[Fix] Use zero as default value of thrs in metrics. (open-mmlab#341)

* Use zero as default value of `thrs` in metrics. And it accepcts a number
instead of float now.

* Fix unit test comment

* Don't pass thrs if no thrs.

mmcls/core/evaluation/eval_metrics.py

@@ -1,3 +1,5 @@
+from numbers import Number
+
 import numpy as np
 import torch
 
@@ -36,7 +38,7 @@ def calculate_confusion_matrix(pred, target):
     return confusion_matrix
 
 
-def precision_recall_f1(pred, target, average_mode='macro', thrs=None):
+def precision_recall_f1(pred, target, average_mode='macro', thrs=0.):
     """Calculate precision, recall and f1 score according to the prediction and
     target.
 
@@ -49,8 +51,8 @@ def precision_recall_f1(pred, target, average_mode='macro', thrs=None):
             class are returned. If 'macro', calculate metrics for each class,
             and find their unweighted mean.
             Defaults to 'macro'.
-        thrs (float | tuple[float], optional): Predictions with scores under
-            the thresholds are considered negative. Default to None.
+        thrs (Number | tuple[Number], optional): Predictions with scores under
+            the thresholds are considered negative. Default to 0.
 
     Returns:
         tuple: tuple containing precision, recall, f1 score.
@@ -78,16 +80,14 @@ class are returned. If 'macro', calculate metrics for each class,
         (f'pred and target should be torch.Tensor or np.ndarray, '
          f'but got {type(pred)} and {type(target)}.')
 
-    if thrs is None:
-        thrs = 0.0
-    if isinstance(thrs, float):
+    if isinstance(thrs, Number):
         thrs = (thrs, )
         return_single = True
     elif isinstance(thrs, tuple):
         return_single = False
     else:
         raise TypeError(
-            f'thrs should be float or tuple, but got {type(thrs)}.')
+            f'thrs should be a number or tuple, but got {type(thrs)}.')
 
     label = np.indices(pred.shape)[1]
     pred_label = np.argsort(pred, axis=1)[:, -1]
@@ -123,7 +123,7 @@ class are returned. If 'macro', calculate metrics for each class,
         return precisions, recalls, f1_scores
 
 
-def precision(pred, target, average_mode='macro', thrs=None):
+def precision(pred, target, average_mode='macro', thrs=0.):
     """Calculate precision according to the prediction and target.
 
     Args:
@@ -135,8 +135,8 @@ def precision(pred, target, average_mode='macro', thrs=None):
             class are returned. If 'macro', calculate metrics for each class,
             and find their unweighted mean.
             Defaults to 'macro'.
-        thrs (float | tuple[float], optional): Predictions with scores under
-            the thresholds are considered negative. Default to None.
+        thrs (Number | tuple[Number], optional): Predictions with scores under
+            the thresholds are considered negative. Default to 0.
 
     Returns:
          float | np.array | list[float | np.array]: Precision.
@@ -153,7 +153,7 @@ class are returned. If 'macro', calculate metrics for each class,
     return precisions
 
 
-def recall(pred, target, average_mode='macro', thrs=None):
+def recall(pred, target, average_mode='macro', thrs=0.):
     """Calculate recall according to the prediction and target.
 
     Args:
@@ -165,8 +165,8 @@ def recall(pred, target, average_mode='macro', thrs=None):
             class are returned. If 'macro', calculate metrics for each class,
             and find their unweighted mean.
             Defaults to 'macro'.
-        thrs (float | tuple[float], optional): Predictions with scores under
-            the thresholds are considered negative. Default to None.
+        thrs (Number | tuple[Number], optional): Predictions with scores under
+            the thresholds are considered negative. Default to 0.
 
     Returns:
          float | np.array | list[float | np.array]: Recall.
@@ -183,7 +183,7 @@ class are returned. If 'macro', calculate metrics for each class,
     return recalls
 
 
-def f1_score(pred, target, average_mode='macro', thrs=None):
+def f1_score(pred, target, average_mode='macro', thrs=0.):
     """Calculate F1 score according to the prediction and target.
 
     Args:
@@ -195,8 +195,8 @@ def f1_score(pred, target, average_mode='macro', thrs=None):
             class are returned. If 'macro', calculate metrics for each class,
             and find their unweighted mean.
             Defaults to 'macro'.
-        thrs (float | tuple[float], optional): Predictions with scores under
-            the thresholds are considered negative. Default to None.
+        thrs (Number | tuple[Number], optional): Predictions with scores under
+            the thresholds are considered negative. Default to 0.
 
