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Home Page: https://scripties.uba.uva.nl/search?id=722710
3D VQ-VAE-2 for high-resolution CT scan synthesis
Home Page: https://scripties.uba.uva.nl/search?id=722710
Happy to report I was able to train a VQ-VAE using a dataset. Very cool to see - and kudos for the nice Tensorboard outputs you have in place! ๐
Do you have any suggestions or code for randomly sampling from the decoder in a generative fashion?
Also, If you have a summary of these files and their purpose, that would be very helpful. I would be happy to do a PR with some comments in the repository if that would be helpful.
Questions on:
calc_ssim_from_checkpoint.py # does this calculate SSIM across the dataset โ
decode_embeddings.py # Specifications for db_path โ
extract_embeddings.py # Does this save embedding to disk โ
Ran successfully:
plot_from_checkpoint.py # plots a forward pass from a random sample โ
train.py # trains a model โ
Much appreciated!
-Akshay
Hi,
I'm using your implementation to generate MRIs. I have trained a VQ-VAE to reconstruct 3D MRIs, but I am unsure about which vectors to use for training the PixelCNN for sampling.
I attempted to understand your LMDB implementation, but it would take me a significant amount of time to fully grasp it. I'm not clear on what exactly is being stored in the LMDB database.
Given that the VQ-VAE encoder outputs multiple quantization vectors (one for each encoding block), what should be the specific input for the PixelCNN?
x = torch.randn(4, 3, 128, 128, 64).to('cuda')
decoded, (commitment_loss, quantizations, encoding_idx) = vqvae(x)
I think i'll have to modify the LMDB data module part.
Thank you!
When installing the dependencies, I am getting:
$ conda env create -f environment.yml
Collecting package metadata (repodata.json): done
Solving environment: failed
ResolvePackageNotFound:
- torchvision==0.9.0.dev20210122=py38_cu110
- pytorch==1.8.0.dev20210122=py3.8_cuda11.0.221_cudnn8.0.5_0
Any ideas?
Hello,
I wanted to confirm the steps for training a VQ-VAE on radiology data. Thank you for working on such an interesting and important application of VQ-VAE. Our research group is particularly interested in applications to oncologic imaging.
1. Sample data
2. Training
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