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chamfer for empty pointclouds #1174
Summary: Fix divide by zero for empty pointcloud in chamfer. Also for empty batches. In process, needed to regularize num_points_per_cloud for empty batches. Reviewed By: kjchalup Differential Revision: D36311330 fbshipit-source-id: 3378ab738bee77ecc286f2110a5c8dc445960340
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@ -99,7 +99,7 @@ class ModelDBIR(ImplicitronModelBase, torch.nn.Module):
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mask_fg[is_known_idx],
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)
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pcl_size = int(point_cloud.num_points_per_cloud())
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pcl_size = point_cloud.num_points_per_cloud().item()
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if (self.max_points > 0) and (pcl_size > self.max_points):
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prm = torch.randperm(pcl_size)[: self.max_points]
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point_cloud = Pointclouds(
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@ -197,11 +197,13 @@ def chamfer_distance(
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cham_norm_x = cham_norm_x.sum(1) # (N,)
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cham_norm_y = cham_norm_y.sum(1) # (N,)
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if point_reduction == "mean":
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cham_x /= x_lengths
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cham_y /= y_lengths
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x_lengths_clamped = x_lengths.clamp(min=1)
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y_lengths_clamped = y_lengths.clamp(min=1)
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cham_x /= x_lengths_clamped
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cham_y /= y_lengths_clamped
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if return_normals:
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cham_norm_x /= x_lengths
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cham_norm_y /= y_lengths
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cham_norm_x /= x_lengths_clamped
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cham_norm_y /= y_lengths_clamped
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if batch_reduction is not None:
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# batch_reduction == "sum"
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@ -211,7 +213,7 @@ def chamfer_distance(
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cham_norm_x = cham_norm_x.sum()
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cham_norm_y = cham_norm_y.sum()
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if batch_reduction == "mean":
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div = weights.sum() if weights is not None else N
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div = weights.sum() if weights is not None else max(N, 1)
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cham_x /= div
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cham_y /= div
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if return_normals:
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@ -303,6 +303,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
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# weight each example by the inverse of number of points in the example
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point_to_cloud_idx = pcls.packed_to_cloud_idx() # (sum(P_i), )
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num_points_per_cloud = pcls.num_points_per_cloud() # (N,)
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# pyre-ignore[16]: `torch.Tensor` has no attribute `gather`
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weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
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weights_p = 1.0 / weights_p.float()
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point_to_edge = point_to_edge * weights_p
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@ -377,6 +378,7 @@ def point_mesh_face_distance(
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# weight each example by the inverse of number of points in the example
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point_to_cloud_idx = pcls.packed_to_cloud_idx() # (sum(P_i),)
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num_points_per_cloud = pcls.num_points_per_cloud() # (N,)
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# pyre-ignore[16]: `torch.Tensor` has no attribute `gather`
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weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
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weights_p = 1.0 / weights_p.float()
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point_to_face = point_to_face * weights_p
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@ -185,7 +185,6 @@ class Pointclouds:
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self._points_list = points
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self._N = len(self._points_list)
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self.valid = torch.zeros((self._N,), dtype=torch.bool, device=self.device)
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self._num_points_per_cloud = []
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if self._N > 0:
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self.device = self._points_list[0].device
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@ -208,6 +207,8 @@ class Pointclouds:
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if len(num_points_per_cloud.unique()) == 1:
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self.equisized = True
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self._num_points_per_cloud = num_points_per_cloud
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else:
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self._num_points_per_cloud = torch.tensor([], dtype=torch.int64)
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elif torch.is_tensor(points):
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if points.dim() != 3 or points.shape[2] != 3:
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@ -525,7 +526,7 @@ class Pointclouds:
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self._compute_packed()
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return self._cloud_to_packed_first_idx
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def num_points_per_cloud(self):
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def num_points_per_cloud(self) -> torch.Tensor:
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"""
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Return a 1D tensor x with length equal to the number of clouds giving
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the number of points in each cloud.
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@ -778,6 +778,17 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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with self.assertRaisesRegex(ValueError, "Support for 1 or 2 norm."):
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chamfer_distance(p1, p2, norm=3)
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def test_empty_clouds(self):
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# Check that point_reduction doesn't divide by zero
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points1 = Pointclouds(points=[torch.zeros(0, 3), torch.zeros(10, 3)])
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points2 = Pointclouds(points=torch.ones(2, 40, 3))
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loss, _ = chamfer_distance(points1, points2, batch_reduction=None)
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self.assertClose(loss, torch.tensor([0.0, 6.0]))
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# Check that batch_reduction doesn't divide by zero
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loss2, _ = chamfer_distance(Pointclouds([]), Pointclouds([]))
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self.assertClose(loss2, torch.tensor(0.0))
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@staticmethod
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def chamfer_with_init(
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batch_size: int,
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