Merge pull request #8 from szerintedmi/resolve_0-dim_tensor_warnings

Resolve invalid index of a 0-dim tensor warnings

u2net_test.py

@@ -84,9 +84,12 @@ def main():
     elif(model_name=='u2netp'):
         print("...load U2NEP---4.7 MB")
         net = U2NETP(3,1)
-    net.load_state_dict(torch.load(model_dir))
+
     if torch.cuda.is_available():
+        net.load_state_dict(torch.load(model_dir))
         net.cuda()
+    else:
+        net.load_state_dict(torch.load(model_dir, map_location='cpu'))
     net.eval()
 
     # --------- 4. inference for each image ---------

u2net_train.py

@@ -38,7 +38,7 @@ def muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v):
 	loss6 = bce_loss(d6,labels_v)
 
 	loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5 + loss6
-	print("l0: %3f, l1: %3f, l2: %3f, l3: %3f, l4: %3f, l5: %3f, l6: %3f\n"%(loss0.data[0],loss1.data[0],loss2.data[0],loss3.data[0],loss4.data[0],loss5.data[0],loss6.data[0]))
+	print("l0: %3f, l1: %3f, l2: %3f, l3: %3f, l4: %3f, l5: %3f, l6: %3f\n"%(loss0.data.item(),loss1.data.item(),loss2.data.item(),loss3.data.item(),loss4.data.item(),loss5.data.item(),loss6.data.item()))
 
 	return loss0, loss
 
@@ -144,8 +144,8 @@ def muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v):
         optimizer.step()
 
         # # print statistics
-        running_loss += loss.data[0]
-        running_tar_loss += loss2.data[0]
+        running_loss += loss.data.item()
+        running_tar_loss += loss2.data.item()
 
         # del temporary outputs and loss
         del d0, d1, d2, d3, d4, d5, d6, loss2, loss