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main.py
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from utils.data import *
from utils.metric import *
from argparse import ArgumentParser
import torch
import torch.utils.data as Data
from model.MSHNet import *
from model.loss import *
from torch.optim import Adagrad
from tqdm import tqdm
import os.path as osp
import os
import time
os.environ['CUDA_VISIBLE_DEVICES']="0"
def parse_args():
#
# Setting parameters
#
parser = ArgumentParser(description='Implement of model')
parser.add_argument('--dataset-dir', type=str, default='/dataset/IRSTD-1k')
parser.add_argument('--batch-size', type=int, default=4)
parser.add_argument('--epochs', type=int, default=400)
parser.add_argument('--lr', type=float, default=0.05)
parser.add_argument('--warm-epoch', type=int, default=5)
parser.add_argument('--base-size', type=int, default=256)
parser.add_argument('--crop-size', type=int, default=256)
parser.add_argument('--multi-gpus', type=bool, default=False)
parser.add_argument('--if-checkpoint', type=bool, default=False)
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--weight-path', type=str, default='/MSHNet/weight/IRSTD-1k_weight.tar')
args = parser.parse_args()
return args
class Trainer(object):
def __init__(self, args):
assert args.mode == 'train' or args.mode == 'test'
self.args = args
self.start_epoch = 0
self.mode = args.mode
trainset = IRSTD_Dataset(args, mode='train')
valset = IRSTD_Dataset(args, mode='val')
self.train_loader = Data.DataLoader(trainset, args.batch_size, shuffle=True, drop_last=True)
self.val_loader = Data.DataLoader(valset, 1, drop_last=False)
device = torch.device('cuda')
self.device = device
model = MSHNet(3)
if args.multi_gpus:
if torch.cuda.device_count() > 1:
print('use '+str(torch.cuda.device_count())+' gpus')
model = nn.DataParallel(model, device_ids=[0, 1])
model.to(device)
self.model = model
self.optimizer = Adagrad(filter(lambda p: p.requires_grad, self.model.parameters()), lr=args.lr)
self.down = nn.MaxPool2d(2, 2)
self.loss_fun = SLSIoULoss()
self.PD_FA = PD_FA(1, 10, args.base_size)
self.mIoU = mIoU(1)
self.ROC = ROCMetric(1, 10)
self.best_iou = 0
self.warm_epoch = args.warm_epoch
if args.mode=='train':
if args.if_checkpoint:
check_folder = ''
checkpoint = torch.load(check_folder+'/checkpoint.pkl')
self.model.load_state_dict(checkpoint['net'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.start_epoch = checkpoint['epoch']+1
self.best_iou = checkpoint['iou']
self.save_folder = check_folder
else:
self.save_folder = '/MSHNet/weight/MSHNet-%s'%(time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time())))
if not osp.exists(self.save_folder):
os.mkdir(self.save_folder)
if args.mode=='test':
weight = torch.load(args.weight_path)
self.model.load_state_dict(weight['state_dict'])
'''
# iou_67.87_weight
weight = torch.load(args.weight_path)
self.model.load_state_dict(weight)
'''
self.warm_epoch = -1
def train(self, epoch):
self.model.train()
tbar = tqdm(self.train_loader)
losses = AverageMeter()
tag = False
for i, (data, mask) in enumerate(tbar):
data = data.to(self.device)
labels = mask.to(self.device)
if epoch>self.warm_epoch:
tag = True
masks, pred = self.model(data, tag)
loss = 0
loss = loss + self.loss_fun(pred, labels, self.warm_epoch, epoch)
for j in range(len(masks)):
if j>0:
labels = self.down(labels)
loss = loss + self.loss_fun(masks[j], labels, self.warm_epoch, epoch)
loss = loss / (len(masks)+1)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
losses.update(loss.item(), pred.size(0))
tbar.set_description('Epoch %d, loss %.4f' % (epoch, losses.avg))
def test(self, epoch):
self.model.eval()
self.mIoU.reset()
self.PD_FA.reset()
tbar = tqdm(self.val_loader)
tag = False
with torch.no_grad():
for i, (data, mask) in enumerate(tbar):
data = data.to(self.device)
mask = mask.to(self.device)
if epoch>self.warm_epoch:
tag = True
loss = 0
_, pred = self.model(data, tag)
# loss += self.loss_fun(pred, mask,self.warm_epoch, epoch)
self.mIoU.update(pred, mask)
self.PD_FA.update(pred, mask)
self.ROC.update(pred, mask)
_, mean_IoU = self.mIoU.get()
tbar.set_description('Epoch %d, IoU %.4f' % (epoch, mean_IoU))
FA, PD = self.PD_FA.get(len(self.val_loader))
_, mean_IoU = self.mIoU.get()
ture_positive_rate, false_positive_rate, _, _ = self.ROC.get()
if self.mode == 'train':
if mean_IoU > self.best_iou:
self.best_iou = mean_IoU
torch.save(self.model.state_dict(), self.save_folder+'/weight.pkl')
with open(osp.join(self.save_folder, 'metric.log'), 'a') as f:
f.write('{} - {:04d}\t - IoU {:.4f}\t - PD {:.4f}\t - FA {:.4f}\n' .
format(time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time())),
epoch, self.best_iou, PD[0], FA[0] * 1000000))
all_states = {"net":self.model.state_dict(), "optimizer":self.optimizer.state_dict(), "epoch": epoch, "iou":self.best_iou}
torch.save(all_states, self.save_folder+'/checkpoint.pkl')
elif self.mode == 'test':
print('mIoU: '+str(mean_IoU)+'\n')
print('Pd: '+str(PD[0])+'\n')
print('Fa: '+str(FA[0]*1000000)+'\n')
if __name__ == '__main__':
args = parse_args()
trainer = Trainer(args)
if trainer.mode=='train':
for epoch in range(trainer.start_epoch, args.epochs):
trainer.train(epoch)
trainer.test(epoch)
else:
trainer.test(1)