fix bug in recall and precision (#112)

mmcls/models/losses/eval_metrics.py

@@ -34,7 +34,7 @@ def precision(pred, target):
     confusion_matrix = calculate_confusion_matrix(pred, target)
     with torch.no_grad():
         res = confusion_matrix.diag() / torch.clamp(
-            confusion_matrix.sum(1), min=1)
+            confusion_matrix.sum(0), min=1)
         res = res.mean().item() * 100
     return res
 
@@ -52,7 +52,7 @@ def recall(pred, target):
     confusion_matrix = calculate_confusion_matrix(pred, target)
     with torch.no_grad():
         res = confusion_matrix.diag() / torch.clamp(
-            confusion_matrix.sum(0), min=1)
+            confusion_matrix.sum(1), min=1)
         res = res.mean().item() * 100
     return res
 

tests/test_dataset.py

@@ -68,16 +68,19 @@ def test_dataset_evaluation():
         dict(gt_label=0),
         dict(gt_label=1),
         dict(gt_label=2),
-        dict(gt_label=1)
+        dict(gt_label=1),
+        dict(gt_label=0)
     ]
-    fake_results = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]])
+    fake_results = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1],
+                             [0, 0, 1]])
     eval_results = dataset.evaluate(
         fake_results, metric=['precision', 'recall', 'f1_score'])
     assert eval_results['precision'] == pytest.approx(
-        (1 + 1 + 1 / 2) / 3 * 100.0)
-    assert eval_results['recall'] == pytest.approx((1 + 1 / 2 + 1) / 3 * 100.0)
+        (1 + 1 + 1 / 3) / 3 * 100.0)
+    assert eval_results['recall'] == pytest.approx(
+        (1 / 2 + 1 / 2 + 1) / 3 * 100.0)
     assert eval_results['f1_score'] == pytest.approx(
-        (1 + 2 / 3 + 2 / 3) / 3 * 100.0)
+        (2 / 3 + 2 / 3 + 1 / 2) / 3 * 100.0)
 
 
 @patch.multiple(BaseDataset, __abstractmethods__=set())