logs, output affine, boolean exit_on_error, and changes for cpu/gpu run

ai-med / quickNAT_pytorch

PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty

MIT License

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logs, output affine, boolean exit_on_error, and changes for cpu/gpu run #21

Closed joshicola closed 4 years ago

joshicola commented 4 years ago

Added logging for real-time non-buffered output
create affine matrix from header and apply (3D Slicer will crash with the discrepancy)
parameter to exit_on_error (or not) if only evaluating on a single image
edits to utils/evaluator.py to enable switching between GPU/CPU evaluations.

These patches are active in https://github.com/flywheel-apps/quicknat-gear (when public).

Use, change, or discard as you see fit.