(model, eval_dataloader, post_process_class)
| 842 | |
| 843 | |
| 844 | def get_center(model, eval_dataloader, post_process_class): |
| 845 | pbar = tqdm(total=len(eval_dataloader), desc="get center:") |
| 846 | max_iter = ( |
| 847 | len(eval_dataloader) - 1 |
| 848 | if platform.system() == "Windows" |
| 849 | else len(eval_dataloader) |
| 850 | ) |
| 851 | char_center = dict() |
| 852 | for idx, batch in enumerate(eval_dataloader): |
| 853 | if idx >= max_iter: |
| 854 | break |
| 855 | images = batch[0] |
| 856 | start = time.time() |
| 857 | preds = model(images) |
| 858 | |
| 859 | batch = [item.numpy() for item in batch] |
| 860 | # Obtain usable results from post-processing methods |
| 861 | post_result = post_process_class(preds, batch[1]) |
| 862 | |
| 863 | # update char_center |
| 864 | char_center = update_center(char_center, post_result, preds) |
| 865 | pbar.update(1) |
| 866 | |
| 867 | pbar.close() |
| 868 | for key in char_center.keys(): |
| 869 | char_center[key] = char_center[key][0] |
| 870 | return char_center |
| 871 | |
| 872 | |
| 873 | def preprocess(is_train=False): |
nothing calls this directly
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