hdhfds 44

inference.py

@@ -45,8 +45,8 @@ class InferenceService:
         # Disable gradients
         for m in [self.resnet, self.vit]:
             if m is not None:
-            for p in m.parameters():
-                p.requires_grad_(False)
+                for p in m.parameters():
+                    p.requires_grad_(False)
 
         # Update overall status
         self.models_loaded = self.resnet_loaded and self.vit_loaded
@@ -177,8 +177,8 @@ class InferenceService:
         # Disable gradients
         for m in [self.resnet, self.vit]:
             if m is not None:
-            for p in m.parameters():
-                p.requires_grad_(False)
+                for p in m.parameters():
+                    p.requires_grad_(False)
         
         # Update overall status
         self.models_loaded = self.resnet_loaded and self.vit_loaded
@@ -202,12 +202,12 @@ class InferenceService:
             return []
             
         try:
-        batch = torch.stack([self.transform(img) for img in images])
-        batch = batch.to(self.device, memory_format=torch.channels_last)
-        use_amp = (self.device == "cuda")
-        with torch.autocast(device_type=("cuda" if use_amp else "cpu"), enabled=use_amp):
-            emb = self.resnet(batch)
-        emb = nn.functional.normalize(emb, dim=-1)
+            batch = torch.stack([self.transform(img) for img in images])
+            batch = batch.to(self.device, memory_format=torch.channels_last)
+            use_amp = (self.device == "cuda")
+            with torch.autocast(device_type=("cuda" if use_amp else "cpu"), enabled=use_amp):
+                emb = self.resnet(batch)
+            emb = nn.functional.normalize(emb, dim=-1)
             result = [e.detach().cpu().numpy().astype(np.float32) for e in emb]
             print(f"🔍 DEBUG: Successfully generated {len(result)} embeddings")
             return result
@@ -658,8 +658,8 @@ class InferenceService:
         # Disable gradients
         for m in [self.resnet, self.vit]:
             if m is not None:
-                for p in m.parameters():
-                    p.requires_grad_(False)
+                    for p in m.parameters():
+                        p.requires_grad_(False)
         
         # Update overall status
         self.models_loaded = self.resnet_loaded and self.vit_loaded