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Describe the bug
The features may not have to be compiled using exactly the same settings for the datamodule as on which the feature extractor is trained.
This is a problem, as it might perform bad on images without a mask or tiles with a different size or mpp.
To Reproduce
Steps to reproduce the behavior:
Train a feature extractor
Compile features with a new PMCHHGImageDataset
Desktop (please complete the following information):
DPAT version 4.3.2
Additional context
A solution might be to save the pytorch lightning hyperparameters with save_hyperparameters or ask for the specific config file the feature extractor trained with, and use those variables for instantiation of PMCHHGImageDataset
The text was updated successfully, but these errors were encountered:
Describe the bug
The features may not have to be compiled using exactly the same settings for the datamodule as on which the feature extractor is trained.
This is a problem, as it might perform bad on images without a mask or tiles with a different size or mpp.
To Reproduce
Steps to reproduce the behavior:
PMCHHGImageDataset
Desktop (please complete the following information):
Additional context
A solution might be to save the pytorch lightning hyperparameters with
save_hyperparameters
or ask for the specific config file the feature extractor trained with, and use those variables for instantiation ofPMCHHGImageDataset
The text was updated successfully, but these errors were encountered: