KITS results mismatch with paper about flamby HOT 13 CLOSED

Hi @jeandut,
I confirm that's the exact command I ran.
I executed already all the other benchmarks (but LIDC-IDRI) and everything is fine. This is the problematic one

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We will be looking into it. Thanks for reporting.

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@luciainnocenti Thanks for reporting.

@jeandut Sure. Let me take a look and get back to you.

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@luciainnocenti I am able to reproduce the results on my end for the pooled training method (seed 42) by running the command Jean mentioned above. Could you confirm if other methods such as FedAvg, and FedProx are working on your end for the KiTS19 dataset?

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Hi @jeandut, I tried to follow your tip on the preprocessing.
I uninstalled and re-installed everything, downloaded the data again from scratch, and now the results are comparable with the paper.
So not sure if the pre-processing wasn't complete or what was the problem. I will check the libraries version just to make sure everything matches

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Hi @luciainnocenti,
Thank you for your interest, could you give the exact command that you ran ?
The results should be obtained by running:

cd flamby/benchmarks
python fed_benchmark.py --seed 42 -cfp ../config_kits19.json

@ErumMushtaq can you take a look ?

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I did not try that yet because the training is quite long and expensive, so I checked only the pooled version to make sureit worked.
I will try it now.

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I am re-downloading and re-processing the dataset on my end as well to confirm if I can reproduce what @ErumMushtaq did with the current version of the repo on a fresh env.
I'll try to add a hash of the dataset files so we can double-check your data @luciainnocenti.
@luciainnocenti the pooled version should be alright as well I don't see any reason why it wouldn't be.
If it's not the data or preprocessing I don't know what it can be frankly.

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Let's check the data first and then we'll make sure every single version of packages match including torch.

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@luciainnocenti have you been able to make it work ? I don't have much bandwidth currently but this on my TODO in the next 3 months.

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Hello @luciainnocenti I just reproduced the results with the exact command I sent you for pooled training:

python fed_benchmark.py --seed 42 -cfp ../config_kits19.json

As a reference the old results in the repository are quite close (although there is some variability). We changed some things in the code to fix reproducibility in some cases and the versions of the deep learning packages were updated to the new ones (notably torch==2.0.1 which did not exist back then) which might have slightly affected the results.

Are you sure you followed all the exact steps indicated in here including the preprocessing ?
I modified the instructions so that some steps are easier to understand.
Here is the result of my pip freeze if it helps:

absl-py==1.4.0
alabaster==0.7.13
albumentations==1.3.1
astor==0.8.1
autograd==1.6.2
autograd-gamma==0.5.0
Babel==2.12.1
batchgenerators==0.25
cachetools==5.3.1
certifi==2023.5.7
cfgv==3.3.1
charset-normalizer==3.1.0
click==8.1.3
cloudpickle==2.2.1
cmake==3.26.4
contourpy==1.1.0
cycler==0.11.0
dask==2023.6.1
dicom-numpy==0.6.5
dicom2nifti==2.4.8
distlib==0.3.6
docutils==0.17.1
efficientnet-pytorch==0.7.1
exceptiongroup==1.1.2
filelock==3.12.2
-e git+https://github.com/owkin/FLamby.git@f169811ee2832329198a78726f4faa6d6f00d4c3#egg=flamby
fonttools==4.40.0
formulaic==0.6.3
fsspec==2023.6.0
future==0.18.3
google-api-core==2.11.1
google-api-python-client==2.91.0
google-auth==2.21.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==1.0.0
googleapis-common-protos==1.59.1
grpcio==1.56.0
histolab==0.6.0
httplib2==0.22.0
identify==2.5.24
idna==3.4
imageio==2.31.1
imagesize==1.4.1
importlib-metadata==6.7.0
importlib-resources==5.12.0
iniconfig==2.0.0
interface-meta==1.3.0
Jinja2==3.1.2
joblib==1.3.1
kiwisolver==1.4.4
large-image==1.23.0
large-image-source-openslide==1.23.0
lifelines==0.27.7
linecache2==1.0.0
lit==16.0.6
llvmlite==0.40.1
locket==1.0.0
Markdown==3.4.3
MarkupSafe==2.1.3
matplotlib==3.7.1
MedPy==0.4.0
monai==1.2.0
mpmath==1.3.0
networkx==3.1
nibabel==3.2.2
nnunet==1.7.0
nodeenv==1.8.0
numba==0.57.1
numpy==1.23.0
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
opacus==1.4.0
opencv-python-headless==4.8.0.74
openslide-python==1.2.0
opt-einsum==3.3.0
packaging==23.1
palettable==3.3.3
pandas==2.0.3
partd==1.4.0
Pillow==9.5.0
platformdirs==3.8.0
pluggy==1.2.0
pre-commit==3.3.3
protobuf==4.23.3
pyasn1==0.5.0
pyasn1-modules==0.3.0
pydicom==2.4.1
Pygments==2.15.1
pynndescent==0.5.10
pyparsing==3.1.0
pytest==7.4.0
python-dateutil==2.8.2
python-gdcm==3.0.22
pytz==2023.3
PyWavelets==1.4.1
PyYAML==6.0
qudida==0.0.4
requests==2.31.0
requests-oauthlib==1.3.1
rsa==4.9
scikit-image==0.19.3
scikit-learn==1.3.0
scipy==1.8.1
seaborn==0.12.2
SimpleITK==2.2.1
six==1.16.0
sklearn==0.0.post5
snowballstemmer==2.2.0
Sphinx==4.5.0
sphinx-rtd-theme==1.0.0
sphinxcontrib-applehelp==1.0.4
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
sympy==1.12
tensorboard==2.13.0
tensorboard-data-server==0.7.1
threadpoolctl==3.1.0
tifffile==2023.4.12
tifftools==1.3.12
tomli==2.0.1
toolz==0.12.0
torch==2.0.1
torchvision==0.15.2
tqdm==4.65.0
traceback2==1.4.0
triton==2.0.0
typing_extensions==4.7.1
tzdata==2023.3
umap-learn==0.5.3
unittest2==1.1.0
uritemplate==4.1.1
urllib3==1.26.16
virtualenv==20.23.1
Werkzeug==2.3.6
wget==3.2
wrapt==1.15.0
zipp==3.15.0

Can you check the length of the FedKits19 dataset when it is instantiated ? Can you add a Tensorboard to monitor the loss it seems there is something very wrong somewhere that must explain this discrepancy.

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As a side note, @luciainnocenti, we will probably very soon update the benchmarking guidelines with new scripts which we hope will be more easy to reproduce and with a nicer interface for results (and training curves!).
In the mean time make sure your environment is matching and that you are using the GPU (it wasn't tested on CPUs only).

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Super cool !
Let me close this issue then !
Do not hesitate if you run into other weird problems !

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