-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
153 lines (132 loc) · 6.58 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
import datetime
import argparse
import torch
import random
import numpy as np
from configs.config import MyConfiguration
from Trainer import Trainer
from Tester import Tester
from data.dataset_list import MyDataset
from torch.utils.data import DataLoader
from models import CPS_Network
def for_train(model,
config,
args,
train_data_loader,
train_unsup_data_loader0,
train_unsup_data_loader1,
valid_data_loader,
begin_time,
resume_file):
myTrainer = Trainer(model=model, config=config, args=args,
train_data_loader=train_data_loader,
valid_data_loader=valid_data_loader,
train_unsup_data_loader0=train_unsup_data_loader0,
train_unsup_data_loader1=train_unsup_data_loader1,
begin_time=begin_time,
resume_file=resume_file)
myTrainer.train()
print(" Training Done ! ")
def for_test(model, config, args, test_data_loader, class_name, begin_time, resume_file):
myTester = Tester(model=model, config=config, args=args,
test_data_loader=test_data_loader,
class_name=class_name,
begin_time=begin_time,
resume_file=resume_file)
myTester.eval_and_predict()
print(" Evaluation Done ! ")
def main(config, args):
# model initialization
model = CPS_Network.FCNs_CPS_ASPP_4conv1(in_ch=config.input_channel, out_ch=1, backbone='vgg16_bn',
pretrained=True)
if hasattr(model, 'name'):
config.config.set("Directory", "model_name", model.name + '_RandMix')
# obtain the maximum number of samples
temp_datset_sup = MyDataset(config=config, args=args, subset='train')
temp_datset_unsup = MyDataset(config=config, args=args, subset='train_unsup')
l_sup = len(temp_datset_sup)
l_unsup = len(temp_datset_unsup)
max_samples = max(l_sup, l_unsup)
del temp_datset_unsup, temp_datset_sup
# initialize the training dataset
train_dataset = MyDataset(config=config, args=args, subset='train', file_length=max_samples)
train_unsup_dataset = MyDataset(config=config, args=args, subset='train_unsup', file_length=max_samples)
valid_dataset = MyDataset(config=config, args=args, subset='val')
test_dataset = MyDataset(config=config, args=args, subset='test')
# initialize the training Dataloader
train_data_loader = DataLoader(dataset=train_dataset,
batch_size=config.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.threads,
drop_last=True)
train_unsup_data_loader0 = DataLoader(dataset=train_unsup_dataset,
batch_size=config.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.threads,
drop_last=True)
train_unsup_data_loader1 = DataLoader(dataset=train_unsup_dataset,
batch_size=config.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.threads,
drop_last=True)
valid_data_loader = DataLoader(dataset=valid_dataset,
batch_size=config.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.threads,
drop_last=False)
test_data_loader = DataLoader(dataset=test_dataset,
batch_size=config.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.threads,
drop_last=False)
begin_time = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
if config.use_gpu:
model = model.cuda(device=args.gpu)
for_train(model=model, config=config, args=args,
train_data_loader=train_data_loader,
valid_data_loader=valid_data_loader,
train_unsup_data_loader0=train_unsup_data_loader0,
train_unsup_data_loader1=train_unsup_data_loader1,
begin_time=begin_time,
resume_file=args.weight)
for_test(model=model, config=config, args=args,
test_data_loader=test_data_loader,
class_name=test_dataset.class_names,
begin_time=begin_time,
resume_file=None
)
if __name__ == '__main__':
config = MyConfiguration('configs/config.cfg')
parser = argparse.ArgumentParser(description="Model Training")
parser.add_argument('-input', metavar='input', type=str, default=config.root_dir,
help='root path to directory containing input images, including train & valid & test')
parser.add_argument('-output', metavar='output', type=str, default=config.save_dir,
help='root path to directory containing all the output, including predictions, logs and ckpt')
parser.add_argument('-weight', metavar='weight', type=str, default=None,
help='path to ckpt which will be loaded')
parser.add_argument('-threads', metavar='threads', type=int, default=2,
help='number of thread used for DataLoader')
parser.add_argument('-is_test', action='store_true', default=False,
help='in train mode, is_test=False')
if config.use_gpu:
parser.add_argument('-gpu', metavar='gpu', type=int, default=0,
help='gpu id to be used for prediction')
else:
parser.add_argument('-gpu', metavar='gpu', type=int, default=-1,
help='gpu id to be used for prediction')
args = parser.parse_args()
if config.use_seed:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(config.random_seed)
torch.manual_seed(config.random_seed)
random.seed(config.random_seed)
np.random.seed(config.random_seed)
else:
torch.backends.cudnn.benchmark = True
main(config=config, args=args)