quality_control: refactor confusion matrix initialization

The existing code creates a row and column for each category in the dataset,
plus an extra row/column at the end for unmatched annotations. However, it's
not immediately clear what order these rows/columns are in, because all
access is done indirectly through the `confusion_matrix_labels_rmap`
dictionary.

IMO, this is an unnecessary level of indirection, which obscures the
structure of the matrix. It's easier to just assign rows/columns 0..(N-1) to
the categories and N to "unmatched".

Do just that. Additionally, factor out the code creating the matrix into a
function, because it's used in two places.

cvat/apps/quality_control/quality_reports.py

@@ -1874,26 +1874,16 @@ def _find_closest_unmatched_shape(shape: dm.Annotation):
         invalid_labels_count = len(mismatches)
         total_labels_count = valid_labels_count + invalid_labels_count
 
-        confusion_matrix_labels = {
-            i: label.name
-            for i, label in enumerate(self._gt_dataset.categories()[dm.AnnotationType.label])
-            if not label.parent
-        }
-        confusion_matrix_labels[None] = "unmatched"
-        confusion_matrix_labels_rmap = {k: i for i, k in enumerate(confusion_matrix_labels.keys())}
-        confusion_matrix_label_count = len(confusion_matrix_labels)
-        confusion_matrix = np.zeros(
-            (confusion_matrix_label_count, confusion_matrix_label_count), dtype=int
-        )
+        confusion_matrix_labels, confusion_matrix = self._make_zero_confusion_matrix()
         for gt_ann, ds_ann in itertools.chain(
             # fully matched annotations - shape, label, attributes
             matches,
             mismatches,
             zip(itertools.repeat(None), ds_unmatched),
             zip(gt_unmatched, itertools.repeat(None)),
         ):
-            ds_label_idx = confusion_matrix_labels_rmap[ds_ann.label if ds_ann else ds_ann]
-            gt_label_idx = confusion_matrix_labels_rmap[gt_ann.label if gt_ann else gt_ann]
+            ds_label_idx = ds_ann.label if ds_ann else self._UNMATCHED_IDX
+            gt_label_idx = gt_ann.label if gt_ann else self._UNMATCHED_IDX
             confusion_matrix[ds_label_idx, gt_label_idx] += 1
 
         matched_ann_counts = np.diag(confusion_matrix)
@@ -1906,15 +1896,11 @@ def _find_closest_unmatched_shape(shape: dm.Annotation):
         label_recalls = _arr_div(matched_ann_counts, gt_ann_counts)
 
         valid_annotations_count = np.sum(matched_ann_counts)
-        missing_annotations_count = np.sum(confusion_matrix[confusion_matrix_labels_rmap[None], :])
-        extra_annotations_count = np.sum(confusion_matrix[:, confusion_matrix_labels_rmap[None]])
+        missing_annotations_count = np.sum(confusion_matrix[self._UNMATCHED_IDX, :])
+        extra_annotations_count = np.sum(confusion_matrix[:, self._UNMATCHED_IDX])
         total_annotations_count = np.sum(confusion_matrix)
-        ds_annotations_count = (
-            np.sum(ds_ann_counts) - ds_ann_counts[confusion_matrix_labels_rmap[None]]
-        )
-        gt_annotations_count = (
-            np.sum(gt_ann_counts) - gt_ann_counts[confusion_matrix_labels_rmap[None]]
-        )
+        ds_annotations_count = np.sum(ds_ann_counts[: self._UNMATCHED_IDX])
+        gt_annotations_count = np.sum(gt_ann_counts[: self._UNMATCHED_IDX])
 
         self._frame_results[frame_id] = ComparisonReportFrameSummary(
             annotations=ComparisonReportAnnotationsSummary(
@@ -1925,7 +1911,7 @@ def _find_closest_unmatched_shape(shape: dm.Annotation):
                 ds_count=ds_annotations_count,
                 gt_count=gt_annotations_count,
                 confusion_matrix=ConfusionMatrix(
-                    labels=list(confusion_matrix_labels.values()),
+                    labels=confusion_matrix_labels,
                     rows=confusion_matrix,
                     precision=label_precisions,
                     recall=label_recalls,
@@ -1953,6 +1939,22 @@ def _find_closest_unmatched_shape(shape: dm.Annotation):
 
