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copy the climategan repo in here

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  1. .gitattributes +6 -0
  2. .gitignore +143 -0
  3. Contributing.md +56 -0
  4. LICENSE +674 -0
  5. README.md +166 -5
  6. USAGE.md +328 -0
  7. app.py +2 -4
  8. apply_events.py +634 -0
  9. climategan/__init__.py +9 -0
  10. climategan/blocks.py +398 -0
  11. climategan/bn_fusion.py +137 -0
  12. climategan/data.py +539 -0
  13. climategan/deeplab/__init__.py +101 -0
  14. climategan/deeplab/deeplab_v2.py +198 -0
  15. climategan/deeplab/deeplab_v3.py +271 -0
  16. climategan/deeplab/mobilenet_v3.py +324 -0
  17. climategan/deeplab/resnet101_v3.py +203 -0
  18. climategan/deeplab/resnetmulti_v2.py +136 -0
  19. climategan/depth.py +230 -0
  20. climategan/discriminator.py +361 -0
  21. climategan/eval_metrics.py +635 -0
  22. climategan/fid.py +561 -0
  23. climategan/fire.py +133 -0
  24. climategan/generator.py +411 -0
  25. climategan/logger.py +445 -0
  26. climategan/losses.py +620 -0
  27. climategan/masker.py +234 -0
  28. climategan/norms.py +186 -0
  29. climategan/optim.py +291 -0
  30. climategan/painter.py +171 -0
  31. climategan/strings.py +99 -0
  32. climategan/trainer.py +1932 -0
  33. climategan/transforms.py +626 -0
  34. climategan/tutils.py +721 -0
  35. climategan/utils.py +1063 -0
  36. eval_masker.py +796 -0
  37. figures/ablation_comparison.py +394 -0
  38. figures/bootstrap_ablation.py +562 -0
  39. figures/bootstrap_ablation_summary.py +361 -0
  40. figures/human_evaluation.py +208 -0
  41. figures/labels.py +200 -0
  42. figures/metrics.py +676 -0
  43. figures/metrics_onefig.py +772 -0
  44. images/flood.png +3 -0
  45. images/smog.png +3 -0
  46. images/wildfire.png +3 -0
  47. inputs/streetview.jpeg +3 -0
  48. notebooks/metrics-dev.ipynb +3 -0
  49. notebooks/plot_metrics.ipynb +3 -0
  50. requirements-3.8.2.txt +91 -0
.gitattributes CHANGED
@@ -26,3 +26,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ notebooks/metrics-dev.ipynb filter=lfs diff=lfs merge=lfs -text
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+ notebooks/plot_metrics.ipynb filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
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+ omnienv/
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+ example_data/
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+ .vscode/
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+ .comet.config
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+ .DS_Store
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+ config/
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+ tests/not_committed/
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+ *.hydra
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+ outputs/
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+ eval_folder*
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+
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ pip-wheel-metadata/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ *.spec
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+
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+ # Installer logs
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+ pip-log.txt
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+ pip-delete-this-directory.txt
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+
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+ # Unit test / coverage reports
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+ htmlcov/
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+ .tox/
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+ .nox/
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+ .coverage
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+ .coverage.*
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+ .cache
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+ nosetests.xml
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+ coverage.xml
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+ *.cover
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+ *.py,cover
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+ .hypothesis/
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+ .pytest_cache/
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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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+ # Django stuff:
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+
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+ # Scrapy stuff:
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+ # Sphinx documentation
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+
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+ # PyBuilder
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+
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+ # IPython
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+ profile_default/
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+ ipython_config.py
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+
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+ # pyenv
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+ .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
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+ __pypackages__/
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+
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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+
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+ # SageMath parsed files
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+ *.sage.py
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+
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+ # Environments
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+ .env
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+ .venv
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+ env/
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+ venv/
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+ ENV/
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+ env.bak/
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+ venv.bak/
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+
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+ # Spyder project settings
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+ .spyderproject
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+ .spyproject
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+
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+ # Rope project settings
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+ .ropeproject
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+
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+ # mkdocs documentation
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+ /site
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+
