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import torch
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from ultralytics.yolo.engine.validator import BaseValidator
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class ClassificationValidator(BaseValidator):
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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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acc = torch.stack((self.correct[:, 0], self.correct.max(1).values), dim=1) # (top1, top5) accuracy
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top1, top5 = acc.mean(0).tolist()
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return {"top1": top1, "top5": top5, "fitness": top5}
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@property
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def metric_keys(self):
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return ["top1", "top5"]
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