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@@ -0,0 +1 @@ +/pytorchyolo-1.8.0.tar.gz diff --git a/python-pytorchyolo.spec b/python-pytorchyolo.spec new file mode 100644 index 0000000..6fd07d3 --- /dev/null +++ b/python-pytorchyolo.spec @@ -0,0 +1,686 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pytorchyolo +Version: 1.8.0 +Release: 1 +Summary: Minimal PyTorch implementation of YOLO +License: GPL-3.0 +URL: https://github.com/eriklindernoren/PyTorch-YOLOv3 +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fc/f0/13da945adfda462d6407b68375bdb02b9952fea1c208b14b594873aebd20/pytorchyolo-1.8.0.tar.gz +BuildArch: noarch + +Requires: python3-torch +Requires: python3-torchvision +Requires: python3-matplotlib +Requires: python3-tensorboard +Requires: python3-terminaltables +Requires: python3-Pillow +Requires: python3-tqdm +Requires: python3-urllib3 +Requires: python3-urllib3 +Requires: python3-scipy +Requires: python3-scipy +Requires: python3-imgaug +Requires: python3-torchsummary +Requires: python3-numpy + +%description +# PyTorch YOLO +A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation. + +YOLOv4 and YOLOv7 weights are also compatible with this implementation. + +[](https://github.com/eriklindernoren/PyTorch-YOLOv3/actions/workflows/main.yml) [](https://pypi.python.org/pypi/pytorchyolo/) [](LICENSE) + +## Installation +### Installing from source + +For normal training and evaluation we recommend installing the package from source using a poetry virtual environment. + +```bash +git clone https://github.com/eriklindernoren/PyTorch-YOLOv3 +cd PyTorch-YOLOv3/ +pip3 install poetry --user +poetry install +``` + +You need to join the virtual environment by running `poetry shell` in this directory before running any of the following commands without the `poetry run` prefix. +Also have a look at the other installing method, if you want to use the commands everywhere without opening a poetry-shell. + +#### Download pretrained weights + +```bash +./weights/download_weights.sh +``` + +#### Download COCO + +```bash +./data/get_coco_dataset.sh +``` + +### Install via pip + +This installation method is recommended, if you want to use this package as a dependency in another python project. +This method only includes the code, is less isolated and may conflict with other packages. +Weights and the COCO dataset need to be downloaded as stated above. +See __API__ for further information regarding the packages API. +It also enables the CLI tools `yolo-detect`, `yolo-train`, and `yolo-test` everywhere without any additional commands. + +```bash +pip3 install pytorchyolo --user +``` + +## Test +Evaluates the model on COCO test dataset. +To download this dataset as well as weights, see above. + +```bash +poetry run yolo-test --weights weights/yolov3.weights +``` + +| Model | mAP (min. 50 IoU) | +| ----------------------- |:-----------------:| +| YOLOv3 608 (paper) | 57.9 | +| YOLOv3 608 (this impl.) | 57.3 | +| YOLOv3 416 (paper) | 55.3 | +| YOLOv3 416 (this impl.) | 55.5 | + +## Inference +Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card. + +| Backbone | GPU | FPS | +| ----------------------- |:--------:|:--------:| +| ResNet-101 | Titan X | 53 | +| ResNet-152 | Titan X | 37 | +| Darknet-53 (paper) | Titan X | 76 | +| Darknet-53 (this impl.) | 1080ti | 74 | + +```bash +poetry run yolo-detect --images data/samples/ +``` + +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/giraffe.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/dog.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/traffic.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/messi.png" width="480"\></p> + +## Train +For argument descriptions have a look at `poetry run yolo-train --help` + +#### Example (COCO) +To train on COCO using a Darknet-53 backend pretrained on ImageNet run: + +```bash +poetry run yolo-train --data config/coco.data --pretrained_weights weights/darknet53.conv.74 +``` + +#### Tensorboard +Track training progress in Tensorboard: +* Initialize training +* Run the command below +* Go to http://localhost:6006/ + +```bash +poetry run tensorboard --logdir='logs' --port=6006 +``` + +Storing the logs on a slow drive possibly leads to a significant training speed decrease. + +You can adjust the log directory using `--logdir <path>` when running `tensorboard` and `yolo-train`. + +## Train on Custom Dataset + +#### Custom model +Run the commands below to create a custom model definition, replacing `<num-classes>` with