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77 lines
2.9 KiB
77 lines
2.9 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, LOGGER
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from ultralytics.yolo.utils.metrics import ClassifyMetrics, ConfusionMatrix
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class ClassificationValidator(BaseValidator):
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.args.task = 'classify'
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self.metrics = ClassifyMetrics()
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self.save_dir = save_dir
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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.names = model.names
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self.nc = len(model.names)
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self.confusion_matrix = ConfusionMatrix(nc=self.nc, task='classify')
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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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n5 = min(len(self.model.names), 5)
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self.pred.append(preds.argsort(1, descending=True)[:, :n5])
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self.targets.append(batch['cls'])
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def finalize_metrics(self, *args, **kwargs):
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self.confusion_matrix.process_cls_preds(self.pred, self.targets)
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self.metrics.speed = self.speed
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self.metrics.confusion_matrix = self.confusion_matrix
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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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augment=False,
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shuffle=False,
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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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LOGGER.info(pf % ('all', self.metrics.top1, self.metrics.top5))
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if self.args.plots:
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self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
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def val(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
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data = cfg.data or 'mnist160'
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args = dict(model=model, data=data)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).val(**args)
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else:
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validator = ClassificationValidator(args=args)
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validator(model=args['model'])
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if __name__ == '__main__':
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val()
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