凍結 UNet encoder 部分，只微調 decoder（上採樣模組）, 實際上操作細節

2025-04-162025-04-18

很好，這個策略「凍結 UNet 的 encoder，只微調 decoder（上採樣模組）」，是遷移學習中非常常見的技巧，尤其適合你現在這種「替換上採樣模組」的場景。

✅ 為什麼這樣做有效？

Encoder 學到的特徵提取能力已經不錯，不需要重新訓練。
Decoder 是新加入（例如 PixelShuffle 版），需要調整來適應。
節省訓練時間，穩定訓練過程。

🧩 假設 UNet 架構如下（簡化版）：

class UNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(1, 64, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, padding=1),
            nn.ReLU(),
        )
        self.bottleneck = nn.Sequential(
            nn.Conv2d(128, 256, 3, padding=1),
            nn.ReLU(),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
            nn.ReLU(),
        )
        self.output_layer = nn.Conv2d(64, 1, 1)

    def forward(self, x):
        x = self.encoder(x)
        x = self.bottleneck(x)
        x = self.decoder(x)
        return self.output_layer(x)

🔧 操作細節：凍結 encoder & bottleneck，只訓練 decoder

✅ 方法 1：設定 `requires_grad = False`

for param in model.encoder.parameters():
    param.requires_grad = False

for param in model.bottleneck.parameters():
    param.requires_grad = False

你也可以寫一個 helper：

def freeze_module(module):
    for param in module.parameters():
        param.requires_grad = False

然後：

freeze_module(model.encoder)
freeze_module(model.bottleneck)

🔎 確認只有 decoder 被訓練：

trainable_params = [name for name, param in model.named_parameters() if param.requires_grad]
print("Trainable parameters:", trainable_params)

你應該只會看到類似：

Trainable parameters: ['decoder.0.weight', 'decoder.0.bias', 'decoder.2.weight', 'decoder.2.bias', 'output_layer.weight', 'output_layer.bias']

🧪 optimizer 也只需要傳 trainable params：

optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)

💡 額外提示：

如果你要微調的是換了 PixelShuffle 版本的 decoder，務必確保 decoder 的 channel size 接得上 encoder 的輸出。
可以加個 assert 驗證尺寸匹配。
如果 encoder/bottleneck 層 batchnorm 存在，建議設為 eval 模式避免 batch statistics 更新：

model.encoder.eval()
model.bottleneck.eval()

如果你願意，我可以幫你把這一段凍結 & 切換上採樣寫成你現有 UNet 架構可插拔的形式，例如：

UNet(upsample_mode="pixelshuffle", freeze_encoder=True)

要嗎？我可以直接幫你整合。

你這個修改方向非常清楚，而且設計得很好！👍
下面我幫你整理幾個建議與修正，以增強你的 UNetSkipConnectionBlock 模組實用性與穩定性：

✅ 建議與修改項目：

1. `use_bias` 建議改名

你定義了 use_bias=True，但這個名稱會讓人誤解是應用在整個 block 上，實際上只用在 Conv2d，建議更具體命名：

conv_bias: bool = True  # or conv2d_bias

2. PixelShuffle 模式需注意通道數倍數

PixelShuffle 需要 in_channels = out_channels * upscale_factor²，你這段已經考慮得很好。不過：

建議加入錯誤提示（你有 warning，但也許 raise 會更安全）

if in_channels % (scale_factor ** 2) != 0:
    raise ValueError(f"in_channels ({in_channels}) must be divisible by scale_factor² ({scale_factor ** 2}) for pixelshuffle")

3. 可選擇是否凍結 Upsample 模組

既然你想要能夠「凍結」上採樣模組，可以在 __init__ 加一個參數來控制：

freeze_upsample: bool = False

然後在 setup_upsample 後面加：

if self.freeze_upsample:
    for param in self.upsample.parameters():
        param.requires_grad = False

4. 改成類別儲存 Upsample 模式名稱

目前 upsample_mode 是字串，也可以考慮用 Enum 或 Literal 顯式定義型別（較安全）：

from typing import Literal

upsample_mode: Literal["conv", "upsample", "pixelshuffle"] = "upsample"

5. init 上建議加入印出確認訊息（debug 用）

方便訓練時確定模式是否正確：

print(f"[UNetBlock] upsample_mode={self.upsample_mode}, scale_factor={scale_factor}, freeze={self.freeze_upsample}")

✅ 總結改進後的 `init` 樣板參考：

def __init__(
    self,
    in_channels: int,
    out_channels: int,
    upsample_mode: Literal["conv", "upsample", "pixelshuffle"] = "upsample",
    scale_factor: int = 2,
    conv_bias: bool = True,
    freeze_upsample: bool = False,
):
    super().__init__()
    self.upsample_mode = upsample_mode
    self.scale_factor = scale_factor
    self.freeze_upsample = freeze_upsample

    self.upsample = self.setup_upsample(in_channels, out_channels, scale_factor, conv_bias)

    if self.freeze_upsample:
        for param in self.upsample.parameters():
            param.requires_grad = False

    print(f"[UNetBlock] upsample_mode={upsample_mode}, scale={scale_factor}, freeze={freeze_upsample}")

