Want to Convert to Pytorch

[goldblum]

Hi! I want to run some tests on this model and adapt it with some techniques using Pytorch. I don't have the computational resources to retrain the model. Is there a way for me to convert this model into Pytorch, loading the parameters and all? Thanks for your help!

[ppwwyyxx]

You can load the pre-trained weights by np.load. The graph architecture is written in the code (nets.py and resnet_model.py). So you have enough information to convert the model to Pytorch.

As far as I'm aware, there is currently no "automatic" way to translate a tensorflow model to pytorch. ONNX may get you half-way there? (but I'm not familiar with it)

[a1600012888]

Hi, I have successfully converted the ResNet152 Baseline model to Pytorch. (I measured the accuracy under different attacks to verify it.) I can provide the code and the model.

[goldblum]

Awesome. I would appreciate that very much. This is the ResNet152 with adversarial training but no feature denoising?

[a1600012888]

Awesome. I would appreciate that very much. This is the ResNet152 with adversarial training but no feature denoising?

Yes. I only translated the ResNet152 part. I hope sharing the code with you helps for converting the feature denoising part. code and model

[goldblum]

Great. Thank you so much for your help.

On Fri, Apr 12, 2019 at 9:51 AM Tianyuan Zhang notifications@github.com wrote:

Awesome. I would appreciate that very much. This is the ResNet152 with adversarial training but no feature denoising?

Yes. I only translated the ResNet152 part. I hope sharing the code with you helps for converting the feature denoising part. code and model https://drive.google.com/open?id=1KQSe91znWWwaUPG1TSpqUyfyw0o0VgMV

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[anianruoss]

Hi @a1600012888, thank you for your script. I have been trying to reproduce the model accuracy but I can't get it to work.

I load the model with:

model = models.resnet152()
model.load_state_dict(
    torch.load('res152-adv-pytorch.model', map_location=lambda storage, loc: storage)
)
model.eval()

and ImageNet data as:

data_loader = torch.utils.data.DataLoader(
    datasets.ImageFolder(
            'ImageNet/validation',
            transform=transforms.Compose([
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor().
                transforms.Normalize(mean=[.5,.5,.5], std=[.5,.5,.5])
            ])
        ),
    batch_size=batch_size,
    shuffle=True
)

but I always get 0% accuracy. I made sure to convert the images from RGB to BGR but that doesn't work either. Do you mind sharing how you managed to reproduce the accuracy?

[a1600012888]

Hi, @anianruoss How about the BN statistics? The model I uploaded did not contains the running mean and running var of the BatchNorm layer.

[anianruoss]

@a1600012888 how did you perform this?

[anianruoss]

I tried to load the BN statistics like this:

    buffers = dict()                                                            

    for name, param in net.named_buffers():                                     
        try:                                                                    
            tf_param = torch_buffer_dic[name]                                   
        except KeyError:                                                        
            print(name, param.shape)                                            
            continue                                                            
        buffers[name] = torch.tensor(tf_param, dtype=param.dtype)               
        #print(name, 'buffer shape:{},  is_right:{}'.format(param.shape, param.shape == tf_param.shape))

    # check the number of weights.                                              
    print(len(torch_weight_dic), num_param)                                     
    net.load_state_dict(torch_weight_dic, strict=False)  # note that my code did not convert the buffer part. e.g.: running mean in BN

    model_dict = net.state_dict()                                               
    model_dict.update(buffers)                                                  
    net.load_state_dict(model_dict)    

but without success. Did you do something else to make it work?

[a1600012888]

@a1600012888 how did you perform this?

I let this model runs on the training set with learning rate being zero. (With training mode.) You can carry out a more sophisticated BN post processing layer by layer.

[anianruoss]

@a1600012888 I don't have the computational resources to run the model on the training set. Could you upload the post-processed model if you have already performed this step?

[luluvi]

Awesome. I would appreciate that very much. This is the ResNet152 with adversarial training but no feature denoising?

hello，I also want to convert to pytorch,Can you successfully add feature denoising on pytorch, can you share it with me? Grateful. thanks!

[luluvi]

Awesome. I would appreciate that very much. This is the ResNet152 with adversarial training but no feature denoising?

Yes. I only translated the ResNet152 part. I hope sharing the code with you helps for converting the feature denoising part. code and model

Can you share your code again,I cannot open your link,thank you very much

[Jack-lx-jiang]

@a1600012888 Could you share more details about the evaluation? I can only get 56% accuracy on clean examples and 0% accuracy on adversarial examples. This is my date transformation code:

transforms.Compose([
        transforms.Resize(256, PIL.Image.BICUBIC),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])

and I transpose the image from RGB to BGR by: images = images[:, [2, 1, 0], :, :]

About the bn statistics, I tried two ways:

1. load the weights of running mean and running variance directly from the weight file.
2. Run on the training set with train mode and without backpropagation.

Unfortunately, I cannot get more than 56% accuracy either way. I do not know whether my data processing or the weight conversion goes wrong? Any advice is welcome! Thanks!

[Equationliu]

I cannot get more than 56% accuracy either

I cannot get 60%+ accuracy either, have you solved this?