b88d36cd9c8056e15607a40f5d10a9072ab84b22,train_imgreid_xent.py,,main,#,95

Before Change

    print("Initializing model: {}".format(args.arch))
    model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={"xent"}, use_gpu=use_gpu)
    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)
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)

After Change

    print("Initializing model: {}".format(args.arch))
    model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={"xent"}, use_gpu=use_gpu)
    print("Model size: {:.3f} M".format(count_num_param(model)))

    criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
    optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)

Instances