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