Open Elegantice opened 4 years ago
In detail,i can not find the out, out2, out 3, out 4.just can find the sr.so idont have the complete loss.because i cant get the loss1, loss2,loss3. The reason is that I can only get one result from the self.model(lr, idx_scale).look forward to your reply.
@Elegantice I also don't know. I guss L1 loss? but i can't reproduce the results in paper...
L 1 loss, and you can check with the pre-trained model , or send me your problem.发自我的华为手机-------- 原始邮件 --------发件人: Yuxiang Chen notifications@github.com日期: 2020年1月6日周一 傍晚5:27收件人: Sudo-Biao/s-LWSR s-LWSR@noreply.github.com抄送: Subscribed subscribed@noreply.github.com主 题: Re: [Sudo-Biao/s-LWSR] about the loss (#3)@Elegantice I also don't know. I guss L1 loss? but i can't reproduce the results in paper...
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thanks for your reply! I have some questions below: *I found in your code that the input images is not divided by 255. Is there any difference between this and dividing by 255 to map to 0 ~ 1? I think there is no difference, so my code is mapped to 0 ~ 1.
What has data enhancement done specifically? How much do these actions affect the final result?
The value range of the output image is not exactly 0-255, is it 0 if it is less than 0, or 255 If it is greater than 255 (clamp operation)?
How many epochs are trained by the results shown in the paper?
Thank you for taking the time to read and give advice.
yuxiang chen
Biao notifications@github.com 于2020年1月6日周一 下午5:44写道:
L 1 loss, and you can check with the pre-trained model , or send me your problem.发自我的华为手机-------- 原始邮件 --------发件人: Yuxiang Chen < notifications@github.com>日期: 2020年1月6日周一 傍晚5:27收件人: Sudo-Biao/s-LWSR < s-LWSR@noreply.github.com>抄送: Subscribed subscribed@noreply.github.com主 题: Re: [Sudo-Biao/s-LWSR] about the loss (#3)@Elegantice I also don't know. I guss L1 loss? but i can't reproduce the results in paper...
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sorry, maybe i dont read your article in detail. i cant find the introduce about the loss.And your code has much loss. i think they are the different loss of different model. can u give me a introduce for your loss? thank u very much.
Sorry for the mistake. I update the right code now.
@Elegantice I also don't know. I guss L1 loss? but i can't reproduce the results in paper...
Sorry for the mistake, and I have updated the right code. You can try it.
sorry, maybe i dont read your article in detail. i cant find the introduce about the loss.And your code has much loss. i think they are the different loss of different model. can u give me a introduce for your loss? thank u very much.