     Returns:
          float | np.array | list[float | np.array]: F1 score.

mmcls/datasets/base_dataset.py

@@ -157,7 +157,10 @@ def evaluate(self,
         average_mode = metric_options.get('average_mode', 'macro')
 
         if 'accuracy' in metrics:
-            acc = accuracy(results, gt_labels, topk=topk, thrs=thrs)
+            if thrs is not None:
+                acc = accuracy(results, gt_labels, topk=topk, thrs=thrs)
+            else:
+                acc = accuracy(results, gt_labels, topk=topk)
             if isinstance(topk, tuple):
                 eval_results_ = {
                     f'accuracy_top-{k}': a
@@ -183,8 +186,12 @@ def evaluate(self,
 
         precision_recall_f1_keys = ['precision', 'recall', 'f1_score']
         if len(set(metrics) & set(precision_recall_f1_keys)) != 0:
-            precision_recall_f1_values = precision_recall_f1(
-                results, gt_labels, average_mode=average_mode, thrs=thrs)
+            if thrs is not None:
+                precision_recall_f1_values = precision_recall_f1(
+                    results, gt_labels, average_mode=average_mode, thrs=thrs)
+            else:
+                precision_recall_f1_values = precision_recall_f1(
+                    results, gt_labels, average_mode=average_mode)
             for key, values in zip(precision_recall_f1_keys,
                                    precision_recall_f1_values):
                 if key in metrics:

mmcls/models/losses/accuracy.py

@@ -1,19 +1,19 @@
+from numbers import Number
+
 import numpy as np
 import torch
 import torch.nn as nn
 
 
-def accuracy_numpy(pred, target, topk=1, thrs=None):
-    if thrs is None:
-        thrs = 0.0
-    if isinstance(thrs, float):
+def accuracy_numpy(pred, target, topk=1, thrs=0.):
+    if isinstance(thrs, Number):
         thrs = (thrs, )
         res_single = True
     elif isinstance(thrs, tuple):
         res_single = False
     else:
         raise TypeError(
-            f'thrs should be float or tuple, but got {type(thrs)}.')
+            f'thrs should be a number or tuple, but got {type(thrs)}.')
 
     res = []
     maxk = max(topk)
@@ -36,17 +36,15 @@ def accuracy_numpy(pred, target, topk=1, thrs=None):
     return res
 
 
-def accuracy_torch(pred, target, topk=1, thrs=None):
-    if thrs is None:
-        thrs = 0.0
-    if isinstance(thrs, float):
+def accuracy_torch(pred, target, topk=1, thrs=0.):
+    if isinstance(thrs, Number):
         thrs = (thrs, )
         res_single = True
     elif isinstance(thrs, tuple):
         res_single = False
     else:
         raise TypeError(
-            f'thrs should be float or tuple, but got {type(thrs)}.')
+            f'thrs should be a number or tuple, but got {type(thrs)}.')
 
     res = []
     maxk = max(topk)
@@ -68,7 +66,7 @@ def accuracy_torch(pred, target, topk=1, thrs=None):
     return res
 
 
-def accuracy(pred, target, topk=1, thrs=None):
+def accuracy(pred, target, topk=1, thrs=0.):
     """Calculate accuracy according to the prediction and target.
 
     Args:
@@ -77,9 +75,8 @@ def accuracy(pred, target, topk=1, thrs=None):
         topk (int | tuple[int]): If the predictions in ``topk``
             matches the target, the predictions will be regarded as
             correct ones. Defaults to 1.
-        thrs (float, optional): thrs (float | tuple[float], optional):
-            Predictions with scores under the thresholds are considered
-            negative. Default to None.
+        thrs (Number | tuple[Number], optional): Predictions with scores under
+            the thresholds are considered negative. Default to 0.
 
     Returns:
         float | list[float] | list[list[float]]: Accuracy

tests/test_data/test_datasets/test_common.py

@@ -132,7 +132,7 @@ def test_dataset_evaluation():
     assert eval_results['f1_score'] == pytest.approx(
         (1 / 2 + 0 + 1 / 2) / 3 * 100.0)
     assert eval_results['accuracy'] == pytest.approx(2 / 6 * 100)
-    # thrs must be a float, tuple or None
+    # thrs must be a number or tuple
     with pytest.raises(TypeError):
         eval_results = dataset.evaluate(
             fake_results,