def forward()

in drqv2.py [0:0]


    def forward(self, x):
        n, c, h, w = x.size()
        assert h == w
        padding = tuple([self.pad] * 4)
        x = F.pad(x, padding, 'replicate')
        eps = 1.0 / (h + 2 * self.pad)
        arange = torch.linspace(-1.0 + eps,
                                1.0 - eps,
                                h + 2 * self.pad,
                                device=x.device,
                                dtype=x.dtype)[:h]
        arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
        base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
        base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1)

        shift = torch.randint(0,
                              2 * self.pad + 1,
                              size=(n, 1, 1, 2),
                              device=x.device,
                              dtype=x.dtype)
        shift *= 2.0 / (h + 2 * self.pad)

        grid = base_grid + shift
        return F.grid_sample(x,
                             grid,
                             padding_mode='zeros',
                             align_corners=False)