py-cpuinfo Exception context manager fix (#14814)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

.github/workflows/publish.yml

@@ -168,6 +168,7 @@ jobs:
           PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
           INDEXNOW_KEY: ${{ secrets.INDEXNOW_KEY_DOCS }}
         run: |
+          pip install black
           export JUPYTER_PLATFORM_DIRS=1
           python docs/build_docs.py
           git clone https://github.com/ultralytics/docs.git docs-repo

docs/en/models/index.md

@@ -21,7 +21,7 @@ Here are some of the key models supported:
 7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
 8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
 9. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
-10. **[Segment Anything Model 2 (SAM2)](sam2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
+10. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
 11. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
 12. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
 13. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.

docs/en/models/sam-2.md

@@ -112,7 +112,7 @@ pip install ultralytics
 The following table details the available SAM 2 models, their pre-trained weights, supported tasks, and compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md).
 
 | Model Type  | Pre-trained Weights                                                                   | Tasks Supported                              | Inference | Validation | Training | Export |
-| ---------- | ------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
+| ----------- | ------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
 | SAM 2 base  | [sam2_b.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅        | ❌         | ❌       | ❌     |
 | SAM 2 large | [sam2_l.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/sam2_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅        | ❌         | ❌       | ❌     |
 
@@ -129,10 +129,10 @@ SAM2 can be utilized across a broad spectrum of tasks, including real-time video
     === "Python"
 
         ```python
-        from ultralytics import SAM2
+        from ultralytics import SAM
 
         # Load a model
-        model = SAM2("sam2_b.pt")
+        model = SAM("sam2_b.pt")
 
         # Display model information (optional)
         model.info()
@@ -153,10 +153,10 @@ SAM2 can be utilized across a broad spectrum of tasks, including real-time video
     === "Python"
 
         ```python
-        from ultralytics import SAM2
+        from ultralytics import SAM
 
         # Load a model
-        model = SAM2("sam2_b.pt")
+        model = SAM("sam2_b.pt")
 
         # Display model information (optional)
         model.info()
@@ -261,10 +261,10 @@ If SAM2 is a crucial part of your research or development work, please cite it u
     === "BibTeX"
 
         ```bibtex
-        @article{kirillov2024sam2,
+        @article{ravi2024sam2,
-          title={SAM2: Segment Anything Model 2},
+          title={SAM 2: Segment Anything in Images and Videos},
-          author={Alexander Kirillov and others},
+          author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
-          journal={arXiv preprint arXiv:2401.12741},
+          journal={arXiv preprint},
           year={2024}
         }
         ```
@@ -296,10 +296,10 @@ SAM2 can be utilized for real-time video segmentation by leveraging its promptab
     === "Python"
 
         ```python
-        from ultralytics import SAM2
+        from ultralytics import SAM
 
         # Load a model
-        model = SAM2("sam2_b.pt")
+        model = SAM("sam2_b.pt")
 
         # Display model information (optional)
         model.info()
@@ -311,7 +311,7 @@ SAM2 can be utilized for real-time video segmentation by leveraging its promptab
         results = model("path/to/image.jpg", points=[150, 150], labels=[1])
         ```
 
-For more comprehensive usage, refer to the [How to Use SAM2](#how-to-use-sam2-versatility-in-image-and-video-segmentation) section.
+For more comprehensive usage, refer to the [How to Use SAM 2](#how-to-use-sam-2-versatility-in-image-and-video-segmentation) section.
 
 ### What datasets are used to train SAM 2, and how do they enhance its performance?
 

mkdocs.yml

@@ -239,7 +239,7 @@ nav:
       - YOLOv9: models/yolov9.md
       - YOLOv10: models/yolov10.md
       - SAM (Segment Anything Model): models/sam.md
-      - SAM2 (Segment Anything Model 2): models/sam2.md
+      - SAM2 (Segment Anything Model 2): models/sam-2.md
       - MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
       - FastSAM (Fast Segment Anything Model): models/fast-sam.md
       - YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
@@ -659,6 +659,7 @@ plugins:
         sdk.md: index.md
         hub/inference_api.md: hub/inference-api.md
         usage/hyperparameter_tuning.md: integrations/ray-tune.md
+        models/sam2.md: models/sam-2.md
         reference/base_pred.md: reference/engine/predictor.md
         reference/base_trainer.md: reference/engine/trainer.md
         reference/exporter.md: reference/engine/exporter.md

ultralytics/utils/torch_utils.py

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-
+import contextlib
 import gc
 import math
 import os
@@ -101,13 +101,16 @@ def autocast(enabled: bool, device: str = "cuda"):
 
 def get_cpu_info():
     """Return a string with system CPU information, i.e. 'Apple M2'."""
+    with contextlib.suppress(Exception):
         import cpuinfo  # pip install py-cpuinfo
 
-    k = "brand_raw", "hardware_raw", "arch_string_raw"  # info keys sorted by preference (not all keys always available)
+        k = "brand_raw", "hardware_raw", "arch_string_raw"  # keys sorted by preference (not all keys always available)
         info = cpuinfo.get_cpu_info()  # info dict
         string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
         return string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")
 
+    return "unknown"
+
 
 def select_device(device="", batch=0, newline=False, verbose=True):
     """