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Callback function to log Masked Autoencoder reconstructions to WandB #88
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A CLI and library for interacting with the Weights and Biases API!
Created a custom callback function to log visualizations of the input and output images to the Masked Autoencoder. Only showing the RGB bands of Sentinel-2 for now. A sample of 6 image pairs (original + reconstructed, so 12 in total) is uploaded to Weights and Biases. Example LightningCLI command: `python trainer.py fit --trainer.max_epochs=20 --data.data_path=data/32VLM --trainer.logger=WandbLogger --trainer.logger.project=clay --trainer.logger.save_dir=checkpoints --trainer.callbacks+=LogMAEReconstructedImage`.
Image processing in Python!
Enhance low contrast images by applying a histogram equalization stretching algorithm on the RGB images, instead of dividing by a magic number like 6000.
More samples to look at! Also only running einsum conversion on as many samples as needed rather than the whole batch, and handling cases where num_samples may be more than the batch_size.
Allows for `from src.callback_wandb import LogMAEReconstruction` to run, even without wandb being installed. Helpful if someone doesn't want to install wandb for whatever reason.
Testing that the LogMAEReconstruction callback works to save a set of images to WandB. Testing this in offline mode only, with checks that artifacts are saved locally, and that the wandb images have the correct caption and format.
Order of the folders could change, so using set instead of list.
Unsure why the unit test passes on GitHub Actions, but causes an `Error: Process completed with exit code 255`. Maybe a smaller batch size would help?
Turn off stdout / stderr logging by setting WANDB_CONSOLE=off to see if it helps with the failing GitHub Actions.
Another attempt to see if it helps prevent exit code 255 on GitHub Actions.
Running out of ideas on why pytest has exit code 255 on GitHub Actions...
Trying to figure out what's going on.
Setting WANDB_MODE="disabled", so no files are logged to disk, though the wandb.Image(s) are still created. See if this helps to resolve the exit code 255 issue on GitHub Actions.
Minor changes to the docstring of the on_validation_batch_end method, and a typo fix.
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Sunk wayyy too much time trying to debug why the unit test was failing on GitHub Actions but not locally (see below), so didn't get to do the SAR and DEM plots. Will implement those in a follow-up PR instead.
# Check that wandb saved some log files to the temporary directory | ||
# assert os.path.exists(path := f"{tmpdirname}/wandb/latest-run/") | ||
# assert set(os.listdir(path=path)) == set( | ||
# [ | ||
# f"run-{trainer.logger.version}.wandb", | ||
# "tmp", | ||
# "files", | ||
# "logs", | ||
# ] | ||
# ) |
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Setting WANDB_MODE="disabled"
in this unit test and commenting these lines that check for log files being saved, because GitHub Actions keeps failing with an error like Error: Process completed with exit code 255
, even though all the unit tests pass on pytest 😕 The unit test does work locally though when I uncomment this block and use WANDB_MODE="offline"
, so not sure what's going on.
Gonna merge this in first and combine with the other wandb callback being developed at #47. |
…88) * ➕ Add wandb A CLI and library for interacting with the Weights and Biases API! * 🔊 Log Masked Autoencoder reconstructions to WandB Created a custom callback function to log visualizations of the input and output images to the Masked Autoencoder. Only showing the RGB bands of Sentinel-2 for now. A sample of 6 image pairs (original + reconstructed, so 12 in total) is uploaded to Weights and Biases. Example LightningCLI command: `python trainer.py fit --trainer.max_epochs=20 --data.data_path=data/32VLM --trainer.logger=WandbLogger --trainer.logger.project=clay --trainer.logger.save_dir=checkpoints --trainer.callbacks+=LogMAEReconstructedImage`. * ➕ Add scikit-image Image processing in Python! * 📸 Apply histogram equalization to RGB images Enhance low contrast images by applying a histogram equalization stretching algorithm on the RGB images, instead of dividing by a magic number like 6000. * 🔧 Increase default sample size from 6 to 8 More samples to look at! Also only running einsum conversion on as many samples as needed rather than the whole batch, and handling cases where num_samples may be more than the batch_size. * 🧑💻 Make wandb a somewhat optional dependency Allows for `from src.callback_wandb import LogMAEReconstruction` to run, even without wandb being installed. Helpful if someone doesn't want to install wandb for whatever reason. * ✅ Add unit test for LogMAEReconstruction Testing that the LogMAEReconstruction callback works to save a set of images to WandB. Testing this in offline mode only, with checks that artifacts are saved locally, and that the wandb images have the correct caption and format. * 🐛 Compare expected folders using set instead of list Order of the folders could change, so using set instead of list. * 🧪 Prevent WandB logger from saving logs to local drive for now Setting WANDB_MODE="disabled", so no files are logged to disk, though the wandb.Image(s) are still created. See if this helps to resolve the exit code 255 issue on GitHub Actions. * 📝 Fix a typo and improve docstring Minor changes to the docstring of the on_validation_batch_end method, and a typo fix.
To visually inspect how the Masked Autoencoder is performing over the training run in terms of reconstructing to original image.
Implemented as a Lightning Callback function which runs at the end of a validation loop's mini-batch (
on_validation_batch_end
). A sample of 6 image pairs (original + reconstructed, so total of 12) are uploaded online.Example usage with LightningCLI:
Samples of histogram equalized RGB images, and the reconstructed outputs (only random noise as this is early in the training).
TODO:
TODO in the future:
References: