torch C API warnings

Summary: This is mostly replacing the old PackedTensorAccessor with the new PackedTensorAccessor64.

Reviewed By: gkioxari

Differential Revision: D21088773

fbshipit-source-id: 5973e5a29d934eafb7c70ec5ec154ca076b64d27

pytorch3d/csrc/compositing/alpha_composite.cu

@@ -12,10 +12,10 @@
 // Currently, support is for floats only.
 __global__ void alphaCompositeCudaForwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> result,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> result,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
@@ -61,12 +61,12 @@ __global__ void alphaCompositeCudaForwardKernel(
 // Currently, support is for floats only.
 __global__ void alphaCompositeCudaBackwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> grad_features,
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_alphas,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_outputs,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> grad_features,
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_alphas,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_outputs,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
@@ -149,10 +149,10 @@ torch::Tensor alphaCompositeCudaForward(
   // doubles. Currently, support is for floats only.
   alphaCompositeCudaForwardKernel<<<numBlocks, threadsPerBlock>>>(
       // clang-format off
-      result.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      result.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return result;
@@ -175,12 +175,12 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeCudaBackward(
   // doubles. Currently, support is for floats only.
   alphaCompositeCudaBackwardKernel<<<numBlocks, threadsPerBlock>>>(
       // clang-format off
-      grad_features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      grad_alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      grad_outputs.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      grad_features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      grad_alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      grad_outputs.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return std::make_tuple(grad_features, grad_alphas);

pytorch3d/csrc/compositing/norm_weighted_sum.cu

@@ -14,10 +14,10 @@ __constant__ const float kEpsilon = 1e-4;
 // Currently, support is for floats only.
 __global__ void weightedSumNormCudaForwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> result,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> result,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
@@ -76,12 +76,12 @@ __global__ void weightedSumNormCudaForwardKernel(
 // Currently, support is for floats only.
 __global__ void weightedSumNormCudaBackwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> grad_features,
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_alphas,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_outputs,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> grad_features,
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_alphas,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_outputs,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
@@ -164,10 +164,10 @@ torch::Tensor weightedSumNormCudaForward(
   // doubles. Currently, support is for floats only.
   // clang-format off
   weightedSumNormCudaForwardKernel<<<numBlocks, threadsPerBlock>>>(
-      result.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      result.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return result;
@@ -190,12 +190,12 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumNormCudaBackward(
   // doubles. Currently, support is for floats only.
   weightedSumNormCudaBackwardKernel<<<numBlocks, threadsPerBlock>>>(
       // clang-format off
-      grad_features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      grad_alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      grad_outputs.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      grad_features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      grad_alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      grad_outputs.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return std::make_tuple(grad_features, grad_alphas);

pytorch3d/csrc/compositing/weighted_sum.cu

@@ -12,10 +12,10 @@
 // Currently, support is for floats only.
 __global__ void weightedSumCudaForwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> result,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> result,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
@@ -58,12 +58,12 @@ __global__ void weightedSumCudaForwardKernel(
 // Currently, support is for floats only.
 __global__ void weightedSumCudaBackwardKernel(
     // clang-format off
-    torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> grad_features,
-    torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_alphas,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> grad_outputs,
-    const torch::PackedTensorAccessor<float, 2, torch::RestrictPtrTraits, size_t> features,
-    const torch::PackedTensorAccessor<float, 4, torch::RestrictPtrTraits, size_t> alphas,
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> points_idx) {
+    torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> grad_features,
+    torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_alphas,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> grad_outputs,
+    const torch::PackedTensorAccessor64<float, 2, torch::RestrictPtrTraits> features,
+    const torch::PackedTensorAccessor64<float, 4, torch::RestrictPtrTraits> alphas,
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> points_idx) {
   // clang-format on
   const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
@@ -123,10 +123,10 @@ torch::Tensor weightedSumCudaForward(
   // doubles. Currently, support is for floats only.
   weightedSumCudaForwardKernel<<<numBlocks, threadsPerBlock>>>(
       // clang-format off
-      result.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      result.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return result;
@@ -149,12 +149,12 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumCudaBackward(
   // doubles. Currently, support is for floats only.
   weightedSumCudaBackwardKernel<<<numBlocks, threadsPerBlock>>>(
       // clang-format off
-      grad_features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      grad_alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      grad_outputs.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      features.packed_accessor<float, 2, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<float, 4, torch::RestrictPtrTraits, size_t>(),
-      points_idx.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>());
+      grad_features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      grad_alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      grad_outputs.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      features.packed_accessor64<float, 2, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<float, 4, torch::RestrictPtrTraits>(),
+      points_idx.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>());
   // clang-format on
 
   return std::make_tuple(grad_features, grad_alphas);

pytorch3d/csrc/face_areas_normals/face_areas_normals_cpu.cpp

@@ -6,7 +6,6 @@
 std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForwardCpu(
     const at::Tensor verts,
     const at::Tensor faces) {
-  const int V = verts.size(0);
   const int F = faces.size(0);
 
   at::Tensor areas = at::empty({F}, verts.options());

pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor_cpu.cpp

@@ -38,7 +38,6 @@ at::Tensor PaddedToPackedCpu(
     const at::Tensor first_idxs,
     const int64_t num_inputs) {
   const int64_t batch_size = inputs_padded.size(0);
-  const int64_t max_size = inputs_padded.size(1);
 
   AT_ASSERTM(
       inputs_padded.dim() == 3, "inputs_padded must be a 3-dimensional tensor");

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -415,7 +415,6 @@ torch::Tensor RasterizeMeshesCoarseCpu(
   auto opts = face_verts.options().dtype(torch::kInt32);
   torch::Tensor faces_per_bin = torch::zeros({N, BH, BW}, opts);
   torch::Tensor bin_faces = torch::full({N, BH, BW, M}, -1, opts);
-  auto faces_per_bin_a = faces_per_bin.accessor<int32_t, 3>();
   auto bin_faces_a = bin_faces.accessor<int32_t, 4>();
 
   // Precompute all face bounding boxes.