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ultralytics 8.0.67 Pose speeds, Comet and ClearML updates (#1871)
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Victor Sonck <victor.sonck@gmail.com> Co-authored-by: Danny Kim <dh031200@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
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commit
2725545090
20
README.md
20
README.md
@ -183,12 +183,12 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
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dataset.
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@ -214,16 +214,16 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
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<a href="https://cutt.ly/yolov5-readme-clearml">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-readme-comet2">
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<a href="https://bit.ly/yolov8-readme-comet">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-neuralmagic">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
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</div>
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
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## <div align="center">Ultralytics HUB</div>
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@ -169,12 +169,12 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
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dataset.
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@ -200,16 +200,16 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
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<a href="https://cutt.ly/yolov5-readme-clearml">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-readme-comet2">
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<a href="https://bit.ly/yolov8-readme-comet">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-neuralmagic">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
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</div>
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| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 |
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| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |
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| 将您的自定义数据集进行标注并直接导出到 YOLOv8 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv8 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv8 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv8 推理的速度最高可提高6倍 |
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| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 |
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| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |
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| 将您的自定义数据集进行标注并直接导出到 YOLOv8 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv8 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov8-readme-comet)可让您保存 YOLOv8 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv8 推理的速度最高可提高6倍 |
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## <div align="center">Ultralytics HUB</div>
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@ -25,12 +25,12 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
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| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
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| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
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| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
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| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
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dataset.
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@ -123,7 +123,7 @@ Use a trained YOLOv8n-pose model to run predictions on images.
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=== "CLI"
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```bash
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yolo pose predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo pose 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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@ -131,7 +131,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
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## Export
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
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!!! example ""
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@ -141,7 +141,7 @@ 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('yolov8n-pose.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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@ -150,7 +150,7 @@ 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 export model=yolov8n.pt format=onnx # export official model
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yolo export model=yolov8n-pose.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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@ -2,7 +2,9 @@
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from pathlib import Path
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from ultralytics import YOLO
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.engine.exporter import Exporter
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, SETTINGS
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from ultralytics.yolo.v8 import classify, detect, segment
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@ -14,20 +16,38 @@ MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
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SOURCE = ROOT / 'assets'
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def test_func(model=None):
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print('callback test passed')
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def test_export():
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exporter = Exporter()
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exporter.add_callback('on_export_start', test_func)
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assert test_func in exporter.callbacks['on_export_start'], 'callback test failed'
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f = exporter(model=YOLO(CFG_DET).model)
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YOLO(f)(SOURCE) # exported model inference
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def test_detect():
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overrides = {'data': 'coco8.yaml', 'model': CFG_DET, 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'coco8.yaml'
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# Trainer
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trainer = detect.DetectionTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = detect.DetectionValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
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pred.add_callback('on_predict_start', test_func)
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
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result = pred(source=SOURCE, model=f'{MODEL}.pt')
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assert len(result), 'predictor test failed'
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@ -50,14 +70,20 @@ def test_segment():
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# trainer
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trainer = segment.SegmentationTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = segment.SegmentationValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
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pred.add_callback('on_predict_start', test_func)
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
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result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
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assert len(result), 'predictor test failed'
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@ -81,13 +107,19 @@ def test_classify():
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# Trainer
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trainer = classify.ClassificationTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = classify.ClassificationValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best)
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# Predictor
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pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
|
||||
pred.add_callback('on_predict_start', test_func)
|
||||
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
|
||||
result = pred(source=SOURCE, model=trainer.best)
|
||||
assert len(result), 'predictor test failed'
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
__version__ = '8.0.66'
|
||||
__version__ = '8.0.67'
|
||||
|
||||
from ultralytics.hub import start
|
||||
from ultralytics.yolo.engine.model import YOLO
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
import requests
|
||||
|
||||
from ultralytics.hub.utils import PREFIX, split_key
|
||||
from ultralytics.yolo.utils import LOGGER
|
||||
from ultralytics.yolo.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
|
||||
|
||||
|
||||
def login(api_key=''):
|
||||
@ -15,7 +15,7 @@ def login(api_key=''):
|
||||
|
||||
Example:
|
||||
from ultralytics import hub
|
||||
hub.login('your_api_key')
|
||||
hub.login('API_KEY')
|
||||
"""
|
||||
from ultralytics.hub.auth import Auth
|
||||
Auth(api_key)
|
||||
@ -23,13 +23,15 @@ def login(api_key=''):
|
||||
|
||||
def logout():
|
||||
"""
|
||||
Logout Ultralytics HUB
|
||||
Log out of Ultralytics HUB by removing the API key from the settings file. To log in again, use 'yolo hub login'.
|
||||
|
||||
Example:
|
||||
from ultralytics import hub
|
||||
hub.logout()
|
||||
"""
|
||||
LOGGER.warning('WARNING ⚠️ This method is not yet implemented.')
|
||||
SETTINGS['api_key'] = ''
|
||||
yaml_save(USER_CONFIG_DIR / 'settings.yaml', SETTINGS)
|
||||
LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo hub login'.")
