from torch.utils.tensorboard import SummaryWriter
import torchvision
if __name__ == '__main__':
opt = gather_options()
print_options(opt)
device = torch.device('cuda:{}'.format(
opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
trainloader, testloader = loadData(opt)
dataset_size = len(trainloader)
print('#training images = %d' % dataset_size)
net = Resnet(opt.input_nc,
num_classes=opt.num_classes,
norm=opt.norm,
nl=opt.nl)
net = init_net(net, init_type='normal', gpu_ids=[0])
if opt.continue_train:
load_networks(opt, net)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=opt.lr, momentum=0.9)
scheduler = get_scheduler(optimizer, opt)
iter = 0
running_loss = 0.0
correct = 0.0
total = 0
gamma=0.1,
last_epoch=checkpoint['epoch'])
print("Loading from epoch:", checkpoint['epoch'], 'schedular:',
self.scheduler.last_epoch, 'map:', checkpoint['map'], 'rank1:',
checkpoint['rank'][0])
return checkpoint['epoch'], checkpoint['rank'], checkpoint['map']
if __name__ == '__main__':
mp.set_start_method(opt.start_method, True)
if opt.model_name == 'MGN':
model = MGN()
loss = Loss_MGN()
elif opt.model_name == 'Resnet':
model = Resnet()
loss = Loss_Resnet()
elif opt.model_name == 'CGN':
model = CGN()
loss = Loss_CGN()
elif opt.model_name == 'SN':
model = SN()
loss = Loss_SN()
elif opt.model_name == 'FPN':
model = FPN()
loss = Loss_FPN()
elif opt.model_name == 'AN':
model = AN()
loss = Loss_AN()
if opt.mode == 'train':