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+ # mypy
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+ .mypy_cache/
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+ .dmypy.json
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+ dmypy.json
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+
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+ # Pyre type checker
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+ .pyre/
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+
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+ # local visualize tool
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+ visualizedEval.py
Contributing.md ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1. Understand the file structure:
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+ 1. architectures in `discriminator.py` `generator.py` `classifier.py`
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+ 2. data loading in `data.py`
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+ 3. data transformation `transforms.py`
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+ 4. optimizers in `optim.py`
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+ 5. utilities in `utils.py`
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+ 6. training procedure in `trainer.py`
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+ 2. Write **tests** in `tests/`
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+ 1. your file should match `test_*.py`
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+ 2. update existing tests when adding functionalities
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+ 3. run tests regularly to check you haven't broken anything `python tests/run.py`
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+ 3. Add **WIP** in your PR's title when not ready to merge
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+ 5. Open an Issue if something's odd, or to assign yourself a todo
14
+ 6. **Format your code** with [black](https://github.com/psf/black)
15
+ 7. Only update `trainer/defaults.yaml` with values that should be shared across runs and users
16
+ 1. use `config/trainer/local_tests.yaml` or any other to setup your particular config overriding `trainer/defaults.yaml`
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+
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+ ## Running tests
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+
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+ As per `7.` you should set your particular config in `config/local_tests.yaml`. Mine looks like:
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+
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+ ```yaml
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+ output_path: /Users/victor/Documents/ccai/github/climategan/example_data
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+ # -------------------
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+ # ----- Tasks -----
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+ # -------------------
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+ #tasks: [a, d, h, s, t, w]
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+ tasks: [a, d, s, t] # for now no h or w
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+ # ----------------
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+ # ----- Data -----
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+ # ----------------
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+ data:
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+ files: # if one is not none it will override the dirs location
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+ base: /Users/victor/Documents/ccai/github/climategan/example_data
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+ transforms:
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+ - name: hflip
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+ ignore: false
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+ p: 0.5
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+ - name: resize
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+ ignore: false
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+ new_size: 256
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+ - name: crop
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+ ignore: false
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+ height: 64
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+ width: 64
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+ gen:
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+ encoder:
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+ n_res: 1
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+ default:
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+ n_res: 1
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+
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+ train:
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+ log_level: 1
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+ ```
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+
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+ Setting `n_res` to 1 is important to run tests faster and with less memory
LICENSE ADDED
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README.md CHANGED
@@ -1,8 +1,8 @@
1
  ---
2
- title: Climategan
3
- emoji: 😻
4
- colorFrom: red
5
- colorTo: purple
6
  sdk: gradio
7
  sdk_version: 2.9.4
8
  app_file: app.py
@@ -10,4 +10,165 @@ pinned: false
10
  license: gpl-3.0
11
  ---
12
 
13
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ title: ClimateGAN
3
+ emoji: 🌎
4
+ colorFrom: blue
5
+ colorTo: green
6
  sdk: gradio
7
  sdk_version: 2.9.4
8
  app_file: app.py
 
10
  license: gpl-3.0
11
  ---
12
 
13
+ # ClimateGAN: Raising Awareness about Climate Change by Generating Images of Floods
14
+
15
+ This repository contains the code used to train the model presented in our **[paper](https://arxiv.org/abs/2110.02871)**.
16
+
17
+ It is not simply a presentation repository but the code we have used over the past 30 months to come to our final architecture. As such, you will find many scripts, classes, blocks and options which we actively use for our own development purposes but are not directly relevant to reproduce results or use pretrained weights.
18
+
19
+ ![flood processing](images/flood.png)
20
+
21
+ ## Using pre-trained weights
22
+
23
+ In the paper, we present ClimateGAN as a solution to produce images of floods. It can actually do **more**:
24
+
25
+ * reusing the segmentation map, we are able to isolate the sky, turn it red and in a few more steps create an image resembling the consequences of a wildfire on a neighboring area, similarly to the [California wildfires](https://www.google.com/search?q=california+wildfires+red+sky&source=lnms&tbm=isch&sa=X&ved=2ahUKEwisws-hx7zxAhXxyYUKHQyKBUwQ_AUoAXoECAEQBA&biw=1680&bih=917&dpr=2).
