RAP for different output shape

[syomantak]

Hi, while running the main.py for resnet50 with 365 output classes, I ran into the following error

torch.Size([1, 2048]) torch.Size([365, 2048]) torch.Size([1, 1000])

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-3-02abbf51b5f9> in <module>
      7 #    Res = model.relprop(R = output * T, alpha= 1).sum(dim=1, keepdim=True)
      8 #else:
----> 9 RAP = model.RAP_relprop(R=T)
     10 Res = (RAP).sum(dim=1, keepdim=True)
     11 # Check relevance value preserved

/home/SharedData3/ushasi/tub/gan/modules/resnet.py in RAP_relprop(self, R)
    324         return R
    325     def RAP_relprop(self, R):
--> 326         R = self.fc.RAP_relprop(R)
    327         R = R.reshape_as(self.avgpool.Y)
    328         R = self.avgpool.RAP_relprop(R)

/home/SharedData3/ushasi/tub/gan/modules/layers.py in RAP_relprop(self, R_p)
    372             pd = R_p
    373 
--> 374             Rp_tmp = first_prop(pd, px, nx, pw, nw)
    375             A =  redistribute(Rp_tmp)
    376 

/home/SharedData3/ushasi/tub/gan/modules/layers.py in first_prop(pd, px, nx, pw, nw)
    317             #print(px,pw)
    **318             print(px.shape,pw.shape,pd.shape)**
--> 319             Rpp = F.linear(px, pw) * pd
    320             Rpn = F.linear(px, nw) * pd
    321             Rnp = F.linear(nx, pw) * pd

RuntimeError: The size of tensor a (365) must match the size of tensor b (1000) at non-singleton dimension 1

The top 3 shapes are a result of the print statement I added (in bold). If I change my T = (T[:, np.newaxis] == np.arange(1000)) * 1.0 to T = (T[:, np.newaxis] == np.arange(365)) * 1.0 in compute_pred function, then the error goes away. I just wanted to confirm that this is indeed the right way to fix the error, I hope I am not doing something random to get rid of the error and in the process, giving the wrong output.

[Surayuth]

I think you are doing it the right way. That line exists to prepare to input tensor for RAP where its size depends on the number of classes.