def main():
#create model
best_prec1 = 0
if args.basenet == 'se_resnet152':
model = MultiModalNet('se_resnet152', 'DPN26', 0.5)
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
elif args.basenet == 'multiscale_se_resnext':
model = MultiModalNet1('multiscale_se_resnext', 'DPN26', 0.5)
elif args.basenet == 'densenet201':
model = MultiModalNet2('densenet201', 'DPN26', 0.5)
elif args.basenet == 'oct_resnet101':
model = oct_resnet101()
# print("load pretrained model from /home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_4.pth")
# pre='/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_1.pth'
# model.load_state_dict(torch.load(pre))
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
# if torch.cuda.device_count() > 1:
# print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
# model = nn.DataParallel(model)
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
model.to(device)
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='MM_1_train',
transform=Aug)
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
32,
num_workers=4,
shuffle=True,
pin_memory=True)
Dataset_val = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=32,
num_workers=4,
shuffle=True,
pin_memory=True)
# criterion = nn.CrossEntropyLoss(weight = weights).cuda()
criterion = nn.CrossEntropyLoss().to(device)
# Optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr = args.lr, momentum = args.momentum,
# weight_decay = args.weight_decay)
Optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
#_,_ = validate(Dataloader_val, model, criterion) #prec1 = validate(Dataloader_val, Network, criterion)
# remember best prec@1 and save checkpoint
#is_best = prec1 > best_prec1
#best_prec1 = max(prec1, best_prec1)
#if is_best:
if epoch % 1 == 0:
torch.save(
model.state_dict(), 'weights/' + args.basenet +
'_se_resnext50_32x4d_resample_pretrained_80w_1/' +
'BDXJTU2019_SGD_' + repr(epoch) + '.pth')
def main():
###Enter Main Func:
mp.set_start_method('spawn')
#create model
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
MODEL_NAME = 'multiscale_se_resnext_HR'
MODEL_DIR = op.join('weights', MODEL_NAME)
BEST_DIR = op.join('weights', 'best_models')
if not op.isdir(MODEL_DIR):
os.mkdir(MODEL_DIR)
if not op.isdir(BEST_DIR):
os.mkdir(BEST_DIR)
# if args.basenet == 'MultiModal':
model = MultiModalNet(MODEL_NAME, 'DPN26', 0.5)
# #net = Networktorch.nn.DataParallel(Network, device_ids=[0])
# elif args.basenet == 'oct_resnet101':
# model = oct_resnet101()
# #net = Networktorch.nn.DataParallel(Network, device_ids=[0])
model = model.cuda()
cudnn.benchmark = True
RESUME = False
# MODEL_PATH = './weights/best_models/se_resnext50_32x4d_SGD_w_46.pth'
pthlist = [i for i in os.listdir(MODEL_DIR) if i[-4:] == '.pth']
if len(pthlist) > 0:
pthlist.sort(key=lambda x: eval(re.findall(r'\d+', x)[-1]))
MODEL_PATH = op.join(MODEL_DIR, pthlist[-1])
model.load_state_dict(torch.load(MODEL_PATH))
RESUME = True
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root=args.dataset_root,
mode='train',
transform=Aug,
TRAIN_IMAGE_DIR='train_image_raw')
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
Dataset_val = MM_BDXJTU2019(root=args.dataset_root,
mode='val',
TRAIN_IMAGE_DIR='train_image_raw')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=64,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
criterion = nn.CrossEntropyLoss(weight=weights).cuda()
Optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
args.start_epoch = eval(re.findall(r'\d+',
MODEL_PATH)[-1]) if RESUME else 0
best_pred1, best_preds = 0, {}
if RESUME and op.isfile(best_log):
best_preds = json.load(open(best_log))
best_pred1 = best_preds['best_pred1']
elif len(os.listdir(BEST_DIR)) > 0:
best_model = MultiModalNet(MODEL_NAME, 'DPN26', 0.5).cuda()
pthlist = [i for i in os.listdir(BEST_DIR) if i[-4:] == '.pth']
pthlist.sort(key=lambda x: eval(re.findall(r'\d+', x)[-1]))
best_model.load_state_dict(torch.load(op.join(BEST_DIR, pthlist[-1])))
best_pred1 = validate(Dataloader_val,
best_model,
criterion,
printable=False)[0]
log_dict(eval(re.findall(r'\d+', pthlist[-1])[-1]), best_pred1)
log('#Resume: Another Start from Epoch {}'.format(args.start_epoch + 1))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
pred1, pred5 = validate(
Dataloader_val, model,
criterion) #pred1 = validate(Dataloader_val, Network, criterion)
# remember best pred@1 and save checkpoint
COMMON_MODEL_PATH = op.join(MODEL_DIR,
'SGD_fold1_{}.pth'.format(epoch + 1))
BEST_MODEL_PATH = op.join(
BEST_DIR, '{}_SGD_fold1_{}.pth'.format(MODEL_NAME, epoch + 1))
torch.save(model.state_dict(), COMMON_MODEL_PATH)
if pred1 > best_pred1:
best_pred1 = max(pred1, best_pred1)
torch.save(model.state_dict(), BEST_MODEL_PATH)
log_dict(epoch + 1, best_pred1)
log('Epoch:{}\tpred1:{}\tBest_pred1:{}\n'.format(
epoch + 1, pred1, best_pred1))
def main():
#create model
best_prec1 = 0
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
if args.basenet == 'MultiModal':
model = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
elif args.basenet == 'oct_resnet101':
model = oct_resnet101()
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
model = model.cuda()
cudnn.benchmark = True
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root=args.dataset_root,
mode='MM_1_train',
transform=Aug)
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
Dataset_val = BDXJTU2019(root=args.dataset_root, mode='val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=8,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
criterion = nn.CrossEntropyLoss(weight=weights).cuda()
Optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
#_,_ = validate(Dataloader_val, model, criterion) #prec1 = validate(Dataloader_val, Network, criterion)
# remember best prec@1 and save checkpoint
#is_best = prec1 > best_prec1
#best_prec1 = max(prec1, best_prec1)
#if is_best:
if epoch % 1 == 0:
torch.save(
model.state_dict(), 'weights/' + args.basenet +
'_se_resnext50_32x4d_resample_pretrained_80w_1/' +
'BDXJTU2019_SGD_' + repr(epoch) + '.pth')