Reference for ultralytics/models/yolo/detect/val.py

def build_dataset(self, img_path, mode="val", batch=None):
    """
    Build YOLO Dataset.

    Args:
        img_path (str): Path to the folder containing images.
        mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
        batch (int, optional): Size of batches, this is for `rect`.

    Returns:
        (Dataset): YOLO dataset.
    """
    return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)

def eval_json(self, stats):
    """
    Evaluate YOLO output in JSON format and return performance statistics.

    Args:
        stats (dict): Current statistics dictionary.

    Returns:
        (dict): Updated statistics dictionary with COCO/LVIS evaluation results.
    """
    if self.args.save_json and (self.is_coco or self.is_lvis) and len(self.jdict):
        pred_json = self.save_dir / "predictions.json"  # predictions
        anno_json = (
            self.data["path"]
            / "annotations"
            / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
        )  # annotations
        pkg = "pycocotools" if self.is_coco else "lvis"
        LOGGER.info(f"\nEvaluating {pkg} mAP using {pred_json} and {anno_json}...")
        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            for x in pred_json, anno_json:
                assert x.is_file(), f"{x} file not found"
            check_requirements("pycocotools>=2.0.6" if self.is_coco else "lvis>=0.5.3")
            if self.is_coco:
                from pycocotools.coco import COCO  # noqa
                from pycocotools.cocoeval import COCOeval  # noqa

                anno = COCO(str(anno_json))  # init annotations api
                pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
                val = COCOeval(anno, pred, "bbox")
            else:
                from lvis import LVIS, LVISEval

                anno = LVIS(str(anno_json))  # init annotations api
                pred = anno._load_json(str(pred_json))  # init predictions api (must pass string, not Path)
                val = LVISEval(anno, pred, "bbox")
            val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # images to eval
            val.evaluate()
            val.accumulate()
            val.summarize()
            if self.is_lvis:
                val.print_results()  # explicitly call print_results
            # update mAP50-95 and mAP50
            stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = (
                val.stats[:2] if self.is_coco else [val.results["AP50"], val.results["AP"]]
            )
        except Exception as e:
            LOGGER.warning(f"{pkg} unable to run: {e}")
    return stats

def finalize_metrics(self, *args, **kwargs):
    """
    Set final values for metrics speed and confusion matrix.

    Args:
        *args (Any): Variable length argument list.
        **kwargs (Any): Arbitrary keyword arguments.
    """
    self.metrics.speed = self.speed
    self.metrics.confusion_matrix = self.confusion_matrix

def get_dataloader(self, dataset_path, batch_size):
    """
    Construct and return dataloader.

    Args:
        dataset_path (str): Path to the dataset.
        batch_size (int): Size of each batch.

    Returns:
        (torch.utils.data.DataLoader): Dataloader for validation.
    """
    dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
    return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1)  # return dataloader

def get_desc(self):
    """Return a formatted string summarizing class metrics of YOLO model."""
    return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")

def get_stats(self):
    """
    Calculate and return metrics statistics.

    Returns:
        (dict): Dictionary containing metrics results.
    """
    stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()}  # to numpy
    self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=self.nc)
    self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
    stats.pop("target_img", None)
    if len(stats):
        self.metrics.process(**stats, on_plot=self.on_plot)
    return self.metrics.results_dict

def init_metrics(self, model):
    """
    Initialize evaluation metrics for YOLO detection validation.

    Args:
        model (torch.nn.Module): Model to validate.
    """
    val = self.data.get(self.args.split, "")  # validation path
    self.is_coco = (
        isinstance(val, str)
        and "coco" in val
        and (val.endswith(f"{os.sep}val2017.txt") or val.endswith(f"{os.sep}test-dev2017.txt"))
    )  # is COCO
    self.is_lvis = isinstance(val, str) and "lvis" in val and not self.is_coco  # is LVIS
    self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1, len(model.names) + 1))
    self.args.save_json |= self.args.val and (self.is_coco or self.is_lvis) and not self.training  # run final val
    self.names = model.names
    self.nc = len(model.names)
    self.end2end = getattr(model, "end2end", False)
    self.metrics.names = self.names
    self.metrics.plot = self.args.plots
    self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf)
    self.seen = 0
    self.jdict = []
    self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])

def plot_predictions(self, batch, preds, ni):
    """
    Plot predicted bounding boxes on input images and save the result.

    Args:
        batch (dict): Batch containing images and annotations.
        preds (List[torch.Tensor]): List of predictions from the model.
        ni (int): Batch index.
    """
    plot_images(
        batch["img"],
        *output_to_target(preds, max_det=self.args.max_det),
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_pred.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )  # pred

def plot_val_samples(self, batch, ni):
    """
    Plot validation image samples.

