| | import numpy as np |
| | import torch |
| | import matplotlib.pyplot as plt |
| | import time |
| |
|
| | plt.switch_backend('agg') |
| |
|
| |
|
| | def adjust_learning_rate(optimizer, epoch, args): |
| | |
| | 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 == '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') |
| |
|
| | def test_params_flop(model,x_shape): |
| | """ |
| | If you want to thest former's flop, you need to give default value to inputs in model.forward(), the following code can only pass one argument to forward() |
| | """ |
| | model_params = 0 |
| | for parameter in model.parameters(): |
| | model_params += parameter.numel() |
| | print('INFO: Trainable parameter count: {:.2f}M'.format(model_params / 1000000.0)) |
| | from ptflops import get_model_complexity_info |
| | with torch.cuda.device(0): |
| | macs, params = get_model_complexity_info(model.cuda(), x_shape, as_strings=True, print_per_layer_stat=True) |
| | |
| | |
| | print('{:<30} {:<8}'.format('Computational complexity: ', macs)) |
| | print('{:<30} {:<8}'.format('Number of parameters: ', params)) |
