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Mar 4, 2020
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g3 sync fix: be more specific on keras packages #2813

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74e1bbf
be more specific on keras packages
pyu10055 Mar 3, 2020
2e75af2
Merge branch 'master' into g3_sync_errors
pyu10055 Mar 4, 2020
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13 changes: 6 additions & 7 deletions tfjs-converter/python/tensorflowjs/converters/converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Artifact conversion to and from Python TensorFlow and Keras."""
"""Artifact conversion to and from Python TensorFlow and tf.keras."""

from __future__ import absolute_import
from __future__ import division
Expand All @@ -28,7 +28,6 @@
import h5py
import numpy as np
import tensorflow as tf
from tensorflow import keras

from tensorflowjs import quantization
from tensorflowjs import version
Expand All @@ -49,7 +48,7 @@ def dispatch_keras_h5_to_tfjs_layers_model_conversion(
- A weights-only HDF5 (e.g., generated with Keras Model's `save_weights()`
method),
- A topology+weights combined HDF5 (e.g., generated with
`keras.model.save_model`).
`tf.keras.model.save_model`).

Args:
h5_path: path to an HDF5 file containing keras model data as a `str`.
Expand Down Expand Up @@ -130,7 +129,7 @@ def dispatch_keras_h5_to_tfjs_graph_model_conversion(
'directory: %s' % h5_path)

temp_savedmodel_dir = tempfile.mktemp(suffix='.savedmodel')
model = keras.models.load_model(h5_path, compile=False)
model = tf.keras.models.load_model(h5_path, compile=False)
model.save(temp_savedmodel_dir, include_optimizer=False, save_format='tf')

# NOTE(cais): This cannot use `tf.compat.v1` because
Expand All @@ -155,7 +154,7 @@ def dispatch_keras_saved_model_to_tensorflowjs_conversion(
"""Converts keras model saved in the SavedModel format to tfjs format.

Note that the SavedModel format exists in keras, but not in
keras-team/keras.
keras-team/tf.keras.

Args:
keras_saved_model_path: path to a folder in which the
Expand Down Expand Up @@ -455,7 +454,7 @@ def get_arg_parser():
help='Input format. '
'For "keras", the input path can be one of the two following formats:\n'
' - A topology+weights combined HDF5 (e.g., generated with'
' `keras.model.save_model()` method).\n'
' `tf.keras.model.save_model()` method).\n'
' - A weights-only HDF5 (e.g., generated with Keras Model\'s '
' `save_weights()` method). \n'
'For "keras_saved_model", the input_path must point to a subfolder '
Expand Down Expand Up @@ -533,7 +532,7 @@ def convert(arguments):
if args.show_version:
print('\ntensorflowjs %s\n' % version.version)
print('Dependency versions:')
print(' keras %s' % keras.__version__)
print(' keras %s' % tf.keras.__version__)
print(' tensorflow %s' % tf.__version__)
return

Expand Down
93 changes: 46 additions & 47 deletions tfjs-converter/python/tensorflowjs/converters/converter_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Unit tests for artifact conversion to and from Python keras."""
"""Unit tests for artifact conversion to and from Python tf.keras."""

from __future__ import absolute_import
from __future__ import division
Expand All @@ -27,7 +27,6 @@

import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow import keras

from tensorflowjs import version
from tensorflowjs.converters import converter
Expand All @@ -50,13 +49,13 @@ def tearDown(self):

def testWeightsOnly(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='MyDense1')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False, kernel_initializer='ones', name='MyDense2')(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 All @@ -80,14 +79,14 @@ def testWeightsOnly(self):

def testConvertSavedKerasModelNoSplitByLayer(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='MergedDense1')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False,
kernel_initializer='ones', name='MergedDense2')(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)

Expand All @@ -101,7 +100,7 @@ def testConvertSavedKerasModelNoSplitByLayer(self):
self.assertIn('layers', model_json['model_config']['config'])

# Check the loaded weights.
self.assertEqual(keras.__version__, model_json['keras_version'])
self.assertEqual(tf.keras.__version__, model_json['keras_version'])
self.assertEqual('tensorflow', model_json['backend'])
self.assertEqual(1, len(groups))
self.assertEqual(3, len(groups[0]))
Expand All @@ -116,14 +115,14 @@ def testConvertSavedKerasModelNoSplitByLayer(self):

def testConvertSavedKerasModelSplitByLayer(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='MergedDense1')(input_tensor)
output = keras.layers.Dense(
output = tf.keras.layers.Dense(
2, use_bias=False,
kernel_initializer='ones', name='MergedDense2')(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)

Expand All @@ -137,7 +136,7 @@ def testConvertSavedKerasModelSplitByLayer(self):
self.assertIn('layers', model_json['model_config']['config'])

