mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 05:24:22 +08:00
Update YOLOv5 YAMLs to 'u' YAMLs (#800)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
0d182e80f1
commit
21ae321bc2
@ -59,18 +59,18 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
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### Docstrings
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Not all functions or classes require docstrings but when they do, we follow [google-stlye docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
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Not all functions or classes require docstrings but when they do, we follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
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```python
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"""
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What the function does - performs nms on given detection predictions
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What the function does. Performs NMS on given detection predictions.
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Args:
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arg1: The description of the 1st argument
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arg2: The description of the 2nd argument
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Returns:
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What the function returns. Empty if nothing is returned
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What the function returns. Empty if nothing is returned.
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Raises:
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Exception Class: When and why this exception can be raised by the function.
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@ -1,6 +1,6 @@
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<div align="center">
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<p>
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<a align="center" href="https://ultralytics.com/yolov8" target="_blank">
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<a href="https://ultralytics.com/yolov8" target="_blank">
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
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</p>
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@ -191,7 +191,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
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## <div align="center">Integrations</div>
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<br>
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<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
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<br>
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<br>
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@ -220,7 +220,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
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🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on
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your iOS or Android device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
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<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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## <div align="center">Contribute</div>
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@ -1,6 +1,6 @@
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<div align="center">
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<p>
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<a align="center" href="https://ultralytics.com/yolov8" target="_blank">
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<a href="https://ultralytics.com/yolov8" target="_blank">
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<img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
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</p>
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@ -167,7 +167,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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## <div align="center">模块集成</div>
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<br>
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<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
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<br>
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<br>
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@ -194,7 +194,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们⭐ **新**的无代码解决方案，用于可视化数据集，训练 YOLOv8🚀 模型，并以无缝体验方式部署到现实世界。现在开始**免费**! 还可以通过下载 [Ultralytics App](https://ultralytics.com/app_install) 在你的 iOS 或 Android 设备上运行 YOLOv8 模型!
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<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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## <div align="center">贡献</div>
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@ -13,7 +13,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
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ENV DEBIAN_FRONTEND noninteractive
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RUN apt update
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RUN TZ=Etc/UTC apt install -y tzdata
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RUN apt install --no-install-recommends -y git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
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RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
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# RUN alias python=python3
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# Create working directory
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@ -125,7 +125,7 @@ given task.
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| show | False | show results if possible |
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| save_txt | False | save results as .txt file |
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| save_conf | False | save results with confidence scores |
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| save_crop | Fasle | save cropped images with results |
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| save_crop | False | save cropped images with results |
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| hide_labels | False | hide labels |
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| hide_conf | False | hide confidence scores |
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| vid_stride | False | video frame-rate stride |
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@ -3,12 +3,12 @@ executors. Let's take a look at the Trainer engine.
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## BaseTrainer
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BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overidding
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BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding
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the required functions or operations as long the as correct formats are followed. For example, you can support your own
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custom model and dataloder by just overriding these functions:
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custom model and dataloader by just overriding these functions:
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* `get_model(cfg, weights)` - The function that builds the model to be trained
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* `get_dataloder()` - The function that builds the dataloder
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* `get_dataloder()` - The function that builds the dataloader
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More details and source code can be found in [`BaseTrainer` Reference](reference/base_trainer.md)
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## DetectionTrainer
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@ -78,6 +78,6 @@ To know more about Callback triggering events and entry point, checkout our Call
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## Other engine components
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There are other componenets that can be customized similarly like `Validators` and `Predictors`
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There are other components that can be customized similarly like `Validators` and `Predictors`
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See Reference section for more information on these.
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@ -80,7 +80,7 @@ training! <a href="https://colab.research.google.com/github/ultralytics/hub/blob
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Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
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models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
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<a align="center" href="https://ultralytics.com/app_install" target="_blank">
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<a href="https://ultralytics.com/app_install" target="_blank">
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<img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
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## ❓ Issues
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@ -1,4 +1,4 @@
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All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimzation routine
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All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimization routine
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boilerplate. You can override any function of these Trainers to suit your needs.
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---
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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__version__ = "8.0.27"
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__version__ = "8.0.28"
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from ultralytics.yolo.engine.model import YOLO
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from ultralytics.yolo.utils import ops
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@ -175,7 +175,9 @@ class Traces:
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cfg = vars(cfg) # convert type from IterableSimpleNamespace to dict
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if not all_keys: # filter cfg
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include_keys = {'task', 'mode'} # always include
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cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None) or k in include_keys}
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cfg = {
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k: (v.split(os.sep)[-1] if isinstance(v, str) and os.sep in v else v)
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for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None) or k in include_keys}
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trace = {'uuid': SETTINGS['uuid'], 'cfg': cfg, 'metadata': self.metadata}
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# Send a request to the HUB API to sync analytics
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@ -456,4 +456,5 @@ class Classify(nn.Module):
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def forward(self, x):
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if isinstance(x, list):
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x = torch.cat(x, 1)
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return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
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x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
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return x if self.training else x.softmax(1)
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@ -70,7 +70,7 @@ from ultralytics.nn.modules import Detect, Segment
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from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, guess_model_task
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
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from ultralytics.yolo.data.utils import check_det_dataset
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from ultralytics.yolo.data.utils import check_det_dataset, IMAGENET_MEAN, IMAGENET_STD
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
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from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
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from ultralytics.yolo.utils.files import file_size
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@ -185,8 +185,8 @@ class Exporter:
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if self.args.half and not coreml and not xml:
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im, model = im.half(), model.half() # to FP16
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shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
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LOGGER.info(
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f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
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f"output shape {shape} ({file_size(file):.1f} MB)")
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# Warnings
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warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
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@ -384,12 +384,18 @@ class Exporter:
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = self.file.with_suffix('.mlmodel')
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task = self.model.task
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if self.model.task == 'classify':
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bias = [-x for x in IMAGENET_MEAN]
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scale = 1 / 255 / (sum(IMAGENET_STD) / 3)
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classifier_config = ct.ClassifierConfig(list(self.model.names.values()))
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else:
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bias = [0.0, 0.0, 0.0]
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scale = 1 / 255
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classifier_config = None
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model = iOSModel(self.model, self.im).eval() if self.args.nms else self.model
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ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
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classifier_config = ct.ClassifierConfig(list(model.names.values())) if task == 'classify' else None
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ct_model = ct.convert(ts,
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inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])],
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inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
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classifier_config=classifier_config)
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bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
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if bits < 32:
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@ -162,6 +162,8 @@ class YOLO:
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.data = data or args.data
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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args.imgsz = check_imgsz(args.imgsz, max_dim=1)
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validator = self.ValidatorClass(args=args)
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@ -180,6 +182,8 @@ class YOLO:
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overrides.update(kwargs)
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.task = self.task
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if args.imgsz == DEFAULT_CFG.imgsz:
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args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed
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exporter = Exporter(overrides=args)
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exporter(model=self.model)
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@ -120,9 +120,6 @@ class BasePredictor:
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pass
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def setup_source(self, source):
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if not self.model:
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raise Exception("Model not initialized!")
