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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| import torch | |
| from ultralytics.data import ClassificationDataset, build_dataloader | |
| from ultralytics.engine.validator import BaseValidator | |
| from ultralytics.utils import DEFAULT_CFG, LOGGER | |
| from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix | |
| from ultralytics.utils.plotting import plot_images | |
| class ClassificationValidator(BaseValidator): | |
| def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None): | |
| """Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar.""" | |
| super().__init__(dataloader, save_dir, pbar, args, _callbacks) | |
| self.args.task = 'classify' | |
| self.metrics = ClassifyMetrics() | |
| def get_desc(self): | |
| """Returns a formatted string summarizing classification metrics.""" | |
| return ('%22s' + '%11s' * 2) % ('classes', 'top1_acc', 'top5_acc') | |
| def init_metrics(self, model): | |
| """Initialize confusion matrix, class names, and top-1 and top-5 accuracy.""" | |
| self.names = model.names | |
| self.nc = len(model.names) | |
| self.confusion_matrix = ConfusionMatrix(nc=self.nc, task='classify') | |
| self.pred = [] | |
| self.targets = [] | |
| def preprocess(self, batch): | |
| """Preprocesses input batch and returns it.""" | |
| 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): | |
| """Updates running metrics with model predictions and batch targets.""" | |
| 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): | |
| """Finalizes metrics of the model such as confusion_matrix and speed.""" | |
| self.confusion_matrix.process_cls_preds(self.pred, self.targets) | |
| if self.args.plots: | |
| for normalize in True, False: | |
| self.confusion_matrix.plot(save_dir=self.save_dir, | |
| names=self.names.values(), | |
| normalize=normalize, | |
| on_plot=self.on_plot) | |
| self.metrics.speed = self.speed | |
| self.metrics.confusion_matrix = self.confusion_matrix | |
| def get_stats(self): | |
| """Returns a dictionary of metrics obtained by processing targets and predictions.""" | |
| self.metrics.process(self.targets, self.pred) | |
| return self.metrics.results_dict | |
| def build_dataset(self, img_path): | |
| return ClassificationDataset(root=img_path, args=self.args, augment=False) | |
| def get_dataloader(self, dataset_path, batch_size): | |
| """Builds and returns a data loader for classification tasks with given parameters.""" | |
| dataset = self.build_dataset(dataset_path) | |
| return build_dataloader(dataset, batch_size, self.args.workers, rank=-1) | |
| def print_results(self): | |
| """Prints evaluation metrics for YOLO object detection model.""" | |
| pf = '%22s' + '%11.3g' * len(self.metrics.keys) # print format | |
| LOGGER.info(pf % ('all', self.metrics.top1, self.metrics.top5)) | |
| def plot_val_samples(self, batch, ni): | |
| """Plot validation image samples.""" | |
| plot_images( | |
| images=batch['img'], | |
| batch_idx=torch.arange(len(batch['img'])), | |
| cls=batch['cls'].view(-1), # warning: use .view(), not .squeeze() for Classify models | |
| fname=self.save_dir / f'val_batch{ni}_labels.jpg', | |
| names=self.names, | |
| on_plot=self.on_plot) | |
| def plot_predictions(self, batch, preds, ni): | |
| """Plots predicted bounding boxes on input images and saves the result.""" | |
| plot_images(batch['img'], | |
| batch_idx=torch.arange(len(batch['img'])), | |
| cls=torch.argmax(preds, dim=1), | |
| fname=self.save_dir / f'val_batch{ni}_pred.jpg', | |
| names=self.names, | |
| on_plot=self.on_plot) # pred | |
| def val(cfg=DEFAULT_CFG, use_python=False): | |
| """Validate YOLO model using custom data.""" | |
| 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() | |