Logs

app.py

@@ -121,6 +121,7 @@ def evaluate_model(model, dataloader, device):
     all_preds = []
     all_labels = []
 
+    test = True
     # Disable gradient calculations
     with torch.no_grad():
         for batch in dataloader:
@@ -134,6 +135,10 @@ def evaluate_model(model, dataloader, device):
 
             # Get predictions
             preds = torch.argmax(logits, dim=-1).cpu().numpy()
+            if test:
+                test = False
+                print(preds)
+                print(labels)
             
             all_preds.extend(preds)
             all_labels.extend(labels.cpu().numpy())
@@ -142,10 +147,6 @@ def evaluate_model(model, dataloader, device):
     print("evaluate_model sizes")
     print(len(all_preds[0]))
     print(len(all_labels[0]))
-    for p in all_preds:
-        if len(p) != len(all_preds[0]):
-            print(len(p))
-            print(p)
     all_preds = np.asarray(all_preds, dtype=np.float32)
     all_labels = np.asarray(all_labels, dtype=np.float32)
     print("Flattened sizes")