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bugfix

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Summary: It seemed that even though the chamfer diff was rebased on top of the knn autograd diff, some of the final updates did not get applied. I'm really surprised that the sandcastle tests did not fail and prevent the diff from landing.

Reviewed By: gkioxari

Differential Revision: D21066156

fbshipit-source-id: 5216efe95180c1b6082d0bac404fa1920cfb7b02
This commit is contained in:
Nikhila Ravi 2020-04-16 11:56:23 -07:00 committed by Facebook GitHub Bot
parent b530b0af32
commit b5eb33b36c
1 changed files with 6 additions and 6 deletions
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12
pytorch3d/loss/chamfer.py
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@ -145,11 +145,11 @@ def chamfer_distance(
cham_norm_x = x.new_zeros(())
cham_norm_y = x.new_zeros(())
x_dists, x_idx = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
y_dists, y_idx = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
y_nn = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
cham_x = x_dists[..., 0] # (N, P1)
cham_y = y_dists[..., 0] # (N, P2)
cham_x = x_nn.dists[..., 0] # (N, P1)
cham_y = y_nn.dists[..., 0] # (N, P2)
if is_x_heterogeneous:
cham_x[x_mask] = 0.0
@ -162,8 +162,8 @@ def chamfer_distance(
if return_normals:
# Gather the normals using the indices and keep only value for k=0
x_normals_near = knn_gather(y_normals, x_idx, y_lengths)[..., 0, :]
y_normals_near = knn_gather(x_normals, y_idx, x_lengths)[..., 0, :]
x_normals_near = knn_gather(y_normals, x_nn.idx, y_lengths)[..., 0, :]
y_normals_near = knn_gather(x_normals, y_nn.idx, x_lengths)[..., 0, :]
cham_norm_x = 1 - torch.abs(
F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)