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57 lines
2.0 KiB
57 lines
2.0 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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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_CFG
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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.args.task = 'classify'
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self.metrics = ClassifyMetrics()
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def get_desc(self):
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return ('%22s' + '%11s' * 2) % ('classes', 'top1_acc', 'top5_acc')
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def init_metrics(self, model):
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self.pred = []
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self.targets = []
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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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self.pred.append(preds.argsort(1, descending=True)[:, :5])
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self.targets.append(batch["cls"])
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def get_stats(self):
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self.metrics.process(self.targets, self.pred)
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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,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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workers=self.args.workers)
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def print_results(self):
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pf = '%22s' + '%11.3g' * len(self.metrics.keys) # print format
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self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5))
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def val(cfg=DEFAULT_CFG):
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cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18"
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cfg.data = cfg.data or "imagenette160"
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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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