Update app.py

app.py

@@ -137,8 +137,8 @@ def infer():
     model = model.eval()
 
     list_of_flows = model(img1_batch.to(device), img2_batch.to(device))
-    print(f"type = {type(list_of_flows)}")
-    print(f"length = {len(list_of_flows)} = number of iterations of the model")
+    print(f"list_of_flows type = {type(list_of_flows)}")
+    print(f"list_of_flows length = {len(list_of_flows)} = number of iterations of the model")
 
 ####################################
 # The RAFT model outputs lists of predicted flows where each entry is a
@@ -154,9 +154,9 @@ def infer():
 # Note that the predicted flows are in "pixel" unit, they are not normalized
 # w.r.t. the dimensions of the images.
     predicted_flows = list_of_flows[-1]
-    print(f"dtype = {predicted_flows.dtype}")
-    print(f"shape = {predicted_flows.shape} = (N, 2, H, W)")
-    print(f"min = {predicted_flows.min()}, max = {predicted_flows.max()}")
+    print(f"predicted_flows dtype = {predicted_flows.dtype}")
+    print(f"predicted_flows shape = {predicted_flows.shape} = (N, 2, H, W)")
+    print(f"predicted_flows min = {predicted_flows.min()}, predicted_flows max = {predicted_flows.max()}")
 
 
 ####################################