Comments (7)
@machanic Hi, can you share us your code of PyTorch version of PixelRL? Thank you very much.
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Thank you for your question.
Why needs the pre-trained FCN weight?
The pre-trained weights are used in order to make the training of the network stable.
Can I train the FCN directly without loading?
Maybe yes, but the training will be unstable especially at the beginning.
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@rfuruta I have an idea of my paper to use your code, but the reward is only a scalar value for the whole image, rather than the pixel-wise rewards. Can I use PixelRL? Maybe the difficult is that many agent & action has to determine one reward.
Thank you.
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I do not think PixelRL will work in such a situation. Training PixelRL with one reward is a remaining problem.
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@rfuruta Why doesn't work? Does it because that so many actions have to be made to obtain one reward?
I have implemented that idea, but it works poorly, maybe my implementation is incorrect?
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I guessed it would not work because the reward value is determined by many actions.
But, my guess may be wrong if your idea is working.
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我注意到您使用
chainer.serializers.load_npz('../denoise_with_convGRU/model/pretrained_15.npz', net)
加载预训练的 FCN 重吗?为什么需要预训练的 FCN 重? 我现在将代码翻译成 PyTorch 版本,我可以直接训练 FCN 而不是加载预训练的重吗? 谢谢!
can you share us your code of PyTorch version of PixelRL? Thank you very much.
from pixelrl.
Related Issues (13)
- Image Restoration and Color Enhancement? HOT 3
- image compression using PixelRL? HOT 1
- Color images? HOT 1
- 3 channel image restoration HOT 1
- What is the moved_image used for? Can you explain the function of state.step() a bit more? HOT 3
- Have you used any pre-trained weights for the colour Enhancement part? HOT 4
- Can pixelRL be combined with GAN? HOT 3
- How to run the code in google colab
- How to run in Colab HOT 6
- Regarding Testing Setup HOT 4
- Why did you augment the test set as well as the training set? And why did that boost the performance of your proposed method? HOT 1
- Reward functions used in image denoising
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