(self)
| 104 | class FromSessionTest(TestModels, parameterized.TestCase): |
| 105 | |
| 106 | def testFloat(self): |
| 107 | with ops.Graph().as_default(): |
| 108 | in_tensor = array_ops.placeholder( |
| 109 | shape=[1, 16, 16, 3], dtype=dtypes.float32) |
| 110 | out_tensor = in_tensor + in_tensor |
| 111 | sess = session.Session() |
| 112 | |
| 113 | # Convert model and ensure model is not None. |
| 114 | converter = lite.TFLiteConverter.from_session(sess, [in_tensor], |
| 115 | [out_tensor]) |
| 116 | tflite_model = converter.convert() |
| 117 | self.assertTrue(tflite_model) |
| 118 | |
| 119 | # Check values from converted model. |
| 120 | interpreter = Interpreter(model_content=tflite_model) |
| 121 | interpreter.allocate_tensors() |
| 122 | |
| 123 | input_details = interpreter.get_input_details() |
| 124 | self.assertEqual(1, len(input_details)) |
| 125 | self.assertEqual('Placeholder', input_details[0]['name']) |
| 126 | self.assertEqual(np.float32, input_details[0]['dtype']) |
| 127 | self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all()) |
| 128 | self.assertEqual((0., 0.), input_details[0]['quantization']) |
| 129 | |
| 130 | output_details = interpreter.get_output_details() |
| 131 | self.assertEqual(1, len(output_details)) |
| 132 | self.assertEqual('add', output_details[0]['name']) |
| 133 | self.assertEqual(np.float32, output_details[0]['dtype']) |
| 134 | self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) |
| 135 | self.assertEqual((0., 0.), output_details[0]['quantization']) |
| 136 | |
| 137 | def testString(self): |
| 138 | with ops.Graph().as_default(): |
nothing calls this directly
no test coverage detected