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name: CI testing
# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
on:
# Trigger the workflow on push or pull request, but only for the master branch
push:
branches:
- master
pull_request:
branches:
- master
jobs:
pytest:
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-20.04, macOS-10.15, windows-2019]
python-version: [3.7]
# Timeout: https://stackoverflow.com/a/59076067/4521646
timeout-minutes: 35
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
# Github Actions: Run step on specific OS: https://stackoverflow.com/a/57948488/4521646
- name: Setup macOS
if: runner.os == 'macOS'
run: |
brew install libomp # https://github.com/pytorch/pytorch/issues/20030
# Note: This uses an internal pip API and may not always work
# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
- name: Get pip cache
id: pip-cache
run: |
python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
- name: Cache pip
uses: actions/cache@v2
with:
path: ${{ steps.pip-cache.outputs.dir }}
key: ${{ runner.os }}-py${{ matrix.python-version }}-${{ hashFiles('requirements.txt') }}
restore-keys: |
${{ runner.os }}-py${{ matrix.python-version }}-
- name: Install dependencies
run: |
pip install --requirement requirements.txt --upgrade --quiet --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --use-feature=2020-resolver
pip install --requirement tests/requirements.txt --quiet --use-feature=2020-resolver
python --version
pip --version
pip list
shell: bash
- name: Tests
run: |
coverage run --source project -m py.test project tests -v --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}.xml
- name: Statistics
if: success()
run: |
coverage report
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
.github
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# Lightning /research
test_tube_exp/
tests/tests_tt_dir/
tests/save_dir
default/
data/
test_tube_logs/
test_tube_data/
datasets/
model_weights/
tests/save_dir
tests/tests_tt_dir/
processed/
raw/
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# IDEs
.idea
.vscode
# seed project
lightning_logs/
MNIST
.DS_Store
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Apache License
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http://www.apache.org/licenses/
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### Deep learning project seed
Use this seed to start new deep learning / ML projects.
- Built in setup.py
- Built in requirements
- Examples with MNIST
- Badges
- Bibtex
#### Goals
The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.
### DELETE EVERYTHING ABOVE FOR YOUR PROJECT
---
<div align="center">
# Your Project Name
[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539)
[![Conference](http://img.shields.io/badge/NeurIPS-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[![Conference](http://img.shields.io/badge/ICLR-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
<!--
ARXIV
[![Paper](http://img.shields.io/badge/arxiv-math.co:1480.1111-B31B1B.svg)](https://www.nature.com/articles/nature14539)
-->
![CI testing](https://github.com/PyTorchLightning/deep-learning-project-template/workflows/CI%20testing/badge.svg?branch=master&event=push)
<!--
Conference
-->
</div>
## Description
What it does
## How to run
First, install dependencies
```bash
# clone project
git clone https://github.com/YourGithubName/deep-learning-project-template
# install project
cd deep-learning-project-template
pip install -e .
pip install -r requirements.txt
```
Next, navigate to any file and run it.
```bash
# module folder
cd project
# run module (example: mnist as your main contribution)
python lit_classifier_main.py
```
## Imports
This project is setup as a package which means you can now easily import any file into any other file like so:
```python
from project.datasets.mnist import mnist
from project.lit_classifier_main import LitClassifier
from pytorch_lightning import Trainer
# model
model = LitClassifier()
# data
train, val, test = mnist()
# train
trainer = Trainer()
trainer.fit(model, train, val)
