Windows fix for marching cubes #1398

Summary: See #1398 .

Reviewed By: davidsonic

Differential Revision: D42139493

fbshipit-source-id: 972fc33b9c3017554ce704f2f10190eba406b7c8

pytorch3d/csrc/marching_cubes/marching_cubes.cu

@@ -13,7 +13,6 @@
 #include <thrust/scan.h>
 #include <cstdio>
 #include "marching_cubes/tables.h"
-#include "utils/pytorch3d_cutils.h"
 
 /*
 Parallelized marching cubes for pytorch extension
@@ -267,13 +266,12 @@ __global__ void CompactVoxelsKernel(
 //    isolevel: threshold to determine isosurface intersection
 //
 __global__ void GenerateFacesKernel(
-    torch::PackedTensorAccessor32<float, 2, torch::RestrictPtrTraits> verts,
-    torch::PackedTensorAccessor<int64_t, 2, torch::RestrictPtrTraits> faces,
-    torch::PackedTensorAccessor<int64_t, 1, torch::RestrictPtrTraits> ids,
-    torch::PackedTensorAccessor32<int, 1, torch::RestrictPtrTraits>
+    at::PackedTensorAccessor32<float, 2, at::RestrictPtrTraits> verts,
+    at::PackedTensorAccessor<int64_t, 2, at::RestrictPtrTraits> faces,
+    at::PackedTensorAccessor<int64_t, 1, at::RestrictPtrTraits> ids,
+    at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits>
         compactedVoxelArray,
-    torch::PackedTensorAccessor32<int, 1, torch::RestrictPtrTraits>
-        numVertsScanned,
+    at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits> numVertsScanned,
     const uint activeVoxels,
     const at::PackedTensorAccessor32<float, 3, at::RestrictPtrTraits> vol,
     const at::PackedTensorAccessor32<int, 2, at::RestrictPtrTraits> faceTable,
@@ -436,15 +434,15 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   // transfer _FACE_TABLE data to device
-  torch::Tensor face_table_tensor = torch::zeros(
-      {256, 16}, torch::TensorOptions().dtype(at::kInt).device(at::kCPU));
+  at::Tensor face_table_tensor = at::zeros(
+      {256, 16}, at::TensorOptions().dtype(at::kInt).device(at::kCPU));
   auto face_table_a = face_table_tensor.accessor<int, 2>();
   for (int i = 0; i < 256; i++) {
     for (int j = 0; j < 16; j++) {
       face_table_a[i][j] = _FACE_TABLE[i][j];
     }
   }
-  torch::Tensor faceTable = face_table_tensor.to(vol.device());
+  at::Tensor faceTable = face_table_tensor.to(vol.device());
 
   // get numVoxels
   int threads = 128;
@@ -458,10 +456,10 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   }
 
   auto d_voxelVerts =
-      torch::zeros({numVoxels}, torch::TensorOptions().dtype(at::kInt))
+      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
   auto d_voxelOccupied =
-      torch::zeros({numVoxels}, torch::TensorOptions().dtype(at::kInt))
+      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
 
   // Execute "ClassifyVoxelKernel" kernel to precompute
@@ -480,7 +478,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   // If the number of active voxels is 0, return zero tensor for verts and
   // faces.
   auto d_voxelOccupiedScan =
-      torch::zeros({numVoxels}, torch::TensorOptions().dtype(at::kInt))
+      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
   ThrustScanWrapper(
       d_voxelOccupiedScan.data_ptr<int>(),
@@ -493,23 +491,21 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   int activeVoxels = lastElement + lastScan;
 
   const int device_id = vol.device().index();
-  auto opt =
-      torch::TensorOptions().dtype(torch::kInt).device(torch::kCUDA, device_id);
-  auto opt_long = torch::TensorOptions()
-                      .dtype(torch::kInt64)
-                      .device(torch::kCUDA, device_id);
+  auto opt = at::TensorOptions().dtype(at::kInt).device(at::kCUDA, device_id);
+  auto opt_long =
+      at::TensorOptions().dtype(at::kLong).device(at::kCUDA, device_id);
 
   if (activeVoxels == 0) {
     int ntris = 0;
-    torch::Tensor verts = torch::zeros({ntris * 3, 3}, vol.options());
-    torch::Tensor faces = torch::zeros({ntris, 3}, opt_long);
-    torch::Tensor ids = torch::zeros({ntris}, opt_long);
+    at::Tensor verts = at::zeros({ntris * 3, 3}, vol.options());
+    at::Tensor faces = at::zeros({ntris, 3}, opt_long);
+    at::Tensor ids = at::zeros({ntris}, opt_long);
     return std::make_tuple(verts, faces, ids);
   }
 
   // Execute "CompactVoxelsKernel" kernel to compress voxels for accleration.
   // This allows us to run triangle generation on only the occupied voxels.
-  auto d_compVoxelArray = torch::zeros({activeVoxels}, opt);
+  auto d_compVoxelArray = at::zeros({activeVoxels}, opt);
   CompactVoxelsKernel<<<grid, threads, 0, stream>>>(
       d_compVoxelArray.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
       d_voxelOccupied.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
@@ -519,7 +515,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   cudaDeviceSynchronize();
 
   // Scan d_voxelVerts array to generate offsets of vertices for each voxel
-  auto d_voxelVertsScan = torch::zeros({numVoxels}, opt);
+  auto d_voxelVertsScan = at::zeros({numVoxels}, opt);
   ThrustScanWrapper(
       d_voxelVertsScan.data_ptr<int>(),
       d_voxelVerts.data_ptr<int>(),
@@ -533,10 +529,10 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   // Execute "GenerateFacesKernel" kernel
   // This runs only on the occupied voxels.
   // It looks up the field values and generates the triangle data.
-  torch::Tensor verts = torch::zeros({totalVerts, 3}, vol.options());
-  torch::Tensor faces = torch::zeros({totalVerts / 3, 3}, opt_long);
+  at::Tensor verts = at::zeros({totalVerts, 3}, vol.options());
+  at::Tensor faces = at::zeros({totalVerts / 3, 3}, opt_long);
 
-  torch::Tensor ids = torch::zeros({totalVerts}, opt_long);
+  at::Tensor ids = at::zeros({totalVerts}, opt_long);
 
   dim3 grid2((activeVoxels + threads - 1) / threads, 1, 1);
   if (grid2.x > 65535) {