chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
@@ -0,0 +1,87 @@
+def calculate_measure(tp, fn, fp):
+    # avoid nan
+    if tp == 0:
+        return 0, 0, 0
+
+    p = tp * 1.0 / (tp + fp)
+    r = tp * 1.0 / (tp + fn)
+    if (p + r) > 0:
+        f1 = 2.0 * (p * r) / (p + r)
+    else:
+        f1 = 0
+    return p, r, f1
+
+
+class Measure(object):
+    def __init__(self, num_classes, target_class):
+        """
+
+        Args:
+            num_classes: number of classes.
+            target_class: target class we focus on, used to print info and do early stopping.
+        """
+        self.num_classes = num_classes
+        self.target_class = target_class
+        self.true_positives = {}
+        self.false_positives = {}
+        self.false_negatives = {}
+        self.target_best_f1 = 0.0
+        self.target_best_f1_epoch = 0
+        self.reset_info()
+
+    def reset_info(self):
+        """
+        reset info after each epoch.
+        """
+        self.true_positives = {
+            cur_class: [] for cur_class in range(self.num_classes)
+        }
+        self.false_positives = {
+            cur_class: [] for cur_class in range(self.num_classes)
+        }
+        self.false_negatives = {
+            cur_class: [] for cur_class in range(self.num_classes)
+        }
+
+    def append_measures(self, predictions, labels):
+        predicted_classes = predictions.argmax(dim=1)
+        for cl in range(self.num_classes):
+            cl_indices = labels == cl
+            pos = predicted_classes == cl
+            hits = predicted_classes[cl_indices] == labels[cl_indices]
+
+            tp = hits.sum()
+            fn = hits.size(0) - tp
+            fp = pos.sum() - tp
+
+            self.true_positives[cl].append(tp.cpu())
+            self.false_negatives[cl].append(fn.cpu())
+            self.false_positives[cl].append(fp.cpu())
+
+    def get_each_timestamp_measure(self):
+        precisions = []
+        recalls = []
+        f1s = []
+        for i in range(len(self.true_positives[self.target_class])):
+            tp = self.true_positives[self.target_class][i]
+            fn = self.false_negatives[self.target_class][i]
+            fp = self.false_positives[self.target_class][i]
+
+            p, r, f1 = calculate_measure(tp, fn, fp)
+            precisions.append(p)
+            recalls.append(r)
+            f1s.append(f1)
+        return precisions, recalls, f1s
+
+    def get_total_measure(self):
+        tp = sum(self.true_positives[self.target_class])
+        fn = sum(self.false_negatives[self.target_class])
+        fp = sum(self.false_positives[self.target_class])
+
+        p, r, f1 = calculate_measure(tp, fn, fp)
+        return p, r, f1
+
+    def update_best_f1(self, cur_f1, cur_epoch):
+        if cur_f1 > self.target_best_f1:
+            self.target_best_f1 = cur_f1
+            self.target_best_f1_epoch = cur_epoch