use custom model?

[dvschultz]

Hi, I see its possible to use models from the modelzoo, is it possible to use a custom trained model? Ay documentation or direction would be appreciated.

[humzaiqbal]

Yeah, you can. Notice in the ModelZoo Colab Notebook they have the following code

from torchvision.models import resnet50

resnet50_model = resnet50(pretrained=True).to(device).eval()
_ = render.render_vis(resnet50_model, "layer4_1_conv1:121", show_inline=True)

You can treat out of the box models the exact same way as modelzoo ones

[mhongg]

I am trying to visualize a model that receives 1 channel input, but it shows

AssertionError: There are no saved feature maps. Make sure to put the model in eval mode, like so: model.to(device).eval(). See README for example.

It works fine if the model receives 3 channels input. How can I modify the code so that it can be used on a custom model that receives 1 channel input?

[greentfrapp]

I'll have an update over the weekend!

[mhongg]

Greatly appreciate your efforts!

[greentfrapp]

Hi @MHong1001, thanks for your interest in Lucent!

If your model only receives 1 channel input, you will need to disable preprocess and explicitly set transforms=[] (or your desired transformations. You should also initialize your own param_f input with channels=1.

Here's a code snippet that should help. The model here is simply a conv layer that takes a 1 channel input.

model = torch.nn.Sequential(torch.nn.Conv2d(1, 10, (3, 3))).to(device).eval()
param_f = lambda: param.image(128, channels=1)
# Alternatively, use pixel_image: param_f = lambda: param.pixel_image(shape=(1, 1, 128, 128))
_ = render.render_vis(model, "0:0", param_f, transforms=[], preprocess=False, show_inline=True)

[miratshah27]

Please help out with this....

[HTalNachmani]

You certainly don't need the help anymore, but I'll leave this here in case that others get into the same problem. I was trying to run Lucent on a custom model, and I kept getting the same error "AssertionError: There are no saved feature maps". What solved it for me was to run an inference on the model once after initializing it and loading in the weights. After that Lucent worked normally! :)

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