experiments/STT-tensorflow
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Remove explicit line "experimental_new_converter = True" the converter launched and is now the default.

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PiperOrigin-RevId: 318401041
Change-Id: If14b594d3d2a1997dfbdd615b55db0a06ff22809
This commit is contained in:
Karim Nosir 2020-06-25 20:18:40 -07:00 committed by TensorFlower Gardener
parent 6e39967e51
commit 49efec606f
4 changed files with 0 additions and 26 deletions
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3
tensorflow/lite/examples/experimental_new_converter/keras_lstm.ipynb
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@ -156,9 +156,6 @@
"outputs": [],
"source": [
"converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
"# Note: It will NOT work without enabling the experimental converter!\n",
"# `experimental_new_converter` flag.\n",
"converter.experimental_new_converter = True\n",
"tflite_model = converter.convert()"
]
},

2
tensorflow/lite/examples/experimental_new_converter/stack_trace_example.py
View File

@ -63,7 +63,6 @@ def test_from_saved_model():
# load the model and convert
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
converter.experimental_new_converter = True
converter.convert()
@ -78,7 +77,6 @@ def test_from_concrete_function():
func = model.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([func])
converter.experimental_new_converter = True
converter.convert()

7
tensorflow/lite/python/lite_test.py
View File

@ -435,7 +435,6 @@ class FromSessionTest(TestModels, parameterized.TestCase):
# Test None after 1st dimension.
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -666,8 +665,6 @@ class FromSessionTest(TestModels, parameterized.TestCase):
[out_tensor])
log_dir = self.get_temp_dir()
converter.conversion_summary_dir = log_dir
# Conversion logs will only be generated when the mlir converter is enabled.
converter.experimental_new_converter = True
tflite_model = converter.convert()
self.assertTrue(tflite_model)
@ -1390,7 +1387,6 @@ class FromSessionTest(TestModels, parameterized.TestCase):
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -1797,7 +1793,6 @@ class FromSavedModelTest(TestModels):
'If you encountered a problem')
# Convert model and ensure model is not None.
converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.experimental_new_converter = True
tflite_model = converter.convert()
self.assertTrue(tflite_model)
self.assertIn(optout_message, log.getvalue())
@ -2385,8 +2380,6 @@ class GrapplerTest(TestModels, parameterized.TestCase):
# Convert model.
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
# Only disable this path in MLIR conversion for toco compatibility.
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.

14
tensorflow/lite/python/lite_v2_test.py
View File

@ -830,7 +830,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -857,7 +856,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -887,7 +885,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -911,7 +908,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -941,7 +937,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_keras_model(model)
converter.experimental_new_converter = True
tflite_model = converter.convert()
actual_value = self._evaluateTFLiteModel(tflite_model, [input_data])[0]
@ -963,7 +958,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_keras_model(model)
converter.experimental_new_converter = True
tflite_model = converter.convert()
actual_value = self._evaluateTFLiteModel(tflite_model, [input_data])[0]
@ -987,7 +981,6 @@ class ControlFlowTest(lite_v2_test_util.ModelTest):
# Convert model.
converter = lite.TFLiteConverterV2.from_keras_model(model)
converter.experimental_new_converter = True
tflite_model = converter.convert()
actual_value = self._evaluateTFLiteModel(tflite_model, [input_data])[0]
@ -1024,7 +1017,6 @@ class GrapplerTest(lite_v2_test_util.ModelTest):
np.testing.assert_almost_equal(expected_value.numpy(), actual_value[0])
# Enable hybrid quantization, same result
converter.experimental_new_converter = True
converter.optimizations = [lite.Optimize.DEFAULT]
hybrid_tflite_model = converter.convert()
actual_value = self._evaluateTFLiteModel(hybrid_tflite_model, [input_data])
@ -1048,7 +1040,6 @@ class UnknownShapes(lite_v2_test_util.ModelTest):
concrete_func = model.get_concrete_function()
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.
@ -1090,12 +1081,10 @@ class UnknownShapes(lite_v2_test_util.ModelTest):
concrete_func, _ = self._getQuantizedModel()
float_converter = lite.TFLiteConverterV2.from_concrete_functions(
[concrete_func])
float_converter.experimental_new_converter = True
float_tflite_model = float_converter.convert()
quantized_converter = lite.TFLiteConverterV2.from_concrete_functions(
[concrete_func])
quantized_converter.experimental_new_converter = True
quantized_converter.optimizations = [lite.Optimize.DEFAULT]
quantized_tflite_model = quantized_converter.convert()
@ -1115,14 +1104,12 @@ class UnknownShapes(lite_v2_test_util.ModelTest):
concrete_func, calibration_gen = self._getQuantizedModel()
float_converter = lite.TFLiteConverterV2.from_concrete_functions(
[concrete_func])
float_converter.experimental_new_converter = True
float_tflite_model = float_converter.convert()
quantized_converter = lite.TFLiteConverterV2.from_concrete_functions(
[concrete_func])
quantized_converter.optimizations = [lite.Optimize.DEFAULT]
quantized_converter.representative_dataset = calibration_gen
quantized_converter.experimental_new_converter = True
quantized_tflite_model = quantized_converter.convert()
# The default input and output types should be float.
@ -1152,7 +1139,6 @@ class UnknownShapes(lite_v2_test_util.ModelTest):
concrete_func = model.get_concrete_function()
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()
# Check values from converted model.