before down 強化 skip connection 或加入細節補償

你的理解幾乎是對的，但我們來精準分析一下：

🔍 關鍵點：

你提到的是想在 encoder 每層加 ResBlock 或 Attention，這就取決於 你希望模組套用在什麼資料上。

你目前的流程是：

encoded = self.down(x)  # 這是 encoder（通常是 conv）
if self.attn_block:
    encoded = self.attn_block(encoded)

這代表：

  • attn_block 是加在 down 之後的 encoded feature map 上。
  • 所以現在 attention 是套用在 下採樣後的特徵 上。

✅ 如果你想 在 skip connection 前 加上 ResBlock 或 Attention：

那你想調整的是下圖中的位置：

   x  ─┐
       ↓
   [ ResBlock / Attention ]
       ↓
   [ Down ]
       ↓

這種做法的確會讓 skip connection（例如 concat）能接收到經過強化處理的特徵。

✅ 你該怎麼改？

你要將 resblock 或 attn_block 放在 down 之前，像這樣：

def forward(self, x, style=None):
    # 加入 pre-encoder 的處理
    if hasattr(self, 'resblock'):
        x = self.resblock(x)
    if self.attn_block:
        x = self.attn_block(x)

    encoded = self.down(x)  # DownSampling happens AFTER ResBlock or Attention

這樣做會讓你調整的模組作用於 encoder input，而不是下採樣後的 feature map。這樣 skip connection 接收的是更有表達力的資訊，而不是僅做過 conv down 的粗略特徵。

🎯 額外建議：

  • 若你做的是字體遷移任務，很多細節都在 skip connection 中保留，skip 中加入 attention/resblock 會幫助保持筆劃形狀、避免轉換後失真。
  • 你可以設計： if self.use_pre_attn: x = self.attn_block(x)

需要我幫你補整段調整後的 forward 邏輯嗎？或者你想測試不同位置的效果？

當然可以！這裡是你原本的 forward 方法，加上 在 encoder 前（也就是 skip connection 輸出之前）插入 ResBlock 或 Attention 的版本。已包含可選的 resblock 和 attn_block：

✅ 調整後 forward 方法：

def forward(self, x, style=None):
    # 如果有 pre-attention 或 ResBlock，就先對輸入進行處理
    if hasattr(self, 'resblock') and self.resblock is not None:
        x = self.resblock(x)
    if hasattr(self, 'attn_block') and self.attn_block is not None:
        x = self.attn_block(x)

    # downsampling 之後的特徵
    encoded = self.down(x)

    if self.innermost:
        if hasattr(self, 'transformer_block'):
            encoded = self.transformer_block(encoded)
        if hasattr(self, 'film') and self.film is not None:
            encoded = self.film(encoded, style)

        decoded = self.up(encoded)

        # 尺寸對齊
        if decoded.shape[2:] != x.shape[2:]:
            decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)

        # ResSkip
        if self.res_skip:
            decoded = self.res_skip(decoded)

        # concat 原始輸入與上採樣結果，並回傳 skip feature
        return torch.cat([x, decoded], 1), encoded.contiguous().view(x.shape[0], -1)

    else:
        sub_output, encoded_real_A = self.submodule(encoded, style)
        decoded = self.up(sub_output)

        if decoded.shape[2:] != x.shape[2:]:
            decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)

        if self.res_skip:
            decoded = self.res_skip(decoded)

        if self.outermost:
            return decoded, encoded_real_A
        else:
            return torch.cat([x, decoded], 1), encoded_real_A

✅ 建議你的 __init__ 中這樣初始化：

if use_pre_resblock:
    self.resblock = YourResBlockModule(in_channels)
else:
    self.resblock = None

if use_pre_attention:
    self.attn_block = YourAttentionModule(in_channels)
else:
    self.attn_block = None

這樣整個模組就具有彈性，可以依據需求開關不同模組。

需要我幫你補上對 ResBlock / Attention 模組的定義範例嗎？或者你想測試 concat skip vs residual skip 結構差異？

當然可以！以下是針對 UNet encoder 中使用的 ResBlock 和 輕量 Attention 模組（使用 Linear Attention 為例） 的定義範例，簡潔而實用，方便你插入 encoder 前的 skip connection。

🔧 1. ResBlock 範例（可搭配 FiLM）

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResBlock(nn.Module):
    def __init__(self, channels, norm_layer=nn.BatchNorm2d):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.norm1 = norm_layer(channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.norm2 = norm_layer(channels)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.norm1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.norm2(out)
        return self.relu(out + identity)

