[FIX] bug of generator, which lead metric to nan when pre_eval=False

Signed-off-by: FreyWang <[email protected]>

mmseg/core/evaluation/metrics.py

@@ -112,8 +112,6 @@ def total_intersect_and_union(results,
          ndarray: The prediction histogram on all classes.
          ndarray: The ground truth histogram on all classes.
     """
-    num_imgs = len(results)
-    assert len(list(gt_seg_maps)) == num_imgs
     total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)

tests/test_data/test_dataset.py

@@ -213,6 +213,14 @@ def test_custom_dataset():
     assert 'mPrecision' in eval_results
     assert 'mRecall' in eval_results
 
+    assert not np.isnan(eval_results['mIoU'])
+    assert not np.isnan(eval_results['mDice'])
+    assert not np.isnan(eval_results['mAcc'])
+    assert not np.isnan(eval_results['aAcc'])
+    assert not np.isnan(eval_results['mFscore'])
+    assert not np.isnan(eval_results['mPrecision'])
+    assert not np.isnan(eval_results['mRecall'])
+
     # test evaluation with pre-eval and the dataset.CLASSES is necessary
     train_dataset.CLASSES = tuple(['a'] * 7)
     pseudo_results = []
@@ -250,6 +258,14 @@ def test_custom_dataset():
     assert 'mPrecision' in eval_results
     assert 'mRecall' in eval_results
 
+    assert not np.isnan(eval_results['mIoU'])
+    assert not np.isnan(eval_results['mDice'])
+    assert not np.isnan(eval_results['mAcc'])
+    assert not np.isnan(eval_results['aAcc'])
+    assert not np.isnan(eval_results['mFscore'])
+    assert not np.isnan(eval_results['mPrecision'])
+    assert not np.isnan(eval_results['mRecall'])
+
 
 @pytest.mark.parametrize('separate_eval', [True, False])
 def test_eval_concat_custom_dataset(separate_eval):