83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
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import numpy as np
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import torch
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import json
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def adjust_learning_rate(optimizer, epoch, args):
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if args.lradj=='type1':
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lr_adjust = {2: args.learning_rate * 0.5 ** 1, 4: args.learning_rate * 0.5 ** 2,
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6: args.learning_rate * 0.5 ** 3, 8: args.learning_rate * 0.5 ** 4,
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10: args.learning_rate * 0.5 ** 5}
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elif args.lradj=='type2':
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lr_adjust = {5: args.learning_rate * 0.5 ** 1, 10: args.learning_rate * 0.5 ** 2,
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15: args.learning_rate * 0.5 ** 3, 20: args.learning_rate * 0.5 ** 4,
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25: args.learning_rate * 0.5 ** 5}
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else:
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lr_adjust = {}
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if epoch in lr_adjust.keys():
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lr = lr_adjust[epoch]
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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print('Updating learning rate to {}'.format(lr))
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class EarlyStopping:
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def __init__(self, patience=7, verbose=False, delta=0):
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self.patience = patience
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self.verbose = verbose
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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self.val_loss_min = np.Inf
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self.delta = delta
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def __call__(self, val_loss, model, path):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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self.save_checkpoint(val_loss, model, path)
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elif score < self.best_score + self.delta:
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self.counter += 1
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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self.save_checkpoint(val_loss, model, path)
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self.counter = 0
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def save_checkpoint(self, val_loss, model, path):
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if self.verbose:
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print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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torch.save(model.state_dict(), path+'/'+'checkpoint.pth')
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self.val_loss_min = val_loss
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class StandardScaler():
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def __init__(self, mean=0., std=1.):
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self.mean = mean
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self.std = std
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def fit(self, data):
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self.mean = data.mean(0)
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self.std = data.std(0)
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def transform(self, data):
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mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
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std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
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return (data - mean) / std
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def inverse_transform(self, data):
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mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
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std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
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return (data * std) + mean
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def load_args(filename):
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with open(filename, 'r') as f:
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args = json.load(f)
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return args
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def string_split(str_for_split):
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str_no_space = str_for_split.replace(' ', '')
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str_split = str_no_space.split(',')
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value_list = [eval(x) for x in str_split]
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return value_list
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