run.py forward() takes 2 positional arguments but 3 were given

[pgodwin]

I'm attempting to use run.py - but getting the below error. Any ideas?

Loading TOFlow Net... Done.
Processing...
Traceback (most recent call last):
  File "run.py", line 131, in <module>
    predict = Estimate(net, Firstfilename=frameFirstName, Secondfilename=frameSecondName, cuda_flag=CUDA)
  File "run.py", line 88, in Estimate
    input=net(tensorPreprocessedFirst, tensorPreprocessedSecond),
  File "C:\Users\pete\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
TypeError: forward() takes 2 positional arguments but 3 were given

[Coldog2333]

Thank you for finding this bug.
Actually, I didn't check the codes under ./unstable, you have to debug if you want to use them.
As the question you proposed, you should revise this line to
input = net(torch.stack([tensorPreprocessedFirst, tensorPreprocessedSecond], dim=1))
Hope this works. And if it works, please tell me and I will update the code in this repository.

[pgodwin]

Thanks @Coldog2333. I've been using this to help denoise VHS captures and I think I've successfully trained a model.

Having some issues saving during evaluation though:
plt.imsave(os.path.join(out_img_dir, video, sep, 'out.png'),normalize(predicted_img.permute(1, 2, 0).cpu().detach().numpy()))

Traceback (most recent call last):
  File "evaluate2.py", line 172, in <module>
    vimeo_evaluate(dataset_dir, './test-result', pathlistfile, task=task, cuda_flag=cuda_flag)
  File "evaluate2.py", line 164, in vimeo_evaluate
    plt.imsave(os.path.join(out_img_dir, str_format % count),predicted_img.permute(1, 2, 0).cpu().detach().numpy())
  File "/home/pete/.local/lib/python3.6/site-packages/matplotlib/pyplot.py", line 2133, in imsave
    return matplotlib.image.imsave(fname, arr, **kwargs)
  File "/home/pete/.local/lib/python3.6/site-packages/matplotlib/image.py", line 1496, in imsave
    rgba = sm.to_rgba(arr, bytes=True)
  File "/home/pete/.local/lib/python3.6/site-packages/matplotlib/cm.py", line 271, in to_rgba
    raise ValueError("Floating point image RGB values "
ValueError: Floating point image RGB values must be in the 0..1 range.

I'm guessing there's a way to scale the result but unsure the best method yet.

I used:

def normalize(x):
    """
    Normalize a list of sample image data in the range of 0 to 1
    : x: List of image data.  The image shape is (32, 32, 3)
    : return: Numpy array of normalized data
    """
    return np.array((x - np.min(x)) / (np.max(x) - np.min(x)))

But this seemed to return variations of brightness levels between frames.

[pgodwin]

Normalize wasn't the right approach. I've just clipped the values outside of bounds.

plt.imsave(os.path.join(out_img_dir, video, sep, 'out.png'),np.clip(predicted_img.permute(1, 2, 0).cpu().detach().numpy(),0,1))

[Coldog2333]

Please see Issue#6 for the image RGB value restriction problem if needed.