需要我幫你整合成一個完整的 UNetSkipConnectionBlock 類別範例嗎？或者你也可以分享完整類別，我可以幫你一起補上這些建議。

你說得完全對，目標是要切換上採樣方式，所以應該是要**凍結 encoder（下採樣路徑）**的訓練參數，而不是凍結上採樣模組！
剛才的「凍結上採樣」的方向是錯的，感謝你指出！我們現在來釐清整個設計：

🎯 目標：支援上採樣方式的「切換 + encoder 凍結」

✅ 你要實現的效果是：

在訓練時切換 upsample_mode（例如從 conv ➜ pixelshuffle）
但**不改動 encoder 部分（下採樣與中間層）**的參數
因此應該要 凍結 encoder 區塊的參數（不讓其梯度更新）

✅ 正確的實作方向應該是：

🔧 1. Encoder 模組中加入凍結參數

class UNetSkipConnectionBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        ...,
        freeze_encoder: bool = False,
        ...
    ):
        super().__init__()
        self.freeze_encoder = freeze_encoder
        
        # Encoder 路徑部分
        self.encoder = nn.Sequential(
            nn.Conv2d(...),
            nn.ReLU(),
            ...
        )

        # Decoder 路徑部分
        self.upsample = self.setup_upsample(...)

        # 若指定凍結 encoder，停用參數更新
        if self.freeze_encoder:
            for param in self.encoder.parameters():
                param.requires_grad = False

🧩 2. Decoder（upsample）保留可訓練

也就是 僅 freeze encoder 的參數，讓 decoder（例如不同上採樣方法）可以自由學習。

🧪 3. 切換 `upsample_mode` 做法（可選）

如果你想在訓練中動態切換 upsample_mode（例如做 ablation），可以設計一個 setter 函式：

def switch_upsample_mode(self, mode: str):
    self.upsample_mode = mode
    self.upsample = self.setup_upsample(...)

🔁 4. 如果你有整個 UNet 類別（非單一 block），建議在 encoder 與 decoder 間明確分開設計：

self.encoder_blocks = nn.ModuleList([...])
self.decoder_blocks = nn.ModuleList([...])

# 凍結 encoder 所有參數
if freeze_encoder:
    for block in self.encoder_blocks:
        for param in block.parameters():
            param.requires_grad = False

✅ 結論

項目	正確處理方向
需要凍結的部分	encoder（下採樣與 bottleneck）
需要允許訓練的部分	decoder（尤其是 upsampling 模組）
切換 upsample 方式目的	比較不同上採樣方式對輸出的影響
是否要凍結 decoder	❌ 不要凍結，否則無法訓練新的 upsampling 結果

需要我幫你改寫一版完整 UNet 類別，支援 freeze_encoder 和 switch_upsample_mode() 嗎？

好的！我們來改寫 UNetSkipConnectionBlock 與 UNetGenerator 架構，讓 freeze_encoder() 和 switch_upsample_mode() 也成為「可插拔的功能模組」，透過類別參數注入（plug-in）的形式，未來可以更彈性地擴充不同的凍結/切換策略。

✅ 改寫重點：

抽象化功能模組：定義 EncoderFreezer 和 UpsampleSwitcher 類別。
透過初始化參數注入功能模組：在 UNetGenerator 中注入這些 plug-in 並調用。
保持原有功能不變：預設 plug-in 可以為 None，不會影響現有使用方式。

🔁 改寫後的程式碼

import torch
import torch.nn as nn

# === Plug-in 模組 ===

class EncoderFreezer:
    def freeze(self, module):
        if isinstance(module, UNetSkipConnectionBlock):
            if module.outermost:
                for param in module.downconv.parameters():
                    param.requires_grad = False
                if module.downnorm is not None:
                    for param in module.downnorm.parameters():
                        param.requires_grad = False
            if module.submodule is not None:
                self.freeze(module.submodule)

class UpsampleSwitcher:
    def __init__(self, upsample_mode='conv'):
        self.upsample_mode = upsample_mode

    def switch(self, module):
        if isinstance(module, UNetSkipConnectionBlock):
            module.upsample_mode = self.upsample_mode
            module.upsample = module._make_upsample_layer()
            if not module.outermost:
                module.upconv = nn.Conv2d(module.in_channels, module.out_channels, kernel_size=3, stride=1, padding=1)
                module.upnorm = nn.BatchNorm2d(module.out_channels)
            if module.submodule is not None:
                self.switch(module.submodule)

# === UNet 架構 ===

class UNetSkipConnectionBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        submodule=None,
        outermost=False,
        innermost=False,
        use_dropout=False,
        upsample_mode='conv'
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.submodule = submodule
        self.outermost = outermost
        self.innermost = innermost
        self.use_dropout = use_dropout
        self.upsample_mode = upsample_mode

        self.downconv = nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)
        self.downnorm = nn.BatchNorm2d(out_channels) if not outermost else None
        self.relu = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(0.5) if use_dropout else None

        self.upsample = self._make_upsample_layer()
        self.upconv = nn.Conv2d(out_channels * 2, in_channels, kernel_size=3, stride=1, padding=1) if not outermost else None
        self.upnorm = nn.BatchNorm2d(in_channels) if not outermost else None
        self.final_layer = nn.Conv2d(out_channels * 2, in_channels, kernel_size=1) if outermost else None