|
||||
|
||||
|
||||
def start(key=''):
|
||||
|
||||
@ -89,12 +89,12 @@ Available Models:
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
|
||||
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
|
||||
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
|
||||
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
|
||||
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
|
||||
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
|
||||
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
|
||||
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
|
||||
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
|
||||
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
|
||||
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
|
||||
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
@ -17,7 +17,7 @@ from PIL import ExifTags, Image, ImageOps
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.nn.autobackend import check_class_names
|
||||
from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, clean_url, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
|
||||
from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file
|
||||
from ultralytics.yolo.utils.ops import segments2boxes
|
||||
@ -241,7 +241,7 @@ def check_det_dataset(dataset, autodownload=True):
|
||||
if val:
|
||||
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
|
||||
if not all(x.exists() for x in val):
|
||||
name = str(dataset).split('?')[0] # dataset name with URL auth stripped
|
||||
name = clean_url(dataset) # dataset name with URL auth stripped
|
||||
m = f"\nDataset '{name}' images not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]
|
||||
if s and autodownload:
|
||||
LOGGER.warning(m)
|
||||
|
||||
@ -53,7 +53,6 @@ import platform
|
||||
import subprocess
|
||||
import time
|
||||
import warnings
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
@ -130,7 +129,7 @@ class Exporter:
|
||||
save_dir (Path): Directory to save results.
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
"""
|
||||
Initializes the Exporter class.
|
||||
|
||||
@ -139,7 +138,7 @@ class Exporter:
|
||||
overrides (dict, optional): Configuration overrides. Defaults to None.
|
||||
"""
|
||||
self.args = get_cfg(cfg, overrides)
|
||||
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
|
||||
self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
|
||||
callbacks.add_integration_callbacks(self)
|
||||
|
||||
@smart_inference_mode()
|
||||
@ -854,6 +853,12 @@ class Exporter:
|
||||
LOGGER.info(f'{prefix} pipeline success')
|
||||
return model
|
||||
|
||||
def add_callback(self, event: str, callback):
|
||||
"""
|
||||
Appends the given callback.
|
||||
"""
|
||||
self.callbacks[event].append(callback)
|
||||
|
||||
def run_callbacks(self, event: str):
|
||||
for callback in self.callbacks.get(event, []):
|
||||
callback(self)
|
||||
|
||||
@ -78,7 +78,7 @@ class YOLO:
|
||||
task (Any, optional): Task type for the YOLO model. Defaults to None.
|
||||
|
||||
"""
|
||||
self._reset_callbacks()
|
||||
self.callbacks = callbacks.get_default_callbacks()
|
||||
self.predictor = None # reuse predictor
|
||||
self.model = None # model object
|
||||
self.trainer = None # trainer object
|
||||
@ -238,7 +238,7 @@ class YOLO:
|
||||
overrides['save'] = kwargs.get('save', False) # not save files by default
|
||||
if not self.predictor:
|
||||
self.task = overrides.get('task') or self.task
|
||||
self.predictor = TASK_MAP[self.task][3](overrides=overrides)
|
||||
self.predictor = TASK_MAP[self.task][3](overrides=overrides, _callbacks=self.callbacks)
|
||||
self.predictor.setup_model(model=self.model, verbose=is_cli)
|
||||
else: # only update args if predictor is already setup
|
||||
self.predictor.args = get_cfg(self.predictor.args, overrides)
|
||||
@ -277,7 +277,7 @@ class YOLO:
|
||||
args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
|
||||
args.imgsz = check_imgsz(args.imgsz, max_dim=1)
|
||||
|
||||
validator = TASK_MAP[self.task][2](args=args)
|
||||
validator = TASK_MAP[self.task][2](args=args, _callbacks=self.callbacks)
|
||||
validator(model=self.model)
|
||||
self.metrics = validator.metrics
|
||||
|
||||
@ -316,7 +316,7 @@ class YOLO:
|
||||
args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
|
||||
if args.batch == DEFAULT_CFG.batch:
|
||||
args.batch = 1 # default to 1 if not modified
|
||||
return Exporter(overrides=args)(model=self.model)
|
||||
return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
|
||||
|
||||
def train(self, **kwargs):
|
||||
"""
|
||||
@ -344,7 +344,7 @@ class YOLO:
|
||||
overrides['resume'] = self.ckpt_path
|
||||
|
||||
self.task = overrides.get('task') or self.task
|
||||
self.trainer = TASK_MAP[self.task][1](overrides=overrides)
|
||||
self.trainer = TASK_MAP[self.task][1](overrides=overrides, _callbacks=self.callbacks)
|
||||
if not overrides.get('resume'): # manually set model only if not resuming
|
||||
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
|
||||
self.model = self.trainer.model
|
||||
@ -387,19 +387,17 @@ class YOLO:
|
||||
"""
|
||||
return self.model.transforms if hasattr(self.model, 'transforms') else None
|
||||
|
||||
@staticmethod
|
||||
def add_callback(event: str, func):
|
||||
def add_callback(self, event: str, func):
|
||||
"""
|
||||
Add callback
|
||||
"""
|
||||
callbacks.default_callbacks[event].append(func)
|
||||
self.callbacks[event].append(func)
|
||||
|
||||
@staticmethod
|
||||
def _reset_ckpt_args(args):
|
||||
include = {'imgsz', 'data', 'task', 'single_cls'} # only remember these arguments when loading a PyTorch model
|
||||
return {k: v for k, v in args.items() if k in include}
|
||||
|
||||
@staticmethod
|
||||
def _reset_callbacks():
|
||||
def _reset_callbacks(self):
|
||||
for event in callbacks.default_callbacks.keys():
|
||||
callbacks.default_callbacks[event] = [callbacks.default_callbacks[event][0]]
|
||||
self.callbacks[event] = [callbacks.default_callbacks[event][0]]
|
||||
|
||||
@ -28,7 +28,6 @@ Usage - formats:
|
||||
yolov8n_paddle_model # PaddlePaddle
|
||||
"""
|
||||
import platform
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
@ -75,7 +74,7 @@ class BasePredictor:
|
||||
data_path (str): Path to data.