# test using the best model!
trainer.test(test_dataloaders=test)
```
### Citation
```
@article{YourName,
title={Your Title},
author={Your team},
journal={Location},
year={Year}
}
```
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from argparse import ArgumentParser
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from torch.utils.data import random_split
from torchvision.datasets.mnist import MNIST
from torchvision import transforms
class LitAutoEncoder(pl.LightningModule):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(28 * 28, 64),
nn.ReLU(),
nn.Linear(64, 3)
)
self.decoder = nn.Sequential(
nn.Linear(3, 64),
nn.ReLU(),
nn.Linear(64, 28 * 28)
)
def forward(self, x):
# in lightning, forward defines the prediction/inference actions
embedding = self.encoder(x)
return embedding
def training_step(self, batch, batch_idx):
x, y = batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = F.mse_loss(x_hat, x)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--hidden_dim', type=int, default=128)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# ------------
# data
# ------------
dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
# ------------
# model
# ------------
model = LitAutoEncoder()
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model, train_loader, val_loader)
# ------------
# testing
# ------------
result = trainer.test(test_dataloaders=test_loader)
print(result)
if __name__ == '__main__':
cli_main()
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from argparse import ArgumentParser
import torch
import pytorch_lightning as pl
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchvision.datasets.mnist import MNIST
from torchvision import transforms
class Backbone(torch.nn.Module):
def __init__(self, hidden_dim=128):
super().__init__()
self.l1 = torch.nn.Linear(28 * 28, hidden_dim)
self.l2 = torch.nn.Linear(hidden_dim, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = torch.relu(self.l1(x))
x = torch.relu(self.l2(x))
return x
class LitClassifier(pl.LightningModule):
def __init__(self, backbone, learning_rate=1e-3):
super().__init__()
self.save_hyperparameters()
self.backbone = backbone
def forward(self, x):
# use forward for inference/predictions
embedding = self.backbone(x)
return embedding
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self.backbone(x)
loss = F.cross_entropy(y_hat, y)
self.log('train_loss', loss, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.backbone(x)
loss = F.cross_entropy(y_hat, y)
self.log('valid_loss', loss, on_step=True)
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self.backbone(x)
loss = F.cross_entropy(y_hat, y)
self.log('test_loss', loss)
def configure_optimizers(self):
# self.hparams available because we called self.save_hyperparameters()
return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--learning_rate', type=float, default=0.0001)
return parser
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--hidden_dim', type=int, default=128)
parser = pl.Trainer.add_argparse_args(parser)
parser = LitClassifier.add_model_specific_args(parser)
args = parser.parse_args()
# ------------
# data
# ------------
dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
# ------------
# model
# ------------
model = LitClassifier(Backbone(hidden_dim=args.hidden_dim), args.learning_rate)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model, train_loader, val_loader)
# ------------
# testing
# ------------
result = trainer.test(test_dataloaders=test_loader)
print(result)
if __name__ == '__main__':
cli_main()
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from argparse import ArgumentParser
import torch
import pytorch_lightning as pl
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchvision.datasets.mnist import MNIST
from torchvision import transforms
class LitClassifier(pl.LightningModule):
def __init__(self, hidden_dim=128, learning_rate=1e-3):
super().__init__()
self.save_hyperparameters()
self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = torch.relu(self.l1(x))
x = torch.relu(self.l2(x))
return x
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
self.log('valid_loss', loss)
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
self.log('test_loss', loss)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--hidden_dim', type=int, default=128)
parser.add_argument('--learning_rate', type=float, default=0.0001)
return parser
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
parser = pl.Trainer.add_argparse_args(parser)
parser = LitClassifier.add_model_specific_args(parser)
args = parser.parse_args()
# ------------
# data
# ------------
dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
# ------------
# model
# ------------
model = LitClassifier(args.hidden_dim, args.learning_rate)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model, train_loader, val_loader)
# ------------
# testing
# ------------
trainer.test(test_dataloaders=test_loader)
if __name__ == '__main__':
cli_main()
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pytorch-lightning >= 1.0.0rc2
torch >= 1.3.0
torchvision >= 0.6.0
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[tool:pytest]
norecursedirs =
.git
dist
build
addopts =
--strict
--doctest-modules
--durations=0
[coverage:report]
exclude_lines =
pragma: no-cover
pass
[flake8]
max-line-length = 120
exclude = .tox,*.egg,build,temp
select = E,W,F
doctests = True
verbose = 2
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
format = pylint
# see: https://www.flake8rules.com/
ignore =
E731 # Do not assign a lambda expression, use a def
W504 # Line break occurred after a binary operator
F401 # Module imported but unused
F841 # Local variable name is assigned to but never used
W605 # Invalid escape sequence 'x'
# setup.cfg or tox.ini
[check-manifest]
ignore =
*.yml
.github
.github/*
[metadata]
license_file = LICENSE
description-file = README.md
# long_description = file:README.md
# long_description_content_type = text/markdown
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#!/usr/bin/env python
from setuptools import setup, find_packages
setup(
name='project',
version='0.0.0',
description='Describe Your Cool Project',
author='',
author_email='',
# REPLACE WITH YOUR OWN GITHUB PROJECT LINK
url='https://github.com/PyTorchLightning/pytorch-lightning-conference-seed',
install_requires=['pytorch-lightning'],
packages=find_packages(),
)
tests/__init__.py deleted 100644 → 0
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− 0
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tests/requirements.txt deleted 100644 → 0
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coverage
codecov>=2.1
pytest>=3.0.5
pytest-cov
pytest-flake8
flake8
check-manifest
twine==1.13.0
\ No newline at end of file
tests/test_classifier.py deleted 100644 → 0
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from pytorch_lightning import Trainer, seed_everything
from project.lit_mnist import LitClassifier
from project.datasets.mnist import mnist
def test_lit_classifier():
seed_everything(1234)
model = LitClassifier()
train, val, test = mnist()
trainer = Trainer(limit_train_batches=50, limit_val_batches=20, max_epochs=2)
trainer.fit(model, train, val)
results = trainer.test(test_dataloaders=test)
assert results[0]['test_acc'] > 0.7
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