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CLI Simplification (#449)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
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14
README.md
14
README.md
@ -91,7 +91,7 @@ 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 task=detect mode=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 a full list
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@ -158,10 +158,10 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage ex
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
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<br>Reproduce by `yolo val detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
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<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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@ -178,10 +178,10 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for us
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo mode=val task=segment data=coco.yaml device=0`
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<br>Reproduce by `yolo val segment data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu`
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<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
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</details>
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@ -198,10 +198,10 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
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<br>Reproduce by `yolo mode=val task=classify data=path/to/ImageNet device=0`
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<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu`
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<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
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</details>
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@ -67,7 +67,7 @@ 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 task=detect mode=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)中可用`yolo`[参数](https://docs.ultralytics.com/config/)的完整列表。
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@ -124,9 +124,9 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
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<br>复现命令 `yolo mode=val task=detect data=coco.yaml device=0`
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<br>复现命令 `yolo val detect data=coco.yaml device=0`
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- **推理速度**使用 COCO 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
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<br>复现命令 `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
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<br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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@ -141,9 +141,9 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
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<br>复现命令 `yolo mode=val task=segment data=coco.yaml device=0`
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<br>复现命令 `yolo val segment data=coco.yaml device=0`
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- **推理速度**使用 COCO 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
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<br>复现命令 `yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu`
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<br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
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</details>
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@ -158,9 +158,9 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
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- **acc** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。
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<br>复现命令 `yolo mode=val task=classify data=path/to/ImageNet device=0`
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<br>复现命令 `yolo val classify data=path/to/ImageNet device=0`
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- **推理速度**使用 ImageNet 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
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<br>复现命令 `yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu`
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<br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
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</details>
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50
docs/cli.md
50
docs/cli.md
@ -4,37 +4,37 @@ YOLO command line interface is the easiest way to get started.
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!!! tip "Syntax"
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```bash
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yolo task=detect mode=train model=yolov8n.yaml epochs=1 ...
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... ... ...
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segment predict yolov8n-seg.pt
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classify val yolov8n-cls.pt
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yolo task=detect mode=train model=yolov8n.yaml args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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```
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The experiment arguments can be overridden directly by pass `arg=val` covered in the next section. You can run any
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supported task by setting `task` and `mode` in cli.
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The default arguments can be overridden directly by passing custom `arg=val` covered in the next section. You can run
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any supported task by setting `task` and `mode` in CLI.
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=== "Training"
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| | `task` | snippet |
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|------------------|------------|------------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo task=detect mode=train </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo task=segment mode=train </code></pre> |
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| Classification | `classify` | <pre><code>yolo task=classify mode=train </code></pre> |
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| Detection | `detect` | <pre><code>yolo detect train </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment train </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify train </code></pre> |
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=== "Prediction"
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| | `task` | snippet |
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|------------------|------------|--------------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo task=detect mode=predict </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo task=segment mode=predict </code></pre> |
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| Classification | `classify` | <pre><code>yolo task=classify mode=predict </code></pre> |
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| Detection | `detect` | <pre><code>yolo detect predict </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment predict </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify predict </code></pre> |
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=== "Validation"
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| | `task` | snippet |
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|------------------|------------|-----------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo task=detect mode=val </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo task=segment mode=val </code></pre> |
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| Classification | `classify` | <pre><code>yolo task=classify mode=val </code></pre> |
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| Detection | `detect` | <pre><code>yolo detect val </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment val </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify val </code></pre> |
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!!! note ""
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@ -44,19 +44,19 @@ supported task by setting `task` and `mode` in cli.
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## Overriding default config arguments
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All global default arguments can be overriden by simply passing them as arguments in the cli.
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Default arguments can be overriden by simply passing them as arguments in the CLI.
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!!! tip ""
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=== "Syntax"
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```bash
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yolo task= ... mode= ... {++ arg=val ++}
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yolo task mode arg=val...
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```
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=== "Example"
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Perform detection training for `10 epochs` with `learning_rate` of `0.01`
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```bash
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yolo task=detect mode=train {++ epochs=10 lr0=0.01 ++}
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yolo detect train epochs=10 lr0=0.01
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```
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---
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@ -67,23 +67,19 @@ You can override config file entirely by passing a new file. You can create a co
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current working dir as follows:
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```bash
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yolo task=init
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yolo copy-config
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```
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You can then use `cfg=name.yaml` command to pass the new config file
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You can then use `cfg=default_copy.yaml` command to pass the new config file along with any addition args:
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```bash
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yolo cfg=default.yaml
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yolo cfg=default_copy.yaml args...
