修改了算法代码,并建立了一个简单的训练脚本.修改bert处理二维输入,移除PPO的permute参数

Algorithm/bert.py

@@ -28,17 +28,26 @@ class Bert(nn.Module):
         self.classifier.train()
 
     def forward(self, x, mask=None):
+        # x可以是2D (batch_size, input_dim) 或 3D (batch_size, seq_len, feature_dim)
+        is_2d_input = (x.dim() == 2)
+        
+        if is_2d_input:
+            # 如果输入是2D,添加一个序列维度
+            x = x.unsqueeze(1)  # (batch_size, 1, input_dim)
+        
         # x: (batch_size, seq_len, input_dim)
         # 线性投影
-        x = self.projection(x)  # (batch_size, input_dim, embed_dim)
+        x = self.projection(x)  # (batch_size, seq_len, embed_dim)
 
         batch_size = x.size(0)
         if self.CLS:
             cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-            x = torch.cat([cls_tokens, x], dim=1)  # (batch_size, 29, embed_dim)
+            x = torch.cat([cls_tokens, x], dim=1)  # (batch_size, seq_len+1, embed_dim)
 
-        # 添加位置编码
-        x = x + self.pos_embed
+        # 添加位置编码(截断或扩展以匹配序列长度)
+        seq_len = x.size(1)
+        pos_embed = self.pos_embed[:, :seq_len, :]
+        x = x + pos_embed
 
         # 转置为(seq_len, batch_size, embed_dim)
         x = x.permute(1, 0, 2)