mirror of
https://github.com/wassname/ETSformer.git
synced 2026-06-27 17:28:43 +08:00
gorold
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
import numpy as np
|
|
import torch
|
|
import matplotlib.pyplot as plt
|
|
import math
|
|
|
|
plt.switch_backend('agg')
|
|
|
|
|
|
def adjust_learning_rate(optimizer, epoch, args):
|
|
for param_group in optimizer.param_groups:
|
|
if param_group['name'] == 'smoothing':
|
|
continue
|
|
elif param_group['name'] == 'damping':
|
|
continue
|
|
else:
|
|
learning_rate = args.learning_rate
|
|
|
|
if args.lradj == 'exponential':
|
|
lr_adjust = {epoch: learning_rate * (0.5 ** ((epoch - 1) // 1))}
|
|
elif args.lradj == 'schedule':
|
|
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 == 'cos':
|
|
lr_adjust = {epoch: learning_rate * 0.5 * (1. + math.cos(math.pi * epoch / args.train_epochs))}
|
|
elif args.lradj == 'cos_with_warmup':
|
|
if epoch <= args.warmup_epochs:
|
|
lr = args.min_lr + (learning_rate - args.min_lr) * (epoch / (args.warmup_epochs + 1))
|
|
else:
|
|
curr_epoch = epoch - args.warmup_epochs
|
|
total_epochs = args.train_epochs - args.warmup_epochs
|
|
lr = learning_rate * 0.5 * (1. + math.cos(math.pi * curr_epoch / total_epochs))
|
|
lr_adjust = {epoch: lr}
|
|
elif args.lradj == 'exponential_with_warmup':
|
|
if epoch <= args.warmup_epochs:
|
|
lr = args.min_lr + (learning_rate - args.min_lr) * (epoch / (args.warmup_epochs + 1))
|
|
else:
|
|
curr_epoch = epoch - args.warmup_epochs
|
|
lr = learning_rate * (0.5 ** ((curr_epoch - 1) // 1))
|
|
lr_adjust = {epoch: lr}
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
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')
|