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Update Keras code to use public TF file API.

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1. Update to use v2 public API from their v1 version.
2. Use the Gfile API to read/write file content for save_model code.

PiperOrigin-RevId: 328159642
Change-Id: I38373fb16449ab8d19f15f4e22ad99d3c598266b
This commit is contained in:
Scott Zhu 2020-08-24 10:02:44 -07:00 committed by TensorFlower Gardener
parent a4219770e9
commit ac4e209f8c
20 changed files with 64 additions and 58 deletions
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2
tensorflow/python/keras/applications/densenet.py
View File

@ -192,7 +192,7 @@ def DenseNet(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/efficientnet.py
View File

@ -269,7 +269,7 @@ def EfficientNet(
if blocks_args == 'default': if blocks_args == 'default':
blocks_args = DEFAULT_BLOCKS_ARGS blocks_args = DEFAULT_BLOCKS_ARGS
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/inception_resnet_v2.py
View File

@ -112,7 +112,7 @@ def InceptionResNetV2(include_top=True,
layers = VersionAwareLayers() layers = VersionAwareLayers()
if kwargs: if kwargs:
raise ValueError('Unknown argument(s): %s' % (kwargs,)) raise ValueError('Unknown argument(s): %s' % (kwargs,))
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/inception_v3.py
View File

@ -108,7 +108,7 @@ def InceptionV3(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/mobilenet.py
View File

@ -164,7 +164,7 @@ def MobileNet(input_shape=None,
layers = VersionAwareLayers() layers = VersionAwareLayers()
if kwargs: if kwargs:
raise ValueError('Unknown argument(s): %s' % (kwargs,)) raise ValueError('Unknown argument(s): %s' % (kwargs,))
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/mobilenet_v2.py
View File

@ -180,7 +180,7 @@ def MobileNetV2(input_shape=None,
layers = VersionAwareLayers() layers = VersionAwareLayers()
if kwargs: if kwargs:
raise ValueError('Unknown argument(s): %s' % (kwargs,)) raise ValueError('Unknown argument(s): %s' % (kwargs,))
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/mobilenet_v3.py
View File

@ -158,7 +158,7 @@ def MobileNetV3(stack_fn,
pooling=None, pooling=None,
dropout_rate=0.2, dropout_rate=0.2,
classifier_activation='softmax'): classifier_activation='softmax'):
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/nasnet.py
View File

@ -150,7 +150,7 @@ def NASNet(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/resnet.py
View File

@ -137,7 +137,7 @@ def ResNet(stack_fn,
layers = VersionAwareLayers() layers = VersionAwareLayers()
if kwargs: if kwargs:
raise ValueError('Unknown argument(s): %s' % (kwargs,)) raise ValueError('Unknown argument(s): %s' % (kwargs,))
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/vgg16.py
View File

@ -113,7 +113,7 @@ def VGG16(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/vgg19.py
View File

@ -113,7 +113,7 @@ def VGG19(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

2
tensorflow/python/keras/applications/xception.py
View File

@ -113,7 +113,7 @@ def Xception(
ValueError: if `classifier_activation` is not `softmax` or `None` when ValueError: if `classifier_activation` is not `softmax` or `None` when
using a pretrained top layer. using a pretrained top layer.
""" """
if not (weights in {'imagenet', None} or file_io.file_exists(weights)): if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
raise ValueError('The `weights` argument should be either ' raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` ' '`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), ' '(pre-training on ImageNet), '

11
tensorflow/python/keras/callbacks.py
View File

@ -1399,9 +1399,10 @@ class ModelCheckpoint(Callback):
def _checkpoint_exists(self, filepath): def _checkpoint_exists(self, filepath):
"""Returns whether the checkpoint `filepath` refers to exists.""" """Returns whether the checkpoint `filepath` refers to exists."""
if filepath.endswith('.h5'): if filepath.endswith('.h5'):
return file_io.file_exists(filepath) return file_io.file_exists_v2(filepath)
tf_saved_model_exists = file_io.file_exists(filepath) tf_saved_model_exists = file_io.file_exists_v2(filepath)
tf_weights_only_checkpoint_exists = file_io.file_exists(filepath + '.index') tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
filepath + '.index')
return tf_saved_model_exists or tf_weights_only_checkpoint_exists return tf_saved_model_exists or tf_weights_only_checkpoint_exists
def _get_most_recently_modified_file_matching_pattern(self, pattern): def _get_most_recently_modified_file_matching_pattern(self, pattern):
@ -1466,7 +1467,7 @@ class ModelCheckpoint(Callback):
n_file_with_latest_mod_time = 0 n_file_with_latest_mod_time = 0
file_path_with_largest_file_name = None file_path_with_largest_file_name = None
if file_io.file_exists(dir_name): if file_io.file_exists_v2(dir_name):
for file_name in os.listdir(dir_name): for file_name in os.listdir(dir_name):
# Only consider if `file_name` matches the pattern. # Only consider if `file_name` matches the pattern.
if re.match(base_name_regex, file_name): if re.match(base_name_regex, file_name):
@ -2505,7 +2506,7 @@ class CSVLogger(Callback):
def on_train_begin(self, logs=None): def on_train_begin(self, logs=None):
if self.append: if self.append:
if file_io.file_exists(self.filename): if file_io.file_exists_v2(self.filename):
with open(self.filename, 'r' + self.file_flags) as f: with open(self.filename, 'r' + self.file_flags) as f:
self.append_header = not bool(len(f.readline())) self.append_header = not bool(len(f.readline()))
mode = 'a' mode = 'a'

32
tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py
View File

@ -37,9 +37,10 @@ from tensorflow.python.platform import test
def checkpoint_exists(filepath): def checkpoint_exists(filepath):
"""Returns whether the checkpoint `filepath` refers to exists.""" """Returns whether the checkpoint `filepath` refers to exists."""
if filepath.endswith('.h5'): if filepath.endswith('.h5'):
return file_io.file_exists(filepath) return file_io.file_exists_v2(filepath)
tf_saved_model_exists = file_io.file_exists(filepath) tf_saved_model_exists = file_io.file_exists_v2(filepath)
tf_weights_only_checkpoint_exists = file_io.file_exists(filepath + '.index') tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
filepath + '.index')
return tf_saved_model_exists or tf_weights_only_checkpoint_exists return tf_saved_model_exists or tf_weights_only_checkpoint_exists
@ -145,7 +146,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
num_epoch = 2 num_epoch = 2
# The saving_filepath shouldn't exist at the beginning (as it's unique). # The saving_filepath shouldn't exist at the beginning (as it's unique).
test_obj.assertFalse(file_io.file_exists(saving_filepath)) test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
model.fit( model.fit(
x=train_ds, x=train_ds,
@ -153,7 +154,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
steps_per_epoch=steps, steps_per_epoch=steps,
callbacks=[callbacks.ModelCheckpoint(filepath=saving_filepath)]) callbacks=[callbacks.ModelCheckpoint(filepath=saving_filepath)])
test_obj.assertTrue(file_io.file_exists(saving_filepath)) test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint') saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint')
@ -185,7 +186,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
num_epoch = 4 num_epoch = 4
# The saving_filepath shouldn't exist at the beginning (as it's unique). # The saving_filepath shouldn't exist at the beginning (as it's unique).
test_obj.assertFalse(file_io.file_exists(saving_filepath)) test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
bar_dir = os.path.join(os.path.dirname(saving_filepath), 'backup') bar_dir = os.path.join(os.path.dirname(saving_filepath), 'backup')
try: try:
@ -204,8 +205,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
backup_filepath = os.path.join(bar_dir, 'checkpoint') backup_filepath = os.path.join(bar_dir, 'checkpoint')
test_obj.assertTrue(file_io.file_exists(backup_filepath)) test_obj.assertTrue(file_io.file_exists_v2(backup_filepath))
test_obj.assertTrue(file_io.file_exists(saving_filepath)) test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
model.fit( model.fit(
x=train_ds, x=train_ds,
@ -217,8 +218,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
AssertCallback() AssertCallback()
]) ])
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
test_obj.assertFalse(file_io.file_exists(backup_filepath)) test_obj.assertFalse(file_io.file_exists_v2(backup_filepath))
test_obj.assertTrue(file_io.file_exists(saving_filepath)) test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint') saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint')
@ -244,7 +245,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
'logfile_%s_%d' % (task_config['type'], task_config['index'])) 'logfile_%s_%d' % (task_config['type'], task_config['index']))
# The saving_filepath shouldn't exist at the beginning (as it's unique). # The saving_filepath shouldn't exist at the beginning (as it's unique).
test_obj.assertFalse(file_io.file_exists(saving_filepath)) test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
model.fit( model.fit(
x=train_ds, x=train_ds,
@ -257,7 +258,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
# `file_io.list_directory()` since the directory may be created at this # `file_io.list_directory()` since the directory may be created at this
# point. # point.
test_obj.assertEqual( test_obj.assertEqual(
bool(file_io.list_directory(saving_filepath)), test_base.is_chief()) bool(file_io.list_directory_v2(saving_filepath)),
test_base.is_chief())
multi_process_runner.run( multi_process_runner.run(
proc_tensorboard_saves_on_chief_but_not_otherwise, proc_tensorboard_saves_on_chief_but_not_otherwise,
@ -280,7 +282,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
# Verifies that even if `saving_filepath_for_temp` exists, tensorboard # Verifies that even if `saving_filepath_for_temp` exists, tensorboard
# can still save to temporary directory. # can still save to temporary directory.
test_obj.assertTrue(file_io.file_exists(saving_filepath_for_temp)) test_obj.assertTrue(file_io.file_exists_v2(saving_filepath_for_temp))
model.fit( model.fit(
x=train_ds, x=train_ds,
@ -301,7 +303,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
num_epoch = 2 num_epoch = 2
# The saving_filepath shouldn't exist at the beginning (as it's unique). # The saving_filepath shouldn't exist at the beginning (as it's unique).
test_obj.assertFalse(file_io.file_exists(saving_filepath)) test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
@ -313,7 +315,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
test_obj.assertTrue(file_io.list_directory(saving_filepath)) test_obj.assertTrue(file_io.list_directory_v2(saving_filepath))
saving_filepath = os.path.join(self.get_temp_dir(), 'logfile') saving_filepath = os.path.join(self.get_temp_dir(), 'logfile')

9
tensorflow/python/keras/distribute/multi_worker_tutorial_test.py
View File

@ -159,9 +159,10 @@ class MultiWorkerTutorialTest(parameterized.TestCase, test.TestCase):
# Make sure chief finishes saving before non-chief's assertions. # Make sure chief finishes saving before non-chief's assertions.
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
if not file_io.file_exists(model_path): if not file_io.file_exists_v2(model_path):
raise RuntimeError() raise RuntimeError()
if file_io.file_exists(write_model_path) != _is_chief(task_type, task_id): if file_io.file_exists_v2(write_model_path) != _is_chief(
task_type, task_id):
raise RuntimeError() raise RuntimeError()
loaded_model = keras.saving.save.load_model(model_path) loaded_model = keras.saving.save.load_model(model_path)
@ -179,9 +180,9 @@ class MultiWorkerTutorialTest(parameterized.TestCase, test.TestCase):
# Make sure chief finishes saving before non-chief's assertions. # Make sure chief finishes saving before non-chief's assertions.
multi_process_runner.barrier().wait() multi_process_runner.barrier().wait()
if not file_io.file_exists(checkpoint_dir): if not file_io.file_exists_v2(checkpoint_dir):
raise RuntimeError() raise RuntimeError()
if file_io.file_exists(write_checkpoint_dir) != _is_chief( if file_io.file_exists_v2(write_checkpoint_dir) != _is_chief(
task_type, task_id): task_type, task_id):
raise RuntimeError() raise RuntimeError()

8
tensorflow/python/keras/distribute/worker_training_state.py
View File

@ -112,12 +112,12 @@ class WorkerTrainingState(object):
successfully finishes. successfully finishes.
""" """
# pylint: disable=protected-access # pylint: disable=protected-access
for pathname in file_io.get_matching_files( for pathname in file_io.get_matching_files_v2(
self.write_checkpoint_manager._prefix + '*'): self.write_checkpoint_manager._prefix + '*'):
file_io.delete_recursively(pathname) file_io.delete_recursively_v2(pathname)
for pathname in file_io.get_matching_files( for pathname in file_io.get_matching_files_v2(
os.path.join(self.write_checkpoint_manager.directory, 'checkpoint')): os.path.join(self.write_checkpoint_manager.directory, 'checkpoint')):
file_io.delete_recursively(pathname) file_io.delete_recursively_v2(pathname)
def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode): def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):
"""Maybe load initial epoch from ckpt considering possible worker recovery. """Maybe load initial epoch from ckpt considering possible worker recovery.

8
tensorflow/python/keras/distribute/worker_training_state_test.py
View File

@ -48,7 +48,7 @@ class ModelCheckpointTest(test_base.IndependentWorkerTestBase,
callbacks.ModelCheckpoint( callbacks.ModelCheckpoint(
filepath=saving_filepath, save_weights_only=save_weights_only) filepath=saving_filepath, save_weights_only=save_weights_only)
] ]
self.assertFalse(file_io.file_exists(saving_filepath)) self.assertFalse(file_io.file_exists_v2(saving_filepath))
try: try:
model.fit( model.fit(
@ -56,9 +56,9 @@ class ModelCheckpointTest(test_base.IndependentWorkerTestBase,
except NotFoundError as e: except NotFoundError as e:
if 'Failed to create a NewWriteableFile' in e.message: if 'Failed to create a NewWriteableFile' in e.message:
self.skipTest('b/138941852, path not found error in Windows py35.') self.skipTest('b/138941852, path not found error in Windows py35.')
tf_saved_model_exists = file_io.file_exists(saving_filepath) tf_saved_model_exists = file_io.file_exists_v2(saving_filepath)
tf_weights_only_checkpoint_exists = file_io.file_exists(saving_filepath + tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
'.index') saving_filepath + '.index')
self.assertTrue(tf_saved_model_exists or tf_weights_only_checkpoint_exists) self.assertTrue(tf_saved_model_exists or tf_weights_only_checkpoint_exists)

6
tensorflow/python/keras/saving/hdf5_format_test.py
View File

@ -1276,7 +1276,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
prefix = 'ackpt' prefix = 'ackpt'
self.evaluate(v.assign(42.)) self.evaluate(v.assign(42.))
m.save_weights(prefix) m.save_weights(prefix)
self.assertTrue(file_io.file_exists('ackpt.index')) self.assertTrue(file_io.file_exists_v2('ackpt.index'))
self.evaluate(v.assign(1.)) self.evaluate(v.assign(1.))
m.load_weights(prefix) m.load_weights(prefix)
self.assertEqual(42., self.evaluate(v)) self.assertEqual(42., self.evaluate(v))
@ -1284,7 +1284,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
prefix = 'subdir/ackpt' prefix = 'subdir/ackpt'
self.evaluate(v.assign(43.)) self.evaluate(v.assign(43.))
m.save_weights(prefix) m.save_weights(prefix)
self.assertTrue(file_io.file_exists('subdir/ackpt.index')) self.assertTrue(file_io.file_exists_v2('subdir/ackpt.index'))
self.evaluate(v.assign(2.)) self.evaluate(v.assign(2.))
m.load_weights(prefix) m.load_weights(prefix)
self.assertEqual(43., self.evaluate(v)) self.assertEqual(43., self.evaluate(v))
@ -1292,7 +1292,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
prefix = 'ackpt/' prefix = 'ackpt/'
self.evaluate(v.assign(44.)) self.evaluate(v.assign(44.))
m.save_weights(prefix) m.save_weights(prefix)
self.assertTrue(file_io.file_exists('ackpt/.index')) self.assertTrue(file_io.file_exists_v2('ackpt/.index'))
self.evaluate(v.assign(3.)) self.evaluate(v.assign(3.))
m.load_weights(prefix) m.load_weights(prefix)
self.assertEqual(44., self.evaluate(v)) self.assertEqual(44., self.evaluate(v))

8
tensorflow/python/keras/saving/saved_model_experimental.py
View File

@ -30,8 +30,8 @@ from tensorflow.python.keras.saving import model_config
from tensorflow.python.keras.saving import saving_utils from tensorflow.python.keras.saving import saving_utils
from tensorflow.python.keras.utils import mode_keys from tensorflow.python.keras.utils import mode_keys
from tensorflow.python.keras.utils.generic_utils import LazyLoader from tensorflow.python.keras.utils.generic_utils import LazyLoader
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import variables from tensorflow.python.ops import variables
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.saved_model import builder as saved_model_builder from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import constants from tensorflow.python.saved_model import constants
@ -152,7 +152,8 @@ def _export_model_json(model, saved_model_path):
model_json_filepath = os.path.join( model_json_filepath = os.path.join(
saved_model_utils.get_or_create_assets_dir(saved_model_path), saved_model_utils.get_or_create_assets_dir(saved_model_path),
compat.as_text(constants.SAVED_MODEL_FILENAME_JSON)) compat.as_text(constants.SAVED_MODEL_FILENAME_JSON))
file_io.write_string_to_file(model_json_filepath, model_json) with gfile.Open(model_json_filepath, 'w') as f:
f.write(model_json)
def _export_model_variables(model, saved_model_path): def _export_model_variables(model, saved_model_path):
@ -417,7 +418,8 @@ def load_from_saved_model(saved_model_path, custom_objects=None):
compat.as_bytes(saved_model_path), compat.as_bytes(saved_model_path),
compat.as_bytes(constants.ASSETS_DIRECTORY), compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON)) compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
model_json = file_io.read_file_to_string(model_json_filepath) with gfile.Open(model_json_filepath, 'r') as f:
model_json = f.read()
model = model_config.model_from_json( model = model_config.model_from_json(
model_json, custom_objects=custom_objects) model_json, custom_objects=custom_objects)

16
tensorflow/python/keras/utils/vis_utils_test.py
View File

@ -38,8 +38,8 @@ class ModelToDotFormatTest(test.TestCase):
try: try:
vis_utils.plot_model( vis_utils.plot_model(
model, to_file=dot_img_file, show_shapes=True, show_dtype=True) model, to_file=dot_img_file, show_shapes=True, show_dtype=True)
self.assertTrue(file_io.file_exists(dot_img_file)) self.assertTrue(file_io.file_exists_v2(dot_img_file))
file_io.delete_file(dot_img_file) file_io.delete_file_v2(dot_img_file)
except ImportError: except ImportError:
pass pass
@ -68,8 +68,8 @@ class ModelToDotFormatTest(test.TestCase):
show_shapes=True, show_shapes=True,
show_dtype=True, show_dtype=True,
expand_nested=True) expand_nested=True)
self.assertTrue(file_io.file_exists(dot_img_file)) self.assertTrue(file_io.file_exists_v2(dot_img_file))
file_io.delete_file(dot_img_file) file_io.delete_file_v2(dot_img_file)
except ImportError: except ImportError:
pass pass
@ -86,8 +86,8 @@ class ModelToDotFormatTest(test.TestCase):
show_shapes=True, show_shapes=True,
show_dtype=True, show_dtype=True,
expand_nested=True) expand_nested=True)
self.assertTrue(file_io.file_exists(dot_img_file)) self.assertTrue(file_io.file_exists_v2(dot_img_file))
file_io.delete_file(dot_img_file) file_io.delete_file_v2(dot_img_file)
except ImportError: except ImportError:
pass pass
@ -102,8 +102,8 @@ class ModelToDotFormatTest(test.TestCase):
show_shapes=True, show_shapes=True,
show_dtype=True, show_dtype=True,
expand_nested=True) expand_nested=True)
self.assertTrue(file_io.file_exists(dot_img_file)) self.assertTrue(file_io.file_exists_v2(dot_img_file))
file_io.delete_file(dot_img_file) file_io.delete_file_v2(dot_img_file)
except ImportError: except ImportError:
pass pass