Bu repo SAHI uygulamasını mantığını öğreniyoruz.

Overview

SAHI-Learn: SAHI'den Beraber Kodlamak İster Misiniz

teaser

Herkese merhabalar ben Kadir Nar. SAHI kütüphanesine gönüllü geliştiriciyim. Bu repo SAHI kütüphanesine yeni bir model nasıl ekleneceğini anlattım.

Geliştiriciler için SAHI Yol Haritası

1. DetectionModel(Detection)

Class ismini oluştururkan model isminin yanına DetectionModel(Detection) yazıyoruz.

Örnekler:

1.1 Mmdet:

class MmdetDetectionModel(DetectionModel)

1.2 Yolov5:

class Yolov5DetectionModel(DetectionModel):

1.3 Detectron2:

class Detectron2DetectionModel(DetectionModel)

1.4 TorchVision:

class TorchVisionDetectionModel(DetectionModel)

2.load_model():

Bu fonksiyon 3 aşamadan oluşmaktadır.

a. Kütüphaneyi import ediyoruz. PYPI desteği olmayan kütüphanelerin kurulumunu desteklenmiyor.

b. Modele girecek resimlerin image_size değerlerini güncellemeniz gerekiyor.

c. category_mapping değişkenini {"1": "pedestrian"} bu formatta olması gerekiyor.

Örnekler:

2.1 Mmdet:

def load_model(self):
    """
    Detection model is initialized and set to self.model.
    """
    try:
        import mmdet
    except ImportError:
        raise ImportError(
            'Please run "pip install -U mmcv mmdet" ' "to install MMDetection first for MMDetection inference."
        )

    from mmdet.apis import init_detector

    # create model
    model = init_detector(
        config=self.config_path,
        checkpoint=self.model_path,
        device=self.device,
    )

    # update model image size
    if self.image_size is not None:
        model.cfg.data.test.pipeline[1]["img_scale"] = (self.image_size, self.image_size)

    # set self.model
    self.model = model

    # set category_mapping
    if not self.category_mapping:
        category_mapping = {str(ind): category_name for ind, category_name in enumerate(self.category_names)}
        self.category_mapping = category_mapping

2.2 Yolov5:

    def load_model(self):
        """
        Detection model is initialized and set to self.model.
        """
        try:
            import yolov5
        except ImportError:
            raise ImportError('Please run "pip install -U yolov5" ' "to install YOLOv5 first for YOLOv5 inference.")

        # set model
        try:
            model = yolov5.load(self.model_path, device=self.device)
            model.conf = self.confidence_threshold
            self.model = model
        except Exception as e:
            TypeError("model_path is not a valid yolov5 model path: ", e)

        # set category_mapping
        if not self.category_mapping:
            category_mapping = {str(ind): category_name for ind, category_name in enumerate(self.category_names)}
            self.category_mapping = category_mapping

2.3 Detectron2:

def load_model(self):
    try:
        import detectron2
    except ImportError:
        raise ImportError(
            "Please install detectron2. Check "
            "`https://detectron2.readthedocs.io/en/latest/tutorials/install.html` "
            "for instalattion details."
        )

    from detectron2.config import get_cfg
    from detectron2.data import MetadataCatalog
    from detectron2.engine import DefaultPredictor
    from detectron2.model_zoo import model_zoo

    cfg = get_cfg()
    cfg.MODEL.DEVICE = self.device

    try:  # try to load from model zoo
        config_file = model_zoo.get_config_file(self.config_path)
        cfg.merge_from_file(config_file)
        cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(self.config_path)
    except Exception as e:  # try to load from local
        print(e)
        if self.config_path is not None:
            cfg.merge_from_file(self.config_path)
        cfg.MODEL.WEIGHTS = self.model_path
    # set input image size
    if self.image_size is not None:
        cfg.INPUT.MIN_SIZE_TEST = self.image_size
        cfg.INPUT.MAX_SIZE_TEST = self.image_size
    # init predictor
    model = DefaultPredictor(cfg)

    self.model = model

    # detectron2 category mapping
    if self.category_mapping is None:
        try:  # try to parse category names from metadata
            metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
            category_names = metadata.thing_classes
            self.category_names = category_names
            self.category_mapping = {
                str(ind): category_name for ind, category_name in enumerate(self.category_names)
            }
        except Exception as e:
            logger.warning(e)
            # https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html#update-the-config-for-new-datasets
            if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
                num_categories = cfg.MODEL.RETINANET.NUM_CLASSES
            else:  # fasterrcnn/maskrcnn etc
                num_categories = cfg.MODEL.ROI_HEADS.NUM_CLASSES
            self.category_names = [str(category_id) for category_id in range(num_categories)]
            self.category_mapping = {
                str(ind): category_name for ind, category_name in enumerate(self.category_names)
            }
    else:
        self.category_names = list(self.category_mapping.values())

