| | 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 |
