def resolve(handle):
"""Resolves a module handle into a path.
This function works both for plain TF2 SavedModels and the legacy TF1 Hub
format.
Resolves a module handle into a path by downloading and caching in
location specified by TF_HUB_CACHE_DIR if needed.
Currently, three types of module handles are supported:
1) Smart URL resolvers such as tfhub.dev, e.g.:
https://tfhub.dev/google/nnlm-en-dim128/1.
2) A directory on a file system supported by Tensorflow containing module
files. This may include a local directory (e.g. /usr/local/mymodule) or a
Google Cloud Storage bucket (gs://mymodule).
3) A URL pointing to a TGZ archive of a module, e.g.
https://example.com/mymodule.tar.gz.
Args:
handle: (string) the Module handle to resolve.
Returns:
A string representing the Module path.
"""
return registry.resolver(handle)
def load_module_spec(path):
"""Loads a ModuleSpec from a TF Hub service or the filesystem.
Warning: Deprecated. This belongs to the hub.Module API and TF1 Hub format.
For TF2, switch to plain SavedModels and hub.load(); see also hub.resolve().
THIS FUNCTION IS DEPRECATED.
Args:
path: string describing the location of a module. There are several
supported path encoding schemes:
a) A URL like "https://tfhub.dev/the/module/1" referring to tfhub.dev or
another service implementing https://www.tensorflow.org/hub/hosting.
b) A URL like "https://example.com/module.tar.gz" that points to a
compressed tarball directly, as long as that web server ignores
the query parameters added by https://www.tensorflow.org/hub/hosting.
c) Any filesystem location of a module directory (e.g. /module_dir
for a local filesystem). All filesystems implementations provided
by Tensorflow are supported.
d) Private name resolution schemes added by the maintainer of your
local installation of the tensorflow_hub library (usually none).
Returns:
A ModuleSpec.
Raises:
ValueError: on unexpected values in the module spec.
tf.errors.OpError: on file handling exceptions.
"""
path = registry.resolver(path)
return registry.loader(path)
def build(self):
"""Builds the class. Used for lazy initialization."""
if self.is_built:
return
self.vocab_file = os.path.join(
registry.resolver(self.uri), 'assets', 'vocab.txt')
self.tokenizer = tokenization.FullTokenizer(self.vocab_file,
self.do_lower_case)
def resolve(handle):
"""Resolves a module handle into a path.
Resolves a module handle into a path by downloading and caching in
location specified by TF_HUB_CACHE_DIR if needed.
Args:
handle: (string) the Module handle to resolve.
Returns:
A string representing the Module path.
"""
return registry.resolver(handle)
def load_module_spec(path):
"""Loads a ModuleSpec from the filesystem.
Args:
path: string describing the location of a module. There are several
supported path encoding schemes:
a) URL location specifying an archived module
(e.g. http://domain/module.tgz)
b) Any filesystem location of a module directory (e.g. /module_dir
for a local filesystem). All filesystems implementations provided
by Tensorflow are supported.
Returns:
A ModuleSpec.
Raises:
ValueError: on unexpected values in the module spec.
tf.OpError: on file handling exceptions.
"""
path = registry.resolver(path)
return registry.loader(path)
def load_module_spec(path):
"""Loads a ModuleSpec from the filesystem.
Args:
path: string describing the location of a module. There are several
supported path encoding schemes:
a) URL location specifying an archived module
(e.g. http://domain/module.tgz)
b) Any filesystem location of a module directory (e.g. /module_dir
for a local filesystem). All filesystems implementations provided
by Tensorflow are supported.
Returns:
A ModuleSpec.
Raises:
ValueError: on unexpected values in the module spec.
tf.OpError: on file handling exceptions.
"""
path = registry.resolver(path)
return registry.loader(path)
def load_module_spec(path):
"""Loads a ModuleSpec from the filesystem.
Args:
path: string describing the location of a module. There are several
supported path encoding schemes:
a) URL location specifying an archived module
(e.g. http://domain/module.tgz)
b) Any filesystem location of a module directory (e.g. /module_dir
for a local filesystem). All filesystems implementations provided
by Tensorflow are supported.
