[superglue] Fixed the way batch mask was applied to the scores before match assignment computation (#39968)

fix: mask filling to score was wrong

src/transformers/models/superglue/modeling_superglue.py

@@ -676,8 +676,10 @@ class SuperGlueForKeypointMatching(SuperGluePreTrainedModel):
 
         if mask is not None:
             mask = mask.reshape(batch_size, 2, num_keypoints)
-            mask0 = mask[:, 0].unsqueeze(-1).expand(-1, -1, num_keypoints)
-            scores = scores.masked_fill(mask0 == 0, -1e9)
+            mask0 = mask[:, 0].unsqueeze(2)
+            mask1 = mask[:, 1].unsqueeze(1)
+            mask = torch.logical_and(mask0, mask1)
+            scores = scores.masked_fill(mask == 0, torch.finfo(scores.dtype).min)
 
         # Run the optimal transport.
         scores = log_optimal_transport(scores, self.bin_score, iterations=self.config.sinkhorn_iterations)

tests/models/superglue/test_modeling_superglue.py

@@ -423,3 +423,5 @@ class SuperGlueModelIntegrationTest(unittest.TestCase):
             torch.sum(~torch.isclose(predicted_matching_scores_values, expected_matching_scores_values, atol=1e-2)) < 4
         )
         self.assertTrue(torch.sum(predicted_matches_values != expected_matches_values) < 4)
+        self.assertTrue(torch.all(outputs.matches[0, 1] < torch.sum(outputs.mask[0, 0])))
+        self.assertTrue(torch.all(outputs.matches[0, 0] < torch.sum(outputs.mask[0, 1])))