-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrain.py
194 lines (162 loc) · 7.54 KB
/
train.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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
import os
from config import Config
import torch
torch.backends.cudnn.benchmark = True
from SSIM import SSIM
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import random
import time
import numpy as np
import utils
from data_RGB import get_training_data, get_validation_data
from MFDNet import HPCNet as mfdnet
import losses
from tqdm import tqdm
if __name__ == "__main__":
opt = Config('training.yml')
gpus = ','.join([str(i) for i in opt.GPU])
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
torch.cuda.is_available()
file_psnr = 'MFD_PSNR.txt'
file_loss = 'MFD_LOSS.txt'
# Set Seeds
random.seed(1234)
np.random.seed(1234)
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
start_epoch = 1
session = 'MFDNet'
result_dir = os.path.join(opt.TRAINING.SAVE_DIR, 'results', session)
model_dir = os.path.join(opt.TRAINING.SAVE_DIR, 'models', session)
utils.mkdir(result_dir)
utils.mkdir(model_dir)
train_dir = opt.TRAINING.TRAIN_DIR
val_dir = opt.TRAINING.VAL_DIR
# Model
model_restoration = mfdnet()
model_restoration.cuda()
device_ids = [i for i in range(torch.cuda.device_count())]
if torch.cuda.device_count() > 0:
print("\n\nLet's use", torch.cuda.device_count(), "GPUs!\n\n")
new_lr = opt.OPTIM.LR_INITIAL
optimizer = optim.Adam(model_restoration.parameters(), lr=new_lr, betas=(0.9, 0.999), eps=1e-8)
# Scheduler
scheduler = optim.lr_scheduler.StepLR(step_size=60, gamma=0.8, optimizer=optimizer)
scheduler.step()
# Resume
if opt.TRAINING.RESUME:
path_chk_rest = utils.get_last_path(model_dir, '_latest.pth')
utils.load_checkpoint(model_restoration, path_chk_rest)
start_epoch = utils.load_start_epoch(path_chk_rest) + 1
utils.load_optim(optimizer, path_chk_rest)
for i in range(1, start_epoch):
scheduler.step()
new_lr = scheduler.get_lr()[0]
print('------------------------------------------------------------------------------')
print("==> Resuming Training with learning rate:", new_lr)
print('------------------------------------------------------------------------------')
if len(device_ids) > 1:
model_restoration = nn.DataParallel(model_restoration, device_ids=device_ids)
# Loss
criterion_char = losses.CharbonnierLoss()
criterion_edge = losses.EdgeLoss()
criterion_SSIM = SSIM()
# DataLoaders
train_dataset = get_training_data(train_dir, {'patch_size': opt.TRAINING.TRAIN_PS})
train_loader = DataLoader(dataset=train_dataset, batch_size=opt.OPTIM.BATCH_SIZE, shuffle=True,
num_workers=4, drop_last=False, pin_memory=True)
val_dataset = get_validation_data(val_dir, {'patch_size': opt.TRAINING.VAL_PS})
val_loader = DataLoader(dataset=val_dataset, batch_size=opt.OPTIM.BATCH_SIZE, shuffle=False,
num_workers=4, drop_last=False, pin_memory=True)
print('===> Start Epoch {} End Epoch {}'.format(start_epoch, opt.OPTIM.NUM_EPOCHS + 1))
print('===> Loading datasets')
best_psnr = 0
best_epoch = 0
model_restoration.train().cuda()
for epoch in range(start_epoch, opt.OPTIM.NUM_EPOCHS + 1):
epoch_start_time = time.time()
epoch_loss = 0
SSIM_all = 0
train_id = 1
train_sample = 0
# model_restoration.train()
# model_restoration.train().cuda()
for i, data in enumerate(tqdm(train_loader), 0):
# zero_grad
for param in model_restoration.parameters():
param.grad = None
target = data[0].cuda()
input_ = data[1].cuda()
criterion_char.cuda()
criterion_edge.cuda()
criterion_SSIM.cuda()
restored = model_restoration(input_)
restored[0] = restored[0].cuda()
restored[1] = restored[1].cuda()
# Compute loss at each stage
loss_char0 = criterion_char(restored[0], target)
loss_char1 = criterion_char(restored[1], input_)
loss_edge0 = criterion_edge(restored[0], target)
loss_edge1 = criterion_edge(restored[1], input_)
loss_SSIM0 = criterion_SSIM(restored[0], target)
loss_SSIM1 = criterion_SSIM(restored[1], input_)
loss = 0.3 * (loss_char0 + 0.2 * loss_char1) + (0.2 * (loss_edge0)) - (
0.15 * (loss_SSIM0 + 0.2 * loss_SSIM1)) # 0.05,0.2
loss.backward()
optimizer.step()
epoch_loss += loss.item()
SSIM_all += loss_SSIM0.item()
train_sample += 1
SSIM = SSIM_all / train_sample
# Evaluation
if epoch % opt.TRAINING.VAL_AFTER_EVERY == 0:
model_restoration.eval()
psnr_val_rgb = []
for ii, data_val in enumerate(val_loader, 0):
target = data_val[0].cuda()
input_ = data_val[1].cuda()
with torch.no_grad():
restored = model_restoration(input_)
restored = restored[0].cuda()
for res, tar in zip(restored, target):
psnr_val_rgb.append(utils.torchPSNR(res, tar))
psnr_val_rgb = torch.stack(psnr_val_rgb).mean().item()
if psnr_val_rgb > best_psnr:
best_psnr = psnr_val_rgb
best_epoch = epoch
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer': optimizer.state_dict()
}, os.path.join(model_dir, "model_best.pth"))
print(
"[epoch %d PSNR: %.4f --- best_epoch %d Best_PSNR %.4f]" % (epoch, psnr_val_rgb, best_epoch, best_psnr))
format_str1 = 'Epoch: %d, PSNR: %.4f, best_epoch: %d, Best_PSNR: %.4f'
a = str(format_str1 % (epoch, psnr_val_rgb, best_epoch, best_psnr))
PSNR_file = open(file_psnr, 'a+')
PSNR_file.write(a)
PSNR_file.write('\n')
PSNR_file.close()
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer': optimizer.state_dict()
}, os.path.join(model_dir, f"model_epoch_{epoch}.pth"))
scheduler.step()
print("------------------------------------------------------------------")
print("Epoch: {}\tTime: {:.4f}\tLoss: {:.4f}\tSSIM: {:.4f}\tLearningRate {:.8f}".format(epoch,
time.time() - epoch_start_time,
epoch_loss, SSIM,
scheduler.get_lr()[0]))
print("------------------------------------------------------------------")
format_str = 'Epoch: %d, Time: %.4f, Loss: %.4f, SSIM: %.4f, LearningRate: %.8f'
a = str(format_str % (epoch, time.time() - epoch_start_time, epoch_loss, SSIM, scheduler.get_lr()[0]))
loss_file = open(file_loss, 'a+')
loss_file.write(a)
loss_file.write('\n')
loss_file.close()
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer': optimizer.state_dict()
}, os.path.join(model_dir, "model_latest"))