    def _make_upsample_layer(self):
        if self.upsample_mode == 'conv':
            return nn.ConvTranspose2d(self.out_channels, self.out_channels, kernel_size=4, stride=2, padding=1)
        elif self.upsample_mode == 'upsample':
            return nn.Sequential(
                nn.Upsample(scale_factor=2, mode='nearest'),
                nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, padding=1)
            )
        elif self.upsample_mode == 'pixelshuffle':
            return nn.Sequential(
                nn.Conv2d(self.out_channels, self.out_channels * 4, kernel_size=3, padding=1),
                nn.PixelShuffle(2)
            )
        else:
            raise ValueError(f"Unsupported upsample_mode: {self.upsample_mode}")

    def forward(self, x):
        down = self.downconv(x)
        if self.downnorm is not None:
            down = self.downnorm(down)
        down = self.relu(down)

        if self.submodule is not None:
            down = self.submodule(down)

        if self.dropout is not None:
            down = self.dropout(down)

        up = self.upsample(down)
        if not self.outermost:
            up = self.upconv(up)
            if self.upnorm is not None:
                up = self.upnorm(up)
            return torch.cat([x, up], dim=1)
        else:
            return self.final_layer(torch.cat([x, up], dim=1))

class UNetGenerator(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        num_downs,
        ngf=64,
        use_dropout=False,
        upsample_mode='conv',
        encoder_freezer=None,
        upsample_switcher=None,
    ):
        super().__init__()
        # 建立最底層的模組
        unet_block = UNetSkipConnectionBlock(ngf * 8, ngf * 8, innermost=True, upsample_mode=upsample_mode)

        # 疊加中間層
        for _ in range(num_downs - 5):
            unet_block = UNetSkipConnectionBlock(ngf * 8, ngf * 8, submodule=unet_block, use_dropout=use_dropout, upsample_mode=upsample_mode)

        # 中階層
        unet_block = UNetSkipConnectionBlock(ngf * 4, ngf * 8, submodule=unet_block, upsample_mode=upsample_mode)
        unet_block = UNetSkipConnectionBlock(ngf * 2, ngf * 4, submodule=unet_block, upsample_mode=upsample_mode)
        unet_block = UNetSkipConnectionBlock(ngf, ngf * 2, submodule=unet_block, upsample_mode=upsample_mode)

        # 最外層
        self.model = UNetSkipConnectionBlock(
            in_channels, ngf, submodule=unet_block, outermost=True, upsample_mode=upsample_mode
        )

        self.encoder_freezer = encoder_freezer
        self.upsample_switcher = upsample_switcher

    def forward(self, x):
        return self.model(x)

    def freeze_encoder(self):
        if self.encoder_freezer is not None:
            self.encoder_freezer.freeze(self.model)

    def switch_upsample_mode(self, new_mode):
        if self.upsample_switcher is not None:
            self.upsample_switcher.upsample_mode = new_mode
            self.upsample_switcher.switch(self.model)

✅ 使用方式

# 建立 plug-in 物件
encoder_freezer = EncoderFreezer()
upsample_switcher = UpsampleSwitcher(upsample_mode='pixelshuffle')

# 注入 plug-in 並建立模型
model = UNetGenerator(
    in_channels=1,
    out_channels=1,
    num_downs=7,
    ngf=64,
    encoder_freezer=encoder_freezer,
    upsample_switcher=upsample_switcher
)

# 切換上採樣方式
model.switch_upsample_mode('conv')

# 凍結 Encoder
model.freeze_encoder()

這樣的架構具備：

模組化、可插拔：功能邏輯抽象成類別，不綁定在模型內部。
擴充方便：可未來再加 EncoderUnfreezer、SelectiveFreezer、ProgressiveSwitcher 等功能。
測試容易：每個功能模組可以單獨測試與替換。

需要我幫你把這部分加到現有的訓練流程裡，或是包成可測試的單元嗎？

結論來說 chatGPT一直鬼打牆，反而是 gemini 一次就正確搞懂需求.

