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validate lengths in chamfer and farthest_points

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Summary: Fixes #1326

Reviewed By: kjchalup

Differential Revision: D39259697

fbshipit-source-id: 51392f4cc4a956165a62901cb115fcefe0e17277
This commit is contained in:
Jeremy Reizenstein 2022-09-08 15:03:36 -07:00 committed by Facebook GitHub Bot
parent 6e25fe8cb3
commit cb7bd33e7f
2 changed files with 15 additions and 10 deletions
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9
pytorch3d/loss/chamfer.py
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@ -48,10 +48,11 @@ def _handle_pointcloud_input(
if points.ndim != 3: if points.ndim != 3:
raise ValueError("Expected points to be of shape (N, P, D)") raise ValueError("Expected points to be of shape (N, P, D)")
X = points X = points
if lengths is not None and ( if lengths is not None:
lengths.ndim != 1 or lengths.shape[0] != X.shape[0] if lengths.ndim != 1 or lengths.shape[0] != X.shape[0]:
): raise ValueError("Expected lengths to be of shape (N,)")
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: if lengths is None:
lengths = torch.full( lengths = torch.full(
(X.shape[0],), X.shape[1], dtype=torch.int64, device=points.device (X.shape[0],), X.shape[1], dtype=torch.int64, device=points.device

16
pytorch3d/ops/sample_farthest_points.py
View File

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