test & compilation fixes

Summary:
Fixes mostly related to the "main" build on circleci.
-Avoid error to do with tuple copy from initializer_list which is `explicit` on old compiler.
-Add better reporting to copyright test.
-Move to PackedTensorAccessor64 from the deprecated PackedTensorAccessor
-Avoid some warnings about mismatched comparisons.

The "main" build is the only one that runs the test_build stuff. In that area
-Fix my bad copyright fix D26275931 (https://github.com/facebookresearch/pytorch3d/commit/3463f418b8a3e3c3e6b6a20007b38eb71938ec7f) / 965c9c
-Add test that all tutorials are valid json.

Reviewed By: nikhilaravi

Differential Revision: D26366466

fbshipit-source-id: c4ab8b7e6647987069f7cb7144aa6ab7c24bcdac

projects/nerf/__init__.py

-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

pytorch3d/csrc/blending/sigmoid_alpha_blend.cu

@@ -9,9 +9,9 @@
 template <typename scalar_t>
 __global__ void SigmoidAlphaBlendForwardKernel(
     // clang-format off
-    const torch::PackedTensorAccessor<scalar_t, 4, torch::RestrictPtrTraits, size_t> distances, // (N, H, W, K)
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> pix_to_face, // (N, H, W, K)
-    torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> alphas, // (N, H, W)
+    const torch::PackedTensorAccessor64<scalar_t, 4, torch::RestrictPtrTraits> distances, // (N, H, W, K)
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> pix_to_face, // (N, H, W, K)
+    torch::PackedTensorAccessor64<scalar_t, 3, torch::RestrictPtrTraits> alphas, // (N, H, W)
     // clang-format on
     const scalar_t sigma,
     const int N,
@@ -93,9 +93,9 @@ torch::Tensor SigmoidAlphaBlendForwardCuda(
       distances.scalar_type(), "sigmoid_alpha_blend_kernel", ([&] {
         // clang-format off
       SigmoidAlphaBlendForwardKernel<scalar_t><<<blocks, threads, 0, stream>>>(
-      distances.packed_accessor<scalar_t, 4, torch::RestrictPtrTraits, size_t>(),
-      pix_to_face.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>(),
-      alphas.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),
+      distances.packed_accessor64<scalar_t, 4, torch::RestrictPtrTraits>(),
+      pix_to_face.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>(),
+      alphas.packed_accessor64<scalar_t, 3, torch::RestrictPtrTraits>(),
       sigma,
       N,
       H,
@@ -111,11 +111,11 @@ torch::Tensor SigmoidAlphaBlendForwardCuda(
 template <typename scalar_t>
 __global__ void SigmoidAlphaBlendBackwardKernel(
     // clang-format off
-    const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> grad_alphas, // (N, H, W)
-    const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> alphas, // (N, H, W)
-    const torch::PackedTensorAccessor<scalar_t, 4, torch::RestrictPtrTraits, size_t> distances, // (N, H, W, K)
-    const torch::PackedTensorAccessor<int64_t, 4, torch::RestrictPtrTraits, size_t> pix_to_face, // (N, H, W, K)
-    torch::PackedTensorAccessor<scalar_t, 4, torch::RestrictPtrTraits, size_t> grad_distances, // (N, H, W)
+    const torch::PackedTensorAccessor64<scalar_t, 3, torch::RestrictPtrTraits> grad_alphas, // (N, H, W)
+    const torch::PackedTensorAccessor64<scalar_t, 3, torch::RestrictPtrTraits> alphas, // (N, H, W)
+    const torch::PackedTensorAccessor64<scalar_t, 4, torch::RestrictPtrTraits> distances, // (N, H, W, K)
+    const torch::PackedTensorAccessor64<int64_t, 4, torch::RestrictPtrTraits> pix_to_face, // (N, H, W, K)
+    torch::PackedTensorAccessor64<scalar_t, 4, torch::RestrictPtrTraits> grad_distances, // (N, H, W)
     // clang-format on
     const scalar_t sigma,
     const int N,
@@ -192,11 +192,11 @@ torch::Tensor SigmoidAlphaBlendBackwardCuda(
         SigmoidAlphaBlendBackwardKernel<scalar_t>
             <<<blocks, threads, 0, stream>>>(
                 // clang-format off
-            grad_alphas.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),
-            alphas.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),
-            distances.packed_accessor<scalar_t, 4, torch::RestrictPtrTraits, size_t>(),
-            pix_to_face.packed_accessor<int64_t, 4, torch::RestrictPtrTraits, size_t>(),
-            grad_distances.packed_accessor<scalar_t, 4, torch::RestrictPtrTraits, size_t>(),
+            grad_alphas.packed_accessor64<scalar_t, 3, torch::RestrictPtrTraits>(),
+            alphas.packed_accessor64<scalar_t, 3, torch::RestrictPtrTraits>(),
+            distances.packed_accessor64<scalar_t, 4, torch::RestrictPtrTraits>(),
+            pix_to_face.packed_accessor64<int64_t, 4, torch::RestrictPtrTraits>(),
+            grad_distances.packed_accessor64<scalar_t, 4, torch::RestrictPtrTraits>(),
                 // clang-format on
                 sigma,
                 N,

pytorch3d/csrc/pulsar/pytorch/renderer.cpp

@@ -214,57 +214,63 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
     batch_processing = true;
     batch_size = vert_pos.size(0);
     THArgCheck(
-        vert_col.ndimension() == 3 && vert_col.size(0) == batch_size,
+        vert_col.ndimension() == 3 &&
+            vert_col.size(0) == static_cast<int64_t>(batch_size),
         2,
         "vert_col needs to have batch size.");
     THArgCheck(
-        vert_radii.ndimension() == 2 && vert_radii.size(0) == batch_size,
+        vert_radii.ndimension() == 2 &&
+            vert_radii.size(0) == static_cast<int64_t>(batch_size),
         3,
         "vert_radii must be specified per batch.");
     THArgCheck(
-        cam_pos.ndimension() == 2 && cam_pos.size(0) == batch_size,
+        cam_pos.ndimension() == 2 &&
+            cam_pos.size(0) == static_cast<int64_t>(batch_size),
         4,
         "cam_pos must be specified per batch and have the correct batch size.");
     THArgCheck(
         pixel_0_0_center.ndimension() == 2 &&
-            pixel_0_0_center.size(0) == batch_size,
+            pixel_0_0_center.size(0) == static_cast<int64_t>(batch_size),
         5,
         "pixel_0_0_center must be specified per batch.");
     THArgCheck(
-        pixel_vec_x.ndimension() == 2 && pixel_vec_x.size(0) == batch_size,
+        pixel_vec_x.ndimension() == 2 &&
+            pixel_vec_x.size(0) == static_cast<int64_t>(batch_size),
         6,
         "pixel_vec_x must be specified per batch.");
     THArgCheck(
-        pixel_vec_y.ndimension() == 2 && pixel_vec_y.size(0) == batch_size,
+        pixel_vec_y.ndimension() == 2 &&
+            pixel_vec_y.size(0) == static_cast<int64_t>(batch_size),
         7,
         "pixel_vec_y must be specified per batch.");
     THArgCheck(
-        focal_length.ndimension() == 1 && focal_length.size(0) == batch_size,
+        focal_length.ndimension() == 1 &&
+            focal_length.size(0) == static_cast<int64_t>(batch_size),
         8,
         "focal_length must be specified per batch.");
     THArgCheck(
         principal_point_offsets.ndimension() == 2 &&
-            principal_point_offsets.size(0) == batch_size,
+            principal_point_offsets.size(0) == static_cast<int64_t>(batch_size),
         9,
         "principal_point_offsets must be specified per batch.");
     if (opacity.has_value()) {
       THArgCheck(
           opacity.value().ndimension() == 2 &&
-              opacity.value().size(0) == batch_size,
+              opacity.value().size(0) == static_cast<int64_t>(batch_size),
           13,
           "Opacity needs to be specified batch-wise.");
     }
     // Check all parameters are for a matching number of points.
     n_points = vert_pos.size(1);
     THArgCheck(
-        vert_col.size(1) == n_points,
+        vert_col.size(1) == static_cast<int64_t>(n_points),
         2,
         ("The number of points for vertex positions (" +
          std::to_string(n_points) + ") and vertex colors (" +
          std::to_string(vert_col.size(1)) + ") doesn't agree.")
             .c_str());
     THArgCheck(
-        vert_radii.size(1) == n_points,
+        vert_radii.size(1) == static_cast<int64_t>(n_points),
         3,
         ("The number of points for vertex positions (" +
          std::to_string(n_points) + ") and vertex radii (" +
@@ -272,7 +278,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
             .c_str());
     if (opacity.has_value()) {
       THArgCheck(
-          opacity.value().size(1) == n_points,
+          opacity.value().size(1) == static_cast<int64_t>(n_points),
           13,
           "Opacity needs to be specified per point.");
     }
@@ -352,14 +358,14 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
     // Check each.
     n_points = vert_pos.size(0);
     THArgCheck(
-        vert_col.size(0) == n_points,
+        vert_col.size(0) == static_cast<int64_t>(n_points),
         2,
         ("The number of points for vertex positions (" +
          std::to_string(n_points) + ") and vertex colors (" +
          std::to_string(vert_col.size(0)) + ") doesn't agree.")
             .c_str());
     THArgCheck(
-        vert_radii.size(0) == n_points,
+        vert_radii.size(0) == static_cast<int64_t>(n_points),
         3,
         ("The number of points for vertex positions (" +
          std::to_string(n_points) + ") and vertex radii (" +
@@ -367,7 +373,7 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
             .c_str());
     if (opacity.has_value()) {
       THArgCheck(
-          opacity.value().size(0) == n_points,
+          opacity.value().size(0) == static_cast<int64_t>(n_points),
           12,
           "Opacity needs to be specified per point.");
     }
@@ -958,12 +964,15 @@ Renderer::backward(
   }
   if (batch_processing) {
     THArgCheck(
-        grad_im.size(0) == batch_size,
+        grad_im.size(0) == static_cast<int64_t>(batch_size),
         1,
         "Gradient image batch size must agree.");
-    THArgCheck(image.size(0) == batch_size, 2, "Image batch size must agree.");
     THArgCheck(
-        forw_info.size(0) == batch_size,
+        image.size(0) == static_cast<int64_t>(batch_size),
+        2,
+        "Image batch size must agree.");
+    THArgCheck(
+        forw_info.size(0) == static_cast<int64_t>(batch_size),
         3,
         "forward info must have batch size.");
   }