the number of classes in your dataset. + +```bash +./config/create_custom_model.sh <num-classes> # Will create custom model 'yolov3-custom.cfg' +``` + +#### Classes +Add class names to `data/custom/classes.names`. This file should have one row per class name. + +#### Image Folder +Move the images of your dataset to `data/custom/images/`. + +#### Annotation Folder +Move your annotations to `data/custom/labels/`. The dataloader expects that the annotation file corresponding to the image `data/custom/images/train.jpg` has the path `data/custom/labels/train.txt`. Each row in the annotation file should define one bounding box, using the syntax `label_idx x_center y_center width height`. The coordinates should be scaled `[0, 1]`, and the `label_idx` should be zero-indexed and correspond to the row number of the class name in `data/custom/classes.names`. + +#### Define Train and Validation Sets +In `data/custom/train.txt` and `data/custom/valid.txt`, add paths to images that will be used as train and validation data respectively. + +#### Train +To train on the custom dataset run: + +```bash +poetry run yolo-train --model config/yolov3-custom.cfg --data config/custom.data +``` + +Add `--pretrained_weights weights/darknet53.conv.74` to train using a backend pretrained on ImageNet. + + +## API + +You are able to import the modules of this repo in your own project if you install the pip package `pytorchyolo`. + +An example prediction call from a simple OpenCV python script would look like this: + +```python +import cv2 +from pytorchyolo import detect, models + +# Load the YOLO model +model = models.load_model( + "<PATH_TO_YOUR_CONFIG_FOLDER>/yolov3.cfg", + "<PATH_TO_YOUR_WEIGHTS_FOLDER>/yolov3.weights") + +# Load the image as a numpy array +img = cv2.imread("<PATH_TO_YOUR_IMAGE>") + +# Convert OpenCV bgr to rgb +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + +# Runs the YOLO model on the image +boxes = detect.detect_image(model, img) + +print(boxes) +# Output will be a numpy array in the following format: +# [[x1, y1, x2, y2, confidence, class]] +``` + +For more advanced usage look at the method's doc strings. + +## Credit + +### YOLOv3: An Incremental Improvement +_Joseph Redmon, Ali Farhadi_ <br> + +**Abstract** <br> +We present some updates to YOLO! We made a bunch +of little design changes to make it better. We also trained +this new network that’s pretty swell. It’s a little bigger than +last time but more accurate. It’s still fast though, don’t +worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, +as accurate as SSD but three times faster. When we look +at the old .5 IOU mAP detection metric YOLOv3 is quite +good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared +to 57.5 AP50 in 198 ms by RetinaNet, similar performance +but 3.8× faster. As always, all the code is online at +https://pjreddie.com/yolo/. + +[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [[Project Webpage]](https://pjreddie.com/darknet/yolo/) [[Authors' Implementation]](https://github.com/pjreddie/darknet) + +``` +@article{yolov3, + title={YOLOv3: An Incremental Improvement}, + author={Redmon, Joseph and Farhadi, Ali}, + journal = {arXiv}, + year={2018} +} +``` + +## Other + +### YOEO — You Only Encode Once + +[YOEO](https://github.com/bit-bots/YOEO) extends this repo with the ability to train an additional semantic segmentation decoder. The lightweight example model is mainly targeted towards embedded real-time applications. + + +%package -n python3-pytorchyolo +Summary: Minimal PyTorch implementation of YOLO +Provides: python-pytorchyolo +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pytorchyolo +# PyTorch YOLO +A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation. + +YOLOv4 and YOLOv7 weights are also compatible with this implementation. + +[](https://github.com/eriklindernoren/PyTorch-YOLOv3/actions/workflows/main.yml) [](https://pypi.python.org/pypi/pytorchyolo/) [](LICENSE) + +## Installation +### Installing from source + +For normal training and evaluation we recommend installing the package from source using a poetry virtual environment. + +```bash +git clone https://github.com/eriklindernoren/PyTorch-YOLOv3 +cd PyTorch-YOLOv3/ +pip3 install poetry --user +poetry install +``` + +You need to join the virtual environment by running `poetry shell` in this directory before running any of the following commands without the `poetry run` prefix. +Also have a look at the other installing method, if you want to use the commands everywhere without opening a poetry-shell. + +#### Download pretrained weights + +```bash +./weights/download_weights.sh +``` + +#### Download COCO + +```bash +./data/get_coco_dataset.sh +``` + +### Install via pip + +This installation method is recommended, if you want to use this package as a dependency in another python project. +This method only includes the code, is less isolated and may conflict with other packages. +Weights and the COCO dataset need to be downloaded as stated above. +See __API__ for further information regarding the packages API. +It also enables the CLI tools `yolo-detect`, `yolo-train`, and `yolo-test` everywhere without any additional commands. + +```bash +pip3 install pytorchyolo --user +``` + +## Test +Evaluates the model on COCO test dataset. +To download this dataset as well as weights, see above. + +```bash +poetry run yolo-test --weights weights/yolov3.weights +``` + +| Model | mAP (min. 50 IoU) | +| ----------------------- |:-----------------:| +| YOLOv3 608 (paper) | 57.9 | +| YOLOv3 608 (this impl.) | 57.3 | +| YOLOv3 416 (paper) | 55.3 | +| YOLOv3 416 (this impl.) | 55.5 | + +## Inference +Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card. + +| Backbone | GPU | FPS | +| ----------------------- |:--------:|:--------:| +| ResNet-101 | Titan X | 53 | +| ResNet-152 | Titan X | 37 | +| Darknet-53 (paper) | Titan X | 76 | +| Darknet-53 (this impl.) | 1080ti | 74 | + +```bash +poetry run yolo-detect --images data/samples/ +``` + +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/giraffe.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/dog.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/traffic.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/messi.png" width="480"\></p> + +## Train +For argument descriptions have a look at `poetry run yolo-train --help` + +#### Example (COCO) +To train on COCO using a Darknet-53 backend pretrained on ImageNet run: + +```bash +poetry run yolo-train --data config/coco.data --pretrained_weights weights/darknet53.conv.74 +``` + +#### Tensorboard +Track training progress in Tensorboard: +* Initialize training +* Run the command below +* Go to http://localhost:6006/ + +```bash +poetry run tensorboard --logdir='logs' --port=6006 +``` + +Storing the logs on a slow drive possibly leads to a significant training speed decrease. + +You can adjust the log directory using `--logdir <path>` when running `tensorboard` and `yolo-train`. + +## Train on Custom Dataset + +#### Custom model +Run the commands below to create a custom model definition, replacing `<num-classes>` with the number of classes in your dataset. + +```bash +./config/create_custom_model.sh <num-classes> # Will create custom model 'yolov3-custom.cfg' +``` + +#### Classes +Add class names to `data/custom/classes.names`. This file should have one row per class name. + +#### Image Folder +Move the images of your dataset to `data/custom/images/`. + +#### Annotation Folder +Move your annotations to `data/custom/labels/`. The dataloader expects that the annotation file corresponding to the image `data/custom/images/train.jpg` has the path `data/custom/labels/train.txt`. Each row in the annotation file should define one bounding box, using the syntax `label_idx x_center y_center width height`. The coordinates should be scaled `[0, 1]`, and the `label_idx` should be zero-indexed and correspond to the row number of the class name in `data/custom/classes.names`. + +#### Define Train and Validation Sets +In `data/custom/train.txt` and `data/custom/valid.txt`, add paths to images that will be used as train and validation data respectively. + +#### Train +To train on the custom dataset run: + +```bash +poetry run yolo-train --model config/yolov3-custom.cfg --data config/custom.data +``` + +Add `--pretrained_weights weights/darknet53.conv.74` to train using a backend pretrained on ImageNet. + + +## API + +You are able to import the modules of this repo in your own project if you install the pip package `pytorchyolo`. + +An example prediction call from a simple OpenCV python script would look like this: + +```python +import cv2 +from pytorchyolo import detect, models + +# Load the YOLO model +model = models.load_model( + "<PATH_TO_YOUR_CONFIG_FOLDER>/yolov3.cfg", + "<PATH_TO_YOUR_WEIGHTS_FOLDER>/yolov3.weights") + +# Load the image as a numpy array +img = cv2.imread("<PATH_TO_YOUR_IMAGE>") + +# Convert OpenCV bgr to rgb +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + +# Runs the YOLO model on the image +boxes = detect.detect_image(model, img) + +print(boxes) +# Output will be a numpy array in the following format: +# [[x1, y1, x2, y2, confidence, class]] +``` + +For more advanced usage look at the method's doc strings. + +## Credit + +### YOLOv3: An Incremental Improvement +_Joseph Redmon, Ali Farhadi_ <br> + +**Abstract** <br> +We present some updates to YOLO! We made a bunch +of little design changes to make it better. We also trained +this new network that’s pretty swell. It’s a little bigger than +last time but more accurate. It’s still fast though, don’t +worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, +as accurate as SSD but three times faster. When we look +at the old .5 IOU mAP detection metric YOLOv3 is quite +good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared +to 57.5 AP50 in 198 ms by RetinaNet, similar performance +but 3.8× faster. As always, all the code is online at +https://pjreddie.com/yolo/. + +[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [[Project Webpage]](https://pjreddie.com/darknet/yolo/) [[Authors' Implementation]](https://github.com/pjreddie/darknet) + +``` +@article{yolov3, + title={YOLOv3: An Incremental Improvement}, + author={Redmon, Joseph and Farhadi, Ali}, + journal = {arXiv}, + year={2018} +} +``` + +## Other + +### YOEO — You Only Encode Once + +[YOEO](https://github.com/bit-bots/YOEO) extends this repo with the ability to train an additional semantic segmentation decoder. The lightweight example model is mainly targeted towards embedded real-time applications. + + +%package help +Summary: Development documents and examples for pytorchyolo +Provides: python3-pytorchyolo-doc +%description help +# PyTorch YOLO +A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation. + +YOLOv4 and YOLOv7 weights are also compatible with this implementation. + +[](https://github.com/eriklindernoren/PyTorch-YOLOv3/actions/workflows/main.yml) [](https://pypi.python.org/pypi/pytorchyolo/) [](LICENSE) + +## Installation +### Installing from source + +For normal training and evaluation we recommend installing the package from source using a poetry virtual environment. + +```bash +git clone https://github.com/eriklindernoren/PyTorch-YOLOv3 +cd PyTorch-YOLOv3/ +pip3 install poetry --user +poetry install +``` + +You need to join the virtual environment by running `poetry shell` in this directory before running any of the following commands without the `poetry run` prefix. +Also have a look at the other installing method, if you want to use the commands everywhere without opening a poetry-shell. + +#### Download pretrained weights + +```bash +./weights/download_weights.sh +``` + +#### Download COCO + +```bash +./data/get_coco_dataset.sh +``` + +### Install via pip + +This installation method is recommended, if you want to use this package as a dependency in another python project. +This method only includes the code, is less isolated and may conflict with other packages. +Weights and the COCO dataset need to be downloaded as stated above. +See __API__ for further information regarding the packages API. +It also enables the CLI tools `yolo-detect`, `yolo-train`, and `yolo-test` everywhere without any additional commands. + +```bash +pip3 install pytorchyolo --user +``` + +## Test +Evaluates the model on COCO test dataset. +To download this dataset as well as weights, see above. + +```bash +poetry run yolo-test --weights weights/yolov3.weights +``` + +| Model | mAP (min. 50 IoU) | +| ----------------------- |:-----------------:| +| YOLOv3 608 (paper) | 57.9 | +| YOLOv3 608 (this impl.) | 57.3 | +| YOLOv3 416 (paper) | 55.3 | +| YOLOv3 416 (this impl.) | 55.5 | + +## Inference +Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card. + +| Backbone | GPU | FPS | +| ----------------------- |:--------:|:--------:| +| ResNet-101 | Titan X | 53 | +| ResNet-152 | Titan X | 37 | +| Darknet-53 (paper) | Titan X | 76 | +| Darknet-53 (this impl.) | 1080ti | 74 | + +```bash +poetry run yolo-detect --images data/samples/ +``` + +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/giraffe.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/dog.