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yinboc / liif

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)
https://yinboc.github.io/liif/
BSD 3-Clause "New" or "Revised" License
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crash in demo #5

Closed stiv-yakovenko closed 3 years ago

stiv-yakovenko commented 3 years ago 
stiv2@gaidar:~/liif$ python3 demo.py --input jap.png --model ./rdn-liif.pth --resolution 450,600 --output output.png --gpu 0
Traceback (most recent call last):
  File "demo.py", line 34, in <module>
    coord.unsqueeze(0), cell.unsqueeze(0), bsize=30000)[0]
  File "/home/stiv2/liif/test.py", line 18, in batched_predict
    model.gen_feat(inp)
  File "/home/stiv2/liif/models/liif.py", line 34, in gen_feat
    self.feat = self.encoder(inp)
  File "/home/stiv2/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/stiv2/liif/models/rdn.py", line 99, in forward
    f__1 = self.SFENet1(x)
  File "/home/stiv2/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/stiv2/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 419, in forward
    return self._conv_forward(input, self.weight)
  File "/home/stiv2/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 416, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[1, 4, 45, 60] to have 3 channels, but got 4 channels instead

how can i fix this?

yinboc commented 3 years ago

It seems your input image has 4 channels, adding a .convert('RGB') after Image.open() should fix. (updated in the main branch)