jingyunliang / hcflow Goto Github PK

Hello, I have a question to ask you. This error will appear in the middle of running the code. Could you give me some guidance?

Maybe this is a stupid question, but I have been puzzled for quite a long time. In the image super-resolution task, the input and output have different dimensions. How to build an invertible mapping between them? I notice that you calculate the determinant of the Jacobian, so I thought the mapping here is strictly invertible?

Great work! During the training process, we found that the output NLL is negative. But theoretically, NLL should be positive. Is there any explanation for this?

Hi,
I have run performance tests with the Matlab versions of the NIQE and BRISQUE codes and found deviations from the values reported in the paper.
Could you please provide a link to the code you used?
thanks a lot~

Hi, I've encountered the error when I trained the HCFlowNet. I changed my ".png" dataset to ".pklv4" dataset. I was trained on the platform of windows 10 with 1 single GPU. Could you please help me find the error? Thanks a lot.

Hi, your work is excellent, but there is one thing I don't understand.

What is written in the paper is:

"A diagonal covariance matrix with all diagonal elements close to zero"

But the code implementation in HCFlowNet_SR_arch.py line 64 is:
basic. Gaussian diag.logp (LR, - torch. Ones_ like(lr)*6, fake_ lr_ from_ hr)

why use - torch. Ones_ like(lr)*6 as covariance matrix? This seems to be inconsistent with the description in the paper

When i trained HCFlow model with DF2K dataset, it showed NaN in nll loss
(I used DF2K-tr.pklv4 & DF2K-tr_X4.pklv4 from SRFlow)

Hello,

MSU Graphics & Media Lab Video Group has recently launched two new Super-Resolution Benchmarks.

• Video Upscalers Benchmark: Quality Enhancement determines the best upscaling methods for increasing video resolution and improving visual quality.
• Super-Resolution for Video Compression benchmark aims to test Super-Resolution methods on compressed videos and select the best model for each video codec standard.

If you are interested in participating, you can add your algorithm following the submission steps:

• Submit for Video Upscalers Benchmark: Quality Enhancement
• Submit for Super-Resolution for Video Compression benchmark

We would be grateful for your feedback on our work!

Thanks for your nice work!

I want to know how much time do you need to train and inference with your models.

Furthermore, will information about params / FLOPs be reported?

Thanks.

ImportError: /home/hbw/gcc-build-5.4.0/lib64/libstdc++.so.6: version `GLIBCXX_3.4.22' not found (required by /home/hbw/anaconda3/lib/python3.8/site-packages/scipy/fft/_pocketfft/pypocketfft.cpython-38-x86_64-linux-gnu.so)

Is this error due to my GCC version being too low, and your version is?
looking forward to your reply!

Is there a way to run the test without GT? I just want to infer the model. I found a mode called LQ which -I think- should only load the images in LR directory. But this mode gives me the error:
assert real_crop * self.opt['scale'] * 2 > self.opt['kernel_size']
TypeError: '>' not supported between instances of 'int' and 'NoneType'

in LQ_dataset.py", line 88

Maybe this is a stupid question, but I have been puzzled for quite a long time. In the image super-resolution task, the input and output have different dimensions. How to build such an invertible mapping between them ?
Take an example:
If I have a low-resolution(LR) image x, and I have had an invertible function G. I can feed LR image x into G, and generate an HR image y. But can you ensure that we could obtain an output the same as x when we feed y into G_inverse?

y = G(x)
x' = G_inverse(y) =? x

I would appreciate it if you could offer some help.

how to deploy to onnx or torchscript.