Tests and docstrings improvements (#4475)

ultralytics/utils/__init__.py

@@ -341,10 +341,10 @@ def yaml_load(file='data.yaml', append_filename=False):
 
 def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
     """
-    Pretty prints a yaml file or a yaml-formatted dictionary.
+    Pretty prints a YAML file or a YAML-formatted dictionary.
 
     Args:
-        yaml_file: The file path of the yaml file or a yaml-formatted dictionary.
+        yaml_file: The file path of the YAML file or a YAML-formatted dictionary.
 
     Returns:
         None

ultralytics/utils/callbacks/clearml.py

@@ -29,8 +29,7 @@ def _log_debug_samples(files, title='Debug Samples') -> None:
         files (list): A list of file paths in PosixPath format.
         title (str): A title that groups together images with the same values.
     """
-    task = Task.current_task()
-    if task:
+    if task := Task.current_task():
         for f in files:
             if f.exists():
                 it = re.search(r'_batch(\d+)', f.name)
@@ -63,8 +62,7 @@ def _log_plot(title, plot_path) -> None:
 def on_pretrain_routine_start(trainer):
     """Runs at start of pretraining routine; initializes and connects/ logs task to ClearML."""
     try:
-        task = Task.current_task()
-        if task:
+        if task := Task.current_task():
             # Make sure the automatic pytorch and matplotlib bindings are disabled!
             # We are logging these plots and model files manually in the integration
             PatchPyTorchModelIO.update_current_task(None)
@@ -86,21 +84,19 @@ def on_pretrain_routine_start(trainer):
 
 
 def on_train_epoch_end(trainer):
-    task = Task.current_task()
-
-    if task:
-        """Logs debug samples for the first epoch of YOLO training."""
+    """Logs debug samples for the first epoch of YOLO training and report current training progress."""
+    if task := Task.current_task():
+        # Log debug samples
         if trainer.epoch == 1:
             _log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
-        """Report the current training progress."""
+        # Report the current training progress
         for k, v in trainer.validator.metrics.results_dict.items():
             task.get_logger().report_scalar('train', k, v, iteration=trainer.epoch)
 
 
 def on_fit_epoch_end(trainer):
     """Reports model information to logger at the end of an epoch."""
-    task = Task.current_task()
-    if task:
+    if task := Task.current_task():
         # You should have access to the validation bboxes under jdict
         task.get_logger().report_scalar(title='Epoch Time',
                                         series='Epoch Time',
@@ -120,8 +116,7 @@ def on_val_end(validator):
 
 def on_train_end(trainer):
     """Logs final model and its name on training completion."""
-    task = Task.current_task()
-    if task:
+    if task := Task.current_task():
         # Log final results, CM matrix + PR plots
         files = [
             'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',

ultralytics/utils/callbacks/dvc.py

@@ -40,7 +40,7 @@ def _log_images(path, prefix=''):
 
         # Group images by batch to enable sliders in UI
         if m := re.search(r'_batch(\d+)', name):
-            ni = m.group(1)
+            ni = m[1]
             new_stem = re.sub(r'_batch(\d+)', '_batch', path.stem)
             name = (Path(new_stem) / ni).with_suffix(path.suffix)
 

ultralytics/utils/downloads.py

@@ -93,7 +93,7 @@ def zip_directory(directory, compress=True, exclude=('.DS_Store', '__MACOSX'), p
         raise FileNotFoundError(f"Directory '{directory}' does not exist.")
 
     # Unzip with progress bar
-    files_to_zip = [f for f in directory.rglob('*') if f.is_file() and not any(x in f.name for x in exclude)]
+    files_to_zip = [f for f in directory.rglob('*') if f.is_file() and all(x not in f.name for x in exclude)]
     zip_file = directory.with_suffix('.zip')
     compression = ZIP_DEFLATED if compress else ZIP_STORED
     with ZipFile(zip_file, 'w', compression) as f:
@@ -185,11 +185,9 @@ def check_disk_space(url='https://ultralytics.com/assets/coco128.zip', sf=1.5, h
                 f'Please free {data * sf - free:.1f} GB additional disk space and try again.')
         if hard:
             raise MemoryError(text)
-        else:
-            LOGGER.warning(text)
-            return False
+        LOGGER.warning(text)
+        return False
 
-            # Pass if error
     return True
 
 
@@ -332,6 +330,9 @@ def get_github_assets(repo='ultralytics/assets', version='latest', retry=False):
     r = requests.get(url)  # github api
     if r.status_code != 200 and retry:
         r = requests.get(url)  # try again
+    if r.status_code != 200:
+        LOGGER.warning(f'⚠️ GitHub assets check failure for {url}: {r.status_code} {r.reason}')
+        return '', []
     data = r.json()
     return data['tag_name'], [x['name'] for x in data['assets']]  # tag, assets
 

ultralytics/utils/metrics.py

@@ -382,7 +382,7 @@ def compute_ap(recall, precision):
     """
     Compute the average precision (AP) given the recall and precision curves.
 
-    Arguments:
+    Args:
         recall (list): The recall curve.
         precision (list): The precision curve.
 

ultralytics/utils/ops.py

@@ -140,7 +140,7 @@ def non_max_suppression(
     """
     Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
 
-    Arguments:
+    Args:
         prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
             containing the predicted boxes, classes, and masks. The tensor should be in the format
             output by a model, such as YOLO.