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Yolo world

YOLOWorld

Bases: RoboflowCoreModel

YOLO-World class for zero-shot object detection.

Attributes:

Name Type Description
model

The YOLO-World model.
Source code in inference/models/yolo_world/yolo_world.py 
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class YOLOWorld(RoboflowCoreModel):
    """YOLO-World class for zero-shot object detection.

    Attributes:
        model: The YOLO-World model.
    """

    task_type = "object-detection"

    def __init__(self, *args, model_id="yolo_world/l", **kwargs):
        """Initializes the YOLO-World model.

        Args:
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """

        super().__init__(*args, model_id=model_id, **kwargs)

        self.model = YOLO(self.cache_file("yolo-world.pt"))
        logger.debug("Loading CLIP ViT-B/32")
        clip_model = Clip(model_id="clip/ViT-B-32")
        logger.debug("CLIP loaded")
        self.clip_model = clip_model
        self.class_names = None

    def preproc_image(self, image: Any):
        """Preprocesses an image.

        Args:
            image (InferenceRequestImage): The image to preprocess.

        Returns:
            np.array: The preprocessed image.
        """
        np_image = load_image_rgb(image)
        return np_image[:, :, ::-1]

    def infer_from_request(
        self,
        request: YOLOWorldInferenceRequest,
    ) -> ObjectDetectionInferenceResponse:
        """
        Perform inference based on the details provided in the request, and return the associated responses.
        """
        result = self.infer(**request.dict())
        return result

    def infer(
        self,
        image: Any = None,
        text: list = None,
        confidence: float = DEFAULT_CONFIDENCE,
        max_detections: Optional[int] = DEFAUlT_MAX_DETECTIONS,
        iou_threshold: float = DEFAULT_IOU_THRESH,
        max_candidates: int = DEFAULT_MAX_CANDIDATES,
        class_agnostic_nms=DEFAULT_CLASS_AGNOSTIC_NMS,
        **kwargs,
    ):
        """
        Run inference on a provided image.

        Args:
            image - can be a BGR numpy array, filepath, InferenceRequestImage, PIL Image, byte-string, etc.
            class_filter (Optional[List[str]]): A list of class names to filter, if provided.

        Returns:
            GroundingDINOInferenceRequest: The inference response.
        """
        logger.debug("YOLOWorld infer() - image preprocessing.")
        t1 = perf_counter()
        image = self.preproc_image(image)
        logger.debug("YOLOWorld infer() - image ready.")
        img_dims = image.shape

        if text is not None and text != self.class_names:
            logger.debug("YOLOWorld infer() - classes embeddings are calculated.")
            self.set_classes(text)
            logger.debug("YOLOWorld infer() - classes embeddings are ready.")
        if self.class_names is None:
            raise ValueError(
                "Class names not set and not provided in the request. Must set class names before inference or provide them via the argument `text`."
            )
        logger.debug("YOLOWorld infer() - prediction starts.")
        results = self.model.predict(
            image,
            conf=confidence,
            verbose=False,
        )[0]
        logger.debug("YOLOWorld infer() - predictions ready.")
        t2 = perf_counter() - t1

        logger.debug("YOLOWorld infer() - post-processing starting")
        if len(results) > 0:
            bbox_array = np.array([box.xywh.tolist()[0] for box in results.boxes])
            conf_array = np.array([[float(box.conf)] for box in results.boxes])
            cls_array = np.array(
                [
                    self.get_cls_conf_array(
                        max_class_id=int(box.cls),
                        max_class_confidence=float(box.conf),
                    )
                    for box in results.boxes
                ]
            )

