britney-code / tf_to_pytorch_model

Convert tensorflow model to pytorch model via [MMdnn](https://github.com/microsoft/MMdnn) for adversarial attacks.

tf_to_torch_model

In this repo, we convert some common Tensorflow models used in adversarial attacks to PyTorch models and provide the resultant models. Since these models are converted from their Tensorflow version, the inputs need the same normalization, i.e., [-1,1]. We have already done this, so you can use it directly.

model = nn.Sequential(
    # Images for inception classifier are normalized to be in [-1, 1] interval.
    Normalize(mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5]), 
    net.KitModel(model_path).eval().cuda())

logit = model(input)

We also provide the PyTorch code for you to implement attacks on our converted models, e.g., I-FGSM (run the following command):

python torch_attack.py

File Description

dataset: Test images.

nets: Original tensorflow models.

nets_weight: Put the original Tensorflow network weight file into this directory.

torch_nets: Converted torch model.

torch_nets_weight: Put the converted Pytorch network weight file into this directory. (You can find them in Releases)

tf_attack.py: Sample attack method with tensorflow.

torch_attack.py: Sample attack method with PyTorch.

Model Accuracy

The following table shows the source of the converted model and the accuracy of the model on the 1000 test pictures (selected from Imagenet) given.

Converted model	Model source	torch Accuracy(%)	tf Accuracy(%)	input size
tf2torch_inception_v3	inception_v3_2016_08_28	96.20	96.20	299*299
tf2torch_inception_v4	inception_v4_2016_09_09	97.40	97.40	299*299
tf2torch_resnet_v2_50	resnet_v2_50_2017_04_14	94.90	94.90	299*299
tf2torch_resnet_v2_101	resnet_v2_101_2017_04_14	96.30	96.30	299*299
tf2torch_resnet_v2_152	resnet_v2_152_2017_04_14	95.80	95.80	299*299
tf2torch_inc_res_v2	inception_resnet_v2_2016_08_30	99.80	99.80	299*299
tf2torch_adv_inception_v3	adv_inception_v3_2017_08_18	94.90	94.90	299*299
tf2torch_ens3_adv_inc_v3	ens3_adv_inception_v3_2017_08_18	93.70	93.70	299*299
tf2torch_ens4_adv_inc_v3	ens4_adv_inception_v3_2017_08_18	91.60	91.60	299*299
tf2torch_ens_adv_inc_res_v2	ens_adv_inception_resnet_v2_2017_08_18	97.60	97.60	299*299

Implementation of sample attack

This table shows our result / paper result ("*" indicates white-box attack). The paper result is from Patch-wise Attack for Fooling Deep Neural Network, and we can see that we have obtained similar results with the converted model. The specific parameter settings can be found in the paper.

attack method	inc_v3*	inc_v4	resnet_v2_152	inc_res_v2	ens3_adv_inc_v3	ens4_adv_inc_v3	ens_adv_inc_res_v2
FGSM	81.0/80.9	37.4/38.0	33.0/33.1	33.9/33.1	16.9/16.8	15.7/15.8	8.2/8.3
I-FGSM	100.0/100.0	30.1/29.6	19.4/19.4	21.4/20.3	12.0/11.7	12.4/12.1	5.5/5.5
MI-FGSM	100.0/100.0	55.1/54.1	42.8/43.5	51.7/50.9	22.2/21.9	21.6/21.1	11.2/10.5
DI-FGSM	99.7/99.8	55.3/54.2	33.4/32.1	43.5/43.6	15.9/15.0	16.4/16.2	8.6/7.1
TI-FGSM	/	/	/	/	31.2/30.8	31.1/30.6	22.9/22.7
PI-FGSM	100.0/100.0	57.5/58.6	47.6/45.0	52.2/51.3	38.4/39.3	39.0/39.5	28.0/28.8

Note !

1. If you want to use aux_logits, using aux_logits=True to create the model:

model = nn.Sequential(
    # Images for inception classifier are normalized to be in [-1, 1] interval.
    Normalize(mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5]), 
    net.KitModel(model_path, aux_logits=True).eval().cuda())
    
logits, aux_logits = model(input)

2. Models with aux_logits:

   • tf2torch_inception_v3,
   • tf2torch_inception_v4,
   • tf2torch_inc_res_v2,
   • tf2torch_adv_inception_v3,
   • tf2torch_ens3_adv_inc_v3,
   • tf2torch_ens4_adv_inc_v3.