26
+ * reusing the depth map, we can simulate the consequences of a smog event on an image, scaling the intensity of the filter by the distance of an object to the camera, as per [HazeRD](http://www2.ece.rochester.edu/~gsharma/papers/Zhang_ICIP2017_HazeRD.pdf)
27
+
28
+ ![image of wildfire processing](images/wildfire.png)
29
+ ![image of smog processing](images/smog.png)
30
+
31
+ In this section we'll explain how to produce the `Painted Input` along with the Smog and Wildfire outputs of a pre-trained ClimateGAN model.
32
+
33
+ ### Installation
34
+
35
+ This repository and associated model have been developed using Python 3.8.2 and **Pytorch 1.7.0**.
36
+
37
+ ```bash
38
+ $ git clone git@github.com:cc-ai/climategan.git
39
+ $ cd climategan
40
+ $ pip install -r requirements-3.8.2.txt # or `requirements-any.txt` for other Python versions (not tested but expected to be fine)
41
+ ```
42
+
43
+ Our pipeline uses [comet.ml](https://comet.ml) to log images. You don't *have* to use their services but we recommend you do as images can be uploaded on your workspace instead of being written to disk.
44
+
45
+ If you want to use Comet, make sure you have the [appropriate configuration in place (API key and workspace at least)](https://www.comet.ml/docs/python-sdk/advanced/#non-interactive-setup)
46
+
47
+ ### Inference
48
+
49
+ 1. Download and unzip the weights [from this link](https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K&export=download) (checkout [`gdown`](https://github.com/wkentaro/gdown) for a commandline interface) and put them in `config/`
50
+
51
+ ```
52
+ $ pip install gdown
53
+ $ mkdir config
54
+ $ cd config
55
+ $ gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K
56
+ $ unzip release-github-v1.zip
57
+ $ cd ..
58
+ ```
59
+
60
+ 2. Run from the repo's root:
61
+
62
+ 1. With `comet`:
63
+
64
+ ```bash
65
+ python apply_events.py --batch_size 4 --half --images_paths path/to/a/folder --resume_path config/model/masker --upload
66
+ ```
67
+
68
+ 2. Without `comet` (and shortened args compared to the previous example):
69
+
70
+ ```bash
71
+ python apply_events.py -b 4 --half -i path/to/a/folder -r config/model/masker --output_path path/to/a/folder
72
+ ```
73
+
74
+ The `apply_events.py` script has many options, for instance to use a different output size than the default systematic `640 x 640` pixels, look at the code or `python apply_events.py --help`.
75
+
76
+ ## Training from scratch
77
+
78
+ ClimateGAN is split in two main components: the Masker producing a binary mask of where water should go and the Painter generating water within this mask given an initial image's context.
79
+
80
+ ### Configuration
81
+
82
+ The code is structured to use `shared/trainer/defaults.yaml` as default configuration. There are 2 ways of overriding those for your purposes (without altering that file):
83
+
84
+ 1. By providing an alternative configuration as command line argument `config=path/to/config.yaml`
85
+
86
+ 1. The code will first load `shared/trainer/defaults.yaml`
87
+ 2. *then* update the resulting dictionary with values read in the provided `config` argument.
88
+ 3. The folder `config/` is NOT tracked by git so you would typically put them there
89
+
90
+ 2. By overwriting specific arguments from the command-line like `python train.py data.loaders.batch_size=8`
91
+
92
+
93
+ ### Data
94
+
95
+ #### Masker
96
+
97
+ ##### Real Images
98
+
99
+ Because of copyrights issues we are not able to share the real images scrapped from the internet. You would have to do that yourself. In the `yaml` config file, the code expects a key pointing to a `json` file like `data.files.<train or val>.r: <path/to/a/json/file>`. This `json` file should be a list of dictionaries with tasks as keys and files as values. Example:
100
+
101
+ ```json
102
+ [
103
+ {
104
+ "x": "path/to/a/real/image",
105
+ "s": "path/to/a/segmentation_map",
106
+ "d": "path/to/a/depth_map"
107
+ },
108
+ ...
