Why does 2d tokens become 1d tokens and then become 2d tokens?

[BeautySilly]

Hi, I have a question,in the van.py file of VAN-Segmentation：‘why does 2d tokens become 1d tokens and then become 2d tokens?’, in line 105, line 110 and line 223? The details are as follows: In block class:

def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.permute(0, 2, 1).view(B, C, H, W)          # <---------this
        x = x + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
                               * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                               * self.mlp(self.norm2(x)))
        x = x.view(B, C, N).permute(0, 2, 1).               # <---------this
        return x

In VAN class:

def forward(self, x):
        B = x.shape[0]
        outs = []

        for i in range(self.num_stages):
            patch_embed = getattr(self, f"patch_embed{i + 1}")
            block = getattr(self, f"block{i + 1}")
            norm = getattr(self, f"norm{i + 1}")
            x, H, W = patch_embed(x)
            for blk in block:
                x = blk(x, H, W)
            x = norm(x)
            x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()          # <---------this
            outs.append(x)

        return outs

My teacher thinks this has a very profound meaning, and I think the author arbitrarily set it up to keep it consistent with the traditional ViT, there is no special meaning, but my explanation can't convince my teacher, so I seek the author's help. Looking forward to your reply! Best！

[XuRuihan]

Because the LayerNorm is performed on the last dimension (which need 1d tokens) and Conv is performed on the last two dimensions (which need 2d tokens)