ci: add github actions (#16)

* chore: update pre-commit versions

* ci: remove old configurations

* ci: copy workflow from prototorch

* ci: run precommit for all files

* ci: add examples CPU test

* ci(test): failing example test

* ci: fix workflow definition

* ci(test): repeat failing example test

* ci: fix workflow definition

* ci(test): repeat failing example test II

* ci: fix test command

* ci: cleanup example test

* ci: remove travis badge
This commit is contained in:
Alexander Engelsberger 2022-01-11 18:28:50 +01:00 committed by GitHub
parent 62c5974a85
commit 1a17193b35
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GPG Key ID: 4AEE18F83AFDEB23
24 changed files with 202 additions and 77 deletions

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@ -1,15 +0,0 @@
# To validate the contents of your configuration file
# run the following command in the folder where the configuration file is located:
# codacy-analysis-cli validate-configuration --directory `pwd`
# To analyse, run:
# codacy-analysis-cli analyse --tool remark-lint --directory `pwd`
---
engines:
pylintpython3:
exclude_paths:
- config/engines.yml
remark-lint:
exclude_paths:
- config/engines.yml
exclude_paths:
- 'tests/**'

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@ -1,2 +0,0 @@
comment:
require_changes: yes

25
.github/workflows/examples.yml vendored Normal file
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@ -0,0 +1,25 @@
# Thi workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
name: examples
on:
push:
paths:
- 'examples/**.py'
jobs:
cpu:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.9
uses: actions/setup-python@v2
with:
python-version: 3.9
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install .[all]
- name: Run examples
run: |
./tests/test_examples.sh examples/

73
.github/workflows/pythonapp.yml vendored Normal file
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@ -0,0 +1,73 @@
# This workflow will install Python dependencies, run tests and lint with a single version of Python
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
name: tests
on:
push:
pull_request:
branches: [ master ]
jobs:
style:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.9
uses: actions/setup-python@v2
with:
python-version: 3.9
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install .[all]
- uses: pre-commit/action@v2.0.3
compatibility:
needs: style
strategy:
fail-fast: false
matrix:
python-version: ["3.7", "3.8", "3.9"]
os: [ubuntu-latest, windows-latest]
exclude:
- os: windows-latest
python-version: "3.7"
- os: windows-latest
python-version: "3.8"
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install .[all]
- name: Test with pytest
run: |
pytest
publish_pypi:
if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
needs: compatibility
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.9
uses: actions/setup-python@v2
with:
python-version: "3.9"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install .[all]
pip install wheel
- name: Build package
run: python setup.py sdist bdist_wheel
- name: Publish a Python distribution to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}

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@ -3,7 +3,7 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.0.1
rev: v4.1.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
@ -18,19 +18,19 @@ repos:
- id: autoflake
- repo: http://github.com/PyCQA/isort
rev: 5.8.0
rev: 5.10.1
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v0.902
rev: v0.931
hooks:
- id: mypy
files: prototorch
additional_dependencies: [types-pkg_resources]
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.31.0
rev: v0.32.0
hooks:
- id: yapf
@ -42,7 +42,7 @@ repos:
- id: python-check-blanket-noqa
- repo: https://github.com/asottile/pyupgrade
rev: v2.19.4
rev: v2.31.0
hooks:
- id: pyupgrade

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@ -1,44 +0,0 @@
dist: bionic
sudo: false
language: python
python:
- 3.9
- 3.8
- 3.7
- 3.6
cache:
directories:
- "$HOME/.cache/pip"
- "./tests/artifacts"
- "$HOME/datasets"
install:
- pip install git+git://github.com/si-cim/prototorch@dev --progress-bar off
- pip install .[all] --progress-bar off
script:
- coverage run -m pytest
- ./tests/test_examples.sh examples/
after_success:
- bash <(curl -s https://codecov.io/bash)
# Publish on PyPI
jobs:
include:
- stage: build
python: 3.9
script: echo "Starting Pypi build"
deploy:
provider: pypi
username: __token__
distributions: "sdist bdist_wheel"
password:
secure: 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
on:
tags: true
skip_existing: true
# The password is encrypted with:
# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
# See https://docs.travis-ci.com/user/deployment/pypi and
# https://github.com/travis-ci/travis.rb#installation
# for more details
# Note: The encrypt command does not work well in ZSH.

