Comments (3)
The behavior of tf.image.convert_image_dtype
is quite confusing. When I called this, it returned [0.0, 255.0] instead of [0.0, 1.0].
There is an issue related to it: tensorflow/tensorflow#1763
As you point out in #38, I may misuse this function.
#37 and #38 should be fixed together.
Thank you for your feedback!
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Yea in my opinion its sort of a dangerous function that tries to be too smart. They should probably break it into 2 functions and raise an error if your input isnt in some specific format
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@lucasgreene @vanhuyz
great work! So the current wrong code should be the same with the right code, and I don't need to fix them?
update:
I found that tensorflow process images in a different way from scipy
I performed same operation with scipy on images : (as in the code )
- resize (bilinear)
- / 127.5 - 1
The images processed by tensorflow show more noise, while the images processed by scipy are much better.
It may be the cause for the obvious noise and artifact on those sample appleoranges.
I don't know how to solve it, do you have any idea?
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Related Issues (20)
- Missing the ngf parameter for F generator HOT 2
- CPU 100%, but training never starts HOT 2
- verbosity error HOT 4
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- Control dependencies in CycleGAN optimizer
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- 怎么使用预训练的模型啊
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