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train.py
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from datetime import datetime
import os
import os.path as osp
# PyTorch includes
import torch
from torchvision import transforms
from torch.utils.data import DataLoader
import argparse
import yaml
from train_process import Trainer
# Custom includes
from dataloaders import fundus_dataloader as DL
from dataloaders import custom_transforms as tr
from networks.deeplabv3 import *
from networks.GAN import BoundaryDiscriminator, UncertaintyDiscriminator
here = osp.dirname(osp.abspath(__file__))
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument('-g', '--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--resume', default=None, help='checkpoint path')
# configurations (same configuration as original work)
# https://github.com/shelhamer/fcn.berkeleyvision.org
parser.add_argument(
'--datasetS', type=str, default='refuge', help='test folder id contain images ROIs to test'
)
parser.add_argument(
'--datasetT', type=str, default='Drishti-GS', help='refuge / Drishti-GS/ RIM-ONE_r3'
)
parser.add_argument(
'--batch-size', type=int, default=8, help='batch size for training the model'
)
parser.add_argument(
'--group-num', type=int, default=1, help='group number for group normalization'
)
parser.add_argument(
'--max-epoch', type=int, default=200, help='max epoch'
)
parser.add_argument(
'--stop-epoch', type=int, default=200, help='stop epoch'
)
parser.add_argument(
'--warmup-epoch', type=int, default=-1, help='warmup epoch begin train GAN'
)
parser.add_argument(
'--interval-validate', type=int, default=10, help='interval epoch number to valide the model'
)
parser.add_argument(
'--lr-gen', type=float, default=1e-3, help='learning rate',
)
parser.add_argument(
'--lr-dis', type=float, default=2.5e-5, help='learning rate',
)
parser.add_argument(
'--lr-decrease-rate', type=float, default=0.1, help='ratio multiplied to initial lr',
)
parser.add_argument(
'--weight-decay', type=float, default=0.0005, help='weight decay',
)
parser.add_argument(
'--momentum', type=float, default=0.99, help='momentum',
)
parser.add_argument(
'--data-dir',
default='/home/sjwang/ssd1T/fundus/domain_adaptation/',
help='data root path'
)
parser.add_argument(
'--pretrained-model',
default='../../../models/pytorch/fcn16s_from_caffe.pth',
help='pretrained model of FCN16s',
)
parser.add_argument(
'--out-stride',
type=int,
default=16,
help='out-stride of deeplabv3+',
)
parser.add_argument(
'--sync-bn',
type=bool,
default=True,
help='sync-bn in deeplabv3+',
)
parser.add_argument(
'--freeze-bn',
type=bool,
default=False,
help='freeze batch normalization of deeplabv3+',
)
args = parser.parse_args()
args.model = 'FCN8s'
now = datetime.now()
args.out = osp.join(here, 'logs', args.datasetT, now.strftime('%Y%m%d_%H%M%S.%f'))
os.makedirs(args.out)
with open(osp.join(args.out, 'config.yaml'), 'w') as f:
yaml.safe_dump(args.__dict__, f, default_flow_style=False)
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
cuda = torch.cuda.is_available()
torch.manual_seed(1337)
if cuda:
torch.cuda.manual_seed(1337)
# 1. dataset
composed_transforms_tr = transforms.Compose([
tr.RandomScaleCrop(512),
tr.RandomRotate(),
tr.RandomFlip(),
tr.elastic_transform(),
tr.add_salt_pepper_noise(),
tr.adjust_light(),
tr.eraser(),
tr.Normalize_tf(),
tr.ToTensor()
])
composed_transforms_ts = transforms.Compose([
tr.RandomCrop(512),
tr.Normalize_tf(),
tr.ToTensor()
])
domain = DL.FundusSegmentation(base_dir=args.data_dir, dataset=args.datasetS, split='train',
transform=composed_transforms_tr)
domain_loaderS = DataLoader(domain, batch_size=args.batch_size, shuffle=True, num_workers=2, pin_memory=True)
domain_T = DL.FundusSegmentation(base_dir=args.data_dir, dataset=args.datasetT, split='train',
transform=composed_transforms_tr)
domain_loaderT = DataLoader(domain_T, batch_size=args.batch_size, shuffle=False, num_workers=2, pin_memory=True)
domain_val = DL.FundusSegmentation(base_dir=args.data_dir, dataset=args.datasetT, split='train',
transform=composed_transforms_ts)
domain_loader_val = DataLoader(domain_val, batch_size=args.batch_size, shuffle=False, num_workers=2, pin_memory=True)
# 2. model
model_gen = DeepLab(num_classes=2, backbone='mobilenet', output_stride=args.out_stride,
sync_bn=args.sync_bn, freeze_bn=args.freeze_bn).cuda()
model_dis = BoundaryDiscriminator().cuda()
model_dis2 = UncertaintyDiscriminator().cuda()
start_epoch = 0
start_iteration = 0
# 3. optimizer
optim_gen = torch.optim.Adam(
model_gen.parameters(),
lr=args.lr_gen,
betas=(0.9, 0.99)
)
optim_dis = torch.optim.SGD(
model_dis.parameters(),
lr=args.lr_dis,
momentum=args.momentum,
weight_decay=args.weight_decay
)
optim_dis2 = torch.optim.SGD(
model_dis2.parameters(),
lr=args.lr_dis,
momentum=args.momentum,
weight_decay=args.weight_decay
)
if args.resume:
checkpoint = torch.load(args.resume)
pretrained_dict = checkpoint['model_state_dict']
model_dict = model_gen.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model_gen.load_state_dict(model_dict)
pretrained_dict = checkpoint['model_dis_state_dict']
model_dict = model_dis.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model_dis.load_state_dict(model_dict)
pretrained_dict = checkpoint['model_dis2_state_dict']
model_dict = model_dis2.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model_dis2.load_state_dict(model_dict)
start_epoch = checkpoint['epoch'] + 1
start_iteration = checkpoint['iteration'] + 1
optim_gen.load_state_dict(checkpoint['optim_state_dict'])
optim_dis.load_state_dict(checkpoint['optim_dis_state_dict'])
optim_dis2.load_state_dict(checkpoint['optim_dis2_state_dict'])
trainer = Trainer.Trainer(
cuda=cuda,
model_gen=model_gen,
model_dis=model_dis,
model_uncertainty_dis=model_dis2,
optimizer_gen=optim_gen,
optimizer_dis=optim_dis,
optimizer_uncertainty_dis=optim_dis2,
lr_gen=args.lr_gen,
lr_dis=args.lr_dis,
lr_decrease_rate=args.lr_decrease_rate,
val_loader=domain_loader_val,
domain_loaderS=domain_loaderS,
domain_loaderT=domain_loaderT,
out=args.out,
max_epoch=args.max_epoch,
stop_epoch=args.stop_epoch,
interval_validate=args.interval_validate,
batch_size=args.batch_size,
warmup_epoch=args.warmup_epoch,
)
trainer.epoch = start_epoch
trainer.iteration = start_iteration
trainer.train()
if __name__ == '__main__':
main()