Row 77593

Row ID: 77593 | Dataset Entry | Axioma AXP Content Repository

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I made a 2 step model with a u net and a gan as 2 consecutive steps.

the output that i get from the u net , i apply thresholding to get a mask , and pass the output and mask to the gan for inpainting. i want to make the threshold also learnable . i kept the threshold as nn.Parameter() , and also set required\_grad = True , but then when I checkked while training the model , the parameter value is not getting updated at all.

The same init value of 0.5 is only coming.

class Combined\_Model(nn.Module):

def \_\_init\_\_(self , options):

super(Combined\_Model, self).\_\_init\_\_()

​

self.pretrained\_state\_dict = torch.load(os.path.join(options.pretrained, '[G0000000.pt](https://G0000000.pt)'), map\_location=torch.device('cuda'))

self.unet = UNet().to(options.device)

​

​

if options.with\_prompts:

self.inpainter = Prompted\_InpaintGenerator(options)

self.org\_gan = InpaintGenerator(options)

\#self.inpainter.load\_state\_dict(load\_pretrained\_weights(self.org\_gan, self.pretrained\_state\_dict), strict=False)

self.inpainter.load\_state\_dict(load\_pretrained\_weights(self.org\_gan , self.inpainter) , strict=True)

else:

self.inpainter = InpaintGenerator(options)

self.inpainter.load\_state\_dict(torch.load(os.path.join(options.pretrained, '[G0000000.pt](https://G0000000.pt)'), map\_location=options.device), strict=False)

​

self.models = \[self.unet, self.inpainter\]

​

self.learnable\_threshold = nn.Parameter(torch.tensor(0.5), requires\_grad=True)

​

def forward(self , x):

​

unet\_output = self.unet(x)

unet\_output\_gray = tensor\_to\_cv2\_gray(unet\_output)

flary\_img\_gray = tensor\_to\_cv2\_gray(x)

print(self.learnable\_threshold)

difference = (torch.from\_numpy(flary\_img\_gray) - torch.from\_numpy(unet\_output\_gray))

\#difference\_tensor = torch.tensor(difference, dtype=torch.float32).to(options.device)

difference\_tensor = difference.clone().to(options.device)

binary\_mask = torch.where(difference\_tensor > self.learnable\_threshold, torch.tensor(1.0).to(options.device), torch.tensor(0.0).to(options.device))

binary\_mask = binary\_mask.unsqueeze(1)

​

inpainted\_output = self.inpainter(unet\_output , binary\_mask)

​

return inpainted\_output

​

FieldValue
text I made a 2 step model with a u net and a gan as 2 consecutive steps. the output that i get from the u net , i apply thresholding to get a mask , and pass the output and mask to the gan for inpainting. i want to make the threshold also learnable . i kept the threshold as nn.Parameter() , and also set required\_grad = True , but then when I checkked while training the model , the parameter value is not getting updated at all. The same init value of 0.5 is only coming. class Combined\_Mod…
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-05-24
username_encoded Z0FBQUFBQm5Lak1qSk1ycnA3YjJRNWRQODFqT2xib1RzX0N4Q1lRejU3MnVKZGN1d1JMb2RfMHhaa200WkR1WXY2OFUxUFhjNlg3OWVPdGM1bWxCaGZXVHpmRzdDNGhjOS1TS1h6aDhONUYxUFBsZnRXQVNPalU9
url_encoded Z0FBQUFBQm5Lak8waVFLaVl1UFc3M2NURm9sc2RaY3dZUWc3ME1wRlR1LTFZdHh6NEd2RzY4dDF5amp1WFAtMlRQOEtFdndRbHphMXZMUWg1Qld6b1ZHWWlqWl9OXzBfTXpSc1ZpRE9WckV2OGxJdGlFTHgzc2FvUi10WmxHSkZ6TDlzRldEbUNwTVotSm5RdTdwT2l2Tl9XNEpNRTN2U3FTRGNBX3I3ZzRWaUtUOS0xQWR0N0Frb0JRWGNva2tOb3BnUHF0bUhEeHBw

