diff --git a/README.zh-CN.md b/README.zh-CN.md index a0dd8b7..accfd30 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -95,7 +95,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 -##
Models
+##
模型
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
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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
(像素) | mAPval
50-95 | 速度
CPU ONNX
(ms) | 速度
A100 TensorRT
(ms) | 参数
(M) | FLOPs
(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 | -- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. -
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. -
Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu` +- **mAPval** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 +
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。 +
通过 `yolo val detect data=coco128.yaml batch=1 device=0|cpu` 复现 @@ -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
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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
(像素) | mAPbox
50-95 | mAPmask
50-95 | 速度
CPU ONNX
(ms) | 速度
A100 TensorRT
(ms) | 参数
(M) | FLOPs
(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 | -- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. -
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. -
Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu` +- **mAPval** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 +
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。 +
通过 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu` 复现 @@ -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
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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
(像素) | acc
top1 | acc
top5 | 速度
CPU ONNX
(ms) | 速度
A100 TensorRT
(ms) | 参数
(M) | FLOPs
(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. -
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. -
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。 +
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。 +
通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现 @@ -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
(pixels) | mAPpose
50-95 | mAPpose
50 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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 | - -- **mAPval** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) - dataset. -
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. -
Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` +| 模型 | 尺寸
(像素) | mAPpose
50-95 | mAPpose
50 | 速度
CPU ONNX
(ms) | 速度
A100 TensorRT
(ms) | 参数
(M) | FLOPs
(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 | + +- **mAPval** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。 +
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。 +
通过 `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` 复现 -##
Integrations
+##
集成

@@ -212,7 +211,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 -##
Contribute
+##
贡献
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 -##
License
+##
许可证
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。 -##
Contact
+##
联系方式
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/)。 diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 61c768f..37cef40 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -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 diff --git a/ultralytics/datasets/VisDrone.yaml b/ultralytics/datasets/VisDrone.yaml index a481066..c56bbba 100644 --- a/ultralytics/datasets/VisDrone.yaml +++ b/ultralytics/datasets/VisDrone.yaml @@ -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 diff --git a/ultralytics/yolo/engine/results.py b/ultralytics/yolo/engine/results.py index b6e780a..b47448b 100644 --- a/ultralytics/yolo/engine/results.py +++ b/ultralytics/yolo/engine/results.py @@ -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 diff --git a/ultralytics/yolo/utils/__init__.py b/ultralytics/yolo/utils/__init__.py index e9daf7c..a517a07 100644 --- a/ultralytics/yolo/utils/__init__.py +++ b/ultralytics/yolo/utils/__init__.py @@ -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 diff --git a/ultralytics/yolo/utils/checks.py b/ultralytics/yolo/utils/checks.py index 0198090..f178d3e 100644 --- a/ultralytics/yolo/utils/checks.py +++ b/ultralytics/yolo/utils/checks.py @@ -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 diff --git a/ultralytics/yolo/utils/metrics.py b/ultralytics/yolo/utils/metrics.py index d7da8e6..d00d1c7 100644 --- a/ultralytics/yolo/utils/metrics.py +++ b/ultralytics/yolo/utils/metrics.py @@ -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) diff --git a/ultralytics/yolo/utils/plotting.py b/ultralytics/yolo/utils/plotting.py index 6338822..40b0014 100644 --- a/ultralytics/yolo/utils/plotting.py +++ b/ultralytics/yolo/utils/plotting.py @@ -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 diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py index 165079a..bc2faf2 100644 --- a/ultralytics/yolo/v8/segment/train.py +++ b/ultralytics/yolo/v8/segment/train.py @@ -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)