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/traffic.png" width="480"\></p> +<p align="center"><img src="https://github.com/eriklindernoren/PyTorch-YOLOv3/raw/master/assets/messi.png" width="480"\></p> + +## Train +For argument descriptions have a look at `poetry run yolo-train --help` + +#### Example (COCO) +To train on COCO using a Darknet-53 backend pretrained on ImageNet run: + +```bash +poetry run yolo-train --data config/coco.data --pretrained_weights weights/darknet53.conv.74 +``` + +#### Tensorboard +Track training progress in Tensorboard: +* Initialize training +* Run the command below +* Go to http://localhost:6006/ + +```bash +poetry run tensorboard --logdir='logs' --port=6006 +``` + +Storing the logs on a slow drive possibly leads to a significant training speed decrease. + +You can adjust the log directory using `--logdir <path>` when running `tensorboard` and `yolo-train`. + +## Train on Custom Dataset + +#### Custom model +Run the commands below to create a custom model definition, replacing `<num-classes>` with the number of classes in your dataset. + +```bash +./config/create_custom_model.sh <num-classes> # Will create custom model 'yolov3-custom.cfg' +``` + +#### Classes +Add class names to `data/custom/classes.names`. This file should have one row per class name. + +#### Image Folder +Move the images of your dataset to `data/custom/images/`. + +#### Annotation Folder +Move your annotations to `data/custom/labels/`. The dataloader expects that the annotation file corresponding to the image `data/custom/images/train.jpg` has the path `data/custom/labels/train.txt`. Each row in the annotation file should define one bounding box, using the syntax `label_idx x_center y_center width height`. The coordinates should be scaled `[0, 1]`, and the `label_idx` should be zero-indexed and correspond to the row number of the class name in `data/custom/classes.names`. + +#### Define Train and Validation Sets +In `data/custom/train.txt` and `data/custom/valid.txt`, add paths to images that will be used as train and validation data respectively. + +#### Train +To train on the custom dataset run: + +```bash +poetry run yolo-train --model config/yolov3-custom.cfg --data config/custom.data +``` + +Add `--pretrained_weights weights/darknet53.conv.74` to train using a backend pretrained on ImageNet. + + +## API + +You are able to import the modules of this repo in your own project if you install the pip package `pytorchyolo`. + +An example prediction call from a simple OpenCV python script would look like this: + +```python +import cv2 +from pytorchyolo import detect, models + +# Load the YOLO model +model = models.load_model( + "<PATH_TO_YOUR_CONFIG_FOLDER>/yolov3.cfg", + "<PATH_TO_YOUR_WEIGHTS_FOLDER>/yolov3.weights") + +# Load the image as a numpy array +img = cv2.imread("<PATH_TO_YOUR_IMAGE>") + +# Convert OpenCV bgr to rgb +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + +# Runs the YOLO model on the image +boxes = detect.detect_image(model, img) + +print(boxes) +# Output will be a numpy array in the following format: +# [[x1, y1, x2, y2, confidence, class]] +``` + +For more advanced usage look at the method's doc strings. + +## Credit + +### YOLOv3: An Incremental Improvement +_Joseph Redmon, Ali Farhadi_ <br> + +**Abstract** <br> +We present some updates to YOLO! We made a bunch +of little design changes to make it better. We also trained +this new network that’s pretty swell. It’s a little bigger than +last time but more accurate. It’s still fast though, don’t +worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, +as accurate as SSD but three times faster. When we look +at the old .5 IOU mAP detection metric YOLOv3 is quite +good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared +to 57.5 AP50 in 198 ms by RetinaNet, similar performance +but 3.8× faster. As always, all the code is online at +https://pjreddie.com/yolo/. + +[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [[Project Webpage]](https://pjreddie.com/darknet/yolo/) [[Authors' Implementation]](https://github.com/pjreddie/darknet) + +``` +@article{yolov3, + title={YOLOv3: An Incremental Improvement}, + author={Redmon, Joseph and Farhadi, Ali}, + journal = {arXiv}, + year={2018} +} +``` + +## Other + +### YOEO — You Only Encode Once + +[YOEO](https://github.com/bit-bots/YOEO) extends this repo with the ability to train an additional semantic segmentation decoder. The lightweight example model is mainly targeted towards embedded real-time applications. + + +%prep +%autosetup -n pytorchyolo-1.8.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-pytorchyolo -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.0-1 +- Package Spec generated @@ -0,0 +1 @@ +fa1d773f5d1bcf8d3e36ff32109fd08b pytorchyolo-1.8.0.tar.gz |