(model, device, loader, optimizer, scheduler, args)
| 27 | |
| 28 | |
| 29 | def train(model, device, loader, optimizer, scheduler, args): |
| 30 | model.train() |
| 31 | loss_accum_dict = defaultdict(float) |
| 32 | pbar = tqdm(loader, desc="Iteration") |
| 33 | for step, batch in enumerate(pbar): |
| 34 | batch = batch.to(device) |
| 35 | |
| 36 | if batch.x.shape[0] == 1 or batch.batch[-1] == 0: |
| 37 | pass |
| 38 | else: |
| 39 | atom_pred_list, extra_output = model(batch) |
| 40 | optimizer.zero_grad() |
| 41 | |
| 42 | loss, loss_dict = model.compute_loss(atom_pred_list, extra_output, batch, args) |
| 43 | loss.backward() |
| 44 | optimizer.step() |
| 45 | scheduler.step() |
| 46 | |
| 47 | for k, v in loss_dict.items(): |
| 48 | loss_accum_dict[k] += v.detach().item() |
| 49 | |
| 50 | if step % args.log_interval == 0: |
| 51 | description = f"Iteration loss: {loss_accum_dict['loss'] / (step + 1):6.4f} lr: {scheduler.get_last_lr()[0]:.5e}" |
| 52 | # for k in loss_accum_dict.keys(): |
| 53 | # description += f" {k}: {loss_accum_dict[k]/(step+1):6.4f}" |
| 54 | |
| 55 | pbar.set_description(description) |
| 56 | |
| 57 | for k in loss_accum_dict.keys(): |
| 58 | loss_accum_dict[k] /= step + 1 |
| 59 | return loss_accum_dict |
| 60 | |
| 61 | |
| 62 | def get_rmsd_min(inputargs): |
no test coverage detected