File size: 3,320 Bytes
093b0a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import numpy as np
import torch
import os
from copy import deepcopy
def adjust_learning_rate(optimizer, epoch, args):
# lr = args.learning_rate * (0.2 ** (epoch // 2))
lr_adjust = {}
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(f"Updating learning rate to {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} -->"
f" {val_loss:.6f}). Saving model ..."
)
torch.save(model.state_dict(), os.path.join(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__
def __deepcopy__(self, memo=None):
return dotdict(deepcopy(dict(self), memo=memo))
class StandardScaler:
def __init__(self):
self.mean = 0.0
self.std = 1.0
def fit(self, data, scale_mean=False):
self.mean = data.mean(0) if scale_mean else 0.0
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.tensor(self.mean).type_as(data).to(data.device)
if torch.is_tensor(data)
else self.mean
)
std = (
torch.tensor(self.std).type_as(data).to(data.device)
if torch.is_tensor(data)
else self.std
)
if mean.shape and data.shape[-1] != mean.shape[-1]:
mean = mean[-1:]
if std.shape and data.shape[-1] != std.shape[-1]:
std = std[-1:]
res = (data * std) + mean
return res
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