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Add docformatter to pre-commit (#5279)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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Glenn Jocher 2023-10-09 02:25:22 +02:00 • committed by GitHub
parent c7aa83da31
commit 7517667a33
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90 changed files with 1396 additions and 497 deletions
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22
ultralytics/utils/checks.py
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@ -64,8 +64,8 @@ def parse_requirements(file_path=ROOT.parent / 'requirements.txt', package=''):
def parse_version(version='0.0.0') -> tuple:
"""
Convert a version string to a tuple of integers, ignoring any extra non-numeric string attached to the version.
This function replaces deprecated 'pkg_resources.parse_version(v)'
Convert a version string to a tuple of integers, ignoring any extra non-numeric string attached to the version. This
function replaces deprecated 'pkg_resources.parse_version(v)'.
Args:
version (str): Version string, i.e. '2.0.1+cpu'
@ -372,8 +372,10 @@ def check_torchvision():
Checks the installed versions of PyTorch and Torchvision to ensure they're compatible.
This function checks the installed versions of PyTorch and Torchvision, and warns if they're incompatible according
to the provided compatibility table based on https://github.com/pytorch/vision#installation. The
compatibility table is a dictionary where the keys are PyTorch versions and the values are lists of compatible
to the provided compatibility table based on:
https://github.com/pytorch/vision#installation.
The compatibility table is a dictionary where the keys are PyTorch versions and the values are lists of compatible
Torchvision versions.
"""
@ -527,9 +529,9 @@ def collect_system_info():
def check_amp(model):
"""
This function checks the PyTorch Automatic Mixed Precision (AMP) functionality of a YOLOv8 model.
If the checks fail, it means there are anomalies with AMP on the system that may cause NaN losses or zero-mAP
results, so AMP will be disabled during training.
This function checks the PyTorch Automatic Mixed Precision (AMP) functionality of a YOLOv8 model. If the checks
fail, it means there are anomalies with AMP on the system that may cause NaN losses or zero-mAP results, so AMP will
be disabled during training.
Args:
model (nn.Module): A YOLOv8 model instance.
@ -606,7 +608,8 @@ def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
def cuda_device_count() -> int:
"""Get the number of NVIDIA GPUs available in the environment.
"""
Get the number of NVIDIA GPUs available in the environment.
Returns:
(int): The number of NVIDIA GPUs available.
@ -626,7 +629,8 @@ def cuda_device_count() -> int:
def cuda_is_available() -> bool:
"""Check if CUDA is available in the environment.
"""
Check if CUDA is available in the environment.
Returns:
(bool): True if one or more NVIDIA GPUs are available, False otherwise.