def build_model()

in Dassl.pytorch/dassl/engine/da/cdac.py [0:0]

• 36 lines of code
• 6 McCabe index (conditional complexity)

    def build_model(self):
        cfg = self.cfg

        # Custom LR Scheduler for CDAC
        if self.cfg.TRAIN.COUNT_ITER == "train_x":
            self.num_batches = len(self.train_loader_x)
        elif self.cfg.TRAIN.COUNT_ITER == "train_u":
            self.num_batches = len(self.len_train_loader_u)
        elif self.cfg.TRAIN.COUNT_ITER == "smaller_one":
            self.num_batches = min(
                len(self.train_loader_x), len(self.train_loader_u)
            )
        self.max_iter = self.max_epoch * self.num_batches
        print("Max Iterations: %d" % self.max_iter)

        print("Building F")
        self.F = SimpleNet(cfg, cfg.MODEL, 0)
        self.F.to(self.device)
        print("# params: {:,}".format(count_num_param(self.F)))
        self.optim_F = build_optimizer(self.F, cfg.OPTIM)
        custom_lr_F = partial(
            custom_scheduler, max_iter=self.max_iter, init_lr=cfg.OPTIM.LR
        )
        self.sched_F = LambdaLR(self.optim_F, custom_lr_F)
        self.register_model("F", self.F, self.optim_F, self.sched_F)

        print("Building C")
        self.C = Prototypes(self.F.fdim, self.num_classes)
        self.C.to(self.device)
        print("# params: {:,}".format(count_num_param(self.C)))
        self.optim_C = build_optimizer(self.C, cfg.OPTIM)

        # Multiply the learning rate of C by lr_multi
        for group_param in self.optim_C.param_groups:
            group_param['lr'] *= self.lr_multi
        custom_lr_C = partial(
            custom_scheduler,
            max_iter=self.max_iter,
            init_lr=cfg.OPTIM.LR * self.lr_multi
        )
        self.sched_C = LambdaLR(self.optim_C, custom_lr_C)
        self.register_model("C", self.C, self.optim_C, self.sched_C)