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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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val.py
+38 -39
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@@ -66,8 +66,8 @@ def save_one_txt(predn, save_conf, shape, file):
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.
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.
@@ -77,8 +77,8 @@ def save_one_txt(predn, save_conf, shape, file):
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.
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
@@ -99,33 +99,33 @@ def save_one_json(predn, jdict, path, class_map):
Saves a single JSON detection result, including image ID, category ID, bounding box, and confidence score.
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.
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.
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}
]
```
```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.
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
@@ -146,15 +146,15 @@ def process_batch(detections, labels, iouv):
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].
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.
Returns:
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.
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
@@ -220,7 +220,7 @@ def run(
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,
weights (str | list[str], optional): Path to the model weights file(s). Supports various formats including 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.
@@ -473,15 +473,14 @@ def parse_opt():
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'.
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.
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.
@@ -552,10 +551,10 @@ def main(opt):
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.
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