Raw Record

{
  "text": " \n\nI made a 2 step model with a u net and a gan as 2 consecutive steps.\n\nthe output that i get from the u net , i apply thresholding to get a mask , and pass the output and mask to the gan for inpainting.  \ni want to make the threshold also learnable .  \ni kept the threshold as nn.Parameter() , and also set required\\_grad = True , but then when I checkked while training the model , the parameter value is not getting updated at all.\n\nThe same init value of 0.5 is only coming.  \n\n\nclass Combined\\_Model(nn.Module):  \n\n\ndef \\_\\_init\\_\\_(self , options):\n\nsuper(Combined\\_Model, self).\\_\\_init\\_\\_()\n\n​\n\nself.pretrained\\_state\\_dict = torch.load(os.path.join(options.pretrained, '[G0000000.pt](https://G0000000.pt)'), map\\_location=torch.device('cuda'))\n\nself.unet = UNet().to(options.device)\n\n​\n\n​\n\nif options.with\\_prompts:\n\nself.inpainter = Prompted\\_InpaintGenerator(options)\n\nself.org\\_gan = InpaintGenerator(options)\n\n\\#self.inpainter.load\\_state\\_dict(load\\_pretrained\\_weights(self.org\\_gan, self.pretrained\\_state\\_dict), strict=False)\n\nself.inpainter.load\\_state\\_dict(load\\_pretrained\\_weights(self.org\\_gan , self.inpainter) , strict=True)\n\nelse:\n\nself.inpainter = InpaintGenerator(options)\n\nself.inpainter.load\\_state\\_dict(torch.load(os.path.join(options.pretrained, '[G0000000.pt](https://G0000000.pt)'), map\\_location=options.device), strict=False)\n\n​\n\nself.models = \\[self.unet, self.inpainter\\]\n\n​\n\nself.learnable\\_threshold = nn.Parameter(torch.tensor(0.5), requires\\_grad=True)\n\n​\n\ndef forward(self , x):\n\n​\n\nunet\\_output = self.unet(x)\n\nunet\\_output\\_gray = tensor\\_to\\_cv2\\_gray(unet\\_output)\n\nflary\\_img\\_gray = tensor\\_to\\_cv2\\_gray(x)\n\nprint(self.learnable\\_threshold)\n\ndifference = (torch.from\\_numpy(flary\\_img\\_gray) - torch.from\\_numpy(unet\\_output\\_gray))\n\n\\#difference\\_tensor = torch.tensor(difference, dtype=torch.float32).to(options.device)\n\ndifference\\_tensor = difference.clone().to(options.device)\n\nbinary\\_mask = torch.where(difference\\_tensor > self.learnable\\_threshold, torch.tensor(1.0).to(options.device), torch.tensor(0.0).to(options.device))\n\nbinary\\_mask = binary\\_mask.unsqueeze(1)\n\n​\n\ninpainted\\_output = self.inpainter(unet\\_output , binary\\_mask)\n\n​\n\nreturn inpainted\\_output\n\n​",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-05-24",
  "username_encoded": "Z0FBQUFBQm5Lak1qSk1ycnA3YjJRNWRQODFqT2xib1RzX0N4Q1lRejU3MnVKZGN1d1JMb2RfMHhaa200WkR1WXY2OFUxUFhjNlg3OWVPdGM1bWxCaGZXVHpmRzdDNGhjOS1TS1h6aDhONUYxUFBsZnRXQVNPalU9",
  "url_encoded": "Z0FBQUFBQm5Lak8waVFLaVl1UFc3M2NURm9sc2RaY3dZUWc3ME1wRlR1LTFZdHh6NEd2RzY4dDF5amp1WFAtMlRQOEtFdndRbHphMXZMUWg1Qld6b1ZHWWlqWl9OXzBfTXpSc1ZpRE9WckV2OGxJdGlFTHgzc2FvUi10WmxHSkZ6TDlzRldEbUNwTVotSm5RdTdwT2l2Tl9XNEpNRTN2U3FTRGNBX3I3ZzRWaUtUOS0xQWR0N0Frb0JRWGNva2tOb3BnUHF0bUhEeHBw"
}
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