add setuptools_scm and build and publish workflow (#3)

* add setuptools_scm and build and publish workflow

* add version to __init__.py

* update README and .gitignore

.github/workflows/build-and-publish.yml

@@ -0,0 +1,21 @@
+name: Build and publish python package
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.9"
+      - name: Build wheel
+        run: pip install build && python -m build
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -9,4 +9,5 @@ __pycache__/
 wandb/
 models/
 .vscode
-*.egg-info
+*.egg-info
+events.out.tfevents*

README.md

@@ -1,27 +1,37 @@
 # neurve
 
-This is the repository to accompany the paper [*Self-supervised representation learning on manifolds*](https://openreview.net/forum?id=EofGDIGAhvR), to be presented at the *ICLR 2021 Workshop on Geometrical and Topological Representation Learning*.
+This is the repository to accompany the paper [_Self-supervised representation learning on manifolds_](https://openreview.net/forum?id=EofGDIGAhvR), to be presented at the _ICLR 2021 Workshop on Geometrical and Topological Representation Learning_.
 
 Additionally, we implement a manifold version of triplet training, which will be expounded on in an upcoming preprint.
 
 ## Notebooks
+
 [MSimCLR Inference](https://github.com/ekorman/neurve/blob/master/notebooks/msimclr-inference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ekorman/neurve/blob/master/notebooks/msimclr-inference.ipynb)
 
 This notebook will run inference using a pre-trained Manifold SimCLR model (trained on either CIFAR10, FashionMNIST, or MNIST).
 
 ## Installation
 
-Install by cloning this repository and then running, from the repo root, the command
-```
-pip install .
+Install via
+
+```shell
+pip install neurve
 ```
 
 or, to install with [Weights & Biases](https://wandb.ai/) support, run:
+
+```shell
+pip install "neurve[wandb]"
 ```
-pip install .[wandb]
+
+You can also install from source by cloning this repository and then running, from the repo root, the command
+
+```shell
+pip install . # or pip install .[wandb]
 ```
 
 The dependencies are
+
 ```
 numpy>=1.17.4
 torch>=1.3.1
@@ -32,18 +42,22 @@ tensorboardX
 ```
 
 ### Datasets
+
 To get the datasets for metric learning (the datasets we use for representation learning are included in `torchvision.datasets`):
 
-* CUB dataset: Download the file `CUB_200_2011.tgz` from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html and decompress in the `data` folder. The folder structure should be `data/CUB_200_2011/images/`.
-* cars196 dataset: run `make data/cars`.
+- CUB dataset: Download the file `CUB_200_2011.tgz` from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html and decompress in the `data` folder. The folder structure should be `data/CUB_200_2011/images/`.
+- cars196 dataset: run `make data/cars`.
 
 ## Training commands
 
 ### Tracking with Weights & Biases
+
 To use [Weights & Biases](https://wandb.ai/) to log training/validation metrics and for storing model checkpoints, set the environment variable `NEURVE_TRACKER` to `wandb`. Otherwise [tensorboardX](https://github.com/lanpa/tensorboardX) will be used for metric logging and model checkpoints will be saved locally.
 
 ### Manifold SimCLR
+
 For self-supervised training, run the command
+
 ```bash
 python experiments/simclr.py \
               --dataset $DATASET \
@@ -54,16 +68,18 @@ python experiments/simclr.py \
               --tau $TAU \
               --out_path $OUT_PATH # if not using Weights & Biases for tracking
 ```
+
 where
 
-* `$DATASET` is one of `"cifar"`, `"mnist"`, `"fashion_mnist"`.
-* `$BACKBONE` is the name of the backbone network (in the paper we used `"resnet50"` for CIFAR10 and `"resnet18"` for MNIST and FashionMNIST).
-* `$DIM_Z` and `$N_CHARTS` are the dimension and number of charts, respectively, for the manifold.
-* `$N_EPOCHS` is the number of epochs to train for (in the paper we used 1,000 for CIFAR10 and 100 for MNIST and FashionMNIST).
-* `$TAU` is the temperature parameter for the contrastive loss function (in the paper we used 0.5 for CIFAR10 and 1.0 for MNIST and FashionMNIST).
-* `$OUT_PATH` is the path to save model checkpoints and tensorboard output.
+- `$DATASET` is one of `"cifar"`, `"mnist"`, `"fashion_mnist"`.
+- `$BACKBONE` is the name of the backbone network (in the paper we used `"resnet50"` for CIFAR10 and `"resnet18"` for MNIST and FashionMNIST).
+- `$DIM_Z` and `$N_CHARTS` are the dimension and number of charts, respectively, for the manifold.
+- `$N_EPOCHS` is the number of epochs to train for (in the paper we used 1,000 for CIFAR10 and 100 for MNIST and FashionMNIST).
+- `$TAU` is the temperature parameter for the contrastive loss function (in the paper we used 0.5 for CIFAR10 and 1.0 for MNIST and FashionMNIST).
+- `$OUT_PATH` is the path to save model checkpoints and tensorboard output.
 
 ### Manifold metric learning
+
 To train metric learning, run the command
 
 ```bash
@@ -73,11 +89,12 @@ python experiments/triplet.py \
               --n_charts $N_CHARTS \
               --out_path $OUT_PATH # if not using Weights & Biases for tracking
 ```
+
 where
 
-* `$DATA_ROOT` is the path to the data (e.g. `data/CUB_200_2011/images/` or `data/cars/`), which should be a folder of subfolders, where each subfolder has the images for one class.
-* `$DIM_Z` and `$N_CHARTS` are the dimension and number of charts, respectively, for the manifold.
-* `$OUT_PATH` is the path to save model checkpoints and tensorboard output.
+- `$DATA_ROOT` is the path to the data (e.g. `data/CUB_200_2011/images/` or `data/cars/`), which should be a folder of subfolders, where each subfolder has the images for one class.
+- `$DIM_Z` and `$N_CHARTS` are the dimension and number of charts, respectively, for the manifold.
+- `$OUT_PATH` is the path to save model checkpoints and tensorboard output.
 
 ## Citation
 

neurve/__init__.py

@@ -0,0 +1,7 @@
+from importlib import metadata
+
+try:
+    __version__ = metadata.version("neurve")
+except Exception:
+    __version__ = "0.0.0-dev"
+del metadata

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "neurve"
-version = "0.1.0"
+dynamic = ["version"]
 description = "atlas learning"
 readme = "README.md"
 requires-python = ">=3.8"
@@ -16,7 +16,7 @@ dependencies = [
 [project.optional-dependencies]
 test = ["pytest"]
 wandb = ["wandb"]
-dev = ["pre-commit"]
+dev = ["setuptools_scm", "pre-commit"]
 
 [build-system]
 requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"]
@@ -25,6 +25,8 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools]
 packages = ["neurve"]
 
+[tool.setuptools_scm]
+
 [tool.black]
 line-length = 79