| 5 | |
| 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): |
| 28 | return storage.cuda() |
| 29 | |
| 30 | if os.path.isfile(path): |
| 31 | if rank == 0: |
| 32 | print("=> loading checkpoint '{}'".format(path)) |
| 33 | |
| 34 | checkpoint = torch.load(path, map_location=map_func) |
| 35 | self.model.load_state_dict(checkpoint["state_dict"], strict=False) |
| 36 | |
| 37 | ckpt_keys = set(checkpoint["state_dict"].keys()) |
| 38 | own_keys = set(model.state_dict().keys()) |
| 39 | missing_keys = own_keys - ckpt_keys |
| 40 | for k in missing_keys: |
| 41 | print("caution: missing keys from checkpoint {}: {}".format(path, k)) |
| 42 | else: |
| 43 | print("=> no checkpoint found at '{}'".format(path)) |
| 44 | |
| 45 | def step(self, data, num_agent, batch_size, loss=True): |
| 46 | bev = data["bev_seq"] |
| 47 | labels = data["labels"] |
| 48 | self.optimizer.zero_grad() |
| 49 | bev = bev.permute(0, 3, 1, 2).contiguous() |
| 50 | |
| 51 | if not self.com: |
| 52 | filtered_bev = [] |
| 53 | filtered_label = [] |
| 54 | for i in range(bev.size(0)): |
| 55 | if torch.sum(bev[i]) > 1e-4: |
| 56 | filtered_bev.append(bev[i]) |
| 57 | filtered_label.append(labels[i]) |
| 58 | bev = torch.stack(filtered_bev, 0) |
| 59 | labels = torch.stack(filtered_label, 0) |
| 60 | |
| 61 | if self.kd_flag: |
| 62 | data["bev_seq_teacher"] = ( |
| 63 | data["bev_seq_teacher"].permute(0, 3, 1, 2).contiguous() |
| 64 | ) |