Renamed old MetricsFactory into SegmentationMetricsFactory

Author: SakhinetiPraveena

detectionmetrics/models/tensorflow.py

@@ -438,7 +438,7 @@ def eval(
             ignored_label_indices.append(dataset_ontology[ignored_class]["idx"])
 
         # Init metrics
-        metrics_factory = um.MetricsFactory(self.n_classes)
+        metrics_factory = um.SegmentationMetricsFactory(self.n_classes)
 
         # Evaluation loop
         pbar = tqdm(dataset.dataset)
@@ -481,7 +481,7 @@ def eval(
                         sample_valid_mask = (
                             valid_mask[i] if valid_mask is not None else None
                         )
-                        sample_mf = um.MetricsFactory(n_classes=self.n_classes)
+                        sample_mf = um.SegmentationMetricsFactory(n_classes=self.n_classes)
                         sample_mf.update(sample_pred, sample_label, sample_valid_mask)
                         sample_df = um.get_metrics_dataframe(sample_mf, self.ontology)
                         sample_df.to_csv(

detectionmetrics/models/torch_segmentation.py

@@ -530,7 +530,7 @@ def eval(
         )
 
         # Init metrics
-        metrics_factory = um.MetricsFactory(self.n_classes)
+        metrics_factory = um.SegmentationMetricsFactory(self.n_classes)
 
         # Evaluation loop
         with torch.no_grad():
@@ -585,7 +585,7 @@ def eval(
                             sample_valid_mask = (
                                 valid_mask[i] if valid_mask is not None else None
                             )
-                            sample_mf = um.MetricsFactory(n_classes=self.n_classes)
+                            sample_mf = um.SegmentationMetricsFactory(n_classes=self.n_classes)
                             sample_mf.update(
                                 sample_pred, sample_label, sample_valid_mask
                             )
@@ -805,7 +805,7 @@ def eval(
         )
 
         # Init metrics
-        metrics_factory = um.MetricsFactory(self.n_classes)
+        metrics_factory = um.SegmentationMetricsFactory(self.n_classes)
 
         # Evaluation loop
         end_th = self.model_cfg.get("end_th", 0.5)
@@ -885,7 +885,7 @@ def eval(
                             sample_valid_mask = (
                                 valid_mask[i] if valid_mask is not None else None
                             )
-                            sample_mf = um.MetricsFactory(n_classes=self.n_classes)
+                            sample_mf = um.SegmentationMetricsFactory(n_classes=self.n_classes)
                             sample_mf.update(
                                 sample_pred, sample_label, sample_valid_mask
                             )

detectionmetrics/utils/metrics.py …ionmetrics/utils/segmentation_metrics.pydetectionmetrics/utils/metrics.py renamed to detectionmetrics/utils/segmentation_metrics.py detectionmetrics/utils/metrics.py renamed to detectionmetrics/utils/segmentation_metrics.py

@@ -6,8 +6,8 @@
 import pandas as pd
 
 
-class MetricsFactory:
-    """'Factory' class to accumulate results and compute metrics
+class SegmentationMetricsFactory:
+    """'Factory' class to accumulate results and compute metrics for segmentation tasks
 
     :param n_classes: Number of classes to evaluate
     :type n_classes: int
@@ -256,12 +256,12 @@ def get_metric_per_name(
 
 
 def get_metrics_dataframe(
-    metrics_factory: MetricsFactory, ontology: dict
+    metrics_factory: SegmentationMetricsFactory, ontology: dict
 ) -> pd.DataFrame:
     """Build a DataFrame with all metrics (global and per class) plus confusion matrix
 
-    :param metrics_factory: Properly updated MetricsFactory object
-    :type metrics_factory: MetricsFactory
+    :param metrics_factory: Properly updated SegmentationMetricsFactory object
+    :type metrics_factory: SegmentationMetricsFactory
     :param ontology: Ontology dictionary
     :type ontology: dict
     :return: DataFrame with all metrics

examples/tutorial_image_segmentation.ipynb

@@ -314,7 +314,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "detectionmetrics-cJs_3AVd-py3.10",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -328,7 +328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

tests/test_metrics.py

@@ -1,12 +1,12 @@
 import numpy as np
 import pytest
-from detectionmetrics.utils.metrics import MetricsFactory
+from detectionmetrics.utils.metrics import SegmentationMetricsFactory
 
 
 @pytest.fixture
 def metrics_factory():
-    """Fixture to create a MetricsFactory instance for testing"""
-    return MetricsFactory(n_classes=3)
+    """Fixture to create a SegmentationMetricsFactory instance for testing"""
+    return SegmentationMetricsFactory(n_classes=3)
 
 
 def test_update_confusion_matrix(metrics_factory):
@@ -94,7 +94,7 @@ def test_edge_cases(metrics_factory):
     with pytest.raises(AssertionError):
         metrics_factory.update(pred, gt)
 
-    empty_metrics_factory = MetricsFactory(n_classes=3)
+    empty_metrics_factory = SegmentationMetricsFactory(n_classes=3)
 
     assert np.isnan(empty_metrics_factory.get_precision(per_class=False))
     assert np.isnan(empty_metrics_factory.get_recall(per_class=False))