add it to compose

inference.py

@@ -55,7 +55,8 @@ transforms = Compose(
         EnsureTyped(keys=keys),
         ConcatItemsd(keys=("t2"), name=CommonKeys.IMAGE, dim=0),
         ConcatItemsd(keys=("t2_anatomy_reader1"), name=CommonKeys.LABEL, dim=0),
-    ]
+    ],
+    allow_missing_keys=True,
 )
 
 
@@ -68,7 +69,8 @@ postprocessing = Compose(
             keys=CommonKeys.PRED, 
             applied_labels=list(range(1, 3))
         ),
-    ]
+    ],
+    allow_missing_keys=True,
 )
 inferer = monai.inferers.SlidingWindowInferer(
     roi_size=(96, 96, 96),
@@ -133,19 +135,19 @@ def make_inference(data_dict:list) -> str:
     test_ds = Dataset(
         data=data_dict,
         transform=transforms,
-        allow_missing_keys=True,
     )
     model.eval()
     with torch.no_grad():
         example = test_ds[0]
-        label = example["t2_anatomy_reader1"]
+        # label = example["t2_anatomy_reader1"]
         input_tensor = example["t2"].unsqueeze(0)
         input_tensor = input_tensor.to(device)
         output_tensor = inferer(input_tensor, model)
         output_tensor = output_tensor.argmax(dim=1, keepdim=False)
         output_tensor = output_tensor.squeeze(0).to(torch.device("cpu"))
 
-        output_tensor = postprocessing({"pred": output_tensor, "label": label})["pred"]
+        # output_tensor = postprocessing({"pred": output_tensor, "label": label})["pred"]
+        output_tensor = postprocessing({"pred": output_tensor})["pred"]
         output_tensor = output_tensor.numpy().astype(np.uint8)
         target_shape = example["t2_meta_dict"]["spatial_shape"]
         output_tensor = resize_image(output_tensor, target_shape)