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Merged
merged 2 commits into from
Mar 3, 2020
Merged

fix g3 sync errors #2810

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ced27fd
fix g3 sync errors
pyu10055 Mar 3, 2020
f4e79cd
Merge branch 'master' into g3_sync_errors
pyu10055 Mar 3, 2020
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1 change: 1 addition & 0 deletions tfjs-converter/python/tensorflowjs/converters/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -129,6 +129,7 @@ py_test(
srcs_version = "PY2AND3",
deps = [
":fuse_depthwise_conv2d",
":tf_saved_model_conversion_v2",
"//tensorflowjs:expect_numpy_installed",
"//tensorflowjs:expect_tensorflow_installed",
],
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10 changes: 5 additions & 5 deletions tfjs-converter/python/tensorflowjs/converters/converter_test.py
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Expand Up @@ -426,7 +426,7 @@ def testConvertTfKerasSequentialSavedAsSavedModel(self):
model = self._createSimpleSequentialModel()
old_model_json = json.loads(model.to_json())
old_weights = model.get_weights()
tf.keras.models.save_model(model, self._tmp_dir)
tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')

# Convert the keras SavedModel to tfjs format.
tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
Expand Down Expand Up @@ -457,7 +457,7 @@ def testConvertTfKerasSequentialCompiledAndSavedAsSavedModel(self):

old_model_json = json.loads(model.to_json())
old_weights = model.get_weights()
tf.keras.models.save_model(model, self._tmp_dir)
tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')

# Convert the keras SavedModel to tfjs format.
tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
Expand Down Expand Up @@ -485,7 +485,7 @@ def testConvertTfKerasSequentialCompiledAndSavedAsSavedModel(self):
def testWrongConverterRaisesCorrectErrorMessage(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
model = self._createSimpleSequentialModel()
tf.keras.models.save_model(model, self._tmp_dir)
tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')

# Convert the keras SavedModel to tfjs format.
tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
Expand Down Expand Up @@ -532,7 +532,7 @@ def testConvertTfKerasFunctionalModelWithWeightsSavedAsSavedModel(self):
model = self._createFunctionalModelWithWeights()
old_model_json = json.loads(model.to_json())
old_weights = model.get_weights()
tf.keras.models.save_model(model, self._tmp_dir)
tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')

# Convert the keras SavedModel to tfjs format.
tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
Expand Down Expand Up @@ -560,7 +560,7 @@ def testConvertTfKerasFunctionalModelWithWeightsSavedAsSavedModel(self):
def testConvertTfKerasSequentialSavedAsSavedModelWithQuantization(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
model = self._createSimpleSequentialModel()
tf.keras.models.save_model(model, self._tmp_dir)
tf.keras.models.save_model(model, self._tmp_dir, save_format='tf')

# Convert the keras SavedModel to tfjs format.
tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs')
Expand Down
2 changes: 1 addition & 1 deletion tfjs-converter/python/tensorflowjs/converters/generate_test_model.py
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Expand Up @@ -65,4 +65,4 @@ def compute(self, x):

if __name__ == '__main__':
args, unparsed = parse_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
tf.compat.v1.app.run(main=main, argv=[sys.argv[0]] + unparsed)
105 changes: 52 additions & 53 deletions tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@
import h5py
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow.compat.v2 import keras

from tensorflowjs import version
from tensorflowjs.converters import keras_h5_conversion as conversion
Expand All @@ -45,13 +44,13 @@ def tearDown(self):
super(ConvertH5WeightsTest, self).tearDown()

def testConvertWeightsFromSimpleModelNoSplitByLayer(self):
input_tensor = keras.layers.Input((3,))
dense1 = keras.layers.Dense(
input_tensor = tf.keras.layers.Input((3,))
dense1 = tf.keras.layers.Dense(
4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
name='MyDense10')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False, kernel_initializer='ones', name='MyDense20')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
model.save_weights(h5_path)

Expand Down Expand Up @@ -80,13 +79,13 @@ def testConvertWeightsFromSimpleModelNoSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))

def testConvertWeightsFromSimpleModelSplitByLayer(self):
input_tensor = keras.layers.Input((3,))
dense1 = keras.layers.Dense(
input_tensor = tf.keras.layers.Input((3,))
dense1 = tf.keras.layers.Dense(
4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
name='MyDense30')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False, kernel_initializer='ones', name='MyDense40')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
model.save_weights(h5_path)

Expand Down Expand Up @@ -118,13 +117,13 @@ def testConvertWeightsFromSimpleModelSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))

def testConvertModelWithNestedLayerNames(self):
model = keras.Sequential()
model = tf.keras.Sequential()