            pred_array = np.concatenate([bbox_array, conf_array, cls_array], axis=1)
            pred_array = np.expand_dims(pred_array, axis=0)
            pred_array = w_np_non_max_suppression(
                pred_array,
                conf_thresh=confidence,
                iou_thresh=iou_threshold,
                class_agnostic=class_agnostic_nms,
                max_detections=max_detections,
                max_candidate_detections=max_candidates,
                box_format="xywh",
            )[0]
        else:
            pred_array = []
        predictions = []
        logger.debug("YOLOWorld infer() - post-processing done")
        for i, pred in enumerate(pred_array):
            predictions.append(
                ObjectDetectionPrediction(
                    **{
                        "x": (pred[0] + pred[2]) / 2,
                        "y": (pred[1] + pred[3]) / 2,
                        "width": pred[2] - pred[0],
                        "height": pred[3] - pred[1],
                        "confidence": pred[4],
                        "class": self.class_names[int(pred[6])],
                        "class_id": int(pred[6]),
                    }
                )
            )

        responses = ObjectDetectionInferenceResponse(
            predictions=predictions,
            image=InferenceResponseImage(width=img_dims[1], height=img_dims[0]),
            time=t2,
        )
        return responses

    def set_classes(self, text: list):
        """Set the class names for the model.

        Args:
            text (list): The class names.
        """
        class_names_to_calculate_embeddings = []
        classes_embeddings = {}
        for class_name in text:
            class_name_hash = f"clip-embedding:{get_text_hash(text=class_name)}"
            embedding_for_class = cache.get_numpy(class_name_hash)
            if embedding_for_class is not None:
                logger.debug(f"Cache hit for class: {class_name}")
                classes_embeddings[class_name] = embedding_for_class
            else:
                logger.debug(f"Cache miss for class: {class_name}")
                class_names_to_calculate_embeddings.append(class_name)
        if len(class_names_to_calculate_embeddings) > 0:
            logger.debug(
                f"Calculating CLIP embeddings for {len(class_names_to_calculate_embeddings)} class names"
            )
            cache_miss_embeddings = self.clip_model.embed_text(
                text=class_names_to_calculate_embeddings
            )
        else:
            cache_miss_embeddings = []
        for missing_class_name, calculated_embedding in zip(
            class_names_to_calculate_embeddings, cache_miss_embeddings
        ):
            classes_embeddings[missing_class_name] = calculated_embedding
            missing_class_name_hash = (
                f"clip-embedding:{get_text_hash(text=missing_class_name)}"
            )
            cache.set_numpy(  # caching vectors of shape (512,)
                missing_class_name_hash,
                calculated_embedding,
                expire=EMBEDDINGS_EXPIRE_TIMEOUT,
            )
        embeddings_in_order = np.stack(
            [classes_embeddings[class_name] for class_name in text], axis=0
        )
        txt_feats = torch.from_numpy(embeddings_in_order)
        txt_feats = txt_feats / txt_feats.norm(p=2, dim=-1, keepdim=True)
        self.model.model.txt_feats = txt_feats.reshape(
            -1, len(text), txt_feats.shape[-1]
        ).detach()
        self.model.model.model[-1].nc = len(text)
        self.class_names = text

    def get_infer_bucket_file_list(self) -> list:
        """Get the list of required files for inference.

        Returns:
            list: A list of required files for inference, e.g., ["model.pt"].
        """
        return ["yolo-world.pt"]

    def get_cls_conf_array(
        self, max_class_id: int, max_class_confidence: float
    ) -> List[float]:
        arr = [0.0] * len(self.class_names)
        arr[max_class_id] = max_class_confidence
        return arr

__init__(*args, model_id='yolo_world/l', **kwargs)

Initializes the YOLO-World model.

Parameters:

Name Type Description Default
*args

Variable length argument list.

 ()
**kwargs

Arbitrary keyword arguments.

 {}
Source code in inference/models/yolo_world/yolo_world.py 
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def __init__(self, *args, model_id="yolo_world/l", **kwargs):
    """Initializes the YOLO-World model.

