删除残差路径和shortcut，镜像问题仍存在

models/swiftformer_temporal.py

@@ -11,7 +11,7 @@ from timm.layers import DropPath, trunc_normal_
 
 
 class DecoderBlock(nn.Module):
-    """Upsampling block for frame prediction decoder with residual connections"""
+    """Upsampling block for frame prediction decoder without residual connections"""
     def __init__(self, in_channels, out_channels, kernel_size=3, stride=2, padding=1, output_padding=1):
         super().__init__()
         # 主路径：反卷积 + 两个卷积层
@@ -31,28 +31,6 @@ class DecoderBlock(nn.Module):
                               kernel_size=3, padding=1, bias=False)
         self.bn3 = nn.BatchNorm2d(out_channels)
         
-        # 残差路径：如果需要改变通道数或空间尺寸
-        self.shortcut = nn.Identity()
-        if in_channels != out_channels or stride != 1:
-            # 使用1x1卷积调整通道数，如果需要上采样则使用反卷积
-            if stride == 1:
-                self.shortcut = nn.Sequential(
-                    nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
-                    nn.BatchNorm2d(out_channels)
-                )
-            else:
-                self.shortcut = nn.Sequential(
-                    nn.ConvTranspose2d(
-                        in_channels, out_channels,
-                        kernel_size=1,
-                        stride=stride,
-                        padding=0,
-                        output_padding=output_padding,
-                        bias=False
-                    ),
-                    nn.BatchNorm2d(out_channels)
-                )
-        
         # 使用ReLU激活函数
         self.activation = nn.ReLU(inplace=True)
         
@@ -67,13 +45,6 @@ class DecoderBlock(nn.Module):
         nn.init.kaiming_normal_(self.conv1.weight, mode='fan_out', nonlinearity='relu')
         nn.init.kaiming_normal_(self.conv2.weight, mode='fan_out', nonlinearity='relu')
         
-        # 初始化shortcut
-        if not isinstance(self.shortcut, nn.Identity):
-            # shortcut现在是Sequential，需要初始化其中的卷积层
-            for module in self.shortcut:
-                if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
-                    nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
-        
         # 初始化BN层（使用默认初始化）
         for m in self.modules():
             if isinstance(m, nn.BatchNorm2d):
@@ -81,8 +52,6 @@ class DecoderBlock(nn.Module):
                 nn.init.constant_(m.bias, 0)
         
     def forward(self, x):
-        identity = self.shortcut(x)
-        
         # 主路径
         x = self.conv_transpose(x)
         x = self.bn1(x)
@@ -94,9 +63,6 @@ class DecoderBlock(nn.Module):
         
         x = self.conv2(x)
         x = self.bn3(x)
-        
-        # 残差连接
-        x = x + identity
         x = self.activation(x)
         return x
 
@@ -105,28 +71,28 @@ class FramePredictionDecoder(nn.Module):
     """Improved decoder for frame prediction"""
     def __init__(self, embed_dims, output_channels=1):
         super().__init__()
-        # Reverse the embed_dims for decoder
-        decoder_dims = embed_dims[::-1]
+        # Define decoder dimensions independently (no skip connections)
+        start_dim = embed_dims[-1]
+        decoder_dims = [start_dim // (2 ** i) for i in range(4)]  # e.g., [220, 110, 55, 27] for XS
         
         self.blocks = nn.ModuleList()
         
-        # 调整顺序：将stride=4放在倒数第二的位置
-        # 第一个block：stride=2 (220 -> 112)
+        # 第一个block：stride=2 (decoder_dims[0] -> decoder_dims[1])
         self.blocks.append(DecoderBlock(
             decoder_dims[0], decoder_dims[1],
             kernel_size=3, stride=2, padding=1, output_padding=1
         ))
-        # 第二个block：stride=2 (112 -> 56)
+        # 第二个block：stride=2 (decoder_dims[1] -> decoder_dims[2])
         self.blocks.append(DecoderBlock(
             decoder_dims[1], decoder_dims[2],
             kernel_size=3, stride=2, padding=1, output_padding=1
         ))
-        # 第三个block：stride=2 (56 -> 48)
+        # 第三个block：stride=2 (decoder_dims[2] -> decoder_dims[3])
         self.blocks.append(DecoderBlock(
             decoder_dims[2], decoder_dims[3],
             kernel_size=3, stride=2, padding=1, output_padding=1
         ))
-        # 第四个block：stride=4 (48 -> 64)，放在倒数第二的位置
+        # 第四个block：stride=4 (decoder_dims[3] -> 64)，放在倒数第二的位置
         self.blocks.append(DecoderBlock(
             decoder_dims[3], 64,
             kernel_size=3, stride=4, padding=1, output_padding=3  # stride=4放在这里
@@ -138,7 +104,7 @@ class FramePredictionDecoder(nn.Module):
             nn.Conv2d(64, 32, kernel_size=3, padding=1, bias=True),
             nn.ReLU(inplace=True),
             nn.Conv2d(32, output_channels, kernel_size=3, padding=1, bias=True),
-            nn.Tanh()  # 添加Tanh激活函数，约束输出在[-1, 1]范围内
+            nn.Tanh()
         )
         
     def forward(self, x):