(self, model, teacher, config, optimizer, kd_flag)
| 6 | |
| 7 | class SegModule(object): |
| 8 | def __init__(self, model, teacher, config, optimizer, kd_flag): |
| 9 | self.config = config |
| 10 | self.model = model |
| 11 | self.optimizer = optimizer |
| 12 | self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( |
| 13 | optimizer, T_max=self.config.nepoch |
| 14 | ) |
| 15 | # self.scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 100, 150, 200], gamma=0.5) |
| 16 | self.criterion = nn.CrossEntropyLoss() |
| 17 | self.teacher = teacher |
| 18 | if kd_flag: |
| 19 | for k, v in self.teacher.named_parameters(): |
| 20 | v.requires_grad = False # fix parameters |
| 21 | |
| 22 | self.kd_flag = kd_flag |
| 23 | |
| 24 | self.com = config.com |
| 25 | |
| 26 | def resume(self, path): |
| 27 | def map_func(storage, location): |
nothing calls this directly
no outgoing calls
no test coverage detected