    Args:
        *args: Variable length argument list.
        **kwargs: Arbitrary keyword arguments.
    """

    super().__init__(*args, model_id=model_id, **kwargs)

    self.model = YOLO(self.cache_file("yolo-world.pt"))
    logger.debug("Loading CLIP ViT-B/32")
    clip_model = Clip(model_id="clip/ViT-B-32")
    logger.debug("CLIP loaded")
    self.clip_model = clip_model
    self.class_names = None

get_infer_bucket_file_list()

Get the list of required files for inference.

Returns:

Name Type Description
list list

A list of required files for inference, e.g., ["model.pt"].
Source code in inference/models/yolo_world/yolo_world.py 
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def get_infer_bucket_file_list(self) -> list:
    """Get the list of required files for inference.

    Returns:
        list: A list of required files for inference, e.g., ["model.pt"].
    """
    return ["yolo-world.pt"]

infer(image=None, text=None, confidence=DEFAULT_CONFIDENCE, max_detections=DEFAUlT_MAX_DETECTIONS, iou_threshold=DEFAULT_IOU_THRESH, max_candidates=DEFAULT_MAX_CANDIDATES, class_agnostic_nms=DEFAULT_CLASS_AGNOSTIC_NMS, **kwargs)

Run inference on a provided image.

Parameters:

Name Type Description Default
class_filter Optional[List[str]]

A list of class names to filter, if provided.

 required

Returns:

Name Type Description
GroundingDINOInferenceRequest

The inference response.
Source code in inference/models/yolo_world/yolo_world.py 
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def infer(
    self,
    image: Any = None,
    text: list = None,
    confidence: float = DEFAULT_CONFIDENCE,
    max_detections: Optional[int] = DEFAUlT_MAX_DETECTIONS,
    iou_threshold: float = DEFAULT_IOU_THRESH,
    max_candidates: int = DEFAULT_MAX_CANDIDATES,
    class_agnostic_nms=DEFAULT_CLASS_AGNOSTIC_NMS,
    **kwargs,
):
    """
    Run inference on a provided image.

    Args:
        image - can be a BGR numpy array, filepath, InferenceRequestImage, PIL Image, byte-string, etc.
        class_filter (Optional[List[str]]): A list of class names to filter, if provided.

    Returns:
        GroundingDINOInferenceRequest: The inference response.
    """
    logger.debug("YOLOWorld infer() - image preprocessing.")
    t1 = perf_counter()
    image = self.preproc_image(image)
    logger.debug("YOLOWorld infer() - image ready.")
    img_dims = image.shape

    if text is not None and text != self.class_names:
        logger.debug("YOLOWorld infer() - classes embeddings are calculated.")
        self.set_classes(text)
        logger.debug("YOLOWorld infer() - classes embeddings are ready.")
    if self.class_names is None:
        raise ValueError(
            "Class names not set and not provided in the request. Must set class names before inference or provide them via the argument `text`."
        )
    logger.debug("YOLOWorld infer() - prediction starts.")
    results = self.model.predict(
        image,
        conf=confidence,
        verbose=False,
    )[0]
    logger.debug("YOLOWorld infer() - predictions ready.")
    t2 = perf_counter() - t1

    logger.debug("YOLOWorld infer() - post-processing starting")
    if len(results) > 0:
        bbox_array = np.array([box.xywh.tolist()[0] for box in results.boxes])
        conf_array = np.array([[float(box.conf)] for box in results.boxes])
        cls_array = np.array(
            [
                self.get_cls_conf_array(
                    max_class_id=int(box.cls),
                    max_class_confidence=float(box.conf),
                )
                for box in results.boxes
            ]
        )