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```
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??? example
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=== "Command"
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```bash
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yolo task=init
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yolo cfg=default.yaml
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yolo copy-config
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yolo cfg=default_copy.yaml args...
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```
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=== "Results"
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TODO: add terminal output
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@ -38,13 +38,13 @@ include train, val, and predict.
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- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
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production or when deploying the model to users.
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| Key | Value | Description |
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|--------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
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| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
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| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
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| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
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| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
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| Key | Value | Description |
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|--------|----------|--------------------------------------------------------------------------------------------------------|
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| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify` |
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| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict`, `export` |
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| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
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| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
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| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
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### Training
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@ -197,6 +197,6 @@ it easier to debug and optimize the training process.
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|-----------|---------|---------------------------------------------------------------------------------------------|
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| project: | 'runs' | The project name |
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| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
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| exist_ok: | `False` | ??? |
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| exist_ok: | `False` | Will replace current directory contents if set to True and output directory exists. |
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| plots | `False` | **Validation**: Save plots while validation |
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| nosave | `False` | Don't save any plots, models or files |
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| save | `False` | Save any plots, models or files |
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@ -36,11 +36,11 @@ CLI requires no customization or code. You can simply run all tasks from the ter
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=== "Example training"
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```bash
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yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=0
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yolo detect train model=yolov8n.pt data=coco128.yaml device=0
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```
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=== "Example Multi-GPU training"
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```bash
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yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
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yolo detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
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```
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[CLI Guide](cli.md){ .md-button .md-button--primary}
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@ -35,7 +35,7 @@ see the [Configuration](../config.md) page.
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=== "CLI"
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```bash
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yolo task=classify mode=train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
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yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
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```
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## Val
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@ -60,8 +60,8 @@ it's training `data` and arguments as model attributes.
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=== "CLI"
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```bash
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yolo task=classify mode=val model=yolov8n-cls.pt # val official model
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yolo task=classify mode=val model=path/to/best.pt # val custom model
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yolo classify val model=yolov8n-cls.pt # val official model
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yolo classify val model=path/to/best.pt # val custom model
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```
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## Predict
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@ -85,8 +85,8 @@ Use a trained YOLOv8n-cls model to run predictions on images.
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=== "CLI"
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```bash
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yolo task=classify mode=predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo task=classify mode=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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## Export
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@ -110,8 +110,8 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
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=== "CLI"
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```bash
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yolo mode=export model=yolov8n-cls.pt format=onnx # export official model
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yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
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yolo export model=yolov8n-cls.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-cls export formats include:
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@ -35,7 +35,7 @@ the [Configuration](../config.md) page.
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=== "CLI"
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```bash
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yolo task=detect mode=train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Val
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@ -60,8 +60,8 @@ training `data` and arguments as model attributes.
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=== "CLI"
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```bash
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yolo task=detect mode=val model=yolov8n.pt # val official model
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yolo task=detect mode=val model=path/to/best.pt # val custom model
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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## Predict
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@ -85,8 +85,8 @@ Use a trained YOLOv8n model to run predictions on images.
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=== "CLI"
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```bash
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yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo task=detect mode=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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## Export
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@ -110,8 +110,8 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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=== "CLI"
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```bash
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yolo mode=export model=yolov8n.pt format=onnx # export official model
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yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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||||
Available YOLOv8 export formats include:
|
||||
|
||||
@ -35,7 +35,7 @@ arguments see the [Configuration](../config.md) page.
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
|
||||
yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Val
|
||||
@ -60,8 +60,8 @@ retains it's training `data` and arguments as model attributes.
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=val model=yolov8n-seg.pt # val official model
|
||||
yolo task=segment mode=val model=path/to/best.pt # val custom model
|
||||
yolo segment val model=yolov8n-seg.pt # val official model
|
||||
yolo segment val model=path/to/best.pt # val custom model
|
||||
```
|
||||
|
||||
## Predict
|
||||
@ -85,8 +85,8 @@ Use a trained YOLOv8n-seg model to run predictions on images.
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo task=segment mode=predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
||||
yolo task=segment mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
||||
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
|
||||
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
|
||||
```
|
||||
|
||||
## Export
|
||||
@ -110,8 +110,8 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo mode=export model=yolov8n-seg.pt format=onnx # export official model
|
||||
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
|
||||
yolo export model=yolov8n-seg.pt format=onnx # export official model
|
||||
yolo export model=path/to/best.pt format=onnx # export custom trained model
|
||||
```
|
||||
|
||||
Available YOLOv8-seg export formats include:
|
||||
|
||||
13
mkdocs.yml
13
mkdocs.yml
@ -75,18 +75,18 @@ plugins:
|
||||
|
||||
# Primary navigation
|
||||
nav:
|
||||
- Home: home.md
|
||||
- Home: index.md
|
||||
- Quickstart: quickstart.md
|
||||
- Tasks:
|
||||
- Detection: tasks/detection.md
|
||||
- Segmentation: tasks/segmentation.md
|
||||
- Classification: tasks/classification.md
|
||||
- Usage:
|
||||
- CLI: cli.md
|
||||
- Python: python.md
|
||||
- Predict: predict.md
|
||||
- Configuration: config.md
|
||||
- Customization Guide: engine.md
|
||||
- CLI: cli.md
|
||||
- Python: python.md
|
||||
- Predict: predict.md
|
||||
- Configuration: config.md
|
||||
- Customization Guide: engine.md
|
||||
- Ultralytics HUB: hub.md
|
||||
- iOS and Android App: app.md
|
||||
- Reference:
|
||||
@ -99,4 +99,3 @@ nav:
|
||||
- Results: reference/results.md
|
||||
- ultralytics.nn: reference/nn.md
|
||||
- Operations: reference/ops.md
|
||||
- Docs: README.md
|
||||
|
||||
6
setup.py
6
setup.py
@ -11,6 +11,7 @@ FILE = Path(__file__).resolve()
|
||||
PARENT = FILE.parent # root directory
|
||||
README = (PARENT / "README.md").read_text(encoding="utf-8")
|
||||
REQUIREMENTS = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements((PARENT / 'requirements.txt').read_text())]
|
||||
PKG_REQUIREMENTS = ['sentry_sdk'] # pip-only requirements
|
||||
|
||||
|
||||
def get_version():
|
||||
@ -35,7 +36,7 @@ setup(
|
||||
author_email='hello@ultralytics.com',
|
||||
packages=find_packages(), # required
|
||||
include_package_data=True,
|
||||
install_requires=REQUIREMENTS,
|
||||
install_requires=REQUIREMENTS + PKG_REQUIREMENTS,
|
||||
extras_require={
|
||||
'dev':
|
||||
['check-manifest', 'pytest', 'pytest-cov', 'coverage', 'mkdocs', 'mkdocstrings[python]', 'mkdocs-material']},
|
||||
@ -49,4 +50,5 @@ setup(
|
||||
"Topic :: Scientific/Engineering :: Image Recognition", "Operating System :: POSIX :: Linux",
|
||||
"Operating System :: MacOS", "Operating System :: Microsoft :: Windows"],
|
||||
keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics",
|
||||
entry_points={'console_scripts': ['yolo = ultralytics.yolo.cli:cli', 'ultralytics = ultralytics.yolo.cli:cli']})
|
||||
entry_points={
|
||||
'console_scripts': ['yolo = ultralytics.yolo.cli:entrypoint', 'ultralytics = ultralytics.yolo.cli:entrypoint']})
|
||||
|
||||
@ -14,30 +14,32 @@ def run(cmd):
|
||||
subprocess.run(cmd.split(), check=True)
|
||||
|
||||
|
||||
def test_checks():
|
||||
run('yolo mode=checks')
|
||||
def test_special_modes():
|
||||
run('yolo checks')
|
||||
run('yolo settings')
|
||||
run('yolo help')
|
||||
|
||||
|
||||
# Train checks ---------------------------------------------------------------------------------------------------------
|
||||
def test_train_det():
|
||||
run(f'yolo mode=train task=detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1')
|
||||
run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1')
|
||||
|
||||
|
||||
def test_train_seg():
|
||||
run(f'yolo mode=train task=segment model={CFG}-seg.yaml data=coco8-seg.yaml imgsz=32 epochs=1')
|
||||
run(f'yolo train segment model={CFG}-seg.yaml data=coco8-seg.yaml imgsz=32 epochs=1')
|
||||
|
||||
|
||||
def test_train_cls():
|
||||
run(f'yolo mode=train task=classify model={CFG}-cls.yaml data=mnist160 imgsz=32 epochs=1')
|
||||
run(f'yolo train classify model={CFG}-cls.yaml data=mnist160 imgsz=32 epochs=1')
|
||||
|
||||
|
||||