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
"""
|
||||
Initializes the BasePredictor class.
|
||||
|
||||
@ -104,7 +103,7 @@ class BasePredictor:
|
||||
self.data_path = None
|
||||
self.source_type = None
|
||||
self.batch = None
|
||||
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
|
||||
self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
|
||||
callbacks.add_integration_callbacks(self)
|
||||
|
||||
def preprocess(self, img):
|
||||
@ -283,3 +282,9 @@ class BasePredictor:
|
||||
def run_callbacks(self, event: str):
|
||||
for callback in self.callbacks.get(event, []):
|
||||
callback(self)
|
||||
|
||||
def add_callback(self, event: str, func):
|
||||
"""
|
||||
Add callback
|
||||
"""
|
||||
self.callbacks[event].append(func)
|
||||
|
||||
@ -8,7 +8,6 @@ Usage:
|
||||
import os
|
||||
import subprocess
|
||||
import time
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
@ -26,7 +25,7 @@ from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
|
||||
from ultralytics.yolo.cfg import get_cfg
|
||||
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, ONLINE, RANK, ROOT, SETTINGS, TQDM_BAR_FORMAT, __version__,
|
||||
callbacks, colorstr, emojis, yaml_save)
|
||||
callbacks, clean_url, colorstr, emojis, yaml_save)
|
||||
from ultralytics.yolo.utils.autobatch import check_train_batch_size
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
|
||||
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
|
||||
@ -72,7 +71,7 @@ class BaseTrainer:
|
||||
csv (Path): Path to results CSV file.
|
||||
"""
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
"""
|
||||
Initializes the BaseTrainer class.
|
||||
|
||||
@ -124,7 +123,7 @@ class BaseTrainer:
|
||||
if 'yaml_file' in self.data:
|
||||
self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
|
||||
except Exception as e:
|
||||
raise RuntimeError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
|
||||
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
|
||||
|
||||
self.trainset, self.testset = self.get_dataset(self.data)
|
||||
self.ema = None
|
||||
@ -143,7 +142,7 @@ class BaseTrainer:
|
||||
self.plot_idx = [0, 1, 2]
|
||||
|
||||
# Callbacks
|
||||
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
|
||||
self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
|
||||
if RANK in (-1, 0):
|
||||
callbacks.add_integration_callbacks(self)
|
||||
|
||||
|
||||
@ -19,7 +19,6 @@ Usage - formats:
|
||||
yolov8n_paddle_model # PaddlePaddle
|
||||
"""
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
@ -55,7 +54,7 @@ class BaseValidator:
|
||||
save_dir (Path): Directory to save results.
|
||||
"""
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
"""
|
||||
Initializes a BaseValidator instance.
|
||||
|
||||
@ -85,7 +84,7 @@ class BaseValidator:
|
||||
if self.args.conf is None:
|
||||
self.args.conf = 0.001 # default conf=0.001
|
||||
|
||||
self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks
|
||||
self.callbacks = _callbacks if _callbacks else callbacks.get_default_callbacks()
|
||||
|
||||
@smart_inference_mode()
|
||||
def __call__(self, trainer=None, model=None):
|
||||
@ -195,6 +194,12 @@ class BaseValidator:
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
|
||||
return stats
|
||||
|
||||
def add_callback(self, event: str, callback):
|
||||
"""
|
||||
Appends the given callback.
|
||||
"""
|
||||
self.callbacks[event].append(callback)
|
||||
|
||||
def run_callbacks(self, event: str):
|
||||
for callback in self.callbacks.get(event, []):
|
||||
callback(self)
|
||||
|
||||
@ -10,6 +10,7 @@ import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import threading
|
||||
import urllib
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
@ -165,7 +166,7 @@ class IterableSimpleNamespace(SimpleNamespace):
|
||||
def set_logging(name=LOGGING_NAME, verbose=True):
|
||||
# sets up logging for the given name
|
||||
rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
|
||||
level = logging.INFO if verbose and rank in (-1, 0) else logging.ERROR
|
||||
level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
|
||||
logging.config.dictConfig({
|
||||
'version': 1,
|
||||
'disable_existing_loggers': False,
|
||||
@ -649,10 +650,20 @@ def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
|
||||
|
||||
def deprecation_warn(arg, new_arg, version=None):
|
||||
if not version:
|
||||
version = float(__version__[0:3]) + 0.2 # deprecate after 2nd major release
|
||||
LOGGER.warning(
|
||||
f'WARNING: `{arg}` is deprecated and will be removed in upcoming major release {version}. Use `{new_arg}` instead'
|
||||
)
|
||||
version = float(__version__[:3]) + 0.2 # deprecate after 2nd major release
|
||||
LOGGER.warning(f"WARNING ⚠️ '{arg}' is deprecated and will be removed in 'ultralytics {version}' in the future. "
|
||||
f"Please use '{new_arg}' instead.")
