fix bug in model's parameters

ChEB-AI · Dec 12, 2023 · ffaa2c6 · ffaa2c6
1 parent 29fc9d4
commit ffaa2c6
Showing 1 changed file with 12 additions and 12 deletions.
diff --git a/chebai/models/electra.py b/chebai/models/electra.py
@@ -483,17 +483,17 @@ def __init__(self, hidden_size=None, embeddings_to_points_hidden_size=None, embe
         super().__init__(**kwargs)
 
         self.config = ElectraConfig(**kwargs["config"], output_attentions=True)
-        in_dim = self.config.hidden_size
-        hidden_dim = self.config.embeddings_to_points_hidden_size
-        out_dim = self.config.embeddings_dimensions
+        self.in_dim = self.config.hidden_size
+        self.hidden_dim = self.config.embeddings_to_points_hidden_size
+        self.out_dim = self.config.embeddings_dimensions
 
         #self.boxes = nn.Parameter(torch.rand((self.config.num_labels, out_dim, 2)))
-        self.boxes = nn.Parameter(torch.rand((self.config.num_labels, out_dim, 2)) * 3 )
+        self.boxes = nn.Parameter(torch.rand((self.config.num_labels, self.out_dim, 2)) * 3 )
 
         self.embeddings_to_points = nn.Sequential(
-            nn.Linear(in_dim, hidden_dim),
+            nn.Linear(self.in_dim, self.hidden_dim),
             nn.ReLU(),
-            nn.Linear(hidden_dim, out_dim)
+            nn.Linear(self.hidden_dim, self.out_dim)
         )
 
     def forward(self, data, **kwargs):
@@ -530,16 +530,16 @@ def __init__(self, hidden_size=None, embeddings_to_points_hidden_size=None, embe
         super().__init__(**kwargs)
 
         self.config = ElectraConfig(**kwargs["config"], output_attentions=True)
-        in_dim = self.config.hidden_size
-        hidden_dim = self.config.embeddings_to_points_hidden_size
-        out_dim = self.config.embeddings_dimensions
+        self.in_dim = self.config.hidden_size
+        self.hidden_dim = self.config.embeddings_to_points_hidden_size
+        self.out_dim = self.config.embeddings_dimensions
 
-        self.boxes = nn.Parameter(torch.rand((self.config.num_labels, out_dim, 2)))
+        self.boxes = nn.Parameter(torch.rand((self.config.num_labels, self.out_dim, 2)))
 
         self.embeddings_to_points = nn.Sequential(
-            nn.Linear(in_dim, hidden_dim),
+            nn.Linear(self.in_dim, self.hidden_dim),
             nn.ReLU(),
-            nn.Linear(hidden_dim, out_dim)
+            nn.Linear(self.hidden_dim, self.out_dim)
         )
 
     def forward(self, data, **kwargs):