cdvae
cdvae copied to clipboard
txie-93
InstantiationException: a leaf Variable that requires grad is being used in an in-place operation
Followed the faster conda installation instructions from the README on WSL2 (files and environment localized to the WSL2 storage) and got the same error as in https://github.com/txie-93/cdvae/issues/2#issuecomment-1153064071.
Exception has occurred: InstantiationException (note: full exception trace is shown but execution is paused at: _run_module_as_main)
Error in call to target 'cdvae.pl_modules.model.CDVAE':
InstantiationException("Error in call to target 'cdvae.pl_modules.gnn.DimeNetPlusPlusWrap':\nRuntimeError('a leaf Variable that requires grad is being used in an in-place operation.')\nfull_key: encoder")
full_key: model
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
return _target_(*args, **kwargs)
File "/home/sgbaird/cdvae/cdvae/pl_modules/gnn.py", line 327, in __init__
super(DimeNetPlusPlusWrap, self).__init__(
File "/home/sgbaird/cdvae/cdvae/pl_modules/gnn.py", line 223, in __init__
self.rbf = BesselBasisLayer(num_radial, cutoff, envelope_exponent)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/torch_geometric/nn/models/dimenet.py", line 59, in __init__
self.reset_parameters()
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/torch_geometric/nn/models/dimenet.py", line 62, in reset_parameters
torch.arange(1, self.freq.numel() + 1, out=self.freq).mul_(PI)
The above exception was the direct cause of the following exception:
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
return _target_(*args, **kwargs)
File "/home/sgbaird/cdvae/cdvae/pl_modules/model.py", line 140, in __init__
self.encoder = hydra.utils.instantiate(
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node
return _call_target(_target_, partial, args, kwargs, full_key)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target
raise InstantiationException(msg) from e
The above exception was the direct cause of the following exception:
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target
raise InstantiationException(msg) from e
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node
return _call_target(_target_, partial, args, kwargs, full_key)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate
return instantiate_node(
File "/home/sgbaird/cdvae/cdvae/run.py", line 94, in run
model: pl.LightningModule = hydra.utils.instantiate(
File "/home/sgbaird/cdvae/cdvae/run.py", line 166, in main
run(cfg)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/site-packages/hydra/main.py", line 90, in decorated_main
_run_hydra(
File "/home/sgbaird/cdvae/cdvae/run.py", line 170, in <module>
main()
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/sgbaird/miniconda3/envs/cdvae/lib/python3.9/runpy.py", line 197, in _run_module_as_main (Current frame)
return _run_code(code, main_globals, None,
The following Colab notebooks all act as reproducers of the same error:
"Error in call to target 'cdvae.pl_modules.gnn.DimeNetPlusPlusWrap':\nRuntimeError('a leaf Variable that requires grad is being used in an in-place operation.')\nfull_key: encoder"
I am having the same problem, is there anyone who managed to find a solution?
I heard back from Peder Lyngby over email in response to my query:
... how did you get CDVAE running? Is there a specific commit that you used? Are you still able to run it via a fresh clone of CDVAE? Or did you have to modify the codebase / resolve errors to get it working? ...
Response:
I am using commit: 65c98702416a60c35a809943a325bbe75de6bc0f (from Dec 14, 2021)
As far as I remember I did not make any significant changes to the code. I had to change a few package names, as it was fixed in the commit 9b70abe7ad8bb90c6b45daf0c8428b5034f503cd (from Dec 18, 2021)
I am running CDVAE on a HPC server running CentOS 7.9 linux and using a RTX 3090 GPU.
Here's the link to the preprint: https://arxiv.org/abs/2206.12159
So trying out with the commit mentioned above (65c98702416a60c35a809943a325bbe75de6bc0f) might shed some light on the issue.
@txie-93
@SillyUglyPig @sgbaird Any luck resolving this issue? It persisted for me even when I try to use the code from commit 65c9870. @txie-93
@sgbaird @SillyUglyPig The error stopped occurring for me if I upgraded to pyg-nightly. However, I haven't been able to run to code for other reasons. But I think they are unrelated to pyg.
@sgbaird Hi, I solved the problem. The error message
InstantiationException("Error in call to target 'cdvae.pl_modules.gnn.DimeNetPlusPlusWrap':\nRuntimeError('a leaf Variable that requires grad is being used in an in-place operation.')\nfull_key: encoder")
is due to the pyg version.
