Uv migration

.github/ISSUE_TEMPLATE/1_bug_report.md

@@ -19,7 +19,7 @@ assignees: ''
 ## Necessary Checks
 <!--- Please ensure, you have completed the following checks. This helps to give insight into the issue and prevent already resolved issues. -->
 - [ ] The issue occurred on the newest version
-<!--- If installed manually, run: 'git pull && poetry install'  -->
+<!--- If installed manually, run: 'git pull && uv sync'  -->
 <!--- If installed via PIP, run: 'pip install --upgrade yoeo' -->
 - [ ] I couldn't find a similar issue here on this project's github repo
 - [ ] If the issue is CUDA related (CUDA error), I have tested and provided the traceback also when CUDA is turned off <!--- For linux, rerun your steps with the prefix CUDA_VISIBLE_DEVICES="" -->

.github/pull_request_template.md

@@ -7,7 +7,7 @@
 <!--- If fixing a bug, there should be an issue describing it with steps to reproduce -->
 
 ## Necessary checks
-- [ ] Update poetry package version [semantically](https://semver.org/)
+- [ ] Update package version [semantically](https://semver.org/)
 - [ ] Write documentation
 - [ ] Create issues for future work
 - [ ] Test on your machine

.github/workflows/main.yml

@@ -11,33 +11,32 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Install Poetry
-        run: pipx install poetry
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
 
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
           python-version: 3.x
-          cache: "poetry"
 
       - name: Install Dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
 
       # Prints the help pages of all scripts to see if the imports etc. work
       - name: Test the help pages
         run: |
-          poetry run yoeo-train -h
-          poetry run yoeo-test -h
-          poetry run yoeo-detect -h
+          uv run yoeo-train -h
+          uv run yoeo-test -h
+          uv run yoeo-detect -h
 
       - name: Demo Training
-        run: poetry run yoeo-train --data config/custom.data --epochs 30
+        run: uv run yoeo-train --data config/custom.data --epochs 30
 
       - name: Demo Evaluate
-        run: poetry run yoeo-test --data config/custom.data --weights checkpoints/yoeo_checkpoint_29.pth
+        run: uv run yoeo-test --data config/custom.data --weights checkpoints/yoeo_checkpoint_29.pth
 
       - name: Demo Detect
-        run: poetry run yoeo-detect --batch_size 2 --weights checkpoints/yoeo_checkpoint_29.pth
+        run: uv run yoeo-detect --batch_size 2 --weights checkpoints/yoeo_checkpoint_29.pth
 
   linter:
     runs-on: ubuntu-22.04

README.md

@@ -13,17 +13,17 @@ This project is based upon [PyTorch-YOLOv3](https://github.com/eriklindernoren/P
 ## Installation
 ### Installing from source
 
-For normal training and evaluation we recommend installing the package from source using a poetry virtual environment.
+For normal training and evaluation we recommend installing the package from source using a uv virtual environment.
 
 ```bash
 git clone https://github.com/bit-bots/YOEO
 cd YOEO/
-pip3 install poetry --user
-poetry install
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv sync
 ```
 
-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.
+You need to join the virtual environment by running `source .venv/bin/activate` in this directory before running any of the following commands without the `uv run` prefix.
+Also have a look at the other installing method, if you want to use the commands everywhere without activating the virtual environment.
 
 #### Download pretrained weights
 
@@ -36,20 +36,20 @@ Evaluates the model on the test dataset.
 See help page for more details.
 
 ```bash
-poetry run yoeo-test -h
+uv run yoeo-test -h
 ```
 
 ## Inference
 Uses pretrained weights to make predictions on images.
 
 ```bash
-poetry run yoeo-detect --images data/samples/
+uv run yoeo-detect --images data/samples/
 ```
 
 <p align="center"><img src="https://user-images.githubusercontent.com/15075613/131503350-3e232e91-016b-4034-8bda-15e6619b0f98.png" width="480"\></p>
 
 ## Train
-For argument descriptions have a look at `poetry run yoeo-train --h`
+For argument descriptions have a look at `uv run yoeo-train --h`
 
 #### Tensorboard
 Track training progress in Tensorboard:
@@ -58,7 +58,7 @@ Track training progress in Tensorboard:
 * Go to http://localhost:6006/
 
 ```bash
-poetry run tensorboard --logdir='logs' --port=6006
+uv run tensorboard --logdir='logs' --port=6006
 ```
 
 Storing the logs on a slow drive possibly leads to a significant training speed decrease.
@@ -73,7 +73,7 @@ Add class names to `data/custom/yoeo_names.yaml`.
 Run the following command to adapt the model file (cfg) to the new number of classes:
 
 ```bash
-poetry run yoeo-custiomize-cfg -c config/yoeo.cfg -d config/custom.data -o config/yoeo-custom.cfg
+uv run yoeo-custiomize-cfg -c config/yoeo.cfg -d config/custom.data -o config/yoeo-custom.cfg
 ```
 
 This changes the layers of the model to fit the number of classes in your dataset.
@@ -94,7 +94,7 @@ In `data/custom/train.txt` and `data/custom/valid.txt`, add paths to images that
 To train on the custom dataset run:
 
 ```bash
-poetry run yoeo-train --model config/yoeo-custom.cfg --data config/custom.data
+uv run yoeo-train --model config/yoeo-custom.cfg --data config/custom.data
 ```
 
 ## API
@@ -138,7 +138,7 @@ For more advanced usage look at the method's doc strings.
 To convert your YOEO model to an ONNX model, you can use the following command:
 
 ```bash
-poetry run yoeo-to-onnx config/yoeo.cfg  # Replace path with your .cfg file
+uv run yoeo-to-onnx config/yoeo.cfg  # Replace path with your .cfg file
 ```
 
 For more information on ONNX, read the [ONNX runtime website](https://onnxruntime.ai/).
@@ -148,7 +148,7 @@ For more information on ONNX, read the [ONNX runtime website](https://onnxruntim
 After successful conversion of your YOEO model to an ONNX model using [this guide](#convert-your-yoeo-model-to-an-onnx-model), you can move on with the next conversion to an OpenVino IR model (intermediate representation) model using the following command:
 
 ```bash
-poetry run yoeo-onnx-to-openvino config/yoeo.onnx  # Replace path with your .onnx file
+uv run yoeo-onnx-to-openvino config/yoeo.onnx  # Replace path with your .onnx file
 ```
 
 For more information on OpenVino, read the [OpenVino documentation](https://docs.openvino.ai).