Returns:
A ModuleSpec.
Raises:
ValueError: on unexpected values in the module spec.
tf.OpError: on file handling exceptions.
"""
path = registry.resolver(path)
module_def_path = _get_module_proto_path(path)
module_def_proto = module_def_pb2.ModuleDef()
with tf.gfile.Open(module_def_path, "rb") as f:
module_def_proto.ParseFromString(f.read())
if module_def_proto.format != module_def_pb2.ModuleDef.FORMAT_V3:
raise ValueError("Unsupported module def format: %r" %
module_def_proto.format)
required_features = set(module_def_proto.required_features)
unsupported_features = (required_features - _MODULE_V3_SUPPORTED_FEATURES)
if unsupported_features:
raise ValueError("Unsupported features: %r" % list(unsupported_features))
saved_model_handler = saved_model_lib.load(path)
checkpoint_filename = saved_model_lib.get_variables_path(path)
return _ModuleSpec(saved_model_handler, checkpoint_filename)
def load_module_spec(path):
"""Loads a ModuleSpec from the filesystem.
DEPRECATION NOTE: This belongs to the hub.Module API and file format for TF1.
For TF2, switch to plain SavedModels and hub.load().
Args:
path: string describing the location of a module. There are several
supported path encoding schemes:
a) URL location specifying an archived module
(e.g. http://domain/module.tgz)
b) Any filesystem location of a module directory (e.g. /module_dir
for a local filesystem). All filesystems implementations provided
by Tensorflow are supported.
Returns:
A ModuleSpec.
Raises:
ValueError: on unexpected values in the module spec.
tf.errors.OpError: on file handling exceptions.
"""
path = registry.resolver(path)
return registry.loader(path)
def __init__(
self,
uri='https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1',
model_dir=None,
seq_len=128,
dropout_rate=0.1,
initializer_range=0.02,
learning_rate=3e-5,
distribution_strategy='mirrored',
num_gpus=-1,
tpu='',
trainable=True,
do_lower_case=True,
is_tf2=True):
"""Initialze an instance with model paramaters.
Args:
uri: TF-Hub path/url to Bert module.
model_dir: The location of the model checkpoint files.
seq_len: Length of the sequence to feed into the model.
dropout_rate: The rate for dropout.
initializer_range: The stdev of the truncated_normal_initializer for
initializing all weight matrices.
learning_rate: The initial learning rate for Adam.
distribution_strategy: A string specifying which distribution strategy to
use. Accepted values are 'off', 'one_device', 'mirrored',
'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case
insensitive. 'off' means not to use Distribution Strategy; 'tpu' means
to use TPUStrategy using `tpu_address`.
num_gpus: How many GPUs to use at each worker with the
DistributionStrategies API. The default is -1, which means utilize all
available GPUs.
tpu: TPU address to connect to.
trainable: boolean, whether pretrain layer is trainable.
do_lower_case: boolean, whether to lower case the input text. Should be
True for uncased models and False for cased models.
is_tf2: boolean, whether the hub module is in TensorFlow 2.x format.
"""
if compat.get_tf_behavior() not in self.compat_tf_versions:
raise ValueError(
'Incompatible versions. Expect {}, but got {}.'.format(
self.compat_tf_versions, compat.get_tf_behavior()))
self.seq_len = seq_len
self.dropout_rate = dropout_rate
self.initializer_range = initializer_range
self.learning_rate = learning_rate
self.trainable = trainable
self.model_dir = model_dir
if self.model_dir is None:
self.model_dir = tempfile.mkdtemp()
num_gpus = get_num_gpus(num_gpus)
self.strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=distribution_strategy,
num_gpus=num_gpus,
tpu_address=tpu)
self.tpu = tpu
self.uri = uri
self.is_tf2 = is_tf2
self.vocab_file = os.path.join(registry.resolver(uri), 'assets',
'vocab.txt')
self.do_lower_case = do_lower_case
self.tokenizer = tokenization.FullTokenizer(self.vocab_file,
self.do_lower_case)
self.bert_config = bert_configs.BertConfig(
0,
initializer_range=self.initializer_range,
hidden_dropout_prob=self.dropout_rate)
