PyTorch implementations of dense and sparse UResNet
This README is very rough and will be completed soon. Stay tuned!
Singularity containers are available on https://www.singularity-hub.org/containers/6596.
LArTPC simulation dataset are publicly available on https://osf.io/9b3cv/.
All options can be found in uresnet/flags.py
.
To train sparse U-ResNet you can use for example:
python bin/uresnet.py train -chks 500 -wp weights/snapshot -io larcv_sparse -bs 1 --gpus 0 -nc 5 -rs 1 -ss 512 -dd 3 -uns 5 -uf 16 -dkeys data,fivetypes -mn uresnet_sparse -it 10 -ld log -if your_data.root
To run the inference:
python bin/uresnet.py inference --full -mp weights/snapshot-1000.ckpt -io larcv_sparse -bs 1 --gpus 0 -nc 5 -rs 1 -ss 512 -dd 3 -uns 5 -uf 16 -dkeys data,fivetypes -mn uresnet_sparse -it 10 -ld log -if your_data.root
Main command-line parameters:
-mn
model name, can beuresnet_dense
oruresnet_sparse
-io
I/O type, can belarcv_sparse
orlarcv_dense
-nc
number of classes-chks
save checkpoint every N iterations-wp
weights directory-bs
batch size--gpus
list gpus-rs
report every N steps in stdout-ss
spatial size of images-dd
data dimension (2 or 3)-uns
U-ResNet depth-uf
U-ResNet initial number of filters-dkeys
data keys in LArCV ROOT file-it
number of iterations-ld
log directory-if
input file-mp
weight files to load for inference
Laura Domine & Kazuhiro Terao