# Val checks -----------------------------------------------------------------------------------------------------------
|
||||
def test_val_detect():
|
||||
run(f'yolo mode=val task=detect model={MODEL}.pt data=coco8.yaml imgsz=32 epochs=1')
|
||||
run(f'yolo val detect model={MODEL}.pt data=coco8.yaml imgsz=32 epochs=1')
|
||||
|
||||
|
||||
def test_val_segment():
|
||||
run(f'yolo mode=val task=segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32 epochs=1')
|
||||
run(f'yolo val segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32 epochs=1')
|
||||
|
||||
|
||||
def test_val_classify():
|
||||
@ -46,11 +48,11 @@ def test_val_classify():
|
||||
|
||||
# Predict checks -------------------------------------------------------------------------------------------------------
|
||||
def test_predict_detect():
|
||||
run(f"yolo mode=predict task=detect model={MODEL}.pt source={ROOT / 'assets'}")
|
||||
run(f"yolo predict detect model={MODEL}.pt source={ROOT / 'assets'}")
|
||||
|
||||
|
||||
def test_predict_segment():
|
||||
run(f"yolo mode=predict task=segment model={MODEL}-seg.pt source={ROOT / 'assets'}")
|
||||
run(f"yolo predict segment model={MODEL}-seg.pt source={ROOT / 'assets'}")
|
||||
|
||||
|
||||
def test_predict_classify():
|
||||
@ -59,12 +61,12 @@ def test_predict_classify():
|
||||
|
||||
# Export checks --------------------------------------------------------------------------------------------------------
|
||||
def test_export_detect_torchscript():
|
||||
run(f'yolo mode=export model={MODEL}.pt format=torchscript')
|
||||
run(f'yolo export model={MODEL}.pt format=torchscript')
|
||||
|
||||
|
||||
def test_export_segment_torchscript():
|
||||
run(f'yolo mode=export model={MODEL}-seg.pt format=torchscript')
|
||||
run(f'yolo export model={MODEL}-seg.pt format=torchscript')
|
||||
|
||||
|
||||
def test_export_classify_torchscript():
|
||||
run(f'yolo mode=export model={MODEL}-cls.pt format=torchscript')
|
||||
run(f'yolo export model={MODEL}-cls.pt format=torchscript')
|
||||
|
||||
@ -1,18 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import argparse
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import hydra
|
||||
from hydra import compose, initialize
|
||||
|
||||
from ultralytics import hub, yolo
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER, PREFIX, print_settings, yaml_load
|
||||
|
||||
DIR = Path(__file__).parent
|
||||
|
||||
|
||||
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent.relative_to(DIR)), config_name=DEFAULT_CONFIG.name)
|
||||
def cli(cfg):
|
||||
"""
|
||||
Run a specified task and mode with the given configuration.
|
||||
@ -21,21 +20,13 @@ def cli(cfg):
|
||||
cfg (DictConfig): Configuration for the task and mode.
|
||||
"""
|
||||
# LOGGER.info(f"{colorstr(f'Ultralytics YOLO v{ultralytics.__version__}')}")
|
||||
from ultralytics.yolo.configs import get_config
|
||||
|
||||
if cfg.cfg:
|
||||
LOGGER.info(f"Overriding default config with {cfg.cfg}")
|
||||
LOGGER.info(f"{PREFIX}Overriding default config with {cfg.cfg}")
|
||||
cfg = get_config(cfg.cfg)
|
||||
task, mode = cfg.task.lower(), cfg.mode.lower()
|
||||
|
||||
# Special case for initializing the configuration
|
||||
if task == "init":
|
||||
shutil.copy2(DEFAULT_CONFIG, Path.cwd())
|
||||
LOGGER.info(f"""
|
||||
{colorstr("YOLO:")} configuration saved to {Path.cwd() / DEFAULT_CONFIG.name}.
|
||||
To run experiments using custom configuration:
|
||||
yolo cfg=config_file.yaml
|
||||
""")
|
||||
return
|
||||
|
||||
# Mapping from task to module
|
||||
task_module_map = {"detect": yolo.v8.detect, "segment": yolo.v8.segment, "classify": yolo.v8.classify}
|
||||
module = task_module_map.get(task)
|
||||
@ -47,10 +38,66 @@ def cli(cfg):
|
||||
"train": module.train,
|
||||
"val": module.val,
|
||||
"predict": module.predict,
|
||||
"export": yolo.engine.exporter.export,
|
||||
"checks": hub.checks}
|
||||
"export": yolo.engine.exporter.export}
|
||||
func = mode_func_map.get(mode)
|
||||
if not func:
|
||||
raise SyntaxError(f"mode not recognized. Choices are {', '.join(mode_func_map.keys())}")
|
||||
|
||||
func(cfg)
|
||||
|
||||
|
||||
def entrypoint():
|
||||
"""
|
||||
This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
|
||||
to the package. It's a combination of argparse and hydra.