2.4 TorchVision:

def load_model(self):
    try:
        import torchvision
    except ImportError:
        raise ImportError(
            "torchvision is not installed. Please run 'pip install -U torchvision to use this "
            "torchvision models'"
        )

    # set model
    try:
        from sahi.utils.torch import torch

        model = self.config_path
        model.load_state_dict(torch.load(self.model_path))
        model.eval()
        model = model.to(self.device)
        self.model = model
    except Exception as e:
        raise Exception(f"Failed to load model from {self.model_path}. {e}")

    # set category_mapping
    from sahi.utils.torchvision import COCO_CLASSES

    if self.category_mapping is None:
        category_names = {str(i): COCO_CLASSES[i] for i in range(len(COCO_CLASSES))}
        self.category_mapping = category_names

3.perform_inference():

Bu fonksiyonda 3 aşamada oluşmaktadır.

a. Kütüphanenin import edilmesi gerekiyor.

b. Resimlerin size değerinin güncellenmesi lazım.

c. Modelin tahmin kodlarının yazılması gerekiyor.

3.1 Mmdet:

def perform_inference(self, image: np.ndarray, image_size: int = None):
    """
    Prediction is performed using self.model and the prediction result is set to self._original_predictions.
    Args:
        image: np.ndarray
            A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
        image_size: int
            Inference input size.
    """
    try:
        import mmdet
    except ImportError:
        raise ImportError(
            'Please run "pip install -U mmcv mmdet" ' "to install MMDetection first for MMDetection inference."
        )

    # Confirm model is loaded
    assert self.model is not None, "Model is not loaded, load it by calling .load_model()"

    # Supports only batch of 1
    from mmdet.apis import inference_detector

    # update model image size
    if image_size is not None:
        warnings.warn("Set 'image_size' at DetectionModel init.", DeprecationWarning)
        self.model.cfg.data.test.pipeline[1]["img_scale"] = (image_size, image_size)

    # perform inference
    if isinstance(image, np.ndarray):
        # https://github.com/obss/sahi/issues/265
        image = image[:, :, ::-1]
    # compatibility with sahi v0.8.15
    if not isinstance(image, list):
        image = [image]
    prediction_result = inference_detector(self.model, image)

    self._original_predictions = prediction_result

3.2 Yolov5:

def perform_inference(self, image: np.ndarray, image_size: int = None):
    """
    Prediction is performed using self.model and the prediction result is set to self._original_predictions.
    Args:
        image: np.ndarray
            A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
        image_size: int
            Inference input size.
    """
    try:
        import yolov5
    except ImportError:
        raise ImportError('Please run "pip install -U yolov5" ' "to install YOLOv5 first for YOLOv5 inference.")

    # Confirm model is loaded
    assert self.model is not None, "Model is not loaded, load it by calling .load_model()"

    if image_size is not None:
        warnings.warn("Set 'image_size' at DetectionModel init.", DeprecationWarning)
        prediction_result = self.model(image, size=image_size)
    elif self.image_size is not None:
        prediction_result = self.model(image, size=self.image_size)
    else:
        prediction_result = self.model(image)

    self._original_predictions = prediction_result

3.3 Detectron2:

def perform_inference(self, image: np.ndarray, image_size: int = None):
    """
    Prediction is performed using self.model and the prediction result is set to self._original_predictions.
    Args:
        image: np.ndarray
            A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
    """
    try:
        import detectron2
    except ImportError:
        raise ImportError("Please install detectron2 via `pip install detectron2`")

    # confirm image_size is not provided
    if image_size is not None:
        warnings.warn("Set 'image_size' at DetectionModel init.")

    # Confirm model is loaded
    if self.model is None:
        raise RuntimeError("Model is not loaded, load it by calling .load_model()")

    if isinstance(image, np.ndarray) and self.model.input_format == "BGR":
        # convert RGB image to BGR format
        image = image[:, :, ::-1]

    prediction_result = self.model(image)

    self._original_predictions = prediction_result

3.4 TorchVision:

def perform_inference(self, image: np.ndarray, image_size: int = None):
    """
    Prediction is performed using self.model and the prediction result is set to self._original_predictions.
    Args:
        image: np.ndarray
            A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
        image_size: int
            Inference input size.
    """
    if self.model is None:
        raise ValueError("model not loaded.")

    from sahi.utils.torchvision import numpy_to_torch, resize_image

    if self.image_size is not None:
        image = resize_image(image, self.image_size)
        image = numpy_to_torch(image)
        prediction_result = self.model([image])

    else:
        prediction_result = self.model([image])

    self._original_predictions = prediction_result

4.num_categories():

Bu fonksiyonda tahmin edilen kategorilerin sayısını döndürmesi isteniyor.

4.1 Mmdet:

def num_categories(self):
    """
    Returns number of categories
    """
    if isinstance(self.model.CLASSES, str):
        num_categories = 1
    else:
        num_categories = len(self.model.CLASSES)
    return num_categories

4.2 Yolov5:

def num_categories(self):
    """
    Returns number of categories
    """
    return len(self.model.names)

4.3 Detectron2:

def num_categories(self):
    """
    Returns number of categories
    """
    num_categories = len(self.category_mapping)
    return num_categories

4.4 TorchVision:

def num_categories(self):
    """
    Returns number of categories
    """
    return len(self.category_mapping)

5.has_mask():

Bu fonksiyonda tahmin edilen kategorilerin maskleri olup olmadığını döndürmesi isteniyor.

5.1 Mmdet:

def has_mask(self):
    """
    Returns if model output contains segmentation mask
    """
    has_mask = self.model.with_mask
    return has_mask```
5.2 Yolov5:

5.2 Yolov5:

def has_mask(self):
    """
    Returns if model output contains segmentation mask
    """
    has_mask = self.model.with_mask
    return has_mask

5.3 Detectron2:

if get_bbox_from_bool_mask(mask) is not None:
    bbox = None
else:
    continue

5.4 TorchVision:

def has_mask(self):
    """
    Returns if model output contains segmentation mask
    """
    return self.model.with_mask

6.category_names():

Bu fonksiyonda tahmin edilen kategorilerin isimlerini döndürmesi isteniyor.

6.1 Mmdet:

def category_names(self):
    if type(self.model.CLASSES) == str:
        # https://github.com/open-mmlab/mmdetection/pull/4973
        return (self.model.CLASSES,)
    else:
        return self.model.CLASSES

6.2 Yolov5:

def category_names(self):
    return self.model.names

6.3 Detectron2:

# detectron2 category mapping
if self.category_mapping is None:
    try:  # try to parse category names from metadata
        metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
        category_names = metadata.thing_classes
        self.category_names = category_names

6.4 TorchVision:

def category_names(self):
    return self.category_mapping

7._create_object_prediction_list_from_original_predictions():

Bu fonksiyon da bir şablon üzerinden kodlama yapmanız sizin için daha rahat olacaktır. Fonksiyonunu altına direk bunu yazabilirsiniz.

original_predictions = self._original_predictions

# compatilibty for sahi v0.8.15
if isinstance(shift_amount_list[0], int):
    shift_amount_list = [shift_amount_list]
if full_shape_list is not None and isinstance(full_shape_list[0], int):
    full_shape_list = [full_shape_list]

Bundan sonra modeliniz tahminleme yaptıktan sonra bbox,mask,category_id, category_name ve score değerleri döndürmesi isteniyor. Bu değerleri object_prediction değişkeninin içindeki none değerleri yerine yazmanız gerekiyor. Aşağıdaki şablon yapısını da bozmamanız istenmektedir.

    object_prediction = ObjectPrediction(
        bbox=None,
        bool_mask=None,
        category_id=None,
        category_name=sNone,
        shift_amount=shift_amount,
        score=None,
        full_shape=full_shape,
    )
    object_prediction_list.append(object_prediction)

# detectron2 DefaultPredictor supports single image
object_prediction_list_per_image = [object_prediction_list]

self._object_prediction_list_per_image = object_prediction_list_per_image

7.1 Mmdet:

def _create_object_prediction_list_from_original_predictions(
    self,
    shift_amount_list: Optional[List[List[int]]] = [[0, 0]],
    full_shape_list: Optional[List[List[int]]] = None,
):
    """
    self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
    self._object_prediction_list_per_image.
    Args:
        shift_amount_list: list of list
            To shift the box and mask predictions from sliced image to full sized image, should
            be in the form of List[[shift_x, shift_y],[shift_x, shift_y],...]
        full_shape_list: list of list
            Size of the full image after shifting, should be in the form of
            List[[height, width],[height, width],...]
    """
    original_predictions = self._original_predictions
    category_mapping = self.category_mapping

