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YOLOv8 Distributed

" YoloV8 base detector class. Modified to support PyTorch DDP framework

YOLOV8_Distributed

Bases: BaseDetector

Distributed YoloV8 detector class. This class provides utility methods for loading the model, generating results, and performing batch image detections.

Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_distributed.py 
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class YOLOV8_Distributed(BaseDetector):
    """
    Distributed YoloV8 detector class.
    This class provides utility methods for loading the model, generating results,
    and performing batch image detections.
    """

    def __init__(self, weights=None, device="cpu", url=None, transform=None):
        """
        Initialize the YOLOV8 detector.

        Args:
            weights (str, optional): 
                Path to the model weights. Defaults to None.
            device (str, optional): 
                Device for model inference. Defaults to "cpu".
            url (str, optional): 
                URL to fetch the model weights. Defaults to None.
        """
        self.transform = transform
        super(YOLOV8_Distributed, self).__init__(weights=weights, device=device, url=url)
        self._load_model(weights, self.device, url)

    def _load_model(self, weights=None, device="cpu", url=None):
        """
        Load the YOLOV8 model weights.

        Args:
            weights (str, optional): 
                Path to the model weights. Defaults to None.
            device (str, optional): 
                Device for model inference. Defaults to "cpu".
            url (str, optional): 
                URL to fetch the model weights. Defaults to None.
        Raises:
            Exception: If weights are not provided.
        """

        if self.MODEL_NAME == 'MDV6b-rtdetrl.pt':
            self.predictor = rtdetr.RTDETRPredictor()
        else:
            self.predictor = yolo.detect.DetectionPredictor()
        # self.predictor.args.device = device # Will uncomment later
        self.predictor.args.imgsz = self.IMAGE_SIZE
        self.predictor.args.save = False # Will see if we want to use ultralytics native inference saving functions.

        if weights:
            self.predictor.setup_model(weights)
        elif url:
            if not os.path.exists(os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)):
                os.makedirs(os.path.join(torch.hub.get_dir(), "checkpoints"), exist_ok=True)
                weights = wget.download(url, out=os.path.join(torch.hub.get_dir(), "checkpoints"))
            else:
                weights = os.path.join(torch.hub.get_dir(), "checkpoints", self.MODEL_NAME)
            self.predictor.setup_model(weights)
        else:
            raise Exception("Need weights for inference.")

        if not self.transform:
            self.transform = pw_trans.MegaDetector_v5_Transform(target_size=self.IMAGE_SIZE,
                                                                stride=self.STRIDE)

    def results_generation(self, preds, img_id, id_strip=None) -> dict:
        """
        Generate results for detection based on model predictions.

        Args:
            preds (ultralytics.engine.results.Results): 
                Model predictions.
            img_id (str): 
                Image identifier.
            id_strip (str, optional): 
                Strip specific characters from img_id. Defaults to None.

        Returns:
            dict: Dictionary containing image ID, detections, and labels.
        """
        xyxy = preds.boxes.xyxy.cpu().numpy()
        confidence = preds.boxes.conf.cpu().numpy()
        class_id = preds.boxes.cls.cpu().numpy().astype(int)

        results = {"img_id": str(img_id).strip(id_strip)}
        # results["detections"] = sv.Detections(
        #     xyxy=xyxy,
        #     confidence=confidence,
        #     class_id=class_id
        # )
        results["detections_xyxy"] = xyxy
        results["detections_confidence"] = confidence
        results["detections_class_id"] = class_id

        # results["labels"] = [
        #     f"{self.CLASS_NAMES[class_id]} {confidence:0.2f}"  
        #     for _, _, confidence, class_id, _, _ in results["detections"] 
        # ]

        results["labels"] = [
            f"{self.CLASS_NAMES[cls_id]} {conf:0.2f}"  
            for cls_id, conf in zip(class_id, confidence)
        ]

        results["n_animal_detected"] = np.sum(class_id == 0)

        return results

    def batch_image_detection(self, loader, batch_size, global_rank, local_rank, output_dir, det_conf_thres=0.2, checkpoint_frequency = 1000):

        """
        Perform batch image detection using the YOLOV8 model.

