Merge pull request #426 from KevinMusgrave/dev

v.1.1.2

Author: KevinMusgrave

conda_build/pytorch-metric-learning/meta.yaml

@@ -1,13 +1,13 @@
 {% set name = "pytorch-metric-learning" %}
-{% set version = "1.1.1" %}
+{% set version = "1.1.2" %}
 
 package:
   name: "{{ name|lower }}"
   version: "{{ version }}"
 
 source:
   url: "https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz"
-  sha256: 6e572dc54179c762abc333fc4c6f68fcd909e800f9519ca1463235d14b9f5c44
+  sha256: aa2a28b7eb6a3a72f2ab14f59073de286832acc5433863de3c8cfc8e8fed38f4
 
 build:
   number: 0

src/pytorch_metric_learning/__init__.py

@@ -1 +1 @@
-__version__ = "1.1.1"
+__version__ = "1.1.2"

src/pytorch_metric_learning/utils/logging_presets.py

@@ -54,7 +54,7 @@ def end_of_iteration_hook(self, trainer):
                 trainer.loss_tracker.loss_weights,
                 {"parent_name": "loss_weights"},
             ],
-            [trainer.loss_funcs, {"recursive_types": [torch.nn.Module]}],
+            [trainer.loss_funcs, {"recursive_types": [torch.nn.Module, dict]}],
             [trainer.mining_funcs, {}],
             [trainer.models, {}],
             [trainer.optimizers, {"custom_attr_func": self.optimizer_custom_attr_func}],

src/pytorch_metric_learning/utils/module_with_records.py

@@ -4,9 +4,11 @@
 
 
 class ModuleWithRecords(torch.nn.Module):
-    def __init__(self, collect_stats=c_f.COLLECT_STATS):
+    def __init__(self, collect_stats=None):
         super().__init__()
-        self.collect_stats = collect_stats
+        self.collect_stats = (
+            c_f.COLLECT_STATS if collect_stats is None else collect_stats
+        )
 
     def add_to_recordable_attributes(
         self, name=None, list_of_names=None, is_stat=False

tests/trainers/test_metric_loss_only.py

@@ -8,6 +8,7 @@
 from torchvision import datasets, transforms
 
 from pytorch_metric_learning.losses import NTXentLoss
+from pytorch_metric_learning.reducers import AvgNonZeroReducer
 from pytorch_metric_learning.samplers import MPerClassSampler
 from pytorch_metric_learning.testers import GlobalEmbeddingSpaceTester
 from pytorch_metric_learning.trainers import MetricLossOnly
@@ -36,7 +37,7 @@ def test_metric_loss_only(self):
             )
         )
 
-        loss_fn = NTXentLoss()
+        loss_fn = NTXentLoss(reducer=AvgNonZeroReducer())
 
         normalize_transform = transforms.Normalize(
             mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
@@ -166,6 +167,18 @@ def test_metric_loss_only(self):
 
                 num_epochs = 2
                 trainer.train(num_epochs=num_epochs)
+
+                for record_name in [
+                    "metric_loss_NTXentLoss",
+                    "metric_loss_NTXentLoss__modules_distance_CosineSimilarity",
+                    "metric_loss_NTXentLoss__modules_reducer_AvgNonZeroReducer",
+                ]:
+                    self.assertTrue(record_keeper.table_exists(record_name))
+                    self.assertTrue(
+                        len(record_keeper.query(f"SELECT * FROM {record_name}"))
+                        == num_epochs * iterations_per_epoch / log_freq
+                    )
+
                 best_epoch, best_accuracy = hooks.get_best_epoch_and_accuracy(
                     tester, "val"
                 )

tests/utils/test_common_functions.py

@@ -57,10 +57,15 @@ def test_torch_standard_scaler(self):
 
     def test_collect_stats_flag(self):
         self.assertTrue(c_f.COLLECT_STATS == WITH_COLLECT_STATS)
-        loss_fn = TripletMarginLoss()
-        self.assertTrue(loss_fn.collect_stats == WITH_COLLECT_STATS)
-        self.assertTrue(loss_fn.distance.collect_stats == WITH_COLLECT_STATS)
-        self.assertTrue(loss_fn.reducer.collect_stats == WITH_COLLECT_STATS)
+        for x in [True, False, True, False, WITH_COLLECT_STATS]:
+            c_f.COLLECT_STATS = x
+            loss_fn = TripletMarginLoss()
+            self.assertTrue(loss_fn.collect_stats == x)
+            self.assertTrue(loss_fn.distance.collect_stats == x)
+            self.assertTrue(loss_fn.reducer.collect_stats == x)
+        for x in [True, False]:
+            loss_fn = TripletMarginLoss(collect_stats=x)
+            self.assertTrue(loss_fn.collect_stats == x)
 
     def test_check_shapes(self):
         embeddings = torch.randn(32, 512, 3)