You can find this problem at #https://github.com/pyg-team/pytorch_geometric/pull/4424 and solved by #https://github.com/pyg-team/pytorch_geometric/pull/4424/files/67a1babccfd8509df7acc5dae326b9728b697c69#diff-902b09f3ae69a1b7cd16a3c3e592cce64922bb3b3fe25a4b95d741b5e6830b3b
So I think you can fix the error by downloading fixed-version of pyg, but I simply modified the dimenet code of pytorch geometric of version 2.0.4.
I modified the BesselBasisLayer in torch_geometric.nn.models.dimenet.py as follows:
class BesselBasisLayer(torch.nn.Module):
def __init__(self, num_radial: int, cutoff: float = 5.0,
envelope_exponent: int = 5):
super().__init__()
self.cutoff = cutoff
self.envelope = Envelope(envelope_exponent)
self.freq = torch.nn.Parameter(torch.empty(num_radial))
self.reset_parameters()
def reset_parameters(self):
with torch.no_grad():
torch.arange(1, self.freq.numel() + 1, out=self.freq).mul_(PI)
self.freq.requires_grad_()
def forward(self, dist):
dist = dist.unsqueeze(-1) / self.cutoff
return self.envelope(dist) * (self.freq * dist).sin()
# class BesselBasisLayer(torch.nn.Module):
# def __init__(self, num_radial, cutoff=5.0, envelope_exponent=5):
# super().__init__()
# self.cutoff = cutoff
# self.envelope = Envelope(envelope_exponent)
# self.freq = torch.nn.Parameter(torch.Tensor(num_radial))
# self.reset_parameters()
# def reset_parameters(self):
# torch.arange(1, self.freq.numel() + 1, out=self.freq).mul_(PI)
# def forward(self, dist):
# dist = dist.unsqueeze(-1) / self.cutoff
# return self.envelope(dist) * (self.freq * dist).sin()
And I also found another import version conflicts of wandb, pytorch-lightning and torchmetrics. I used latest version of wandb (0.15.8) and downgraded pytorch-lightning=1.3.8 and torchmetrics=0.7.3.
Package Version Editable project location
----------------------------- ---------------- -----------------------------
absl-py 1.4.0
aiohttp 3.8.5
aiosignal 1.3.1
altair 5.0.1
antlr4-python3-runtime 4.8
anyio 3.7.1
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
arrow 1.2.3
ase 3.22.1
astor 0.8.1
astroid 2.15.6
asttokens 2.2.1
async-lru 2.0.4
async-timeout 4.0.2
attrs 23.1.0
autopep8 2.0.2
Babel 2.12.1
backcall 0.2.0
backports.functools-lru-cache 1.6.5
backports.zoneinfo 0.2.1
base58 2.1.1
beautifulsoup4 4.12.2
bleach 6.0.0
blinker 1.6.2
brotlipy 0.7.0
cached-property 1.5.2
cachetools 5.3.1
cdvae 0.0.1 /home/holywater2/24ICLR/cdvae
certifi 2023.7.22
cffi 1.15.1
charset-normalizer 3.2.0
click 8.1.6
cmake 3.27.0
colorama 0.4.6
comm 0.1.4
configparser 6.0.0
contourpy 1.1.0
cryptography 41.0.2
cycler 0.11.0
debugpy 1.6.8
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.7
docker-pycreds 0.4.0
emmet-core 0.63.1
entrypoints 0.4
exceptiongroup 1.1.2
executing 1.2.0
fastjsonschema 2.18.0
filelock 3.12.2
Flask 2.3.2
flit_core 3.9.0
fonttools 4.42.0
fqdn 1.5.1
frozenlist 1.4.0
fsspec 2023.6.0
future 0.18.3
gitdb 4.0.10
GitPython 3.1.32
google-auth 2.22.0
google-auth-oauthlib 1.0.0
googledrivedownloader 0.4
grpcio 1.56.2
higher 0.2.1
hydra-core 1.1.0
hydra-joblib-launcher 1.1.5
idna 3.4
importlib-metadata 6.8.0
importlib-resources 6.0.0
iniconfig 2.0.0
ipykernel 6.25.0
ipython 8.12.0
ipywidgets 8.1.0
isodate 0.6.1
isoduration 20.11.0
isort 5.12.0
itsdangerous 2.1.2
jedi 0.19.0
Jinja2 3.1.2
joblib 1.3.1
json5 0.9.14
jsonpointer 2.0
jsonschema 4.18.6
jsonschema-specifications 2023.7.1
