def pre_process(self, img):
        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
            self.img = self.img.half()

        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
            self.mod_scale = 4
        if self.mod_scale is not None:
            self.mod_pad_h, self.mod_pad_w = 0, 0
            _, _, h, w = self.img.size()
            if (h % self.mod_scale != 0):
                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
            if (w % self.mod_scale != 0):
                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        
scripts/upsampler.py