Running under windows?
Opened this issue ยท 5 comments
Hey, just discovered this work and it looks extremely great! I'd be very excited if it was possible to explore possible integrations into our current research. I tried installing under windows but a few requirements are linux only if I am correct.
So when running
conda env create --name diffusion_net -f environment.yml
I get
Solving environment: failed
ResolvePackageNotFound:
- ncurses=6.2
- libgcc-ng=9.1.0
- ld_impl_linux-64=2.33.1
- libstdcxx-ng=9.1.0
- readline=8.1
If you could give me your opinion on if it would be possible to run under windows at all, I would be very thankful! If you could drop any hints on how, it would be even more appreciated.
In any case: Thank you for your great work and best wishes!
Hi! Thanks for the heads up on this.
To my knowledge there should be no issues with running on Windows, I suspect this is just an issue of bad versions in the conda environment file.
If you don't mind, I would try running DiffusionNet in a clean environment, and just adding the default versions of packages as the are needed. Everything is just standard Pytorch and other popular packages, so I don't think it should be much of an issue.
I will also give this a shot myself next time I am on a Windows machine, but I'm not sure when that will be! Please do let me know here if you hit any other big obstacles with packages.
Hi again and thank you for your reply!
You are right, I just tested it by initializing a new conda env and running the human_segmentation_original
experiment by calling
python human_segmentation_original.py --input_features=xyz
I then looked at the missing package errors and installed their default versions using pip. This worked, so I think it is really just the environment.yml
file that needs a little correction :)
Hey @nmwsharp @pvnieo
I just did some tries again. Here is an environment.yml
that mostly did the trick for me:
https://github.com/fstwn/diffusion-net/blob/3cdeb8a7a0b98c12c6bb8c48c2a650a49090792e/environment.yml
I ran all your experiments with the following results:
classification_shrec11
- Training: โ
functional_correspondence
- Training: โ (see below)
- Pretrained Model: โ (see below)
human_segmentation
- Training: โ (see below)
- Pretrained Model: โ
rna_mesh_segmentation
- Training: โ
sampling_invariance
- Training: โ
- Pretrained Model: โ (see below)
The functional_correspondence
experiment always throws the following error for me:
Traceback (most recent call last):
File "functional_correspondence.py", line 216, in <module>
train_loss = train_epoch(epoch)
File "functional_correspondence.py", line 116, in train_epoch
for data in tqdm(train_loader):
File "...\lib\site-packages\tqdm\std.py", line 1167, in __iter__
for obj in iterable:
File "...\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
data = self._next_data()
File "...\lib\site-packages\torch\utils\data\dataloader.py", line 561, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "...\lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in fetch
data = self.dataset[possibly_batched_index]
File "...\diffusion-net\experiments\functional_correspondence\faust_scape_dataset.py", line 188, in __getitem__
evec_1_a, evec_2_a = evec_1[vts1,:], evec_2[vts2,:]
IndexError: tensors used as indices must be long, byte or bool tensors
The human_segmentation_original
throws this error on training:
Traceback (most recent call last):
File "human_segmentation_original.py", line 204, in <module>
train_acc = train_epoch(epoch)
File "human_segmentation_original.py", line 136, in train_epoch
loss = torch.nn.functional.nll_loss(preds, labels)
File "...\lib\site-packages\torch\nn\functional.py", line 2532, in nll_loss
return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: "nll_loss_forward_reduce_cuda_kernel_2d_index" not implemented for 'Int'
The sampling_invariance
experiment produces a load of these errors on evaluation:
Computing all-pairs geodesic distance (warning: SLOW!)
0%| | 0/120 [00:02<?, ?it/s]
Traceback (most recent call last):
File "../../src\diffusion_net\geometry.py", line 862, in get_all_pairs_geodesic_distance
pool = Pool(None) # on 8 processors
File "...\lib\multiprocessing\context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "...\lib\multiprocessing\pool.py", line 212, in __init__
self._repopulate_pool()
File "...\lib\multiprocessing\pool.py", line 303, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "...\lib\multiprocessing\pool.py", line 326, in _repopulate_pool_static
w.start()
File "...\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "...\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "...\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "...\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "...\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "...\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "...\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "...\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "...\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "...\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "...\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "...\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "...\diffusion-net\experiments\sampling_invariance\sampling_invariance.py", line 244, in <module>
test_acc = test(with_geodesic_error=True)
File "...\diffusion-net\experiments\sampling_invariance\sampling_invariance.py", line 215, in test
errors = diffusion_net.geometry.geodesic_label_errors(verts_ref, faces_ref, pred_labels, labels, normalization='diameter', geodesic_cache_dir=geodesic_cache_dir)
File "../../src\diffusion_net\geometry.py", line 768, in geodesic_label_errors
dists = get_all_pairs_geodesic_distance(target_verts, target_faces, geodesic_cache_dir)
File "../../src\diffusion_net\geometry.py", line 866, in get_all_pairs_geodesic_distance
pool.close()
UnboundLocalError: local variable 'pool' referenced before assignment
I hope this information is useful for your further development! Thank you for your great work, I really admire and appreciate it ๐
Wow, thank you so much for this information, it is super helpful! I may refer other Windows users to this post, and we can also try to track down and fix some of these issues on Windows. I really appreciate it!
Always glad to help and contribute ๐