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48 lines
1.7 KiB
48 lines
1.7 KiB
import hydra
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import torch
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from ultralytics.yolo.data import build_classification_dataloader
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from ultralytics.yolo.engine.validator import BaseValidator
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from ultralytics.yolo.utils import DEFAULT_CONFIG
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from ultralytics.yolo.utils.metrics import ClassifyMetrics
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class ClassificationValidator(BaseValidator):
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def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
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super().__init__(dataloader, save_dir, pbar, logger, args)
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self.metrics = ClassifyMetrics()
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def init_metrics(self, model):
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self.correct = torch.tensor([], device=next(model.parameters()).device)
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def preprocess(self, batch):
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batch["img"] = batch["img"].to(self.device, non_blocking=True)
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batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
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batch["cls"] = batch["cls"].to(self.device)
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return batch
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def update_metrics(self, preds, batch):
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targets = batch["cls"]
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correct_in_batch = (targets[:, None] == preds).float()
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self.correct = torch.cat((self.correct, correct_in_batch))
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def get_stats(self):
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self.metrics.process(self.correct)
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return self.metrics.results_dict
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def get_dataloader(self, dataset_path, batch_size):
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return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def val(cfg):
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cfg.data = cfg.data or "imagenette160"
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cfg.model = cfg.model or "resnet18"
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validator = ClassificationValidator(args=cfg)
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validator(model=cfg.model)
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if __name__ == "__main__":
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val()
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