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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 <[email protected]>

* Update export.py

Signed-off-by: Glenn Jocher <[email protected]>

* Add missing Python function docstrings

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

* Update export.py

Signed-off-by: Glenn Jocher <[email protected]>

* Add missing Python function docstrings

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

---------

Signed-off-by: Glenn Jocher <[email protected]>
Co-authored-by: UltralyticsAssistant <[email protected]>
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@glenn-jocher @UltralyticsAssistant
glenn-jocher and UltralyticsAssistant authored Jul 8, 2024
1 parent 8257e0a commit ff06328
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24 changes: 21 additions & 3 deletions benchmarks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ def run(
imgsz (int): Inference size in pixels (default: 640).
batch_size (int): Batch size for inference (default: 1).
data (Path | str): Path to the dataset.yaml file (default: ROOT / "data/coco128.yaml").
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: None).
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: "").
half (bool): Use FP16 half-precision inference (default: False).
test (bool): Test export formats only (default: False).
pt_only (bool): Test PyTorch format only (default: False).
Expand Down Expand Up @@ -175,6 +175,24 @@ def test(
Returns:
pd.DataFrame: DataFrame containing the results of the export tests, including format names and export statuses.
Examples:
```python
$ python benchmarks.py --weights yolov5s.pt --img 640
```
Notes:
Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js are unsupported.
Usage:
Install required packages:
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU support
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU support
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
Run export tests:
$ python benchmarks.py --weights yolov5s.pt --img 640
"""
y, t = [], time.time()
device = select_device(device)
Expand Down Expand Up @@ -213,8 +231,8 @@ def parse_opt():
half (bool): Use FP16 half-precision inference. This is a flag and defaults to False.
test (bool): Test exports only. This is a flag and defaults to False.
pt_only (bool): Test PyTorch only. This is a flag and defaults to False.
hard_fail (bool|str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum metric
floor, i.e., '0.29'. Defaults to False.
hard_fail (bool | str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum
metric floor, e.g., '0.29'. Defaults to False.
Returns:
argparse.Namespace: Parsed command-line arguments encapsulated in an argparse Namespace object.
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74 changes: 37 additions & 37 deletions detect.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,40 +101,40 @@ def run(
Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.
Args:
weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index.
Default is 'data/images'.
data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
conf_thres (float): Confidence threshold for detections. Default is 0.25.
iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
max_det (int): Maximum number of detections per image. Default is 1000.
device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which
uses the best available device.
view_img (bool): If True, display inference results using OpenCV. Default is False.
save_txt (bool): If True, save results in a text file. Default is False.
save_csv (bool): If True, save results in a CSV file. Default is False.
save_conf (bool): If True, include confidence scores in the saved results. Default is False.
save_crop (bool): If True, save cropped prediction boxes. Default is False.
nosave (bool): If True, do not save inference images or videos. Default is False.
classes (list[int]): List of class indices to filter detections by. Default is None.
agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
augment (bool): If True, use augmented inference. Default is False.
visualize (bool): If True, visualize feature maps. Default is False.
update (bool): If True, update all models' weights. Default is False.
project (str | Path): Directory to save results. Default is 'runs/detect'.
name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
False.
line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
half (bool): If True, use FP16 half-precision inference. Default is False.
dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam index.
Default is 'data/images'.
data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
conf_thres (float): Confidence threshold for detections. Default is 0.25.
iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
max_det (int): Maximum number of detections per image. Default is 1000.
device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which
uses the best available device.
view_img (bool): If True, display inference results using OpenCV. Default is False.
save_txt (bool): If True, save results in a text file. Default is False.
save_csv (bool): If True, save results in a CSV file. Default is False.
save_conf (bool): If True, include confidence scores in the saved results. Default is False.
save_crop (bool): If True, save cropped prediction boxes. Default is False.
nosave (bool): If True, do not save inference images or videos. Default is False.
classes (list[int]): List of class indices to filter detections by. Default is None.
agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
augment (bool): If True, use augmented inference. Default is False.
visualize (bool): If True, visualize feature maps. Default is False.
update (bool): If True, update all models' weights. Default is False.
project (str | Path): Directory to save results. Default is 'runs/detect'.
name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
False.
line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
half (bool): If True, use FP16 half-precision inference. Default is False.
dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
Returns:
None
None
Examples:
```python
Expand Down Expand Up @@ -397,15 +397,15 @@ def main(opt):
Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running.
Args:
opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
Returns:
None
None
Note:
This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified options.
Refer to the usage guide and examples for more information about different sources and formats at:
https://github.com/ultralytics/ultralytics
This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified options.
Refer to the usage guide and examples for more information about different sources and formats at:
https://github.com/ultralytics/ultralytics
Example usage:
Expand Down
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