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@ -1,6 +1,5 @@
# ProtoTorch Models
[![Build Status](https://api.travis-ci.com/si-cim/prototorch_models.svg?branch=main)](https://travis-ci.com/github/si-cim/prototorch_models)
[![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/prototorch_models?color=yellow&label=version)](https://github.com/si-cim/prototorch_models/releases)
[![PyPI](https://img.shields.io/pypi/v/prototorch_models)](https://pypi.org/project/prototorch_models/)
[![GitHub license](https://img.shields.io/github/license/si-cim/prototorch_models)](https://github.com/si-cim/prototorch_models/blob/master/LICENSE)

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@ -10,6 +10,7 @@ from prototorch.utils.colors import hex_to_rgb
class Vis2DColorSOM(pl.Callback):
def __init__(self, data, title="ColorSOMe", pause_time=0.1):
super().__init__()
self.title = title

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@ -8,6 +8,7 @@ import torch
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size

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@ -8,6 +8,7 @@ import torch
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size

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@ -8,6 +8,7 @@ import torch
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size

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@ -19,9 +19,23 @@ from .glvq import (
SiameseGTLVQ,
)
from .knn import KNN
from .lvq import LVQ1, LVQ21, MedianLVQ
from .probabilistic import CELVQ, PLVQ, RSLVQ, SLVQ
from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
from .lvq import (
LVQ1,
LVQ21,
MedianLVQ,
)
from .probabilistic import (
CELVQ,
PLVQ,
RSLVQ,
SLVQ,
)
from .unsupervised import (
GrowingNeuralGas,
HeskesSOM,
KohonenSOM,
NeuralGas,
)
from .vis import *
__version__ = "0.3.0"

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@ -14,6 +14,7 @@ from ..nn.wrappers import LambdaLayer
class ProtoTorchBolt(pl.LightningModule):
"""All ProtoTorch models are ProtoTorch Bolts."""
def __init__(self, hparams, **kwargs):
super().__init__()
@ -52,6 +53,7 @@ class ProtoTorchBolt(pl.LightningModule):
class PrototypeModel(ProtoTorchBolt):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -81,6 +83,7 @@ class PrototypeModel(ProtoTorchBolt):
class UnsupervisedPrototypeModel(PrototypeModel):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -103,6 +106,7 @@ class UnsupervisedPrototypeModel(PrototypeModel):
class SupervisedPrototypeModel(PrototypeModel):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -178,6 +182,7 @@ class ProtoTorchMixin(object):
class NonGradientMixin(ProtoTorchMixin):
"""Mixin for custom non-gradient optimization."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.automatic_optimization = False
@ -188,6 +193,7 @@ class NonGradientMixin(ProtoTorchMixin):
class ImagePrototypesMixin(ProtoTorchMixin):
"""Mixin for models with image prototypes."""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
"""Constrain the components to the range [0, 1] by clamping after updates."""
self.proto_layer.components.data.clamp_(0.0, 1.0)

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@ -11,6 +11,7 @@ from .extras import ConnectionTopology
class PruneLoserPrototypes(pl.Callback):
def __init__(self,
threshold=0.01,
idle_epochs=10,
@ -67,6 +68,7 @@ class PruneLoserPrototypes(pl.Callback):
class PrototypeConvergence(pl.Callback):
def __init__(self, min_delta=0.01, idle_epochs=10, verbose=False):
self.min_delta = min_delta
self.idle_epochs = idle_epochs # epochs to wait
@ -89,6 +91,7 @@ class GNGCallback(pl.Callback):
Based on "A Growing Neural Gas Network Learns Topologies" by Bernd Fritzke.
"""
def __init__(self, reduction=0.1, freq=10):
self.reduction = reduction
self.freq = freq