🧠 2. LinearAttentionBlock（輕量 Attention）

這裡用的是簡單版本的線性 Attention，適合放在中低層中節省記憶體：

class LinearAttentionBlock(nn.Module):
    def __init__(self, dim, heads=4):
        super().__init__()
        self.heads = heads
        self.scale = dim ** -0.5

        self.to_q = nn.Conv2d(dim, dim, 1, bias=False)
        self.to_k = nn.Conv2d(dim, dim, 1, bias=False)
        self.to_v = nn.Conv2d(dim, dim, 1, bias=False)

        self.to_out = nn.Sequential(
            nn.Conv2d(dim, dim, 1),
            nn.BatchNorm2d(dim)
        )

    def forward(self, x):
        b, c, h, w = x.shape
        q = self.to_q(x).reshape(b, self.heads, c // self.heads, h * w)
        k = self.to_k(x).reshape(b, self.heads, c // self.heads, h * w)
        v = self.to_v(x).reshape(b, self.heads, c // self.heads, h * w)

        k = k.softmax(dim=-1)  # attention weights
        context = torch.einsum('bhcd,bhce->bhde', k, v)  # [b, h, c//h, c//h]

        out = torch.einsum('bhde,bhcd->bhce', context, q)  # [b, h, c//h, hw]
        out = out.reshape(b, c, h, w)
        return self.to_out(out + x)

🔌 如何用在 UNet 模組中

if use_pre_resblock:
    self.resblock = ResBlock(in_channels)
else:
    self.resblock = None

if use_pre_attention:
    self.attn_block = LinearAttentionBlock(in_channels)
else:
    self.attn_block = None

這樣你就可以：

  • 在 encoder 的每層加入 feature enhancement。
  • 選擇是否加入 ResBlock / Attention。
  • 用更低計算量的 LinearAttentionBlock，取代原本的 Self-Attention。

如果你還想讓這兩個模組支援 FiLM 調節（使用 style embedding），我也可以幫你補上對應版本，要嗎？

好，這邊是加上 FiLM 調節（Feature-wise Linear Modulation） 的版本，讓 ResBlock 和 LinearAttentionBlock 都可以根據 style embedding 動態調整輸出特徵，進一步強化風格遷移的能力。

🎛 FiLM 模組定義（可共用）

class FiLM(nn.Module):
    def __init__(self, embedding_dim, num_features):
        super(FiLM, self).__init__()
        self.gamma = nn.Linear(embedding_dim, num_features)
        self.beta = nn.Linear(embedding_dim, num_features)

    def forward(self, x, style):
        # x: [B, C, H, W], style: [B, D]
        gamma = self.gamma(style).unsqueeze(2).unsqueeze(3)
        beta = self.beta(style).unsqueeze(2).unsqueeze(3)
        return gamma * x + beta

🔧 FiLM 化 ResBlock

class FiLMResBlock(nn.Module):
    def __init__(self, channels, style_dim, norm_layer=nn.BatchNorm2d):
        super(FiLMResBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.norm1 = norm_layer(channels)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.norm2 = norm_layer(channels)
        self.relu = nn.ReLU(inplace=True)
        self.film = FiLM(style_dim, channels)

    def forward(self, x, style):
        identity = x
        out = self.conv1(x)
        out = self.norm1(out)
        out = self.film(out, style)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.norm2(out)
        out = self.film(out, style)

        return self.relu(out + identity)

🧠 FiLM 化 LinearAttentionBlock

class FiLMLinearAttentionBlock(nn.Module):
    def __init__(self, dim, style_dim, heads=4):
        super().__init__()
        self.heads = heads
        self.scale = dim ** -0.5

        self.to_q = nn.Conv2d(dim, dim, 1, bias=False)
        self.to_k = nn.Conv2d(dim, dim, 1, bias=False)
        self.to_v = nn.Conv2d(dim, dim, 1, bias=False)

        self.to_out = nn.Sequential(
            nn.Conv2d(dim, dim, 1),
            nn.BatchNorm2d(dim)
        )

        self.film = FiLM(style_dim, dim)

    def forward(self, x, style):
        b, c, h, w = x.shape
        q = self.to_q(x).reshape(b, self.heads, c // self.heads, h * w)
        k = self.to_k(x).reshape(b, self.heads, c // self.heads, h * w)
        v = self.to_v(x).reshape(b, self.heads, c // self.heads, h * w)

        k = k.softmax(dim=-1)
        context = torch.einsum('bhcd,bhce->bhde', k, v)
        out = torch.einsum('bhde,bhcd->bhce', context, q).reshape(b, c, h, w)
        out = self.to_out(out)

        out = self.film(out, style)
        return out + x

✅ 使用方式範例

在 UNet block 的 forward() 裡呼叫時：

if self.resblock is not None:
    x = self.resblock(x, style)

if self.attn_block is not None:
    x = self.attn_block(x, style)

需要我幫你整合進 UNet Block 的初始化與 forward 嗎？還是你想先試著插入？

—

以下是整合了 FiLM-ResBlock 與 FiLM-Attention 模組的 UNet block 範例（適用於 Encoder / Decoder Block 中），會在 skip 之前先經過 resblock 與 attn_block，並可根據 style embedding 調節特徵表現。

🧱 UNetBlock（整合 FiLM-ResBlock + FiLM-Attn）

class UNetBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        style_dim=None,
        down=True,
        submodule=None,
        innermost=False,
        outermost=False,
        use_resblock=False,
        use_attn=False,
    ):
        super(UNetBlock, self).__init__()
        self.innermost = innermost
        self.outermost = outermost
        self.submodule = submodule
        self.style_dim = style_dim

