发生错误TypeError: forward() missing 1 required positional argument: 'x_size'如何解决

在 `FeedForward` 类的 `forward` 方法中，`dwconv` 需要一个额外的参数 `x_size`。这个参数在调用 `dwconv` 时必须提供。你可以通过以下几种方式来解决这个问题：

### 方案一：修改 `FeedForward` 类的 `forward` 方法
确保在调用 `dwconv` 时传递 `x_size` 参数。例如：

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, act_layer=nn.GELU, dropout=0.):
        super().__init__()
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.act = act_layer()
        self.before_add = emptyModule()
        self.after_add = emptyModule()
        self.dwconv = dwconv(hidden_dim=hidden_dim)
        self.fc2 = nn.Linear(dim, hidden_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, x_size):
        x = self.fc1(x)
        x = self.act(x)
        x = self.before_add(x)
        x = x + self.dwconv(x, x_size)  # 添加 x_size 参数
        x = self.after_add(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x

### 方案二：在调用 `FeedForward` 时传递 `x_size`
确保在调用 `FeedForward` 的 `forward` 方法时传递 `x_size` 参数。例如，在 `Transformer` 类的 `forward` 方法中：

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.conv8 = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1)
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            b, h, w = x.size()
            x7 = x.reshape(b, 1, 7, 64)
            x8 = self.conv8(x7)
            x = attn(x) + x
            x = ff(x, (h, w)) + x  # 传递 x_size 参数
            x8 = x8.reshape(b, 7, 64)
            x = x + x8
        return x

### 方案三：在 `ViT` 类的 `forward` 方法中传递 `x_size`
确保在调用 `Transformer` 的 `forward` 方法时传递 `x_size` 参数。例如：

class ViT(nn.Module):
    def __init__(self, *, image_height, image_width, patch_height, patch_width, num_classes, dim, depth, heads, mlp_dim, channels, pool='mean', dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()
        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),
            nn.Linear(patch_dim, dim),
        )
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(emb_dropout)
        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout=0.)
        self.pool = pool
        self.to_latent = nn.Identity()
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        x = self.to_patch_embedding(img)
        b, n, _ = x.shape
        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]
        x_0 = self.dropout(x)
        x_1 = self.transformer(x_0, (int(n**0.5), int(n**0.5)))  # 传递 x_size 参数
        x_2 = self.transformer(x_1, (int(n**0.5), int(n**0.5)))
        diff_1 = x_2 - x_1
        diff_1_1 = diff_1 + x_2
        x_3 = self.transformer(diff_1_1, (int(n**0.5), int(n**0.5)))
        diff_2 = x_3 - x_2
        diff_2_2 = diff_2 + x_3
        x_4 = self.transformer(diff_2_2, (int(n**0.5), int(n**0.5))) * 0.2
        x = x_0 + x_4
        x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
        x = self.to_latent(x)
        return self.mlp_head(x)

以上三种方案都可以解决 `TypeError: forward() missing 1 required positional argument: 'x_size'` 错误。选择适合你代码结构的一种进行修改即可。