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ultralytics 8.0.54 TFLite export improvements and fixes (#1447)
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
30fc4b537f
commit
701fba4770
@ -79,7 +79,7 @@ pip install ultralytics
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YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
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@ -71,7 +71,7 @@ pip install ultralytics
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YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
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```bash
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yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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`yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)
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@ -22,11 +22,11 @@ export arguments.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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=== "CLI"
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@ -26,9 +26,9 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official detection model
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model = YOLO("yolov8n-seg.pt") # load an official segmentation model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official detection model
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model = YOLO('yolov8n-seg.pt') # load an official segmentation model
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model = YOLO('path/to/best.pt') # load a custom model
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# Track with the model
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
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@ -60,7 +60,7 @@ to [predict page](https://docs.ultralytics.com/modes/predict/).
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt")
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
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```
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=== "CLI"
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@ -82,7 +82,7 @@ any configurations(expect the `tracker_type`) you need to.
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt")
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
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```
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=== "CLI"
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@ -21,16 +21,24 @@ training arguments.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml') # build a new model from YAML
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Build a new model from YAML and start training from scratch
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yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
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```
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## Arguments
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@ -21,8 +21,8 @@ training `data` and arguments as model attributes. See Arguments section below f
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -60,14 +60,14 @@ classification into their Python projects using YOLOv8.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Use the model
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results = model.train(data="coco128.yaml", epochs=3) # train the model
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results = model.train(data='coco128.yaml', epochs=3) # train the model
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results = model.val() # evaluate model performance on the validation set
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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success = model.export(format="onnx") # export the model to ONNX format
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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success = model.export(format='onnx') # export the model to ONNX format
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```
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[Python Guide](usage/python.md){.md-button .md-button--primary}
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@ -26,11 +26,11 @@ see the [Configuration](../usage/cfg.md) page.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-cls.yaml") # build a new model from scratch
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model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n-cls.yaml') # build a new model from scratch
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model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data="mnist160", epochs=100, imgsz=64)
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model.train(data='mnist160', epochs=100, imgsz=64)
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```
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=== "CLI"
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@ -51,8 +51,8 @@ it's training `data` and arguments as model attributes.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -78,17 +78,17 @@ Use a trained YOLOv8n-cls model to run predictions on images.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -105,11 +105,11 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('yolov8n-cls.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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=== "CLI"
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@ -26,11 +26,11 @@ the [Configuration](../usage/cfg.md) page.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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@ -51,8 +51,8 @@ training `data` and arguments as model attributes.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -80,17 +80,17 @@ Use a trained YOLOv8n model to run predictions on images.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -107,11 +107,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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=== "CLI"
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@ -28,11 +28,11 @@ train an OpenPose model on a custom dataset, see the OpenPose Training page.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n.yaml') # build a new model from scratch
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model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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model.train(data='coco128.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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@ -53,8 +53,8 @@ training `data` and arguments as model attributes.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -82,17 +82,17 @@ Use a trained YOLOv8n model to run predictions on images.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -109,11 +109,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format="onnx")
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model.export(format='onnx')
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```
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=== "CLI"
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@ -26,11 +26,11 @@ arguments see the [Configuration](../usage/cfg.md) page.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-seg.yaml") # build a new model from scratch
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model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
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model = YOLO('yolov8n-seg.yaml') # build a new model from scratch
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model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
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model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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@ -51,8 +51,8 @@ retains it's training `data` and arguments as model attributes.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-seg.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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@ -84,17 +84,17 @@ Use a trained YOLOv8n-seg model to run predictions on images.
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n-seg.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo segment predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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@ -111,11 +111,11 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
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||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n-seg.pt") # load an official model
|
||||
model = YOLO("path/to/best.pt") # load a custom trained
|
||||
model = YOLO('yolov8n-seg.pt') # load an official model
|
||||
model = YOLO('path/to/best.pt') # load a custom trained
|
||||
|
||||
# Export the model
|
||||
model.export(format="onnx")
|
||||
model.export(format='onnx')
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
|
||||
@ -17,7 +17,7 @@ def on_predict_batch_end(predictor):
|
||||
im0s = im0s if isinstance(im0s, list) else [im0s]
|
||||
predictor.results = zip(predictor.results, im0s)
|
||||
|
||||
model = YOLO(f"yolov8n.pt")
|
||||
model = YOLO(f'yolov8n.pt')
|
||||
model.add_callback("on_predict_batch_end", on_predict_batch_end)
|
||||
for (result, frame) in model.track/predict():
|
||||
pass
|
||||
|
||||
@ -59,8 +59,8 @@ Use a trained YOLOv8n model to run predictions on images.
