(self)
| 44 | class FromSessionTest(test_util.TensorFlowTestCase): |
| 45 | |
| 46 | def testFloat(self): |
| 47 | with ops.Graph().as_default(): |
| 48 | in_tensor = array_ops.placeholder( |
| 49 | shape=[1, 16, 16, 3], dtype=dtypes.float32) |
| 50 | out_tensor = in_tensor + in_tensor |
| 51 | sess = session.Session() |
| 52 | |
| 53 | # Convert model and ensure model is not None. |
| 54 | converter = lite.TFLiteConverter.from_session(sess, [in_tensor], |
| 55 | [out_tensor]) |
| 56 | converter.experimental_enable_mlir_converter = True |
| 57 | tflite_model = converter.convert() |
| 58 | |
| 59 | # Check values from converted model. |
| 60 | interpreter = Interpreter(model_content=tflite_model) |
| 61 | interpreter.allocate_tensors() |
| 62 | |
| 63 | input_details = interpreter.get_input_details() |
| 64 | self.assertEqual(1, len(input_details)) |
| 65 | self.assertEqual('Placeholder', input_details[0]['name']) |
| 66 | self.assertEqual(np.float32, input_details[0]['dtype']) |
| 67 | self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all()) |
| 68 | self.assertEqual((0., 0.), input_details[0]['quantization']) |
| 69 | |
| 70 | output_details = interpreter.get_output_details() |
| 71 | self.assertEqual(1, len(output_details)) |
| 72 | self.assertEqual('add', output_details[0]['name']) |
| 73 | self.assertEqual(np.float32, output_details[0]['dtype']) |
| 74 | self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) |
| 75 | self.assertEqual((0., 0.), output_details[0]['quantization']) |
| 76 | |
| 77 | def testString(self): |
| 78 | with ops.Graph().as_default(): |
nothing calls this directly
no test coverage detected