| import json |
| import numpy as np |
| from omegaconf import open_dict |
| from fvcore.common.registry import Registry |
|
|
| from common.misc import gather_dict |
|
|
| EVALUATOR_REGISTRY = Registry("EVALUATOR") |
|
|
|
|
| class BaseEvaluator(): |
| def __init__(self, cfg, accelerator): |
| self.accelerator = accelerator |
| self.best_result = -np.inf |
| self.save = cfg.eval.save |
| self.save_dir.mkdir(parents=True, exist_ok=True) |
| self.reset() |
|
|
| def reset(self): |
| self.eval_results = [] |
| self.eval_dict = {} |
|
|
| def batch_metrics(self, data_dict, include_count=False): |
| raise NotImplementedError("Per batch metrics calculation is required for evaluation") |
|
|
| def update(self, data_dict): |
| metrics = self.batch_metrics(data_dict, include_count=True) |
| for key in metrics.keys(): |
| if key not in self.eval_dict: |
| self.eval_dict[key] = [] |
| self.eval_dict[key].append(metrics[key]) |
|
|
| def record(self): |
| self.eval_dict = gather_dict(self.accelerator, self.eval_dict) |
| for k, metrics in self.eval_dict.items(): |
| if not isinstance(metrics, list): |
| continue |
| |
| total_value = sum(x[0] for x in metrics) |
| total_count = sum(x[1] for x in metrics) |
| self.eval_dict[k] = total_value / max(total_count, 1) |
| print(k, total_value, total_count) |
| |
| if self.save and self.accelerator.is_main_process: |
| with (self.save_dir / "results.json").open("w") as f: |
| json.dump(self.eval_results, f) |
| |
| self.eval_dict['target_metric'] = self.eval_dict[self.target_metric] |
| if self.eval_dict["target_metric"] > self.best_result: |
| is_best = True |
| self.best_result = self.eval_dict["target_metric"] |
| else: |
| is_best = False |
| self.eval_dict['best_result'] = self.best_result |
| return is_best, self.eval_dict |
|
|
|
|
| def get_eval(name, cfg, accelerator, **kwargs): |
| """Get an evaluator or a list of evaluators.""" |
| if isinstance(name, str): |
| eval = EVALUATOR_REGISTRY.get(name)(cfg, accelerator, **kwargs) |
| else: |
| eval = [EVALUATOR_REGISTRY.get(i)(cfg, accelerator, **kwargs) for i in name] |
| return eval |
|
|
| def build_eval(cfg, accelerator, **kwargs): |
| if cfg.eval.get("train", None) is not None: |
| train_eval = get_eval(cfg.eval.train.name, cfg, accelerator, **kwargs) |
| val_eval = get_eval(cfg.eval.val.name, cfg, accelerator, **kwargs) |
| return {"train": train_eval, "val": val_eval} |
| elif cfg.eval.get("name", None) is not None: |
| return get_eval(cfg.eval.name, cfg, accelerator, **kwargs) |
| else: |
| with open_dict(cfg): |
| cfg.eval.name = [cfg.data.get(dataset).evaluator for dataset in cfg.data.val] |
| return get_eval(cfg.eval.name, cfg, accelerator, **kwargs) |