|
||||
!!! example ""
|
||||
|
||||
```bash
|
||||
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
||||
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
||||
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
|
||||
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
|
||||
```
|
||||
|
||||
## Export
|
||||
|
||||
@ -6,7 +6,7 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolov8n.pt") # pass any model type
|
||||
model = YOLO('yolov8n.pt') # pass any model type
|
||||
model.train(epochs=5)
|
||||
```
|
||||
|
||||
@ -14,8 +14,8 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolov8n.yaml")
|
||||
model.train(data="coco128.yaml", epochs=5)
|
||||
model = YOLO('yolov8n.yaml')
|
||||
model.train(data='coco128.yaml', epochs=5)
|
||||
```
|
||||
|
||||
=== "Resume"
|
||||
@ -31,8 +31,8 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolov8n.yaml")
|
||||
model.train(data="coco128.yaml", epochs=5)
|
||||
model = YOLO('yolov8n.yaml')
|
||||
model.train(data='coco128.yaml', epochs=5)
|
||||
model.val() # It'll automatically evaluate the data you trained.
|
||||
```
|
||||
|
||||
@ -44,7 +44,7 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
|
||||
# It'll use the data yaml file in model.pt if you don't set data.
|
||||
model.val()
|
||||
# or you can set the data you want to val
|
||||
model.val(data="coco128.yaml")
|
||||
model.val(data='coco128.yaml')
|
||||
```
|
||||
|
||||
!!! example "Predict"
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
|
||||
# Base ----------------------------------------
|
||||
matplotlib>=3.2.2
|
||||
numpy>=1.18.5
|
||||
numpy>=1.21.6
|
||||
opencv-python>=4.6.0
|
||||
Pillow>=7.1.2
|
||||
PyYAML>=5.3.1
|
||||
|
||||
@ -207,9 +207,9 @@ def test_predict_callback_and_setup():
|
||||
def test_result():
|
||||
model = YOLO('yolov8n-seg.pt')
|
||||
res = model([SOURCE, SOURCE])
|
||||
res[0].cpu().numpy()
|
||||
res[0].plot(show_conf=False)
|
||||
print(res[0].path)
|
||||
res[0] = res[0].cpu().numpy()
|
||||
print(res[0].path, res[0].masks.masks)
|
||||
|
||||
model = YOLO('yolov8n.pt')
|
||||
res = model(SOURCE)
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
__version__ = '8.0.53'
|
||||
__version__ = '8.0.54'
|
||||
|
||||
from ultralytics.yolo.engine.model import YOLO
|
||||
from ultralytics.yolo.utils.checks import check_yolo as checks
|
||||
|
||||
@ -411,12 +411,12 @@ class Detect(nn.Module):
|
||||
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
|
||||
self.shape = shape
|
||||
|
||||
if self.export and self.format == 'edgetpu': # FlexSplitV ops issue
|
||||
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
|
||||
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
|
||||
if self.export and self.format in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'): # avoid TF FlexSplitV ops
|
||||
box = x_cat[:, :self.reg_max * 4]
|
||||
cls = x_cat[:, self.reg_max * 4:]
|
||||
else:
|
||||
box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
|
||||
box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
|
||||
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
|
||||
y = torch.cat((dbox, cls.sigmoid()), 1)
|
||||
return y if self.export else (y, x)
|
||||
|
||||
@ -11,8 +11,8 @@ import torch.nn as nn
|
||||
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
|
||||
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
|
||||
GhostBottleneck, GhostConv, Segment)
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_yaml
|
||||
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
|
||||
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
|
||||
|
||||
@ -151,15 +151,19 @@ class BaseModel(nn.Module):
|
||||
m.strides = fn(m.strides)
|
||||
return self
|
||||
|
||||
def load(self, weights):
|
||||
"""
|
||||
This function loads the weights of the model from a file
|
||||
def load(self, weights, verbose=True):
|
||||
"""Load the weights into the model.
|
||||
|
||||
Args:
|
||||
weights (str): The weights to load into the model.
|
||||
weights (dict) or (torch.nn.Module): The pre-trained weights to be loaded.
|
||||
verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
|
||||
"""
|
||||
# Force all tasks to implement this function
|
||||
raise NotImplementedError('This function needs to be implemented by derived classes!')
|
||||
model = weights['model'] if isinstance(weights, dict) else weights # torchvision models are not dicts
|
||||
csd = model.float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
||||
self.load_state_dict(csd, strict=False) # load
|
||||
if verbose:
|
||||
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
|
||||
|
||||
|
||||
class DetectionModel(BaseModel):
|
||||
@ -234,13 +238,6 @@ class DetectionModel(BaseModel):
|
||||
y[-1] = y[-1][..., i:] # small
|
||||
return y
|
||||
|
||||
def load(self, weights, verbose=True):
|
||||
csd = weights.float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
||||
self.load_state_dict(csd, strict=False) # load
|
||||
if verbose and RANK == -1:
|
||||
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
|
||||
|
||||
|
||||
class SegmentationModel(DetectionModel):
|
||||
# YOLOv8 segmentation model
|
||||
@ -293,12 +290,6 @@ class ClassificationModel(BaseModel):
|
||||
self.names = {i: f'{i}' for i in range(self.yaml['nc'])} # default names dict
|
||||
self.info()
|
||||
|
||||
def load(self, weights):
|
||||
model = weights['model'] if isinstance(weights, dict) else weights # torchvision models are not dicts
|
||||
csd = model.float().state_dict()
|
||||
csd = intersect_dicts(csd, self.state_dict()) # intersect
|
||||
self.load_state_dict(csd, strict=False) # load
|
||||
|
||||
@staticmethod
|
||||
def reshape_outputs(model, nc):
|
||||
# Update a TorchVision classification model to class count 'n' if required
|
||||
@ -338,6 +329,7 @@ def torch_safe_load(weight):
|
||||
"""
|
||||
from ultralytics.yolo.utils.downloads import attempt_download_asset
|
||||
|
||||
check_suffix(file=weight, suffix='.pt')
|
||||
file = attempt_download_asset(weight) # search online if missing locally
|
||||
try:
|
||||
return torch.load(file, map_location='cpu'), file # load
|
||||
|
||||
@ -54,11 +54,10 @@ CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay',
|
||||
'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou') # fractional floats limited to 0.0 - 1.0
|
||||
CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
|
||||
'line_thickness', 'workspace', 'nbs', 'save_period')
|
||||
CFG_BOOL_KEYS = ('save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect',
|
||||
'cos_lr', 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show',
|
||||
'save_txt', 'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment',
|
||||
'agnostic_nms', 'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms',
|
||||
'v5loader')
|
||||
CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr',
|
||||
'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt',
|
||||
'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms',
|
||||
'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
|
||||
|
||||
# Define valid tasks and modes
|
||||
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
|
||||
@ -290,6 +289,8 @@ def entrypoint(debug=''):
|
||||
from ultralytics.yolo.engine.model import YOLO
|
||||
overrides['model'] = model
|
||||
model = YOLO(model, task=task)
|
||||
if isinstance(overrides.get('pretrained'), str):
|
||||
model.load(overrides['pretrained'])
|
||||
|
||||
# Task Update
|
||||
if task != model.task:
|
||||
|
||||
@ -188,7 +188,7 @@ class Exporter:
|
||||
m.dynamic = self.args.dynamic
|
||||
m.export = True
|
||||
m.format = self.args.format
|
||||
elif isinstance(m, C2f) and not edgetpu:
|
||||
elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
|
||||
# EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
|
||||
m.forward = m.forward_split
|
||||
|
||||
|
||||
@ -8,8 +8,8 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
|
||||
guess_model_task, nn)
|
||||
from ultralytics.yolo.cfg import get_cfg
|
||||
from ultralytics.yolo.engine.exporter import Exporter
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, ONLINE, RANK, ROOT,
|
||||
callbacks, is_git_dir, is_pip_package, yaml_load)
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
|
||||
is_git_dir, yaml_load)
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
|
||||
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
|
||||
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
|
||||
@ -153,16 +153,10 @@ class YOLO:
|
||||
f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
|
||||
f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
|
||||
|
||||
def _check_pip_update(self):
|
||||
@smart_inference_mode()
|
||||
def reset_weights(self):
|
||||
"""
|
||||
Inform user of ultralytics package update availability
|
||||
"""
|
||||
if ONLINE and is_pip_package():
|
||||
check_pip_update_available()
|
||||
|
||||
def reset(self):
|
||||
"""
|
||||
Resets the model modules.
