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के लिए संदर्भ ultralytics/models/yolo/segment/val.py

नोट

यह फ़ाइल यहाँ उपलब्ध है https://github.com/ultralytics/ultralytics/बूँद/मुख्य/ultralytics/मॉडल/yolo/segment/val.py का उपयोग करें। यदि आप कोई समस्या देखते हैं तो कृपया पुल अनुरोध का योगदान करके इसे ठीक करने में मदद करें 🛠️। 🙏 धन्यवाद !



ultralytics.models.yolo.segment.val.SegmentationValidator

का रूप: DetectionValidator

एक वर्ग जो विभाजन मॉडल के आधार पर सत्यापन के लिए DetectionValidator वर्ग का विस्तार करता है।

उदाहरण
from ultralytics.models.yolo.segment import SegmentationValidator

args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
validator = SegmentationValidator(args=args)
validator()
में स्रोत कोड ultralytics/models/yolo/segment/val.py
class SegmentationValidator(DetectionValidator):
    """
    A class extending the DetectionValidator class for validation based on a segmentation model.

    Example:
        ```python
        from ultralytics.models.yolo.segment import SegmentationValidator

        args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
        validator = SegmentationValidator(args=args)
        validator()
        ```
    """

    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)

    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

    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")
            self.process = ops.process_mask_upsample  # more accurate
        else:
            self.process = ops.process_mask  # faster
        self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])

    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)",
        )

    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

    def _prepare_batch(self, si, batch):
        """Prepares a batch for training or inference by processing images and targets."""
        prepared_batch = super()._prepare_batch(si, batch)
        midx = [si] if self.args.overlap_mask else batch["batch_idx"] == si
        prepared_batch["masks"] = batch["masks"][midx]
        return prepared_batch

    def _prepare_pred(self, pred, pbatch, proto):
        """Prepares a batch for training or inference by processing images and targets."""
        predn = super()._prepare_pred(pred, pbatch)
        pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch["imgsz"])
        return predn, pred_masks

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

    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

    def _process_batch(self, detections, gt_bboxes, gt_cls, pred_masks=None, gt_masks=None, overlap=False, masks=False):
        """
        Return correct prediction matrix.

        Args:
            detections (array[N, 6]), x1, y1, x2, y2, conf, class
            labels (array[M, 5]), class, x1, y1, x2, y2

        Returns:
            correct (array[N, 10]), for 10 IoU levels
        """
        if masks:
            if overlap:
                nl = len(gt_cls)
                index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
                gt_masks = gt_masks.repeat(nl, 1, 1)  # shape(1,640,640) -> (n,640,640)
                gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
            if gt_masks.shape[1:] != pred_masks.shape[1:]:
                gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
                gt_masks = gt_masks.gt_(0.5)
            iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
        else:  # boxes
            iou = box_iou(gt_bboxes, detections[:, :4])

        return self.match_predictions(detections[:, 5], gt_cls, iou)

    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,
        )

    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()

    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],
                }
            )

    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

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

SegmentationValidator को इनिशियलाइज़ करें और टास्क को 'segment', मेट्रिक्स को SegmentMetrics पर सेट करें.

में स्रोत कोड 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(stats)

COCO-शैली ऑब्जेक्ट डिटेक्शन मूल्यांकन मेट्रिक्स लौटाएं।

में स्रोत कोड ultralytics/models/yolo/segment/val.py
250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268269270 271 272 273 274 275 276277
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(*args, **kwargs)

मूल्यांकन मीट्रिक के लिए गति और भ्रम मैट्रिक्स सेट करता है।

में स्रोत कोड 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()

मूल्यांकन मीट्रिक का स्वरूपित वर्णन लौटाएं.

में स्रोत कोड 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(model)

मैट्रिक्स को इनिशियलाइज़ करें और फ़्लैग के आधार पर मास्क प्रोसेसिंग फ़ंक्शन save_json चयन करें।

में स्रोत कोड 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")
        self.process = ops.process_mask_upsample  # more accurate
    else:
        self.process = ops.process_mask  # faster
    self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])

plot_predictions(batch, preds, ni)

मास्क और बाउंडिंग बॉक्स के साथ प्लॉट बैच भविष्यवाणियां।

में स्रोत कोड ultralytics/models/yolo/segment/val.py
204 205 206 207 208209 210 211 212 213 214215
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(batch, ni)

बाउंडिंग बॉक्स लेबल के साथ प्लॉट सत्यापन नमूने।

में स्रोत कोड ultralytics/models/yolo/segment/val.py
190 191 192 193 194 195 196 197 198 199 200201202
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(preds)

पोस्ट-प्रक्रियाएं YOLO भविष्यवाणियां और प्रोटो के साथ आउटपुट डिटेक्शन लौटाता है।

में स्रोत कोड 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(predn, filename, pred_masks)

एक JSON परिणाम सहेजें।

उदाहरण:

>>> result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
में स्रोत कोड ultralytics/models/yolo/segment/val.py
217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246247 248
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(batch)

मास्क को फ्लोट में परिवर्तित करके और डिवाइस पर भेजकर बैच को प्रीप्रोसेस करता है।

में स्रोत कोड 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

update_metrics(preds, batch)

मैट्रिक्स।

में स्रोत कोड ultralytics/models/yolo/segment/val.py
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150151 152 153 154155
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
        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:
            pred_masks = ops.scale_image(
                pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
                pbatch["ori_shape"],
                ratio_pad=batch["ratio_pad"][si],
            )
            self.pred_to_json(predn, batch["im_file"][si], pred_masks)





2023-11-12 बनाया गया, अपडेट किया गया 2023-11-25
लेखक: ग्लेन-जोचर (3)