        Args:
            loader (torch.utils.data.DataLoader): 
                DataLoader for input images.
            batch_size (int):
                Size of the batch for detection.
            global_rank (int): 
                Global rank of the process.
            local_rank (int): 
                Local rank of the process.
            output_dir (str): 
                Directory to save detection results.
            det_conf_thres (float, optional): 
                Confidence threshold for detections. Defaults to 0.2.
            checkpoint_frequency (int, optional): 
                Frequency of saving intermediate results. Defaults to 1000.
        """
        os.makedirs(output_dir, exist_ok=True)
        self.predictor.args.batch = batch_size
        self.predictor.args.conf = det_conf_thres
        self.predictor.args.device = local_rank


        # Create checkpoint directory
        # Track batches and processed items
        results = {
            "img_id": [],
            "detections_xyxy": [],
            "detections_confidence": [],
            "detections_class_id": [],
            "labels": [],
            "n_animal_detected": [],
            "normalized_coords": []
        }

        checkpoint_dir = os.path.join(output_dir, f"checkpoints_rank{global_rank}")
        os.makedirs(checkpoint_dir, exist_ok=True)
        batch_counter = 0
        processed_count = 0
        start_time = time.time()

        for uuids, images in loader:
            batch_counter += 1
            processed_count += len(images)
            # images: tensor of shape [batch_size, 3, H, W]
            # Assuming images are transformed & Standardized
            det_results = self.predictor.stream_inference(images)

            for idx, preds in enumerate(det_results):
                res = self.results_generation(preds, uuids[idx])

                size = preds.orig_shape
                normalized_coords = [[x1 / size[1], y1 / size[0], x2 / size[1], y2 / size[0]] for x1, y1, x2, y2 in res["detections_xyxy"]]
                res["normalized_coords"] = normalized_coords

                #results.append(res)
                results["img_id"].append(res["img_id"])
                results["detections_xyxy"].append(res["detections_xyxy"].tolist())
                results["detections_confidence"].append(res["detections_confidence"].tolist())
                results["detections_class_id"].append(res["detections_class_id"].tolist())
                results["labels"].append(res["labels"])
                results["n_animal_detected"].append(int(res["n_animal_detected"]))
                results["normalized_coords"].append(res["normalized_coords"])

            if batch_counter % checkpoint_frequency == 0:
                elapsed = time.time() - start_time
                print(f"[Rank {global_rank}] Processed {processed_count} images in {elapsed}")

                # Save intermediate results
                checkpoint_path = os.path.join(
                    checkpoint_dir, 
                    f"checkpoint_{batch_counter:06d}.parquet"
                )

                df = pd.DataFrame({
                    "img_id": results["img_id"],
                    "n_animal_detected": results["n_animal_detected"]
                })
                df.to_parquet(checkpoint_path, index=False)
                print(f"[Rank {global_rank}] Saved checkpoint to {checkpoint_path}")

        # Save results to disk
        os.makedirs(output_dir, exist_ok=True)
        df = pd.DataFrame({
                    "img_id": results["img_id"],
                    "n_animal_detected": results["n_animal_detected"]
                })
        out_path = os.path.join(output_dir, f"predictions_rank{global_rank}.parquet")
        df.to_parquet(out_path, index=False)
        print(f"[rank {global_rank}] Saved predictions to {out_path}")

        return results

__init__(weights=None, device='cpu', url=None, transform=None)

Initialize the YOLOV8 detector.

Parameters:

Name Type Description Default
weights str

Path to the model weights. Defaults to None.

 None
device str

Device for model inference. Defaults to "cpu".

 'cpu'
url str

URL to fetch the model weights. Defaults to None.

 None
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_distributed.py 
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def __init__(self, weights=None, device="cpu", url=None, transform=None):
    """
    Initialize the YOLOV8 detector.

    Args:
        weights (str, optional): 
            Path to the model weights. Defaults to None.
        device (str, optional): 
            Device for model inference. Defaults to "cpu".
        url (str, optional): 
            URL to fetch the model weights. Defaults to None.
    """
    self.transform = transform
    super(YOLOV8_Distributed, self).__init__(weights=weights, device=device, url=url)
    self._load_model(weights, self.device, url)

batch_image_detection(loader, batch_size, global_rank, local_rank, output_dir, det_conf_thres=0.2, checkpoint_frequency=1000)

Perform batch image detection using the YOLOV8 model.

Parameters:

Name Type Description Default
loader DataLoader

DataLoader for input images.

 required
batch_size int

Size of the batch for detection.

 required
global_rank int

Global rank of the process.

 required
local_rank int

Local rank of the process.

 required
output_dir str

Directory to save detection results.

 required
det_conf_thres float

Confidence threshold for detections. Defaults to 0.2.

 0.2
checkpoint_frequency int

Frequency of saving intermediate results. Defaults to 1000.

 1000
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_distributed.py 
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def batch_image_detection(self, loader, batch_size, global_rank, local_rank, output_dir, det_conf_thres=0.2, checkpoint_frequency = 1000):

    """
    Perform batch image detection using the YOLOV8 model.

