dev(MeanIoU): store confusion matries of each batch

mmeval/segmentation/mean_iou.py

@@ -91,7 +91,7 @@ def num_classes(self) -> int:
 
         The number of classes should be set during initialization, otherwise it
         will be obtained from the 'classes' or 'num_classes' field in
-        `self.dataset_meta`.
+        ``self.dataset_meta``.
 
         Raises:
             RuntimeError: If the num_classes is not set.
@@ -114,22 +114,16 @@ def num_classes(self) -> int:
     def add(self, predictions: Sequence, labels: Sequence) -> None:  # type: ignore # yapf: disable # noqa: E501
         """Process one batch of data and predictions.
 
-        Calculate the confusion matrix from the inputs and update the total
-        confusion matrix stored in `self._results[0]`.
+        Calculate the confusion matrix from the inputs and store in
+        ``self._results``.
 
         Args:
             data_batch (Sequence): A batch of data from the dataloader.
             predictions (Sequence): A batch of outputs from the model.
         """
         cm = self.compute_confusion_matrix(predictions, labels,
                                            self.num_classes)
-        # update the total confusion matrix stored in `self._results[0]`
-        if len(self._results) == 0:
-            self._results.append(cm)
-        else:
-            # Cumulative the confusion matrix.
-            total_cm = self._results[0]
-            self._results[0] = total_cm + cm
+        self._results.append(cm)
 
     @overload  # type: ignore
     @dispatch
@@ -315,34 +309,3 @@ def compute_metric(
             - Keys from `self._compute_core` if in verbose results mode.
         """
         return self._compute_metric(results)
-
-
-# The code below is temporary for test, will be removed.
-
-if __name__ == '__main__':
-
-    from _miou import IoUMetric as _IoUMetric
-    torch.manual_seed(0)
-
-    num_classes = 2
-    high, width = (224, 224)
-    batch_size = 48
-    np_miou = MeanIoU(num_classes=num_classes, verbose_results=True)
-    miou = MeanIoU(
-        dataset_meta={'classes': [i for i in range(num_classes)]},
-        verbose_results=True)
-    _miou = _IoUMetric(
-        iou_metrics=['mIoU', 'mDice', 'mFscore'],
-        dataset_meta={'classes': [i for i in range(num_classes)]})
-
-    for i in range(10):
-        labels = torch.randint(0, num_classes, size=(batch_size, high, width))
-        predicts = torch.randint(
-            0, num_classes, size=(batch_size, high, width))
-        np_miou.add(predicts.numpy(), labels.numpy())
-        miou.add(predicts, labels)
-        _miou.add(predicts, labels)
-
-    print(miou.compute())
-    print(np_miou.compute())
-    print(_miou.compute())