|
||||
|
||||
This function allows for:
|
||||
- passing mandatory YOLO args as a list of strings
|
||||
- specifying the task to be performed, either 'detect', 'segment' or 'classify'
|
||||
- specifying the mode, either 'train', 'val', 'test', or 'predict'
|
||||
- running special modes like 'checks'
|
||||
- passing overrides to the package's configuration
|
||||
|
||||
It uses the package's default config and initializes it using the passed overrides.
|
||||
Then it calls the CLI function with the composed config
|
||||
"""
|
||||
parser = argparse.ArgumentParser(description='YOLO parser')
|
||||
parser.add_argument('args', type=str, nargs='+', help='YOLO args')
|
||||
args = parser.parse_args().args
|
||||
|
||||
tasks = 'detect', 'segment', 'classify'
|
||||
modes = 'train', 'val', 'predict', 'export'
|
||||
special_modes = {
|
||||
'checks': hub.checks,
|
||||
'help': lambda: LOGGER.info(HELP_MSG),
|
||||
'settings': print_settings,
|
||||
'copy-config': copy_default_config}
|
||||
|
||||
overrides = [] # basic overrides, i.e. imgsz=320
|
||||
defaults = yaml_load(DEFAULT_CONFIG)
|
||||
for a in args:
|
||||
if '=' in a:
|
||||
overrides.append(a)
|
||||
elif a in tasks:
|
||||
overrides.append(f'task={a}')
|
||||
elif a in modes:
|
||||
overrides.append(f'mode={a}')
|
||||
elif a in special_modes:
|
||||
special_modes[a]()
|
||||
return
|
||||
elif a in defaults and defaults[a] is False:
|
||||
overrides.append(f'{a}=True') # auto-True for default False args, i.e. yolo show
|
||||
else:
|
||||
raise (SyntaxError(f"'{a}' is not a valid yolo argument\n{HELP_MSG}"))
|
||||
|
||||
with initialize(version_base=None, config_path=str(DEFAULT_CONFIG.parent.relative_to(DIR)), job_name="YOLO"):
|
||||
cfg = compose(config_name=DEFAULT_CONFIG.name, overrides=overrides)
|
||||
cli(cfg)
|
||||
|
||||
|
||||
# Special modes --------------------------------------------------------------------------------------------------------
|
||||
def copy_default_config():
|
||||
new_file = Path.cwd() / DEFAULT_CONFIG.name.replace('.yaml', '_copy.yaml')
|
||||
shutil.copy2(DEFAULT_CONFIG, new_file)
|
||||
LOGGER.info(f"{PREFIX}{DEFAULT_CONFIG} copied to {new_file}\n"
|
||||
f"Usage for running YOLO with this new custom config:\nyolo cfg={new_file} args...")
|
||||
|
||||
@ -160,7 +160,7 @@ class BasePredictor:
|
||||
return list(chain(*list(self.stream_inference(source, model, verbose)))) # merge list of Result into one
|
||||
|
||||
def predict_cli(self):
|
||||
# Method used for cli prediction. It uses always generator as outputs as not required by cli mode
|
||||
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
|
||||
gen = self.stream_inference(verbose=True)
|
||||
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
|
||||
pass
|
||||
|
||||
@ -10,6 +10,7 @@ import tempfile
|
||||
import threading
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
import cv2
|
||||
import git
|
||||
@ -41,12 +42,15 @@ HELP_MSG = \
|
||||
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO('yolov8n.yaml') # build a new model from scratch
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for best training results)
|
||||
results = model.train(data='coco128.yaml') # train the model
|
||||
results = model.val() # evaluate model performance on the validation set
|
||||
results = model.predict(source='bus.jpg') # predict on an image
|
||||
success = model.export(format='onnx') # export the model to ONNX format
|
||||
# Load a model
|
||||
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
|
||||
|
||||
3. Use the command line interface (CLI):
|
||||
|
||||
@ -161,12 +165,12 @@ def is_pip_package(filepath: str = __name__) -> bool:
|
||||
return spec is not None and spec.origin is not None
|
||||
|
||||
|
||||
def is_dir_writeable(dir_path: str) -> bool:
|
||||
def is_dir_writeable(dir_path: Union[str, Path]) -> bool:
|
||||
"""
|
||||
Check if a directory is writeable.