jupyter_client 8.3.0
jupyter_core 4.12.0
jupyter-events 0.7.0
jupyter-lsp 2.2.0
jupyter_server 2.7.0
jupyter_server_terminals 0.4.4
jupyterlab 4.0.3
jupyterlab-pygments 0.2.2
jupyterlab_server 2.24.0
jupyterlab-widgets 3.0.8
kiwisolver 1.4.4
latexcodec 2.0.1
lazy-object-proxy 1.9.0
lightning-utilities 0.9.0
lit 16.0.6
Markdown 3.4.4
MarkupSafe 2.1.3
matplotlib 3.7.1
matplotlib-inline 0.1.6
mccabe 0.7.0
mistune 3.0.0
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
monty 2023.5.8
mp-api 0.33.3
mpmath 1.3.0
msgpack 1.0.5
multidict 6.0.4
multiprocess 0.70.15
munkres 1.1.4
nbclient 0.8.0
nbconvert 7.7.3
nbformat 5.9.2
nest-asyncio 1.5.6
networkx 3.1
notebook_shim 0.2.3
numpy 1.24.3
nvidia-cublas-cu11 11.10.3.66
nvidia-cuda-cupti-cu11 11.7.101
nvidia-cuda-nvrtc-cu11 11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11 8.5.0.96
nvidia-cufft-cu11 10.9.0.58
nvidia-curand-cu11 10.2.10.91
nvidia-cusolver-cu11 11.4.0.1
nvidia-cusparse-cu11 11.7.4.91
nvidia-nccl-cu11 2.14.3
nvidia-nvtx-cu11 11.7.91
oauthlib 3.2.2
omegaconf 2.1.2
overrides 7.3.1
p-tqdm 1.3.3
packaging 23.1
palettable 3.3.3
pandas 2.0.3
pandocfilters 1.5.0
parso 0.8.3
pathos 0.3.1
pathtools 0.1.2
patsy 0.5.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 10.0.0
pip 23.2.1
pkgutil_resolve_name 1.3.10
platformdirs 3.10.0
plotly 5.15.0
pluggy 1.2.0
ply 3.11
pooch 1.7.0
pox 0.3.3
ppft 1.7.6.7
prometheus-client 0.17.1
promise 2.3
prompt-toolkit 3.0.39
protobuf 4.23.3
psutil 5.9.5
ptyprocess 0.7.0
pure-eval 0.2.2
pyarrow 12.0.1
pyasn1 0.4.8
pyasn1-modules 0.2.7
pybtex 0.24.0
pycodestyle 2.11.0
pycparser 2.21
pydantic 1.10.12
pydeck 0.8.1b0
pyDeprecate 0.3.0
pyg-lib 0.2.0+pt20cu117
Pygments 2.15.1
PyJWT 2.8.0
pylint 2.17.5
pymatgen 2023.7.20
pyOpenSSL 23.2.0
pyparsing 3.1.1
PyQt5 5.15.9
PyQt5-sip 12.12.2
PySocks 1.7.1
pytest 7.4.0
python-dateutil 2.8.2
python-dotenv 1.0.0
python-json-logger 2.0.7
python-louvain 0.16
pytorch-lightning 1.3.8
pytz 2023.3
pyu2f 0.1.5
PyYAML 5.4.1
pyzmq 25.1.0
rdflib 7.0.0
referencing 0.30.0
requests 2.31.0
requests-oauthlib 1.3.1
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rpds-py 0.9.2
rsa 4.9
ruamel.yaml 0.17.32
ruamel.yaml.clib 0.2.7
scikit-learn 1.3.0
scipy 1.10.1
seaborn 0.12.2
Send2Trash 1.8.2
sentry-sdk 1.29.2
setproctitle 1.3.2
setuptools 68.0.0
shortuuid 1.0.11
sip 6.7.11
six 1.16.0
SMACT 2.2.1
smmap 5.0.0
sniffio 1.3.0
soupsieve 2.3.2.post1
spglib 2.0.2
stack-data 0.6.2
statsmodels 0.14.0
streamlit 0.79.0
subprocess32 3.5.4
sympy 1.12
tabulate 0.9.0
tenacity 8.2.2
tensorboard 2.13.0
tensorboard-data-server 0.7.0
terminado 0.17.1
threadpoolctl 3.2.0
tinycss2 1.2.1
toml 0.10.2
tomli 2.0.1
tomlkit 0.12.1
toolz 0.12.0
torch 2.0.1
torch-cluster 1.6.1+pt20cu117
torch-geometric 2.0.4
torch-scatter 2.1.1+pt20cu117
torch-sparse 0.6.17+pt20cu117
torch-spline-conv 1.2.2+pt20cu117
torchdiffeq 0.0.1
torchmetrics 0.7.3
tornado 6.3.2
tqdm 4.65.0
traitlets 5.9.0
triton 2.0.0
typing_extensions 4.7.1
typing-utils 0.1.0
tzdata 2023.3
tzlocal 5.0.1
uncertainties 3.1.7
unicodedata2 15.0.0
uri-template 1.3.0
urllib3 1.26.15
validators 0.20.0
wandb 0.15.8
watchdog 3.0.0
wcwidth 0.2.6
webcolors 1.13
webencodings 0.5.1
websocket-client 1.6.1
Werkzeug 2.3.6
wheel 0.41.0
widgetsnbextension 4.0.8
wrapt 1.15.0
yarl 1.9.2
zipp 3.16.2
@holywater2 wow! Nice work. I might loop back to this and try to prepare a Colab notebook.
