type() deprecated

Summary:
Replace `tensor.type().is_cuda()` with the preferred `tensor.is_cuda()`.
Replace `AT_DISPATCH_FLOATING_TYPES(tensor.type(), ...` with `AT_DISPATCH_FLOATING_TYPES(tensor.scalar_type(), ...`.
These avoid deprecation warnings in future pytorch.

Reviewed By: nikhilaravi

Differential Revision: D20646565

fbshipit-source-id: 1a0c15978c871af816b1dd7d4a7ea78242abd95e

pytorch3d/csrc/compositing/alpha_composite.h

@@ -58,7 +58,7 @@ torch::Tensor alphaCompositeForward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (features.type().is_cuda()) {
+  if (features.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(features);
     CHECK_CONTIGUOUS_CUDA(alphas);
@@ -86,7 +86,7 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeBackward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (grad_outputs.type().is_cuda()) {
+  if (grad_outputs.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(grad_outputs);
     CHECK_CONTIGUOUS_CUDA(features);

pytorch3d/csrc/compositing/norm_weighted_sum.h

@@ -56,7 +56,7 @@ torch::Tensor weightedSumNormForward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (features.type().is_cuda()) {
+  if (features.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(features);
     CHECK_CONTIGUOUS_CUDA(alphas);
@@ -85,7 +85,7 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumNormBackward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (grad_outputs.type().is_cuda()) {
+  if (grad_outputs.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(grad_outputs);
     CHECK_CONTIGUOUS_CUDA(features);

pytorch3d/csrc/compositing/weighted_sum.h

@@ -56,7 +56,7 @@ torch::Tensor weightedSumForward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (features.type().is_cuda()) {
+  if (features.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(features);
     CHECK_CONTIGUOUS_CUDA(alphas);
@@ -84,7 +84,7 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumBackward(
   alphas = alphas.contiguous();
   points_idx = points_idx.contiguous();
 
-  if (grad_outputs.type().is_cuda()) {
+  if (grad_outputs.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(grad_outputs);
     CHECK_CONTIGUOUS_CUDA(features);

pytorch3d/csrc/face_areas_normals/face_areas_normals.cu

@@ -219,7 +219,7 @@ std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForwardCuda(
   const int blocks = 64;
   const int threads = 512;
   AT_DISPATCH_FLOATING_TYPES(
-      verts.type(), "face_areas_normals_forward_cuda", ([&] {
+      verts.scalar_type(), "face_areas_normals_forward_cuda", ([&] {
         FaceAreasNormalsForwardKernel<scalar_t><<<blocks, threads>>>(
             verts.data_ptr<scalar_t>(),
             faces.data_ptr<int64_t>(),

pytorch3d/csrc/face_areas_normals/face_areas_normals.h

@@ -44,7 +44,7 @@ at::Tensor FaceAreasNormalsBackwardCuda(
 std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForward(
     const at::Tensor verts,
     const at::Tensor faces) {
-  if (verts.type().is_cuda() && faces.type().is_cuda()) {
+  if (verts.is_cuda() && faces.is_cuda()) {
 #ifdef WITH_CUDA
     return FaceAreasNormalsForwardCuda(verts, faces);
 #else
@@ -60,7 +60,7 @@ at::Tensor FaceAreasNormalsBackward(
     const at::Tensor grad_normals,
     const at::Tensor verts,
     const at::Tensor faces) {
-  if (verts.type().is_cuda() && faces.type().is_cuda()) {
+  if (verts.is_cuda() && faces.is_cuda()) {
 #ifdef WITH_CUDA
     return FaceAreasNormalsBackwardCuda(grad_areas, grad_normals, verts, faces);
 #else

pytorch3d/csrc/gather_scatter/gather_scatter.h

@@ -32,7 +32,7 @@ at::Tensor gather_scatter(
     const at::Tensor edges,
     bool directed,
     bool backward) {
-  if (input.type().is_cuda() && edges.type().is_cuda()) {
+  if (input.is_cuda() && edges.is_cuda()) {
 #ifdef WITH_CUDA
     return gather_scatter_cuda(input, edges, directed, backward);
 #else

pytorch3d/csrc/nearest_neighbor_points/nearest_neighbor_points.cu

@@ -228,11 +228,11 @@ at::Tensor NearestNeighborIdxCuda(at::Tensor p1, at::Tensor p2) {
 
