Closed luomomo closed 6 years ago
Hello.
xxx in the last row is just a mean-shifting module (It only has biases, and convolution kernel is a identity kernel.)
However, I have no idea where the xxx from the second row came from.
If you let me know how did you convert our baseline model to pytorch model, I will try and give you an answer.
Thank you.
Also, we have a pytorch version of EDSR, too (This is an unofficial version.)
For some reason, it is difficult to open the entire pytorch code, but I think I can share the model code.
If you want that code, please tell me.
Here is the link convert link: https://github.com/clcarwin/convert_torch_to_pytorch And there is the model when nResblock=2 baseline ( (conv_in): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (residual): NetworkBlock ( (layer): Sequential ( (0): BasicBlock ( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (relu): ReLU (inplace) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (1): BasicBlock ( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (relu): ReLU (inplace) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) (conv_mid): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (shuffle): PixelShuffle (upscale_factor=2) (conv_out): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) )
That is great if you can share the model code.
I checked the converting code, and found nothing wrong.
I think it is much better for you to use my code.
Here is a link for the pytorch version of EDSR
https://drive.google.com/open?id=0B_riD-4WK4WwMTNEQTFNdjBJUVE
I finally know the difference! Your have two meanShift modules which lead to two more conv weights and bias in the model parameters.
Hi, I am trying to recurrence baseline model x2 in pytorch. I think I create my model totally the same to the paper and baseline.lua. I convert your model baseline.x2.t7 to baseline.x2.pth and found that the parameters are different to my pytorch model's. Did you add 2 more conv() in your training?Or is there someting wrong in my steps?