bugfix

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

pytorch3d/loss/chamfer.py

@@ -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)