|
||||
Resets the model modules parameters to randomly initialized values, losing all training information.
|
||||
"""
|
||||
self._check_is_pytorch_model()
|
||||
for m in self.model.modules():
|
||||
@ -170,6 +164,18 @@ class YOLO:
|
||||
m.reset_parameters()
|
||||
for p in self.model.parameters():
|
||||
p.requires_grad = True
|
||||
return self
|
||||
|
||||
@smart_inference_mode()
|
||||
def load(self, weights='yolov8n.pt'):
|
||||
"""
|
||||
Transfers parameters with matching names and shapes from 'weights' to model.
|
||||
"""
|
||||
self._check_is_pytorch_model()
|
||||
if isinstance(weights, (str, Path)):
|
||||
weights, self.ckpt = attempt_load_one_weight(weights)
|
||||
self.model.load(weights)
|
||||
return self
|
||||
|
||||
def info(self, verbose=False):
|
||||
"""
|
||||
@ -299,7 +305,7 @@ class YOLO:
|
||||
**kwargs (Any): Any number of arguments representing the training configuration.
|
||||
"""
|
||||
self._check_is_pytorch_model()
|
||||
self._check_pip_update()
|
||||
check_pip_update_available()
|
||||
overrides = self.overrides.copy()
|
||||
overrides.update(kwargs)
|
||||
if kwargs.get('cfg'):
|
||||
|
||||
@ -48,7 +48,7 @@ class Results:
|
||||
self.probs = probs if probs is not None else None
|
||||
self.names = names
|
||||
self.path = path
|
||||
self._keys = [k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None]
|
||||
self._keys = ('boxes', 'masks', 'probs')
|
||||
|
||||
def pandas(self):
|
||||
pass
|
||||
@ -56,7 +56,7 @@ class Results:
|
||||
|
||||
def __getitem__(self, idx):
|
||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
setattr(r, k, getattr(self, k)[idx])
|
||||
return r
|
||||
|
||||
@ -70,30 +70,30 @@ class Results:
|
||||
|
||||
def cpu(self):
|
||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
setattr(r, k, getattr(self, k).cpu())
|
||||
return r
|
||||
|
||||
def numpy(self):
|
||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
setattr(r, k, getattr(self, k).numpy())
|
||||
return r
|
||||
|
||||
def cuda(self):
|
||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
setattr(r, k, getattr(self, k).cuda())
|
||||
return r
|
||||
|
||||
def to(self, *args, **kwargs):
|
||||
r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
setattr(r, k, getattr(self, k).to(*args, **kwargs))
|
||||
return r
|
||||
|
||||
def __len__(self):
|
||||
for k in self._keys:
|
||||
for k in self.keys:
|
||||
return len(getattr(self, k))
|
||||
|
||||
def __str__(self):
|
||||
@ -107,6 +107,10 @@ class Results:
|
||||
name = self.__class__.__name__
|
||||
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
return [k for k in self._keys if getattr(self, k) is not None]
|
||||
|
||||
def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
||||
"""
|
||||
Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.
|
||||
|
||||
@ -46,14 +46,14 @@ HELP_MSG = \
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolov8n.yaml") # build a new model from scratch
|
||||
model = YOLO('yolov8n.yaml') # build a new model from scratch
|
||||
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Use the model
|
||||
results = model.train(data="coco128.yaml", epochs=3) # train the model
|
||||
results = model.val() # evaluate model performance on the validation set
|
||||
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
|
||||
success = model.export(format="onnx") # export the model to ONNX format
|
||||
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
|
||||
success = model.export(format='onnx') # export the model to ONNX format
|
||||
|
||||
3. Use the command line interface (CLI):
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
AutoBatch utils
|
||||
Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch.
|
||||
"""
|
||||
|
||||
from copy import deepcopy
|
||||
@ -13,18 +13,35 @@ from ultralytics.yolo.utils.torch_utils import profile
|
||||
|
||||
|
||||
def check_train_batch_size(model, imgsz=640, amp=True):
|
||||
# Check YOLOv5 training batch size
|
||||
"""
|
||||
Check YOLO training batch size using the autobatch() function.
|
||||
|
||||
Args:
|
||||
model (torch.nn.Module): YOLO model to check batch size for.
|
||||
imgsz (int): Image size used for training.
