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Sorry for the later response. The following command can be used to run testing: python -u predict_ada_hist.py -w checkpoints/BCSS/ds_True_Ada_scse/0_0.001_Ada_scse -o scse -l 1234_pred -v soft -d BCSS
-w path to the checkpoints, where you save the trained models.
-o feature re-calibration in skip connection, option: [scse, None].
-l specify the output from the ADS_UNet. option:
1234_pred, Collecting softmax prediction from each base $UNet^d$
1, Only the output of the UNet$^1$ is returned
...
4, Only the output of the UNet$^4$ is returned
-v voting strategy, option:
soft, $\alpha$ voting
mean, average voting
-d dataset, option: [BCSS, CRAG, Kumar]
Still working on cleaning the code and will update the README later, apologize for bring you confusion.
Hi @ynulonger,
Thank you for sharing your code.
Can you share the code for testing on BCSS dataset?
Best,
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