    Args:
        loader (torch.utils.data.DataLoader): 
            DataLoader for input images.
        batch_size (int):
            Size of the batch for detection.
        global_rank (int): 
            Global rank of the process.
        local_rank (int): 
            Local rank of the process.
        output_dir (str): 
            Directory to save detection results.
        det_conf_thres (float, optional): 
            Confidence threshold for detections. Defaults to 0.2.
        checkpoint_frequency (int, optional): 
            Frequency of saving intermediate results. Defaults to 1000.
    """
    os.makedirs(output_dir, exist_ok=True)
    self.predictor.args.batch = batch_size
    self.predictor.args.conf = det_conf_thres
    self.predictor.args.device = local_rank


    # Create checkpoint directory
    # Track batches and processed items
    results = {
        "img_id": [],
        "detections_xyxy": [],
        "detections_confidence": [],
        "detections_class_id": [],
        "labels": [],
        "n_animal_detected": [],
        "normalized_coords": []
    }

    checkpoint_dir = os.path.join(output_dir, f"checkpoints_rank{global_rank}")
    os.makedirs(checkpoint_dir, exist_ok=True)
    batch_counter = 0
    processed_count = 0
    start_time = time.time()

    for uuids, images in loader:
        batch_counter += 1
        processed_count += len(images)
        # images: tensor of shape [batch_size, 3, H, W]
        # Assuming images are transformed & Standardized
        det_results = self.predictor.stream_inference(images)

        for idx, preds in enumerate(det_results):
            res = self.results_generation(preds, uuids[idx])

            size = preds.orig_shape
            normalized_coords = [[x1 / size[1], y1 / size[0], x2 / size[1], y2 / size[0]] for x1, y1, x2, y2 in res["detections_xyxy"]]
            res["normalized_coords"] = normalized_coords

            #results.append(res)
            results["img_id"].append(res["img_id"])
            results["detections_xyxy"].append(res["detections_xyxy"].tolist())
            results["detections_confidence"].append(res["detections_confidence"].tolist())
            results["detections_class_id"].append(res["detections_class_id"].tolist())
            results["labels"].append(res["labels"])
            results["n_animal_detected"].append(int(res["n_animal_detected"]))
            results["normalized_coords"].append(res["normalized_coords"])

        if batch_counter % checkpoint_frequency == 0:
            elapsed = time.time() - start_time
            print(f"[Rank {global_rank}] Processed {processed_count} images in {elapsed}")

            # Save intermediate results
            checkpoint_path = os.path.join(
                checkpoint_dir, 
                f"checkpoint_{batch_counter:06d}.parquet"
            )

            df = pd.DataFrame({
                "img_id": results["img_id"],
                "n_animal_detected": results["n_animal_detected"]
            })
            df.to_parquet(checkpoint_path, index=False)
            print(f"[Rank {global_rank}] Saved checkpoint to {checkpoint_path}")

    # Save results to disk
    os.makedirs(output_dir, exist_ok=True)
    df = pd.DataFrame({
                "img_id": results["img_id"],
                "n_animal_detected": results["n_animal_detected"]
            })
    out_path = os.path.join(output_dir, f"predictions_rank{global_rank}.parquet")
    df.to_parquet(out_path, index=False)
    print(f"[rank {global_rank}] Saved predictions to {out_path}")

    return results

results_generation(preds, img_id, id_strip=None)

Generate results for detection based on model predictions.

Parameters:

Name Type Description Default
preds Results

Model predictions.

 required
img_id str

Image identifier.

 required
id_strip str

Strip specific characters from img_id. Defaults to None.

 None

Returns:

Name Type Description
dict dict

Dictionary containing image ID, detections, and labels.
Source code in PytorchWildlife/models/detection/ultralytics_based/yolov8_distributed.py 
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def results_generation(self, preds, img_id, id_strip=None) -> dict:
    """
    Generate results for detection based on model predictions.

    Args:
        preds (ultralytics.engine.results.Results): 
            Model predictions.
        img_id (str): 
            Image identifier.
        id_strip (str, optional): 
            Strip specific characters from img_id. Defaults to None.

    Returns:
        dict: Dictionary containing image ID, detections, and labels.
    """
    xyxy = preds.boxes.xyxy.cpu().numpy()
    confidence = preds.boxes.conf.cpu().numpy()
    class_id = preds.boxes.cls.cpu().numpy().astype(int)

    results = {"img_id": str(img_id).strip(id_strip)}
    # results["detections"] = sv.Detections(
    #     xyxy=xyxy,
    #     confidence=confidence,
    #     class_id=class_id
    # )
    results["detections_xyxy"] = xyxy
    results["detections_confidence"] = confidence
    results["detections_class_id"] = class_id

    # results["labels"] = [
    #     f"{self.CLASS_NAMES[class_id]} {confidence:0.2f}"  
    #     for _, _, confidence, class_id, _, _ in results["detections"] 
    # ]

    results["labels"] = [
        f"{self.CLASS_NAMES[cls_id]} {conf:0.2f}"  
        for cls_id, conf in zip(class_id, confidence)
    ]

    results["n_animal_detected"] = np.sum(class_id == 0)

    return results