# Add a layer with a nested layer name, i.e., a layer name with slash(es)
# in it.
model.add(keras.layers.Dense(2, input_shape=[12], name='dense'))
model.add(keras.layers.Dense(8, name='foo/dense'))
model.add(keras.layers.Dense(4, name='foo/bar/dense'))
model.add(tf.keras.layers.Dense(2, input_shape=[12], name='dense'))
model.add(tf.keras.layers.Dense(8, name='foo/dense'))
model.add(tf.keras.layers.Dense(4, name='foo/bar/dense'))
tfjs_path = os.path.join(self._tmp_dir, 'nested_layer_names_model')
conversion.save_keras_model(model, tfjs_path)

Expand All @@ -135,7 +134,7 @@ def testConvertModelWithNestedLayerNames(self):
# Check meta-data in the artifact JSON.
self.assertEqual(model_json['format'], 'layers-model')
self.assertEqual(model_json['generatedBy'],
'keras v%s' % keras.__version__)
'keras v%s' % tf.keras.__version__)
self.assertEqual(
model_json['convertedBy'],
'TensorFlow.js Converter v%s' % version.version)
Expand All @@ -159,14 +158,14 @@ def testConvertModelWithNestedLayerNames(self):
self.assertEqual([4], weight_shapes['foo/bar/dense/bias'])

def testConvertMergedModelFromSimpleModelNoSplitByLayer(self):
input_tensor = keras.layers.Input((3,))
dense1 = keras.layers.Dense(
input_tensor = tf.keras.layers.Input((3,))
dense1 = tf.keras.layers.Dense(
4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
name='MergedDense10')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False,
kernel_initializer='ones', name='MergedDense20')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
model.save(h5_path)
config_json = json.loads(model.to_json(), encoding='utf8')
Expand All @@ -184,7 +183,7 @@ def testConvertMergedModelFromSimpleModelNoSplitByLayer(self):
# By default, all weights of the model ought to be put in the same group.
self.assertEqual(1, len(groups))

self.assertEqual(keras.__version__, out['keras_version'])
self.assertEqual(tf.keras.__version__, out['keras_version'])
self.assertEqual('tensorflow', out['backend'])
weight_group = groups[0]
self.assertEqual(3, len(weight_group))
Expand All @@ -205,14 +204,14 @@ def testConvertMergedModelFromSimpleModelNoSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))

def testConvertMergedModelFromSimpleModelSplitByLayer(self):
input_tensor = keras.layers.Input((3,))
dense1 = keras.layers.Dense(
input_tensor = tf.keras.layers.Input((3,))
dense1 = tf.keras.layers.Dense(
4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
name='MergedDense30')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False,
kernel_initializer='ones', name='MergedDense40')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
model = tf.keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
model.save(h5_path)
config_json = json.loads(model.to_json(), encoding='utf8')
Expand All @@ -231,7 +230,7 @@ def testConvertMergedModelFromSimpleModelSplitByLayer(self):
# because the model has two layers.
self.assertEqual(2, len(groups))

self.assertEqual(keras.__version__, out['keras_version'])
self.assertEqual(tf.keras.__version__, out['keras_version'])
self.assertEqual('tensorflow', out['backend'])
self.assertEqual(2, len(groups[0]))
kernel1 = groups[0][0]
Expand All @@ -252,11 +251,11 @@ def testConvertMergedModelFromSimpleModelSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([4, 2]), kernel2['data']))

def testConvertWeightsFromSequentialModelNoSplitByLayer(self):
sequential_model = keras.models.Sequential([
keras.layers.Dense(
sequential_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(
3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
name='Dense10'),
keras.layers.Dense(
tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer='ones', name='Dense20')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
Expand Down Expand Up @@ -286,11 +285,11 @@ def testConvertWeightsFromSequentialModelNoSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([3, 1]).tolist(), kernel2['data']))

def testConvertWeightsFromSequentialModelSplitByLayer(self):
sequential_model = keras.models.Sequential([
keras.layers.Dense(
sequential_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(
3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
name='Dense30'),
keras.layers.Dense(
tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer='ones', name='Dense40')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
Expand Down Expand Up @@ -323,10 +322,10 @@ def testConvertWeightsFromSequentialModelSplitByLayer(self):
self.assertTrue(np.allclose(np.ones([3, 1]).tolist(), kernel2['data']))

def testSaveModelSucceedsForNonSequentialModel(self):
t_input = keras.Input([2])
dense_layer = keras.layers.Dense(3)
t_input = tf.keras.Input([2])
dense_layer = tf.keras.layers.Dense(3)
t_output = dense_layer(t_input)
model = keras.Model(t_input, t_output)
model = tf.keras.Model(t_input, t_output)
conversion.save_keras_model(model, self._tmp_dir)

