Add CLI YoloV10 support

README.md

@@ -41,7 +41,7 @@ docker compose run tools-cli shared_with_container/models/yolov6nr4.pt
 # Building the package
 pip install .
 # Running the package
-tools --model shared_with_container/models/yolov6nr4.pt --imgsz "416"
+tools shared_with_container/models/yolov6nr4.pt --imgsz "416"
 ```
 
 ### Arguments

tools/main.py

@@ -13,6 +13,7 @@
     YOLOV6R4_CONVERSION,
     YOLOV7_CONVERSION,
     YOLOV8_CONVERSION,
+    YOLOV10_CONVERSION,
     Config,
     detect_version,
     upload_file_to_remote,
@@ -35,7 +36,8 @@
     YOLOV6R3_CONVERSION,
     YOLOV6R4_CONVERSION,
     YOLOV7_CONVERSION,
-    YOLOV8_CONVERSION
+    YOLOV8_CONVERSION,
+    YOLOV10_CONVERSION,
 ]
 
 
@@ -110,6 +112,9 @@ def convert(
         elif version == YOLOV8_CONVERSION:
             from tools.yolo.yolov8_exporter import YoloV8Exporter
             exporter = YoloV8Exporter(str(model_path), config.imgsz, config.use_rvc2)
+        elif version == YOLOV10_CONVERSION:
+            from tools.yolo.yolov10_exporter import YoloV10Exporter
+            exporter = YoloV10Exporter(str(model_path), config.imgsz, config.use_rvc2)
         else:
             logger.error("Unrecognized model version.")
             raise typer.Exit(code=1)

tools/modules/__init__.py

@@ -13,6 +13,7 @@
     ClassifyV8,
     DetectV5,
     DetectV7,
+    DetectV10,
 )
 from .exporter import Exporter
 from .stage2 import Multiplier
@@ -33,4 +34,5 @@
     "Multiplier",
     "DetectV5",
     "DetectV7",
+    "DetectV10",
 ]

tools/modules/heads.py

@@ -624,3 +624,10 @@ def forward(self, x):
             x = torch.cat(x, 1)
         x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
         return x
+
+class DetectV10(DetectV8):
+    """YOLOv10 Detect head for detection models."""
+    def __init__(self, old_detect, use_rvc2):
+        super().__init__(old_detect, use_rvc2)
+        self.cv2 = old_detect.one2one_cv2
+        self.cv3 = old_detect.one2one_cv3

tools/utils/__init__.py

@@ -17,6 +17,7 @@
     YOLOV6R4_CONVERSION,
     YOLOV7_CONVERSION,
     YOLOV8_CONVERSION,
+    YOLOV10_CONVERSION,
     detect_version,
 )
 from .in_channels import get_first_conv2d_in_channels
@@ -31,6 +32,7 @@
     "YOLOV6R4_CONVERSION",
     "YOLOV7_CONVERSION",
     "YOLOV8_CONVERSION",
+    "YOLOV10_CONVERSION",
     "GOLD_YOLO_CONVERSION",
     "UNRECOGNIZED",
     "resolve_path",

tools/utils/version_detection.py

@@ -10,6 +10,7 @@
 YOLOV6R4_CONVERSION = "yolov6r4"
 YOLOV7_CONVERSION = "yolov7"
 YOLOV8_CONVERSION = "yolov8"
+YOLOV10_CONVERSION = "yolov10"
 GOLD_YOLO_CONVERSION = "goldyolo"
 UNRECOGNIZED = "none"
 
@@ -61,6 +62,8 @@ def detect_version(path: str, debug: bool = False) -> str:
                 return YOLOV6R3_CONVERSION
             elif "yolov7" in content:
                 return YOLOV7_CONVERSION
+            elif "yolov10" in content:
+                return YOLOV10_CONVERSION
             elif (
                 "SPPF" in content
                 or "yolov5" in content

tools/yolo/yolov10_exporter.py

@@ -0,0 +1,82 @@
+import sys
+
+sys.path.append("./tools/yolo/ultralytics")
+
+from ultralytics.nn.modules import Detect
+from ultralytics.nn.tasks import attempt_load_one_weight
+import torch
+from typing import Tuple, List, Optional
+
+from tools.modules import Exporter, DetectV10
+from tools.utils import get_first_conv2d_in_channels
+
+class YoloV10Exporter(Exporter):
+    def __init__(
+        self,
+        model_path: str,
+        imgsz: Tuple[int, int],
+        use_rvc2: bool,
+    ):
+        super().__init__(
+            model_path,
+            imgsz,
+            use_rvc2,
+            subtype="yolov10",
+            output_names=["output1_yolov10", "output2_yolov10", "output3_yolov10"],
+        )
+        self.load_model()
+
+    def load_model(self):
+        # load the model
+        model, _ = attempt_load_one_weight(
+            self.model_path, device="cpu", inplace=True, fuse=True
+        )
+
+        if isinstance(model.model[-1], (Detect)):
+            model.model[-1] = DetectV10(model.model[-1], self.use_rvc2)
+
+
+
+        self.names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+        # check num classes and labels
+        assert model.yaml["nc"] == len(self.names), f'Model class count {model.yaml["nc"]} != len(names) {len(self.names)}'
+
+        try:
+            self.number_of_channels = get_first_conv2d_in_channels(model)
+            # print(f"Number of channels: {self.number_of_channels}")
+        except Exception as e:
+            print(f"Error while getting number of channels: {e}")
+
+        # check if image size is suitable
+        gs = max(int(model.stride.max()), 32)  # model stride
+        if isinstance(self.imgsz, int):
+            self.imgsz = [self.imgsz, self.imgsz]
+        for sz in self.imgsz:
+            if sz % gs != 0:
+                raise ValueError(f"Image size is not a multiple of maximum stride {gs}")
+
+        # ensure correct length
+        if len(self.imgsz) != 2:
+            raise ValueError("Image size must be of length 1 or 2.")
+
+        model.eval()
+        self.model = model
+
+
+    def export_nn_archive(self, class_names: Optional[List[str]] = None):
+        """
+        Export the model to NN archive format.
+
+        Args:
+            class_list (Optional[List[str]], optional): List of class names. Defaults to None.
+        """
+        names = list(self.model.names.values())
+
+        if class_names is not None:
+            assert len(class_names) == len(names), f"Number of the given class names {len(class_names)} does not match number of classes {len(names)} provided in the model!"
+            names = class_names
+
+        self.f_nn_archive = (self.output_folder / f"{self.model_name}.tar.xz").resolve()
+
+        self.make_nn_archive(names, self.model.model[-1].nc)
+