Add `retry` option to `get_github_assets()` function (#4148)

docs/modes/predict.md

@@ -410,7 +410,7 @@ All Ultralytics `predict()` calls will return a list of `Results` objects:
 | `save_crop()`   | `None`          | Save cropped predictions to `save_dir/cls/file_name.jpg`.                           |
 | `tojson()`      | `None`          | Convert the object to JSON format.                                                  |
 
-For more details see the `Results` class [documentation](../reference/engine/results.md#-results).
+For more details see the `Results` class [documentation](../reference/engine/results.md).
 
 ### Boxes
 
@@ -448,7 +448,7 @@ Here is a table for the `Boxes` class methods and properties, including their na
 | `xyxyn`   | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
 | `xywhn`   | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
 
-For more details see the `Boxes` class [documentation](../reference/engine/results.md#boxes).
+For more details see the `Boxes` class [documentation](../reference/engine/results.md).
 
 ### Masks
 
@@ -473,7 +473,7 @@ For more details see the `Boxes` class [documentation](../reference/engine/resul
 Here is a table for the `Masks` class methods and properties, including their name, type, and description:
 
 | Name      | Type                      | Description                                                     |
-|------------|---------------------------|-----------------------------------------------------------------|
+|-----------|---------------------------|-----------------------------------------------------------------|
 | `cpu()`   | Method                    | Returns the masks tensor on CPU memory.                         |
 | `numpy()` | Method                    | Returns the masks tensor as a numpy array.                      |
 | `cuda()`  | Method                    | Returns the masks tensor on GPU memory.                         |
@@ -481,7 +481,7 @@ Here is a table for the `Masks` class methods and properties, including their na
 | `xyn`     | Property (`torch.Tensor`) | A list of normalized segments represented as tensors.           |
 | `xy`      | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
 
-For more details see the `Masks` class [documentation](../reference/engine/results.md#masks).
+For more details see the `Masks` class [documentation](../reference/engine/results.md).
 
 ### Keypoints
 
@@ -515,7 +515,7 @@ Here is a table for the `Keypoints` class methods and properties, including thei
 | `xy`      | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors.  |
 | `conf`    | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None.   |
 
-For more details see the `Keypoints` class [documentation](../reference/engine/results.md#keypoints).
+For more details see the `Keypoints` class [documentation](../reference/engine/results.md).
 
 ### Probs
 
@@ -540,21 +540,21 @@ For more details see the `Keypoints` class [documentation](../reference/engine/r
 Here's a table summarizing the methods and properties for the `Probs` class:
 
 | Name       | Type                      | Description                                                             |
-|------------|-------------------------|-------------------------------------------------------------------------|
+|------------|---------------------------|-------------------------------------------------------------------------|
 | `cpu()`    | Method                    | Returns a copy of the probs tensor on CPU memory.                       |
 | `numpy()`  | Method                    | Returns a copy of the probs tensor as a numpy array.                    |
 | `cuda()`   | Method                    | Returns a copy of the probs tensor on GPU memory.                       |
 | `to()`     | Method                    | Returns a copy of the probs tensor with the specified device and dtype. |
-| `top1`     | Property `int`          | Index of the top 1 class.                                               |
+| `top1`     | Property (`int`)          | Index of the top 1 class.                                               |
-| `top5`     | Property `list[int]`    | Indices of the top 5 classes.                                           |
+| `top5`     | Property (`list[int]`)    | Indices of the top 5 classes.                                           |
-| `top1conf` | Property `torch.Tensor` | Confidence of the top 1 class.                                          |
+| `top1conf` | Property (`torch.Tensor`) | Confidence of the top 1 class.                                          |
-| `top5conf` | Property `torch.Tensor` | Confidences of the top 5 classes.                                       |
+| `top5conf` | Property (`torch.Tensor`) | Confidences of the top 5 classes.                                       |
 
-For more details see the `Probs` class [documentation](../reference/engine/results.md#probs).
+For more details see the `Probs` class [documentation](../reference/engine/results.md).
 
 ## Plotting Results
 
-You can the `plot()` method of a `Result` objects to plot predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object.
+You can use the `plot()` method of a `Result` objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object onto a numpy array that can then be shown or saved.
 
 !!! example "Plotting"
 
@@ -570,8 +570,10 @@ You can the `plot()` method of a `Result` objects to plot predictions. It plots
 
     # Show the results
     for r in results:
-        im = r.plot()  # plot a BGR numpy array of predictions
+        im_array = r.plot()  # plot a BGR numpy array of predictions
-        Image.fromarray(im[..., ::-1]).show()  # show RGB image
+        im = Image.fromarray(im[..., ::-1])  # RGB PIL image
+        im.show()  # show image
+        im.save('results.jpg')  # save image
     ```
     
     The `plot()` method has the following arguments available:

ultralytics/engine/exporter.py

@@ -422,7 +422,7 @@ class Exporter:
                 f'{prefix} WARNING ⚠️ PNNX not found. Attempting to download binary file from '
                 'https://github.com/pnnx/pnnx/.\nNote PNNX Binary file must be placed in current working directory '
                 f'or in {ROOT}. See PNNX repo for full installation instructions.')
-            _, assets = get_github_assets(repo='pnnx/pnnx')
+            _, assets = get_github_assets(repo='pnnx/pnnx', retry=True)
             asset = [x for x in assets if ('macos' if MACOS else 'ubuntu' if LINUX else 'windows') in x][0]
             attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
             unzip_dir = Path(asset).with_suffix('')

ultralytics/engine/results.py

@@ -208,8 +208,10 @@ class Results(SimpleClass):
             model = YOLO('yolov8n.pt')
             results = model('bus.jpg')  # results list
             for r in results:
-                im = r.plot()  # BGR numpy array
+                im_array = r.plot()  # plot a BGR numpy array of predictions
-                Image.fromarray(im[..., ::-1]).show()  # show RGB image
+                im = Image.fromarray(im[..., ::-1])  # RGB PIL image
+                im.show()  # show image
+                im.save('results.jpg')  # save image
             ```
         """
         if img is None and isinstance(self.orig_img, torch.Tensor):

ultralytics/utils/downloads.py

@@ -202,12 +202,16 @@ def safe_download(url,
         return unzip_dir
 
 
-def get_github_assets(repo='ultralytics/assets', version='latest'):
+def get_github_assets(repo='ultralytics/assets', version='latest', retry=False):
     """Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])."""
     if version != 'latest':
         version = f'tags/{version}'  # i.e. tags/v6.2
-    response = requests.get(f'https://api.github.com/repos/{repo}/releases/{version}').json()  # github api
+    url = f'https://api.github.com/repos/{repo}/releases/{version}'
-    return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
+    r = requests.get(url)  # github api
+    if r.status_code != 200 and retry:
+        r = requests.get(url)  # try again
+    data = r.json()
+    return data['tag_name'], [x['name'] for x in data['assets']]  # tag, assets
 
 
 def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):