         return conflicts
 
+    # row/column index in the confusion matrix corresponding to unmatched annotations
+    _UNMATCHED_IDX = -1
+
+    def _make_zero_confusion_matrix(self) -> Tuple[List[str], np.ndarray]:
+        label_names = [
+            label.name
+            for label in self._gt_dataset.categories()[dm.AnnotationType.label]
+            if not label.parent
+        ]
+        label_names.append("unmatched")
+        num_labels = len(label_names)
+
+        confusion_matrix = np.zeros((num_labels, num_labels), dtype=int)
+
+        return label_names, confusion_matrix
+
     def generate_report(self) -> ComparisonReport:
         self._find_gt_conflicts()
 
@@ -1985,7 +1987,8 @@ def generate_report(self) -> ComparisonReport:
             ),
         )
         mean_ious = []
-        confusion_matrices = []
+        confusion_matrix_labels, confusion_matrix = self._make_zero_confusion_matrix()
+
         for frame_id, frame_result in self._frame_results.items():
             intersection_frames.append(frame_id)
             conflicts += frame_result.conflicts
@@ -1994,27 +1997,14 @@ def generate_report(self) -> ComparisonReport:
                 annotations = deepcopy(frame_result.annotations)
             else:
                 annotations.accumulate(frame_result.annotations)
-            confusion_matrices.append(frame_result.annotations.confusion_matrix.rows)
+            confusion_matrix += frame_result.annotations.confusion_matrix.rows
 
             if annotation_components is None:
                 annotation_components = deepcopy(frame_result.annotation_components)
             else:
                 annotation_components.accumulate(frame_result.annotation_components)
             mean_ious.append(frame_result.annotation_components.shape.mean_iou)
 
-        confusion_matrix_labels = {
-            i: label.name
-            for i, label in enumerate(self._gt_dataset.categories()[dm.AnnotationType.label])
-            if not label.parent
-        }
-        confusion_matrix_labels[None] = "unmatched"
-        confusion_matrix_labels_rmap = {k: i for i, k in enumerate(confusion_matrix_labels.keys())}
-        if confusion_matrices:
-            confusion_matrix = np.sum(confusion_matrices, axis=0)
-        else:
-            confusion_matrix = np.zeros(
-                (len(confusion_matrix_labels), len(confusion_matrix_labels)), dtype=int
-            )
         matched_ann_counts = np.diag(confusion_matrix)
         ds_ann_counts = np.sum(confusion_matrix, axis=1)
         gt_ann_counts = np.sum(confusion_matrix, axis=0)
@@ -2025,15 +2015,11 @@ def generate_report(self) -> ComparisonReport:
         label_recalls = _arr_div(matched_ann_counts, gt_ann_counts)
 
         valid_annotations_count = np.sum(matched_ann_counts)
-        missing_annotations_count = np.sum(confusion_matrix[confusion_matrix_labels_rmap[None], :])
-        extra_annotations_count = np.sum(confusion_matrix[:, confusion_matrix_labels_rmap[None]])
+        missing_annotations_count = np.sum(confusion_matrix[self._UNMATCHED_IDX, :])
+        extra_annotations_count = np.sum(confusion_matrix[:, self._UNMATCHED_IDX])
         total_annotations_count = np.sum(confusion_matrix)
-        ds_annotations_count = (
-            np.sum(ds_ann_counts) - ds_ann_counts[confusion_matrix_labels_rmap[None]]
-        )
-        gt_annotations_count = (
-            np.sum(gt_ann_counts) - gt_ann_counts[confusion_matrix_labels_rmap[None]]
-        )
+        ds_annotations_count = np.sum(ds_ann_counts[: self._UNMATCHED_IDX])
+        gt_annotations_count = np.sum(gt_ann_counts[: self._UNMATCHED_IDX])
 
         return ComparisonReport(
             parameters=self.settings,
@@ -2058,7 +2044,7 @@ def generate_report(self) -> ComparisonReport:
                     ds_count=ds_annotations_count,
                     gt_count=gt_annotations_count,
                     confusion_matrix=ConfusionMatrix(
-                        labels=list(confusion_matrix_labels.values()),
+                        labels=confusion_matrix_labels,
                         rows=confusion_matrix,
                         precision=label_precisions,
                         recall=label_recalls,