109
+ ]
110
+ ```
111
+
112
+ Following the [ADVENT](https://github.com/valeoai/ADVENT) procedure, only `x` should be required. We use `s` and `d` inferred from pre-trained models (DeepLab v3+ and MiDAS) to use those pseudo-labels in the first epochs of training (see `pseudo:` in the config file)
113
+
114
+ ##### Simulated Images
115
+
116
+ We share snapshots of the Virtual World we created in the [Mila-Simulated-Flood dataset](). You can download and unzip one water-level and then produce json files similar to that of the real data, with an additional key `"m": "path/to/a/ground_truth_sim_mask"`. Lastly, edit the config file: `data.files.<train or val>.s: <path/to/a/json/file>`
117
+
118
+ #### Painter
119
+
120
+ The painter expects input images and binary masks to train using the [GauGAN](https://github.com/NVlabs/SPADE) training procedure. Unfortunately we cannot share openly the collected data, but similarly as for the Masker's real data you would point to the data using a `json` file as:
121
+
122
+ ```json
123
+ [
124
+ {
125
+ "x": "path/to/a/real/image",
126
+ "m": "path/to/a/water_mask",
127
+ },
128
+ ...
129
+ ]
130
+ ```
131
+
132
+ And put those files as values to `data.files.<train or val>.rf: <path/to/a/json/file>` in the configuration.
133
+
134
+ ## Coding conventions
135
+
136
+ * Tasks
137
+ * `x` is an input image, in [-1, 1]
138
+ * `s` is a segmentation target with `long` classes
139
+ * `d` is a depth map target in R, may be actually `log(depth)` or `1/depth`
140
+ * `m` is a binary mask with 1s where water is/should be
141
+ * Domains
142
+ * `r` is the *real* domain for the masker. Input images are real pictures of urban/suburban/rural areas
143
+ * `s` is the *simulated* domain for the masker. Input images are taken from our Unity world
144
+ * `rf` is the *real flooded* domain for the painter. Training images are pairs `(x, m)` of flooded scenes for which the water should be reconstructed, in the validation data input images are not flooded and we provide a manually labeled mask `m`
145
+ * `kitti` is a special `s` domain to pre-train the masker on [Virtual Kitti 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/)
146
+ * it alters the `trainer.loaders` dict to select relevant data sources from `trainer.all_loaders` in `trainer.switch_data()`. The rest of the code is identical.
147
+ * Flow
148
+ * This describes the call stack for the trainers standard training procedure
149
+ * `train()`
150
+ * `run_epoch()`
151
+ * `update_G()`
152
+ * `zero_grad(G)`
153
+ * `get_G_loss()`
154
+ * `get_masker_loss()`
155
+ * `masker_m_loss()` -> masking loss
156
+ * `masker_s_loss()` -> segmentation loss
157
+ * `masker_d_loss()` -> depth estimation loss
158
+ * `get_painter_loss()` -> painter's loss
159
+ * `g_loss.backward()`
160
+ * `g_opt_step()`
161
+ * `update_D()`
162
+ * `zero_grad(D)`
163
+ * `get_D_loss()`
164
+ * painter's disc losses
165
+ * `masker_m_loss()` -> masking AdvEnt disc loss
166
+ * `masker_s_loss()` -> segmentation AdvEnt disc loss
167
+ * `d_loss.backward()`
168
+ * `d_opt_step()`
169
+ * `update_learning_rates()` -> update learning rates according to schedules defined in `opts.gen.opt` and `opts.dis.opt`
170
+ * `run_validation()`
171
+ * compute val losses
172
+ * `eval_images()` -> compute metrics
173
+ * `log_comet_images()` -> compute and upload inferences
174
+ * `save()`
USAGE.md ADDED
@@ -0,0 +1,328 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # ClimateGAN
2
+ - [ClimateGAN](#climategan)
3
+ - [Setup](#setup)
4
+ - [Coding conventions](#coding-conventions)
5
+ - [updates](#updates)
6
+ - [interfaces](#interfaces)
7
+ - [Logging on comet](#logging-on-comet)
8
+ - [Resources](#resources)
9
+ - [Example](#example)
10
+ - [Release process](#release-process)
11
+
12
+ ## Setup
13
+
14
+ **`PyTorch >= 1.1.0`** otherwise optimizer.step() and scheduler.step() are in the wrong order ([docs](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate))
15
+
16
+ **pytorch==1.6** to use pytorch-xla or automatic mixed precision (`amp` branch).
