205df55987d9c62c13738bebc5ffe18b738f1c50,train_vidreid_xent.py,,main,#,35

Before Change

        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")

After Change

    optimizer = init_optimizer(model.parameters(), **optimizer_kwargs(args))
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)

    if args.fixbase_epoch > 0:
        if hasattr(model, "classifier") and isinstance(model.classifier, nn.Module):
            optimizer_tmp = init_optimizer(model.classifier.parameters(), **optimizer_kwargs(args))
        else:
            print("Warn: model has no attribute "classifier" and fixbase_epoch is reset to 0")
            args.fixbase_epoch = 0
        raise NotImplementedError

    if args.load_weights and check_isfile(args.load_weights):
        // load pretrained weights but ignore layers that don"t match in size
        checkpoint = torch.load(args.load_weights)
        pretrain_dict = checkpoint["state_dict"]

Instances