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Ultralytics Code Refactor https://ultralytics.com/actions (#13175)

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* Refactor code for speed and clarity

* Update export.py

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

* Update export.py

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

* Add missing Python function docstrings

* Auto-format by https://ultralytics.com/actions

* Update export.py

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

* Add missing Python function docstrings

* Auto-format by https://ultralytics.com/actions

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn JocherandUltralyticsAssistant authored and GitHub committed 2024-07-08 22:05:56 +02:00
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commit ff06328498
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train.py
+56 -51
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@@ -546,10 +546,10 @@ def parse_opt(known=False):
Parses command-line arguments for YOLOv5 training, validation, and testing.
Args:
known (bool, optional): If True, parses known arguments, ignoring the unknown. Defaults to False.
known (bool, optional): If True, parses known arguments, ignoring the unknown. Defaults to False.
Returns:
argparse.Namespace: Parsed command-line arguments.
argparse.Namespace: Parsed command-line arguments.
Example:
```python
@@ -559,9 +559,9 @@ def parse_opt(known=False):
```
Links:
Models: https://github.com/ultralytics/yolov5/tree/master/models
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
Models: https://github.com/ultralytics/yolov5/tree/master/models
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
"""
parser = argparse.ArgumentParser()
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
@@ -621,15 +621,15 @@ def main(opt, callbacks=Callbacks()):
Runs training or hyperparameter evolution with specified options and optional callbacks.
Args:
opt (argparse.Namespace): The command-line arguments parsed for YOLOv5 training and evolution.
callbacks (ultralytics.utils.callbacks.Callbacks, optional): Callback functions for various training stages.
Defaults to Callbacks().
opt (argparse.Namespace): The command-line arguments parsed for YOLOv5 training and evolution.
callbacks (ultralytics.utils.callbacks.Callbacks, optional): Callback functions for various training stages.
Defaults to Callbacks().
Returns:
None
None
Note:
For detailed usage, visit:
For detailed usage, visit:
https://github.com/ultralytics/yolov5/tree/master/models
"""
if RANK in {-1, 0}:
@@ -918,51 +918,56 @@ def run(**kwargs):
Executes YOLOv5 training with given options, allowing optional overrides through keyword arguments.
Args:
weights (str): Path to initial weights. Defaults to ROOT / 'yolov5s.pt'.
cfg (str): Path to model YAML configuration. Defaults to an empty string.
data (str): Path to dataset YAML configuration. Defaults to ROOT / 'data/coco128.yaml'.
hyp (str): Path to hyperparameters YAML configuration. Defaults to ROOT / 'data/hyps/hyp.scratch-low.yaml'.
epochs (int): Total number of training epochs. Defaults to 100.
batch_size (int): Total batch size for all GPUs. Use -1 for automatic batch size determination. Defaults to 16.
imgsz (int): Image size (pixels) for training and validation. Defaults to 640.
rect (bool): Use rectangular training. Defaults to False.
resume (bool | str): Resume most recent training with an optional path. Defaults to False.
nosave (bool): Only save final checkpoint. Defaults to False.
noval (bool): Only validate at the final epoch. Defaults to False.
noautoanchor (bool): Disable AutoAnchor. Defaults to False.
noplots (bool): Do not save plot files. Defaults to False.
evolve (int): Evolve hyperparameters for a specified number of generations. Use 300 if provided without a value.
evolve_population (str): Directory for loading population during evolution. Defaults to ROOT / 'data/hyps'.
resume_evolve (str): Resume hyperparameter evolution from the last generation. Defaults to None.
bucket (str): gsutil bucket for saving checkpoints. Defaults to an empty string.
cache (str): Cache image data in 'ram' or 'disk'. Defaults to None.
image_weights (bool): Use weighted image selection for training. Defaults to False.
device (str): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu'. Defaults to an empty string.
multi_scale (bool): Use multi-scale training, varying image size by ±50%. Defaults to False.
single_cls (bool): Train with multi-class data as single-class. Defaults to False.
optimizer (str): Optimizer type, choices are ['SGD', 'Adam', 'AdamW']. Defaults to 'SGD'.
sync_bn (bool): Use synchronized BatchNorm, only available in DDP mode. Defaults to False.
workers (int): Maximum dataloader workers per rank in DDP mode. Defaults to 8.
