improved rf

quick_eval.py

@@ -39,10 +39,10 @@ max_save = 10
 with torch.no_grad():
     for i in tqdm(range(0, len(test_dataset), batch_size), desc="Evaluating"):
         batch = test_dataset[i : i + batch_size]
-        images = rf.sample(batch["x0"].to(device))
+        images = rf.sample(batch["cloud"].to(device))
 
         image = denormalize(images[-1]).clamp(0, 1)
-        original = denormalize(batch["x1"]).clamp(0, 1)
+        original = denormalize(batch["gt"]).clamp(0, 1)
 
         if saved_count < max_save:
             for j in range(min(image.shape[0], max_save - saved_count)):