| from pathlib import Path |
| import torch |
|
|
| from evaluator.build import EVALUATOR_REGISTRY, BaseEvaluator |
|
|
|
|
| @EVALUATOR_REGISTRY.register() |
| class ReferIt3DEval(BaseEvaluator): |
| def __init__(self, cfg, accelerator, **kwargs): |
| self.target_metric = 'og_acc' |
| self.save_dir = Path(cfg.exp_dir) / "eval_results" / self.__class__.__name__ |
| super().__init__(cfg, accelerator, **kwargs) |
|
|
| def batch_metrics(self, data_dict, include_count=False): |
| |
| if len(data_dict['og3d_logits'].shape) == 3: |
| data_dict['tgt_object_id'] = data_dict['tgt_object_id'].flatten(0, 1).unsqueeze(1) |
| data_dict['is_hard'] = data_dict['is_hard'].flatten(0, 1) |
| data_dict['is_view_dependent'] = data_dict['is_view_dependent'].flatten(0, 1) |
| data_dict['og3d_logits'] = data_dict['og3d_logits'].flatten(0, 1) |
|
|
| metrics = {} |
| og_pred = torch.argmax(data_dict['og3d_logits'], dim=-1) |
| total_count = len(og_pred) |
|
|
| |
| hard_count = data_dict['is_hard'].sum().item() |
| easy_count = total_count - hard_count |
|
|
| |
| view_dep_count = data_dict['is_view_dependent'].sum().item() |
| view_indep_count = total_count - view_dep_count |
|
|
| |
| correct_preds = data_dict['tgt_object_id'].flatten() == og_pred |
| correct = correct_preds.sum().item() |
|
|
| |
| hard_correct = (correct_preds & data_dict['is_hard']).sum().item() |
| easy_correct = correct - hard_correct |
|
|
| |
| view_dep_correct = (correct_preds & data_dict['is_view_dependent']).sum().item() |
| view_indep_correct = correct - view_dep_correct |
|
|
| metrics['og_acc_easy'] = (easy_correct, easy_count) |
| metrics['og_acc_hard'] = (hard_correct, hard_count) |
| metrics['og_acc_view_dep'] = (view_dep_correct, view_dep_count) |
| metrics['og_acc_view_indep'] = (view_indep_correct, view_indep_count) |
|
|
| metrics['og_acc'] = (og_pred == data_dict['tgt_object_id'].squeeze(1)).sum().item() |
| if 'txt_cls_logits' in data_dict: |
| metrics['txt_acc'] = (torch.argmax(data_dict['txt_cls_logits'], dim=1) == data_dict["tgt_object_label"].squeeze(1)).sum().item() |
| |
| |
| gt = data_dict['obj_labels'] |
| mask = data_dict['obj_masks'] |
| for key in data_dict.keys(): |
| if key.endswith('logits') and data_dict[key].ndim == 3 and data_dict[key].shape[:2] == data_dict['obj_labels'].shape: |
| new_key = key.replace('logits', 'acc') |
| pred = torch.argmax(data_dict[key], dim=2) |
| metrics[new_key] = ((pred[mask] == gt[mask]).sum().item(), data_dict['obj_masks'].sum().item()) |
|
|
| for key in metrics: |
| if isinstance(metrics[key], tuple): |
| |
| continue |
| metrics[key] = (metrics[key], total_count) |
| |
| if self.save: |
| item_ids = data_dict['data_idx'] |
| for i in range(len(item_ids)): |
| self.eval_results.append({ |
| "scene_id": item_ids[i], |
| "bbox": data_dict['obj_boxes'][i][og_pred[i]].cpu().numpy().tolist(), |
| "correct": og_pred[i].item() == data_dict['tgt_object_id'][i].item() |
| }) |
|
|
| if not include_count: |
| for key, v in metrics.items(): |
| metrics[key] = v[0] / max(v[1], 1) |
|
|
| return metrics |
|
|