One thing that might also help is installing using the env.ci-cu11.yml from https://github.com/knc6/cdvae/tree/pip. I was able to get this code running after installing using packages one by one following the env.ci-cu11.yml. Additionally, I reccommend installing torch-cluster torch-scatter torch-sparse torch-spline-conv all from source. Here is my pip list.
Package Version Editable project location
------------------------- ----------- -------------------------------------
absl-py 1.4.0
aiohttp 3.8.5
aiosignal 1.3.1
antlr4-python3-runtime 4.8
appdirs 1.4.4
ase 3.22.1
async-timeout 4.0.2
attrs 23.1.0
cachetools 5.3.1
cdvae 0.0.1 /home/*/Desktop/Projects/cdvae
certifi 2023.7.22
cffi 1.15.1
charset-normalizer 2.0.12
click 8.1.6
configparser 6.0.0
contourpy 1.1.0
cycler 0.11.0
dill 0.3.7
dnspython 2.4.1
docker-pycreds 0.4.0
filelock 3.9.0
fonttools 4.41.1
frozenlist 1.4.0
fsspec 2023.6.0
future 0.18.3
gitdb 4.0.10
GitPython 3.1.32
gmpy2 2.1.2
google-auth 2.22.0
google-auth-oauthlib 1.0.0
googledrivedownloader 0.4
grpcio 1.56.2
h5py 3.9.0
hydra-core 1.1.0
hydra-joblib-launcher 1.1.5
idna 3.4
imageio 2.31.1
importlib-metadata 6.8.0
importlib-resources 6.0.0
isodate 0.6.1
Jinja2 3.1.2
joblib 1.1.0
jsonschema 4.18.4
jsonschema-specifications 2023.7.1
kiwisolver 1.4.4
latexcodec 2.0.1
lazy_loader 0.3
lightning-utilities 0.9.0
llvmlite 0.40.1
Markdown 3.4.4
MarkupSafe 2.1.1
matminer 0.7.3
matplotlib 3.7.2
mkl-fft 1.3.6
mkl-random 1.2.2
mkl-service 2.4.0
monty 2023.5.8
mpmath 1.2.1
multidict 6.0.4
multiprocess 0.70.15
networkx 2.8.4
numba 0.57.1
numpy 1.21.5
oauthlib 3.2.2
omegaconf 2.1.2
p-tqdm 1.3.3
packaging 21.3
palettable 3.3.3
pandas 2.0.3
pathos 0.3.1
pathtools 0.1.2
Pillow 10.0.0
Pint 0.21.1
pip 23.2.1
pkgutil_resolve_name 1.3.10
plotly 5.15.0
plyfile 1.0.1
pox 0.3.3
ppft 1.7.6.7
promise 2.3
protobuf 3.20.1
psutil 5.9.0
pyasn1 0.5.0
pyasn1-modules 0.3.0
pybtex 0.24.0
pycparser 2.21
pyDeprecate 0.3.0
pymatgen 2022.4.26
pymongo 4.4.1
pyparsing 3.0.9
python-dateutil 2.8.2
python-dotenv 0.20.0
python-louvain 0.16
pytorch-lightning 1.3.8
pytz 2023.3
PyWavelets 1.4.1
PyYAML 5.4.1
rdflib 6.3.2
referencing 0.30.0
requests 2.31.0
requests-oauthlib 1.3.1
rpds-py 0.9.2
rsa 4.9
ruamel.yaml 0.17.32
ruamel.yaml.clib 0.2.7
scikit-learn 1.1.0
scipy 1.7.3
sentry-sdk 1.28.1
setproctitle 1.3.2
setuptools 59.5.0
shortuuid 1.0.11
six 1.16.0
SMACT 2.2.1
smmap 5.0.0
spglib 2.0.2
subprocess32 3.5.4
sympy 1.11.1
tabulate 0.9.0
tenacity 8.2.2
tensorboard 2.13.0
