lint fixes

Summary: Ran `dev/linter.sh`.

Reviewed By: bottler

Differential Revision: D19761062

fbshipit-source-id: 1a49abe4a5f2bc7641b2b46e254aa77e6a48aa7d

pytorch3d/csrc/nearest_neighbor_points/nearest_neighbors_points_cpu.cpp

@@ -3,36 +3,36 @@
 #include <torch/extension.h>
 
 at::Tensor NearestNeighborIdxCpu(at::Tensor p1, at::Tensor p2) {
-    const int N = p1.size(0);
+  const int N = p1.size(0);
-    const int P1 = p1.size(1);
+  const int P1 = p1.size(1);
-    const int D = p1.size(2);
+  const int D = p1.size(2);
-    const int P2 = p2.size(1);
+  const int P2 = p2.size(1);
 
-    auto long_opts = p1.options().dtype(torch::kInt64);
+  auto long_opts = p1.options().dtype(torch::kInt64);
-    torch::Tensor out = torch::empty({N, P1}, long_opts);
+  torch::Tensor out = torch::empty({N, P1}, long_opts);
 
-    auto p1_a = p1.accessor<float, 3>();
+  auto p1_a = p1.accessor<float, 3>();
-    auto p2_a = p2.accessor<float, 3>();
+  auto p2_a = p2.accessor<float, 3>();
-    auto out_a = out.accessor<int64_t, 2>();
+  auto out_a = out.accessor<int64_t, 2>();
 
-    for (int n = 0; n < N; ++n) {
+  for (int n = 0; n < N; ++n) {
-        for (int i1 = 0; i1 < P1; ++i1) {
+    for (int i1 = 0; i1 < P1; ++i1) {
-            // TODO: support other floating-point types?
+      // TODO: support other floating-point types?
-            float min_dist = -1;
+      float min_dist = -1;
-            int64_t min_idx = -1;
+      int64_t min_idx = -1;
-            for (int i2 = 0; i2 < P2; ++i2) {
+      for (int i2 = 0; i2 < P2; ++i2) {
-                float dist = 0;
+        float dist = 0;
-                for (int d = 0; d < D; ++d) {
+        for (int d = 0; d < D; ++d) {
-                    float diff = p1_a[n][i1][d] - p2_a[n][i2][d];
+          float diff = p1_a[n][i1][d] - p2_a[n][i2][d];
-                    dist += diff * diff;
+          dist += diff * diff;
-                }
-                if (min_dist == -1 || dist < min_dist) {
-                    min_dist = dist;
-                    min_idx = i2;
-                }
-            }
-            out_a[n][i1] = min_idx;
         }
+        if (min_dist == -1 || dist < min_dist) {
+          min_dist = dist;
+          min_idx = i2;
+        }
+      }
+      out_a[n][i1] = min_idx;
     }
-    return out;
+  }
+  return out;
 }

scripts/parse_tutorials.py

@@ -9,7 +9,6 @@ import nbformat
 from bs4 import BeautifulSoup
 from nbconvert import HTMLExporter, ScriptExporter
 
-
 TEMPLATE = """const CWD = process.cwd();
 
 const React = require('react');
@@ -43,7 +42,9 @@ def gen_tutorials(repo_dir: str) -> None:
     Also create ipynb and py versions of tutorial in Docusaurus site for
     download.
     """
-    with open(os.path.join(repo_dir, "website", "tutorials.json"), "r") as infile:
+    with open(
+        os.path.join(repo_dir, "website", "tutorials.json"), "r"
+    ) as infile:
         tutorial_config = json.loads(infile.read())
 
     tutorial_ids = {x["id"] for v in tutorial_config.values() for x in v}
@@ -52,7 +53,9 @@ def gen_tutorials(repo_dir: str) -> None:
         print("Generating {} tutorial".format(tid))
 
         # convert notebook to HTML
-        ipynb_in_path = os.path.join(repo_dir, "docs", "tutorials", "{}.ipynb".format(tid))
+        ipynb_in_path = os.path.join(
+            repo_dir, "docs", "tutorials", "{}.ipynb".format(tid)
+        )
         with open(ipynb_in_path, "r") as infile:
             nb_str = infile.read()
             nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)
@@ -105,7 +108,10 @@ if __name__ == "__main__":
         description="Generate JS, HTML, ipynb, and py files for tutorials."
     )
     parser.add_argument(
-        "--repo_dir", metavar="path", required=True, help="Pytorch3D repo directory."
+        "--repo_dir",
+        metavar="path",
+        required=True,
+        help="Pytorch3D repo directory.",
     )
     args = parser.parse_args()
-    gen_tutorials(args.repo_dir)
+    gen_tutorials(args.repo_dir)

tests/test_nearest_neighbor_points.py

@@ -43,21 +43,21 @@ class TestNearestNeighborPoints(unittest.TestCase):
         """
         Test cuda output vs naive python implementation.
         """
-        device = torch.device('cuda:0')
+        device = torch.device("cuda:0")
         self._test_nn_helper(device)
 
     def test_nn_cpu(self):
         """
         Test cpu output vs naive python implementation
         """
-        device = torch.device('cpu')
+        device = torch.device("cpu")
         self._test_nn_helper(device)
 
     @staticmethod
     def bm_nn_points_cpu_with_init(
         N: int = 4, D: int = 4, P1: int = 128, P2: int = 128
     ):
-        device = torch.device('cpu')
+        device = torch.device("cpu")
         x = torch.randn(N, P1, D, device=device)
         y = torch.randn(N, P2, D, device=device)