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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
# metrics is a list of (value, count)
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) |