project (str): Directory for saving training runs. Defaults to ROOT / 'runs/train'.
name (str): Name for saving the training run. Defaults to 'exp'.
exist_ok (bool): Allow existing project/name without incrementing. Defaults to False.
quad (bool): Use quad dataloader. Defaults to False.
cos_lr (bool): Use cosine learning rate scheduler. Defaults to False.
label_smoothing (float): Label smoothing epsilon value. Defaults to 0.0.
patience (int): Patience for early stopping, measured in epochs without improvement. Defaults to 100.
freeze (list): Layers to freeze, e.g., backbone=10, first 3 layers = [0, 1, 2]. Defaults to [0].
save_period (int): Frequency in epochs to save checkpoints. Disabled if < 1. Defaults to -1.
seed (int): Global training random seed. Defaults to 0.
local_rank (int): Automatic DDP Multi-GPU argument. Do not modify. Defaults to -1.
weights (str, optional): Path to initial weights. Defaults to ROOT / 'yolov5s.pt'.
cfg (str, optional): Path to model YAML configuration. Defaults to an empty string.
data (str, optional): Path to dataset YAML configuration. Defaults to ROOT / 'data/coco128.yaml'.
hyp (str, optional): Path to hyperparameters YAML configuration. Defaults to ROOT / 'data/hyps/hyp.scratch-low.yaml'.
epochs (int, optional): Total number of training epochs. Defaults to 100.
batch_size (int, optional): Total batch size for all GPUs. Use -1 for automatic batch size determination. Defaults to 16.
imgsz (int, optional): Image size (pixels) for training and validation. Defaults to 640.
rect (bool, optional): Use rectangular training. Defaults to False.
resume (bool | str, optional): Resume most recent training with an optional path. Defaults to False.
nosave (bool, optional): Only save the final checkpoint. Defaults to False.
noval (bool, optional): Only validate at the final epoch. Defaults to False.
noautoanchor (bool, optional): Disable AutoAnchor. Defaults to False.
noplots (bool, optional): Do not save plot files. Defaults to False.
evolve (int, optional): Evolve hyperparameters for a specified number of generations. Use 300 if provided without a value.
evolve_population (str, optional): Directory for loading population during evolution. Defaults to ROOT / 'data/hyps'.
resume_evolve (str, optional): Resume hyperparameter evolution from the last generation. Defaults to None.
bucket (str, optional): gsutil bucket for saving checkpoints. Defaults to an empty string.
cache (str, optional): Cache image data in 'ram' or 'disk'. Defaults to None.
image_weights (bool, optional): Use weighted image selection for training. Defaults to False.
device (str, optional): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu'. Defaults to an empty string.
multi_scale (bool, optional): Use multi-scale training, varying image size by ±50%. Defaults to False.
single_cls (bool, optional): Train with multi-class data as single-class. Defaults to False.
optimizer (str, optional): Optimizer type, choices are ['SGD', 'Adam', 'AdamW']. Defaults to 'SGD'.
sync_bn (bool, optional): Use synchronized BatchNorm, only available in DDP mode. Defaults to False.
workers (int, optional): Maximum dataloader workers per rank in DDP mode. Defaults to 8.
project (str, optional): Directory for saving training runs. Defaults to ROOT / 'runs/train'.
name (str, optional): Name for saving the training run. Defaults to 'exp'.
exist_ok (bool, optional): Allow existing project/name without incrementing. Defaults to False.
quad (bool, optional): Use quad dataloader. Defaults to False.
cos_lr (bool, optional): Use cosine learning rate scheduler. Defaults to False.
label_smoothing (float, optional): Label smoothing epsilon value. Defaults to 0.0.
patience (int, optional): Patience for early stopping, measured in epochs without improvement. Defaults to 100.
freeze (list, optional): Layers to freeze, e.g., backbone=10, first 3 layers = [0, 1, 2]. Defaults to [0].
save_period (int, optional): Frequency in epochs to save checkpoints. Disabled if < 1. Defaults to -1.
seed (int, optional): Global training random seed. Defaults to 0.
local_rank (int, optional): Automatic DDP Multi-GPU argument. Do not modify. Defaults to -1.
Returns:
None: The function initiates YOLOv5 training or hyperparameter evolution based on the provided options.
None: The function initiates YOLOv5 training or hyperparameter evolution based on the provided options.
Examples:
```python
import train
train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
```
```python
import train
train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
```
Notes:
- Models: https://github.com/ultralytics/yolov5/tree/master/models
- Datasets: https://github.com/ultralytics/yolov5/tree/master/data
- Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
"""
opt = parse_opt(True)
for k, v in kwargs.items():