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`ultralytics 8.0.75` fixes and updates (#1967)

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Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Jonathan Rayner <jonathan.j.rayner@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
pull/1987/head v8.0.75
Glenn Jocher 2 years ago committed by GitHub
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  1. 103
      README.zh-CN.md
  2. 2
      ultralytics/__init__.py
  3. 2
      ultralytics/datasets/VisDrone.yaml
  4. 59
      ultralytics/yolo/engine/results.py
  5. 36
      ultralytics/yolo/utils/__init__.py
  6. 3
      ultralytics/yolo/utils/checks.py
  7. 5
      ultralytics/yolo/utils/metrics.py
  8. 16
      ultralytics/yolo/utils/plotting.py
  9. 2
      ultralytics/yolo/v8/segment/train.py

103
README.zh-CN.md
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@ -95,7 +95,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
</details>
## <div align="center">Models</div>
## <div align="center">模型</div>
所有的 YOLOv8 预训练模型都可以在此找到。检测、分割和姿态模型在 [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) 数据集上进行预训练，而分类模型在 [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) 数据集上进行预训练。
@ -105,18 +105,18 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
查看 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些模型的示例。
| Model | size<br><sup>(pixels) | mAP<sup>val<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](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------------- | -------------------------------- | -------------- | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val detect data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val detect data=coco128.yaml batch=1 device=0|cpu` 复现
</details>
@ -124,18 +124,18 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
查看 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些模型的示例。
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<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-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](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 |
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | --------------------------- | -------------------------------- | -------------- | ----------------- |
| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](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 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val segment data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu` 复现
</details>
@ -143,18 +143,18 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
查看 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些模型的示例。
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](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 |
| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | --------------------------- | -------------------------------- | -------------- | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](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 |
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。
<br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。
<br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
</details>
@ -162,24 +162,23 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
查看 [姿态文档](https://docs.ultralytics.com/tasks/) 以获取使用这些模型的示例。
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 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 | 79.7 | 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 | 85.8 | 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 | 88.8 | 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 | 89.9 | 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 | 90.4 | 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 | 91.3 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
dataset.
<br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
<br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<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 | 79.7 | 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 | 85.8 | 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 | 88.8 | 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 | 89.9 | 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 | 90.4 | 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 | 91.3 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。
<br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
<br>通过 `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` 复现
</details>
## <div align="center">Integrations</div>
## <div align="center">集成</div>
<br>
<a href="https://bit.ly/ultralytics_hub" target="_blank">
@ -212,7 +211,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
## <div align="center">Contribute</div>
## <div align="center">贡献</div>
我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](CONTRIBUTING.md)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
@ -221,14 +220,14 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
## <div align="center">License</div>
## <div align="center">许可证</div>
YOLOv8 提供两种不同的许可证：
- **GPL-3.0 许可证**：详细信息请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
- **企业许可证**：为商业产品开发提供更大的灵活性，无需遵循 GPL-3.0 的开源要求。典型的用例是将 Ultralytics 软件和 AI 模型嵌入商业产品和应用中。在 [Ultralytics 授权](https://ultralytics.com/license) 处申请企业许可证。
## <div align="center">Contact</div>
## <div align="center">联系方式</div>
如需报告 YOLOv8 的错误或提出功能需求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) 或 [Ultralytics 社区论坛](https://community.ultralytics.com/)。

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.74'
__version__ = '8.0.75'
from ultralytics.hub import start
from ultralytics.yolo.engine.model import YOLO

2
ultralytics/datasets/VisDrone.yaml
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@ -56,7 +56,7 @@ download: |
cls = int(row[5]) - 1
box = convert_box(img_size, tuple(map(int, row[:4])))
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
fl.writelines(lines) # write label.txt