|
||||
|
||||
|
||||
def clean_url(url):
|
||||
# Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt
|
||||
url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
return urllib.parse.unquote(url).split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
|
||||
|
||||
def url2file(url):
|
||||
# Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
|
||||
return Path(clean_url(url)).name
|
||||
|
||||
|
||||
# Run below code on yolo/utils init ------------------------------------------------------------------------------------
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
from .base import add_integration_callbacks, default_callbacks
|
||||
from .base import add_integration_callbacks, default_callbacks, get_default_callbacks
|
||||
|
||||
__all__ = 'add_integration_callbacks', 'default_callbacks'
|
||||
__all__ = 'add_integration_callbacks', 'default_callbacks', 'get_default_callbacks'
|
||||
|
||||
@ -2,6 +2,8 @@
|
||||
"""
|
||||
Base callbacks
|
||||
"""
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
# Trainer callbacks ----------------------------------------------------------------------------------------------------
|
||||
@ -143,6 +145,10 @@ default_callbacks = {
|
||||
'on_export_end': [on_export_end]}
|
||||
|
||||
|
||||
def get_default_callbacks():
|
||||
return defaultdict(list, deepcopy(default_callbacks))
|
||||
|
||||
|
||||
def add_integration_callbacks(instance):
|
||||
from .clearml import callbacks as clearml_callbacks
|
||||
from .comet import callbacks as comet_callbacks
|
||||
|
||||
@ -1,10 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
import re
|
||||
|
||||
import matplotlib.image as mpimg
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
||||
|
||||
try:
|
||||
import clearml
|
||||
from clearml import Task
|
||||
from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
|
||||
from clearml.binding.matplotlib_bind import PatchedMatplotlib
|
||||
|
||||
assert hasattr(clearml, '__version__') # verify package is not directory
|
||||
assert not TESTS_RUNNING # do not log pytest
|
||||
@ -12,21 +19,61 @@ except (ImportError, AssertionError):
|
||||
clearml = None
|
||||
|
||||
|
||||
def _log_images(imgs_dict, group='', step=0):
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
for k, v in imgs_dict.items():
|
||||
task.get_logger().report_image(group, k, step, v)
|
||||
def _log_debug_samples(files, title='Debug Samples'):
|
||||
"""
|
||||
Log files (images) as debug samples in the ClearML task.
|
||||
|
||||
arguments:
|
||||
files (List(PosixPath)) a list of file paths in PosixPath format
|
||||
title (str) A title that groups together images with the same values
|
||||
"""
|
||||
for f in files:
|
||||
if f.exists():
|
||||
it = re.search(r'_batch(\d+)', f.name)
|
||||
iteration = int(it.groups()[0]) if it else 0
|
||||
Task.current_task().get_logger().report_image(title=title,
|
||||
series=f.name.replace(it.group(), ''),
|
||||
local_path=str(f),
|
||||
iteration=iteration)
|
||||
|
||||
|
||||
def _log_plot(title, plot_path):
|
||||
"""
|
||||
Log image as plot in the plot section of ClearML
|
||||
|
||||
arguments:
|
||||
title (str) Title of the plot
|
||||
plot_path (PosixPath or str) Path to the saved image file
|
||||
"""
|
||||
img = mpimg.imread(plot_path)
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks
|
||||
ax.imshow(img)
|
||||
|
||||
Task.current_task().get_logger().report_matplotlib_figure(title, '', figure=fig, report_interactive=False)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
# TODO: reuse existing task
|
||||
try:
|
||||
task = Task.init(project_name=trainer.args.project or 'YOLOv8',
|
||||
task_name=trainer.args.name,
|
||||
tags=['YOLOv8'],
|
||||
output_uri=True,
|
||||
reuse_last_task_id=False,
|
||||
auto_connect_frameworks={'pytorch': False})
|
||||
if Task.current_task():
|
||||
task = Task.current_task()
|
||||
|
||||
# Make sure the automatic pytorch and matplotlib bindings are disabled!
|
||||
# We are logging these plots and model files manually in the integration
|
||||
PatchPyTorchModelIO.update_current_task(None)
|
||||
PatchedMatplotlib.update_current_task(None)
|
||||
else:
|
||||
task = Task.init(project_name=trainer.args.project or 'YOLOv8',
|
||||
task_name=trainer.args.name,
|
||||
tags=['YOLOv8'],
|
||||
output_uri=True,
|
||||
reuse_last_task_id=False,
|
||||
auto_connect_frameworks={
|
||||
'pytorch': False,
|
||||
'matplotlib': False})
|
||||
LOGGER.warning('ClearML Initialized a new task. If you want to run remotely, '
|
||||
'please add clearml-init and connect your arguments before initializing YOLO.')
