Method forward

modules/VIPTRv1.py:162–208

x: (b h w c) mask_h: (n h h) mask_w: (n w w)

(self, x: torch.Tensor, rel_pos, chunkwise_recurrent=False, incremental_state=None)

        nn.init.constant_(self.out_proj.bias, 0.0)

    def forward(self, x: torch.Tensor, rel_pos, chunkwise_recurrent=False, incremental_state=None):
        '''
        x: (b h w c)
        mask_h: (n h h)
        mask_w: (n w w)
        '''
        bsz, h, w, _ = x.size()

        mask_h, mask_w = rel_pos

        q = self.q_proj(x)
        k = self.k_proj(x)
        v = self.v_proj(x)
        lepe = self.lepe(v)

        k *= self.scaling
        qr = q.view(bsz, h, w, self.num_heads, self.key_dim).permute(0, 3, 1, 2, 4)  # (b n h w d1)
        kr = k.view(bsz, h, w, self.num_heads, self.key_dim).permute(0, 3, 1, 2, 4)  # (b n h w d1)

        '''
        qr: (b n h w d1)
        kr: (b n h w d1)
        v: (b h w n*d2)
        '''

        qr_w = qr.transpose(1, 2)  # (b h n w d1)
        kr_w = kr.transpose(1, 2)  # (b h n w d1)
        v = v.reshape(bsz, h, w, self.num_heads, -1).permute(0, 1, 3, 2, 4)  # (b h n w d2)

        qk_mat_w = qr_w @ kr_w.transpose(-1, -2)  # (b h n w w)
        qk_mat_w = qk_mat_w + mask_w  # (b h n w w)
        qk_mat_w = torch.softmax(qk_mat_w, -1)  # (b h n w w)
        v = torch.matmul(qk_mat_w, v)  # (b h n w d2)

        qr_h = qr.permute(0, 3, 1, 2, 4)  # (b w n h d1)
        kr_h = kr.permute(0, 3, 1, 2, 4)  # (b w n h d1)
        v = v.permute(0, 3, 2, 1, 4)  # (b w n h d2)

        qk_mat_h = qr_h @ kr_h.transpose(-1, -2)  # (b w n h h)
        qk_mat_h = qk_mat_h + mask_h  # (b w n h h)
        qk_mat_h = torch.softmax(qk_mat_h, -1)  # (b w n h h)
        output = torch.matmul(qk_mat_h, v)  # (b w n h d2)

        output = output.permute(0, 3, 1, 2, 4).flatten(-2, -1)  # (b h w n*d2)
        output = output + lepe   ####################################################################
        output = self.out_proj(output)
        return output


class MaSA(nn.Module):