-
Notifications
You must be signed in to change notification settings - Fork 0
/
edc_aptos.py
206 lines (170 loc) · 7.77 KB
/
edc_aptos.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
195
196
197
198
199
200
201
202
203
204
205
206
# import needed library
import os
import logging
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
from utils import get_logger, count_parameters, over_write_args_from_file
from train_utils import TBLog, get_optimizer_v2, get_multistep_schedule_with_warmup
from methods.edc1 import EDC
from datasets.dataset import AD_Dataset
from datasets.data_utils import get_data_loader
from models.edc import R50_R50, WR50_WR50
import warnings
warnings.filterwarnings('ignore')
def main_worker(gpu, args):
'''
'''
args.gpu = gpu
assert args.seed is not None
random.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
cudnn.deterministic = True
# cudnn.benchmark = True
# SET save_path and logger
save_path = os.path.join(args.save_dir, args.save_name)
logger_level = "INFO"
tb_log = None
logger = get_logger(args.save_name, save_path, logger_level)
logger.warning(f"USE GPU: {args.gpu} for training")
# Construct Dataset & DataLoader
train_dset = AD_Dataset(name=args.dataset, train=True, data_dir=args.data_dir)
train_dset = train_dset.get_dset()
print('TrainSet Image Number:', len(train_dset))
eval_dset = AD_Dataset(name=args.dataset, train=False, data_dir=args.data_dir)
eval_dset = eval_dset.get_dset()
print('EvalSet Image Number:', len(eval_dset))
loader_dict = {}
dset_dict = {'train': train_dset, 'eval': eval_dset}
generator_lb = torch.Generator()
generator_lb.manual_seed(args.seed)
loader_dict['train'] = get_data_loader(dset_dict['train'],
args.batch_size,
data_sampler=args.train_sampler,
num_iters=args.num_train_iter,
num_workers=args.num_workers,
distributed=False,
generator=generator_lb)
loader_dict['eval'] = get_data_loader(dset_dict['eval'],
args.eval_batch_size,
num_workers=args.num_workers,
drop_last=False)
model = R50_R50(img_size=args.img_size,
train_encoder=True,
stop_grad=True,
reshape=True,
bn_pretrain=False,
)
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.momentum = 0.01
runner = EDC(model=model,
num_eval_iter=args.num_eval_iter,
tb_log=tb_log,
logger=logger)
logger.info(f'Number of Trainable Params: {count_parameters(runner.model)}')
# SET Optimizer & LR Scheduler
optimizer = get_optimizer_v2(runner.model, args.optim, args.lr, args.momentum, lr_encoder=args.lr_encoder,
weight_decay=args.weight_decay)
scheduler = get_multistep_schedule_with_warmup(optimizer, milestones=[1e10], gamma=0.2,
num_warmup_steps=0)
runner.set_optimizer(optimizer, scheduler)
if not torch.cuda.is_available():
raise Exception('ONLY GPU TRAINING IS SUPPORTED')
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
runner.model = runner.model.cuda(args.gpu)
logger.info(f"model_arch: {model}")
logger.info(f"Arguments: {args}")
## set DataLoader
runner.set_data_loader(loader_dict)
# If args.resume, load checkpoints from args.load_path
if args.resume:
runner.load_model(args.load_path)
# START TRAINING
runner.tb_log = TBLog(save_path, 'tb', use_tensorboard=args.use_tensorboard)
runner.train(args)
logging.warning(f"training is FINISHED")
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='')
'''
Saving & loading of the model.
'''
parser.add_argument('--save_dir', type=str, default='./saved_models')
parser.add_argument('-sn', '--save_name', type=str,
default='edcad_aptos_256_r50_r50_m4_bn99_adamw5e4wd1e4_1e5_b32_i1k_cl1_0',
)
parser.add_argument('--resume', action='store_true', default=False)
parser.add_argument('--load_path', type=str, default=None)
parser.add_argument('-o', '--overwrite', action='store_true', default=True)
parser.add_argument('--use_tensorboard', action='store_true', default=True,
help='Use tensorboard to plot and save curves, otherwise save the curves locally.')
'''
Training Configuration
'''
parser.add_argument('--epoch', type=int, default=1)
parser.add_argument('--num_train_iter', type=int, default=1000,
help='total number of training iterations')
parser.add_argument('--num_eval_iter', type=int, default=250,
help='evaluation frequency')
parser.add_argument('-bsz', '--batch_size', type=int, default=32)
parser.add_argument('--eval_batch_size', type=int, default=64,
help='batch size of evaluation data loader (it does not affect the accuracy)')
parser.add_argument('--ema_m', type=float, default=0., help='ema momentum for eval_model')
'''
Optimizer configurations
'''
parser.add_argument('--optim', type=str, default='AdamW')
parser.add_argument('--lr', type=float, default=5e-4)
parser.add_argument('--lr_encoder', type=float, default=1e-5)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=1e-4)
parser.add_argument('--amp', type=str2bool, default=False, help='use mixed precision training or not')
parser.add_argument('--clip', type=float, default=1)
'''
Data Configurations
'''
parser.add_argument('--data_dir', type=str, default="/data/disk2T1/guoj/APTOS")
parser.add_argument('-ds', '--dataset', type=str, default='fundus')
parser.add_argument('--train_sampler', type=str, default='RandomSampler')
parser.add_argument('--img_size', type=int, default=256)
parser.add_argument('--num_workers', type=int, default=4)
'''
multi-GPUs & Distrbitued Training
'''
## args for distributed training (from https://github.com/pytorch/examples/blob/master/imagenet/main.py)
parser.add_argument('--seed', default=0, type=int,
help='seed for initializing training.')
parser.add_argument('--gpu', default='1', type=str,
help='GPU id to use.')
# config file
parser.add_argument('--c', type=str, default='')
args = parser.parse_args()
over_write_args_from_file(args, args.c)
save_path = os.path.join(args.save_dir, args.save_name)
if os.path.exists(save_path) and args.overwrite and args.resume == False:
import shutil
shutil.rmtree(save_path)
if os.path.exists(save_path) and not args.overwrite:
raise Exception('already existing model: {}'.format(save_path))
if args.resume:
if args.load_path is None:
raise Exception('Resume of training requires --load_path in the args')
if os.path.abspath(save_path) == os.path.abspath(args.load_path) and not args.overwrite:
raise Exception('Saving & Loading pathes are same. \
If you want over-write, give --overwrite in the argument.')
main_worker(int(args.gpu), args)