Python

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=64,
use_dropout=False, self_attention=False, attention_type='linear',
blur=False, outermost=False, innermost=False, use_transformer=False,
attn_layers=None, up_mode='conv', freeze_downsample=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
self.innermost = innermost
self.layer = layer
self.attn_layers = attn_layers or []
self._up_mode = up_mode
self.freeze_downsample = freeze_downsample
conv_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.downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=kernel_size, stride=stride, padding=padding, bias=conv_bias)
nn.init.kaiming_normal_(self.downconv.weight, nonlinearity='leaky_relu')
self.downrelu = nn.SiLU(inplace=True)
self.downnorm = norm_layer(inner_nc)
self.uprelu = nn.SiLU(inplace=True)
self.upnorm = norm_layer(outer_nc)
# Define the upsampling layer based on the initial up_mode
self._build_upsample(inner_nc, outer_nc, conv_bias)
self.down = nn.Sequential(self.downconv) if outermost or innermost else nn.Sequential(self.downrelu, self.downconv, self.downnorm)
if innermost:
if use_transformer:
# Assuming TransformerBlock is defined elsewhere
from .transformer_block import TransformerBlock
self.transformer_block = TransformerBlock(inner_nc)
# Assuming FiLMModulation is defined elsewhere
# from .film_layer import FiLMModulation
# self.film = FiLMModulation(inner_nc, embedding_dim)
if hasattr(self, 'film'): # 避免在沒有 FiLM 層時出錯
self.film = FiLMModulation(inner_nc, embedding_dim)
self.up = nn.Sequential(self.uprelu, self.upconv, self.upnorm)
elif outermost:
self.up = nn.Sequential(self.uprelu, self.upconv, nn.Tanh())
else:
self.up = nn.Sequential(self.uprelu, self.upconv, self.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:
# Assuming LinearAttention and SelfAttention are defined elsewhere
from .attention import LinearAttention, SelfAttention
self.attn_block = LinearAttention(inner_nc) if attention_type == 'linear' else SelfAttention(inner_nc)
else:
self.attn_block = None
# Assuming ResSkip is defined elsewhere
# from .residual_skip import ResSkip
# self.res_skip = ResSkip(outer_nc, outer_nc) if not outermost and not innermost and layer in [4, 5, 6, 7] else None
if not outermost and not innermost and layer in [4, 5, 6, 7]:
self.res_skip = ResSkip(outer_nc, outer_nc) if hasattr(self, 'res_skip') else None
# Freeze downsample layers if specified
if self.freeze_downsample:
for param in self.downconv.parameters():
param.requires_grad = False
for param in self.downnorm.parameters():
param.requires_grad = False
def _build_upsample(self, inner_nc, outer_nc, bias):
if self._up_mode == 'conv':
self.upconv = nn.ConvTranspose2d(inner_nc * (2 if not self.innermost and not self.outermost else 1),
outer_nc, kernel_size=4, stride=2, padding=1, output_padding=1, bias=bias)
nn.init.kaiming_normal_(self.upconv.weight)
elif self._up_mode == 'upsample':
self.upconv = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.Conv2d(inner_nc * (2 if not self.innermost and not self.outermost else 1),
outer_nc, kernel_size=3, stride=1, padding=1, bias=bias),
self.upnorm
)
nn.init.kaiming_normal_(self.upconv[1].weight)
elif self._up_mode == 'pixelshuffle':
self.upconv = nn.Sequential(
nn.Conv2d(inner_nc * (2 if not self.innermost and not self.outermost else 1),
outer_nc * 4, kernel_size=3, stride=1, padding=1, bias=bias),
nn.PixelShuffle(2),
self.upnorm
)
nn.init.kaiming_normal_(self.upconv[0].weight)
else:
raise ValueError(f"Unsupported up_mode: {self._up_mode}. Choose 'conv', 'upsample', or 'pixelshuffle'.")
def switch_upsample_mode(self, new_up_mode):
if new_up_mode not in ['conv', 'upsample', 'pixelshuffle']:
raise ValueError(f"Unsupported up_mode: {new_up_mode}. Choose 'conv', 'upsample', or 'pixelshuffle'.")
if new_up_mode == self._up_mode:
return  # No change needed
self._up_mode = new_up_mode
inner_nc = self.downconv.out_channels
outer_nc = self.downconv.in_channels if self.outermost else (self.submodule.downconv.in_channels if self.submodule else inner_nc * 2) # 修正 outer_nc 的判斷
conv_bias = self.upnorm.bias is not None
# Rebuild the upsampling layer
self._build_upsample(inner_nc, self.downconv.in_channels if self.outermost else outer_nc, conv_bias)
# Update the 'up' sequential layer
if self.outermost:
self.up = nn.Sequential(self.uprelu, self.upconv, nn.Tanh())
elif self.innermost:
self.up = nn.Sequential(self.uprelu, self.upconv, self.upnorm)
else:
up_seq = [self.uprelu, self.upconv, self.upnorm]
if hasattr(self.up, 'dropout'):
up_seq.append(self.up.dropout)
self.up = nn.Sequential(*up_seq)
def forward(self, x, style=None):
if hasattr(self, 'attn_block') and self.attn_block is not None:
x = self.attn_block(x)
encoded = self.down(x)
if self.innermost:
if hasattr(self, 'transformer_block'):
encoded = self.transformer_block(encoded)
if hasattr(self, 'film') and 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 hasattr(self, 'res_skip') and self.res_skip is not None:
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)
if decoded.shape[2:] != x.shape[2:]:
decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
if hasattr(self, 'res_skip') and self.res_skip is not None:
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=64,
norm_layer=nn.InstanceNorm2d, use_dropout=False,
self_attention=False, blur=False, attention_type='linear',
attn_layers=None, up_mode='conv', freeze_layers=None):
super(UNetGenerator, self).__init__()
if attn_layers is None:
attn_layers = []
if freeze_layers is None:
freeze_layers = []
self.unet_blocks = nn.ModuleList()
# 創建 UNet 的下採樣部分
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, input_nc=None, submodule=None,
norm_layer=norm_layer, layer=1, embedding_dim=embedding_dim,
self_attention=self_attention, blur=blur, innermost=True,
use_transformer=True, attention_type=attention_type,
attn_layers=attn_layers, up_mode=up_mode,
freeze_downsample=(1 in freeze_layers)
)
self.unet_blocks.append(unet_block)
for i in range(num_downs - 5):
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, input_nc=None, submodule=self.unet_blocks[-1],
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, up_mode=up_mode,
freeze_downsample=(i+2 in freeze_layers)
)
self.unet_blocks.append(unet_block)
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, submodule=self.unet_blocks[-1], norm_layer=norm_layer, layer=5, up_mode=up_mode, freeze_downsample=(5 in freeze_layers))
self.unet_blocks.append(unet_block)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, submodule=self.unet_blocks[-1], norm_layer=norm_layer, layer=6, up_mode=up_mode, freeze_downsample=(6 in freeze_layers))
self.unet_blocks.append(unet_block)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, submodule=self.unet_blocks[-1], norm_layer=norm_layer, layer=7, up_mode=up_mode, freeze_downsample=(7 in freeze_layers))
self.unet_blocks.append(unet_block)
# 創建 UNet 的最外層（輸出層）
self.model = UnetSkipConnectionBlock(
output_nc, ngf, input_nc=input_nc, submodule=self.unet_blocks[-1],
norm_layer=norm_layer, layer=8, outermost=True,
self_attention=self_attention, blur=blur,
attention_type=attention_type, attn_layers=attn_layers, up_mode=up_mode
)
self.unet_blocks.append(self.model)
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_real_A = self.model(x, style)
return fake_B, encoded_real_A
def encode(self, x, style_or_label=None):
style = self._prepare_style(style_or_label)
_, encoded_real_A = self.model(x, style)
return encoded_real_A
def switch_upsample_mode(self, new_up_mode):
for block in self.unet_blocks:
block.switch_upsample_mode(new_up_mode)
# 假設 ResSkip, LinearAttention, SelfAttention, TransformerBlock, FiLMModulation,
# CategoryLoss, PerceptualLoss, EdgeAwareLoss, Discriminator, init_net 這些類別和函數
# 都在其他地方定義並可以被 import。
# 為了讓程式碼可以獨立運行，我將創建一些簡單的佔位符類別。
class ResSkip(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
return self.conv(x) + x
class LinearAttention(nn.Module):
def __init__(self, dim):
super().__init__()
self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=False)
self.proj = nn.Conv2d(dim, dim, 1)
def forward(self, x): return self.proj(torch.softmax(self.qkv(x), dim=1))
class SelfAttention(nn.Module):
def __init__(self, dim):
super().__init__()
self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=False)
self.proj = nn.Conv2d(dim, dim, 1)
def forward(self, x): return self.proj(torch.softmax(self.qkv(x), dim=1))
class TransformerBlock(nn.Module):
def __init__(self, dim):
super().__init__()
self.attn = nn.Conv2d(dim, dim, 1)
self.ffn = nn.Conv2d(dim, dim, 1)
def forward(self, x): return self.ffn(self.attn(x)) + x
class FiLMModulation(nn.Module):
def __init__(self, num_features, embedding_dim):
super().__init__()
self.embed = nn.Linear(embedding_dim, num_features * 2)
def forward(self, x, style):
gamma, beta = self.embed(style).chunk(2, dim=-1)
gamma = gamma.unsqueeze(2).unsqueeze(3).expand_as(x)
beta = beta.unsqueeze(2).unsqueeze(3).expand_as(x)
return x * (1 + gamma) + beta
class CategoryLoss(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.criterion = nn.CrossEntropyLoss()
def forward(self, output, target):
return self.criterion(output, target)
class PerceptualLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, fake, real):
return torch.mean(torch.abs(fake - real)) # 簡單的 L1 距離作為範例
class EdgeAwareLoss(nn.Module):
def __init__(self):
super().__init__()
class Discriminator(nn.Module):
def __init__(self, input_nc, embedding_num, ndf=64, blur=False, norm_layer=nn.BatchNorm2d):
super().__init__()
# 簡單的判別器作為範例
self.model = nn.Sequential(
nn.Conv2d(input_nc, ndf, kernel_size=4, stride=2, padding=1),

