robail-yasrab / rootnav-2.0 Goto Github PK

Hi @mikepound! I'm transferring our email conversation to Github for future clarity. Here is the start of the email chain:

Our group has been trying to install RootNav-2.0 on PC

Both @ADAS-DaveSkirvin and I tried various different approaches to getting the package installed, and neither had any success. All of our issues were around versioning of the 3rd party packages. In particular, it was torchvision causing the most trouble, with (IIRC) conflict with ??tensorboard. I assume this has resulted from version divergence since the most recent RootNav release?

Dave (cc) tried fairly extensively with conda, and I tried extensively using pip natively (since unfortunately, because ADAS is a commercial entity, conda is a paid license for us, and we’ll need to work around this). I started to expect that this was a conda-forge or pytorch channel versioning issue specific to native pip, but as Dave was having the exact same issues in conda, I don’t think this is the root problem. We’re on PCs, if relevant.

I tried forcing the install versions to those in the requirements.txt with ~=, but this also didn’t work. Neither did trying to roll back further by hand, including rolling back pip itself. I also tried a Python 2 install with your alternate version, hoping that the lack of recent Py2 support might have prevented version divergence, but this also didn’t work.

Does this sound like a known issue to you? Is there some manual workaround we can try? I have fairly extensive experience in Python for large scale research software, so can hopefully answer technical questions you might have. [Happy to open an issue on Github if you’d like me to... and here we are]

Hi all, this tool is really cool!

I have some image data that is similar to image data in your preprint. I do not expect RootNav2 to work on this data out-of-the-box without some transfer learning, but I thought I would try inference on some of my images just to assess how far it could get with the pretrained models. I run into an issue though where I always get the error Segmentation fault: 11. I made sure to resize my images to 1024x1024. I'm not sure where in the process it crashes, maybe it's just due to it not detecting the landmark points properly or something? Here's the full output:

python rootnav.py --model arabidopsis_plate ./test_rootnav_inputs/ ./test_rootnav_outputs/
RootNav 2.0
Cuda is not available, switching to CPU
Loading model...Done
Now Reading test_img.tiff
RootNav-2.0/inference/crf/crf.py:23: RuntimeWarning: divide by zero encountered in log
    unary = -np.log(model_softmax)
Segmentation fault: 11

Command----

kamlesh@kamlesh-Y530-15ICH:~/RootNav-2.0/training$ python3 training.py train

Error---

RUNDIR: runs/rootnav2/65093 Dataset Loading from ./OSR_Root_dataset/ /home/kamlesh/.local/lib/python3.8/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead. warnings.warn(warning.format(ret)) Starting training /home/kamlesh/.local/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. " Killed

@mikepound @robail-yasrab
Please look into the problem and help me solve this issue.

Hi,

Would it be possible to have some images to infer on?

Regards