        # 下採樣或上採樣模組
        self.down = nn.Conv2d(in_channels, out_channels, 4, 2, 1) if down else nn.Identity()
        self.up = nn.ConvTranspose2d(out_channels * 2, in_channels, 4, 2, 1) if not outermost else nn.ConvTranspose2d(out_channels, in_channels, 4, 2, 1)

        # 選配模組
        self.resblock = FiLMResBlock(out_channels, style_dim) if use_resblock and style_dim is not None else None
        self.attn_block = FiLMLinearAttentionBlock(out_channels, style_dim) if use_attn and style_dim is not None else None

        # 中間模組 (最底層)
        if innermost:
            self.transformer_block = TransformerBlock(out_channels)  # 如需
            self.film = FiLM(style_dim, out_channels)

        self.res_skip = FiLMResBlock(in_channels, style_dim) if use_resblock and style_dim is not None else None

    def forward(self, x, style=None):
        encoded = self.down(x)

        if self.resblock:
            encoded = self.resblock(encoded, style)
        if self.attn_block:
            encoded = self.attn_block(encoded, style)

        if self.innermost:
            if hasattr(self, 'transformer_block'):
                encoded = self.transformer_block(encoded)
            if hasattr(self, 'film') and self.film is not None:
                encoded = self.film(encoded, style)

            decoded = self.up(encoded)
            if decoded.shape[2:] != x.shape[2:]:
                decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)

            if self.res_skip:
                decoded = self.res_skip(decoded, style)

            return torch.cat([x, decoded], 1), encoded.contiguous().view(x.shape[0], -1)

        else:
            sub_output, encoded_real_A = self.submodule(encoded, style)
            decoded = self.up(sub_output)
            if decoded.shape[2:] != x.shape[2:]:
                decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)

            if self.res_skip:
                decoded = self.res_skip(decoded, style)

            if self.outermost:
                return decoded, encoded_real_A
            else:
                return torch.cat([x, decoded], 1), encoded_real_A

✅ 用法範例（建立 UNet）

block = UNetBlock(
    in_channels=64,
    out_channels=128,
    style_dim=128,
    down=True,
    use_resblock=True,
    use_attn=True,
    submodule=sub_unet_block
)

如果你是用遞迴組裝 U-Net，可以幫你再包裝成組合式建構方法。有需要也能整合進整個 Zi2ZiModel 架構中。要嗎？

import torch
import torch.nn as nn

class FiLM(nn.Module):
def init(self, embedding_dim, num_features):
super(FiLM, self).init()
self.gamma = nn.Linear(embedding_dim, num_features)
self.beta = nn.Linear(embedding_dim, num_features)

def forward(self, x, style):
    gamma = self.gamma(style).unsqueeze(2).unsqueeze(3)
    beta = self.beta(style).unsqueeze(2).unsqueeze(3)
    return gamma * x + beta

class FiLMResBlock(nn.Module):
def init(self, channels, style_dim, norm_layer=nn.BatchNorm2d):
super(FiLMResBlock, self).init()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.norm1 = norm_layer(channels)
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.norm2 = norm_layer(channels)
self.relu = nn.ReLU(inplace=False) # ✅ 改為非 in-place
self.film = FiLM(style_dim, channels)

def forward(self, x, style):
    identity = x
    out = self.conv1(x)
    out = self.norm1(out)
    out = self.film(out, style)
    out = self.relu(out)

    out = self.conv2(out)
    out = self.norm2(out)
    out = self.film(out, style)

    out = out + identity  # ✅ 改為 out = out + identity 而非 in-place
    out = self.relu(out)
    return out

class FiLMLinearAttentionBlock(nn.Module):
def init(self, dim, style_dim, heads=4):
super().init()
self.heads = heads
self.scale = dim ** -0.5

    self.to_q = nn.Conv2d(dim, dim, 1, bias=False)
    self.to_k = nn.Conv2d(dim, dim, 1, bias=False)
    self.to_v = nn.Conv2d(dim, dim, 1, bias=False)

    self.to_out = nn.Sequential(
        nn.Conv2d(dim, dim, 1),
        nn.BatchNorm2d(dim)
    )

    self.film = FiLM(style_dim, dim)

def forward(self, x, style):
    b, c, h, w = x.shape
    q = self.to_q(x).reshape(b, self.heads, c // self.heads, h * w)
    k = self.to_k(x).reshape(b, self.heads, c // self.heads, h * w)
    v = self.to_v(x).reshape(b, self.heads, c // self.heads, h * w)

    k = k.softmax(dim=-1)
    context = torch.einsum('bhcd,bhce->bhde', k, v)
    out = torch.einsum('bhde,bhcd->bhce', context, q).reshape(b, c, h, w)
    out = self.to_out(out)

    out = self.film(out, style)
    out = out + x  # ✅ 非 in-place 加法
    return out

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