        pred_array = np.concatenate([bbox_array, conf_array, cls_array], axis=1)
        pred_array = np.expand_dims(pred_array, axis=0)
        pred_array = w_np_non_max_suppression(
            pred_array,
            conf_thresh=confidence,
            iou_thresh=iou_threshold,
            class_agnostic=class_agnostic_nms,
            max_detections=max_detections,
            max_candidate_detections=max_candidates,
            box_format="xywh",
        )[0]
    else:
        pred_array = []
    predictions = []
    logger.debug("YOLOWorld infer() - post-processing done")
    for i, pred in enumerate(pred_array):
        predictions.append(
            ObjectDetectionPrediction(
                **{
                    "x": (pred[0] + pred[2]) / 2,
                    "y": (pred[1] + pred[3]) / 2,
                    "width": pred[2] - pred[0],
                    "height": pred[3] - pred[1],
                    "confidence": pred[4],
                    "class": self.class_names[int(pred[6])],
                    "class_id": int(pred[6]),
                }
            )
        )

    responses = ObjectDetectionInferenceResponse(
        predictions=predictions,
        image=InferenceResponseImage(width=img_dims[1], height=img_dims[0]),
        time=t2,
    )
    return responses

infer_from_request(request)

Perform inference based on the details provided in the request, and return the associated responses.

Source code in inference/models/yolo_world/yolo_world.py 
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def infer_from_request(
    self,
    request: YOLOWorldInferenceRequest,
) -> ObjectDetectionInferenceResponse:
    """
    Perform inference based on the details provided in the request, and return the associated responses.
    """
    result = self.infer(**request.dict())
    return result

preproc_image(image)

Preprocesses an image.

Parameters:

Name Type Description Default
image InferenceRequestImage

The image to preprocess.

 required

Returns:

Type Description

np.array: The preprocessed image.
Source code in inference/models/yolo_world/yolo_world.py 
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def preproc_image(self, image: Any):
    """Preprocesses an image.

    Args:
        image (InferenceRequestImage): The image to preprocess.

    Returns:
        np.array: The preprocessed image.
    """
    np_image = load_image_rgb(image)
    return np_image[:, :, ::-1]

set_classes(text)

Set the class names for the model.

Parameters:

Name Type Description Default
text list

The class names.

 required
Source code in inference/models/yolo_world/yolo_world.py 
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def set_classes(self, text: list):
    """Set the class names for the model.

    Args:
        text (list): The class names.
    """
    class_names_to_calculate_embeddings = []
    classes_embeddings = {}
    for class_name in text:
        class_name_hash = f"clip-embedding:{get_text_hash(text=class_name)}"
        embedding_for_class = cache.get_numpy(class_name_hash)
        if embedding_for_class is not None:
            logger.debug(f"Cache hit for class: {class_name}")
            classes_embeddings[class_name] = embedding_for_class
        else:
            logger.debug(f"Cache miss for class: {class_name}")
            class_names_to_calculate_embeddings.append(class_name)
    if len(class_names_to_calculate_embeddings) > 0:
        logger.debug(
            f"Calculating CLIP embeddings for {len(class_names_to_calculate_embeddings)} class names"
        )
        cache_miss_embeddings = self.clip_model.embed_text(
            text=class_names_to_calculate_embeddings
        )
    else:
        cache_miss_embeddings = []
    for missing_class_name, calculated_embedding in zip(
        class_names_to_calculate_embeddings, cache_miss_embeddings
    ):
        classes_embeddings[missing_class_name] = calculated_embedding
        missing_class_name_hash = (
            f"clip-embedding:{get_text_hash(text=missing_class_name)}"
        )
        cache.set_numpy(  # caching vectors of shape (512,)
            missing_class_name_hash,
            calculated_embedding,
            expire=EMBEDDINGS_EXPIRE_TIMEOUT,
        )
    embeddings_in_order = np.stack(
        [classes_embeddings[class_name] for class_name in text], axis=0
    )
    txt_feats = torch.from_numpy(embeddings_in_order)
    txt_feats = txt_feats / txt_feats.norm(p=2, dim=-1, keepdim=True)
    self.model.model.txt_feats = txt_feats.reshape(
        -1, len(text), txt_feats.shape[-1]
    ).detach()
    self.model.model.model[-1].nc = len(text)
    self.class_names = text