17
+
18
+ Configuration files use the **YAML** syntax. If you don't know what `&` and `<<` mean, you'll have a hard time reading the files. Have a look at:
19
+
20
+ * https://dev.to/paulasantamaria/introduction-to-yaml-125f
21
+ * https://stackoverflow.com/questions/41063361/what-is-the-double-left-arrow-syntax-in-yaml-called-and-wheres-it-specced/41065222
22
+
23
+ **pip**
24
+
25
+ ```
26
+ $ pip install comet_ml scipy opencv-python torch torchvision omegaconf==1.4.1 hydra-core==0.11.3 scikit-image imageio addict tqdm torch_optimizer
27
+ ```
28
+
29
+ ## Coding conventions
30
+
31
+ * Tasks
32
+ * `x` is an input image, in [-1, 1]
33
+ * `s` is a segmentation target with `long` classes
34
+ * `d` is a depth map target in R, may be actually `log(depth)` or `1/depth`
35
+ * `m` is a binary mask with 1s where water is/should be
36
+ * Domains
37
+ * `r` is the *real* domain for the masker. Input images are real pictures of urban/suburban/rural areas
38
+ * `s` is the *simulated* domain for the masker. Input images are taken from our Unity world
39
+ * `rf` is the *real flooded* domain for the painter. Training images are pairs `(x, m)` of flooded scenes for which the water should be reconstructed, in the validation data input images are not flooded and we provide a manually labeled mask `m`
40
+ * `kitti` is a special `s` domain to pre-train the masker on [Virtual Kitti 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/)
41
+ * it alters the `trainer.loaders` dict to select relevant data sources from `trainer.all_loaders` in `trainer.switch_data()`. The rest of the code is identical.
42
+ * Flow
43
+ * This describes the call stack for the trainers standard training procedure
44
+ * `train()`
45
+ * `run_epoch()`
46
+ * `update_G()`
47
+ * `zero_grad(G)`
48
+ * `get_G_loss()`
49
+ * `get_masker_loss()`
50
+ * `masker_m_loss()` -> masking loss
51
+ * `masker_s_loss()` -> segmentation loss
52
+ * `masker_d_loss()` -> depth estimation loss
53
+ * `get_painter_loss()` -> painter's loss
54
+ * `g_loss.backward()`
55
+ * `g_opt_step()`
56
+ * `update_D()`
57
+ * `zero_grad(D)`
58
+ * `get_D_loss()`
59
+ * painter's disc losses
60
+ * `masker_m_loss()` -> masking AdvEnt disc loss
61
+ * `masker_s_loss()` -> segmentation AdvEnt disc loss
62
+ * `d_loss.backward()`
63
+ * `d_opt_step()`
64
+ * `update_learning_rates()` -> update learning rates according to schedules defined in `opts.gen.opt` and `opts.dis.opt`
65
+ * `run_validation()`
66
+ * compute val losses
67
+ * `eval_images()` -> compute metrics
68
+ * `log_comet_images()` -> compute and upload inferences
69
+ * `save()`
70
+
71
+ ### Resuming
72
+
73
+ Set `train.resume` to `True` in `opts.yaml` and specify where to load the weights:
74
+
75
+ Use a config's `load_path` namespace. It should have sub-keys `m`, `p` and `pm`:
76
+
77
+ ```yaml
78
+ load_paths:
79
+ p: none # Painter weights
80
+ m: none # Masker weights
81
+ pm: none # Painter + Masker weights (single ckpt for both)
82
+ ```
83
+
84
+ 1. any path which leads to a dir will be loaded as `path / checkpoints / latest_ckpt.pth`
85
+ 2. if you want to specify a specific checkpoint (not the latest), it MUST be a `.pth` file
86
+ 3. resuming a `P` **OR** an `M` model, you may only specify 1 of `load_path.p` **OR** `load_path.m`.