59
ultralytics/yolo/engine/results.py
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@ -21,7 +21,7 @@ class BaseTensor(SimpleClass):
"""
Attributes:
tensor (torch.Tensor): A tensor.
data (torch.Tensor): Base tensor.
orig_shape (tuple): Original image size, in the format (height, width).
Methods:
@ -31,20 +31,14 @@ class BaseTensor(SimpleClass):
to(): Returns a copy of the tensor with the specified device and dtype.
"""
def __init__(self, tensor, orig_shape) -> None:
super().__init__()
assert isinstance(tensor, torch.Tensor)
self.tensor = tensor
def __init__(self, data, orig_shape) -> None:
self.data = data
self.orig_shape = orig_shape
@property
def shape(self):
return self.data.shape
@property
def data(self):
return self.tensor
def cpu(self):
return self.__class__(self.data.cpu(), self.orig_shape)
@ -164,7 +158,6 @@ class Results(SimpleClass):
font_size=None,
font='Arial.ttf',
pil=False,
example='abc',
img=None,
img_gpu=None,
kpt_line=True,
@ -183,7 +176,6 @@ class Results(SimpleClass):
font_size (float, optional): The font size of the text. If None, it is scaled to the image size.
font (str): The font to use for the text.
pil (bool): Whether to return the image as a PIL Image.
example (str): An example string to display. Useful for indicating the expected format of the output.
img (numpy.ndarray): Plot to another image. if not, plot to original image.
img_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
kpt_line (bool): Whether to draw lines connecting keypoints.
@ -201,12 +193,16 @@ class Results(SimpleClass):
conf = kwargs['show_conf']
assert type(conf) == bool, '`show_conf` should be of boolean type, i.e, show_conf=True/False'
annotator = Annotator(deepcopy(self.orig_img if img is None else img), line_width, font_size, font, pil,
example)
names = self.names
annotator = Annotator(deepcopy(self.orig_img if img is None else img),
line_width,
font_size,
font,
pil,
example=names)
pred_boxes, show_boxes = self.boxes, boxes
pred_masks, show_masks = self.masks, masks
pred_probs, show_probs = self.probs, probs
names = self.names
keypoints = self.keypoints
if pred_masks and show_masks:
if img_gpu is None:
@ -236,13 +232,13 @@ class Results(SimpleClass):
def verbose(self):
"""
Return log string for each tasks.
Return log string for each task.
"""
log_string = ''
probs = self.probs
boxes = self.boxes
if len(self) == 0:
return log_string if probs is not None else log_string + '(no detections), '
return log_string if probs is not None else f'{log_string}(no detections), '
if probs is not None:
n5 = min(len(self.names), 5)
top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
@ -346,26 +342,26 @@ class Boxes(BaseTensor):
boxes = boxes[None, :]
n = boxes.shape[-1]
assert n in (6, 7), f'expected `n` in [6, 7], but got {n}' # xyxy, (track_id), conf, cls
super().__init__(boxes, orig_shape)
self.is_track = n == 7
self.boxes = boxes
self.orig_shape = torch.as_tensor(orig_shape, device=boxes.device) if isinstance(boxes, torch.Tensor) \
else np.asarray(orig_shape)
@property
def xyxy(self):
return self.boxes[:, :4]
return self.data[:, :4]
@property
def conf(self):
return self.boxes[:, -2]
return self.data[:, -2]
@property
def cls(self):
return self.boxes[:, -1]
return self.data[:, -1]
@property
def id(self):
return self.boxes[:, -3] if self.is_track else None
return self.data[:, -3] if self.is_track else None
@property
@lru_cache(maxsize=2) # maxsize 1 should suffice
@ -386,8 +382,9 @@ class Boxes(BaseTensor):
LOGGER.info('results.pandas() method not yet implemented')
@property
def data(self):
return self.boxes
def boxes(self):
LOGGER.warning("WARNING ⚠️ 'Boxes.boxes' is deprecated. Use 'Boxes.data' instead.")
return self.data
class Masks(BaseTensor):
@ -416,8 +413,7 @@ class Masks(BaseTensor):
def __init__(self, masks, orig_shape) -> None:
if masks.ndim == 2:
masks = masks[None, :]
self.masks = masks # N, h, w
self.orig_shape = orig_shape
super().__init__(masks, orig_shape)
@property
@lru_cache(maxsize=1)
@ -432,17 +428,18 @@ class Masks(BaseTensor):
def xyn(self):
# Segments (normalized)
return [
ops.scale_coords(self.masks.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.masks)]
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.data)]
@property
@lru_cache(maxsize=1)
def xy(self):
# Segments (pixels)
return [
ops.scale_coords(self.masks.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.masks)]
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.data)]
@property
def data(self):
return self.masks
def masks(self):
LOGGER.warning("WARNING ⚠️ 'Masks.masks' is deprecated. Use 'Masks.data' instead.")
return self.data

