feat(utils): add common machine learning utility functions

utils/tools.py

@@ -0,0 +1,106 @@
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import time
+
+plt.switch_backend('agg')
+
+
+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
+        }
+    elif args.lradj == 'type3':
+        lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
+    
+    # Sigmoid learning rate decay
+    elif args.lradj == 'sigmoid':
+        k = 0.5 # logistic growth rate
+        s = 10  # decreasing curve smoothing rate
+        w = 10  # warm-up coefficient
+        lr_adjust = {epoch: args.learning_rate / (1 + np.exp(-k * (epoch - w))) - args.learning_rate / (1 + np.exp(-k/s * (epoch - w*s)))}
+    
+    elif args.lradj == 'constant':
+        lr_adjust = {epoch: args.learning_rate}
+    elif args.lradj == '3':
+        lr_adjust = {epoch: args.learning_rate if epoch < 10 else args.learning_rate*0.1}
+    elif args.lradj == '4':
+        lr_adjust = {epoch: args.learning_rate if epoch < 15 else args.learning_rate*0.1}
+    elif args.lradj == '5':
+        lr_adjust = {epoch: args.learning_rate if epoch < 25 else args.learning_rate*0.1}
+    elif args.lradj == '6':
+        lr_adjust = {epoch: args.learning_rate if epoch < 5 else args.learning_rate*0.1}  
+ 
+    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, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def transform(self, data):
+        return (data - self.mean) / self.std
+
+    def inverse_transform(self, data):
+        return (data * self.std) + self.mean
+
+
+def visual(true, preds=None, name='./pic/test.pdf'):
+    """
+    Results visualization
+    """
+    plt.figure()
+    plt.plot(true, label='GroundTruth', linewidth=2)
+    if preds is not None:
+        plt.plot(preds, label='Prediction', linewidth=2)
+    plt.legend()
+    plt.savefig(name, bbox_inches='tight')