87
+ You may also leave **BOTH** at `none`, in which case `output_path / checkpoints / latest_ckpt.pth`
88
+ will be used
89
+ 4. resuming a P+M model, you may specify (`p` AND `m`) **OR** `pm` **OR** leave all at `none`,
90
+ in which case `output_path / checkpoints / latest_ckpt.pth` will be used to load from
91
+ a single checkpoint
92
+
93
+ ### Generator
94
+
95
+ * **Encoder**:
96
+
97
+ `trainer.G.encoder` Deeplabv2 or v3-based encoder
98
+ * Code borrowed from
99
+ * https://github.com/valeoai/ADVENT/blob/master/advent/model/deeplabv2.py
100
+ * https://github.com/CoinCheung/DeepLab-v3-plus-cityscapes
101
+
102
+ * **Decoders**:
103
+ * `trainer.G.decoders["s"]` -> *Segmentation* -> DLV3+ architecture (ASPP + Decoder)
104
+ * `trainer.G.decoders["d"]` -> *Depth* -> ResBlocks + (Upsample + Conv)
105
+ * `trainer.G.decoders["m"]` -> *Mask* -> ResBlocks + (Upsample + Conv) -> Binary mask: 1 = water should be there
106
+ * `trainer.G.mask()` predicts a mask and optionally applies `sigmoid` from an `x` input or a `z` input
107
+
108
+ * **Painter**: `trainer.G.painter` -> [GauGAN SPADE-based](https://github.com/NVlabs/SPADE)
109
+ * input = masked image
110
+ * `trainer.G.paint(m, x)` higher level function which takes care of masking
111
+ * If `opts.gen.p.paste_original_content` the painter should only create water and not reconstruct outside the mask: the output of `paint()` is `painted * m + x * (1 - m)`
112
+
113
+ High level methods of interest:
114
+
115
+ * `trainer.infer_all()` creates a dictionary of events with keys `flood` `wildfire` and `smog`. Can take in a single image or a batch, of numpy arrays or torch tensors, on CPU/GPU/TPU. This method calls, amongst others:
116
+ * `trainer.G.encode()` to compute the shared latent vector `z`
117
+ * `trainer.G.mask(z=z)` to infer the mask
118
+ * `trainer.compute_fire(x, segmentation)` to create a wildfire image from `x` and inferred segmentation
119
+ * `trainer.compute_smog(x, depth)` to create a smog image from `x` and inferred depth
120
+ * `trainer.compute_flood(x, mask)` to create a flood image from `x` and inferred mask using the painter (`trainer.G.paint(m, x)`)
121
+ * `Trainer.resume_from_path()` static method to resume a trainer from a path
122
+
123
+ ### Discriminator
124
+
125
+ ## updates
126
+
127
+ multi-batch:
128
+
129
+ ```
130
+ multi_domain_batch = {"rf: batch0, "r": batch1, "s": batch2}
131
+ ```
132
+
133
+ ## interfaces
134
+
135
+ ### batches
136
+ ```python
137
+ batch = Dict({
138
+ "data": {
139
+ "d": depthmap,,
140
+ "s": segmentation_map,
141
+ "m": binary_mask
142
+ "x": real_flooded_image,
143
+ },
144
+ "paths":{
145
+ same_keys: path_to_file
146
+ }
147
+ "domain": list(rf | r | s),
148
+ "mode": list(train | val)
149
+ })
150
+ ```
151
+
152
+ ### data
153
+
154
+ #### json files
155
+
156
+ | name | domain | description | author |
157
+ | :--------------------------------------------- | :----: | :------------------------------------------------------------------------- | :-------: |
158
+ | **train_r_full.json, val_r_full.json** | r | MiDaS+ Segmentation pseudo-labels .pt (HRNet + Cityscapes) | Mélisande |
159
+ | **train_s_full.json, val_s_full.json** | s | Simulated data from Unity11k urban + Unity suburban dataset | *** |
160
+ | train_s_nofences.json, val_s_nofences.json | s | Simulated data from Unity11k urban + Unity suburban dataset without fences | Alexia |
161
+ | train_r_full_pl.json, val_r_full_pl.json | r | MegaDepth + Segmentation pseudo-labels .pt (HRNet + Cityscapes) | Alexia |
162
+ | train_r_full_midas.json, val_r_full_midas.json | r | MiDaS+ Segmentation (HRNet + Cityscapes) | Mélisande |
163
+ | train_r_full_old.json, val_r_full_old.json | r | MegaDepth+ Segmentation (HRNet + Cityscapes) | *** |
164
+ | train_r_nopeople.json, val_r_nopeople.json | r | Same training data as above with people removed | Sasha |
165
+ | train_rf_with_sim.json | rf | Doubled train_rf's size with sim data (randomly chosen) | Victor |
166
+ | train_rf.json | rf | UPDATE (12/12/20): added 50 ims & masks from ADE20K Outdoors | Victor |
167
+ | train_allres.json, val_allres.json | rf | includes both lowres and highres from ORCA_water_seg | Tianyu |
168
+ | train_highres_only.json, val_highres_only.json | rf | includes only highres from ORCA_water_seg | Tianyu |
169
+
170
+
171
+ ```yaml
172
+ # data file ; one for each r|s
173
+ - x: /path/to/image
174
+ m: /path/to/mask
175
+ s: /path/to/segmentation map
176
+ - x: /path/to/another image
177
+ d: /path/to/depth map
178
+ m: /path/to/mask
179
+ s: /path/to/segmentation map
180
+ - x: ...