使用 self._up_mode 而不是直接修改 self.up_mode 的原因，主要是為了在類別內部管理上採樣模式的狀態，並在切換模式時觸發相應的操作。這樣做有以下幾個優點：

內部狀態管理： self._up_mode 可以被視為類別 UnetSkipConnectionBlock 內部用於記錄當前上採樣模式的私有變數。使用帶有底線前綴的變數（雖然在 Python 中不是強制的私有，但慣例上表示內部使用）有助於區分外部可訪問的屬性和類別內部使用的狀態。
觸發模式切換邏輯： 當你想要切換上採樣模式時，你會調用 switch_upsample_mode() 方法。在這個方法內部，self._up_mode 會被更新為新的模式，並且會觸發重新構建上採樣層 (self._build_upsample()) 的邏輯。如果直接修改 self.up_mode，就不會有這個額外的步驟來更新網路結構。
避免外部直接修改導致不一致： 如果允許外部直接修改 self.up_mode，例如 block.up_mode = 'pixelshuffle'，那麼上採樣模式的內部狀態雖然改變了，但是實際使用的上採樣層 (self.upconv) 並不會隨之更新，導致網路行為與預期的模式不符。通過提供一個專門的方法 switch_upsample_mode()，可以確保在模式改變時，網路的相關組件也能正確地更新。
封裝性： 使用 self._up_mode 並提供 switch_upsample_mode() 方法是一種更好的封裝方式。它將上採樣模式的切換邏輯封裝在類別內部，使得使用者只需要調用一個方法即可完成模式切換，而不需要關心底層是如何實現的。