    Args:
        batch (dict): Batch containing images and annotations.
        ni (int): Batch index.
    """
    plot_images(
        batch["img"],
        batch["batch_idx"],
        batch["cls"].squeeze(-1),
        batch["bboxes"],
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_labels.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )

def postprocess(self, preds):
    """
    Apply Non-maximum suppression to prediction outputs.

    Args:
        preds (torch.Tensor): Raw predictions from the model.

    Returns:
        (List[torch.Tensor]): Processed predictions after NMS.
    """
    return ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        labels=self.lb,
        nc=self.nc,
        multi_label=True,
        agnostic=self.args.single_cls or self.args.agnostic_nms,
        max_det=self.args.max_det,
        end2end=self.end2end,
        rotated=self.args.task == "obb",
    )

def pred_to_json(self, predn, filename):
    """
    Serialize YOLO predictions to COCO json format.

    Args:
        predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
        filename (str): Image filename.
    """
    stem = Path(filename).stem
    image_id = int(stem) if stem.isnumeric() else stem
    box = ops.xyxy2xywh(predn[:, :4])  # xywh
    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
    for p, b in zip(predn.tolist(), box.tolist()):
        self.jdict.append(
            {
                "image_id": image_id,
                "category_id": self.class_map[int(p[5])],
                "bbox": [round(x, 3) for x in b],
                "score": round(p[4], 5),
            }
        )

def preprocess(self, batch):
    """
    Preprocess batch of images for YOLO validation.

    Args:
        batch (dict): Batch containing images and annotations.

    Returns:
        (dict): Preprocessed batch.
    """
    batch["img"] = batch["img"].to(self.device, non_blocking=True)
    batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
    for k in ["batch_idx", "cls", "bboxes"]:
        batch[k] = batch[k].to(self.device)

    if self.args.save_hybrid and self.args.task == "detect":
        height, width = batch["img"].shape[2:]
        nb = len(batch["img"])
        bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
        self.lb = [
            torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
            for i in range(nb)
        ]

    return batch

def print_results(self):
    """Print training/validation set metrics per class."""
    pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys)  # print format
    LOGGER.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
    if self.nt_per_class.sum() == 0:
        LOGGER.warning(f"WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels")

    # Print results per class
    if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
        for i, c in enumerate(self.metrics.ap_class_index):
            LOGGER.info(
                pf % (self.names[c], self.nt_per_image[c], self.nt_per_class[c], *self.metrics.class_result(i))
            )

    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
            )

def save_one_txt(self, predn, save_conf, shape, file):
    """
    Save YOLO detections to a txt file in normalized coordinates in a specific format.

    Args:
        predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
        save_conf (bool): Whether to save confidence scores.
        shape (tuple): Shape of the original image.
        file (Path): File path to save the detections.
    """
    from ultralytics.engine.results import Results

    Results(
        np.zeros((shape[0], shape[1]), dtype=np.uint8),
        path=None,
        names=self.names,
        boxes=predn[:, :6],
    ).save_txt(file, save_conf=save_conf)

def update_metrics(self, preds, batch):
    """
    Update metrics with new predictions and ground truth.

    Args:
        preds (List[torch.Tensor]): List of predictions from the model.
        batch (dict): Batch data containing ground truth.
    """
    for si, pred in enumerate(preds):
        self.seen += 1
        npr = len(pred)
        stat = dict(
            conf=torch.zeros(0, device=self.device),
            pred_cls=torch.zeros(0, device=self.device),
            tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
        )
        pbatch = self._prepare_batch(si, batch)
        cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
        nl = len(cls)
        stat["target_cls"] = cls
        stat["target_img"] = cls.unique()
        if npr == 0:
            if nl:
                for k in self.stats.keys():
                    self.stats[k].append(stat[k])
                if self.args.plots:
                    self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
            continue

        # Predictions
        if self.args.single_cls:
            pred[:, 5] = 0
        predn = self._prepare_pred(pred, pbatch)
        stat["conf"] = predn[:, 4]
        stat["pred_cls"] = predn[:, 5]

        # Evaluate
        if nl:
            stat["tp"] = self._process_batch(predn, bbox, cls)
        if self.args.plots:
            self.confusion_matrix.process_batch(predn, bbox, cls)
        for k in self.stats.keys():
            self.stats[k].append(stat[k])

        # Save
        if self.args.save_json:
            self.pred_to_json(predn, batch["im_file"][si])
        if self.args.save_txt:
            self.save_one_txt(
                predn,
                self.args.save_conf,
                pbatch["ori_shape"],
                self.save_dir / "labels" / f"{Path(batch['im_file'][si]).stem}.txt",
            )