36
ultralytics/yolo/utils/__init__.py
Unescape View File

@ -17,6 +17,7 @@ from types import SimpleNamespace
from typing import Union
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import yaml
@ -116,7 +117,7 @@ class SimpleClass:
attr = []
for a in dir(self):
v = getattr(self, a)
if not callable(v) and not a.startswith('__'):
if not callable(v) and not a.startswith('_'):
if isinstance(v, SimpleClass):
# Display only the module and class name for subclasses
s = f'{a}: {v.__module__}.{v.__class__.__name__} object'
@ -164,6 +165,39 @@ class IterableSimpleNamespace(SimpleNamespace):
return getattr(self, key, default)
def plt_settings(rcparams={'font.size': 11}, backend='Agg'):
"""
Decorator to temporarily set rc parameters and the backend for a plotting function.
Usage:
decorator: @plt_settings({"font.size": 12})
context manager: with plt_settings({"font.size": 12}):
Args:
rcparams (dict): Dictionary of rc parameters to set.
backend (str, optional): Name of the backend to use. Defaults to 'Agg'.
Returns:
callable: Decorated function with temporarily set rc parameters and backend.
"""
def decorator(func):
def wrapper(*args, **kwargs):
original_backend = plt.get_backend()
plt.switch_backend(backend)
with plt.rc_context(rcparams):
result = func(*args, **kwargs)
plt.switch_backend(original_backend)
return result
return wrapper
return decorator
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

3
ultralytics/yolo/utils/checks.py
Unescape View File

@ -128,7 +128,8 @@ def check_latest_pypi_version(package_name='ultralytics'):
Returns:
str: The latest version of the package.
"""
response = requests.get(f'https://pypi.org/pypi/{package_name}/json')
requests.packages.urllib3.disable_warnings() # Disable the InsecureRequestWarning
response = requests.get(f'https://pypi.org/pypi/{package_name}/json', verify=False)
if response.status_code == 200:
return response.json()['info']['version']
return None

5
ultralytics/yolo/utils/metrics.py
Unescape View File

@ -11,7 +11,7 @@ import numpy as np
import torch
import torch.nn as nn
from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept
from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
@ -234,6 +234,7 @@ class ConfusionMatrix:
return tp[:-1], fp[:-1] # remove background class
@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
@plt_settings()
def plot(self, normalize=True, save_dir='', names=()):
import seaborn as sn
@ -277,6 +278,7 @@ def smooth(y, f=0.05):
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
@plt_settings()
def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
# Precision-recall curve
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
@ -299,6 +301,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
plt.close(fig)
@plt_settings()
def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
# Metric-confidence curve
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)

16
ultralytics/yolo/utils/plotting.py
Unescape View File

@ -5,22 +5,18 @@ import math
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
from PIL import __version__ as pil_version
from ultralytics.yolo.utils import LOGGER, TryExcept, threaded
from ultralytics.yolo.utils import LOGGER, TryExcept, plt_settings, threaded
from .checks import check_font, check_version, is_ascii
from .files import increment_path
from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
matplotlib.rc('font', **{'size': 11})
matplotlib.use('Agg') # for writing to files only
class Colors:
# Ultralytics color palette https://ultralytics.com/
@ -212,6 +208,7 @@ class Annotator:
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
@plt_settings()
def plot_labels(boxes, cls, names=(), save_dir=Path('')):
import pandas as pd
import seaborn as sn
@ -228,7 +225,6 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
plt.close()
# matplotlib labels
matplotlib.use('svg') # faster
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
with contextlib.suppress(Exception): # color histogram bars by class
@ -244,9 +240,9 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
# rectangles
boxes[:, 0:2] = 0.5 # center
boxes = xywh2xyxy(boxes) * 2000
img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
for cls, box in zip(cls[:1000], boxes[:1000]):
boxes = xywh2xyxy(boxes) * 1000
img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
for cls, box in zip(cls[:500], boxes[:500]):
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
ax[1].imshow(img)
ax[1].axis('off')
@ -256,7 +252,6 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
ax[a].spines[s].set_visible(False)
plt.savefig(save_dir / 'labels.jpg', dpi=200)
matplotlib.use('Agg')
plt.close()
@ -400,6 +395,7 @@ def plot_images(images,
annotator.im.save(fname) # save
@plt_settings()
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
import pandas as pd

2
ultralytics/yolo/v8/segment/train.py
Unescape View File

@ -79,7 +79,7 @@ class SegLoss(Loss):
# targets
try:
batch_idx = batch['batch_idx'].view(-1, 1)
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes'].to(dtype)), 1)
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)

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