總結來說：

self._up_mode 是用於追蹤當前上採樣模式的內部狀態變數。
switch_upsample_mode() 方法是一個受控的介面，用於安全地切換上採樣模式。在這個方法內部，self._up_mode 會被更新，並且會觸發重新構建上採樣層的邏輯，從而保證網路結構與所設定的模式一致。

這種設計模式使得程式碼更易於管理、維護和理解，並降低了因外部不當修改導致錯誤的可能性。

上面的版本, 有點複雜, 而且有錯誤, 要改太麻煩, 直接改用下列這個版本:

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=64,
                 use_dropout=False, self_attention=False, attention_type='linear',
                 blur=False, outermost=False, innermost=False, use_transformer=False,
                 attn_layers=None, up_mode='conv', freeze_downsample=False):
        super(UnetSkipConnectionBlock, self).__init__()
        self.outermost = outermost
        self.innermost = innermost
        self.layer = layer
        self.attn_layers = attn_layers or []
        self.up_mode = up_mode
        self.freeze_downsample = freeze_downsample

        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.downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=kernel_size, stride=stride, padding=padding, bias=use_bias)
        nn.init.kaiming_normal_(self.downconv.weight, nonlinearity='leaky_relu')

        self.downrelu = nn.SiLU(inplace=True)
        self.downnorm = norm_layer(inner_nc)
        self.uprelu = nn.SiLU(inplace=True)
        self.upnorm = norm_layer(outer_nc)

        if outermost:
            if self.up_mode == 'conv':
                self.upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, output_padding=1, bias=use_bias)
                nn.init.kaiming_normal_(self.upconv.weight)
            elif self.up_mode == 'upsample':
                self.upconv = nn.Sequential(
                    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
                    nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[1].weight)
            elif self.up_mode == 'pixelshuffle':
                self.upconv = nn.Sequential(
                    nn.Conv2d(inner_nc * 2 if not innermost else inner_nc, outer_nc * 4, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    nn.PixelShuffle(2),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[0].weight)
            else:
                raise ValueError(f"Unsupported up_mode: {self.up_mode}. Choose 'conv', 'upsample', or 'pixelshuffle'.")
            self.down = nn.Sequential(self.downconv)
            self.up = nn.Sequential(self.uprelu, self.upconv, nn.Tanh())
        elif innermost:
            if self.up_mode == 'conv':
                self.upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, output_padding=1, bias=use_bias)
                nn.init.kaiming_normal_(self.upconv.weight)
            elif self.up_mode == 'upsample':
                self.upconv = nn.Sequential(
                    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
                    nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[1].weight)
            elif self.up_mode == 'pixelshuffle':
                self.upconv = nn.Sequential(
                    nn.Conv2d(inner_nc, outer_nc * 4, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    nn.PixelShuffle(2),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[0].weight)
            else:
                raise ValueError(f"Unsupported up_mode: {self.up_mode}. Choose 'conv', 'upsample', or 'pixelshuffle'.")
            self.down = nn.Sequential(self.downrelu, self.downconv)
            self.up = nn.Sequential(self.uprelu, self.upconv, self.upnorm)
            if use_transformer:
                # Assuming TransformerBlock is defined elsewhere
                from .transformer_block import TransformerBlock
                self.transformer_block = TransformerBlock(inner_nc)
            # Assuming FiLMModulation is defined elsewhere
            # from .film_layer import FiLMModulation
            # self.film = FiLMModulation(inner_nc, embedding_dim)
            if hasattr(self, 'film'): # 避免在沒有 FiLM 層時出錯
                self.film = FiLMModulation(inner_nc, embedding_dim)
        else:
            if self.up_mode == 'conv':
                self.upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, output_padding=1, bias=use_bias)
                nn.init.kaiming_normal_(self.upconv.weight)
            elif self.up_mode == 'upsample':
                self.upconv = nn.Sequential(
                    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
                    nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[1].weight)
            elif self.up_mode == 'pixelshuffle':
                self.upconv = nn.Sequential(
                    nn.Conv2d(inner_nc * 2, outer_nc * 4, kernel_size=3, stride=1, padding=1, bias=use_bias),
                    nn.PixelShuffle(2),
                    self.upnorm
                )
                nn.init.kaiming_normal_(self.upconv[0].weight)
            else:
                raise ValueError(f"Unsupported up_mode: {self.up_mode}. Choose 'conv', 'upsample', or 'pixelshuffle'.")
            self.down = nn.Sequential(self.downrelu, self.downconv, self.downnorm)
            self.up = nn.Sequential(self.uprelu, self.upconv, self.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:
            # Assuming LinearAttention and SelfAttention are defined elsewhere
            from .attention import LinearAttention, SelfAttention
            self.attn_block = LinearAttention(inner_nc) if attention_type == 'linear' else SelfAttention(inner_nc)
        else:
            self.attn_block = None

