors[i].data_ptr<long>();
grid_neighbors[i] = torch::zeros({(int64_t)grids[i].seq2grid.size(), 9}, int64_options);
int64_t* nptr = grid_neighbors[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) {
nptr[j] = grids[i].seq2neighbor[j];
}
grid_evencorners[i] = torch::zeros({grids[i].seq2evencorner.size()}, int64_options);
grid_oddcorners[i] = torch::zeros({grids[i].seq2oddcorner.size()}, int64_options);
long* dptr = grid_evencorners[i].data_ptr<long>();
grid_evencorners[i] = torch::zeros({(int64_t)grids[i].seq2evencorner.size()}, int64_options);
grid_oddcorners[i] = torch::zeros({(int64_t)grids[i].seq2oddcorner.size()}, int64_options);
int64_t* dptr = grid_evencorners[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) {
dptr[j] = grids[i].seq2evencorner[j];
}
dptr = grid_oddcorners[i].data_ptr<long>();
dptr = grid_oddcorners[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) {
dptr[j] = grids[i].seq2oddcorner[j];
}
if (i + 1 < grids.size()) {
grid_downsamples[i] = torch::zeros({grids[i].downsample_seq.size()}, int64_options);
long* dptr = grid_downsamples[i].data_ptr<long>();
grid_downsamples[i] = torch::zeros({(int64_t)grids[i].downsample_seq.size()}, int64_options);
int64_t* dptr = grid_downsamples[i].data_ptr<int64_t>();
for (int j = 0; j < grids[i].downsample_seq.size(); ++j) {
dptr[j] = grids[i].downsample_seq[j];
}
Expand Down
8 changes: 4 additions & 4 deletions hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -100,24 +100,24 @@ std::vector<torch::Tensor> rasterize_image_cpu(torch::Tensor V, torch::Tensor F,
auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false);
auto findices = torch::zeros({height, width}, options);
INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
auto z_min = torch::ones({height, width}, INT64_options) * (long)maxint;
auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;

if (!use_depth_prior) {
for (int i = 0; i < num_faces; ++i) {
rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), 0,
(INT64*)z_min.data_ptr<long>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
(INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
}
} else {
for (int i = 0; i < num_faces; ++i)
rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
(INT64*)z_min.data_ptr<long>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
(INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
}

auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
auto barycentric = torch::zeros({height, width, 3}, float_options);
for (int i = 0; i < width * height; ++i)
barycentricFromImgcoordCPU(V.data_ptr<float>(), F.data_ptr<int>(),
findices.data_ptr<int>(), (INT64*)z_min.data_ptr<long>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>(), i);
findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>(), i);

return {findices, barycentric};
}
Expand Down
8 changes: 4 additions & 4 deletions hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu
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Expand Up @@ -108,20 +108,20 @@ std::vector<torch::Tensor> rasterize_image_gpu(torch::Tensor V, torch::Tensor F,
auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false);
auto findices = torch::zeros({height, width}, options);
INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
auto z_min = torch::ones({height, width}, INT64_options) * (long)maxint;
auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;

if (!use_depth_prior) {
rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), 0,
(INT64*)z_min.data_ptr<long>(), occlusion_truncation, width, height, num_vertices, num_faces);
(INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces);
} else {
rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
(INT64*)z_min.data_ptr<long>(), occlusion_truncation, width, height, num_vertices, num_faces);
(INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces);
}

auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false);
auto barycentric = torch::zeros({height, width, 3}, float_options);
barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256>>>(V.data_ptr<float>(), F.data_ptr<int>(),
findices.data_ptr<int>(), (INT64*)z_min.data_ptr<long>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>());
findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>());

return {findices, barycentric};
}