-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_multiDataset_discriminator.py
180 lines (149 loc) · 8.13 KB
/
train_multiDataset_discriminator.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
import time
import argparse
import shutil
import torch.cuda.random
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
from torch.utils.data import DataLoader
from warmup_scheduler import GradualWarmupScheduler
from tensorboardX import SummaryWriter
from skimage.measure import compare_psnr
from model.ELD_UNet import ELD_UNet
from model.DG_UNet import *
from data.dataloader import *
from utils.util import *
from utils.checkpoint import *
from utils.gen_mat import *
from loss.loss import *
ImageFile.LOAD_TRUNCATED_IMAGES = True
torchvision.set_image_backend('accimage')
def train(opt, epoch, ad_net, data_loader, optimizer, scheduler, logger, writer):
t0 = time.time()
epoch_loss = AverageMeter()
epoch_acc = AverageMeter()
ad_net.train()
for iteration, (target, label) in enumerate(data_loader):
target, label = target.cuda(), label.cuda()
target_ad_out = ad_net(target)
target_acc = accuracy(target_ad_out, label)
loss = get_ad_loss(target_ad_out, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss.update(loss.data, target.size(0))
epoch_acc.update(target_acc, target.size(0))
if iteration % opt.print_freq == 0:
logger.info('Train epoch: [{:d}/{:d}]\titeration: [{:d}/{:d}]\tlr={:.6f}\tloss={:.4f}\tacc={:4f}'
.format(epoch, opt.nEpochs, iteration, len(data_loader), scheduler.get_lr()[0], epoch_loss.avg, target_acc))
writer.add_scalar('Train_loss', epoch_loss.avg, epoch)
writer.add_scalar('Learning_rate', scheduler.get_lr()[0], epoch)
writer.add_scalar('Accuracy', epoch_acc.avg, epoch)
logger.info('||==> Train epoch: [{:d}/{:d}]\tlr={:.6f}\tl1_loss={:.4f}\tacc={:4f}\tcost_time={:.4f}'
.format(epoch, opt.nEpochs, scheduler.get_lr()[0], epoch_loss.avg, epoch_acc.avg, time.time() - t0))
return epoch_acc.avg
def main():
parser = argparse.ArgumentParser(description='PyTorch image denoising')
# dataset settings
parser.add_argument('--data_set1', type=str, default='renoir_v2', help='the exact dataset we want to train on')
parser.add_argument('--data_set2', type=str, default='nind', help='the exact dataset we want to train on')
parser.add_argument('--data_set3', type=str, default='rid2021_v2', help='the exact dataset we want to train on')
parser.add_argument('--data_dir', type=str, default='/mnt/lustre/share/yangmingzhuo/processed',
help='the dataset dir')
parser.add_argument('--batch_size', type=int, default=256, help='training batch size: 32')
parser.add_argument('--patch_size', type=int, default=128, help='Size of cropped HR image')
# training settings
parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=1e-1, help='learning rate. default=0.0002')
parser.add_argument('--lr_min', type=float, default=1e-3, help='minimum learning rate. default=0.000001')
parser.add_argument('--start_epoch', type=int, default=1, help='starting epoch')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='weight_decay')
# model settings
parser.add_argument('--model_type', type=str, default='Discriminator_model_v2', help='the name of model')
parser.add_argument('--pretrain_model', type=str, default='', help='pretrain model path')
# general settings
parser.add_argument('--gpus', default='0', type=str, help='id of gpus')
parser.add_argument('--log_dir', default='./logs_disc/', help='Location to save checkpoint models')
parser.add_argument('--seed', type=int, default=0, help='random seed to use. Default=0')
parser.add_argument('--num_workers', type=int, default=8, help='number of workers')
