初始化

pytorchtools.py

@@ -0,0 +1,52 @@
+import numpy as np
+
+
+class EarlyStopping:
+    """Early stops the training if validation loss doesn't improve after a given patience."""
+
+    def __init__(self, patience=7, verbose=False, delta=0, save_path="."):
+        """
+        Args:
+            patience (int): How long to wait after last time validation loss improved.
+                            Default: 7
+            verbose (bool): If True, prints a message for each validation loss improvement.
+                            Default: False
+            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
+                            Default: 0
+        """
+        self.patience = patience
+        self.verbose = verbose
+        self.counter = 0
+        self.best_score = None
+        self.early_stop = False
+        self.val_loss_min = np.Inf
+        self.delta = delta
+        self.save_path = save_path
+
+    def __call__(self, val_loss, model):
+
+        score = -val_loss
+
+        if self.best_score is None:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+        elif score < self.best_score + self.delta:
+            self.counter += 1
+            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
+            if self.counter >= self.patience:
+                self.early_stop = True
+        else:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+            self.counter = 0
+
+    def save_checkpoint(self, val_loss, model):
+        '''Saves model when validation loss decrease.'''
+        if self.verbose:
+            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
+        # save_path = join(self.save_path, "best_model")
+        # if not os.path.exists(save_path):
+        #     os.mkdir(save_path)
+        # model_to_save = model.module if hasattr(model, 'module') else model
+        # model_to_save.save_pretrained(save_path)
+        self.val_loss_min = val_loss