|
||||
task.connect(vars(trainer.args), name='General')
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ ClearML installed but not initialized correctly, not logging this run. {e}')
|
||||
@ -34,27 +81,47 @@ def on_pretrain_routine_start(trainer):
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
if trainer.epoch == 1:
|
||||
_log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, 'Mosaic', trainer.epoch)
|
||||
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
task = Task.current_task()
|
||||
if task and trainer.epoch == 0:
|
||||
# You should have access to the validation bboxes under jdict
|
||||
Task.current_task().get_logger().report_scalar(title='Epoch Time',
|
||||
series='Epoch Time',
|
||||
value=trainer.epoch_time,
|
||||
iteration=trainer.epoch)
|
||||
if trainer.epoch == 0:
|
||||
model_info = {
|
||||
'model/parameters': get_num_params(trainer.model),
|
||||
'model/GFLOPs': round(get_flops(trainer.model), 3),
|
||||
'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
|
||||
task.connect(model_info, name='Model')
|
||||
for k, v in model_info.items():
|
||||
Task.current_task().get_logger().report_single_value(k, v)
|
||||
|
||||
|
||||
def on_val_end(validator):
|
||||
# Log val_labels and val_pred
|
||||
_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
task = Task.current_task()
|
||||
if task:
|
||||
task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)
|
||||
# Log final results, CM matrix + PR plots
|
||||
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
|
||||
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
|
||||
for f in files:
|
||||
_log_plot(title=f.stem, plot_path=f)
|
||||
# Report final metrics
|
||||
for k, v in trainer.validator.metrics.results_dict.items():
|
||||
Task.current_task().get_logger().report_single_value(k, v)
|
||||
# Log the final model
|
||||
Task.current_task().update_output_model(model_path=str(trainer.best),
|
||||
model_name=trainer.args.name,
|
||||
auto_delete_file=False)
|
||||
|
||||
|
||||
callbacks = {
|
||||
'on_pretrain_routine_start': on_pretrain_routine_start,
|
||||
'on_train_epoch_end': on_train_epoch_end,
|
||||
'on_fit_epoch_end': on_fit_epoch_end,
|
||||
'on_val_end': on_val_end,
|
||||
'on_train_end': on_train_end} if clearml else {}
|
||||
|
||||
@ -1,5 +1,8 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
from ultralytics.yolo.utils import LOGGER, TESTS_RUNNING
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, RANK, TESTS_RUNNING, ops
|
||||
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
||||
|
||||
try:
|
||||
@ -10,41 +13,308 @@ try:
|
||||
except (ImportError, AssertionError):
|
||||
comet_ml = None
|
||||
|
||||
COMET_MODE = os.getenv('COMET_MODE', 'online')
|
||||
COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'YOLOv8')
|
||||
# determines how many batches of image predictions to log from the validation set
|
||||
COMET_EVAL_BATCH_LOGGING_INTERVAL = int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
|
||||
# determines whether to log confusion matrix every evaluation epoch
|
||||
COMET_EVAL_LOG_CONFUSION_MATRIX = (os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
|
||||
# determines whether to log image predictions every evaluation epoch
|
||||
COMET_EVAL_LOG_IMAGE_PREDICTIONS = (os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true')
|
||||
COMET_MAX_IMAGE_PREDICTIONS = int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
# ensures certain logging functions only run for supported tasks
|
||||
COMET_SUPPORTED_TASKS = ['detect']
|
||||
# scales reported confidence scores (0.0-1.0) by this value
|
||||
COMET_MAX_CONFIDENCE_SCORE = int(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100))
|
||||
|
||||
# names of plots created by YOLOv8 that are logged to Comet
|
||||
EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
|
||||
LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
|
||||
|
||||
_comet_image_prediction_count = 0
|
||||
|
||||
|
||||
def _get_experiment_type(mode, project_name):
|
||||
if mode == 'offline':
|
||||
return comet_ml.OfflineExperiment(project_name=project_name)
|
||||
|
||||
return comet_ml.Experiment(project_name=project_name)
|
||||
|
||||
|
||||
def _create_experiment(args):
|
||||
# Ensures that the experiment object is only created in a single process during distributed training.
|
||||
if RANK not in (-1, 0):
|
||||
return
|
||||
try:
|
||||
experiment = comet_ml.Experiment(project_name=trainer.args.project or 'YOLOv8')
|
||||
experiment.set_name(trainer.args.name)
|
||||
experiment.log_parameters(vars(trainer.args))
|
||||
experiment = _get_experiment_type(COMET_MODE, args.project)
|
||||
experiment.log_parameters(vars(args))
|
||||
experiment.log_others({
|
||||
'eval_batch_logging_interval': COMET_EVAL_BATCH_LOGGING_INTERVAL,
|
||||
'log_confusion_matrix': COMET_EVAL_LOG_CONFUSION_MATRIX,
|
||||
'log_image_predictions': COMET_EVAL_LOG_IMAGE_PREDICTIONS,
|
||||
'max_image_predictions': COMET_MAX_IMAGE_PREDICTIONS, })
|
||||
experiment.log_other('Created from', 'yolov8')
|
||||
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}')
|
||||
|
||||
|
||||
def _fetch_trainer_metadata(trainer):
|
||||
curr_epoch = trainer.epoch + 1
|
||||
|
||||
train_num_steps_per_epoch = len(trainer.train_loader.dataset) // trainer.batch_size
|
||||
curr_step = curr_epoch * train_num_steps_per_epoch
|
||||
final_epoch = curr_epoch == trainer.epochs
|
||||
|
||||
save = trainer.args.save
|
||||
save_period = trainer.args.save_period
|
||||
save_interval = curr_epoch % save_period == 0
|
||||
save_assets = save and save_period > 0 and save_interval and not final_epoch
|
||||
|
||||
return dict(curr_epoch=curr_epoch, curr_step=curr_step, save_assets=save_assets, final_epoch=final_epoch)
|
||||
|
||||
|
||||
def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
|
||||
"""YOLOv8 resizes images during training and the label values
|
||||
are normalized based on this resized shape. This function rescales the
|
||||
bounding box labels to the original image shape.