tensorboard-data-server 0.7.1
threadpoolctl 3.1.0
tifffile 2023.7.10
torch 1.9.0+cu111
torch-cluster 1.6.0
torch-geometric 1.7.2
torch-scatter 2.0.9
torch-sparse 0.6.12
torch-spline-conv 1.2.1
torchmetrics 0.7.0
tqdm 4.65.0
typing_extensions 4.7.1
tzdata 2023.3
uncertainties 3.1.7
urllib3 1.26.11
wandb 0.10.33
watchdog 3.0.0
Werkzeug 2.3.6
wheel 0.38.4
yarl 1.9.2
zipp 3.16.2
I am running on an NVIDIA A4500 with Ubuntu 22.04 and Cuda 11.7
@AseemGill Hi ! thank you for your guidance. Can I just replace the yml file and use env.ci-cu11.yml? How have you installed packages one by one? Could you explain? I am new so asking.
Should I have to just pip all the packages ?
- pip:
- altair==4.2.0
- antlr4-python3-runtime==4.8
- astor==0.8.1
- base58==2.1.1
- bleach==5.0.0
- configparser==5.2.0
- debugpy==1.6.0
- dill==0.3.4
- docker-pycreds==0.4.0
- fastjsonschema==2.15.3
- gitdb==4.0.9
- gitpython==3.1.27
- googledrivedownloader==0.4
- higher==0.2.1
- hydra-core==1.1.0
- hydra-joblib-launcher==1.1.5
- importlib-resources==5.7.1
- iniconfig==1.1.1
- ipykernel==6.13.0
- ipywidgets==7.7.0
- isodate==0.6.1
- jsonschema==4.5.1
- jupyter-client==7.3.1
- jupyterlab-pygments==0.2.2
- jupyterlab-widgets==1.1.0
- matplotlib-inline==0.1.3
- multiprocess==0.70.12.2
- nbclient==0.6.3
- nbconvert==6.5.0
- nbformat==5.4.0
- networkx==2.8
- omegaconf==2.1.2
- p-tqdm==1.3.3
- pathos==0.2.8
- pathtools==0.1.2
- pluggy==1.0.0
- pox==0.3.0
- ppft==1.6.6.4
- prometheus-client==0.14.1
- promise==2.3
- prompt-toolkit==3.0.29
- psutil==5.9.0
- py==1.11.0
- pyarrow==8.0.0
- pydeck==0.7.1
- pygments==2.12.0
- pymatgen==2022.4.26
- pyrsistent==0.18.1
- pytest==7.1.2
- python-dotenv==0.20.0
- python-louvain==0.16
- rdflib==6.1.1
- scikit-learn==1.1.0
- sentry-sdk==1.5.12
- setuptools==59.5.0
- shortuuid==1.0.9
- smact==2.2.1
- smmap==5.0.0
- soupsieve==2.3.2.post1
- streamlit==0.79.0
- subprocess32==3.5.4
- terminado==0.13.3
- tinycss2==1.1.1
- tomli==2.0.1
- toolz==0.11.2
- torch-cluster==1.6.0
- torch-geometric==1.7.2
- torch-spline-conv==1.2.1
- torchdiffeq==0.0.1
- torchmetrics==0.6.0
- traitlets==5.2.0
- validators==0.19.0
- wandb==0.10.33
- watchdog==2.1.7
- webencodings==0.5.1
- widgetsnbextension==3.6.0
Hi, i think i solve the problem.
i find the error start in the cdvae/cdvae/pl_modules/gnn.py. and it can't import swish
so i change the from torch_geometric.nn.acts import swish to
try: from torch_geometric.nn.acts import swish except ImportError: from torch_geometric.nn.resolver import swish
