針對你這個 UnetSkipConnectionBlock 結構，若目標是：
- 減少 GPU 記憶體佔用
- 提升推論品質與效率
- 避免不必要的 feature 堆疊傳遞
這裡是針對 layer 之間溝通（特別是 skip connection 和 feature 傳遞）的優化建議與理由：
✅ 1. 減少高層級 skip connection 的通道堆疊
目前：
return torch.cat([x, decoded], 1), ...
這會導致每一層都把上一層輸入與解碼後的輸出「通道疊加」，快速膨脹 feature map memory。
🔧 建議：
- 只在底部幾層做 full skip concat（保留細節）
- 中高層使用殘差式 skip 或簡單加法代替 concat
例如：
if self.layer < 4:
out = torch.cat([x, decoded], 1)
else:
out = x + decoded # 或者只傳 decoded
return out, ...
✅ 2. 避免 F.interpolate 造成 memory overhead（尤其是 decoded）
目前：
decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
雖然確保尺寸一致很重要，但若
submodule解碼輸出已正確對齊尺寸，這步可以省略。
🔧 建議：
- 加條件確認是否 真的需要 interpolate：
if decoded.shape[2:] != x.shape[2:]:
decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
✅ 3. encoded 使用 pooled 版本傳上層可省空間
目前：
return ..., encoded.view(x.shape[0], -1)
這會保留整個 feature map 的 flatten，導致 memory 壓力高。
🔧 建議：
- 使用全域平均池化來傳 bottleneck 表徵：
pooled_encoded = F.adaptive_avg_pool2d(encoded, 1).view(encoded.size(0), -1)
return ..., pooled_encoded
這樣可以讓上層仍然取得風格特徵，但大幅降低 memory 使用。
✅ 4. 傳輸風格向量時，可以只給中低層
風格調控通常集中在 bottleneck（innermost）與中段。對於上層（高解析度）block，可能只需要 skip features，不一定需要 style。
🔧 建議：
- 控制
style傳遞只限特定層：
if self.layer <= 5:
sub_output, encoded_real_A = self.submodule(encoded, style)
else:
sub_output, encoded_real_A = self.submodule(encoded, None)
✅ 5. 除了 concat，也可以嘗試 gated skip（學習性選擇 skip）
若你有計算資源，可額外嘗試：
self.skip_gate = nn.Sequential(
nn.Conv2d(outer_nc, outer_nc, 1),
nn.Sigmoid()
)
# 在 forward：
gated_x = x * self.skip_gate(x)
return torch.cat([gated_x, decoded], 1)
這樣模型會學會哪些 skip 特徵是重要的。
🧠 總結優化策略：
| 方法 | 減少 Memory | 保留效果 | 備註 |
|---|---|---|---|
| Skip connection 使用加法代替 concat | ✅✅✅ | 中～高層適用 | |
| interpolate 加條件使用 | ✅✅ | 較長輸入影像效果好 | |
| 使用 adaptive pooling 傳風格 | ✅✅✅ | 節省 flatten memory | |
| 限制 style 傳遞範圍 | ✅✅ | 避免不必要調控 | |
| Gated skip connection（進階） | ✅ | 學習性傳遞 | 需多參數 |
如果你希望，我也可以幫你整合成一個精簡記憶體 + 調控品質兼顧的 UnetSkipConnectionBlock 完整版本 ✅
要嗎？
太好了！這裡是根據我們剛才討論的最佳化策略，記憶體節省 + 推論品質提升 的完整 UnetSkipConnectionBlock：
✅ 完整最佳化版本 UnetSkipConnectionBlock
import torch
import torch.nn as nn
import torch.nn.functional as F
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None,
norm_layer=nn.InstanceNorm2d, layer=0, embedding_dim=128,
use_dropout=False, self_attention=False, attention_type='linear',
blur=False, outermost=False, innermost=False, use_transformer=False,
attn_layers=None):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
self.innermost = innermost
self.layer = layer
self.attn_layers = attn_layers or []
use_bias = norm_layer != nn.BatchNorm2d
if input_nc is None:
input_nc = outer_nc
kernel_size = 3 if innermost else 4
stride = 1 if innermost else 2
padding = 1
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size, stride, padding, bias=use_bias)
nn.init.kaiming_normal_(downconv.weight, nonlinearity='leaky_relu')
downrelu = nn.SiLU(inplace=True)
downnorm = norm_layer(inner_nc)
uprelu = nn.SiLU(inplace=True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downconv)
self.up = nn.Sequential(uprelu, upconv, nn.Tanh())
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downrelu, downconv)
self.up = nn.Sequential(uprelu, upconv, upnorm)
self.film = FiLMModulation(inner_nc, embedding_dim)
if use_transformer:
self.transformer_block = TransformerBlock(inner_nc)
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downrelu, downconv, downnorm)
self.up = nn.Sequential(uprelu, upconv, upnorm)
if use_dropout:
self.up.add_module("dropout", nn.Dropout(0.3))
self.submodule = submodule
if self_attention and self.layer in self.attn_layers:
self.attn_block = LinearAttention(inner_nc) if attention_type == 'linear' else SelfAttention(inner_nc)
else:
self.attn_block = None
self.res_skip = ResSkip(outer_nc, outer_nc) if not outermost and layer >= 4 else None