|
||||
"""
|
||||
|
||||
resized_image_height, resized_image_width = resized_image_shape
|
||||
|
||||
# convert normalized xywh format predictions to xyxy in resized scale format
|
||||
box = ops.xywhn2xyxy(box, h=resized_image_height, w=resized_image_width)
|
||||
# scale box predictions from resized image scale back to original image scale
|
||||
box = ops.scale_boxes(resized_image_shape, box, original_image_shape, ratio_pad)
|
||||
# Convert bounding box format from xyxy to xywh for Comet logging
|
||||
box = ops.xyxy2xywh(box)
|
||||
# adjust xy center to correspond top-left corner
|
||||
box[:2] -= box[2:] / 2
|
||||
box = box.tolist()
|
||||
|
||||
return box
|
||||
|
||||
|
||||
def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, class_name_map=None):
|
||||
indices = batch['batch_idx'] == img_idx
|
||||
bboxes = batch['bboxes'][indices]
|
||||
if len(bboxes) == 0:
|
||||
LOGGER.debug(f'COMET WARNING: Image: {image_path} has no bounding boxes labels')
|
||||
return None
|
||||
|
||||
cls_labels = batch['cls'][indices].squeeze(1).tolist()
|
||||
if class_name_map:
|
||||
cls_labels = [str(class_name_map[label]) for label in cls_labels]
|
||||
|
||||
original_image_shape = batch['ori_shape'][img_idx]
|
||||
resized_image_shape = batch['resized_shape'][img_idx]
|
||||
ratio_pad = batch['ratio_pad'][img_idx]
|
||||
|
||||
data = []
|
||||
for box, label in zip(bboxes, cls_labels):
|
||||
box = _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)
|
||||
data.append({'boxes': [box], 'label': f'gt_{label}', 'score': COMET_MAX_CONFIDENCE_SCORE})
|
||||
|
||||
return {'name': 'ground_truth', 'data': data}
|
||||
|
||||
|
||||
def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
|
||||
stem = image_path.stem
|
||||
image_id = int(stem) if stem.isnumeric() else stem
|
||||
|
||||
predictions = metadata.get(image_id)
|
||||
if not predictions:
|
||||
LOGGER.debug(f'COMET WARNING: Image: {image_path} has no bounding boxes predictions')
|
||||
return None
|
||||
|
||||
data = []
|
||||
for prediction in predictions:
|
||||
boxes = prediction['bbox']
|
||||
score = prediction['score'] * COMET_MAX_CONFIDENCE_SCORE
|
||||
cls_label = prediction['category_id']
|
||||
if class_label_map:
|
||||
cls_label = str(class_label_map[cls_label])
|
||||
|
||||
data.append({'boxes': [boxes], 'label': cls_label, 'score': score})
|
||||
|
||||
return {'name': 'prediction', 'data': data}
|
||||
|
||||
|
||||
def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map):
|
||||
ground_truth_annotations = _format_ground_truth_annotations_for_detection(img_idx, image_path, batch,
|
||||
class_label_map)
|
||||
prediction_annotations = _format_prediction_annotations_for_detection(image_path, prediction_metadata_map,
|
||||
class_label_map)
|
||||
|
||||
annotations = [
|
||||
annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None]
|
||||
return [annotations] if annotations else None
|
||||
|
||||
|
||||
def _create_prediction_metadata_map(model_predictions):
|
||||
pred_metadata_map = {}
|
||||
for prediction in model_predictions:
|
||||
pred_metadata_map.setdefault(prediction['image_id'], [])
|
||||
pred_metadata_map[prediction['image_id']].append(prediction)
|
||||
|
||||
return pred_metadata_map
|
||||
|
||||
|
||||
def _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch):
|
||||
conf_mat = trainer.validator.confusion_matrix.matrix
|
||||
names = list(trainer.data['names'].values()) + ['background']
|
||||
experiment.log_confusion_matrix(
|
||||
matrix=conf_mat,
|
||||
labels=names,
|
||||
max_categories=len(names),
|
||||
epoch=curr_epoch,
|
||||
step=curr_step,
|
||||
)
|
||||
|
||||
|
||||
def _log_images(experiment, image_paths, curr_step, annotations=None):
|
||||
if annotations:
|
||||
for image_path, annotation in zip(image_paths, annotations):
|
||||
experiment.log_image(image_path, name=image_path.stem, step=curr_step, annotations=annotation)
|
||||
|
||||
else:
|
||||
for image_path in image_paths:
|
||||
experiment.log_image(image_path, name=image_path.stem, step=curr_step)
|
||||
|
||||
|
||||
def _log_image_predictions(experiment, validator, curr_step):
|
||||
global _comet_image_prediction_count
|
||||
|
||||
task = validator.args.task
|
||||
if task not in COMET_SUPPORTED_TASKS:
|
||||
return
|
||||
|
||||
jdict = validator.jdict
|
||||
if not jdict:
|
||||
return
|
||||
|
||||
predictions_metadata_map = _create_prediction_metadata_map(jdict)
|
||||
dataloader = validator.dataloader
|
||||
class_label_map = validator.names
|
||||
|
||||
for batch_idx, batch in enumerate(dataloader):
|
||||
if (batch_idx + 1) % COMET_EVAL_BATCH_LOGGING_INTERVAL != 0:
|
||||
continue
|
||||
|
||||
image_paths = batch['im_file']
|
||||
for img_idx, image_path in enumerate(image_paths):
|
||||
if _comet_image_prediction_count >= COMET_MAX_IMAGE_PREDICTIONS:
|
||||
return
|
||||
|
||||
image_path = Path(image_path)
|
||||
annotations = _fetch_annotations(
|
||||
img_idx,
|
||||
image_path,
|
||||
batch,
|
||||
predictions_metadata_map,
|
||||
class_label_map,
|
||||
)
|
||||
_log_images(
|
||||
experiment,
|
||||
[image_path],
|
||||
curr_step,
|
||||
annotations=annotations,
|
||||
)
|
||||
_comet_image_prediction_count += 1
|
||||
|
||||
|
||||
def _log_plots(experiment, trainer):
|
||||
plot_filenames = [trainer.save_dir / f'{plots}.png' for plots in EVALUATION_PLOT_NAMES]
|
||||
_log_images(experiment, plot_filenames, None)
|
||||
|
||||
label_plot_filenames = [trainer.save_dir / f'{labels}.jpg' for labels in LABEL_PLOT_NAMES]
|
||||
_log_images(experiment, label_plot_filenames, None)
|
||||
|
||||
|
||||
def _log_model(experiment, trainer):
|
||||
experiment.log_model(
|
||||
COMET_MODEL_NAME,
|
||||
file_or_folder=str(trainer.best),
|
||||
file_name='best.pt',
|
||||
overwrite=True,
|
||||
)
|
||||
|
||||
|
||||
def on_pretrain_routine_start(trainer):
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if not experiment:
|
||||
