In tensor_name function, don't drop the suffix behind colon if it's not the first tensor. For example, it should make sure:

tensor:0 -> tensor
tensor:1 -> tensor:1
tensor:2 -> tensor:2

PiperOrigin-RevId: 238447941

tensorflow/lite/python/convert.py

@@ -214,7 +214,17 @@ def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str):
 
 
 def tensor_name(x):
-  return x.name.split(":")[0]
+  """Returns name of the input tensor."""
+  parts = x.name.split(":")
+  if len(parts) > 2:
+    raise ValueError("Tensor name invalid. Expect 0 or 1 colon, got {0}".format(
+        len(parts) - 1))
+
+  # To be consistent with the tensor naming scheme in tensorflow, we need
+  # drop the ':0' suffix for the first tensor.
+  if len(parts) > 1 and parts[1] != "0":
+    return x.name
+  return parts[0]
 
 
 # Don't expose these for now.

tensorflow/lite/python/convert_test.py

@@ -55,6 +55,17 @@ class ConvertTest(test_util.TensorFlowTestCase):
     # with self.assertRaisesRegexp(RuntimeError, "!model->operators.empty()"):
     #   result = convert.toco_convert(sess.graph_def, [in_tensor], [in_tensor])
 
+  def testTensorName(self):
+    in_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.float32)
+    # out_tensors should have names: "split:0", "split:1", "split:2", "split:3".
+    out_tensors = array_ops.split(
+        value=in_tensor, num_or_size_splits=[1, 1, 1, 1], axis=0)
+    expect_names = ["split", "split:1", "split:2", "split:3"]
+
+    for i in range(len(expect_names)):
+      got_name = convert.tensor_name(out_tensors[i])
+      self.assertEqual(got_name, expect_names[i])
+
   def testQuantization(self):
     in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
                                       dtype=dtypes.float32)

tensorflow/lite/python/lite_test.py

@@ -597,6 +597,35 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     interpreter = Interpreter(model_content=tflite_model)
     interpreter.allocate_tensors()
 
+  def testMultipleOutputNodeNames(self):
+    """Tests converting a graph with an op that have multiple outputs."""
+    input_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.float32)
+    out0, out1, out2, out3 = array_ops.split(input_tensor, [1, 1, 1, 1], axis=0)
+    sess = session.Session()
+
+    # Convert model and ensure model is not None.
+    converter = lite.TFLiteConverter.from_session(sess, [input_tensor],
+                                                  [out0, out1, out2, out3])
+    tflite_model = converter.convert()
+    self.assertTrue(tflite_model)
+
+    # Check values from converted model.
+    interpreter = Interpreter(model_content=tflite_model)
+    interpreter.allocate_tensors()
+
+    input_details = interpreter.get_input_details()
+    self.assertEqual(1, len(input_details))
+    interpreter.set_tensor(input_details[0]['index'],
+                           np.asarray([1.0, 2.0, 3.0, 4.0], dtype=np.float32))
+    interpreter.invoke()
+
+    output_details = interpreter.get_output_details()
+    self.assertEqual(4, len(output_details))
+    self.assertEqual(1.0, interpreter.get_tensor(output_details[0]['index']))
+    self.assertEqual(2.0, interpreter.get_tensor(output_details[1]['index']))
+    self.assertEqual(3.0, interpreter.get_tensor(output_details[2]['index']))
+    self.assertEqual(4.0, interpreter.get_tensor(output_details[3]['index']))
+
 
 @test_util.run_v1_only('b/120545219')
 class FromFrozenGraphFile(test_util.TensorFlowTestCase):