search

facebookresearch / PoincareMaps

The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.
Other
148 stars 31 forks source link

pytorch version #3

Closed kimcdata closed 4 years ago

kimcdata commented 4 years ago

Hi, thanks for releasing this method, it looks very useful. I'm trying to use it on our data. I am not an experienced python user so I'm having trouble getting all the dependencies in place.

What version of pytorch are you using, please?

I am getting the following error when running main.py:

Traceback (most recent call last): File "main.py", line 179, in color_dict=color_dict) File "/home/rstudio/ms_project/PoincareMaps/train.py", line 41, in train loss = model.lossfn(model(inputs), targets) File "/home/rstudio/miniconda3/envs/mspy/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call_\ result = self.forward(*input, kwargs) File "/home/rstudio/ms_project/PoincareMaps/model.py", line 131, in forward dists = self.dist()(embs_inputs, embs_all).squeeze(-1) File "/home/rstudio/miniconda3/envs/mspy/lib/python3.7/site-packages/torch/autograd/function.py", line 145, in __call_\ "Legacy autograd function with non-static forward method is deprecated. " RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)**

python 3.7.7 on ubuntu 18.04 LTS

Thanks

results of pip freeze > anndata==0.7.3 > annoy==1.16.3 > certifi==2020.4.5.2 > cffi==1.14.0 > cycler==0.10.0 > Cython==0.29.20 > decorator==4.4.2 > fastdtw==0.3.2 > get-version==2.1 > h5py==2.10.0 > imageio==2.8.0 > importlib-metadata==1.6.1 > joblib==0.15.1 > kiwisolver==1.2.0 > legacy-api-wrap==1.2 > leidenalg==0.8.0 > llvmlite==0.33.0 > matplotlib==3.1.3 > mkl-fft==1.0.15 > mkl-random==1.1.0 > mkl-service==2.3.0 > natsort==7.0.1 > networkx==2.4 > numba==0.50.0 > numexpr==2.7.1 > numpy==1.18.1 > packaging==20.4 > pandas==1.0.3 > patsy==0.5.1 > Pillow==7.1.2 > pycairo==1.18.0 > pycparser==2.20 > pyparsing==2.4.7 > python-dateutil==2.8.1 > python-igraph==0.7.1.post7 > pytz==2020.1 > PyWavelets==1.1.1 > scanpy==1.5.1 > scikit-image==0.17.2 > scikit-learn==0.23.1 > scipy==1.4.1 > scrublet==0.2.1 > seaborn==0.10.1 > setuptools-scm==4.1.2 > six==1.15.0 > statsmodels==0.11.1 > tables==3.6.1 > tbb==2020.0.133 > threadpoolctl==2.1.0 > tifffile==2020.6.3 > torch==1.5.0 > tornado==6.0.4 > tqdm==4.46.1 > umap-learn==0.4.4 > zipp==3.1.0
klanita commented 4 years ago

I use Python 3.7.5 and it works. But it looks like the problem with the compatibility with the last pytorch version. I used pytorch 1.3.1 for this code, because wrote the code about 2 years ago.

kimcdata commented 4 years ago

Thanks for the ultra fast reply!

I think I have it working.