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| # Ultralytics YOLO 🚀, GPL-3.0 license | |
| from ultralytics.yolo.data import build_classification_dataloader | |
| from ultralytics.yolo.engine.validator import BaseValidator | |
| from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER | |
| from ultralytics.yolo.utils.metrics import ClassifyMetrics | |
| class ClassificationValidator(BaseValidator): | |
| def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None): | |
| super().__init__(dataloader, save_dir, pbar, args) | |
| self.args.task = 'classify' | |
| self.metrics = ClassifyMetrics() | |
| def get_desc(self): | |
| return ('%22s' + '%11s' * 2) % ('classes', 'top1_acc', 'top5_acc') | |
| def init_metrics(self, model): | |
| self.pred = [] | |
| self.targets = [] | |
| def preprocess(self, batch): | |
| batch['img'] = batch['img'].to(self.device, non_blocking=True) | |
| batch['img'] = batch['img'].half() if self.args.half else batch['img'].float() | |
| batch['cls'] = batch['cls'].to(self.device) | |
| return batch | |
| def update_metrics(self, preds, batch): | |
| n5 = min(len(self.model.names), 5) | |
| self.pred.append(preds.argsort(1, descending=True)[:, :n5]) | |
| self.targets.append(batch['cls']) | |
| def finalize_metrics(self, *args, **kwargs): | |
| self.metrics.speed = self.speed | |
| # self.metrics.confusion_matrix = self.confusion_matrix # TODO: classification ConfusionMatrix | |
| def get_stats(self): | |
| self.metrics.process(self.targets, self.pred) | |
| return self.metrics.results_dict | |
| def get_dataloader(self, dataset_path, batch_size): | |
| return build_classification_dataloader(path=dataset_path, | |
| imgsz=self.args.imgsz, | |
| batch_size=batch_size, | |
| augment=False, | |
| shuffle=False, | |
| workers=self.args.workers) | |
| def print_results(self): | |
| pf = '%22s' + '%11.3g' * len(self.metrics.keys) # print format | |
| LOGGER.info(pf % ('all', self.metrics.top1, self.metrics.top5)) | |
| def val(cfg=DEFAULT_CFG, use_python=False): | |
| model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" | |
| data = cfg.data or 'mnist160' | |
| args = dict(model=model, data=data) | |
| if use_python: | |
| from ultralytics import YOLO | |
| YOLO(model).val(**args) | |
| else: | |
| validator = ClassificationValidator(args=args) | |
| validator(model=args['model']) | |
| if __name__ == '__main__': | |
| val() | |