upd

Author: AlekseySh

oml/functional/metrics.py

@@ -108,7 +108,6 @@ def take_unreduced_metrics_by_mask(metrics: TMetricsDict, mask: BoolTensor) -> T
     output: TMetricsDict = {}
 
     for k, v in metrics.items():
-        print("uuu", v)
         if isinstance(v, (Tensor, np.ndarray)):
             output[k] = v[mask]
         elif isinstance(v, (float, int)):
@@ -449,7 +448,8 @@ def calc_fnmr_at_fmr(pos_dist: FloatTensor, neg_dist: FloatTensor, fmr_vals: Tup
     Example:
         >>> pos_dist = torch.tensor([0, 0, 1, 1, 2, 2, 5, 5, 9, 9])
         >>> neg_dist = torch.tensor([3, 3, 4, 4, 6, 6, 7, 7, 8, 8])
-        >>> calc_fnmr_at_fmr(pos_dist, neg_dist, fmr_vals=(0.1, 0.5))
+        >>> metrics = calc_fnmr_at_fmr(pos_dist, neg_dist, fmr_vals=(0.1, 0.5))
+        >>> [round(m, 4) for metric in metrics]
         [0.4000, 0.2000]
 
     """
@@ -515,7 +515,8 @@ def calc_pcf(embeddings: FloatTensor, pcf_variance: Tuple[float, ...]) -> List[f
         because the number of principal axes is superior to the desired explained variance threshold).
 
         >>> embeddings = torch.eye(4, 10, dtype=torch.float)
-        >>> calc_pcf(embeddings, pcf_variance=(0.5, 1))
+        >>> metrics = calc_pcf(embeddings, pcf_variance=(0.5, 1))
+        >>> [round(m, 4) for m in metrics]
         [0.2000, 0.5000]
 
     """

oml/metrics/embeddings.py

@@ -236,7 +236,6 @@ def compute_metrics(self) -> TMetricsDict_ByLabels:  # type: ignore
             for category in np.unique(query_categories):
                 mask = query_categories == category
 
-                print(metrics[self.overall_categories_key], mask, "zzz")
                 metrics[category] = take_unreduced_metrics_by_mask(metrics[self.overall_categories_key], mask)
 
                 metrics[category].update(

oml/registry/transforms.py

@@ -108,7 +108,6 @@ def save_transforms_as_files(cfg: TCfg) -> List[Tuple[str, str]]:
                     keys_files.append((key, transforms_file))
             except Exception:
                 print(f"We are not able to interpret {key} as albumentations transforms and log them as a file.")
-    print(keys_files, "zzzz")
     return keys_files
 
 

tests/test_oml/test_registry/test_registry.py

@@ -140,12 +140,10 @@ def test_saving_transforms_as_files() -> None:
 
     """
     cfg = yaml.safe_load(cfg)
-    save_transforms_as_files(cfg)
+    names_files = save_transforms_as_files(cfg)
 
-    assert Path("transforms_train.yaml").exists() or Path(".hydra/transforms_train.yaml").exists()
-    assert (not Path("transforms_val.yaml").exists()) and (not Path("transforms_val.yaml").exists())
+    assert len(names_files) == 1, "Check that we only saved train transforms as expected"
 
-    if Path("transforms_train.yaml").exists():
-        Path("transforms_train.yaml").unlink()
-    else:
-        Path(".hydra/transforms_train.yaml").unlink()
+    file = Path(names_files[0][1])
+    assert file.exists()
+    Path(file).unlink()