zoobot 0.0.4

Galaxy morphology classifiers

Zoobot

Zoobot classifies galaxy morphology with deep learning. This code will let you:

• Reproduce and improve the Galaxy Zoo DECaLS automated classifications
• Finetune the classifier for new tasks

For example, you can train a new classifier like so:

model = define_model.get_model(
    output_dim=len(schema.label_cols),  # schema defines the questions and answers
    input_size=initial_size, 
    crop_size=int(initial_size * 0.75),
    resize_size=resize_size
)

model.compile(
    loss=losses.get_multiquestion_loss(schema.question_index_groups),
    optimizer=tf.keras.optimizers.Adam()
)

training_config.train_estimator(
    model, 
    train_config,  # parameters for how to train e.g. epochs, patience
    train_dataset,
    test_dataset
)

You can finetune Zoobot with a free GPU using this Google Colab notebook. To install locally, keep reading.

Installation

Development Use

If you will be making changes to the Zoobot package itself (e.g. to add a new architecture), download the code using git:

# I recommend using a virtual environment, see below
git clone [e-mail]:mwalmsley/zoobot.git

And then install Zoobot using pip, specifying either the pytorch dependencies, the tensorflow dependencies, or both:

pip install -e zoobot[pytorch]  # pytorch dependencies
pip install -e zoobot[tensorflow]  # tensorflow dependencies
pip install -e zoobot[pytorch,tensorflow]  # both

The main branch is for stable-ish releases. The dev branch includes the shiniest features but may change at any time.

Direct Use

I expect most users will make small changes. But if you won't be making any changes to Zoobot itself (e.g. you just want to apply it, or you're in a production environment), you can simply install directly from pip:

pip install zoobot[pytorch]  # pytorch dependencies
# other dependency options as above

Getting Started

To get started, see the documentation. For pretrained model weights, precalculated representations, catalogues, and so forth, see the data notes in particular.

I also include some working examples for you to copy and adapt.

TensorFlow:

• tensorflow/examples/train_model_on_catalog.py (only necessary to train from scratch)
• tensorflow/examples/make_predictions.py
• tensorflow/examples/finetune_minimal.py
• tensorflow/examples/finetune_advanced.py

PyTorch:

• pytorch/examples/train_model_on_catalog.py (only necessary to train from scratch)
• Finetuning examples coming soon (this note added Oct 2022)

I also include some examples which record how the models in W+22a (the GZ DECaLS data release) were trained:

• replication/tensorflow/train_model_on_decals_dr5_splits.py
• replication/pytorch/train_model_on_decals_dr5_splits.py

There's also the gz_decals_data_release_analysis_demo.ipynb, which describes Zoobot's statistical predictions. When trained from scratch, it predicts the parameters for distributions, not simple class labels!

Latest features

• Added to PyPI/pip! Convenient for production or simple use.
• PyTorch version! Integrates with PyTorch Lightning and WandB. Multi-GPU support. Trains on jpeg images, rather than TFRecords, and does not yet have a finetuning example script.
• Train on colour (3-band) images: Add --color (American-friendly) to train_model.py
• Select which EfficientNet variant to train using the get_architecture arg in define_model.py - or replace with a func. returning your own architecture!
• New predict_on_dataset.py and save_predictons.py modules with useful functions for making predictions on large sets of images. Predictions are now saved to .hdf5 by default, which is much more convenient than csv for multi-forward-pass predictions.
• Multi-GPU (single node) training
• Support for Weights and Biases (wandb)
• Worked examples for custom representations
• Colab notebook for GZ predictions and fine-tuning
• Schemas (questions and answers for the decision trees) extended to include DECaLS DR1/2 and DR8, in various combinations. See zoobot.shared.label_metadata.py.
• Test time augmentations are now off by default but can be enabled with --test-time-augs on train_model.py

Contributions are welcome and will be credited in any future work.

Note on Environments

I recommend installing in a virtual environment like anaconda. For example, conda create --name zoobot python=3.7, then conda activate zoobot. Do not install packages directly with anaconda itself (e.g. conda install tensorflow) as Anaconda may install older versions. Use pip instead, as above. Python 3.7 or greater is required.

Replication

For replication of the GZ DECaLS classifier see /replicate. This contains slurm scripts to:

• Create training TFRecords equivalent to those used to train the published classifier
• Train the classifier itself (by calling zoobot/tensorflow/examples/train_model.py)

Citing

If you use this repo for your research, please cite the paper and the code (via Zenodo).