# Check the loaded weights.
self.assertEqual(keras.__version__, model_json['keras_version'])
self.assertEqual(tf.keras.__version__, model_json['keras_version'])
self.assertEqual('tensorflow', model_json['backend'])
self.assertEqual(2, len(groups))
self.assertEqual(2, len(groups[0]))
Expand All @@ -153,8 +152,8 @@ def testConvertSavedKerasModelSplitByLayer(self):

def testConvertSavedKerasModeltoTfLayersModelSharded(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
Expand Down Expand Up @@ -182,11 +181,11 @@ def testConvertSavedKerasModeltoTfLayersModelSharded(self):

def testConvertWeightsFromSequentialModel(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='Dense1'),
keras.layers.Dense(
tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer='ones', name='Dense2')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
Expand All @@ -212,8 +211,8 @@ def testConvertWeightsFromSequentialModel(self):
def testConvertModelForNonexistentDirCreatesDir(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
output_dir = os.path.join(self._tmp_dir, 'foo_model')
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='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
Expand All @@ -234,8 +233,8 @@ def testOutpuDirAsAnExistingFileLeadsToValueError(self):
f.write('\n')

with tf.Graph().as_default(), tf.compat.v1.Session():
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='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
Expand All @@ -248,11 +247,11 @@ def testOutpuDirAsAnExistingFileLeadsToValueError(self):

def testTensorflowjsToKerasConversionSucceeds(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='Dense1'),
keras.layers.Dense(
tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer='ones', name='Dense2')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save(h5_path)
Expand All @@ -267,7 +266,7 @@ def testTensorflowjsToKerasConversionSucceeds(self):

# Load the new H5 and compare the model JSONs.
with tf.Graph().as_default(), tf.compat.v1.Session():
new_model = keras.models.load_model(new_h5_path)
new_model = tf.keras.models.load_model(new_h5_path)
self.assertEqual(old_model_json, new_model.to_json())

def testTensorflowjsToKerasConversionFailsOnDirInputPath(self):
Expand All @@ -278,11 +277,11 @@ def testTensorflowjsToKerasConversionFailsOnDirInputPath(self):

def testTensorflowjsToKerasConversionFailsOnExistingDirOutputPath(self):
with tf.Graph().as_default(), tf.compat.v1.Session():
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='Dense1'),
keras.layers.Dense(
tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer='ones', name='Dense2')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save(h5_path)
Expand Down Expand Up @@ -318,8 +317,8 @@ def tearDown(self):

def testConvertKerasModelToTfGraphModel(self):
output_dir = os.path.join(self._tmp_dir, 'foo_model')
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='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
Expand Down Expand Up @@ -349,8 +348,8 @@ def testConvertKerasModelToTfGraphModel(self):

def testConvertKerasModelToTfGraphModelSharded(self):
output_dir = os.path.join(self._tmp_dir, 'foo_model')
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='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
Expand Down Expand Up @@ -394,29 +393,29 @@ def tearDown(self):
super(ConvertTfKerasSavedModelTest, self).tearDown()

def _createSimpleSequentialModel(self):
model = keras.Sequential()
model.add(keras.layers.Reshape([2, 3], input_shape=[6]))
model.add(keras.layers.LSTM(10))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model = tf.keras.Sequential()
model.add(tf.keras.layers.Reshape([2, 3], input_shape=[6]))
model.add(tf.keras.layers.LSTM(10))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')
model.predict(tf.ones((1, 6)), steps=1)
tf.keras.backend.set_learning_phase(0)
return model

def _createNestedSequentialModel(self):
model = keras.Sequential()
model.add(keras.layers.Dense(6, input_shape=[10], activation='relu'))
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(6, input_shape=[10], activation='relu'))
model.add(self._createSimpleSequentialModel())
model.compile(optimizer='adam', loss='binary_crossentropy')
model.predict(tf.ones((1, 10)), steps=1)
return model

def _createFunctionalModelWithWeights(self):
input1 = keras.Input(shape=[8])
input2 = keras.Input(shape=[10])
y = keras.layers.Concatenate()([input1, input2])
y = keras.layers.Dense(4, activation='softmax')(y)
model = keras.Model([input1, input2], y)
input1 = tf.keras.Input(shape=[8])
input2 = tf.keras.Input(shape=[10])
y = tf.keras.layers.Concatenate()([input1, input2])
y = tf.keras.layers.Dense(4, activation='softmax')(y)
model = tf.keras.Model([input1, input2], y)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.predict([tf.ones((1, 8)), tf.ones((1, 10))], steps=1)
return model
Expand Down
7 changes: 3 additions & 4 deletions tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,6 @@

import six
import tensorflow.compat.v2 as tf
from tensorflow import keras

from tensorflowjs import read_weights
from tensorflowjs.converters import keras_h5_conversion
Expand Down Expand Up @@ -60,9 +59,9 @@ def _deserialize_keras_model(model_topology_json,
unique_name_scope = uuid.uuid4().hex if use_unique_name_scope else None
with tf.compat.v1.name_scope(unique_name_scope):
if is_tf_keras:
model = keras.models.model_from_json(json.dumps(model_topology_json))
model = tf.keras.models.model_from_json(json.dumps(model_topology_json))
else:
model = keras.models.model_from_json(json.dumps(model_topology_json))
model = tf.keras.models.model_from_json(json.dumps(model_topology_json))

if weight_entries:
weights_dict = dict()
Expand Down Expand Up @@ -174,7 +173,7 @@ def load_keras_model(config_json_path,
same TensorFlow Graph or Session context. Default: `False`.

Returns:
The loaded instance of `keras.Model`.
The loaded instance of `tf.keras.Model`.

Raises:
TypeError, if the format of the JSON content of `config_json_path` has an
Expand Down
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