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utils/tools.py

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+import numpy as np
+import torch
+
+def adjust_learning_rate(optimizer, epoch, args):
+    # lr = args.learning_rate * (0.2 ** (epoch // 2))
+    if args.lradj=='type1':
+        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch-1) // 1))}
+    elif args.lradj=='type2':
+        lr_adjust = {
+            2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 
+            10: 5e-7, 15: 1e-7, 20: 5e-8
+        }
+    if epoch in lr_adjust.keys():
+        lr = lr_adjust[epoch]
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+        print('Updating learning rate to {}'.format(lr))
+
+class EarlyStopping:
+    def __init__(self, patience=7, verbose=False, delta=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
+
+    def __call__(self, val_loss, model, path):
+        score = -val_loss
+        if self.best_score is None:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model, path)
+        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, path)
+            self.counter = 0
+
+    def save_checkpoint(self, val_loss, model, path):
+        if self.verbose:
+            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
+        torch.save(model.state_dict(), path+'/'+'checkpoint.pth')
+        self.val_loss_min = val_loss
+
+class dotdict(dict):
+    """dot.notation access to dictionary attributes"""
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+class StandardScaler():
+    def __init__(self):
+        self.mean = 0.
+        self.std = 1.
+    
+    def fit(self, data):
+        self.mean = data.mean(0)
+        self.std = data.std(0)
+
+    def transform(self, data):
+        mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
+        std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
+        return (data - mean) / std
+
+    def inverse_transform(self, data):
+        mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
+        std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
+        if data.shape[-1] != mean.shape[-1]:
+            mean = mean[-1:]
+            std = std[-1:]
+        return (data * std) + mean