Merge pull request #692 from KevinMusgrave/dev

v2.5.0

Author: KevinMusgrave

src/pytorch_metric_learning/__init__.py

@@ -1 +1 @@
-__version__ = "2.4.1"
+__version__ = "2.5.0"

src/pytorch_metric_learning/losses/manifold_loss.py

@@ -90,13 +90,13 @@ def compute_loss(self, embeddings, labels, indices_tuple, ref_emb, ref_labels):
         if self.lambdaC != np.inf:
             F = F[:N, N:]
             loss_int = F - F[torch.arange(N), meta_classes].view(-1, 1) + self.margin
-            loss_int[
-                torch.arange(N), meta_classes
-            ] = -np.inf  # This way avoid numerical cancellation happening   # NoQA
+            loss_int[torch.arange(N), meta_classes] = (
+                -np.inf
+            )  # This way avoid numerical cancellation happening   # NoQA
             # instead with subtraction of margin term           # NoQA
-            loss_int[
-                loss_int < 0
-            ] = -np.inf  # This way no loss for positive correlation with own proxy
+            loss_int[loss_int < 0] = (
+                -np.inf
+            )  # This way no loss for positive correlation with own proxy
 
             loss_int = torch.exp(loss_int)
             loss_int = torch.log(1 + torch.sum(loss_int, dim=1))
@@ -106,9 +106,9 @@ def compute_loss(self, embeddings, labels, indices_tuple, ref_emb, ref_labels):
             F_e, F_p.unsqueeze(1), dim=-1
         ).t()
         loss_ctx += -loss_ctx[torch.arange(N), meta_classes].view(-1, 1) + self.margin
-        loss_ctx[
-            torch.arange(N), meta_classes
-        ] = -np.inf  # This way avoid numerical cancellation happening   # NoQA
+        loss_ctx[torch.arange(N), meta_classes] = (
+            -np.inf
+        )  # This way avoid numerical cancellation happening   # NoQA
         # instead with subtraction of margin term           # NoQA
         loss_ctx[loss_ctx < 0] = -np.inf
 

src/pytorch_metric_learning/testers/base_tester.py

@@ -306,8 +306,10 @@ def test(
                 query_split_name,
                 reference_split_names,
             )
-        self.end_of_testing_hook(self) if self.end_of_testing_hook else c_f.LOGGER.info(
-            self.all_accuracies
+        (
+            self.end_of_testing_hook(self)
+            if self.end_of_testing_hook
+            else c_f.LOGGER.info(self.all_accuracies)
         )
         del self.embeddings_and_labels
         return self.all_accuracies

src/pytorch_metric_learning/utils/loss_and_miner_utils.py

@@ -85,11 +85,57 @@ def neg_pairs_from_tuple(indices_tuple):
 
 
 def get_all_triplets_indices(labels, ref_labels=None):
-    matches, diffs = get_matches_and_diffs(labels, ref_labels)
-    triplets = matches.unsqueeze(2) * diffs.unsqueeze(1)
+    all_matches, all_diffs = get_matches_and_diffs(labels, ref_labels)
+
+    if (
+        all_matches.shape[0] * all_matches.shape[1] * all_matches.shape[1]
+        < torch.iinfo(torch.int32).max
+    ):
+        # torch.nonzero is not supported for tensors with more than INT_MAX elements
+        return get_all_triplets_indices_vectorized_method(all_matches, all_diffs)
+
+    return get_all_triplets_indices_loop_method(labels, all_matches, all_diffs)
+
+
+def get_all_triplets_indices_vectorized_method(all_matches, all_diffs):
+    triplets = all_matches.unsqueeze(2) * all_diffs.unsqueeze(1)
     return torch.where(triplets)
 
 
+def get_all_triplets_indices_loop_method(labels, all_matches, all_diffs):
+    all_matches, all_diffs = all_matches.bool(), all_diffs.bool()
+
+    # Find anchors with at least a positive and a negative
+    indices = torch.arange(0, len(labels), device=labels.device)
+    indices = indices[all_matches.any(dim=1) & all_diffs.any(dim=1)]
+
+    # No triplets found
+    if len(indices) == 0:
+        return (
+            torch.tensor([], device=labels.device, dtype=labels.dtype),
+            torch.tensor([], device=labels.device, dtype=labels.dtype),
+            torch.tensor([], device=labels.device, dtype=labels.dtype),
+        )
+
+    # Compute all triplets
+    anchors = []
+    positives = []
+    negatives = []
+    for i in indices:
+        matches = all_matches[i].nonzero(as_tuple=False).squeeze(1)
+        diffs = all_diffs[i].nonzero(as_tuple=False).squeeze(1)
+        nd = len(diffs)
+        nm = len(matches)
+        matches = matches.repeat_interleave(nd)
+        diffs = diffs.repeat(nm)
+        anchors.append(
+            torch.full((len(matches),), i, dtype=labels.dtype, device=labels.device)
+        )
+        positives.append(matches)
+        negatives.append(diffs)
+    return torch.cat(anchors), torch.cat(positives), torch.cat(negatives)
+
+
 # sample triplets, with a weighted distribution if weights is specified.
 def get_random_triplet_indices(
     labels, ref_labels=None, t_per_anchor=None, weights=None

tests/utils/test_calculate_accuracies.py

@@ -67,9 +67,11 @@ def test_accuracy_calculator(self):
                         "query_labels": query_labels,
                         "label_counts": label_counts,
                         "knn_labels": knn_labels,
-                        "not_lone_query_mask": torch.ones(6, dtype=torch.bool)
-                        if i == 0
-                        else torch.zeros(6, dtype=torch.bool),
+                        "not_lone_query_mask": (
+                            torch.ones(6, dtype=torch.bool)
+                            if i == 0
+                            else torch.zeros(6, dtype=torch.bool)
+                        ),
                     }
 
                     function_dict = AC.get_function_dict()

tests/utils/test_loss_and_miner_utils.py

@@ -291,6 +291,29 @@ def test_remove_self_comparisons_small_ref(self):
         self.assertTrue(torch.equal(a1, correct_a1))
         self.assertTrue(torch.equal(p, correct_p))
 
+    def test_get_all_triplets_indices(self):
+        torch.manual_seed(920)
+        for dtype in TEST_DTYPES:
+            for batch_size in [32, 256, 512]:
+                for ref_labels in [None, torch.randint(0, 5, size=(batch_size // 2,))]:
+                    labels = torch.randint(0, 5, size=(batch_size,))
+
+                    a, p, n = lmu.get_all_triplets_indices(labels, ref_labels)
+                    matches, diffs = lmu.get_matches_and_diffs(labels, ref_labels)
+
+                    a2, p2, n2 = lmu.get_all_triplets_indices_vectorized_method(
+                        matches, diffs
+                    )
+                    a3, p3, n3 = lmu.get_all_triplets_indices_loop_method(
+                        labels, matches, diffs
+                    )
+                    self.assertTrue(
+                        (a == a2).all() and (p == p2).all() and (n == n2).all()
+                    )
+                    self.assertTrue(
+                        (a == a3).all() and (p == p3).all() and (n == n3).all()
+                    )
+
 
 if __name__ == "__main__":
     unittest.main()