There is a bug?(Maybe) when deal with the dataset in the size of 240*240 with the window_size 15

[hujb48]

Hi, Thanks for the great work to solve the SR problem via the multi-scale QKV attention module! When I train the model with my own datasets that the resolution of the LR imgs is 6060, while the HR imgs is 240240, so I need to change the 'window_size' in the options/train/xxx.yml as 15. After this operation, I met some problems in class OCAB(), that in about line 394 in hat_arch.py: def forward(self, x, x_size, rpi): .... kv_windows = self.unfold(kv) .... the kv_widows is in shape of [4, 174240, 9], that nw*b of k and v is 36 that is not be same as q which is 64.

I found that the problem would happend in nn.unfold(), when window size is odd, padding and kernel size could changed as: class OCAB(nn.Module): .... def forward(self, x, x_size, rpi): .... #self.overlap_win_size = int(window_size * overlap_ratio) + window_size self.overlap_win_size = int(math.ceil(window_size * overlap_ratio)) + window_size .... #self.unfold = nn.Unfold(kernel_size=(self.overlap_win_size, self.overlap_win_size), stride=window_size, padding= (self.overlap_win_size-window_size)//2) self.unfold = nn.Unfold(kernel_size=(self.overlap_win_size, self.overlap_win_size), stride=window_size, padding=-(-(self.overlap_win_size-window_size)//2)) .... and class HAT(nn.Module): .... def calculate_rpi_oca(self): #window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) window_size_ext = self.window_size + int(math.ceil(self.overlap_ratio * self.window_size))

The model is trained normally, but I don't know if these corrections are right or not?