`ultralytics 8.0.230` TensorRT export hang fix (#7180)

Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
Co-authored-by: CV & LLM & AIGC er <wenvoi@163.com>
Co-authored-by: Aaron <42322215+aaronllowe@users.noreply.github.com>
Co-authored-by: crypthusiast0 <42322215+crypthusiast0@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -76,6 +76,12 @@ repos:
     hooks:
       - id: docformatter
 
+  - repo: https://github.com/hadialqattan/pycln
+    rev: v2.4.0
+    hooks:
+      - id: pycln
+        args: [--all]
+
 #  - repo: https://github.com/asottile/yesqa
 #    rev: v1.4.0
 #    hooks:

docs/en/guides/heatmaps.md

@@ -183,8 +183,8 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         # Heatmap Init
         heatmap_obj = heatmap.Heatmap()
         heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
-                                     imw=cap.get(4),  # should same as cap height
+                                     imw=im0.shape[1],  # should same as im0 width
-                                     imh=cap.get(3),  # should same as cap width
+                                     imh=im0.shape[0],  # should same as im0 height
                                      view_img=True,
                                      shape="circle")
 

docs/en/integrations/roboflow.md

@@ -8,9 +8,9 @@ keywords: Ultralytics, YOLOv8, Roboflow, vector analysis, confusion matrix, data
 
 [Roboflow](https://roboflow.com/?ref=ultralytics) has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether you’re in need of [data labeling](https://roboflow.com/annotate?ref=ultralytics), [model training](https://roboflow.com/train?ref=ultralytics), or [model deployment](https://roboflow.com/deploy?ref=ultralytics), Roboflow gives you building blocks to bring custom computer vision solutions to your project.
 
-!!! Warning
+!!! Note
 
-    Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.
+    Ultralytics offers two licensing options: the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an OSI-approved open-source license ideal for students and enthusiasts, and the [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services. For more details see [Ultralytics Licensing](https://ultralytics.com/license).
 
 In this guide, we are going to showcase how to find, label, and organize data for use in training a custom Ultralytics YOLOv8 model. Use the table of contents below to jump directly to a specific section:
 

setup.py

@@ -75,7 +75,7 @@ setup(
             'mkdocs-material',
             'mkdocstrings[python]',
             'mkdocs-redirects',  # for 301 redirects
-            'mkdocs-ultralytics-plugin>=0.0.34',  # for meta descriptions and images, dates and authors
+            'mkdocs-ultralytics-plugin>=0.0.35',  # for meta descriptions and images, dates and authors
         ],
         'export': [
             'coremltools>=7.0',

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.229'
+__version__ = '8.0.230'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/exporter.py

@@ -579,6 +579,8 @@ class Exporter:
     def export_engine(self, prefix=colorstr('TensorRT:')):
         """YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt."""
         assert self.im.device.type != 'cpu', "export running on CPU but must be on GPU, i.e. use 'device=0'"
+        f_onnx, _ = self.export_onnx()  # run before trt import https://github.com/ultralytics/ultralytics/issues/7016
+
         try:
             import tensorrt as trt  # noqa
         except ImportError:
@@ -587,8 +589,8 @@ class Exporter:
             import tensorrt as trt  # noqa
 
         check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
+
         self.args.simplify = True
-        f_onnx, _ = self.export_onnx()
 
         LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
         assert Path(f_onnx).exists(), f'failed to export ONNX file: {f_onnx}'