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ultralytics 8.0.158 add benchmarks to coverage (#4432)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
This commit is contained in:
Glenn Jocher
2023-08-20 20:52:30 +02:00
committed by GitHub
parent 495806565d
commit 87ce15d383
51 changed files with 352 additions and 482 deletions
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8
ultralytics/models/yolo/classify/__init__.py
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@ -1,7 +1,7 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict
from ultralytics.models.yolo.classify.train import ClassificationTrainer, train
from ultralytics.models.yolo.classify.val import ClassificationValidator, val
from ultralytics.models.yolo.classify.predict import ClassificationPredictor
from ultralytics.models.yolo.classify.train import ClassificationTrainer
from ultralytics.models.yolo.classify.val import ClassificationValidator
__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'
__all__ = 'ClassificationPredictor', 'ClassificationTrainer', 'ClassificationValidator'

36
ultralytics/models/yolo/classify/predict.py
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@ -4,10 +4,26 @@ import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ASSETS, DEFAULT_CFG
from ultralytics.utils import DEFAULT_CFG
class ClassificationPredictor(BasePredictor):
"""
A class extending the BasePredictor class for prediction based on a classification model.
Notes:
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.classify import ClassificationPredictor
args = dict(model='yolov8n-cls.pt', source=ASSETS)
predictor = ClassificationPredictor(overrides=args)
predictor.predict_cli()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
@ -30,21 +46,3 @@ class ClassificationPredictor(BasePredictor):
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
return results
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Run YOLO model predictions on input images/videos."""
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = ClassificationPredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()

34
ultralytics/models/yolo/classify/train.py
View File

@ -13,6 +13,21 @@ from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_di
class ClassificationTrainer(BaseTrainer):
"""
A class extending the BaseTrainer class for training based on a classification model.
Notes:
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
Example:
```python
from ultralytics.models.yolo.classify import ClassificationTrainer
args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3)
trainer = ClassificationTrainer(overrides=args)
trainer.train()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initialize a ClassificationTrainer object with optional configuration overrides and callbacks."""
@ -137,22 +152,3 @@ class ClassificationTrainer(BaseTrainer):
cls=batch['cls'].view(-1), # warning: use .view(), not .squeeze() for Classify models
fname=self.save_dir / f'train_batch{ni}.jpg',
on_plot=self.on_plot)
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train a YOLO classification model."""
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
data = cfg.data or 'mnist160' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = ClassificationTrainer(overrides=args)
trainer.train()
if __name__ == '__main__':
train()

35
ultralytics/models/yolo/classify/val.py
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@ -4,12 +4,27 @@ import torch
from ultralytics.data import ClassificationDataset, build_dataloader
from ultralytics.engine.validator import BaseValidator
from ultralytics.utils import DEFAULT_CFG, LOGGER
from ultralytics.utils import LOGGER
from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
from ultralytics.utils.plotting import plot_images
class ClassificationValidator(BaseValidator):
"""
A class extending the BaseValidator class for validation based on a classification model.
Notes:
- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
Example:
```python
from ultralytics.models.yolo.classify import ClassificationValidator
args = dict(model='yolov8n-cls.pt', data='imagenet10')
validator = ClassificationValidator(args=args)
validator(model=args['model'])
```
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
@ -92,21 +107,3 @@ class ClassificationValidator(BaseValidator):
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names,
on_plot=self.on_plot) # pred
def val(cfg=DEFAULT_CFG, use_python=False):
"""Validate YOLO model using custom data."""
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
data = cfg.data or 'mnist160'
args = dict(model=model, data=data)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = ClassificationValidator(args=args)
validator(model=args['model'])
if __name__ == '__main__':
val()

8
ultralytics/models/yolo/detect/__init__.py
View File

@ -1,7 +1,7 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .predict import DetectionPredictor, predict
from .train import DetectionTrainer, train
from .val import DetectionValidator, val
from .predict import DetectionPredictor
from .train import DetectionTrainer
from .val import DetectionValidator
__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'
__all__ = 'DetectionPredictor', 'DetectionTrainer', 'DetectionValidator'

33
ultralytics/models/yolo/detect/predict.py
View File

@ -4,10 +4,23 @@ import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
from ultralytics.utils import ops
class DetectionPredictor(BasePredictor):
"""
A class extending the BasePredictor class for prediction based on a detection model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.detect import DetectionPredictor
args = dict(model='yolov8n.pt', source=ASSETS)
predictor = DetectionPredictor(overrides=args)
predictor.predict_cli()
```
"""
def postprocess(self, preds, img, orig_imgs):
"""Post-processes predictions and returns a list of Results objects."""
@ -27,21 +40,3 @@ class DetectionPredictor(BasePredictor):
img_path = path[i] if isinstance(path, list) else path
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO model inference on input image(s)."""
model = cfg.model or 'yolov8n.pt'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = DetectionPredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()

