Refactor code

luxonis · Oct 16, 2024 · acce13c · acce13c
1 parent 1ef8f34
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diff --git a/tools/utils/version_detection.py b/tools/utils/version_detection.py
@@ -82,7 +82,7 @@ def detect_version(path: str, debug: bool = False) -> str:
 
         # Remove the output folder
         subprocess.check_output("rm -r extracted_model", shell=True)
-    except subprocess.CalledProcessError:
-        raise RuntimeError()
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError() from e
 
     return UNRECOGNIZED
diff --git a/tools/yolov7/yolov7/README.md b/tools/yolov7/yolov7/README.md
@@ -17,26 +17,27 @@ Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-t
 
 - Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)
 
-## Performance 
+## Performance
 
 MS COCO
 
-| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
-| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
-| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
-| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
-|  |  |  |  |  |  |  |
-| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
-| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
-| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
-| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
+| Model                                                                                       | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
+| :------------------------------------------------------------------------------------------ | :-------: | :---------------: | :----------------------------: | :----------------------------: | :---------: | :-------------------: |
+| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt)         |    640    |     **51.4%**     |           **69.7%**            |           **55.9%**            |  161 *fps*  |       2.8 *ms*        |
+| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt)      |    640    |     **53.1%**     |           **71.2%**            |           **57.8%**            |  114 *fps*  |       4.3 *ms*        |
+|                                                                                             |           |                   |                                |                                |             |                       |
+| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt)   |   1280    |     **54.9%**     |           **72.6%**            |           **60.1%**            |  84 *fps*   |       7.6 *ms*        |
+| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt)   |   1280    |     **56.0%**     |           **73.5%**            |           **61.2%**            |  56 *fps*   |       12.3 *ms*       |
+| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt)   |   1280    |     **56.6%**     |           **74.0%**            |           **61.8%**            |  44 *fps*   |       15.0 *ms*       |
+| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) |   1280    |     **56.8%**     |           **74.4%**            |           **62.1%**            |  36 *fps*   |       18.7 *ms*       |
 
 ## Installation
 
 Docker environment (recommended)
+
 <details><summary> <b>Expand</b> </summary>
 
-``` shell
+```shell
 # create the docker container, you can change the share memory size if you have more.
 nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
 
@@ -57,7 +58,7 @@ cd /yolov7
 
 [`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
 
-``` shell
+```shell
 python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
 ```
 
@@ -84,15 +85,15 @@ To measure accuracy, download [COCO-annotations for Pycocotools](http://images.c
 
 Data preparation
 
-``` shell
+```shell
 bash scripts/get_coco.sh
 ```
 
-* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) 
+- Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
 
 Single GPU training
 
-``` shell
+```shell
 # train p5 models
 python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
 
@@ -102,7 +103,7 @@ python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml
 
 Multiple GPU training
 
-``` shell
+```shell
 # train p5 models
 python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
 
@@ -116,7 +117,7 @@ python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_a
 
 Single GPU finetuning for custom dataset
 
-``` shell
+```shell
 # finetune p5 models
 python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
 
@@ -131,12 +132,14 @@ See [reparameterization.ipynb](tools/reparameterization.ipynb)
 ## Inference
 
 On video:
-``` shell
+
+```shell
 python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
 ```
 
 On image:
-``` shell
+
+```shell
 python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
 ```
 
@@ -146,12 +149,12 @@ python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inferen
     </a>
 </div>
 
-
 ## Export
 
 **Pytorch to CoreML (and inference on MacOS/iOS)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 
 **Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
 ```shell
 python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
         --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
@@ -168,7 +171,6 @@ python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p
 
 **Pytorch to TensorRT another way** <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
 
-
 ```shell
 wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
 python export.py --weights yolov7-tiny.pt --grid --include-nms
@@ -195,7 +197,6 @@ See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoi
     </a>
 </div>
 
-
 ## Instance segmentation
 
 [`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)
@@ -208,7 +209,6 @@ See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instan
     </a>
 </div>
 
-
 ## Citation
 
 ```
@@ -220,7 +220,6 @@ See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instan
 }
 ```
 
-
 ## Teaser
 
 Yolov7-semantic & YOLOv7-panoptic & YOLOv7-caption
@@ -240,20 +239,19 @@ Yolov7-semantic & YOLOv7-panoptic & YOLOv7-caption
     </a>
 </div>
 
-
 ## Acknowledgements
 
 <details><summary> <b>Expand</b> </summary>
 
-* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
-* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
-* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
-* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
-* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
-* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
-* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
-* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
-* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
-* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
+- [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
+- [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
+- [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
+- [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
+- [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
+- [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
+- [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
+- [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
+- [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
+- [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
 
 </details>