A problem when reproduced on large scale factor super-resolution

[Caoxuheng]

Thank you for your work on Hypertransformer! When I tried HyperTransformer on CAVE dataset with 32-scale factor super-resolution, the loss is so huge. In order to make the code suitable for 32x super resolution tasks, I upsampled the MS_image 4 times and then fed it into the feature extractor in backbone (i.e., the self.SFE in code). The HSIs in CAVE have been normailized into 0~1. But the numerical range of reocnstructed HSI is very huge too, about 3e4.

The size of MS_image: 8x8x31; Size of PAN_image(RGB image): 32x32x3, Batchsize:5;

Training Epoch: 1 Loss: 3612763.764423077
Training Epoch: 2 Loss: 766998.2764423077
Training Epoch: 3 Loss: 350786.46514423075
Training Epoch: 4 Loss: 230237.5733173077
Training Epoch: 5 Loss: 184773.47115384616
Training Epoch: 6 Loss: 160125.31189903847
Training Epoch: 7 Loss: 137537.77524038462
Training Epoch: 8 Loss: 117618.86358173077
Training Epoch: 9 Loss: 106500.88341346153
Training Epoch: 10 Loss: 96507.72115384616

Furthermore, the output is displayed on RGB. I want to known if there is something wrong.
(https://github.com/Caoxuheng/imgs/blob/main/1.png)