db4fc0e5816a504a87e4a2a603b065c179406a04,xnornet_plusplus/main.py,,main,#,69

Before Change

    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        prec1 = validate(val_loader, model, criterion, epoch)
        // train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)

        // evaluate on validation set

After Change

    // define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    param_grp_1 = []
    for m in model.modules():
        if isinstance(m,BinConv2d):
            param_grp_1.append(m.weight)
            param_grp_1.append(m.alpha)
            param_grp_1.append(m.beta)
            param_grp_1.append(m.gamma)
    param_grp_1_ids = list(map(id, param_grp_1))
    param_grp_2 = list(filter(lambda p: id(p) not in param_grp_1_ids, model.parameters()))

    if args.optimizer == "sgd":
        if args.quantize:

Instances