| import torch |
| import numpy as np |
|
|
| from evaluator.build import EVALUATOR_REGISTRY, BaseEvaluator |
|
|
|
|
| @EVALUATOR_REGISTRY.register() |
| class PretrainEval(BaseEvaluator): |
| def __init__(self, cfg, accelerator, **kwargs): |
| self.cfg = cfg |
| self.eval_dict = { |
| "target_metric": [], "og_acc": [], "lang_cls_acc_mask": [], "obj_cls_post_acc": [], "obj_cls_pre_acc": [], |
| "obj_cls_raw_acc": [], "obj_cls_pre_acc_unmask": [], "obj_cls_pre_acc_mask": [], |
| "obj_cls_post_acc_unmask": [], "obj_cls_post_acc_mask": [] |
| } |
| self.accelerator = accelerator |
| self.device = self.accelerator.device |
| self.total_count = 0 |
| self.best_result = -np.inf |
|
|
| def batch_metrics(self, data_dict): |
| metrics = {} |
| txt_token_mask = (data_dict['masked_lm_labels'] != -1) |
| if 'tgt_object_id' in data_dict.keys(): |
| metrics['og_acc'] = (torch.argmax(data_dict['og3d_logits'], dim=-1) == data_dict['tgt_object_id'].squeeze( |
| 1)).sum().item() / float(len(data_dict['tgt_object_id'])) |
| metrics['lang_cls_acc_mask'] = torch.sum( |
| torch.argmax(data_dict['txt_lm_cls_logits'], dim=2)[txt_token_mask] == data_dict['masked_lm_labels'][ |
| txt_token_mask]).item() / float(txt_token_mask.sum().item() + 1e-8) |
| if 'obj_cls_post_logits' in data_dict.keys(): |
| metrics['obj_cls_post_acc'] = torch.sum( |
| torch.argmax(data_dict['obj_cls_post_logits'], dim=2)[data_dict['obj_masks']] == data_dict["obj_labels"][ |
| data_dict['obj_masks']]).item() / float(data_dict['obj_masks'].sum().item() + 1e-8) |
| metrics['obj_cls_post_acc_unmask'] = torch.sum( |
| torch.argmax(data_dict['obj_cls_post_logits'], dim=2)[ |
| data_dict['obj_masks'] * data_dict['obj_sem_masks']] == |
| data_dict["obj_labels"][data_dict['obj_masks'] * data_dict['obj_sem_masks']]).item() / float( |
| (data_dict['obj_masks'] * data_dict['obj_sem_masks']).sum().item() + 1e-8) |
| metrics['obj_cls_post_acc_mask'] = torch.sum(torch.argmax(data_dict['obj_cls_post_logits'], dim=2)[ |
| data_dict['obj_masks'] * data_dict[ |
| 'obj_sem_masks'].logical_not()] == |
| data_dict["obj_labels"][ |
| data_dict['obj_masks'] * data_dict[ |
| 'obj_sem_masks'].logical_not()]).item() / float( |
| (data_dict['obj_masks'] * data_dict['obj_sem_masks'].logical_not()).sum().item() + 1e-8) |
| if 'obj_cls_raw_logits' in data_dict.keys(): |
| metrics['obj_cls_raw_acc'] = torch.sum( |
| torch.argmax(data_dict['obj_cls_raw_logits'], dim=2)[data_dict['obj_masks']] == data_dict["obj_labels"][ |
| data_dict['obj_masks']]).item() / float(data_dict['obj_masks'].sum().item() + 1e-8) |
| if 'obj_cls_pre_logits' in data_dict.keys(): |
| metrics['obj_cls_pre_acc'] = torch.sum( |
| torch.argmax(data_dict['obj_cls_pre_logits'], dim=2)[data_dict['obj_masks']] == data_dict["obj_labels"][ |
| data_dict['obj_masks']]).item() / float(data_dict['obj_masks'].sum().item() + 1e-8) |
| metrics['obj_cls_pre_acc_unmask'] = torch.sum( |
| torch.argmax(data_dict['obj_cls_pre_logits'], dim=2)[data_dict['obj_masks'] * data_dict['obj_sem_masks']] == |
| data_dict["obj_labels"][data_dict['obj_masks'] * data_dict['obj_sem_masks']]).item() / float( |
| (data_dict['obj_masks'] * data_dict['obj_sem_masks']).sum().item() + 1e-8) |
| metrics['obj_cls_pre_acc_mask'] = torch.sum(torch.argmax(data_dict['obj_cls_pre_logits'], dim=2)[ |
| data_dict['obj_masks'] * data_dict[ |
| 'obj_sem_masks'].logical_not()] == data_dict["obj_labels"][ |
| data_dict['obj_masks'] * data_dict[ |
| 'obj_sem_masks'].logical_not()]).item() / float( |
| (data_dict['obj_masks'] * data_dict['obj_sem_masks'].logical_not()).sum().item() + 1e-8) |
| all_acc = [v for k, v in metrics.items()] |
| metrics["target_metric"] = float(sum(all_acc)) / len(all_acc) |
| metrics["total_count"] = data_dict["txt_lm_cls_logits"].shape[0] |
| return metrics |
|
|
| def update(self, data_dict): |
| metrics = self.batch_metrics(data_dict) |
| self.total_count += metrics["total_count"] |
| for key in self.eval_dict.keys(): |
| if key not in metrics.keys(): |
| continue |
| self.eval_dict[key].append(float(metrics[key]) * metrics["total_count"]) |
|
|
| def record(self): |
| |
| for k, v in self.eval_dict.items(): |
| self.eval_dict[k] = sum(v) / self.total_count |
| if self.eval_dict["target_metric"] > self.best_result: |
| is_best = True |
| self.best_result = self.eval_dict["target_metric"] |
| else: |
| is_best = False |
| return is_best, self.eval_dict |
|
|
| def reset(self): |
| for key in self.eval_dict.keys(): |
| self.eval_dict[key] = [] |
| self.total_count = 0 |
