search

petteriTeikari / vesselNN

vesselNN
13 stars 5 forks source link

AttributeError: 'module' object has no attribute 'emio' #2

Open kkhuang1990 opened 7 years ago

kkhuang1990 commented 7 years ago

when I run the command below to train the model python train.py -c ../../configs/ZNN_configs/config_VD2D_tanh.cfg error like this came out Traceback (most recent call last): File "train.py", line 263, in main( args ) File "train.py", line 77, in main smp_trn = zsample.CSamples(config, pars, pars['train_range'], net, outsz, logfile) File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zsample.py", line 551, in init sample = CBoundarySample(config, pars, sid, net, outsz, log) File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zsample.py", line 422, in init log=log, is_forward=is_forward) File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zsample.py", line 58, in init outsz, setsz_in, fov, is_forward=is_forward ) File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zdataset.py", line 377, in init outsz, setsz, mapsz, is_forward=is_forward ) File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zdataset.py", line 229, in init arrlist = self._read_files( fnames ); File "/home/mil/huang/Coronary_Artery_Segmentation/vesselNN/znn-release/python/front_end/zdataset.py", line 305, in _read_files vol = emirt.emio.imread(fl) AttributeError: 'module' object has no attribute 'emio'

I have already downloaded the latest emirt from github and put it under the znn-release/python dir

petteriTeikari commented 6 years ago

Yes the external dependencies are a bit tricky, or can be @kkhuang1990 :S

I think it would be interesting to try the dataset, and the same basic architecture with that DeepMedic for example: https://github.com/Kamnitsask/deepmedic

Or convert the simple 2D+3D into a residual architecture? And as Theano gets discontinued, maybe implement the whole thing from scratch using Tensorflow or PyTorch?

Or just having to redo the configs and all based on the latest code from ZNN guys if you want more memory without the GPU acceleration? https://github.com/seung-lab/znn-release