感谢作者的贡献！我正尝试训练3通道图片，目前遇到几个问题： 1、当训练ECBSR-M4C16时，训练基本正常，但是psnr比论文中低不少，我用了IMDN代码中的测试方法，将图片转成Ycbcr之后，针对Y通道计算psnr和ssim，但是测试结果在set14上psnr只有31.2x左右，在set5上psnr只有34.96左右。如果直接计算3通道图片的psnr和ssim，结果会更低。 2、当训练ECBSR-M16C64时，训练loss波动异常，loss会不定期的变得非常大：

##===========Epoch: 15=============##
Epoch:015, 03200/51200, loss: 4.5040, time: 35.135
Epoch:015, 06400/51200, loss: 4.3543, time: 30.960
Epoch:015, 09600/51200, loss: 4.3308, time: 30.984
Epoch:015, 12800/51200, loss: 4.3714, time: 30.946
Epoch:015, 16000/51200, loss: 4.3591, time: 31.149
Epoch:015, 19200/51200, loss: 498924.5582, time: 31.234
Epoch:015, 22400/51200, loss: 529279.5449, time: 31.021
Epoch:015, 25600/51200, loss: 463260.1697, time: 31.044
Epoch:015, 28800/51200, loss: 411850.2629, time: 30.985
Epoch:015, 32000/51200, loss: 370703.8992, time: 31.002
Epoch:015, 35200/51200, loss: 337032.6480, time: 31.208
Epoch:015, 38400/51200, loss: 308969.3531, time: 31.480
Epoch:015, 41600/51200, loss: 285220.5471, time: 31.479
Epoch:015, 44800/51200, loss: 264861.9574, time: 30.958
Epoch:015, 48000/51200, loss: 247216.6885, time: 30.925
Epoch:015, 51200/51200, loss: 231775.5556, time: 31.046
[set5-X2], PSNR/SSIM: 7.86/0.0751 (Best: 32.21/0.9010, Epoch: 14/14)
[set14-X2], PSNR/SSIM: 9.28/0.1133 (Best: 28.74/0.8311, Epoch: 14/14)

隔几个epoch后loss就会变得异常大，但是ECBSR-M4C16不会。其他训练参数都是默认的，训练数据是DIV2K，只修改了训练时的colors为3，然后forward里y = self.backbone(x) + x进行相应的修改。目前没看出来哪里的问题。

你好，最近我将SR模型转换为SNPE DLC模型的时候出现GPU/DSP runtime不支持pixelshuffle的问题，请教一下如何解决？ 我的具体环境如下： 芯片：865 snpe版本：1.55.0 系统：aarch64_ubuntu1804_rb5

具体问题表述：将pytorch模型转换为onnx，然后将onnx模型转成dlc模型的时候，snpe会自动将pixelshuffle操作分解为reshape、permute等操作，在模型输入尺寸超过320*320的时候转换模型就会出现dsp/gpu runtime不支持的情况。

在您的论文中有提到在DSP上实现了1080p的实时超分，请问一下您有没有遇到这个问题？然后您是怎么解决的呢？ 非常感谢！！！

Thank you for your work. default colors set as 1, when I tried to train 3(RGB) image, error occurred... maybe x need concat more times?

train the model from scratch! ##===========Epoch: 0=============## Traceback (most recent call last): File "/code/ECBSR/train.py", line 143, in sr = model(lr) File "/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/code/ECBSR/models/ecbsr.py", line 28, in forward y = self.backbone(x) + x RuntimeError: The size of tensor a (12) must match the size of tensor b (3) at non-singleton dimension 1

I used the pt file that came out through the learning process you provided. But, the results converted to onnx and executed using pt files are different. The results of the same pre-processing and the same post-processing are different.

Here is Picture [pytorch inference]

[onnx inference] Looking at it, the onnx model shows better results. If it's the opposite situation, I'll understand, but I don't understand that onnx is a better result.

What is this file used for? Convolution parameter fusion in the inference stage is not directly read from the training model, but after the training model is converted by convert.py, then inference is performed?

Thank you very much for your work, it is very helpful to my study! I did not find the code for the inference where shows how to implement the re-parameterizable building during testing?

i wirte the code to merge Ychannel by sr with CbCr upsampled by bicubic, but the final rgb mat contains many blue or green or red pixels.

将超分后的Y通道和直接只用bicubic插值上采样的CbCr转换为rgb格式图片保存，发现有很多红绿蓝的像素点，怎么解决？能分享一下代码吗

Hi, it is a good job to incorporate the Re-parameterization into SR model. I still have some questions about the code and paper.

• In Section 2, Computation Reduction, the paper says

However, quantization of SR models often can hardly maintain the SR quality because of the precision requirement of pixel-wise prediction

The original RepVGG model does not perform well in quantization. How about the error of quantization in low-level Re-parameterization quantization? It seems that there is no further explanation or results for the quantization error in the paper.

• Common SR models are trained on RGB channel and tested on Matlab version Y of YCbCr. However, in the provided code https://github.com/xindongzhang/ECBSR/blob/eac2ff12b4fa24a24c99b6e32deff59d0ad9e3d4/datas/div2k.py#L10 https://github.com/xindongzhang/ECBSR/blob/eac2ff12b4fa24a24c99b6e32deff59d0ad9e3d4/datas/div2k.py#L54 How about the error between different versions of Ycbcr? Thanks!

Thanks for your great work. I have some questions about the ECB block structure diagram in the paper. The ECB block in the paper has conv-33 and sobel-Dy branch but conv-11 and sobel-Dy int the code. Is there someting wrong in the structure diagram (3.1 b) of the paper?

Hi~ one more question.. As you know, the y channel super resolution result has to merge back to all channels' image, which are simple resized up. so, when I compute the PSNR using the color image, result is not good as the evaluation result during training. the x3 model test using DIV2K val DB. the PSNR result drop from 31.0x to 29.x...

anything wrong? thank you.

请问为什么我用 flops, params = profile(model, inputs=(input,)) print('计算量:{}'.format(flops)) print('参数量:{}'.format(params))

统计ecb 参数量和计算量显示是0呢？ 换了其他统计方法也是.

请问你们是用什么工具统计参数量和计算量的

If I don't need deploy the model to mobile phone, only want to remcover SR pictures from LR ,which are RGB pictures, what should Ido? How to train and recover? Thanks!