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@ -13,6 +13,7 @@ from .glvq import SiameseGLVQ
class CBC(SiameseGLVQ):
"""Classification-By-Components."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)

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@ -54,6 +54,7 @@ def ltangent_distance(x, y, omegas):
class GaussianPrior(torch.nn.Module):
def __init__(self, variance):
super().__init__()
self.variance = variance
@ -63,6 +64,7 @@ class GaussianPrior(torch.nn.Module):
class RankScaledGaussianPrior(torch.nn.Module):
def __init__(self, lambd):
super().__init__()
self.lambd = lambd
@ -72,6 +74,7 @@ class RankScaledGaussianPrior(torch.nn.Module):
class ConnectionTopology(torch.nn.Module):
def __init__(self, agelimit, num_prototypes):
super().__init__()
self.agelimit = agelimit

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@ -4,9 +4,17 @@ import torch
from torch.nn.parameter import Parameter
from ..core.competitions import wtac
from ..core.distances import lomega_distance, omega_distance, squared_euclidean_distance
from ..core.distances import (
lomega_distance,
omega_distance,
squared_euclidean_distance,
)
from ..core.initializers import EyeTransformInitializer
from ..core.losses import GLVQLoss, lvq1_loss, lvq21_loss
from ..core.losses import (
GLVQLoss,
lvq1_loss,
lvq21_loss,
)
from ..core.transforms import LinearTransform
from ..nn.wrappers import LambdaLayer, LossLayer
from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
@ -15,6 +23,7 @@ from .extras import ltangent_distance, orthogonalization
class GLVQ(SupervisedPrototypeModel):
"""Generalized Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -99,6 +108,7 @@ class SiameseGLVQ(GLVQ):
transformation pipeline are only learned from the inputs.
"""
def __init__(self,
hparams,
backbone=torch.nn.Identity(),
@ -165,6 +175,7 @@ class LVQMLN(SiameseGLVQ):
rather in the embedding space.
"""
def compute_distances(self, x):
latent_protos, _ = self.proto_layer()
latent_x = self.backbone(x)
@ -180,6 +191,7 @@ class GRLVQ(SiameseGLVQ):
TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -205,6 +217,7 @@ class SiameseGMLVQ(SiameseGLVQ):
Implemented as a Siamese network with a linear transformation backbone.
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -235,6 +248,7 @@ class GMLVQ(GLVQ):
function. This makes it easier to implement a localized variant.
"""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", omega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
@ -269,6 +283,7 @@ class GMLVQ(GLVQ):
class LGMLVQ(GMLVQ):
"""Localized and Generalized Matrix Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", lomega_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
@ -285,6 +300,7 @@ class LGMLVQ(GMLVQ):
class GTLVQ(LGMLVQ):
"""Localized and Generalized Tangent Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", ltangent_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
@ -323,6 +339,7 @@ class SiameseGTLVQ(SiameseGLVQ, GTLVQ):
class GLVQ1(GLVQ):
"""Generalized Learning Vector Quantization 1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.loss = LossLayer(lvq1_loss)
@ -331,6 +348,7 @@ class GLVQ1(GLVQ):
class GLVQ21(GLVQ):
"""Generalized Learning Vector Quantization 2.1."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.loss = LossLayer(lvq21_loss)
@ -362,6 +380,7 @@ class ImageGTLVQ(ImagePrototypesMixin, GTLVQ):
after updates.
"""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
"""Constrain the components to the range [0, 1] by clamping after updates."""
self.proto_layer.components.data.clamp_(0.0, 1.0)

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@ -4,13 +4,17 @@ import warnings
from ..core.competitions import KNNC
from ..core.components import LabeledComponents
from ..core.initializers import LiteralCompInitializer, LiteralLabelsInitializer
from ..core.initializers import (
LiteralCompInitializer,
LiteralLabelsInitializer,
)
from ..utils.utils import parse_data_arg
from .abstract import SupervisedPrototypeModel
class KNN(SupervisedPrototypeModel):
"""K-Nearest-Neighbors classification algorithm."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)

