validate lengths in chamfer and farthest_points

Summary: Fixes #1326

Reviewed By: kjchalup

Differential Revision: D39259697

fbshipit-source-id: 51392f4cc4a956165a62901cb115fcefe0e17277

pytorch3d/loss/chamfer.py

@@ -48,10 +48,11 @@ def _handle_pointcloud_input(
         if points.ndim != 3:
             raise ValueError("Expected points to be of shape (N, P, D)")
         X = points
-        if lengths is not None and (
-            lengths.ndim != 1 or lengths.shape[0] != X.shape[0]
-        ):
+        if lengths is not None:
+            if lengths.ndim != 1 or lengths.shape[0] != X.shape[0]:
                 raise ValueError("Expected lengths to be of shape (N,)")
+            if lengths.max() > X.shape[1]:
+                raise ValueError("A length value was too long")
         if lengths is None:
             lengths = torch.full(
                 (X.shape[0],), X.shape[1], dtype=torch.int64, device=points.device

pytorch3d/ops/sample_farthest_points.py

@@ -56,9 +56,11 @@ def sample_farthest_points(
     # Validate inputs
     if lengths is None:
         lengths = torch.full((N,), P, dtype=torch.int64, device=device)
-
+    else:
         if lengths.shape != (N,):
             raise ValueError("points and lengths must have same batch dimension.")
+        if lengths.max() > P:
+            raise ValueError("A value in lengths was too large.")
 
     # TODO: support providing K as a ratio of the total number of points instead of as an int
     if isinstance(K, int):
@@ -107,9 +109,11 @@ def sample_farthest_points_naive(
     # Validate inputs
     if lengths is None:
         lengths = torch.full((N,), P, dtype=torch.int64, device=device)
-
-    if lengths.shape[0] != N:
+    else:
+        if lengths.shape != (N,):
             raise ValueError("points and lengths must have same batch dimension.")
+        if lengths.max() > P:
+            raise ValueError("Invalid lengths.")
 
     # TODO: support providing K as a ratio of the total number of points instead of as an int
     if isinstance(K, int):