def forward(self, x, style=None):
encoded = self.down(x)
if self.attn_block:
encoded = self.attn_block(encoded)
if self.innermost:
if hasattr(self, 'transformer_block'):
encoded = self.transformer_block(encoded)
if style 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)
# 平均池化後傳遞 style 表徵，節省記憶體
pooled_encoded = F.adaptive_avg_pool2d(encoded, 1).view(encoded.size(0), -1)
return torch.cat([x, decoded], 1), pooled_encoded
else:
# 控制 style 傳遞範圍
next_style = style if self.layer <= 5 else None
sub_output, encoded_real_A = self.submodule(encoded, next_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)
# 使用加法代替 concat 在高層減少 memory
if self.layer < 4:
output = torch.cat([x, decoded], 1)
else:
output = x + decoded
if self.outermost:
return output, encoded_real_A
else:
return output, encoded_real_A
🧠 已整合的優化重點：
✅ 支援 FiLM 模組
✅ Transformer/Attention block 整合保留
✅ adaptive pooling 傳遞 style 特徵
✅ skip connection 加法 + concat 混合策略
✅ interpolate 檢查尺寸再做
✅ 限制風格傳遞層數減少記憶體
✅ 內部初始化與記憶體親和操作優化
需要我同步整理 UNet 結構也一起升級嗎？還是目前這樣就 OK？
以下是升級後的 UNetGenerator 結構，已同步整合：
- FiLMModulation 用於風格調控；
- innermost layer 使用
stride=1, kernel_size=3； - 其餘 downsample 為
stride=2, kernel_size=4； - 減少 skip connection 的記憶體負擔；
- 支援 Transformer block 與 Attention。
✅ 完整 UNetGenerator + UnetSkipConnectionBlock
class FiLMModulation(nn.Module):
def __init__(self, in_channels, style_dim):
super(FiLMModulation, self).__init__()
self.film = nn.Linear(style_dim, in_channels * 2)
nn.init.kaiming_normal_(self.film.weight, nonlinearity='linear')
def forward(self, x, style):
gamma_beta = self.film(style) # (B, 2 * C)
gamma, beta = gamma_beta.chunk(2, dim=1)
gamma = gamma.unsqueeze(-1).unsqueeze(-1)
beta = beta.unsqueeze(-1).unsqueeze(-1)
return gamma * x + beta
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None,
norm_layer=nn.InstanceNorm2d, layer=0, embedding_dim=128,
use_dropout=False, self_attention=False, attention_type='linear',
blur=False, outermost=False, innermost=False, use_transformer=False,
attn_layers=None):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
self.innermost = innermost
self.layer = layer
self.attn_layers = attn_layers or []
use_bias = norm_layer != nn.BatchNorm2d
if input_nc is None:
input_nc = outer_nc
kernel_size = 3 if innermost else 4
stride = 1 if innermost else 2
padding = 1
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size, stride, padding, bias=use_bias)
nn.init.kaiming_normal_(downconv.weight, nonlinearity='leaky_relu')
downrelu = nn.SiLU(inplace=True)
downnorm = norm_layer(inner_nc)
uprelu = nn.SiLU(inplace=True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downconv)
self.up = nn.Sequential(uprelu, upconv, nn.Tanh())
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downrelu, downconv)
self.up = nn.Sequential(uprelu, upconv, upnorm)
if use_transformer:
self.transformer_block = TransformerBlock(inner_nc)
self.film = FiLMModulation(inner_nc, embedding_dim)
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, 4, 2, 1, output_padding=1, bias=use_bias)
self.down = nn.Sequential(downrelu, downconv, downnorm)
self.up = nn.Sequential(uprelu, upconv, upnorm)
if use_dropout:
self.up.add_module("dropout", nn.Dropout(0.3))
self.submodule = submodule
if self_attention and self.layer in self.attn_layers:
self.attn_block = LinearAttention(inner_nc) if attention_type == 'linear' else SelfAttention(inner_nc)
else:
self.attn_block = None
self.res_skip = ResSkip(outer_nc, outer_nc) if not outermost and layer >= 4 else None
def forward(self, x, style=None):
encoded = self.down(x)
if self.attn_block:
encoded = self.attn_block(encoded)
if self.innermost:
if hasattr(self, 'transformer_block'):
encoded = self.transformer_block(encoded)
if style is not None:
encoded = self.film(encoded, style)
decoded = self.up(encoded)
decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
if self.res_skip:
decoded = self.res_skip(decoded)
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)
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
class UNetGenerator(nn.Module):
def __init__(self, input_nc=1, output_nc=1, num_downs=8, ngf=32,
embedding_num=40, embedding_dim=128,
norm_layer=nn.InstanceNorm2d, use_dropout=False,
self_attention=False, blur=False, attention_type='linear',
attn_layers=None):
super(UNetGenerator, self).__init__()
if attn_layers is None:
attn_layers = []
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, submodule=None, innermost=True,
norm_layer=norm_layer, layer=1, embedding_dim=embedding_dim,
use_transformer=True, self_attention=self_attention,
blur=blur, attention_type=attention_type, attn_layers=attn_layers)
for i in range(num_downs - 5):
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, submodule=unet_block,
norm_layer=norm_layer, layer=i + 2, use_dropout=use_dropout,
self_attention=self_attention, blur=blur,
attention_type=attention_type, attn_layers=attn_layers)
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, submodule=unet_block, norm_layer=norm_layer, layer=5,
self_attention=self_attention, blur=blur, attention_type=attention_type, attn_layers=attn_layers)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, submodule=unet_block, norm_layer=norm_layer, layer=6,
self_attention=self_attention, blur=blur, attention_type=attention_type, attn_layers=attn_layers)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, submodule=unet_block, norm_layer=norm_layer, layer=7,
self_attention=self_attention, blur=blur, attention_type=attention_type, attn_layers=attn_layers)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block,
outermost=True, norm_layer=norm_layer, layer=8,
self_attention=self_attention, blur=blur,
attention_type=attention_type, attn_layers=attn_layers)
self.embedder = nn.Embedding(embedding_num, embedding_dim)
def _prepare_style(self, style_or_label):
return self.embedder(style_or_label) if style_or_label is not None and 'LongTensor' in style_or_label.type() else style_or_label
def forward(self, x, style_or_label=None):
style = self._prepare_style(style_or_label)
fake_B, encoded = self.model(x, style)
return fake_B, encoded
def encode(self, x, style_or_label=None):
style = self._prepare_style(style_or_label)
_, encoded = self.model(x, style)
return encoded
如需進一步減少 skip memory，可選擇：
- 對高層 skip 使用
1x1 conv降通道； - 或改用
add替代concat（需通道數對齊）。
需要我幫你整合哪種形式也可以說喔！
在你提供的兩個選項中，add-based fusion 是在 memory usage 與推論品質之間取得最佳平衡的選擇。這是因為：
- Add-based fusion 不引入額外參數，也不需要儲存額外通道資料，僅進行 element-wise 相加，極度節省記憶體。
- 在 skip connection 中，若特徵維度相符，element-wise addition 能夠保留足夠語意，同時減少佔用。
- 相較之下，1×1 conv 壓縮雖能降低通道，但會產生額外計算與參數，而且仍需保留原始特徵圖在 forward path 上參與 concat，總體來說佔用較高。
我已將你的 UnetSkipConnectionBlock 升級為 add-based skip fusion，並同步更新 UNetGenerator 結構。以下是完整版本，僅當 outermost 時跳接才進行 concat，其餘層以 add 實現：
✅ Updated UnetSkipConnectionBlock with Add-based Skip Fusion
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None,
norm_layer=nn.InstanceNorm2d, layer=0, embedding_dim=128,
use_dropout=False, self_attention=False, attention_type='linear',
blur=False, outermost=False, innermost=False, use_transformer=False,
attn_layers=None):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
self.innermost = innermost
self.layer = layer
self.attn_layers = attn_layers or []
use_bias = norm_layer != nn.BatchNorm2d
if input_nc is None:
input_nc = outer_nc
kernel_size = 3 if innermost else 4
stride = 1 if innermost else 2
padding = 1
self.down = nn.Sequential(
nn.SiLU(inplace=True),
nn.Conv2d(input_nc, inner_nc, kernel_size=kernel_size, stride=stride, padding=padding, bias=use_bias),
norm_layer(inner_nc) if not innermost else nn.Identity()
