new structure

.gitlab-ci.yml

+image: "python:3.7"
+
+stages:
+  - Static Analysis
+  - Test
+  # - upload
+  - makepdf
+
+# - testdoc
+# - deploydoc
+
+mypy:
+  stage: Static Analysis
+  allow_failure: true
+  script:
+  - python --version
+  - pip install -r requirements.txt
+  - apt-get update -qq && apt-get install -y -qq pandoc
+  - python3 -m pip install --upgrade mypy
+  - python -m mypy datainformed-prior
+
+flake8:
+  stage: Static Analysis
+  allow_failure: true
+  script:
+  - python --version
+  - pip install -r requirements.txt
+  - apt-get update -qq && apt-get install -y -qq pandoc
+  - python3 -m pip install --upgrade flake8
+  - flake8 --max-line-length=120 datainformed-prior/*.py
+
+pylint:
+  stage: Static Analysis
+  allow_failure: true
+  script:
+  - python --version
+  - pip install -r requirements.txt
+  - apt-get update -qq && apt-get install -y -qq pandoc
+  - python3 -m pip install --upgrade pylint
+  - pylint -d C0301 datainformed-prior/*.py
+
+unit_test:
+  stage: Test
+  allow_failure: true
+  script:
+  - python --version
+  - pip install -r requirements.txt
+  - apt-get update -qq && apt-get install -y -qq pandoc
+  - python3 -m pip install --upgrade pytest pytest-cov
+  - python3 -m pytest
+  - python3 -m pytest --cov-report term-missing --cov=datainformed-prior tests/
+
+pypi:
+  stage: upload
+  allow_failure: true
+  script:
+  - cd python
+  - python --version
+  - pip install -r requirements.txt
+  - apt-get update -qq && apt-get install -y -qq pandoc
+  - python3 -m pip install --upgrade twine
+  - rm -rf dist
+  - echo "[distutils]" >> ~/.pypirc
+  - echo "  index-servers =" >> ~/.pypirc
+  - echo "    mcutility" >> ~/.pypirc
+  - echo "" >> ~/.pypirc
+  - echo "[mcutility]" >> ~/.pypirc
+  - echo "  repository = https://test.pypi.org/legacy/" >> ~/.pypirc
+  - echo "  username = __token__" >> ~/.pypirc
+  - echo "  password = pypi-AgENdGVzdC5weXBpLm9yZwIkODc2YjkyNGMtYWFlMC00YTNkLTlmYzgtY2UzNzI5MGYwMzhlAAI6eyJwZXJtaXNzaW9ucyI6IHsicHJvamVjdHMiOiBbIm1jdXRpbGl0eSJdfSwgInZlcnNpb24iOiAxfQAABiCNlJgWB50pL7DURSrfmJ6Qh1i5JnUjA5mTA8V4UobzkA" >> ~/.pypirc
+  - echo "" >> ~/.pypirc
+  - python3 setup.py check sdist bdist  # This will fail if your creds are bad.
+  # Since create generates a *.linux*.tar.gz file, we have to delete ist
+  # Otherwise twine complains that only one .tar.gz is allowed for upload
+  - rm -rf dist
+  - python3 setup.py sdist bdist_wheel
+  - twine upload -r mcutility dist/*.tar.gz
+  only:
+    - master
+
+# test:
+#   stage: testdoc
+#   script:
+#   - python3 -m pip install -U sphinx nbsphinx nbsphinx-link m2r2 sphinx-rtd-theme pandoc
+#   - sphinx-build -b html docs/ public
+#   only:
+#   - branches
+#   except:
+#   - master
+
+# pages:
+#   stage: deploydoc
+#   script:
+#   - python3 -m pip install -U sphinx nbsphinx nbsphinx-link m2r2 sphinx-rtd-theme pandoc
+#   - sphinx-build -b html docs/ public
+#   artifacts:
+#     paths:
+#     - public
+#   only:
+#   - master
+
+compile_pdf:
+  image: miktex/miktex
+  stage: makepdf
+  script:
+    - apt-get update -y -qq && apt-get install -y -qq inkscape python3-pip python-pygments pandoc texlive-latex-recommended
+    - mpm --update-db --admin
+    - python3 -m pip install -U sphinx nbsphinx nbsphinx-link m2r2 sphinx-rtd-theme pandoc ipython mock numpy scipy matplotlib
+    - python3 -m pip install -r requirements.txt
+    - mkdir documentation
+    - cd docs
+    - make html
+    - make latex
+    - make latexpdf
+    - mv _build/latex/MCUtility.pdf ../../MCUtility.pdf
+    - cp -r _build/html/ ../../documentation/
+  artifacts:
+    paths:
+    - datainformed-prior.pdf
+    - documentation
+  only:
+    - master

