v2.6.0

.github/workflows/base_test_workflow.yml

@@ -18,8 +18,8 @@ jobs:
             pytorch-version: 1.6
             torchvision-version: 0.7
           - python-version: 3.9
-            pytorch-version: 2.1
-            torchvision-version: 0.16
+            pytorch-version: 2.3
+            torchvision-version: 0.18
 
     steps:
     - uses: actions/checkout@v2
@@ -30,7 +30,7 @@ jobs:
     - name: Install dependencies
       run: |
         pip install .[with-hooks-cpu]
-        pip install torch==${{ matrix.pytorch-version }} torchvision==${{ matrix.torchvision-version }} --force-reinstall
+        pip install "numpy<2.0" torch==${{ matrix.pytorch-version }} torchvision==${{ matrix.torchvision-version }} --force-reinstall
         pip install --upgrade protobuf==3.20.1
         pip install six
         pip install packaging

setup.py

@@ -39,7 +39,7 @@
     ],
     python_requires=">=3.0",
     install_requires=[
-        "numpy",
+        "numpy < 2.0",
         "scikit-learn",
         "tqdm",
         "torch >= 1.6.0",

src/pytorch_metric_learning/__init__.py

@@ -1 +1 @@
-__version__ = "2.5.0"
+__version__ = "2.6.0"

src/pytorch_metric_learning/utils/distributed.py

@@ -1,3 +1,5 @@
+import warnings
+
 import torch
 
 from ..losses import BaseMetricLossFunction, CrossBatchMemory
@@ -93,15 +95,28 @@ def __init__(self, loss, efficient=False):
 
     def forward(
         self,
-        emb,
+        embeddings,
         labels=None,
         indices_tuple=None,
         ref_emb=None,
         ref_labels=None,
         enqueue_mask=None,
     ):
+        if not is_distributed():
+            warnings.warn(
+                "DistributedLossWrapper is being used in a non-distributed setting. Returning the loss as is."
+            )
+            return self.loss(embeddings, labels, indices_tuple, ref_emb, ref_labels)
+
         world_size = torch.distributed.get_world_size()
-        common_args = [emb, labels, indices_tuple, ref_emb, ref_labels, world_size]
+        common_args = [
+            embeddings,
+            labels,
+            indices_tuple,
+            ref_emb,
+            ref_labels,
+            world_size,
+        ]
         if isinstance(self.loss, CrossBatchMemory):
             return self.forward_cross_batch(*common_args, enqueue_mask)
         return self.forward_regular_loss(*common_args)