From 75e2a0a14f4a127892477c29044de1f3a6c4b242 Mon Sep 17 00:00:00 2001
From: Feng Liu <fengliuai@google.com>
Date: Fri, 7 Feb 2020 11:50:37 -0800
Subject: [PATCH] Add an integration tests for inference_input_type=uint8 and
 inference_type=float

PiperOrigin-RevId: 293862594
Change-Id: Ic4435ac836e34fec4e5c65bc0fe485020b3ba7ff
---
 tensorflow/lite/python/lite_test.py | 37 +++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/tensorflow/lite/python/lite_test.py b/tensorflow/lite/python/lite_test.py
index 8c1f10af530..7977b30e7ae 100644
--- a/tensorflow/lite/python/lite_test.py
+++ b/tensorflow/lite/python/lite_test.py
@@ -257,6 +257,43 @@ class FromSessionTest(TestModels, parameterized.TestCase):
     self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
     self.assertTrue(output_details[0]['quantization'][0] > 0)  # scale
 
+  def testQuantizedInput(self):
+    with ops.Graph().as_default():
+      in_tensor = array_ops.placeholder(
+          shape=[1, 16, 16, 3], dtype=dtypes.float32)
+      out_tensor = in_tensor + in_tensor
+      sess = session.Session()
+
+    # Convert model and ensure model is not None.
+    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
+                                                  [out_tensor])
+    converter.inference_input_type = lite_constants.QUANTIZED_UINT8
+    converter.inference_type = lite_constants.FLOAT
+    converter.quantized_input_stats = {
+        'Placeholder': (0., 1.)
+    }  # mean, std_dev
+    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.assertLen(input_details, 1)
+    self.assertEqual('Placeholder', input_details[0]['name'])
+    self.assertEqual(np.uint8, input_details[0]['dtype'])
+    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
+    self.assertEqual((1., 0.),
+                     input_details[0]['quantization'])  # scale, zero_point
+
+    output_details = interpreter.get_output_details()
+    self.assertLen(output_details, 1)
+    self.assertEqual('add', output_details[0]['name'])
+    self.assertEqual(np.float32, output_details[0]['dtype'])
+    self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
+    self.assertEqual((0., 0.), output_details[0]['quantization'])  # float
+
   def testQuantizationInvalid(self):
     with ops.Graph().as_default():
       in_tensor_1 = array_ops.placeholder(