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113 lines
4.7 KiB
113 lines
4.7 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import torch |
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from ultralytics.data import ClassificationDataset, build_dataloader |
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from ultralytics.engine.validator import BaseValidator |
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from ultralytics.utils import LOGGER |
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from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix |
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from ultralytics.utils.plotting import plot_images |
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class ClassificationValidator(BaseValidator): |
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""" |
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A class extending the BaseValidator class for validation based on a classification model. |
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Notes: |
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- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. |
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Example: |
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```python |
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from ultralytics.models.yolo.classify import ClassificationValidator |
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args = dict(model='yolov8n-cls.pt', data='imagenet10') |
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validator = ClassificationValidator(args=args) |
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validator() |
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``` |
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""" |
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None): |
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"""Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar.""" |
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super().__init__(dataloader, save_dir, pbar, args, _callbacks) |
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self.targets = None |
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self.pred = None |
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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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"""Returns a formatted string summarizing classification metrics.""" |
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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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"""Initialize confusion matrix, class names, and top-1 and top-5 accuracy.""" |
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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, conf=self.args.conf, 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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"""Preprocesses input batch and returns it.""" |
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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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"""Updates running metrics with model predictions and batch targets.""" |
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n5 = min(len(self.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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"""Finalizes metrics of the model such as confusion_matrix and speed.""" |
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self.confusion_matrix.process_cls_preds(self.pred, self.targets) |
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if self.args.plots: |
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for normalize in True, False: |
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self.confusion_matrix.plot( |
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save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot |
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) |
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self.metrics.speed = self.speed |
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self.metrics.confusion_matrix = self.confusion_matrix |
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self.metrics.save_dir = self.save_dir |
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def get_stats(self): |
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"""Returns a dictionary of metrics obtained by processing targets and predictions.""" |
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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 build_dataset(self, img_path): |
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"""Creates and returns a ClassificationDataset instance using given image path and preprocessing parameters.""" |
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return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split) |
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def get_dataloader(self, dataset_path, batch_size): |
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"""Builds and returns a data loader for classification tasks with given parameters.""" |
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dataset = self.build_dataset(dataset_path) |
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return build_dataloader(dataset, batch_size, self.args.workers, rank=-1) |
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def print_results(self): |
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"""Prints evaluation metrics for YOLO object detection model.""" |
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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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def plot_val_samples(self, batch, ni): |
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"""Plot validation image samples.""" |
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plot_images( |
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images=batch["img"], |
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batch_idx=torch.arange(len(batch["img"])), |
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cls=batch["cls"].view(-1), # warning: use .view(), not .squeeze() for Classify models |
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fname=self.save_dir / f"val_batch{ni}_labels.jpg", |
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names=self.names, |
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on_plot=self.on_plot, |
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) |
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def plot_predictions(self, batch, preds, ni): |
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"""Plots predicted bounding boxes on input images and saves the result.""" |
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plot_images( |
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batch["img"], |
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batch_idx=torch.arange(len(batch["img"])), |
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cls=torch.argmax(preds, dim=1), |
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fname=self.save_dir / f"val_batch{ni}_pred.jpg", |
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names=self.names, |
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on_plot=self.on_plot, |
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) # pred
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