Add docstrings to YOLOv5 functions (#12760)

* Add docstrings to top level files

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* Add docstrings

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* Add docstrings

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* Add docstrings

* Add docstrings

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* Add docstrings

* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Add docstrings

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* Update activations.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn JocherandUltralyticsAssistant authored and GitHub committed 2024-02-25 14:04:01 +01:00
1 parent ca00c34c2a
commit 41603da162
42 files changed
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@@ -76,6 +76,7 @@ GIT_INFO = check_git_info()
def train(opt, device):
"""Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
init_seeds(opt.seed + 1 + RANK, deterministic=True)
save_dir, data, bs, epochs, nw, imgsz, pretrained = (
opt.save_dir,
@@ -306,6 +307,9 @@ def train(opt, device):
def parse_opt(known=False):
"""Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
parsed arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
@@ -333,7 +337,7 @@ def parse_opt(known=False):
def main(opt):
# Checks
"""Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks."""
if RANK in {-1, 0}:
print_args(vars(opt))
check_git_status()
@@ -357,7 +361,11 @@ def main(opt):
def run(**kwargs):
# Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
"""
Executes YOLOv5 model training or inference with specified parameters, returning updated options.
Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
"""
opt = parse_opt(True)
for k, v in kwargs.items():
setattr(opt, k, v)