def check_class(line):
"""Check if class name is valid in Data Base"""
if line == "":
print("** class name missing **")
return False
line = line.split()
if line[0] not in models_dict.keys():
print("** class doesn't exist **")
return False
return True
def main(args):
############ init config ################
model_name = args.model
assert model_name in models_dict.keys(),"The Usage model is not exist !"
print('Usage model :{}'.format(model_name))
#################### init logger ###################################
log_dir = './logs/'+ args.model+'_'+args.note + '/{}'.format(time.strftime('%Y%m%d-%H%M%S'))
logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
logger.info('{}-Train'.format(args.model))
# setting
setting={k: v for k, v in args._get_kwargs()}
logger.info(setting)
args.save_path = log_dir
args.save_tbx_log = args.save_path + '/tbx_log'
writer = SummaryWriter(args.save_tbx_log)
##################### init device #################################
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda= args.gpus>0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
####################### init dataset ###########################################
train_loader=get_dataloder(args,split_flag="train")
val_loader=get_dataloder(args,split_flag="valid")
######################## init model ############################################
# model
logger.info("Model Dict has keys: \n {}".format(models_dict.keys()))
model=get_models(args)
if torch.cuda.device_count() > 1 and args.use_cuda:
logger.info('use: %d gpus', torch.cuda.device_count())
model = nn.DataParallel(model)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss=='bce':
criterion=nn.BCELoss()
elif args.loss=='bcelog':
criterion=nn.BCEWithLogitsLoss()
elif args.loss=="dice":
criterion=DiceLoss()
elif args.loss=="softdice":
criterion=SoftDiceLoss()
elif args.loss=='bcedice':
criterion=BCEDiceLoss()
else:
criterion=nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model=model.to(args.device)
criterion=criterion.to(args.device)
# init optimizer
if args.model_optimizer=="sgd":
#torch.optim.SGD(parametetrs,lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
optimizer=torch.optim.SGD(model.parameters(),lr=args.lr,weight_decay=args.weight_decay,momentum=args.momentum)
else:
optimizer=torch.optim.Adam(model.parameters(),args.lr,[args.beta1, args.beta2],
weight_decay=args.weight_decay)
# init schedulers Steplr
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,args.epoch)
# scheduler=torch.optim.lr_scheduler.StepLR(optimizer=optimizer,step_size=30,gamma=0.1,last_epoch=-1)
############################### check resume #########################
start_epoch=0
if args.resume is not None:
if os.path.isfile(args.resume):
logger.info("Loading model and optimizer from checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
else:
raise FileNotFoundError("No checkpoint found at '{}'".format(args.resume))
#################################### train and val ########################
max_value=0
for epoch in range(start_epoch,args.epoch):
# lr=adjust_learning_rate(args,optimizer,epoch)
scheduler.step()
logger.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# train
mr, ms, mp, mf, mjc, md, macc, mean_loss=train(args, model, criterion, train_loader,
optimizer, epoch, logger)
# write
writer.add_scalar('Train/Loss', mean_loss, epoch)
writer.add_scalar('Train/mAcc', macc, epoch)
writer.add_scalar('Train/Recall', mr, epoch)
writer.add_scalar('Train/Specifi', ms, epoch)
writer.add_scalar('Train/Precision', mp, epoch)
writer.add_scalar('Train/F1', mf, epoch)
writer.add_scalar('Train/Jc', mjc, epoch)
writer.add_scalar('Train/Dice', md, epoch)
# val
vmr, vms, vmp, vmf, vmjc, vmd, vmacc, vmean_loss=val(args, model, criterion, val_loader, epoch, logger)
writer.add_scalar('Val/Loss', vmean_loss, epoch)
writer.add_scalar('Val/mAcc', vmacc, epoch)
writer.add_scalar('Val/Recall', vmr, epoch)
writer.add_scalar('Val/Specifi', vms, epoch)
writer.add_scalar('Val/Precision', vmp, epoch)
writer.add_scalar('Val/F1', vmf, epoch)
writer.add_scalar('Val/Jc', vmjc, epoch)
writer.add_scalar('Val/Dice', vmd, epoch)
is_best=True if (vmjc>=max_value) else False
max_value=max(max_value,vmjc)
state={
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'state_dict': model.state_dict(),
'scheduler': model.state_dict(),
}
logger.info("epoch:{} best:{} max_value:{}".format(epoch,is_best,max_value))
if not is_best:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
else:
torch.save(state,os.path.join(args.save_path,"checkpoint.pth.tar"))
torch.save(state,os.path.join(args.save_path,"model_best.pth.tar"))
writer.close()
def adjust_learning_rate(args,optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr
assert len(args.gammas) == len(args.schedule), "length of gammas and schedule should be equal"
for (gamma, step) in zip(args.gammas, args.schedule):
if (epoch >= step):
lr = lr * gamma
else:
break
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
if __name__ == '__main__':
models_name=models_dict.keys()
datasets_name=datasets_dict.keys()
parser = argparse.ArgumentParser(description='Unet serieas baseline')
# Add default argument
parser.add_argument('--model', type=str, default='unet',choices=models_name,
help='Model to train and evaluation')
parser.add_argument('--note' ,type=str, default='_',
help='model note ')
parser.add_argument('--dataset',type=str, default='cvc',choices=datasets_name,
help='Model to train and evaluation')
parser.add_argument('--base_size', type=int, default=256, help="resize base size")
parser.add_argument('--crop_size', type=int, default=256, help="crop size")
parser.add_argument('--im_channel', type=int, default=3, help="input image channel ")
parser.add_argument('--class_num', type=int, default=1, help="output feature channel")
parser.add_argument('--epoch', type=int, default=1600, help="epochs")
parser.add_argument('--train_batch', type=int, default=8, help="train_batch")