_create_experiment(trainer.args)
|
||||
|
||||
|
||||
def on_train_epoch_end(trainer):
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if experiment:
|
||||
experiment.log_metrics(trainer.label_loss_items(trainer.tloss, prefix='train'), step=trainer.epoch + 1)
|
||||
if trainer.epoch == 1:
|
||||
for f in trainer.save_dir.glob('train_batch*.jpg'):
|
||||
experiment.log_image(f, name=f.stem, step=trainer.epoch + 1)
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
|
||||
experiment.log_metrics(
|
||||
trainer.label_loss_items(trainer.tloss, prefix='train'),
|
||||
step=curr_step,
|
||||
epoch=curr_epoch,
|
||||
)
|
||||
|
||||
if curr_epoch == 1:
|
||||
_log_images(experiment, trainer.save_dir.glob('train_batch*.jpg'), curr_step)
|
||||
|
||||
|
||||
def on_fit_epoch_end(trainer):
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if experiment:
|
||||
experiment.log_metrics(trainer.metrics, step=trainer.epoch + 1)
|
||||
if trainer.epoch == 0:
|
||||
model_info = {
|
||||
'model/parameters': get_num_params(trainer.model),
|
||||
'model/GFLOPs': round(get_flops(trainer.model), 3),
|
||||
'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
|
||||
experiment.log_metrics(model_info, step=trainer.epoch + 1)
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
save_assets = metadata['save_assets']
|
||||
|
||||
experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
|
||||
experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
|
||||
if curr_epoch == 1:
|
||||
model_info = {
|
||||
'model/parameters': get_num_params(trainer.model),
|
||||
'model/GFLOPs': round(get_flops(trainer.model), 3),
|
||||
'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
|
||||
experiment.log_metrics(model_info, step=curr_step, epoch=curr_epoch)
|
||||
|
||||
if not save_assets:
|
||||
return
|
||||
|
||||
_log_model(experiment, trainer)
|
||||
if COMET_EVAL_LOG_CONFUSION_MATRIX:
|
||||
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
|
||||
if COMET_EVAL_LOG_IMAGE_PREDICTIONS:
|
||||
_log_image_predictions(experiment, trainer.validator, curr_step)
|
||||
|
||||
|
||||
def on_train_end(trainer):
|
||||
experiment = comet_ml.get_global_experiment()
|
||||
if experiment:
|
||||
experiment.log_model('YOLOv8', file_or_folder=str(trainer.best), file_name='best.pt', overwrite=True)
|
||||
if not experiment:
|
||||
return
|
||||
|
||||
metadata = _fetch_trainer_metadata(trainer)
|
||||
curr_epoch = metadata['curr_epoch']
|
||||
curr_step = metadata['curr_step']
|
||||
plots = trainer.args.plots
|
||||
|
||||
_log_model(experiment, trainer)
|
||||
if plots:
|
||||
_log_plots(experiment, trainer)
|
||||
|
||||
_log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
|
||||
_log_image_predictions(experiment, trainer.validator, curr_step)
|
||||
experiment.end()
|
||||
|
||||
global _comet_image_prediction_count
|
||||
_comet_image_prediction_count = 0
|
||||
|
||||
|
||||
callbacks = {
|
||||
|
||||
@ -8,7 +8,6 @@ import platform
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import urllib
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
@ -20,8 +19,9 @@ import requests
|
||||
import torch
|
||||
from matplotlib import font_manager
|
||||
|
||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
|
||||
emojis, is_colab, is_docker, is_kaggle, is_online, is_pip_package)
|
||||
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, clean_url, colorstr,
|
||||
downloads, emojis, is_colab, is_docker, is_kaggle, is_online, is_pip_package,
|
||||
url2file)
|
||||
|
||||
|
||||
def is_ascii(s) -> bool:
|
||||
@ -267,9 +267,9 @@ def check_file(file, suffix='', download=True, hard=True):
|
||||
return file
|
||||
elif download and file.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://')): # download
|
||||
url = file # warning: Pathlib turns :// -> :/
|
||||
file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
file = url2file(file) # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
if Path(file).exists():
|
||||
LOGGER.info(f'Found {url} locally at {file}') # file already exists
|
||||
LOGGER.info(f'Found {clean_url(url)} locally at {file}') # file already exists
|
||||
else:
|
||||
downloads.safe_download(url=url, file=file, unzip=False)
|
||||
return file
|
||||
|
||||
@ -12,7 +12,7 @@ import requests
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.yolo.utils import LOGGER, checks, emojis, is_online
|
||||
from ultralytics.yolo.utils import LOGGER, checks, clean_url, emojis, is_online, url2file
|
||||
|
||||
GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \
|
||||
[f'yolov5{k}u.pt' for k in 'nsmlx'] + \
|
||||
@ -43,10 +43,18 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
|
||||
if path is None:
|
||||
path = Path(file).parent # default path
|
||||
with ZipFile(file) as zipObj:
|
||||
for f in zipObj.namelist(): # list all archived filenames in the zip
|
||||
for i, f in enumerate(zipObj.namelist()): # list all archived filenames in the zip
|
||||
# If zip does not expand into a directory create a new directory to expand into
|
||||
if i == 0:
|
||||
info = zipObj.getinfo(f)
|
||||
if info.file_size > 0 or not info.filename.endswith('/'): # element is a file and not a directory
|
||||
path = Path(path) / Path(file).stem # define new unzip directory
|
||||
unzip_dir = path
|
||||
else:
|
||||
unzip_dir = f
|
||||
if all(x not in f for x in exclude):
|
||||
zipObj.extract(f, path=path)
|
||||
return zipObj.namelist()[0] # return unzip dir
|
||||
return unzip_dir # return unzip dir
|
||||
|
||||
|
||||
def safe_download(url,
|
||||
@ -79,8 +87,8 @@ def safe_download(url,
|
||||
f = Path(url) # filename
|
||||
else: # does not exist
|
||||
assert dir or file, 'dir or file required for download'
|
||||
f = dir / Path(url).name if dir else Path(file)
|
||||
desc = f'Downloading {url} to {f}'
|
||||
f = dir / url2file(url) if dir else Path(file)
|
||||
desc = f'Downloading {clean_url(url)} to {f}'
|
||||
LOGGER.info(f'{desc}...')
