This is the code for GFN dropout project , currently it include codes for MLP and resnet for MNIST,CIFAR dataset and transformer for VQA task
This code is partially adapted from :https://github.com/szhang42/Contextual_dropout_release
TO run the expeirment go to GFNDropout/tasks/Scripts/ and run corresponding experiment
for example to run GFN dropout using MLP model on MNIST with "topdown" mask without BNN backbone
the two important settings are "--mask" which types of mask to use and "--BNN" whether to use BNN as backbone
python -u ../image_classification/main.py train
--model=MLP_GFN
--GFN_dropout True
--dropout_rate 0.5
--dataset=mnist
--lambas='[.0,.0,.0,.0]'
--optimizer=adam
--lr=0.001
--add_noisedata=False
--dptype False
--concretedp False
--fixdistrdp False
--ctype "Bernoulli"
--dropout_distribution 'bernoulli'
--mask "topdown"
--BNN False
--model_name "_MNIST_MLP_GFN"
--max_epoch 200 \
run GFN dropout using Resnet18 model on cifar with "topdown" mask without BNN backbone
python ../image_classification/main.py train
--model=ResNet_GFN
--GFN_dropout True
--dropout_rate 0.5
--dataset=cifar10
--lambas=.001
--optimizer=momentum
--lr=0.1
--schedule_milestone="[25, 40]"
--add_noisedata=False
--concretedp False
--dptype False
--fixdistrdp False
--ctype "Bernoulli"
--dropout_distribution 'bernoulli'
--model_name "_CIFAR_ResNet_GFN"
--mask "topdown"
--BNN False
--max_epoch 200 \
To test performance on augmented data :
python -u ../image_classification/main.py test
--model=MLP_GFN
--GFN_dropout True
--dropout_rate 0.2
--dataset=mnist
--lambas='[.0,.0,.0,.0]'
--optimizer=adam
--lr=0.001
--add_noisedata=False
--dptype False
--concretedp False
--fixdistrdp False
--ctype "Bernoulli"
--dropout_distribution 'bernoulli'
--mask "random"
--BNN False
--model_name "_MNIST_MLP_GFN"
--augment_test=True
--load_file="your model file location" \