main_dipr_sisr throws input type RuntimeError

[rishftw]

Hi, I was trying to run the main_dipr_sisr.py(inconsistent naming, the rest say dpir) in colab like this:

!python main_dipr_sisr.py

it gives me this output:

`LogHandlers setup!

20-09-28 07:21:02.662 : model_name:drunet_color, image sigma:0.000, model sigma:0.000
20-09-28 07:21:02.664 : Model path: model_zoo/drunet_color.pth
20-09-28 07:21:02.664 : testsets/srbsd68
20-09-28 07:21:02.669 : --------- sf:2 --k: 0 ---------
Traceback (most recent call last):
File "main_dipr_sisr.py", line 301, in
main()
File "main_dipr_sisr.py", line 222, in main
x = utils_model.test_mode(model, x, mode=3, refield=32, min_size=256, modulo=16)
File "/content/drive/My Drive/dpir/DPIR/utils/utils_model.py", line 36, in test_mode
E = test_x8(model, L, modulo)
File "/content/drive/My Drive/dpir/DPIR/utils/utils_model.py", line 164, in test_x8
E_list = [test_pad(model, util.augment_img_tensor(L, mode=i), modulo=modulo) for i in range(8)]
File "/content/drive/My Drive/dpir/DPIR/utils/utils_model.py", line 164, in
E_list = [test_pad(model, util.augment_img_tensor(L, mode=i), modulo=modulo) for i in range(8)]
File "/content/drive/My Drive/dpir/DPIR/utils/utils_model.py", line 68, in test_pad
E = model(L)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/content/drive/My Drive/dpir/DPIR/models/network_unet.py", line 106, in forward
x1 = self.m_head(x0)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py", line 419, in forward
return self._conv_forward(input, self.weight)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py", line 416, in _conv_forward
self.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.cuda.DoubleTensor) and weight type (torch.cuda.FloatTensor) should be the same
`

I got it to run by adding this

L = L.float()

to the function

test_mode(model, L, mode=0, refield=32, min_size=256, sf=1, modulo=1)

in DPIR/utils/utils_model.py right before the if statements, however I do not know enough to tell if using a simple cast like that causes any internal problems. Thought I'd put this here in case anyone else runs into the issue.