        # Assuming ResSkip is defined elsewhere
        # from .residual_skip import ResSkip
        # self.res_skip = ResSkip(outer_nc, outer_nc) if not outermost and not innermost and layer in [4, 5, 6, 7] else None
        if not outermost and not innermost and layer in [4, 5, 6, 7]:
            self.res_skip = ResSkip(outer_nc, outer_nc) if hasattr(self, 'res_skip') else None

        # Freeze downsample layers if specified
        if self.freeze_downsample:
            for param in self.downconv.parameters():
                param.requires_grad = False
            for param in self.downnorm.parameters():
                param.requires_grad = False

    def forward(self, x, style=None):
        if hasattr(self, 'attn_block') and self.attn_block is not None:
            x = self.attn_block(x)
        encoded = self.down(x)
        if self.innermost:
            if hasattr(self, 'transformer_block'):
                encoded = self.transformer_block(encoded)
            if hasattr(self, 'film') and 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 hasattr(self, 'res_skip') and self.res_skip is not None:
                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)
            if decoded.shape[2:] != x.shape[2:]:
                decoded = F.interpolate(decoded, size=x.shape[2:], mode='bilinear', align_corners=False)
            if hasattr(self, 'res_skip') and self.res_skip is not None:
                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=64,
                 norm_layer=nn.InstanceNorm2d, use_dropout=False,
                 self_attention=False, blur=False, attention_type='linear',
                 attn_layers=None, up_mode='conv', freeze_layers=None):
        super(UNetGenerator, self).__init__()
        if attn_layers is None:
            attn_layers = []
        if freeze_layers is None:
            freeze_layers = []

        # 創建 UNet 的下採樣部分
        unet_block = UnetSkipConnectionBlock(
            ngf * 8, ngf * 8, input_nc=None, submodule=None,
            norm_layer=norm_layer, layer=1, embedding_dim=embedding_dim,
            self_attention=self_attention, blur=blur, innermost=True,
            use_transformer=True, attention_type=attention_type,
            attn_layers=attn_layers, up_mode=up_mode,
            freeze_downsample=(1 in freeze_layers)
        )

        for i in range(num_downs - 5):
            unet_block = UnetSkipConnectionBlock(
                ngf * 8, ngf * 8, input_nc=None, 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, up_mode=up_mode,
                freeze_downsample=(i+2 in freeze_layers)
            )

        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, submodule=unet_block, norm_layer=norm_layer, layer=5, up_mode=up_mode, freeze_downsample=(5 in freeze_layers))
        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, submodule=unet_block, norm_layer=norm_layer, layer=6, up_mode=up_mode, freeze_downsample=(6 in freeze_layers))
        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, submodule=unet_block, norm_layer=norm_layer, layer=7, up_mode=up_mode, freeze_downsample=(7 in freeze_layers))

        # 創建 UNet 的最外層（輸出層）
        self.model = UnetSkipConnectionBlock(
            output_nc, ngf, input_nc=input_nc, submodule=unet_block,
            norm_layer=norm_layer, layer=8, outermost=True,
            self_attention=self_attention, blur=blur,
            attention_type=attention_type, attn_layers=attn_layers, up_mode=up_mode
        )

        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_real_A = self.model(x, style)
        return fake_B, encoded_real_A

    def encode(self, x, style_or_label=None):
        style = self._prepare_style(style_or_label)
        _, encoded_real_A = self.model(x, style)
        return encoded_real_A

# 假設 ResSkip, LinearAttention, SelfAttention, TransformerBlock, FiLMModulation,
# CategoryLoss, PerceptualLoss, EdgeAwareLoss, Discriminator, init_net 這些類別和函數
# 都在其他地方定義並可以被 import。
# 為了讓程式碼可以獨立運行，我將創建一些簡單的佔位符類別。

class ResSkip(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
    def forward(self, x):
        return self.conv(x) + x

class LinearAttention(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=False)
        self.proj = nn.Conv2d(dim, dim, 1)
    def forward(self, x): return self.proj(torch.softmax(self.qkv(x), dim=1))

class SelfAttention(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=False)
        self.proj = nn.Conv2d(dim, dim, 1)
    def forward(self, x): return self.proj(torch.softmax(self.qkv(x), dim=1))

class TransformerBlock(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.attn = nn.Conv2d(dim, dim, 1)
        self.ffn = nn.Conv2d(dim, dim, 1)
    def forward(self, x): return self.ffn(self.attn(x)) + x

class FiLMModulation(nn.Module):
    def __init__(self, num_features, embedding_dim):
        super().__init__()
        self.embed = nn.Linear(embedding_dim, num_features * 2)
    def forward(self, x, style):
        gamma, beta = self.embed(style).chunk(2, dim=-1)
        gamma = gamma.unsqueeze(2).unsqueeze(3).expand_as(x)
        beta = beta.unsqueeze(2).unsqueeze(3).expand_as(x)
        return x * (1 + gamma) + beta

class CategoryLoss(nn.Module):
    def __init__(self, num_classes):
        super().__init__()
        self.criterion = nn.CrossEntropyLoss()
    def forward(self, output, target):
        return self.criterion(output, target)

class PerceptualLoss(nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, fake, real):
        return torch.mean(torch.abs(fake - real)) # 簡單的 L1 距離作為範例

class EdgeAwareLoss(nn.Module):
    def __init__(self):
        super().__init__()