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self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
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self.dataset = load_inference_source(source=source,
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transforms=getattr(self.model.model, 'transforms', None),
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@ -190,6 +187,10 @@ class BasePredictor:
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if self.args.verbose:
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LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
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# Release assets
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if isinstance(self.vid_writer[-1], cv2.VideoWriter):
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self.vid_writer[-1].release() # release final video writer
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# Print results
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if self.args.verbose and self.seen:
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t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
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@ -5,6 +5,7 @@ import inspect
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import math
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import os
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import platform
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import re
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import shutil
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import urllib
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from pathlib import Path
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@ -67,12 +68,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
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f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
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# Apply max_dim
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if max_dim == 1:
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LOGGER.warning(f"WARNING ⚠️ 'train' and 'val' imgsz types must be integer, updating to 'imgsz={max(imgsz)}'. "
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f"'predict' and 'export' imgsz may be list or integer, "
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f"i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'")
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if len(imgsz) > max_dim:
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msg = "'train' and 'val' imgsz must be an integer, while 'predict' and 'export' imgsz may be a [h, w] list " \
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"or an integer, i.e. 'yolo export imgsz=640,480' or 'yolo export imgsz=640'"
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if max_dim != 1:
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raise ValueError(f"imgsz={imgsz} is not a valid image size. {msg}")
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LOGGER.warning(f"WARNING ⚠️ updating to 'imgsz={max(imgsz)}'. {msg}")
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imgsz = [max(imgsz)]
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# Make image size a multiple of the stride
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sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]
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@ -220,10 +222,24 @@ def check_suffix(file='yolov8n.pt', suffix=('.pt',), msg=''):
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assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
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def check_yolov5u_filename(file: str):
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# Replace legacy YOLOv5 filenames with updated YOLOv5u filenames
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if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
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original_file = file
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file = re.sub(r"(.*yolov5([nsmlx]))\.", "\\1u.", file) # i.e. yolov5n.pt -> yolov5nu.pt
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file = re.sub(r"(.*yolov3(|-tiny|-spp))\.", "\\1u.", file) # i.e. yolov3-spp.pt -> yolov3-sppu.pt
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if file != original_file:
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LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
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f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
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f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
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return file
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def check_file(file, suffix=''):
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# Search/download file (if necessary) and return path
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check_suffix(file, suffix) # optional
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file = str(file) # convert to str()
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file = str(file) # convert to string
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file = check_yolov5u_filename(file) # yolov5n -> yolov5nu
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if not file or ('://' not in file and Path(file).is_file()): # exists ('://' check required in Windows Python<3.10)
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return file
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elif file.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://')): # download
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@ -1,7 +1,6 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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import contextlib
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import re
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import subprocess
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from itertools import repeat
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from multiprocessing.pool import ThreadPool
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@ -111,6 +110,7 @@ def safe_download(url,
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def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
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# Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
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from ultralytics.yolo.utils import SETTINGS
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from ultralytics.yolo.utils.checks import check_yolov5u_filename
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def github_assets(repository, version='latest'):
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# Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])
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@ -121,15 +121,7 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
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# YOLOv3/5u updates
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file = str(file)
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if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
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original_file = file
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file = re.sub(r"(.*yolov5([nsmlx]))\.pt", "\\1u.pt", file) # i.e. yolov5n.pt -> yolov5nu.pt
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file = re.sub(r"(.*yolov3(|-tiny|-spp))\.pt", "\\1u.pt", file) # i.e. yolov3-spp.pt -> yolov3-sppu.pt
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if file != original_file:
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LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
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f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
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f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
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file = check_yolov5u_filename(file)
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file = Path(file.strip().replace("'", ''))
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if file.exists():
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return str(file)
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@ -23,7 +23,7 @@ class ClassificationPredictor(BasePredictor):
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results = []
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for i, pred in enumerate(preds):
|
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shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
|
||||
results.append(Results(probs=pred.softmax(0), orig_shape=shape[:2]))
|
||||
results.append(Results(probs=pred, orig_shape=shape[:2]))
|
||||
return results
|
||||
|
||||
def write_results(self, idx, results, batch):
|
||||
|
||||
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Reference in New Issue
Block a user