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -291,8 +291,8 @@ RasterizeMeshesNaiveCpu(
             const float dist_neighbor = std::abs(std::get<2>(neighbor));
             if (dist < dist_neighbor) {
               // Overwrite the neighbor face values.
-              q[idx_top_k] = {
-                  pz, f, signed_dist, bary_clip.x, bary_clip.y, bary_clip.z};
+              q[idx_top_k] = std::make_tuple(
+                  pz, f, signed_dist, bary_clip.x, bary_clip.y, bary_clip.z);
             }
           } else {
             // Handle as a normal face.

tests/test_build.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+import json
 import os
 import unittest
 from collections import Counter
@@ -39,18 +40,33 @@ class TestBuild(unittest.TestCase):
             + " All rights reserved.\n"
         )
 
+        files_missing_copyright_header = []
+
         for extension in extensions:
-            for i in root_dir.glob(f"**/*.{extension}"):
-                if str(i).endswith(
+            for path in root_dir.glob(f"**/*.{extension}"):
+                if str(path).endswith(
                     "pytorch3d/transforms/external/kornia_angle_axis_to_rotation_matrix.py"
                 ):
                     continue
-                if str(i).endswith("pytorch3d/csrc/pulsar/include/fastermath.h"):
+                if str(path).endswith("pytorch3d/csrc/pulsar/include/fastermath.h"):
                     continue
-                with open(i) as f:
+                with open(path) as f:
                     firstline = f.readline()
                     if firstline.startswith(("# -*-", "#!")):
                         firstline = f.readline()
-                    self.assertTrue(
-                        firstline.endswith(expect), f"{i} missing copyright header."
-                    )
+                    if not firstline.endswith(expect):
+                        files_missing_copyright_header.append(str(path))
+
+        if len(files_missing_copyright_header) != 0:
+            self.fail("\n".join(files_missing_copyright_header))
+
+    @unittest.skipIf(in_conda_build, "In conda build")
+    def test_valid_ipynbs(self):
+        # Check that the ipython notebooks are valid json
+        test_dir = Path(__file__).resolve().parent
+        tutorials_dir = test_dir.parent / "docs" / "tutorials"
+        tutorials = sorted(tutorials_dir.glob("*.ipynb"))
+
+        for tutorial in tutorials:
+            with open(tutorial) as f:
+                json.load(f)