print("load_net...")
if args.model == 'mAlexNet':
net = mAlexNet()
elif args.model == 'AlexNet':
net = AlexNet()
elif args.model == "carnet":
net=carNet()
elif args.model=="googlenet":
net=models.googlenet()
num_fc = net.fc.in_features
net.fc = nn.Linear(num_fc, 2)
elif args.model=="vgg16":
net=models.vgg16(pretrained=True)
num_fc = net.classifier[6].in_features
net.classifier[6] = torch.nn.Linear(num_fc, 2)
for param in net.parameters():
param.requires_grad = False
# 但是参数全部固定了，也没法进行学习，所以我们不固定最后一层，即全连接层
for param in net.classifier[6].parameters():
param.requires_grad = True
elif args.model=="Inception_v3":
net=models.inception_v3()
net.AuxLogits.fc=nn.Linear(768,2)
net.fc = nn.Linear(2048, 2)
net.aux_logits=False
# net=net.cuda()
elif args.model=="mobilenet_v3_small":
net=models.mobilenet_v3_small()
net.classifier[3]=nn.Linear(1024,2)
# net.fc = nn.Linear(num_fc, 2)
elif args.model=="mobilenet_v3_large":
setattr(dataloader['base'], 'total_item_len', len(base_set))
# ---------------------------- model ------------------------------------
if config.model_name == 'resnet18':
model = ResNet18(config.code_length, classes, config.class_mask)
elif config.model_name == 'alexnet':
model = AlexNet(config.code_length, classes, config.class_mask)
else:
print('undefined model ! ')
model = nn.DataParallel(model)
model.cuda()
# ---------------------------- loss and opt ------------------------------------
criterion = nn.CrossEntropyLoss()
criterion_hash = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=config.lr, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
# ---------------------------- log and train ------------------------------------
log_file = open(
config.model_name + '_' + config.dataset + '_' +
str(config.code_length) + '.log', 'a')
log_file.write(str(config))
log_file.write('\n')
print('training start ...')
train_model(model, dataloader, criterion, criterion_hash, optimizer,
exp_lr_scheduler, config.epoch, config.code_length, classes,
log_file)
log_file.close()