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Ultralytics Code Refactor https://ultralytics.com/actions (#13174)

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Refactor code for speed and clarity
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Glenn Jocher authored and GitHub committed 2024-07-08 21:19:04 +02:00
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val.py
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@@ -62,7 +62,30 @@ from utils.torch_utils import select_device, smart_inference_mode
def save_one_txt(predn, save_conf, shape, file):
"""Saves one detection result to a txt file in normalized xywh format, optionally including confidence."""
"""
Saves one detection result to a txt file in normalized xywh format, optionally including confidence.
Args:
predn (torch.Tensor): Predicted bounding boxes and associated confidence scores and classes
in xyxy format, tensor of shape (N, 6) where N is the number of detections.
save_conf (bool): If True, saves the confidence scores along with the bounding box coordinates.
shape (tuple): Shape of the original image as (height, width).
file (str | Path): File path where the result will be saved.
Returns:
None
Notes:
The xyxy bounding box format represents the coordinates (xmin, ymin, xmax, ymax).
The xywh format represents the coordinates (center_x, center_y, width, height) and is
normalized by the width and height of the image.
Example:
```python
predn = torch.tensor([[10, 20, 30, 40, 0.9, 1]]) # example prediction
save_one_txt(predn, save_conf=True, shape=(640, 480), file="output.txt")
```
"""
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
@@ -73,9 +96,36 @@ def save_one_txt(predn, save_conf, shape, file):
def save_one_json(predn, jdict, path, class_map):
"""
Saves one JSON detection result with image ID, category ID, bounding box, and score.
Saves a single JSON detection result, including image ID, category ID, bounding box, and confidence score.
Example: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
Args:
predn (torch.Tensor): Predicted detections in xyxy format with shape (n, 6) where n is the number of detections.
The tensor should contain [x_min, y_min, x_max, y_max, confidence, class_id] for each detection.
jdict (list[dict]): List to collect JSON formatted detection results.
path (pathlib.Path): Path object of the image file, used to extract image_id.
class_map (dict[int, int]): Mapping from model class indices to dataset-specific category IDs.
Returns:
None: Appends detection results as dictionaries to `jdict` list in-place.
Example:
```python
predn = torch.tensor([[100, 50, 200, 150, 0.9, 0], [50, 30, 100, 80, 0.8, 1]])
jdict = []
path = Path("42.jpg")
class_map = {0: 18, 1: 19}
save_one_json(predn, jdict, path, class_map)
```
This will append to `jdict`:
```
[
{'image_id': 42, 'category_id': 18, 'bbox': [125.0, 75.0, 100.0, 100.0], 'score': 0.9},
{'image_id': 42, 'category_id': 19, 'bbox': [75.0, 55.0, 50.0, 50.0], 'score': 0.8}
]
```
Notes:
The `bbox` values are formatted as [x, y, width, height], where x and y represent the top-left corner of the box.
"""
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
@@ -93,13 +143,30 @@ def save_one_json(predn, jdict, path, class_map):
def process_batch(detections, labels, iouv):
"""
Return correct prediction matrix.
Return a correct prediction matrix given detections and labels at various IoU thresholds.
Args:
detections (np.ndarray): Array of shape (N, 6) where each row corresponds to a detection with
format [x1, y1, x2, y2, conf, class].
labels (np.ndarray): Array of shape (M, 5) where each row corresponds to a ground truth label with
format [class, x1, y1, x2, y2].
iouv (np.ndarray): Array of IoU thresholds to evaluate at.
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
correct (np.ndarray): A binary array of shape (N, len(iouv)) indicating whether each detection
is a true positive for each IoU threshold. There are 10 IoU levels used in the evaluation.
Example:
```python
detections = np.array([[50, 50, 200, 200, 0.9, 1], [30, 30, 150, 150, 0.7, 0]])
labels = np.array([[1, 50, 50, 200, 200]])
iouv = np.linspace(0.5, 0.95, 10)
correct = process_batch(detections, labels, iouv)
```
Notes:
- This function is used as part of the evaluation pipeline for object detection models.
- IoU (Intersection over Union) is a common evaluation metric for object detection performance.
"""
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
iou = box_iou(labels[:, 1:], detections[:, :4])
@@ -148,7 +215,44 @@ def run(
callbacks=Callbacks(),
compute_loss=None,
):
"""Evaluates model on a dataset and logs performance metrics, results are saved to specific directories."""
"""
Evaluates a YOLOv5 model on a dataset and logs performance metrics.
Args:
data (str | dict): Path to a dataset yaml file or a dataset dictionary.
weights (str | list[str], optional): Path to the model weights file(s). Supports various formats: PyTorch,
TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite,
TensorFlow Edge TPU, and PaddlePaddle.
batch_size (int, optional): Batch size for inference. Default is 32.
imgsz (int, optional): Input image size (pixels). Default is 640.
conf_thres (float, optional): Confidence threshold for object detection. Default is 0.001.
iou_thres (float, optional): IoU threshold for Non-Maximum Suppression (NMS). Default is 0.6.
max_det (int, optional): Maximum number of detections per image. Default is 300.
task (str, optional): Task type - 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
device (str, optional): Device to use for computation, e.g., '0' or '0,1,2,3' for CUDA or 'cpu' for CPU. Default is ''.
workers (int, optional): Number of dataloader workers. Default is 8.
single_cls (bool, optional): Treat dataset as a single class. Default is False.
augment (bool, optional): Enable augmented inference. Default is False.
verbose (bool, optional): Enable verbose output. Default is False.
save_txt (bool, optional): Save results to *.txt files. Default is False.
save_hybrid (bool, optional): Save label and prediction hybrid results to *.txt files. Default is False.
save_conf (bool, optional): Save confidences in --save-txt labels. Default is False.
save_json (bool, optional): Save a COCO-JSON results file. Default is False.
project (str | Path, optional): Directory to save results. Default is ROOT/'runs/val'.
name (str, optional): Name of the run. Default is 'exp'.
exist_ok (bool, optional): Overwrite existing project/name without incrementing. Default is False.
half (bool, optional): Use FP16 half-precision inference. Default is True.
dnn (bool, optional): Use OpenCV DNN for ONNX inference. Default is False.
model (torch.nn.Module, optional): Model object for training. Default is None.
dataloader (torch.utils.data.DataLoader, optional): Dataloader object. Default is None.
save_dir (Path, optional): Directory to save results. Default is Path('').
plots (bool, optional): Plot validation images and metrics. Default is True.
callbacks (utils.callbacks.Callbacks, optional): Callbacks for logging and monitoring. Default is Callbacks().
compute_loss (function, optional): Loss function for training. Default is None.
Returns:
dict: Contains performance metrics including precision, recall, mAP50, and mAP50-95.
"""
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
@@ -364,7 +468,53 @@ def run(
def parse_opt():
"""Parses command-line options for YOLOv5 model inference configuration."""
"""
Parses command-line options for YOLOv5 model inference configuration.
Args:
data (str): Path to the dataset YAML file, default is 'data/coco128.yaml'.
weights (List[str]): List of paths to the model weight files, default is 'yolov5s.pt'.
batch_size (int): Batch size for inference, default is 32.
imgsz (int): Inference image size in pixels, default is 640.
conf_thres (float): Confidence threshold for predictions, default is 0.001.
iou_thres (float): IoU threshold for Non-Max Suppression (NMS), default is 0.6.
max_det (int): Maximum number of detections per image, default is 300.
task (str): Task type - options are 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
device (str): Device to run the model on, e.g., '0' or '0,1,2,3' or 'cpu'. Default is empty to let
the system choose automatically.
workers (int): Maximum number of dataloader workers per rank in DDP mode, default is 8.
single_cls (bool): If set, treats the dataset as a single-class dataset. Default is False.
augment (bool): If set, performs augmented inference. Default is False.
verbose (bool): If set, reports mAP by class. Default is False.
save_txt (bool): If set, saves results to *.txt files. Default is False.
save_hybrid (bool): If set, saves label+prediction hybrid results to *.txt files. Default is False.
save_conf (bool): If set, saves confidences in --save-txt labels. Default is False.
save_json (bool): If set, saves results to a COCO-JSON file. Default is False.
project (str): Project directory to save results to. Default is 'runs/val'.
name (str): Name of the directory to save results to. Default is 'exp'.
exist_ok (bool): If set, existing directory will not be incremented. Default is False.
half (bool): If set, uses FP16 half-precision inference. Default is False.
dnn (bool): If set, uses OpenCV DNN for ONNX inference. Default is False.
Returns:
argparse.Namespace: Parsed command-line options
Notes:
- The '--data' parameter is checked to ensure it ends with 'coco.yaml' if '--save-json' is set.
- The '--save-txt' option is set to True if '--save-hybrid' is enabled.
- Args are printed using `print_args` to facilitate debugging.
Example:
To validate a trained YOLOv5 model on a COCO dataset:
```python
$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
```
Different model formats could be used instead of yolov5s.pt:
```python
$ python val.py --weights yolov5s.pt yolov5s.torchscript yolov5s.onnx yolov5s_openvino_model yolov5s.engine
```
Additional options include saving results in different formats, selecting devices, and more.
"""
parser = argparse.ArgumentParser()
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)")
@@ -397,8 +547,25 @@ def parse_opt():
def main(opt):
"""Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided
options.
"""
Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided options.
Args:
opt (argparse.Namespace): Parsed command-line options.
- This includes values for parameters like 'data', 'weights', 'batch_size', 'imgsz', 'conf_thres', 'iou_thres',
'max_det', 'task', 'device', 'workers', 'single_cls', 'augment', 'verbose', 'save_txt', 'save_hybrid',
'save_conf', 'save_json', 'project', 'name', 'exist_ok', 'half', and 'dnn', essential for configuring
the YOLOv5 tasks.
Returns:
None
Examples:
To validate a trained YOLOv5 model on the COCO dataset with a specific weights file, use:
```python
$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
```
"""
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))