| 27 | |
| 28 | |
| 29 | def train(model, device, loader, optimizer, args, evaluator): |
| 30 | model.train() |
| 31 | |
| 32 | y_true = [] |
| 33 | y_pred = [] |
| 34 | loss_accum = 0 |
| 35 | for step, batch in enumerate(tqdm(loader, desc="Iteration")): |
| 36 | # one b(atch) per device |
| 37 | batch = [b for b in batch if not b.x.shape[0] == 1 and not b.batch[-1] == 0] |
| 38 | if batch: |
| 39 | pred = model(batch) |
| 40 | optimizer.zero_grad() |
| 41 | |
| 42 | trg = torch.cat([b.y.to(device) for b in batch], dim=0) |
| 43 | loss = multicls_criterion(pred, trg.to(torch.long).view(-1,)) |
| 44 | loss.backward() |
| 45 | if args.clip > 0: |
| 46 | torch.nn.utils.clip_grad_norm(model.parameters(), args.clip) |
| 47 | optimizer.step() |
| 48 | |
| 49 | loss_accum += loss.item() |
| 50 | |
| 51 | y_true.append(trg.view(-1,1).detach().cpu()) |
| 52 | y_pred.append(torch.argmax(pred.detach(), dim=1).view(-1, 1).cpu()) |
| 53 | |
| 54 | y_true = torch.cat(y_true, dim=0).numpy() |
| 55 | y_pred = torch.cat(y_pred, dim=0).numpy() |
| 56 | # print(y_true) |
| 57 | # print(y_pred) |
| 58 | input_dict = {"y_true": y_true, "y_pred": y_pred} |
| 59 | return loss_accum / (step + 1), evaluator.eval(input_dict) |
| 60 | |
| 61 | |
| 62 | def eval(model, device, loader, evaluator): |