140 lines
6.6 KiB
Python
140 lines
6.6 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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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This validator handles the validation process for classification models, including metrics calculation,
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confusion matrix generation, and visualization of results.
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Attributes:
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targets (List[torch.Tensor]): Ground truth class labels.
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pred (List[torch.Tensor]): Model predictions.
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metrics (ClassifyMetrics): Object to calculate and store classification metrics.
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names (Dict): Mapping of class indices to class names.
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nc (int): Number of classes.
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confusion_matrix (ConfusionMatrix): Matrix to evaluate model performance across classes.
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Methods:
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get_desc: Return a formatted string summarizing classification metrics.
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init_metrics: Initialize confusion matrix, class names, and tracking containers.
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preprocess: Preprocess input batch by moving data to device.
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update_metrics: Update running metrics with model predictions and batch targets.
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finalize_metrics: Finalize metrics including confusion matrix and processing speed.
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postprocess: Extract the primary prediction from model output.
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get_stats: Calculate and return a dictionary of metrics.
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build_dataset: Create a ClassificationDataset instance for validation.
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get_dataloader: Build and return a data loader for classification validation.
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print_results: Print evaluation metrics for the classification model.
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plot_val_samples: Plot validation image samples with their ground truth labels.
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plot_predictions: Plot images with their predicted class labels.
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Examples:
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>>> from ultralytics.models.yolo.classify import ClassificationValidator
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>>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
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>>> validator = ClassificationValidator(args=args)
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>>> validator()
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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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"""
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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"""Initialize ClassificationValidator with dataloader, save directory, and other parameters."""
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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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"""Return 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 tracking containers for predictions and targets."""
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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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"""Preprocess input batch by moving data to device and converting to appropriate dtype."""
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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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"""Update 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].type(torch.int32).cpu())
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self.targets.append(batch["cls"].type(torch.int32).cpu())
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def finalize_metrics(self, *args, **kwargs):
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"""Finalize metrics including confusion matrix and processing 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 postprocess(self, preds):
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"""Extract the primary prediction from model output if it's in a list or tuple format."""
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return preds[0] if isinstance(preds, (list, tuple)) else preds
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def get_stats(self):
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"""Calculate and return a dictionary of metrics 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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"""Create a ClassificationDataset instance for validation."""
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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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"""Build and return a data loader for classification validation."""
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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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"""Print evaluation metrics for the classification 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 with their ground truth labels."""
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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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"""Plot images with their predicted class labels and save the visualization."""
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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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