Comments (4)
Hi! Would you like to share the command you run as well as the file structure where you are running the command?
We haven't tested with input of that high resolution, but the behavior with different resolution (as long as 8x) should be consistent.
Also, I'm wondering what's the memory size of your machine? 1920 x 1072 is very high resolution as SR input...
Thanks!
from toflow.
@anchen1011 ,thank you for your reply. I use th demo.lua -mode sr -inpath ../data/xxx
to do the SR, and the input is 3840x2144 (RGB, bicubic upsampling from 1920x1072), the expectde output is 3840x2144, while it is 2112x1168 instead。I notice that the size of input is limited in the loader.py: local npMax = 2500000
, when it set to local npMax = 8500000
, it crashed. however, i tried cutting the input images into four squares, and SR each square respectively. Is there any better solution?
from toflow.
The reason why it crashed should be memory outage, since 1920 x 1072 is very high resolution as SR input...
Cutting it into smaller squares should be able to generate a reasonable input. However, this might cause abnormal artifacts in boundaries. Introducing some overlapping, averaging, and smoothening might help.
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Closed due to inactivity. Feel free to reopen if there is further question~
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Related Issues (20)
- Pre-training the flow estimation network HOT 7
- Resolution mismatching for interpolation HOT 2
- the noise level of the pretrained denoise model HOT 2
- vimeo-90k HOT 4
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- Looking forward to the training code. HOT 1
- divide vimeo90K dataset into training set and test set
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- Running toflow on consecutive frames HOT 1
- What is the number of parameters for this algorithm? HOT 1
- the results of sr model is not correct HOT 2
- add noise to input seems to have something wrong HOT 3
- Loading the model:
- Confusion on Image Transformation Module HOT 1
- About the box down-sample kernel HOT 2
- About Vimeo Dataset. HOT 1
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- 2x model
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