Merge pull request #631 from KevinMusgrave/dev

v2.1.2

Author: KevinMusgrave

docs/samplers.md

@@ -1,5 +1,6 @@
 # Samplers
-Samplers are just extensions of the torch.utils.data.Sampler class, i.e. they are passed to a PyTorch Dataloader. The purpose of samplers is to determine how batches should be formed. This is also where any offline pair or triplet miners should exist.
+Samplers are just extensions of the torch.utils.data.Sampler class, i.e. they are passed to a PyTorch Dataloader (specifically as _sampler_ argument, unless otherwise mentioned). 
+The purpose of samplers is to determine how batches should be formed. This is also where any offline pair or triplet miners should exist.
 
 
 ## MPerClassSampler
@@ -87,4 +88,4 @@ samplers.FixedSetOfTriplets(labels, num_triplets)
 **Parameters**:
 
 * **labels**: The list of labels for your dataset, i.e. the labels[x] should be the label of the xth element in your dataset.
-* **num_triplets**: The number of triplets to create.
+* **num_triplets**: The number of triplets to create.

src/pytorch_metric_learning/__init__.py

@@ -1 +1,2 @@
-__version__ = "2.1.1"
+__version__ = "2.1.2"
+

src/pytorch_metric_learning/miners/triplet_margin_miner.py

@@ -37,6 +37,8 @@ def mine(self, embeddings, labels, ref_emb, ref_labels):
             ap_dist - an_dist if self.distance.is_inverted else an_dist - ap_dist
         )
 
+        self.set_stats(ap_dist, an_dist, triplet_margin)
+
         if self.type_of_triplets == "easy":
             threshold_condition = triplet_margin > self.margin
         else:

src/pytorch_metric_learning/samplers/hierarchical_sampler.py

@@ -2,14 +2,14 @@
 from collections import defaultdict
 
 import torch
-from torch.utils.data.sampler import BatchSampler
+from torch.utils.data.sampler import Sampler
 
 from ..utils import common_functions as c_f
 
 
 # Inspired by
 # https://github.com/kunhe/Deep-Metric-Learning-Baselines/blob/master/datasets.py
-class HierarchicalSampler(BatchSampler):
+class HierarchicalSampler(Sampler):
     def __init__(
         self,
         labels,

tests/miners/test_triplet_margin_miner.py

@@ -103,3 +103,12 @@ def test_empty_output(self):
             self.assertTrue(len(a) == 0)
             self.assertTrue(len(p) == 0)
             self.assertTrue(len(n) == 0)
+
+    @unittest.skipUnless(WITH_COLLECT_STATS, "WITH_COLLECT_STATS is false")
+    def test_recordable_attributes(self):
+        miner = TripletMarginMiner()
+        emb, labels = torch.randn(32, 32), torch.randint(0, 3, size=(32,))
+        miner(emb, labels)
+        self.assertNotEqual(miner.avg_triplet_margin, 0)
+        self.assertNotEqual(miner.pos_pair_dist, 0)
+        self.assertNotEqual(miner.neg_pair_dist, 0)