|
||||
|
||||
Args:
|
||||
dir_path (str): The path to the directory.
|
||||
dir_path (str) or (Path): The path to the directory.
|
||||
|
||||
Returns:
|
||||
bool: True if the directory is writeable, False otherwise.
|
||||
@ -179,6 +183,18 @@ def is_dir_writeable(dir_path: str) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def is_pytest_running():
|
||||
"""
|
||||
Returns a boolean indicating if pytest is currently running or not
|
||||
:return: True if pytest is running, False otherwise
|
||||
"""
|
||||
try:
|
||||
import sys
|
||||
return "pytest" in sys.modules
|
||||
except ImportError:
|
||||
return False
|
||||
|
||||
|
||||
def get_git_root_dir():
|
||||
"""
|
||||
Determines whether the current file is part of a git repository and if so, returns the repository root directory.
|
||||
@ -348,6 +364,17 @@ def yaml_load(file='data.yaml', append_filename=False):
|
||||
return {**yaml.safe_load(f), 'yaml_file': str(file)} if append_filename else yaml.safe_load(f)
|
||||
|
||||
|
||||
def set_sentry(dsn=None):
|
||||
"""
|
||||
Initialize the Sentry SDK for error tracking and reporting if pytest is not currently running.
|
||||
"""
|
||||
if dsn and not is_pytest_running():
|
||||
import sentry_sdk # noqa
|
||||
|
||||
import ultralytics
|
||||
sentry_sdk.init(dsn=dsn, traces_sample_rate=1.0, release=ultralytics.__version__, debug=False)
|
||||
|
||||
|
||||
def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
|
||||
"""
|
||||
Loads a global Ultralytics settings YAML file or creates one with default values if it does not exist.
|
||||
@ -364,8 +391,9 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
|
||||
|
||||
is_git = is_git_directory() # True if ultralytics installed via git
|
||||
root = get_git_root_dir() if is_git else Path()
|
||||
datasets_root = (root.parent if (is_git and is_dir_writeable(root.parent)) else root).resolve()
|
||||
defaults = {
|
||||
'datasets_dir': str((root.parent if is_git else root) / 'datasets'), # default datasets directory.
|
||||
'datasets_dir': str(datasets_root / 'datasets'), # default datasets directory.
|
||||
'weights_dir': str(root / 'weights'), # default weights directory.
|
||||
'runs_dir': str(root / 'runs'), # default runs directory.
|
||||
'sync': True, # sync analytics to help with YOLO development
|
||||
@ -393,6 +421,26 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
|
||||
return settings
|
||||
|
||||
|
||||
def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
|
||||
"""
|
||||
Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
|
||||
directories.
|
||||
"""
|
||||
SETTINGS.update(kwargs)
|
||||
yaml_save(file, SETTINGS)
|
||||
|
||||
|
||||
def print_settings():
|
||||
"""
|
||||
Function that prints Ultralytics settings
|
||||
"""
|
||||
import json
|
||||
s = f'\n{PREFIX}Settings:\n'
|
||||
s += json.dumps(SETTINGS, indent=2)
|
||||
s += f"\n\nUpdate settings at {USER_CONFIG_DIR / 'settings.yaml'}"
|
||||
LOGGER.info(s)
|
||||
|
||||
|
||||
# Run below code on utils init -----------------------------------------------------------------------------------------
|
||||
|
||||
# Set logger
|
||||
@ -403,15 +451,7 @@ if platform.system() == 'Windows':
|
||||
setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging
|
||||
|
||||
# Check first-install steps
|
||||
PREFIX = colorstr("Ultralytics: ")
|
||||
SETTINGS = get_settings()
|
||||
DATASETS_DIR = Path(SETTINGS['datasets_dir']) # global datasets directory
|
||||
|
||||
|
||||
def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
|
||||
"""
|
||||
Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
|
||||
directories.
|
||||
"""
|
||||
SETTINGS.update(kwargs)
|
||||
|
||||
yaml_save(file, SETTINGS)
|
||||
set_sentry()
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.configs import hydra_patch # noqa (patch hydra cli)
|
||||
from ultralytics.yolo.configs import hydra_patch # noqa (patch hydra CLI)
|
||||
from ultralytics.yolo.v8 import classify, detect, segment
|
||||
|
||||
__all__ = ["classify", "segment", "detect"]
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user