181
+ ```
182
+
183
+ or
184
+
185
+ ```json
186
+ [
187
+ {
188
+ "x": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000005.jpg",
189
+ "s": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000005.npy",
190
+ "d": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000005_depth.jpg"
191
+ },
192
+ {
193
+ "x": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000006.jpg",
194
+ "s": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000006.npy",
195
+ "d": "/Users/victor/Documents/ccai/github/climategan/example_data/gsv_000006_depth.jpg"
196
+ }
197
+ ]
198
+ ```
199
+
200
+ The json files used are located at `/network/tmp1/ccai/data/climategan/`. In the basenames, `_s` denotes simulated domain data and `_r` real domain data.
201
+ The `base` folder contains json files with paths to images (`"x"`key) and masks (taken as ground truth for the area that should be flooded, `"m"` key).
202
+ The `seg` folder contains json files and keys `"x"`, `"m"` and `"s"` (segmentation) for each image.
203
+
204
+
205
+ loaders
206
+
207
+ ```
208
+ loaders = Dict({
209
+ train: { r: loader, s: loader},
210
+ val: { r: loader, s: loader}
211
+ })
212
+ ```
213
+
214
+ ### losses
215
+
216
+ `trainer.losses` is a dictionary mapping to loss functions to optimize for the 3 main parts of the architecture: generator `G`, discriminators `D`:
217
+
218
+ ```python
219
+ trainer.losses = {
220
+ "G":{ # generator
221
+ "gan": { # gan loss from the discriminators
222
+ "a": GANLoss, # adaptation decoder
223
+ "t": GANLoss # translation decoder
224
+ },
225
+ "cycle": { # cycle-consistency loss
226
+ "a": l1 | l2,,
227
+ "t": l1 | l2,
228
+ },
229
+ "auto": { # auto-encoding loss a.k.a. reconstruction loss
230
+ "a": l1 | l2,
231
+ "t": l1 | l2
232
+ },
233
+ "tasks": { # specific losses for each auxillary task
234
+ "d": func, # depth estimation
235
+ "h": func, # height estimation
236
+ "s": cross_entropy_2d, # segmentation
237
+ "w": func, # water generation
238
+ },
239
+ "classifier": l1 | l2 | CE # loss from fooling the classifier
240
+ },
241
+ "D": GANLoss, # discriminator losses from the generator and true data
242
+ "C": l1 | l2 | CE # classifier should predict the right 1-h vector [rf, rn, sf, sn]
243
+ }
244
+ ```
245
+
246
+ ## Logging on comet
247
+
248
+ Comet.ml will look for api keys in the following order: argument to the `Experiment(api_key=...)` call, `COMET_API_KEY` environment variable, `.comet.config` file in the current working directory, `.comet.config` in the current user's home directory.
249
+
250
+ If your not managing several comet accounts at the same time, I recommend putting `.comet.config` in your home as such:
251
+
252
+ ```
253
+ [comet]
254
+ api_key=<api_key>
255
+ workspace=vict0rsch
256
+ rest_api_key=<rest_api_key>
257
+ ```
258
+
259
+ ### Tests
260
+
261
+ Run tests by executing `python test_trainer.py`. You can add `--no_delete` not to delete the comet experiment at exit and inspect uploads.
262
+
263
+ Write tests as scenarios by adding to the list `test_scenarios` in the file. A scenario is a dict of overrides over the base opts in `shared/trainer/defaults.yaml`. You can create special flags for the scenario by adding keys which start with `__`. For instance, `__doc` is a mandatory key in any scenario describing it succinctly.
264
+
265
+ ## Resources
266
+
267