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HDTree-ICML-checkpoints

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metadata 
library_name: pytorch
license: mit
pipeline_tag: unconditional-image-generation
tags:
  - hdtree
  - pytorch
  - mnist
  - single-cell
  - clustering

HDTree ICML Checkpoints

This repository hosts pretrained checkpoints for the model presented in the paper HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference.

HDTree is a generative modeling framework designed for robust lineage inference. It captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and employs a quantized diffusion process to model continuous cell state transitions.

Files

File Dataset Configuration Notes
checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth MNIST configs/mnist.yaml Best MNIST checkpoint from the full run by checkpoint validation accuracy.
checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth Limb configs/limb.yaml default Limb sweep i10/default checkpoint.

Sample Usage

To validate a trained checkpoint using the official code, you can use the provided validation script:

# Example for MNIST
bash scripts/validate_checkpoint.sh mnist checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth

To compute reconstruction and log-likelihood with diffusion sampling, enable generation using the following command:

python main.py validate \
  -c configs/mnist.yaml \
  --model.init_args.ckpt_path=checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth \
  --model.init_args.training_str=step2_r \
  --model.init_args.gen_data_bool=True

Reported Metrics

MNIST full run summary:

ACC DP LP NMI
0.97310 0.93262 0.97310 0.92999

Limb i10 run summary (batch_size=1000, K=10, exaggeration_lat=0.5, nu_lat=0.3):

ACC DP LP NMI
0.52860 0.41029 0.58370 0.49042

The included logs/ files contain the original run outputs used to record these metrics.

Download 

pip install huggingface_hub
huggingface-cli download zelinzang/HDTree-ICML-checkpoints --local-dir .

Checksums

See SHA256SUMS.