是的，當 freeze_downsample=True 時，你的優化器（optimizer）仍然需要傳遞模型中可訓練的參數。

原因：

freeze_downsample=True 的作用： 這個設定會將 UnetSkipConnectionBlock 中下採樣卷積層 (self.downconv) 和下採樣歸一化層 (self.downnorm) 的 requires_grad 屬性設置為 False。這意味著這些層的參數在反向傳播過程中不會計算梯度，因此也不會被優化器更新。
模型中仍然存在可訓練的參數： 即使你凍結了下採樣層，UNet 模型中仍然存在其他可訓練的參數，例如：
- 上採樣卷積層 (self.upconv) 及其歸一化層 (self.upnorm) 的參數。
- 最外層的輸出卷積層的參數。
- 最內層可能存在的 Transformer 塊或 FiLM 層的參數。
- 注意力機制 (self.attn_block) 的參數（如果使用）。
- 殘差跳躍連接 (self.res_skip) 的卷積層參數（如果使用）。
- UNetGenerator 中的 Embedding 層 (self.embedder) 的參數。
優化器的職責： 優化器的目標是更新模型中所有 requires_grad 為 True 的參數，以最小化損失函數。如果你不將這些可訓練的參數傳遞給優化器，那麼它們將永遠不會被更新，模型的訓練將無法進行或不完整。

正確的做法：

在創建優化器時，你需要遍歷你的 UNetGenerator (self.netG) 的所有參數，並將它們傳遞給優化器。PyTorch 的 torch.optim 模塊會自動識別哪些參數的 requires_grad 為 True 並對它們進行優化。

範例程式碼 (在 Zi2ZiModel.setup() 方法中)：

Python

    def setup(self):
# ... (模型定義部分) ...
# 將 netG 的所有參數傳遞給優化器
self.optimizer_G = torch.optim.AdamW(self.netG.parameters(), lr=self.lr, betas=(self.beta1, 0.999), weight_decay=self.weight_decay)
self.optimizer_D = torch.optim.AdamW(self.netD.parameters(), lr=self.lr, betas=(self.beta1, 0.999), weight_decay=self.weight_decay)
# ... (scheduler 等其他設定) ...

總結：

即使你凍結了部分下採樣層，模型中仍然存在其他需要訓練的參數。因此，在創建優化器時，你應該將整個生成器模型 (self.netG.parameters()) 的參數都傳遞給它。優化器會自動處理只更新 requires_grad 為 True 的參數。

使用 conv 上採樣, 訓練出來的checkpoint 的 l1_loss 已經到5, 但改用 pixelshuffle 進行上採樣訓練, 並 freeze down sample, 持續訓練的 l1_loss 維持到 35 ~ 39, 是不是等l1_loss 低於 10 再解凍 down sample 比較好?

這其實是一個非常合理、實務上常見的策略，特別是在做模型微調或階段性訓練（stage-wise training）時。以下是針對你情況的說明與建議：

🔍 狀況回顧：

使用 conv 上採樣訓練出來的 checkpoint，L1 loss ≈ 5，已經相當低。
切換成 pixelshuffle 上採樣，freeze downsample 區塊，再訓練時，L1 loss ≈ 35 ~ 39，代表新上採樣結構還沒適應現有 features。
擔心 premature 解凍 downsample 會造成 feature drift，使整個模型不穩。

✅ 建議：等 L1 loss < 10 再解凍 downsample 是明智的

這樣做的原因如下：

1️⃣ 先讓 pixelshuffle 的 decoder 適應現有 encoder features

凍結 downsample 可以確保 encoder 提供的 feature 是穩定的（原本 conv 上採樣學來的），
pixelshuffle 的 decoder 有足夠的時間去學會如何解析這些 feature 並產生合適輸出。

2️⃣ 等 decoder 收斂後，再允許 encoder 微調

當 decoder 已經有基本能力後，再解凍 encoder（downsample），可以共同微調，
避免一開始兩邊都亂動，導致 learning signal 擴散或惡化。

3️⃣ 設定一個解凍閾值是穩定策略

建議設一個觸發條件，例如：
- L1 loss < 10 且持續下降
- 或者 validation set 有明顯提升（若有）
屆時再執行：model.unfreeze_encoder() 或類似方法。

💡 額外建議（進階技巧）：

warmup decoder → joint tuning： 你可以視 pixelshuffle decoder 的收斂狀況設一個 epoch/step 門檻，例如： if epoch >= 10 or running_l1_loss < 10: model.unfreeze_encoder()
lr scheduler 可以分開設計： 若使用 optimizer.param_groups，可給 encoder 比較小的 learning rate，讓其慢慢 fine-tune，避免破壞原有特徵。
visual check + loss 追蹤： 可視化生成圖像與 loss trend，會有助於決定最佳的解凍時機。

如果你需要我幫你寫個自動化解凍策略的 training loop callback（比如 L1 loss < 閾值就解凍），也可以隨時說 👌
需要我幫你整理一版嗎？

Max的程式語言筆記