    # compatilibty for sahi v0.8.15
    shift_amount_list = fix_shift_amount_list(shift_amount_list)
    full_shape_list = fix_full_shape_list(full_shape_list)

    # parse boxes and masks from predictions
    num_categories = self.num_categories
    object_prediction_list_per_image = []
    for image_ind, original_prediction in enumerate(original_predictions):
        shift_amount = shift_amount_list[image_ind]
        full_shape = None if full_shape_list is None else full_shape_list[image_ind]

        if self.has_mask:
            boxes = original_prediction[0]
            masks = original_prediction[1]
        else:
            boxes = original_prediction

        object_prediction_list = []

        # process predictions
        for category_id in range(num_categories):
            category_boxes = boxes[category_id]
            if self.has_mask:
                category_masks = masks[category_id]
            num_category_predictions = len(category_boxes)

            for category_predictions_ind in range(num_category_predictions):
                bbox = category_boxes[category_predictions_ind][:4]
                score = category_boxes[category_predictions_ind][4]
                category_name = category_mapping[str(category_id)]

                # ignore low scored predictions
                if score < self.confidence_threshold:
                    continue

                # parse prediction mask
                if self.has_mask:
                    bool_mask = category_masks[category_predictions_ind]
                else:
                    bool_mask = None

                # fix negative box coords
                bbox[0] = max(0, bbox[0])
                bbox[1] = max(0, bbox[1])
                bbox[2] = max(0, bbox[2])
                bbox[3] = max(0, bbox[3])

                # fix out of image box coords
                if full_shape is not None:
                    bbox[0] = min(full_shape[1], bbox[0])
                    bbox[1] = min(full_shape[0], bbox[1])
                    bbox[2] = min(full_shape[1], bbox[2])
                    bbox[3] = min(full_shape[0], bbox[3])

                # ignore invalid predictions
                if not (bbox[0] < bbox[2]) or not (bbox[1] < bbox[3]):
                    logger.warning(f"ignoring invalid prediction with bbox: {bbox}")
                    continue

                object_prediction = ObjectPrediction(
                    bbox=bbox,
                    category_id=category_id,
                    score=score,
                    bool_mask=bool_mask,
                    category_name=category_name,
                    shift_amount=shift_amount,
                    full_shape=full_shape,
                )
                object_prediction_list.append(object_prediction)
        object_prediction_list_per_image.append(object_prediction_list)
    self._object_prediction_list_per_image = object_prediction_list_per_image

7.2 Yolov5:

def _create_object_prediction_list_from_original_predictions(
    self,
    shift_amount_list: Optional[List[List[int]]] = [[0, 0]],
    full_shape_list: Optional[List[List[int]]] = None,
):
    """
    self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
    self._object_prediction_list_per_image.
    Args:
        shift_amount_list: list of list
            To shift the box and mask predictions from sliced image to full sized image, should
            be in the form of List[[shift_x, shift_y],[shift_x, shift_y],...]
        full_shape_list: list of list
            Size of the full image after shifting, should be in the form of
            List[[height, width],[height, width],...]
    """
    original_predictions = self._original_predictions

    # compatilibty for sahi v0.8.15
    shift_amount_list = fix_shift_amount_list(shift_amount_list)
    full_shape_list = fix_full_shape_list(full_shape_list)

    # handle all predictions
    object_prediction_list_per_image = []
    for image_ind, image_predictions_in_xyxy_format in enumerate(original_predictions.xyxy):
        shift_amount = shift_amount_list[image_ind]
        full_shape = None if full_shape_list is None else full_shape_list[image_ind]
        object_prediction_list = []

        # process predictions
        for prediction in image_predictions_in_xyxy_format.cpu().detach().numpy():
            x1 = int(prediction[0])
            y1 = int(prediction[1])
            x2 = int(prediction[2])
            y2 = int(prediction[3])
            bbox = [x1, y1, x2, y2]
            score = prediction[4]
            category_id = int(prediction[5])
            category_name = self.category_mapping[str(category_id)]

            # fix negative box coords
            bbox[0] = max(0, bbox[0])
            bbox[1] = max(0, bbox[1])
            bbox[2] = max(0, bbox[2])
            bbox[3] = max(0, bbox[3])

