How to get pixel-level embeddings

[loris2222]

Hi, I am trying to use your model for research purposes on Explainable AI.
After struggling for more than I'd like to admit I finally managed to get it up and working, however, I can't find an easy way to get the pixel-level embeddings from your framework, since the interfaces are quite convoluted.

Right now I've been able to do so with evaluator._modules['module'].net.get_image_features(image) starting from your notebook. I had to write get_image_features as a modified version of forward that ends at the image features. As such, I don't think this is the best way.

Do you have any suggestion on how to proceed? Maybe some general instructions on how to try to do so?

Thank you in advance!

[Boyi Li]

Hi @loris2222 ,

Thanks for your interest in LSeg!

Yea, LSeg was built one year ago, so you might need to install older version tools. I guess the fast solution is to revise the code.

Hope this helps!

Best,
Boyi

[Yunhan Yang]

Hi @loris2222,
Have you found the way to get pixel-level embeddings in newer version tools?

[loris2222]

Hi @yhyang-myron, yes, I was able to get pixel-level embeddings but the solution is quite hacky and I am not ready to share the full code. I am unsure about what you mean with 'newer version tools' though.

By the way, to get it working, I had to add a function in lseg_net.py that returns the model output after the embeddings. This, however, seems to only be working for batch size = 1 since it must be run through the evaluator. Anyway, here is the code for that function:

def get_image_features(self, x):
	if self.channels_last == True:
		x.contiguous(memory_format=torch.channels_last)
	layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)

	layer_1_rn = self.scratch.layer1_rn(layer_1)
	layer_2_rn = self.scratch.layer2_rn(layer_2)
	layer_3_rn = self.scratch.layer3_rn(layer_3)
	layer_4_rn = self.scratch.layer4_rn(layer_4)

	path_4 = self.scratch.refinenet4(layer_4_rn)
	path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
	path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
	path_1 = self.scratch.refinenet1(path_2, layer_1_rn)

	image_features = self.scratch.head1(path_1)
	imshape = image_features.shape
	image_features = image_features.permute(0, 2, 3, 1).reshape(-1, self.out_c)

	image_features = image_features.view(imshape[0], imshape[2], imshape[3], -1)
	image_features = torch.nn.functional.normalize(image_features, p=2.0, dim=-1) 

	return image_features

[Yunhan Yang]

@loris2222
Thank you!