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import torch
from pathlib import Path
from evaluator.build import EVALUATOR_REGISTRY, BaseEvaluator
@EVALUATOR_REGISTRY.register()
class PretrainObjEval(BaseEvaluator):
def __init__(self, cfg, accelerator, **kwargs):
self.target_metric = "accuracy"
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):
metrics = {}
logits = data_dict["obj_logits"][data_dict["obj_masks"]].view(-1, data_dict["obj_logits"].shape[-1])
labels = data_dict["obj_labels"][data_dict["obj_masks"]].view(-1)
_, pred = torch.max(logits, 1)
metrics["accuracy"] = ((pred == labels.view(-1)).sum().item(), labels.shape[0])
if not include_count:
for key, v in metrics.items():
metrics[key] = v[0] / max(v[1], 1)
return metrics |