(self, eval_loader=None, args=None)
| 336 | |
| 337 | @torch.no_grad() |
| 338 | def evaluate(self, eval_loader=None, args=None): |
| 339 | self.model.eval() |
| 340 | self.ema.apply_shadow() |
| 341 | if eval_loader is None: |
| 342 | eval_loader = self.loader_dict['eval'] |
| 343 | total_loss = 0.0 |
| 344 | total_num = 0.0 |
| 345 | y_true = [] |
| 346 | y_pred = [] |
| 347 | y_logits = [] |
| 348 | for _, x, y in eval_loader: |
| 349 | x, y = x.cuda(args.gpu), y.cuda(args.gpu) |
| 350 | num_batch = x.shape[0] |
| 351 | total_num += num_batch |
| 352 | logits = self.model(x) |
| 353 | loss = F.cross_entropy(logits, y, reduction='mean') |
| 354 | y_true.extend(y.cpu().tolist()) |
| 355 | y_pred.extend(torch.max(logits, dim=-1)[1].cpu().tolist()) |
| 356 | y_logits.extend(torch.softmax(logits, dim=-1).cpu().tolist()) |
| 357 | total_loss += loss.detach() * num_batch |
| 358 | top1 = accuracy_score(y_true, y_pred) |
| 359 | top5 = top_k_accuracy_score(y_true, y_logits, k=5) |
| 360 | precision = precision_score(y_true, y_pred, average='macro') |
| 361 | recall = recall_score(y_true, y_pred, average='macro') |
| 362 | F1 = f1_score(y_true, y_pred, average='macro') |
| 363 | AUC = roc_auc_score(y_true, y_logits, multi_class='ovo') |
| 364 | |
| 365 | cf_mat = confusion_matrix(y_true, y_pred, normalize='true') |
| 366 | self.print_fn('confusion matrix:\n' + np.array_str(cf_mat)) |
| 367 | self.ema.restore() |
| 368 | self.model.train() |
| 369 | return {'eval/loss': total_loss / total_num, 'eval/top-1-acc': top1, 'eval/top-5-acc': top5, |
| 370 | 'eval/precision': precision, 'eval/recall': recall, 'eval/F1': F1, 'eval/AUC': AUC} |
| 371 | |
| 372 | def save_model(self, save_name, save_path): |
| 373 | save_filename = os.path.join(save_path, save_name) |
no test coverage detected