Merge pull request #702 from elisim/master

DistributedLossWrapper enhancements

.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

README.md

@@ -18,16 +18,17 @@
 
 ## News
 
+**April 1**: v2.5.0
+- Improved `get_all_triplets_indices` so that large batch sizes don't trigger the `INT_MAX` error.
+- See the [release notes](https://github.com/KevinMusgrave/pytorch-metric-learning/releases/tag/v2.5.0).
+- Thank you [mkmenta](https://github.com/mkmenta).
+
 **December 15**: v2.4.0
 - Added [DynamicSoftMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#dynamicsoftmarginloss).
 - Added [RankedListLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#rankedlistloss).
 - See the [release notes](https://github.com/KevinMusgrave/pytorch-metric-learning/releases/tag/v2.4.0).
 - Thank you [domenicoMuscill0](https://github.com/domenicoMuscill0), [Puzer](https://github.com/Puzer), [interestingzhuo](https://github.com/interestingzhuo), and [GaetanLepage](https://github.com/GaetanLepage).
 
-**July 25**: v2.3.0
-- Added [HistogramLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#histogramloss)
-- Thank you [domenicoMuscill0](https://github.com/domenicoMuscill0).
-
 ## Documentation
 - [**View the documentation here**](https://kevinmusgrave.github.io/pytorch-metric-learning/)
 - [**View the installation instructions here**](https://github.com/KevinMusgrave/pytorch-metric-learning#installation)
@@ -236,6 +237,7 @@ Thanks to the contributors who made pull requests!
 | [AlenUbuntu](https://github.com/AlenUbuntu) | [CircleLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#circleloss) |
 | [interestingzhuo](https://github.com/interestingzhuo) | [PNPLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#pnploss) |
 | [wconnell](https://github.com/wconnell) | [Learning a scRNAseq Metric Embedding](https://github.com/KevinMusgrave/pytorch-metric-learning/blob/master/examples/notebooks/scRNAseq_MetricEmbedding.ipynb) |
+| [mkmenta](https://github.com/mkmenta) | Improved `get_all_triplets_indices` (fixed the `INT_MAX` error) |
 | [AlexSchuy](https://github.com/AlexSchuy) | optimized ```utils.loss_and_miner_utils.get_random_triplet_indices``` |
 | [JohnGiorgi](https://github.com/JohnGiorgi) | ```all_gather``` in [utils.distributed](https://kevinmusgrave.github.io/pytorch-metric-learning/distributed) |
 | [Hummer12007](https://github.com/Hummer12007) | ```utils.key_checker``` |

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 +95,21 @@ 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)