add trust_remote_code=True,

model.py

@@ -62,6 +62,7 @@ class EvalNet:
         if torch.cuda.is_available():
             self.model = self.model.cuda()
             self.classifier = self.classifier.cuda()
+
         self.model.eval()
 
     def _get_backbone(self, backbone_ver, backbone_list):
@@ -76,6 +77,7 @@ class EvalNet:
             "monetjoe/cv_backbones",
             split=self.imgnet_ver,
             cache_dir="./__pycache__",
+            trust_remote_code=True,
         )
         backbone_info = self._get_backbone(backbone, backbone_list)
         return (
@@ -171,13 +173,11 @@ class EvalNet:
 
         if self.type == "convnext":
             out = self.model(x)
-            out = self.classifier(out).squeeze()
-            return out
+            return self.classifier(out).squeeze()
 
         else:
             out = self.model(x)
             out = out.view(
                 out.size(0), self.out_channel_before_classifier, self.H, self.H
             )
-            out = self.classifier(out).squeeze()
-            return out
+            return self.classifier(out).squeeze()

t_model.py

@@ -79,6 +79,7 @@ class t_EvalNet:
             "monetjoe/cv_backbones",
             split=self.imgnet_ver,
             cache_dir="./__pycache__",
+            trust_remote_code=True,
         )
         backbone_info = self._get_backbone(backbone, backbone_list)
         return (
@@ -140,14 +141,12 @@ class t_EvalNet:
             x = self.model.encoder(x)
             x = x[:, 1:].permute(0, 2, 1)
             x = x.unsqueeze(2)
-            x = self.classifier(x).squeeze()  # x shape: [bsz, hidden_dim, 1, seq_len]
-            return x
+            return self.classifier(x).squeeze()
 
         elif self.type == "swin_transformer":
             x = self.model.features(x)  # [B, H, W, C]
             x = x.permute(0, 3, 1, 2)
             x = self.avgpool(x)  # [B, C, 1, W]
-            x = self.classifier(x).squeeze()
-            return x
+            return self.classifier(x).squeeze()
 
         return None