LICENSE

+# # License
+
+# copyright Manuel Marschall (PTB) 2022
+
+# This software is licensed under the BSD-like license:
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in
+#    the documentation and/or other materials provided with the distribution.
+
+# DISCLAIMER
+# ----------
+# This software was developed at Physikalisch-Technische Bundesanstalt
+# (PTB). The software is made available "as is" free of cost. PTB assumes
+# no responsibility whatsoever for its use by other parties, and makes no
+# guarantees, expressed or implied, about its quality, reliability, safety,
+# suitability or any other characteristic. In no event will PTB be liable
+# for any direct, indirect or consequential damage arising in connection
\ No newline at end of file

experiments_runner.py → datainformed-prior/experiments_runner.py

@@ -17,39 +17,16 @@ available_methods = ["laplace", "thikonov", "gmrf", "mnist_regu", "generic", "ge
                      "latpush_linear", "eb_qmin"]
 
 
-def get_generator(vae_quality):
-    if isinstance(vae_quality, str):
-        # PATH_DET = "deterministic/"
-        PATH_PROB = f"stochastic_{vae_quality}/"
-        PATH_ADD = "onnx_models/"
-
-        onnx_vae_good = ONNX_VAE_STO(PATH_ADD + PATH_PROB + f"{vae_quality}_probVAE_encoder.onnx",
-                                     PATH_ADD + PATH_PROB + f"{vae_quality}_probVAE_decoder.onnx")
-        tf_vae_good = onnx_vae_good.to_tensorflow()
-        prob_generator = ProbabilisticGenerator.from_vae(tf_vae_good, int(28*28), 20)
-
-    elif isinstance(vae_quality, (float, int)):
-        PATH_PROB_GOOD = "stochastic_good/"
-        # PATH_PROB_BAD = "stochastic_bad/"
-        PATH_PROB_BAD = "train_vae_test_no17/cont/"
-        PATH_ADD = "onnx_models/"
-
-        onnx_vae_good = ONNX_VAE_STO(PATH_ADD + PATH_PROB_GOOD + "good_probVAE_encoder.onnx",
-                                     PATH_ADD + PATH_PROB_GOOD + "good_probVAE_decoder.onnx")
-        tf_vae_good = onnx_vae_good.to_tensorflow()
-        prob_generator_good = ProbabilisticGenerator.from_vae(tf_vae_good, int(28*28), 20)
-
-        # onnx_vae_bad = ONNX_VAE_STO(PATH_ADD + PATH_PROB_BAD + "bad_probVAE_encoder.onnx",
-        #                             PATH_ADD + PATH_PROB_BAD + "bad_probVAE_decoder.onnx")
-        # tf_vae_bad = onnx_vae_bad.to_tensorflow()
-        tf_vae_bad = TF_VAE_OWN.from_path(PATH_PROB_BAD)
-        prob_generator_bad = ProbabilisticGenerator.from_vae(tf_vae_bad, int(28*28), 20)
-
-        prob_generator = ConvexProbabilisticGenerator(prob_generator_good, prob_generator_bad, vae_quality)
-    else:
-        raise ValueError(f"unknown quality: {vae_quality}")
-
-    return prob_generator
+def get_generator():
+    vae_quality = "good"
+    PATH_PROB = f"stochastic_{vae_quality}/"
+    PATH_ADD = "onnx_models/"
+
+    onnx_vae_good = ONNX_VAE_STO(PATH_ADD + PATH_PROB + f"{vae_quality}_probVAE_encoder.onnx",
+                                    PATH_ADD + PATH_PROB + f"{vae_quality}_probVAE_decoder.onnx")
+    tf_vae_good = onnx_vae_good.to_tensorflow()
+
+    return ProbabilisticGenerator.from_vae(tf_vae_good, int(28*28), 20)
 
 
 def draw_images(blur_operator_sigma=4,

requirements.txt

+numpy==1.21.2
+scipy==1.7.1
+matplotlib
+packaging
+corner==2.2.1
+imageio==2.13.2
+ipython==7.31.1
+onnx==1.10.2
+onnx-tf==1.9.0
+pandas==1.1.5
+scikit-image==0.19.0
+seaborn==0.11.2
+tensorflow==2.7.0
+tensorflow-probability=0.15.0
\ No newline at end of file