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

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/val.py. If you spot a problem please help fix it by contributing a Pull Request ūüõ†ÔłŹ. Thank you ūüôŹ!


ultralytics.models.yolo.segment.val.SegmentationValidator

SegmentationValidator(dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None)

Bases: DetectionValidator

A class extending the DetectionValidator class for validation based on a segmentation model.

Example
from ultralytics.models.yolo.segment import SegmentationValidator

args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
validator = SegmentationValidator(args=args)
validator()
Source code in ultralytics/models/yolo/segment/val.py
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
    """Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
    self.plot_masks = None
    self.process = None
    self.args.task = "segment"
    self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)

eval_json

eval_json(stats)

Return COCO-style object detection evaluation metrics.

Source code in ultralytics/models/yolo/segment/val.py
def eval_json(self, stats):
    """Return COCO-style object detection evaluation metrics."""
    if self.args.save_json and self.is_coco and len(self.jdict):
        anno_json = self.data["path"] / "annotations/instances_val2017.json"  # annotations
        pred_json = self.save_dir / "predictions.json"  # predictions
        LOGGER.info(f"\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...")
        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            check_requirements("pycocotools>=2.0.6")
            from pycocotools.coco import COCO  # noqa
            from pycocotools.cocoeval import COCOeval  # noqa

            for x in anno_json, pred_json:
                assert x.is_file(), f"{x} file not found"
            anno = COCO(str(anno_json))  # init annotations api
            pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
            for i, eval in enumerate([COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm")]):
                if self.is_coco:
                    eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # im to eval
                eval.evaluate()
                eval.accumulate()
                eval.summarize()
                idx = i * 4 + 2
                stats[self.metrics.keys[idx + 1]], stats[self.metrics.keys[idx]] = eval.stats[
                    :2
                ]  # update mAP50-95 and mAP50
        except Exception as e:
            LOGGER.warning(f"pycocotools unable to run: {e}")
    return stats

finalize_metrics

finalize_metrics(*args, **kwargs)

Sets speed and confusion matrix for evaluation metrics.

Source code in ultralytics/models/yolo/segment/val.py
def finalize_metrics(self, *args, **kwargs):
    """Sets speed and confusion matrix for evaluation metrics."""
    self.metrics.speed = self.speed
    self.metrics.confusion_matrix = self.confusion_matrix

get_desc

get_desc()

Return a formatted description of evaluation metrics.

Source code in ultralytics/models/yolo/segment/val.py
def get_desc(self):
    """Return a formatted description of evaluation metrics."""
    return ("%22s" + "%11s" * 10) % (
        "Class",
        "Images",
        "Instances",
        "Box(P",
        "R",
        "mAP50",
        "mAP50-95)",
        "Mask(P",
        "R",
        "mAP50",
        "mAP50-95)",
    )

init_metrics

init_metrics(model)

Initialize metrics and select mask processing function based on save_json flag.

Source code in ultralytics/models/yolo/segment/val.py
def init_metrics(self, model):
    """Initialize metrics and select mask processing function based on save_json flag."""
    super().init_metrics(model)
    self.plot_masks = []
    if self.args.save_json:
        check_requirements("pycocotools>=2.0.6")
    # more accurate vs faster
    self.process = ops.process_mask_native if self.args.save_json or self.args.save_txt else ops.process_mask
    self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])

plot_predictions

plot_predictions(batch, preds, ni)

Plots batch predictions with masks and bounding boxes.

Source code in ultralytics/models/yolo/segment/val.py
def plot_predictions(self, batch, preds, ni):
    """Plots batch predictions with masks and bounding boxes."""
    plot_images(
        batch["img"],
        *output_to_target(preds[0], max_det=15),  # not set to self.args.max_det due to slow plotting speed
        torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_pred.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )  # pred
    self.plot_masks.clear()

plot_val_samples

plot_val_samples(batch, ni)

Plots validation samples with bounding box labels.

Source code in ultralytics/models/yolo/segment/val.py
def plot_val_samples(self, batch, ni):
    """Plots validation samples with bounding box labels."""
    plot_images(
        batch["img"],
        batch["batch_idx"],
        batch["cls"].squeeze(-1),
        batch["bboxes"],
        masks=batch["masks"],
        paths=batch["im_file"],
        fname=self.save_dir / f"val_batch{ni}_labels.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )

postprocess

postprocess(preds)

Post-processes YOLO predictions and returns output detections with proto.

Source code in ultralytics/models/yolo/segment/val.py
def postprocess(self, preds):
    """Post-processes YOLO predictions and returns output detections with proto."""
    p = ops.non_max_suppression(
        preds[0],
        self.args.conf,
        self.args.iou,
        labels=self.lb,
        multi_label=True,
        agnostic=self.args.single_cls,
        max_det=self.args.max_det,
        nc=self.nc,
    )
    proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
    return p, proto

pred_to_json

pred_to_json(predn, filename, pred_masks)

Save one JSON result.

Examples:

>>> result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
Source code in ultralytics/models/yolo/segment/val.py
def pred_to_json(self, predn, filename, pred_masks):
    """
    Save one JSON result.

    Examples:
         >>> result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
    """
    from pycocotools.mask import encode  # noqa

    def single_encode(x):
        """Encode predicted masks as RLE and append results to jdict."""
        rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
        rle["counts"] = rle["counts"].decode("utf-8")
        return rle

    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
    pred_masks = np.transpose(pred_masks, (2, 0, 1))
    with ThreadPool(NUM_THREADS) as pool:
        rles = pool.map(single_encode, pred_masks)
    for i, (p, b) in enumerate(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),
                "segmentation": rles[i],
            }
        )

preprocess

preprocess(batch)

Preprocesses batch by converting masks to float and sending to device.

Source code in ultralytics/models/yolo/segment/val.py
def preprocess(self, batch):
    """Preprocesses batch by converting masks to float and sending to device."""
    batch = super().preprocess(batch)
    batch["masks"] = batch["masks"].to(self.device).float()
    return batch

save_one_txt

save_one_txt(predn, pred_masks, save_conf, shape, file)

Save YOLO detections to a txt file in normalized coordinates in a specific format.

Source code in ultralytics/models/yolo/segment/val.py
def save_one_txt(self, predn, pred_masks, save_conf, shape, file):
    """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
    from ultralytics.engine.results import Results

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

update_metrics

update_metrics(preds, batch)

Metrics.

Source code in ultralytics/models/yolo/segment/val.py
def update_metrics(self, preds, batch):
    """Metrics."""
    for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
        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),
            tp_m=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

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

        # Evaluate
        if nl:
            stat["tp"] = self._process_batch(predn, bbox, cls)
            stat["tp_m"] = self._process_batch(
                predn, bbox, cls, pred_masks, gt_masks, self.args.overlap_mask, masks=True
            )
            if self.args.plots:
                self.confusion_matrix.process_batch(predn, bbox, cls)

        for k in self.stats.keys():
            self.stats[k].append(stat[k])

        pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
        if self.args.plots and self.batch_i < 3:
            self.plot_masks.append(pred_masks[:15].cpu())  # filter top 15 to plot

        # Save
        if self.args.save_json:
            self.pred_to_json(
                predn,
                batch["im_file"][si],
                ops.scale_image(
                    pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
                    pbatch["ori_shape"],
                    ratio_pad=batch["ratio_pad"][si],
                ),
            )
        if self.args.save_txt:
            self.save_one_txt(
                predn,
                pred_masks,
                self.args.save_conf,
                pbatch["ori_shape"],
                self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
            )





Created 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1)