Added yolov8engine.py for TensorRT-quantized YOLOv8 models

sahi/auto_model.py

@@ -13,6 +13,7 @@
     "yolov5sparse": "Yolov5SparseDetectionModel",
     "yolonas": "YoloNasDetectionModel",
     "yolov8onnx": "Yolov8OnnxDetectionModel",
+    "yolov8engine": "Yolov8EngineDetectionModel"
 }
 
 

sahi/models/yolov8engine.py

@@ -0,0 +1,181 @@
+import json
+import logging
+from typing import Any, Dict, List, Optional
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+from sahi.models.base import DetectionModel
+from sahi.prediction import ObjectPrediction
+from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
+from sahi.utils.import_utils import check_requirements
+
+# Must be passed a config_path to a json with the below fields. 
+# Fields must be obtained from base PyTorch model before quantization.
+# {
+#     "task": "segment", # or 'detect'
+#     "names": [
+#         "dog",
+#         "cat",
+#     ],
+#     "imgsz": [
+#         640,
+#         640
+#     ],
+#     "half": true
+# }
+
+class Yolov8EngineDetectionModel(DetectionModel):
+    def __init__(self):
+        try:
+            with open(self.config_path, 'r') as file:
+                self.cfg = json.load(file)
+        except Exception as e:
+            raise TypeError("config_path is not a valid yolov8engine config path")
+
+        super().__init__()
+
+    def check_dependencies(self) -> None:
+        check_requirements(["ultralytics"])
+
+    def load_model(self):
+        """
+        Detection model is initialized and set to self.model.
+        """
+
+        from ultralytics import YOLO
+
+        try:
+            model = YOLO(self.model_path, task=self.cfg["task"])
+            self.set_model(model)
+        except Exception as e:
+            raise TypeError("model_path is not a valid yolov8engine model path: ", e)
+
+    def set_model(self, model: Any):
+        """
+        Sets the underlying YOLOv8 model.
+        Args:
+            model: Any
+                A YOLOv8 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
+
+    def perform_inference(self, image: np.ndarray):
+        """
+        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.
+        """
+
+        # Confirm model is loaded
+        if self.model is None:
+            raise ValueError("Model is not loaded, load it by calling .load_model()")
+
+        kwargs = {"cfg": None, "verbose": False, "conf": self.confidence_threshold, "device": self.device, "imgsz": self.cfg["imgsz"], "half": self.cfg["half"]}
+
+        if self.image_size is not None:
+            kwargs = {"imgsz": self.image_size, **kwargs}
+
+        prediction_result = self.model.predict(image[:, :, ::-1], **kwargs)  # YOLOv8 expects numpy arrays to have BGR
+
+        # We do not filter results again as confidence threshold is already applied above
+        prediction_result = [result.boxes.data for result in prediction_result]
+
+        self._original_predictions = prediction_result
+
+    @property
+    def category_names(self):
+        return self.cfg["names"]
+
+    @property
+    def num_categories(self):
+        """
+        Returns number of categories
+        """
+        return len(self.cfg["names"])
+
+    @property
+    def has_mask(self):
+        """
+        Returns if model output contains segmentation mask
+        """
+        return False
+
+    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):
+            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 = prediction[0]
+                y1 = prediction[1]
+                x2 = prediction[2]
+                y2 = 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