            # fix out of image box coords
            if full_shape is not None:
                bbox[0] = min(full_shape[1], bbox[0])
                bbox[1] = min(full_shape[0], bbox[1])
                bbox[2] = min(full_shape[1], bbox[2])
                bbox[3] = min(full_shape[0], bbox[3])

            # ignore invalid predictions
            if not (bbox[0] < bbox[2]) or not (bbox[1] < bbox[3]):
                logger.warning(f"ignoring invalid prediction with bbox: {bbox}")
                continue

            object_prediction = ObjectPrediction(
                bbox=bbox,
                category_id=category_id,
                score=score,
                bool_mask=None,
                category_name=category_name,
                shift_amount=shift_amount,
                full_shape=full_shape,
            )
            object_prediction_list.append(object_prediction)
        object_prediction_list_per_image.append(object_prediction_list)

    self._object_prediction_list_per_image = object_prediction_list_per_image

7.3 Detectron2:

def _create_object_prediction_list_from_original_predictions(
    self,
    shift_amount_list: Optional[List[List[int]]] = [[0, 0]],
    full_shape_list: Optional[List[List[int]]] = None,
):
    """
    self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
    self._object_prediction_list_per_image.
    Args:
        shift_amount_list: list of list
            To shift the box and mask predictions from sliced image to full sized image, should
            be in the form of List[[shift_x, shift_y],[shift_x, shift_y],...]
        full_shape_list: list of list
            Size of the full image after shifting, should be in the form of
            List[[height, width],[height, width],...]
    """
    original_predictions = self._original_predictions

    # compatilibty for sahi v0.8.15
    if isinstance(shift_amount_list[0], int):
        shift_amount_list = [shift_amount_list]
    if full_shape_list is not None and isinstance(full_shape_list[0], int):
        full_shape_list = [full_shape_list]

    # parse boxes, masks, scores, category_ids from predictions
    boxes = original_predictions["instances"].pred_boxes.tensor.tolist()
    scores = original_predictions["instances"].scores.tolist()
    category_ids = original_predictions["instances"].pred_classes.tolist()

    # check if predictions contain mask
    try:
        masks = original_predictions["instances"].pred_masks.tolist()
    except AttributeError:
        masks = None

    # create object_prediction_list
    object_prediction_list_per_image = []
    object_prediction_list = []

    # detectron2 DefaultPredictor supports single image
    shift_amount = shift_amount_list[0]
    full_shape = None if full_shape_list is None else full_shape_list[0]

    for ind in range(len(boxes)):
        score = scores[ind]
        if score < self.confidence_threshold:
            continue

        category_id = category_ids[ind]

        if masks is None:
            bbox = boxes[ind]
            mask = None
        else:
            mask = np.array(masks[ind])

            # check if mask is valid
            if get_bbox_from_bool_mask(mask) is not None:
                bbox = None
            else:
                continue

        object_prediction = ObjectPrediction(
            bbox=bbox,
            bool_mask=mask,
            category_id=category_id,
            category_name=self.category_mapping[str(category_id)],
            shift_amount=shift_amount,
            score=score,
            full_shape=full_shape,
        )
        object_prediction_list.append(object_prediction)

    # detectron2 DefaultPredictor supports single image
    object_prediction_list_per_image = [object_prediction_list]

    self._object_prediction_list_per_image = object_prediction_list_per_image

7.4 TorchVision: Not: TorchVision kütüphanesinin geliştirilmeye devam etmektedir.

Issues
  • obss/sahi reposu ile karsilastirma

    obss/sahi reposu ile karsilastirma

    Selamlar Kadir,

    Bu reporunun obss/sahi reposu ile arasindaki farklari yazabilir misin readme'ye? Hangi acilardan daha sade? Ilk gozume carpan: Detectron2 ve MMDetection destegi cikarilmis, baska farklar da var midir?

    documentation 
    opened by fcakyon 4
Owner
Kadir Nar
Junior Deep Learning Engineer at @gesund-ai
Kadir Nar
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Official repo for QHack—the quantum machine learning hackathon

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This repo is customed for VisDrone.

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null 53 Apr 4, 2022
This repo is customed for VisDrone.

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The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

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The repo contains the code of the ACL2020 paper `Dice Loss for Data-imbalanced NLP Tasks`

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This repo contains the official code of our work SAM-SLR which won the CVPR 2021 Challenge on Large Scale Signer Independent Isolated Sign Language Recognition.

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This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

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A repo that contains all the mesh keys needed for mesh backend, along with a code example of how to use them in python

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zhangxian 47 Mar 17, 2022
We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

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null 90 Apr 11, 2022
The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

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