Loading trained model to make prediction of single image

1

I trained a UNET model using data loaders now I want to use the trained model to get predictions for a new random image of the size (256x256) single channel. How do I do that?
I tried:

model.eval()
predb = model(torch.from_numpy(img).float().cuda())

But getting:

v.py:459, in Conv2d._conv_forward(self, input, weight, bias)
    455 if self.padding_mode != 'zeros':
    456     return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    457                     weight, bias, self.stride,
    458                     _pair(0), self.dilation, self.groups)
--> 459 return F.conv2d(input, weight, bias, self.stride,
    460                 self.padding, self.dilation, self.groups)

RuntimeError: GET was unable to find an engine to execute this computation
2

That looks like a cuDNN error and would be unexpected. Could you provide a code snippet that reproduces the issue (e.g., by calling F.conv2d using random data with the same shape)?

3

My model code is here incase that helps! UNET model on GPU runtime error