Merge branch 'main' into jaap/add_torchvision_detector

Author: deruyter92

.github/workflows/python-package.yml

@@ -1,32 +1,59 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-name: Python package
+name: Update pypi release
 
 on:
   push:
-    branches: [ main ]
+    tags:
+      - 'v*.*.*'
   pull_request:
-    branches: [ main ]
-
+    branches:
+      - main
+      - public
+    types:
+      - labeled
+      - opened
+      - edited
+      - synchronize
+      - reopened
 
 jobs:
-  build:
-
+  release:
     runs-on: ubuntu-latest
-    strategy:
-      # You can use PyPy versions in python-version. For example, pypy2 and pypy3
-      matrix:
-        python-version: ["3.10"]
 
     steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-    # You can test your matrix by printing the current Python version
-    - name: Display Python version
-      run: python -c "import sys; print(sys.version)"
-    #test installation of DLC-core dependencies:
-    - name: Install dependencies
-      run: pip install .
+      - name: Setup Python
+        id: setup-python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+
+      - name: Cache dependencies
+        id: pip-cache
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('pyproject.toml', 'requirements.txt', 'setup.cfg', 'setup.py') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-${{ steps.setup-python.outputs.python-version }}-
+            ${{ runner.os }}-pip-
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install wheel
+          pip install "packaging>=24.2"
+          pip install build
+          pip install twine
+
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Build and publish to PyPI
+        if: ${{ github.event_name == 'push' }}
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TWINE_API_KEY }}
+        run: |
+          python -m build
+          ls dist/
+          tar tvf dist/deeplabcut_live-*.tar.gz
+          python3 -m twine upload --verbose dist/*

CITATION.cff

@@ -52,4 +52,4 @@ authors:
 date-released: 2020-08-05
 doi: "10.7554/eLife.61909"
 license: "AGPL-3.0-or-later"
-version:  "1.1.0"
+version:  "1.1.1rc1"

MANIFEST.in

@@ -1 +1,3 @@
-include dlclive/check_install/*
+include dlclive/check_install/*
+include dlclive/modelzoo/model_configs/*.yaml
+include dlclive/modelzoo/project_configs/*.yaml

README.md

@@ -77,14 +77,18 @@ DeepLabCut-live can be installed from PyPI with PyTorch or Tensorflow directly:
    uv pip install deeplabcut-live[pytorch] # or [tf]
    ```
 
+Note: On **Windows**, the `deeplabcut-live[pytorch]` extra will not install the required CUDA-enabled wheels for PyTorch by default. For GPU support, install CUDA-enabled PyTorch first, then install `deeplabcut-live[pytorch]`. 
+
 Please see our instruction manual for more elaborate information on how to install on a [Windows or Linux machine](
 docs/install_desktop.md) or on a [NVIDIA Jetson Development Board](
-docs/install_jetson.md). Note, this code works with PyTorch, TensorFlow 1 or TensorFlow
+docs/install_jetson.md). 
+
+This code works with PyTorch, TensorFlow 1 or TensorFlow
 2 models, but whatever engine you exported your model with, you must import with the 
 same version (i.e., export a PyTorch model, then install PyTorch, export with TF1.13, 
 then use TF1.13 with DlC-Live; export with TF2.3, then use TF2.3 with DLC-live).
 
-Note, you can test your installation by running:
+You can test your installation by running:
 
 ```bash
 dlc-live-test

dlclive/modelzoo/pytorch_model_zoo_export.py

@@ -14,7 +14,18 @@ def export_modelzoo_model(
     detector_name: str | None = None,
 ) -> None:
     """
+    Export a DeepLabCut Model Zoo model to a single .pt file.
 
+    Downloads the model configuration and weights from HuggingFace, bundles them
+    together (optionally with a detector), and saves as a single torch archive.
+    Skips export if the output file already exists.
+
+    Args:
+        export_path: Arbitrary destination path for the exported .pt file.
+        super_animal: Super animal dataset name (e.g. "superanimal_quadruped").
+        model_name: Pose model architecture name (e.g. "resnet_50").
+        detector_name: Optional detector model name. If provided, detector
+            weights are included in the export.
     """
     Path(export_path).parent.mkdir(parents=True, exist_ok=True)
     if Path(export_path).exists():

dlclive/modelzoo/utils.py

@@ -9,6 +9,7 @@
 from pathlib import Path
 
 from dlclibrary.dlcmodelzoo.modelzoo_download import download_huggingface_model
+from dlclibrary.dlcmodelzoo.modelzoo_download import _load_model_names as huggingface_model_paths
 from ruamel.yaml import YAML
 
 from dlclive.modelzoo.resolve_config import update_config
@@ -50,10 +51,7 @@ def list_available_projects() -> list[str]:
 
 
 def list_available_combinations() -> list[str]:
-    models = list_available_models()
-    projects = list_available_projects()
-    combinations = ["_".join([p, m]) for p in projects for m in models]
-    return combinations
+    return list(huggingface_model_paths.keys())
 
 
 def read_config_as_dict(config_path: str | Path) -> dict:

dlclive/version.py

@@ -7,5 +7,5 @@
 """
 
 
-__version__ = "1.1.0"
+__version__ = "1.1.1rc1"
 VERSION = __version__

docs/install_desktop.md

@@ -32,6 +32,19 @@ Install from PyPI with PyTorch or TensorFlow:
    # Or using uv
    uv pip install deeplabcut-live[pytorch] # or [tf]
    ```
+   
+### Windows-users with GPU: 
+On **Windows**, the `deeplabcut-live[pytorch]` extra will not install the required CUDA-enabled wheels for PyTorch by default. Windows users with a CUDA GPU should install CUDA-enabled PyTorch first: 
+
+
+   ```bash
+   # First install PyTorch with CUDA support
+   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
+   
+   # Then install DeepLabCut-live (it will use the existing GPU-enabled PyTorch)
+   pip install deeplabcut-live[pytorch]
+   ```
+
 
 ## 2. Install from Git Repository
 If you want to install from a local clone of the repository, follow these steps:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "deeplabcut-live"
-version = "1.1.0"
+version = "1.1.1rc1"
 description = "Class to load exported DeepLabCut networks and perform pose estimation on single frames (from a camera feed)"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -89,7 +89,11 @@ include-package-data = true
 include = ["dlclive*"]
 
 [tool.setuptools.package-data]
-dlclive = ["check_install/*"]
+dlclive = [
+    "check_install/*",
+    "modelzoo/model_configs/*.yaml",
+    "modelzoo/project_configs/*.yaml",
+]
 
 # [tool.ruff]
 # lint.select = ["E", "F", "B", "I", "UP"]