-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_location_135_Rain100L.py
164 lines (135 loc) · 6.84 KB
/
train_location_135_Rain100L.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
import os
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as utils
from torch.autograd import Variable
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from DerainDataset import *
from utils import *
import cv2
from torch.optim.lr_scheduler import MultiStepLR
import torchvision.transforms.functional as F
import pytorch_ssim
from networks.generator_aid135 import BRN
parser = argparse.ArgumentParser(description="AID")
parser.add_argument("--preprocess", type=bool, default=False, help='run prepare_data or not')
parser.add_argument("--batchSize", type=int, default=12, help="Training batch size")
parser.add_argument("--epochs", type=int, default=100, help="Number of training epochs")
parser.add_argument("--lr", type=float, default=1e-3, help="Initial learning rate") # 1e-3L 5e-4H 5e-5R1400
parser.add_argument("--save_path", type=str, default='./logs/Rain100L/location_TrainL_135/', help='path of log files')
parser.add_argument("--save_freq", type=int, default=1, help='save intermediate model')
parser.add_argument("--data_path", type=str, default='/media/ubuntu/SoftWare1/DerainDataset/RainTrainL/', help='path to training data')
parser.add_argument("--use_GPU", type=bool, default=True, help='use GPU or not')
parser.add_argument("--gpu_id", type=str, default='0', help='GPU id')
parser.add_argument("--inter_iter", type=int, default=8, help='number of inter_iteration')
opt = parser.parse_args()
if opt.use_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
def main():
if not os.path.isdir(opt.save_path):
os.makedirs(opt.save_path)
# Load dataset
print('Loading dataset ...\n')
if (opt.data_path.find('Light') != -1 or opt.data_path.find('Heavy') != -1):
dataset_train = newDataset(data_path=opt.data_path)
else:
dataset_train = Dataset(data_path=opt.data_path)
# dataset_val = Dataset(train=False)
loader_train = DataLoader(dataset=dataset_train, num_workers=4, batch_size=opt.batchSize, shuffle=True)
print("# of training samples: %d\n" % int(len(dataset_train)))
# Build model
net = BRN(recurrent_iter=opt.inter_iter, use_GPU=opt.use_GPU)
net = nn.DataParallel(net)
# criterion = nn.MSELoss(size_average=False)
criterion = pytorch_ssim.SSIM()
# Move to GPU
model = net.cuda()
criterion.cuda()
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
scheduler = MultiStepLR(optimizer, milestones=[30, 50, 80], gamma=0.2) # learning rates
# training
writer = SummaryWriter(opt.save_path)
step = 0
initial_epoch = findLastCheckpoint(save_dir=opt.save_path) # load the last model in matconvnet style
if initial_epoch > 0:
print('resuming by loading epoch %03d' % initial_epoch)
model.load_state_dict(torch.load(os.path.join(opt.save_path, 'net_epoch%d.pth' % initial_epoch)))
for epoch in range(initial_epoch, opt.epochs):
scheduler.step(epoch)
# set learning rate
for param_group in optimizer.param_groups:
# param_group["lr"] = current_lr
print('learning rate %f' % param_group["lr"])
# train
for i, (input, target) in enumerate(loader_train, 0):
# training step
model.train()
model.zero_grad()
optimizer.zero_grad()
# rain = input - target
input_train, target_train = Variable(input.cuda()), Variable(target.cuda())
out_train, _, _, _ = model(input_train)
pixel_loss = criterion(target_train, out_train)
# mse = criterion(input_train1 - target_train, r)
loss = (-pixel_loss) # + mse
loss.backward()
optimizer.step()
# results
model.eval()
with torch.no_grad():
out_train, _, out_r_train, _ = model(input_train)
out_train = torch.clamp(out_train, 0., 1.)
out_r_train = torch.clamp(out_r_train, 0., 1.)
psnr_train = batch_PSNR(out_train, target_train, 1.)
# save_image(epoch, [out_train, target_train, out_r_train], opt.res_path)
# psnr_train_r = batch_PSNR(out_r_train, rain_train, 1.)
print("[epoch %d][%d/%d] loss: %.4f, PSNR_train: %.4f" % (
epoch + 1, i + 1, len(loader_train), loss.item(), psnr_train))
# if you are using older version of PyTorch, you may need to change loss.item() to loss.data[0]
if step % 10 == 0:
# Log the scalar values
writer.add_scalar('loss', loss.item(), step)
writer.add_scalar('PSNR on training data', psnr_train, step)
# writer.add_scalar('loss_r', loss_r.item(), step)
# writer.add_scalar('PSNR_r on training data', psnr_train_r, step)
step += 1
## the end of each epoch
model.eval()
with torch.no_grad():
# log the images
out_train, _, _, _ = model(input_train)
out_train = torch.clamp(out_train, 0., 1.)
# out_r_train = torch.clamp(out_r_train, 0., 1.)
Img = utils.make_grid(target_train.data, nrow=8, normalize=True, scale_each=True)
Imgn = utils.make_grid(input_train.data, nrow=8, normalize=True, scale_each=True)
Irecon = utils.make_grid(out_train.data, nrow=8, normalize=True, scale_each=True)
rainstreak = utils.make_grid(out_r_train.data, nrow=8, normalize=True, scale_each=True)
writer.add_image('clean image', Img, epoch)
writer.add_image('noisy image', Imgn, epoch)
writer.add_image('reconstructed image', Irecon, epoch)
writer.add_image('estimated rain image', rainstreak, epoch)
# save model
torch.save(model.state_dict(), os.path.join(opt.save_path, 'net_latest.pth'))
if epoch % opt.save_freq == 0:
torch.save(model.state_dict(), os.path.join(opt.save_path, 'net_epoch%d.pth' % (epoch + 1)))
if __name__ == "__main__":
if opt.preprocess:
if opt.data_path.find('RainTrainH') != -1:
print(opt.data_path.find('RainTrainH'))
prepare_data_RainTrainH(data_path=opt.data_path, patch_size=100, stride=100)
elif opt.data_path.find('RainTrainL') != -1:
prepare_data_RainTrainL(data_path=opt.data_path, patch_size=100, stride=100)
elif opt.data_path.find('Rain12600') != -1:
prepare_data_Rain12600(data_path=opt.data_path, patch_size=100, stride=100)
elif (opt.data_path.find('Light') != -1 or opt.data_path.find('Heavy') != -1):
prepare_data_newrain(data_path=opt.data_path, patch_size=100, stride=100)
else:
print('unkown datasets: please define prepare data function in DerainDataset.py')
main()