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seg-grad-cam's Introduction

SEG-GRAD-CAM

pytorch implementation of SEG-GRAD-CAM,which based on grad-cam.It also has 3D SEG-GRAD-CAM which is used for video. see the paper Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping

How to use

Dependencies

This tutorial depends on the following libraries:

  • pytorch
  • opencv-python

usage

gradcam.py:which is the formal implementation GRAD-CAM.My work is based on this work.

gradcam_unet.py:which is the implementation SEG-GRAD-CAM.We use the model deep smoke Segmentation(like Unet).

you can run gradcam_unet.py using model BaiDuyun with password "3d7c" or google drive

result/pic_1.jpg result/cam_1.jpg

gradcam_3d.py:which is the implementation SEG-GRAD-CAM based on 3dunet.It's used for video Activation Mapping. because the paper haven't been public.So the model will be release soon.But it is not important if you want to vis activation map based on your own model.

generate_gif.py: the input and output data of gradcam_3d.py is pic.If you want to compose a series of pic to gif,you can run generate_gif.py.

result/video_2.gif result/vis2.gif

##Question if you have any question about the code. Please email me [email protected]

seg-grad-cam's People

Contributors

linhaoqi027 avatar

Stargazers

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Forkers

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seg-grad-cam's Issues

105_checkpoint.pth.tar downloading error

Hello,
I am facing a problem with 105_checkpoint.pth.tar file downloading. I try to create a new account on https://pan.baidu.com/ but they don't accept the overseas mobile number account.
I request you please upload file another platform or dropbox or drive. This is easy for everyone to download a file from there.

Error of a layer with multi input

I want to draw a heatmap for "Attention UNet", which has attention gate need two input. When code run the loop in class FeatureExtractor:

for name, module in self.model._modules.items():
      x = module(x)
      if name in self.target_layers:
          x.register_hook(self.save_gradient)
          outputs += [x]

when it run to attention gate:
TypeError: forward() missing 1 required positional argument: 'x'

Code of attunet is:

    def forward(self, x):
        e1 = self.inc(x)
        e2 = self.down1(e1)
        e3 = self.down2(e2)
        e4 = self.down3(e3)
        e5 = self.down4(e4)

        d4 = self.Up4(e5)
        x4 = self.Att4(g=d4, x=e4) #Error happened
        ……

How to make the code support this network？Hope your reply.

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