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Could you comment whether it's possible to modify only a bit of the code and continue using your pre-trained model to "upscale_two"?
Thank you so much for TecoGAN 🙇
I've successfully upscaled sequences of images with your pre-trained model 🚀
The current code hard-codes upscale_four - quadrupling the width and quadrupling the height.
I have attempted several code changes, starting with changing output_shape to be *2 (not *4) of the input shape.
Needless to say, minor tinkering keeps resulting in incompatible shapes, e.g:
Dimension 1 in both shapes must be equal, but are 288 and 576. Shapes are [1,288,360,3] and [1,576,720,3]. for 'generator/Assign_1' (op: 'Assign') with input shapes: [1,288,360,3], [1,576,720,3].
I suspect I might be trying a fool's errand (since the model was trained to upsample x4 rather than x2). Please let me know 🙇
ps - In your paper you write:
In our case, the outputs have four times the resolution of the inputs.
But based on the code, it seems that you may have increased the resolution 16 times?
pps - minor grammatical error in the paper:
In order to avoid the this undesirable case
The text was updated successfully, but these errors were encountered:
right, although the algorithm is not for a fixed resolution, the implementation here is now fixed to a factor of 4 in each dimension and increases the resolution for 16 times.
The code has to be changed consistently for many places for a factor of 2 and the published model cannot be used. An easier solution is to get the 4x result first, and down-scale it to 2x resolution.
Thank you for the ps and pps! We will update the paper in a future version.
Could you comment whether it's possible to modify only a bit of the code and continue using your pre-trained model to "upscale_two"?
Thank you so much for TecoGAN 🙇
I've successfully upscaled sequences of images with your pre-trained model 🚀
The current code hard-codes
upscale_four
- quadrupling the width and quadrupling the height.I have attempted several code changes, starting with changing
output_shape
to be*2
(not*4
) of the input shape.Needless to say, minor tinkering keeps resulting in incompatible shapes, e.g:
I suspect I might be trying a fool's errand (since the model was trained to upsample x4 rather than x2). Please let me know 🙇
ps - In your paper you write:
But based on the code, it seems that you may have increased the resolution 16 times?
pps - minor grammatical error in the paper:
The text was updated successfully, but these errors were encountered: