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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| import torch | |
| from ultralytics.data import ClassificationDataset, build_dataloader | |
| from ultralytics.engine.validator import BaseValidator | |
| from ultralytics.utils import LOGGER | |
| from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix | |
| from ultralytics.utils.plotting import plot_images | |
| class ClassificationValidator(BaseValidator): | |
| """ | |
| A class extending the BaseValidator class for validation based on a classification model. | |
| Notes: | |
| - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. | |
| Example: | |
| ```python | |
| from ultralytics.models.yolo.classify import ClassificationValidator | |
| args = dict(model="yolov8n-cls.pt", data="imagenet10") | |
| validator = ClassificationValidator(args=args) | |
| validator() | |
| ``` | |
| """ | |
| 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.targets = None | |
| self.pred = None | |
| 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, conf=self.args.conf, 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.names), 5) | |
| self.pred.append(preds.argsort(1, descending=True)[:, :n5].type(torch.int32).cpu()) | |
| self.targets.append(batch["cls"].type(torch.int32).cpu()) | |
| 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 | |
| self.metrics.save_dir = self.save_dir | |
| def postprocess(self, preds): | |
| """Preprocesses the classification predictions.""" | |
| return preds[0] if isinstance(preds, (list, tuple)) else preds | |
| 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): | |
| """Creates and returns a ClassificationDataset instance using given image path and preprocessing parameters.""" | |
| return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split) | |
| 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 | |