|
||||
f.parent.mkdir(parents=True, exist_ok=True) # make directory if missing
|
||||
for i in range(retry + 1):
|
||||
@ -156,9 +164,9 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
|
||||
name = Path(parse.unquote(str(file))).name # decode '%2F' to '/' etc.
|
||||
if str(file).startswith(('http:/', 'https:/')): # download
|
||||
url = str(file).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth...
|
||||
file = url2file(name) # parse authentication https://url.com/file.txt?auth...
|
||||
if Path(file).is_file():
|
||||
LOGGER.info(f'Found {url} locally at {file}') # file already exists
|
||||
LOGGER.info(f'Found {clean_url(url)} locally at {file}') # file already exists
|
||||
else:
|
||||
safe_download(url=url, file=file, min_bytes=1E5)
|
||||
return file
|
||||
|
||||
@ -3,7 +3,6 @@
|
||||
import contextlib
|
||||
import glob
|
||||
import os
|
||||
import urllib
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
@ -80,12 +79,6 @@ def file_size(path):
|
||||
return 0.0
|
||||
|
||||
|
||||
def url2file(url):
|
||||
# Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt
|
||||
url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth
|
||||
|
||||
|
||||
def get_latest_run(search_dir='.'):
|
||||
# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
|
||||
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
|
||||
|
||||
@ -13,11 +13,11 @@ from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer
|
||||
|
||||
class ClassificationTrainer(BaseTrainer):
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
if overrides is None:
|
||||
overrides = {}
|
||||
overrides['task'] = 'classify'
|
||||
super().__init__(cfg, overrides)
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
|
||||
def set_model_attributes(self):
|
||||
self.model.names = self.data['names']
|
||||
|
||||
@ -8,8 +8,8 @@ from ultralytics.yolo.utils.metrics import ClassifyMetrics
|
||||
|
||||
class ClassificationValidator(BaseValidator):
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args)
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
|
||||
self.args.task = 'classify'
|
||||
self.metrics = ClassifyMetrics()
|
||||
|
||||
|
||||
@ -18,8 +18,8 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
|
||||
|
||||
class DetectionValidator(BaseValidator):
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args)
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
|
||||
self.args.task = 'detect'
|
||||
self.is_coco = False
|
||||
self.class_map = None
|
||||
|
||||
@ -20,11 +20,11 @@ from ultralytics.yolo.v8.detect.train import Loss
|
||||
# BaseTrainer python usage
|
||||
class PoseTrainer(v8.detect.DetectionTrainer):
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
if overrides is None:
|
||||
overrides = {}
|
||||
overrides['task'] = 'pose'
|
||||
super().__init__(cfg, overrides)
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
model = PoseModel(cfg, ch=3, nc=self.data['nc'], data_kpt_shape=self.data['kpt_shape'], verbose=verbose)
|
||||
|
||||
@ -14,8 +14,8 @@ from ultralytics.yolo.v8.detect import DetectionValidator
|
||||
|
||||
class PoseValidator(DetectionValidator):
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args)
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
|
||||
self.args.task = 'pose'
|
||||
self.metrics = PoseMetrics(save_dir=self.save_dir)
|
||||
|
||||
|
||||
@ -17,11 +17,11 @@ from ultralytics.yolo.v8.detect.train import Loss
|
||||
# BaseTrainer python usage
|
||||
class SegmentationTrainer(v8.detect.DetectionTrainer):
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
if overrides is None:
|
||||
overrides = {}
|
||||
overrides['task'] = 'segment'
|
||||
super().__init__(cfg, overrides)
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
|
||||
|
||||
@ -16,8 +16,8 @@ from ultralytics.yolo.v8.detect import DetectionValidator
|
||||
|
||||
class SegmentationValidator(DetectionValidator):
|
||||
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args)
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
|
||||
self.args.task = 'segment'
|
||||
self.metrics = SegmentMetrics(save_dir=self.save_dir)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user