33
ultralytics/models/yolo/detect/train.py
View File

@ -8,12 +8,24 @@ from ultralytics.data import build_dataloader, build_yolo_dataset
from ultralytics.engine.trainer import BaseTrainer
from ultralytics.models import yolo
from ultralytics.nn.tasks import DetectionModel
from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
from ultralytics.utils import LOGGER, RANK
from ultralytics.utils.plotting import plot_images, plot_labels, plot_results
from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
class DetectionTrainer(BaseTrainer):
"""
A class extending the BaseTrainer class for training based on a detection model.
Example:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
args = dict(model='yolov8n.pt', data='coco8.yaml', epochs=3)
trainer = DetectionTrainer(overrides=args)
trainer.train()
```
"""
def build_dataset(self, img_path, mode='train', batch=None):
"""
@ -102,22 +114,3 @@ class DetectionTrainer(BaseTrainer):
boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train and optimize YOLO model given training data and device."""
model = cfg.model or 'yolov8n.pt'
data = cfg.data or 'coco8.yaml' # or yolo.ClassificationDataset("mnist")
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = DetectionTrainer(overrides=args)
trainer.train()
if __name__ == '__main__':
train()

32
ultralytics/models/yolo/detect/val.py
View File

@ -8,7 +8,7 @@ import torch
from ultralytics.data import build_dataloader, build_yolo_dataset, converter
from ultralytics.engine.validator import BaseValidator
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
from ultralytics.utils import LOGGER, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
from ultralytics.utils.plotting import output_to_target, plot_images
@ -16,6 +16,18 @@ from ultralytics.utils.torch_utils import de_parallel
class DetectionValidator(BaseValidator):
"""
A class extending the BaseValidator class for validation based on a detection model.
Example:
```python
from ultralytics.models.yolo.detect import DetectionValidator
args = dict(model='yolov8n.pt', data='coco8.yaml')
validator = DetectionValidator(args=args)
validator(model=args['model'])
```
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize detection model with necessary variables and settings."""
@ -254,21 +266,3 @@ class DetectionValidator(BaseValidator):
except Exception as e:
LOGGER.warning(f'pycocotools unable to run: {e}')
return stats
def val(cfg=DEFAULT_CFG, use_python=False):
"""Validate trained YOLO model on validation dataset."""
model = cfg.model or 'yolov8n.pt'
data = cfg.data or 'coco8.yaml'
args = dict(model=model, data=data)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = DetectionValidator(args=args)
validator(model=args['model'])
if __name__ == '__main__':
val()

8
ultralytics/models/yolo/pose/__init__.py
View File

@ -1,7 +1,7 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .predict import PosePredictor, predict
from .train import PoseTrainer, train
from .val import PoseValidator, val
from .predict import PosePredictor
from .train import PoseTrainer
from .val import PoseValidator
__all__ = 'PoseTrainer', 'train', 'PoseValidator', 'val', 'PosePredictor', 'predict'
__all__ = 'PoseTrainer', 'PoseValidator', 'PosePredictor'

33
ultralytics/models/yolo/pose/predict.py
View File

@ -2,10 +2,23 @@
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import ASSETS, DEFAULT_CFG, LOGGER, ops
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
class PosePredictor(DetectionPredictor):
"""
A class extending the DetectionPredictor class for prediction based on a pose model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.pose import PosePredictor
args = dict(model='yolov8n-pose.pt', source=ASSETS)
predictor = PosePredictor(overrides=args)
predictor.predict_cli()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
@ -40,21 +53,3 @@ class PosePredictor(DetectionPredictor):
boxes=pred[:, :6],
keypoints=pred_kpts))
return results
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO to predict objects in an image or video."""
model = cfg.model or 'yolov8n-pose.pt'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = PosePredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()

31
ultralytics/models/yolo/pose/train.py
View File

@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
class PoseTrainer(yolo.detect.DetectionTrainer):
"""
A class extending the DetectionTrainer class for training based on a pose model.
Example:
```python
from ultralytics.models.yolo.pose import PoseTrainer
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
trainer = PoseTrainer(overrides=args)
trainer.train()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initialize a PoseTrainer object with specified configurations and overrides."""
@ -59,22 +71,3 @@ class PoseTrainer(yolo.detect.DetectionTrainer):
def plot_metrics(self):
"""Plots training/val metrics."""
plot_results(file=self.csv, pose=True, on_plot=self.on_plot) # save results.png
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train the YOLO model on the given data and device."""
model = cfg.model or 'yolov8n-pose.yaml'
data = cfg.data or 'coco8-pose.yaml'
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = PoseTrainer(overrides=args)
trainer.train()
if __name__ == '__main__':
train()

