Repository for the semantic WMI loss

Nick Hoernle

Last update: Sep 15, 2022

Related tags

Deep Learning semantic_loss

Overview

Installation:

pip install -e .

Installing DL2:

First clone DL2 in a separate directory and install it using the following commands:

git clone https://github.com/eth-sri/dl2
cd dl2
pip install -r requirements.txt

If you are using a virtual environment then make sure to install DL2 in that environment. Now DL2 can be imported as a python libary. To achieve this just extend the PYTHONPATH variable to also point to the DL2 directory:

export PYTHONPATH="${PYTHONPATH}:{path_to_dl2}"

Execution:

Run CIFAR10 experiment:

run_image_experiments.py cifar10 --layers=10 --widen_factor=1 run

Run CIFAR100 experiment:

run_image_experiments.py cifar100 --layers=10 --widen_factor=1 run

Generate experiment conditions:

cd scripts
python generate_experiments.py

Help functions:

run_image_experiments.py -- --help
run_image_experiments.py cifar10 -- --help
run_image_experiments.py cifar100 -- --help

Acknowledgements

densenet-pytorch
Wide Residual Networks (WideResNets) in PyTorch
Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko and Nikos Komodakis.

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Comments

Reproduce the results for CIFAR-100
Dear authors,

I tried to run the CIFAR-100 experiment with the provided command run_image_experiments.py cifar100 --layers=10 --widen_factor=1 run but the accuracy is as follows. (I tried this command again but the accuracy is even lower.)

... Train [200/200]: Loss 1.046 Acc 69.507 AccSC 79.418 Test [200/200]: Loss 1.646 Acc 58.16 AccSC 70.78 ======== TESTING ON UNSEEN DATA ========= ======== USE FINAL MODEL ========= Test [1/200]: Loss 1.668 Acc 58.43 AccSC 71.07 Test [200/200]: Loss 1.668 Acc 58.43 AccSC 71.07 Final Model accuracy ====> 2.739713430404663 ======== USE BEST MODEL ========= Test [1/200]: Loss 1.67 Acc 58.42 AccSC 71.14 Test [200/200]: Loss 1.67 Acc 58.42 AccSC 71.14 Final Model accuracy ====> 1.0581140518188477

Could you help me with the following questions? Thank you!

How can I reproduce the results in the paper? For example, I cannot get the 74.4 ± (0.2) class accuracy and 85.4 ± (0.3) super-class accuracy, and it's not clear how to print out the constraint satisfaction accuracy.

How can I interpret the Final Model accuracy ====> 1.0581140518188477 in the above result?

How can I run the baseline for comparison?

The notebook https://github.com/NickHoernle/semantic_loss/blob/master/notebooks/cifar100_experiment.ipynb only prints out some pre-defined numbers. When would this notebook be updated?

Your help and time are greatly appreciated!

Best, Zhun
opened by zhunyoung 0

Repository for the semantic WMI loss

Related tags

Overview

Installation:

Installing DL2:

Execution:

Generate experiment conditions:

Help functions:

Acknowledgements

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Reproduce the results for CIFAR-100

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