   if (D == 3) {
     // Use the specialized kernel for D=3.
-    AT_DISPATCH_FLOATING_TYPES(p1.type(), "nearest_neighbor_v3_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(
-                                 size_t shared_size = threads * sizeof(size_t) +
+        p1.scalar_type(), "nearest_neighbor_v3_cuda", ([&] {
-                                     threads * sizeof(int64_t);
+          size_t shared_size =
-                                 NearestNeighborKernelD3<scalar_t>
+              threads * sizeof(size_t) + threads * sizeof(int64_t);
-                                     <<<blocks, threads, shared_size>>>(
+          NearestNeighborKernelD3<scalar_t><<<blocks, threads, shared_size>>>(
               p1.data_ptr<scalar_t>(),
               p2.data_ptr<scalar_t>(),
               idx.data_ptr<int64_t>(),
@@ -243,7 +243,7 @@ at::Tensor NearestNeighborIdxCuda(at::Tensor p1, at::Tensor p2) {
   } else {
     // Use the general kernel for all other D.
     AT_DISPATCH_FLOATING_TYPES(
-        p1.type(), "nearest_neighbor_v3_cuda", ([&] {
+        p1.scalar_type(), "nearest_neighbor_v3_cuda", ([&] {
           // To avoid misaligned memory access, the size of shared buffers
           // need to be rounded to the next even size.
           size_t D_2 = D + (D % 2);

pytorch3d/csrc/nearest_neighbor_points/nearest_neighbor_points.h

@@ -29,7 +29,7 @@ at::Tensor NearestNeighborIdxCuda(at::Tensor p1, at::Tensor p2);
 
 // Implementation which is exposed.
 at::Tensor NearestNeighborIdx(at::Tensor p1, at::Tensor p2) {
-  if (p1.type().is_cuda() && p2.type().is_cuda()) {
+  if (p1.is_cuda() && p2.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(p1);
     CHECK_CONTIGUOUS_CUDA(p2);

pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor.cu

@@ -128,7 +128,7 @@ at::Tensor PackedToPaddedCuda(
   const int blocks = batch_size;
   if (D == 1) {
     AT_DISPATCH_FLOATING_TYPES(
-        inputs_packed.type(), "packed_to_padded_d1_kernel", ([&] {
+        inputs_packed.scalar_type(), "packed_to_padded_d1_kernel", ([&] {
           PackedToPaddedKernelD1<scalar_t><<<blocks, threads>>>(
               inputs_packed.data_ptr<scalar_t>(),
               first_idxs.data_ptr<int64_t>(),
@@ -139,7 +139,7 @@ at::Tensor PackedToPaddedCuda(
         }));
   } else {
     AT_DISPATCH_FLOATING_TYPES(
-        inputs_packed.type(), "packed_to_padded_kernel", ([&] {
+        inputs_packed.scalar_type(), "packed_to_padded_kernel", ([&] {
           PackedToPaddedKernel<scalar_t><<<blocks, threads>>>(
               inputs_packed.data_ptr<scalar_t>(),
               first_idxs.data_ptr<int64_t>(),
@@ -175,7 +175,7 @@ at::Tensor PaddedToPackedCuda(
 
   if (D == 1) {
     AT_DISPATCH_FLOATING_TYPES(
-        inputs_padded.type(), "padded_to_packed_d1_kernel", ([&] {
+        inputs_padded.scalar_type(), "padded_to_packed_d1_kernel", ([&] {
           PaddedToPackedKernelD1<scalar_t><<<blocks, threads>>>(
               inputs_padded.data_ptr<scalar_t>(),
               first_idxs.data_ptr<int64_t>(),
@@ -186,7 +186,7 @@ at::Tensor PaddedToPackedCuda(
         }));
   } else {
     AT_DISPATCH_FLOATING_TYPES(
-        inputs_padded.type(), "padded_to_packed_kernel", ([&] {
+        inputs_padded.scalar_type(), "padded_to_packed_kernel", ([&] {
           PaddedToPackedKernel<scalar_t><<<blocks, threads>>>(
               inputs_padded.data_ptr<scalar_t>(),
               first_idxs.data_ptr<int64_t>(),

pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor.h

@@ -72,7 +72,7 @@ at::Tensor PackedToPadded(
     const at::Tensor inputs_packed,
     const at::Tensor first_idxs,
     const int64_t max_size) {
-  if (inputs_packed.type().is_cuda()) {
+  if (inputs_packed.is_cuda()) {
 #ifdef WITH_CUDA
     return PackedToPaddedCuda(inputs_packed, first_idxs, max_size);
 #else
@@ -87,7 +87,7 @@ at::Tensor PaddedToPacked(
     const at::Tensor inputs_padded,
     const at::Tensor first_idxs,
     const int64_t num_inputs) {
-  if (inputs_padded.type().is_cuda()) {
+  if (inputs_padded.is_cuda()) {
 #ifdef WITH_CUDA
     return PaddedToPackedCuda(inputs_padded, first_idxs, num_inputs);
 #else

pytorch3d/csrc/pytorch3d_cutils.h

@@ -3,8 +3,7 @@
 #pragma once
 #include <torch/extension.h>
 
-#define CHECK_CUDA(x) \
+#define CHECK_CUDA(x) AT_ASSERTM(x.is_cuda(), #x "must be a CUDA tensor.")
-  AT_ASSERTM(x.type().is_cuda(), #x "must be a CUDA tensor.")
 #define CHECK_CONTIGUOUS(x) \
   AT_ASSERTM(x.is_contiguous(), #x "must be contiguous.")
 #define CHECK_CONTIGUOUS_CUDA(x) \

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.h

@@ -82,7 +82,7 @@ RasterizeMeshesNaive(
     const int faces_per_pixel,
     const bool perspective_correct) {
   // TODO: Better type checking.
-  if (face_verts.type().is_cuda()) {
+  if (face_verts.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizeMeshesNaiveCuda(
         face_verts,
@@ -160,7 +160,7 @@ torch::Tensor RasterizeMeshesBackward(
     const torch::Tensor& grad_bary,
     const torch::Tensor& grad_dists,
     const bool perspective_correct) {
-  if (face_verts.type().is_cuda()) {
+  if (face_verts.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizeMeshesBackwardCuda(
         face_verts,
@@ -236,7 +236,7 @@ torch::Tensor RasterizeMeshesCoarse(
     const float blur_radius,
     const int bin_size,
     const int max_faces_per_bin) {
-  if (face_verts.type().is_cuda()) {
+  if (face_verts.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizeMeshesCoarseCuda(
         face_verts,
@@ -322,7 +322,7 @@ RasterizeMeshesFine(
     const int bin_size,
     const int faces_per_pixel,
     const bool perspective_correct) {
-  if (face_verts.type().is_cuda()) {
+  if (face_verts.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizeMeshesFineCuda(
         face_verts,

pytorch3d/csrc/rasterize_points/rasterize_points.h

@@ -63,8 +63,8 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsNaive(
     const int image_size,
     const float radius,
     const int points_per_pixel) {
-  if (points.type().is_cuda() && cloud_to_packed_first_idx.type().is_cuda() &&
+  if (points.is_cuda() && cloud_to_packed_first_idx.is_cuda() &&
-      num_points_per_cloud.type().is_cuda()) {
+      num_points_per_cloud.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizePointsNaiveCuda(
         points,
@@ -137,8 +137,8 @@ torch::Tensor RasterizePointsCoarse(
     const float radius,
     const int bin_size,
     const int max_points_per_bin) {
-  if (points.type().is_cuda() && cloud_to_packed_first_idx.type().is_cuda() &&
+  if (points.is_cuda() && cloud_to_packed_first_idx.is_cuda() &&
-      num_points_per_cloud.type().is_cuda()) {
+      num_points_per_cloud.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizePointsCoarseCuda(
         points,
@@ -206,7 +206,7 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsFine(
     const float radius,
     const int bin_size,
     const int points_per_pixel) {
-  if (points.type().is_cuda()) {
+  if (points.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizePointsFineCuda(
         points, bin_points, image_size, radius, bin_size, points_per_pixel);
@@ -255,7 +255,7 @@ torch::Tensor RasterizePointsBackward(
     const torch::Tensor& idxs,
     const torch::Tensor& grad_zbuf,
     const torch::Tensor& grad_dists) {
-  if (points.type().is_cuda()) {
+  if (points.is_cuda()) {
 #ifdef WITH_CUDA
     return RasterizePointsBackwardCuda(points, idxs, grad_zbuf, grad_dists);
 #else