feat: 更新框架，新增数据及定义和backbone

roboimi/vla/modules/encoders.py

@@ -30,17 +30,17 @@ class MLP(nn.Module):
 class SinusoidalPositionalEncoding(nn.Module):
     def __init__(
             self,
-            emb_dim
+            embed_dim
     ):
         super().__init__()
-        self.emb_dim = emb_dim
+        self.embed_dim = embed_dim
 
     def forward(self, timesteps):
         timesteps = timesteps.float()
         B, T = timesteps.shape
         device = timesteps.device
 
-        half_dim = self.emb_dim // 2
+        half_dim = self.embed_dim // 2
 
         exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
             torch.log(torch.tensor(10000.0)) / half_dim
@@ -58,14 +58,14 @@ class ActionEncoder(nn.Module):
     def __init__(
             self,
             action_dim,
-            emb_dim,
+            embed_dim,
             
     ):
         super().__init__()
-        self.W1 = nn.Linear(action_dim, emb_dim)
+        self.W1 = nn.Linear(action_dim, embed_dim)
         self.W2 = nn.Linear(2 * action_dim, action_dim)
-        self.W3 = nn.Linear(emb_dim, emb_dim)
-        self.pos_encoder = SinusoidalPositionalEncoding(emb_dim)
+        self.W3 = nn.Linear(embed_dim, embed_dim)
+        self.pos_encoder = SinusoidalPositionalEncoding(embed_dim)
 
     def forward(
             self,
@@ -89,13 +89,13 @@ class StateEncoder(nn.Module):
             self,
             state_dim,
             hidden_dim,
-            emb_dim
+            embed_dim
     ):
         super().__init__()
         self.mlp = MLP(
             state_dim,
             hidden_dim,
-            emb_dim
+            embed_dim
         )
 
     def forward(
@@ -103,4 +103,4 @@ class StateEncoder(nn.Module):
             states
     ):
         state_emb = self.mlp(states)
-        return state_emb # [B, 1, emb_dim]
+        return state_emb # [B, 1, embed_dim]