Porting of the X-Ray Latent Diffusion Models model from MONAI Generative Models into model-zoo. #718
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…s repo. Signed-off-by: Virginia <virginia.fernandez@kcl.ac.uk>
… that no longer applies.
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… longer necessary. On inference, modification of the entry point from save_jpg to run to allow for run to be called alone, and truncation of loading statements which are too long. Modification of the README to include command example changes.
…z/model-zoo into xray_classifier_port
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Thanks for the contribution. Would be great to add corresponding test here:
https://github.com/Project-MONAI/model-zoo/tree/dev/ci/unit_tests
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| large_files: | ||
| - path: "models/autoencoder.pth" | ||
| url: "https://drive.google.com/uc?export=download&id=1paDN1m-Q_Oy8d_BanPkRTi3RlNB_Sv_h" | ||
| hash_val: "" |
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Please also include the hash val here.
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| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", | ||
| "version": "1.0.0", | ||
| "changelog": { | ||
| "0.2": "Flipped images fixed" |
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Should also include "1.0.0" here?
added 2 commits
December 17, 2024 08:44
For metadata, adjusted the versions, setting current to Initial Release. I also removed nibabel from the optional package requirements as this CXR produces JPG and does not deal with nifti files.
…z/model-zoo into xray_classifier_port
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| { | |||
| "imports": [ | |||
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Hi @virginiafdez , thanks for the PR. Could you also help to add a test file? The purpose is to ensure the bundle is runnable, and here is an example:
https://github.com/Project-MONAI/model-zoo/blob/dev/ci/unit_tests/test_spleen_ct_segmentation.py#L37
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Description
As part of the porting of MONAI Generative Models, the models present in the model zoo from that repo (https://github.com/Project-MONAI/GenerativeModels/tree/main/model-zoo) need to be ported into the main model zoo from MONAI.
This is one of the models, a latent diffusion model that generates X-Rays.
Status
**Ready
Please ensure all the checkboxes:
./runtests.sh --codeformat.CONTRIBUTING.md).monai,pytorchandnumpyare correct inmetadata.json.eval_metricsof the provided weights and TorchScript modules.large_file.yml./home/your_name/for"bundle_root").