|
||||
amp (bool): If True, use automatic mixed precision (AMP) for training.
|
||||
|
||||
Returns:
|
||||
int: Optimal batch size computed using the autobatch() function.
|
||||
"""
|
||||
|
||||
with torch.cuda.amp.autocast(amp):
|
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return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||||
|
||||
|
||||
def autobatch(model, imgsz=640, fraction=0.7, batch_size=16):
|
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# Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
|
||||
# Usage:
|
||||
# import torch
|
||||
# from utils.autobatch import autobatch
|
||||
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
|
||||
# print(autobatch(model))
|
||||
def autobatch(model, imgsz=640, fraction=0.67, batch_size=16):
|
||||
"""
|
||||
Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.
|
||||
|
||||
Args:
|
||||
model: YOLO model to compute batch size for.
|
||||
imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
|
||||
fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67.
|
||||
batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.
|
||||
|
||||
Returns:
|
||||
int: The optimal batch size.
|
||||
"""
|
||||
|
||||
# Check device
|
||||
prefix = colorstr('AutoBatch: ')
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING, colorstr
|
||||
|
||||
try:
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
@ -18,11 +18,14 @@ def _log_scalars(scalars, step=0):
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
global writer
|
||||
try:
|
||||
writer = SummaryWriter(str(trainer.save_dir))
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
|
||||
if SummaryWriter:
|
||||
try:
|
||||
global writer
|
||||
writer = SummaryWriter(str(trainer.save_dir))
|
||||
prefix = colorstr('TensorBoard: ')
|
||||
LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def on_batch_end(trainer):
|
||||
|
||||
@ -20,8 +20,8 @@ import requests
|
||||
import torch
|
||||
from matplotlib import font_manager
|
||||
|
||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis,
|
||||
is_colab, is_docker, is_jupyter, is_online)
|
||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
|
||||
emojis, is_colab, is_docker, is_jupyter, is_online, is_pip_package)
|
||||
|
||||
|
||||
def is_ascii(s) -> bool:
|
||||
@ -141,12 +141,14 @@ def check_pip_update_available():
|
||||
Returns:
|
||||
bool: True if an update is available, False otherwise.
|
||||
"""
|
||||
from ultralytics import __version__
|
||||
latest = check_latest_pypi_version()
|
||||
if pkg.parse_version(__version__) < pkg.parse_version(latest): # update is available
|
||||
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
|
||||
f"Update with 'pip install -U ultralytics'")
|
||||
return True
|
||||
if ONLINE and is_pip_package():
|
||||
with contextlib.suppress(ConnectionError):
|
||||
from ultralytics import __version__
|
||||
latest = check_latest_pypi_version()
|
||||
if pkg.parse_version(__version__) < pkg.parse_version(latest): # update is available
|
||||
LOGGER.info(f'New https://pypi.org/project/ultralytics/{latest} available 😃 '
|
||||
f"Update with 'pip install -U ultralytics'")
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
@ -235,11 +237,11 @@ def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
|
||||
# Check file(s) for acceptable suffix
|
||||
if file and suffix:
|
||||
if isinstance(suffix, str):
|
||||
suffix = [suffix]
|
||||
suffix = (suffix, )
|
||||
for f in file if isinstance(file, (list, tuple)) else [file]:
|
||||
s = Path(f).suffix.lower() # file suffix
|
||||
if len(s):
|
||||
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}'
|
||||
assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}, not {s}'
|
||||
|
||||
|
||||
def check_yolov5u_filename(file: str, verbose: bool = True):
|
||||
|
||||
@ -76,7 +76,7 @@ class DetectionPredictor(BasePredictor):
|
||||
if self.args.save_crop:
|
||||
save_one_box(d.xyxy,
|
||||
imc,
|
||||
file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||
file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||
BGR=True)
|
||||
|
||||
return log_string
|
||||
|
||||
@ -58,10 +58,9 @@ class DetectionTrainer(BaseTrainer):
|
||||
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
model = DetectionModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
||||
model = DetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
||||
if weights:
|
||||
model.load(weights)
|
||||
|
||||
return model
|
||||
|
||||
def get_validator(self):
|
||||
|
||||
@ -90,7 +90,7 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
if self.args.save_crop:
|
||||
save_one_box(d.xyxy,
|
||||
imc,
|
||||
file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||
file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||
BGR=True)
|
||||
|
||||
return log_string
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user