parser.add_argument('--print_freq', type=int, default=10, help='print freq')
parser.add_argument('--exp_id', type=int, default=0, help='experiment')
opt = parser.parse_args()
# initialize
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
cudnn.benchmark = True
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
epoch_acc_best = 0
epoch_best = 0
# log setting
log_folder = os.path.join(opt.log_dir, "model_{}_gpu_{}_ds_{}_{}_{}_ps_{}_bs_{}_ep_{}_lr_{}_lr_min_{}_exp_id_{}"
.format(opt.model_type, opt.gpus, opt.data_set1, opt.data_set2, opt.data_set3,
opt.patch_size, opt.batch_size, opt.nEpochs, opt.lr, opt.lr_min, opt.exp_id))
output_process(log_folder)
checkpoint_folder = make_dir(os.path.join(log_folder, 'checkpoint'))
writer = SummaryWriter(log_folder)
logger = get_logger(log_folder, 'DGNet_log')
logger.info(opt)
# load dataset
logger.info('Loading datasets {} {} {}, Batch Size: {}, Patch Size: {}'.format(opt.data_set1, opt.data_set2,
opt.data_set3, opt.batch_size,
opt.patch_size))
train_set = LoadMultiDataset_clean(src_path1=os.path.join(opt.data_dir, opt.data_set1, 'train'),
src_path2=os.path.join(opt.data_dir, opt.data_set2, 'train'),
src_path3=os.path.join(opt.data_dir, opt.data_set3, 'train'),
patch_size=opt.patch_size,
train=True)
train_data_loader = DataLoaderX(dataset=train_set, batch_size=opt.batch_size, shuffle=True,
num_workers=opt.num_workers, pin_memory=True)
logger.info('Train dataset length: {}'.format(len(train_data_loader)))
# load network
logger.info('Building model {}'.format(opt.model_type))
ad_net = Discriminator_model_v1()
if torch.cuda.device_count() > 1:
ad_net = torch.nn.DataParallel(ad_net)
logger.info("Push model to data parallel and then gpu!")
else:
logger.info("Push model to one gpu!")
ad_net.cuda()
logger.info('model={}'.format(ad_net))
# loss
logger.info('==> Use CE loss as criterion')
# optimizer and scheduler
t_max = opt.nEpochs
logger.info('Optimizer: Adam, Learning rate: {}, Scheduler: CosineAnnealingLR, T_max: {}'
.format(opt.lr, t_max))
optimizer = optim.Adam(ad_net.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
logger.info('optimizer={}'.format(optimizer))
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, t_max, eta_min=opt.lr_min)
logger.info('scheduler={}'.format(scheduler))
# resume
if opt.pretrain_model != '':
ad_net, start_epoch, optimizer, acc_best = load_ad_net_dp(opt.pretrain_model, ad_net, optimizer, logger)
start_epoch += 1
for i in range(1, start_epoch):
scheduler.step()
logger.info('Resume start epoch: {}, Learning rate:{:.6f}'.format(start_epoch, scheduler.get_lr()[0]))
else:
start_epoch = opt.start_epoch
logger.info('Start epoch: {}, Learning rate:{:.6f}'.format(start_epoch, scheduler.get_lr()[0]))
# training
for epoch in range(start_epoch, opt.nEpochs + 1):
# training
epoch_acc = train(opt, epoch, ad_net, train_data_loader, optimizer, scheduler, logger, writer)
if epoch_acc >= epoch_acc_best:
epoch_acc_best = epoch_acc
epoch_best = epoch
save_ad_net(os.path.join(checkpoint_folder, "ad_net_best.pth"), epoch, ad_net, optimizer, epoch_acc_best, logger)
# save model
save_ad_net(os.path.join(checkpoint_folder, "ad_net_latest.pth"), epoch, ad_net, optimizer, epoch_acc, logger)
scheduler.step()
logger.info('||==> best_epoch = {}, best_acc = {}'.format(epoch_best, epoch_acc_best))
# generate evaluate_mat for SSIM validation
# gen_mat(ELD_UNet(), os.path.join(checkpoint_folder, "model_best.pth"), checkpoint_folder, val_data_loader,
# opt.test_batch_size, opt.test_patch_size, logger)
if __name__ == '__main__':
main()