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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 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])
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
self.metrics.save_dir = self.save_dir
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
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