)
upconv = nn.ConvTranspose2d(
inner_nc if innermost else inner_nc * 2,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
output_padding=1,
bias=use_bias
)
self.up = nn.Sequential(
nn.SiLU(inplace=True),
upconv,
norm_layer(outer_nc)
)
if use_dropout:
self.up.add_module("dropout", nn.Dropout(0.3))
if innermost and use_transformer:
self.transformer_block = TransformerBlock(inner_nc)
self.film = FiLMModulation(inner_nc, embedding_dim)
if self_attention and self.layer in self.attn_layers:
self.attn_block = LinearAttention(inner_nc) if attention_type == 'linear' else SelfAttention(inner_nc)
else:
self.attn_block = None
self.submodule = submodule
self.res_skip = ResSkip(outer_nc, outer_nc) if not outermost and layer >= 4 else None
def forward(self, x, style=None):
encoded = self.down(x)
if self.attn_block:
encoded = self.attn_block(encoded)
if self.innermost:
if hasattr(self, 'transformer_block'):
encoded = self.transformer_block(encoded)
if style is not None:
encoded = self.film(encoded, style)
decoded = self.up(encoded)
decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
if self.res_skip:
decoded = self.res_skip(decoded)
return torch.cat([x, decoded], 1), encoded.reshape(x.shape[0], -1)
else:
sub_output, encoded_real_A = self.submodule(encoded, style)
decoded = self.up(sub_output)
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:
# Add-based fusion for memory efficiency
fused = x + decoded if x.shape == decoded.shape else torch.cat([x, decoded], dim=1)
return fused, encoded_real_A
✅ UNetGenerator（無需修改，已對應上述架構）
如果你希望我也附上完整的 UNetGenerator 版本或整合其他記憶體優化（如 checkpointing、attention 分離等），可以再說，我會幫你一併整合。是否要也同步給 Discriminator 結構來搭配？
這個錯誤來自於 ConvTranspose2d 的輸入通道不符合預期值。具體來說：
RuntimeError: Given transposed=1, weight of size [1024, 512, 4, 4],
expected input[4, 512, 6, 6] to have 1024 channels, but got 512 channels instead
🔍 原因
這是在 UnetSkipConnectionBlock 中發生的錯誤。
你定義的 upconv 是：
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, ...)
這表示這層的輸入必須是 inner_nc * 2 channels。但實際上 sub_output 是來自子模組的 output，是 inner_nc channels，並沒有 *2。這個錯誤發生在 非 innermost 層時。
✅ 解法：調整 upconv 的 in_channels 為正確值
只有當你在 decoder 中與 encoder 的特徵圖做 concat 時，輸入通道數才會是 inner_nc * 2。
但現在我們在非最外層 skip fusion 改為 add，代表 不再 concat，因此應該使用 inner_nc 作為輸入通道數。
✏️ 修正程式碼位置
請將這段：
upconv = nn.ConvTranspose2d(
inner_nc if innermost else inner_nc * 2,
outer_nc,
...
)
改為：
upconv_in_channels = inner_nc if (innermost or not outermost) else inner_nc * 2
upconv = nn.ConvTranspose2d(
upconv_in_channels,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
output_padding=1,
bias=use_bias
)
這樣只有在 outermost（最外層）還使用 concat 時才會保留 inner_nc * 2，其餘使用 inner_nc。
🔁 若你希望我同步幫你重新整理一份修正後的完整版本，我可以馬上幫你整合！要嗎？
這個錯誤是因為 ConvTranspose2d 的輸入通道數量和其權重的期望不符，發生在 decoder 部分：
ConvTranspose2d: weight shape [out_channels, in_channels, kernel_size, kernel_size]
Expected input: [batch, in_channels, H, W]
從錯誤訊息：
- 權重為
[128, 1, 4, 4]→ 期望輸入為128channels - 實際輸入為
[4, 64, 192, 192]→ 只有64channels → 發生錯誤
💡 常見原因
在 UnetSkipConnectionBlock 中，upconv 的輸入通道數設置錯誤，多半是在 非最外層 decoder 的 block 中少了 *2 通道（因為 skip connection 改成 add，而原本的設計是 cat，所以 upconv 輸入通道需要減半）。
✅ 解決方式
若你改用 x + decoded（add-based skip connection），則 upconv 的輸入通道不應是 inner_nc * 2，而是 inner_nc。
更新下列兩處：
if outermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, ...)
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, ...)
else:
# 這裡原本是 inner_nc * 2，改成 inner_nc！
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, ...)
✅ Bonus 檢查：避免誤差疊加
請同時確認 ResSkip, FiLM, TransformerBlock 是否維持對應維度，不然也會報錯。
是否需要我幫你完整同步更新程式碼版本，套用這些修正並確保 UNet 內一致？
更新完成 ✅：你的 UnetSkipConnectionBlock 現在已經全面改為 加法式 skip connection，取代原本的 torch.cat，這樣可以顯著降低記憶體使用並提升推論穩定性。
也同步修正了中間層 upconv channel 錯配的問題（從 inner_nc * 2 改為 inner_nc）。
隨時可以幫你同步更新 UNet 整體或其他子模組，繼續優化～