(self)
| 336 | str(error.exception)) |
| 337 | |
| 338 | def testBatchSizeValid(self): |
| 339 | with ops.Graph().as_default(): |
| 340 | in_tensor = array_ops.placeholder( |
| 341 | shape=[None, 16, 16, 3], dtype=dtypes.float32) |
| 342 | out_tensor = in_tensor + in_tensor |
| 343 | sess = session.Session() |
| 344 | |
| 345 | # Convert model and ensure model is not None. |
| 346 | converter = lite.TFLiteConverter.from_session(sess, [in_tensor], |
| 347 | [out_tensor]) |
| 348 | tflite_model = converter.convert() |
| 349 | self.assertTrue(tflite_model) |
| 350 | |
| 351 | # Check values from converted model. |
| 352 | interpreter = Interpreter(model_content=tflite_model) |
| 353 | interpreter.allocate_tensors() |
| 354 | |
| 355 | input_details = interpreter.get_input_details() |
| 356 | self.assertEqual(1, len(input_details)) |
| 357 | self.assertEqual('Placeholder', input_details[0]['name']) |
| 358 | self.assertEqual(np.float32, input_details[0]['dtype']) |
| 359 | self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all()) |
| 360 | self.assertEqual((0., 0.), input_details[0]['quantization']) |
| 361 | |
| 362 | output_details = interpreter.get_output_details() |
| 363 | self.assertEqual(1, len(output_details)) |
| 364 | self.assertEqual('add', output_details[0]['name']) |
| 365 | self.assertEqual(np.float32, output_details[0]['dtype']) |
| 366 | self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) |
| 367 | self.assertEqual((0., 0.), output_details[0]['quantization']) |
| 368 | |
| 369 | def testBatchSizeNonZero(self): |
| 370 | with ops.Graph().as_default(): |
nothing calls this directly
no test coverage detected