print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={"xent"})
print("Model size: {:.5f}M".format(sum(p.numel() for p in model.parameters())/1000000.0))
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
After Change
print("Train classifier for {} epochs while keeping base network frozen".format(args.fixbase_epoch))
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch, model, criterion, optimizer_tmp, trainloader, use_gpu, freeze_bn=True)
train_time += round(time.time() - start_train_time)
del optimizer_tmp
print("Now open all layers for training")