# Verify the content of the artifacts output directory.
Expand All @@ -346,12 +345,12 @@ def testSaveModelSucceedsForNonSequentialModel(self):
self.assertIn('paths', weights_manifest[0])

def testSaveModelSucceedsForTfKerasNonSequentialModel(self):
t_input = keras.Input([2])
dense_layer = keras.layers.Dense(3)
t_input = tf.keras.Input([2])
dense_layer = tf.keras.layers.Dense(3)
t_output = dense_layer(t_input)
model = keras.Model(t_input, t_output)
model = tf.keras.Model(t_input, t_output)

# `keras.Model`s must be compiled before they can be saved.
# `tf.keras.Model`s must be compiled before they can be saved.
model.compile(loss='mean_squared_error', optimizer='sgd')

conversion.save_keras_model(model, self._tmp_dir)
Expand All @@ -373,12 +372,12 @@ def testSaveModelSucceedsForTfKerasNonSequentialModel(self):
self.assertIn('paths', weights_manifest[0])

def testSaveModelSucceedsForNestedKerasModel(self):
inner_model = keras.Sequential([
keras.layers.Dense(4, input_shape=[3], activation='relu'),
keras.layers.Dense(3, activation='tanh')])
outer_model = keras.Sequential()
inner_model = tf.keras.Sequential([
tf.keras.layers.Dense(4, input_shape=[3], activation='relu'),
tf.keras.layers.Dense(3, activation='tanh')])
outer_model = tf.keras.Sequential()
outer_model.add(inner_model)
outer_model.add(keras.layers.Dense(1, activation='sigmoid'))
outer_model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

conversion.save_keras_model(outer_model, self._tmp_dir)

Expand All @@ -402,9 +401,9 @@ def testSaveModelSucceedsForNestedKerasModel(self):
self.assertEqual(6, len(weight_entries))

def testSaveModelSucceedsForTfKerasSequentialModel(self):
model = keras.Sequential([keras.layers.Dense(1, input_shape=[2])])
model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=[2])])

# `keras.Model`s must be compiled before they can be saved.
# `tf.keras.Model`s must be compiled before they can be saved.
model.compile(loss='mean_squared_error', optimizer='sgd')

conversion.save_keras_model(model, self._tmp_dir)
Expand All @@ -428,8 +427,8 @@ def testSaveModelSucceedsForTfKerasSequentialModel(self):
def testSavedModelSucceedsForExistingDirAndSequential(self):
artifacts_dir = os.path.join(self._tmp_dir, 'artifacts')
os.makedirs(artifacts_dir)
model = keras.Sequential()
model.add(keras.layers.Dense(3, input_shape=[2]))
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(3, input_shape=[2]))
conversion.save_keras_model(model, artifacts_dir)

# Verify the content of the artifacts output directory.
Expand All @@ -452,10 +451,10 @@ def testSavedModelRaisesErrorIfArtifactsDirExistsAsAFile(self):
artifacts_dir = os.path.join(self._tmp_dir, 'artifacts')
with open(artifacts_dir, 'wt') as f:
f.write('foo\n')
t_input = keras.Input([2])
dense_layer = keras.layers.Dense(3)
t_input = tf.keras.Input([2])
dense_layer = tf.keras.layers.Dense(3)
t_output = dense_layer(t_input)
model = keras.Model(t_input, t_output)
model = tf.keras.Model(t_input, t_output)
with self.assertRaisesRegexp( # pylint: disable=deprecated-method
ValueError, r'already exists as a file'):
conversion.save_keras_model(model, artifacts_dir)
Expand All @@ -478,8 +477,8 @@ def testTranslateBatchNormalizationV1ClassName(self):
self.assertEqual(json_object['config']['layers'][2]['class_name'], 'Dense')

# Assert that converted JSON can be reconstituted as a model object.
model = keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, keras.Sequential)
model = tf.keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, tf.keras.Sequential)
self.assertEqual(model.input_shape, (None, 3))
self.assertEqual(model.output_shape, (None, 1))
self.assertEqual(model.layers[0].units, 10)
Expand All @@ -503,8 +502,8 @@ def testTranslateUnifiedGRUAndLSTMClassName(self):
self.assertEqual(json_object['config']['layers'][1]['class_name'], 'LSTM')

# Assert that converted JSON can be reconstituted as a model object.
model = keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, keras.Sequential)
model = tf.keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, tf.keras.Sequential)
self.assertEqual(model.input_shape, (None, 4, 3))
self.assertEqual(model.output_shape, (None, 2))

Expand Down