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