-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
173 lines (140 loc) · 5.19 KB
/
utils.py
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
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import torch
import torch.nn as nn
import logging
import numpy as np
import os
import json
import argparse
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def initialize_logger(file_dir):
logger = logging.getLogger()
fhandler = logging.FileHandler(filename=file_dir, mode='a')
formatter = logging.Formatter('%(asctime)s - %(message)s', "%Y-%m-%d %H:%M:%S")
fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.setLevel(logging.INFO)
return logger
def save_checkpoint(model_path, epoch, iteration, model, optimizer):
state = {
'epoch': epoch,
'iter': iteration,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, os.path.join(model_path, 'net_%depoch.pth' % epoch))
def save_checkpoint_best_psnr(model_path, epoch, iteration, model, optimizer):
state = {
'epoch': epoch,
'iter': iteration,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, os.path.join(model_path, 'best_psnr_%depoch.pth' % epoch))
def save_checkpoint_for_finetune(model_path, epoch, iteration, model, optimizer, train_losses_rmse, train_losses_psnr, valid_losses_rmse, valid_losses_psnr):
state = {
'epoch': epoch,
'iter': iteration,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'train_losses_rmse' : train_losses_rmse,
'train_losses_psnr' : train_losses_psnr,
'valid_losses_rmse' : valid_losses_rmse,
'valid_losses_psnr' : valid_losses_psnr
}
torch.save(state, os.path.join(model_path, 'fine_tune_result_%depoch.pth' % epoch))
class Loss_MRAE(nn.Module):
def __init__(self):
super(Loss_MRAE, self).__init__()
def forward(self, outputs, label):
assert outputs.shape == label.shape
error = torch.abs(outputs - label) / label
mrae = torch.mean(error.contiguous().view(-1))
return mrae
class Loss_MAE(nn.Module):
def __init__(self):
super(Loss_MAE, self).__init__()
def forward(self, outputs, label):
assert outputs.shape == label.shape
error = torch.abs(outputs - label)
mae = torch.mean(error.contiguous().view(-1))
return mae
class Loss_RMSE(nn.Module):
def __init__(self):
super(Loss_RMSE, self).__init__()
def forward(self, outputs, label):
assert outputs.shape == label.shape
error = outputs-label
sqrt_error = torch.pow(error,2)
rmse = torch.sqrt(torch.mean(sqrt_error.contiguous().view(-1)))
return rmse
class Loss_PSNR(nn.Module):
def __init__(self):
super(Loss_PSNR, self).__init__()
def forward(self, im_true, im_fake, data_range=255):
N = im_true.size()[0]
C = im_true.size()[1]
H = im_true.size()[2]
W = im_true.size()[3]
Itrue = im_true.clamp(0., 1.).mul_(data_range).resize_(N, C * H * W)
Ifake = im_fake.clamp(0., 1.).mul_(data_range).resize_(N, C * H * W)
mse = nn.MSELoss(reduce=False)
err = mse(Itrue, Ifake).sum(dim=1, keepdim=True).div_(C * H * W)
psnr = 10. * torch.log((data_range ** 2) / err) / np.log(10.)
return torch.mean(psnr)
def time2file_name(time):
year = time[0:4]
month = time[5:7]
day = time[8:10]
hour = time[11:13]
minute = time[14:16]
second = time[17:19]
time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second
return time_filename
def record_loss(loss_csv, epoch, iteration, epoch_time, lr, train_loss, test_loss):
""" Record many results."""
loss_csv.write('{},{},{},{},{},{}\n'.format(epoch, iteration, epoch_time, lr, train_loss, test_loss))
loss_csv.flush()
loss_csv.close
def save_model(args, model, optimizer, scheduler, epoch, path):
file_name = f'{args.model_name}_{epoch}.pt'
torch.save({
"model": model.state_dict(),
"optimizer" : optimizer.state_dict(),
"scheduler" : scheduler.state_dict()
}, os.path.join(path, file_name))
def load_model(model_path, model):
checkpoint = torch.load(model_path)
model.load_state_dict(checkpoint["model"])
return model
def save_dict(dict_data, path):
with open(path, 'w') as f:
json.dump(dict_data, f, indent=2)
def read_dict(path):
with open(path, 'r') as f:
mydict = json.load(f)
return mydict
def save_args(args, path):
file_name = 'args.txt'
args_path = os.path.join(path, file_name)
with open(args_path, 'w') as f:
json.dump(args.__dict__, f, indent=2)
def open_args(path, ipykernel=True):
argparser = argparse.ArgumentParser()
args = argparser.parse_args(args=[])
file_name = 'args.txt'
args_path = os.path.join(path, file_name)
with open(args_path, 'r') as f:
args.__dict__ = json.load(f)
return args