change name

src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

@@ -244,23 +244,23 @@ def __init__(self, config: ViTPoseBackboneConfig) -> None:
         experts = [nn.Linear(hidden_features, part_features) for _ in range(num_experts)]
         self.experts = nn.ModuleList(experts)
 
-    def forward(self, x, indices):
-        expert_x = torch.zeros_like(x[:, :, -self.part_features :])
+    def forward(self, hidden_state, indices):
+        expert_hidden_state = torch.zeros_like(hidden_state[:, :, -self.part_features :])
 
-        x = self.fc1(x)
-        x = self.act(x)
-        shared_x = self.fc2(x)
+        hidden_state = self.fc1(hidden_state)
+        hidden_state = self.act(hidden_state)
+        shared_hidden_state = self.fc2(hidden_state)
         indices = indices.view(-1, 1, 1)
 
         # to support ddp training
         for i in range(self.num_experts):
             selectedIndex = indices == i
-            current_x = self.experts[i](x) * selectedIndex
-            expert_x = expert_x + current_x
+            current_hidden_state = self.experts[i](hidden_state) * selectedIndex
+            expert_hidden_state = expert_hidden_state + current_hidden_state
 
-        x = torch.cat([shared_x, expert_x], dim=-1)
+        hidden_state = torch.cat([shared_hidden_state, expert_hidden_state], dim=-1)
 
-        return x
+        return hidden_state
 
 
 class ViTPoseBackboneMLP(nn.Module):