Constrained-CNN losses for weakly supervised segmentation
- Pytorch 1.0
Once the zip is in place, everything should be automatic:
make -f acdc.make make -f prostate.make make -f zenodo_spine.make
Usually takes a little bit more than a day per makefile.
This perform in the following order:
- Unpacking of the data
- Remove unwanted big files
- Normalization and slicing of the data
- Training with the different methods
- Plotting of the metrics curves
- Display of a report
- Archiving of the results in an .tar.gz stored in the
The main advantage of the makefile is that it will handle by itself the dependencies between the different parts. For instance, once the data has been pre-processed, it won't do it another time, even if you delete the training results. It is also a good way to avoid overwriting existing results by relaunching the exp by accident.
Of course, parts can be launched separately :
make -f acdc.make data/acdc # Unpack only make -f acdc.make data/MIDL # unpack if needed, then slice the data make -f acdc.make results/acdc/fs # train only with full supervision. Create the data if needed make -f acdc.make results/acdc/val_dice.png # Create only this plot. Do the trainings if needed
The number of option for the main script is fairly dense, but the recipes in the different makefiles should give you a good idea on how to modify the training parameters and create new targets. In case of questions, feel free to contact me.
MIDL/ train/ img/ case_10_0_0.png ... gt/ case_10_0_0.png ... random/ ... ... val/ img/ case_10_0_0.png ... gt/ case_10_0_0.png ... random/ ... ...
The network takes png files as an input. The gt folder contains gray-scale images of the ground-truth, where the gray-scale level are the number of the class (namely, 0 and 1). This is because I often use my segmentation viewer to visualize the results, so that does not really matter. If you want to see it directly in an image viewer, you can either use the remap script, or use imagemagick:
mogrify -normalize data/ISLES/val/gt/*.png
results/ acdc/ fs/ best_epoch/ val/ case_10_0_0.png ... iter000/ val/ ... size_595/ ... best.pkl # best model saved metrics.csv # metrics over time, csv best_epoch.txt # number of the best epoch val_dice.npy # log of all the metric over time for each image and class val_dice.png # Plot over time ... prostate/ ... archives/ $(REPO)-$(DATE)-$(HASH)-$(HOSTNAME)-acdc.tar.gz $(REPO)-$(DATE)-$(HASH)-$(HOSTNAME)-prostate.tar.gz
The losses are defined in the
losses.py file. Explaining the remaining of the code is left as an exercise for the reader.
Remove all assertions from the code. Usually done after making sure it does not crash for one complete epoch:
make -f acdc.make <anything really> CFLAGS=-O
Use a specific python executable:
make -f acdc.make <super target> CC=/path/to/the/executable
Train for only 5 epochs, with a dummy network, and only 10 images per data loader. Useful for debugging:
make -f acdc.make <really> NET=Dimwit EPC=5 DEBUG=--debug
Rebuild everything even if already exist:
make -f acdc.make <a> -B
Only print the commands that will be run (useful to check recipes are properly defined):
make -f acdc.make <a> -n
Create a gif for the predictions over time of a specific patient:
cd results/acdc/fs convert iter*/val/case_14_0_0.png case_14_0_0.gif mogrify -normalize case_14_0_0.gif