32
ultralytics/models/yolo/pose/val.py
View File

@ -6,13 +6,25 @@ import numpy as np
import torch
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
from ultralytics.utils import LOGGER, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou
from ultralytics.utils.plotting import output_to_target, plot_images
class PoseValidator(DetectionValidator):
"""
A class extending the DetectionValidator class for validation based on a pose model.
Example:
```python
from ultralytics.models.yolo.pose import PoseValidator
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
validator = PoseValidator(args=args)
validator(model=args['model'])
```
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
@ -201,21 +213,3 @@ class PoseValidator(DetectionValidator):
except Exception as e:
LOGGER.warning(f'pycocotools unable to run: {e}')
return stats
def val(cfg=DEFAULT_CFG, use_python=False):
"""Performs validation on YOLO model using given data."""
model = cfg.model or 'yolov8n-pose.pt'
data = cfg.data or 'coco8-pose.yaml'
args = dict(model=model, data=data)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = PoseValidator(args=args)
validator(model=args['model'])
if __name__ == '__main__':
val()

8
ultralytics/models/yolo/segment/__init__.py
View File

@ -1,7 +1,7 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .predict import SegmentationPredictor, predict
from .train import SegmentationTrainer, train
from .val import SegmentationValidator, val
from .predict import SegmentationPredictor
from .train import SegmentationTrainer
from .val import SegmentationValidator
__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'
__all__ = 'SegmentationPredictor', 'SegmentationTrainer', 'SegmentationValidator'

33
ultralytics/models/yolo/segment/predict.py
View File

@ -4,10 +4,23 @@ import torch
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
from ultralytics.utils import DEFAULT_CFG, ops
class SegmentationPredictor(DetectionPredictor):
"""
A class extending the DetectionPredictor class for prediction based on a segmentation model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.segment import SegmentationPredictor
args = dict(model='yolov8n-seg.pt', source=ASSETS)
predictor = SegmentationPredictor(overrides=args)
predictor.predict_cli()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
@ -42,21 +55,3 @@ class SegmentationPredictor(DetectionPredictor):
results.append(
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results
def predict(cfg=DEFAULT_CFG, use_python=False):
"""Runs YOLO object detection on an image or video source."""
model = cfg.model or 'yolov8n-seg.pt'
source = cfg.source or ASSETS
args = dict(model=model, source=source)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = SegmentationPredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()

28
ultralytics/models/yolo/segment/train.py
View File

@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
class SegmentationTrainer(yolo.detect.DetectionTrainer):
"""
A class extending the DetectionTrainer class for training based on a segmentation model.
Example:
```python
from ultralytics.models.yolo.segment import SegmentationTrainer
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
trainer = SegmentationTrainer(overrides=args)
trainer.train()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initialize a SegmentationTrainer object with given arguments."""
@ -46,19 +58,11 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
plot_results(file=self.csv, segment=True, on_plot=self.on_plot) # save results.png
def train(cfg=DEFAULT_CFG, use_python=False):
def train(cfg=DEFAULT_CFG):
"""Train a YOLO segmentation model based on passed arguments."""
model = cfg.model or 'yolov8n-seg.pt'
data = cfg.data or 'coco8-seg.yaml'
device = cfg.device if cfg.device is not None else ''
args = dict(model=model, data=data, device=device)
if use_python:
from ultralytics import YOLO
YOLO(model).train(**args)
else:
trainer = SegmentationTrainer(overrides=args)
trainer.train()
args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
trainer = SegmentationTrainer(overrides=args)
trainer.train()
if __name__ == '__main__':

27
ultralytics/models/yolo/segment/val.py
View File

@ -15,6 +15,18 @@ from ultralytics.utils.plotting import output_to_target, plot_images
class SegmentationValidator(DetectionValidator):
"""
A class extending the DetectionValidator class for validation based on a segmentation model.
Example:
```python
from ultralytics.models.yolo.segment import SegmentationValidator
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
validator = SegmentationValidator(args=args)
validator(model=args['model'])
```
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
@ -233,18 +245,11 @@ class SegmentationValidator(DetectionValidator):
return stats
def val(cfg=DEFAULT_CFG, use_python=False):
def val(cfg=DEFAULT_CFG):
"""Validate trained YOLO model on validation data."""
model = cfg.model or 'yolov8n-seg.pt'
data = cfg.data or 'coco8-seg.yaml'
args = dict(model=model, data=data)
if use_python:
from ultralytics import YOLO
YOLO(model).val(**args)
else:
validator = SegmentationValidator(args=args)
validator(model=args['model'])
args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
validator = SegmentationValidator(args=args)
validator(model=args['model'])
if __name__ == '__main__':