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@ -9,6 +9,7 @@ from .glvq import GLVQ
class LVQ1(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
protos, plables = self.proto_layer()
x, y = train_batch
@ -38,6 +39,7 @@ class LVQ1(NonGradientMixin, GLVQ):
class LVQ21(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 2.1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
protos, plabels = self.proto_layer()
@ -70,6 +72,7 @@ class MedianLVQ(NonGradientMixin, GLVQ):
# TODO Avoid computing distances over and over
"""
def __init__(self, hparams, verbose=True, **kwargs):
self.verbose = verbose
super().__init__(hparams, **kwargs)

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@ -11,6 +11,7 @@ from .glvq import GLVQ, SiameseGMLVQ
class CELVQ(GLVQ):
"""Cross-Entropy Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -29,6 +30,7 @@ class CELVQ(GLVQ):
class ProbabilisticLVQ(GLVQ):
def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
super().__init__(hparams, **kwargs)
@ -62,6 +64,7 @@ class ProbabilisticLVQ(GLVQ):
class SLVQ(ProbabilisticLVQ):
"""Soft Learning Vector Quantization."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss = LossLayer(nllr_loss)
@ -70,6 +73,7 @@ class SLVQ(ProbabilisticLVQ):
class RSLVQ(ProbabilisticLVQ):
"""Robust Soft Learning Vector Quantization."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss = LossLayer(rslvq_loss)
@ -81,6 +85,7 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
TODO: Use Backbone LVQ instead
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conditional_distribution = RankScaledGaussianPrior(

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@ -18,6 +18,7 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
TODO Allow non-2D grids
"""
def __init__(self, hparams, **kwargs):
h, w = hparams.get("shape")
# Ignore `num_prototypes`
@ -69,6 +70,7 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
class HeskesSOM(UnsupervisedPrototypeModel):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -78,6 +80,7 @@ class HeskesSOM(UnsupervisedPrototypeModel):
class NeuralGas(UnsupervisedPrototypeModel):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
@ -110,6 +113,7 @@ class NeuralGas(UnsupervisedPrototypeModel):
class GrowingNeuralGas(NeuralGas):
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)

View File

@ -11,6 +11,7 @@ from ..utils.utils import mesh2d
class Vis2DAbstract(pl.Callback):
def __init__(self,
data,
title="Prototype Visualization",
@ -118,6 +119,7 @@ class Vis2DAbstract(pl.Callback):
class VisGLVQ2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
@ -141,6 +143,7 @@ class VisGLVQ2D(Vis2DAbstract):
class VisSiameseGLVQ2D(Vis2DAbstract):
def __init__(self, *args, map_protos=True, **kwargs):
super().__init__(*args, **kwargs)
self.map_protos = map_protos
@ -179,6 +182,7 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
class VisGMLVQ2D(Vis2DAbstract):
def __init__(self, *args, ev_proj=True, **kwargs):
super().__init__(*args, **kwargs)
self.ev_proj = ev_proj
@ -212,6 +216,7 @@ class VisGMLVQ2D(Vis2DAbstract):
class VisCBC2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
@ -235,6 +240,7 @@ class VisCBC2D(Vis2DAbstract):
class VisNG2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
@ -262,6 +268,7 @@ class VisNG2D(Vis2DAbstract):
class VisImgComp(Vis2DAbstract):
def __init__(self,
*args,
random_data=0,

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@ -1,8 +1,23 @@
[isort]
profile = hug
src_paths = isort, test
[yapf]
based_on_style = pep8
spaces_before_comment = 2
split_before_logical_operator = true
[pylint]
disable =
too-many-arguments,
too-few-public-methods,
fixme,
[pycodestyle]
max-line-length = 79
[isort]
profile = hug
src_paths = isort, test
multi_line_output = 3
include_trailing_comma = True
force_grid_wrap = 3
use_parentheses = True
line_length = 79

View File

@ -4,6 +4,7 @@ import unittest
class TestDummy(unittest.TestCase):
def setUp(self):
pass