train_class_num=args.train_class_num,
test_class_num=args.test_class_num,
includes_all_train_class=args.includes_all_train_class)
# data loader
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.bs,
shuffle=True,
num_workers=4)
loss_Dict = {
"CenterLoss":
CenterLoss(centerloss_weight=args.centerloss_weight,
num_classes=args.train_class_num),
"SoftmaxLoss":
SoftmaxLoss(),
"ArcFaceLoss":
ArcFaceLoss(scaling=args.scaling, m=args.m),
"NormFaceLoss":
NormFaceLoss(scaling=args.scaling),
"PSoftmaxLoss":
PSoftmaxLoss()
}
criterion = loss_Dict[args.loss]
criterion = criterion.to(device)
def main():
print(f"\nStart training ...\n")
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
print('==> Building model..')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
trainset = MNIST(root='../../data', train=True, download=True, transform=transform,
train_class_num=args.train_class_num, test_class_num=args.test_class_num,
includes_all_train_class=args.includes_all_train_class)
testset = MNIST(root='../../data', train=False, download=True, transform=transform,
train_class_num=args.train_class_num, test_class_num=args.test_class_num,
includes_all_train_class=args.includes_all_train_class)
# data loader
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.bs, shuffle=True, num_workers=4)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.bs, shuffle=False, num_workers=4)
loss_Dict = {"CenterLoss": CenterLoss(centerloss_weight=args.centerloss_weight, num_classes=args.train_class_num),
"SoftmaxLoss": SoftmaxLoss(),
"ArcFaceLoss": ArcFaceLoss(scaling=args.scaling, m=args.m),
"NormFaceLoss": NormFaceLoss(scaling=args.scaling),
"PSoftmaxLoss": PSoftmaxLoss()}
criterion = loss_Dict[args.loss]
criterion = criterion.to(device)
def main():
print(f"\nStart training ...\n")
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
print('==> Building model..')
net = BuildNet(backbone=args.arch, num_classes=args.train_class_num, embed_dim=args.embed_dim)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)