fix: 由于设置的采样点太小，取消Encoder1D的池化操作

brep2sdf/networks/encoder.py

@@ -77,6 +77,45 @@ class UNetMidBlock1D(nn.Module):
         return x
 
 class Encoder1D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 256,
+        block_out_channels: Tuple[int] = (64, 128, 256),
+        layers_per_block: int = 2,  # 添加这个参数但不使用
+    ):
+        super().__init__()
+        self.conv_in = nn.Conv1d(in_channels, block_out_channels[0], 1)  # 使用1x1卷积
+        
+        self.blocks = nn.ModuleList([])
+        in_ch = block_out_channels[0]
+        for out_ch in block_out_channels:
+            block = nn.Sequential(
+                nn.Conv1d(in_ch, out_ch, 1),  # 1x1卷积
+                nn.BatchNorm1d(out_ch),
+                nn.ReLU(inplace=True)
+            )
+            self.blocks.append(block)
+            in_ch = out_ch
+            
+        # 添加中间块和输出卷积
+        self.mid_block = nn.Sequential(
+            nn.Conv1d(block_out_channels[-1], block_out_channels[-1], 1),
+            nn.BatchNorm1d(block_out_channels[-1]),
+            nn.ReLU(inplace=True)
+        )
+        
+        self.conv_out = nn.Conv1d(block_out_channels[-1], out_channels, 1)
+    
+    def forward(self, x):
+        x = self.conv_in(x)
+        for block in self.blocks:  # 使用self.blocks而不是self.down_blocks
+            x = block(x)
+        x = self.mid_block(x)
+        x = self.conv_out(x)
+        return x
+
+class Encoder1D_(nn.Module):
     """一维编码器"""
     def __init__(
         self,