(self)
| 75 | self.assertEqual((0., 0.), output_details[0]['quantization']) |
| 76 | |
| 77 | def testString(self): |
| 78 | with ops.Graph().as_default(): |
| 79 | in_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.string) |
| 80 | out_tensor = array_ops.reshape(in_tensor, shape=[2, 2]) |
| 81 | sess = session.Session() |
| 82 | |
| 83 | # Convert model and ensure model is not None. |
| 84 | converter = lite.TFLiteConverter.from_session(sess, [in_tensor], |
| 85 | [out_tensor]) |
| 86 | converter.experimental_enable_mlir_converter = True |
| 87 | tflite_model = converter.convert() |
| 88 | |
| 89 | # Check values from converted model. |
| 90 | interpreter = Interpreter(model_content=tflite_model) |
| 91 | interpreter.allocate_tensors() |
| 92 | |
| 93 | input_details = interpreter.get_input_details() |
| 94 | self.assertEqual(1, len(input_details)) |
| 95 | self.assertEqual('Placeholder', input_details[0]['name']) |
| 96 | self.assertEqual(np.string_, input_details[0]['dtype']) |
| 97 | self.assertTrue(([4] == input_details[0]['shape']).all()) |
| 98 | |
| 99 | output_details = interpreter.get_output_details() |
| 100 | self.assertEqual(1, len(output_details)) |
| 101 | self.assertEqual('Reshape', output_details[0]['name']) |
| 102 | self.assertEqual(np.string_, output_details[0]['dtype']) |
| 103 | self.assertTrue(([2, 2] == output_details[0]['shape']).all()) |
| 104 | |
| 105 | def testQuantization(self): |
| 106 | with ops.Graph().as_default(): |
nothing calls this directly
no test coverage detected