def load(cls, basename, **kwargs):
K.clear_session()
model = cls()
model.impl = keras.models.load_model(basename)
state = read_json(basename + '.state')
for prop in ls_props(model):
if prop in state:
setattr(model, prop, state[prop])
inputs = dict({(v.name[:v.name.find(':')], v)
for v in model.impl.inputs})
model.embeddings = dict()
for key, class_name in state['embeddings'].items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
embed_args[key] = inputs[key]
Constructor = eval(class_name)
model.embeddings[key] = Constructor(key, **embed_args)
##model.lengths_key = state.get('lengths_key')
with open(basename + '.labels', 'r') as f:
model.labels = json.load(f)
return model
def _create_remote_model(directory, backend, remote, name, signature_name, beam, preproc='client'):
"""Reads the necessary information from the remote bundle to instatiate
a client for a remote model.
:directory the location of the exported model bundle
:remote a url endpoint to hit
:name the model name, as defined in tf-serving's model.config
:signature_name the signature to use.
:beam used for s2s and found in the kwargs. We default this and pass it in.
:returns a RemoteModel
"""
assets = read_json(os.path.join(directory, 'model.assets'))
model_name = assets['metadata']['exported_model']
labels = read_json(os.path.join(directory, model_name) + '.labels')
lengths_key = assets.get('lengths_key', None)
inputs = assets.get('inputs', [])
if backend == 'tf':
remote_models = import_user_module('baseline.remote')
if remote.startswith('http'):
RemoteModel = remote_models.RemoteModelTensorFlowREST
elif preproc == 'server':
RemoteModel = remote_models.RemoteModelTensorFlowGRPCPreproc
else:
RemoteModel = remote_models.RemoteModelTensorFlowGRPC
model = RemoteModel(remote, name, signature_name, labels=labels, lengths_key=lengths_key, inputs=inputs, beam=beam)
else:
raise ValueError("only Tensorflow is currently supported for remote Services")
return model
def _create_model(self, sess, basename):
labels = read_json(basename + '.labels')
model_params = self.task.config_params["model"]
model_params["sess"] = sess
state = read_json(basename + '.state')
if state.get('constrain_decode', False):
constraint = transition_mask(
labels, self.task.config_params['train']['span_type'],
Offsets.GO, Offsets.EOS, Offsets.PAD)
model_params['constraint'] = constraint
# Re-create the embeddings sub-graph
embeddings = dict()
for key, class_name in state['embeddings'].items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(class_name)
embeddings[key] = Constructor(key, **embed_args)
model = baseline.model.create_model_for(self.task.task_name(),
embeddings, labels,
**model_params)
for prop in ls_props(model):
if prop in state:
setattr(model, prop, state[prop])
model.create_loss()
softmax_output = tf.nn.softmax(model.probs)
values, indices = tf.nn.top_k(softmax_output, 1)
start_np = np.full((1, 1, len(labels)), -1e4, dtype=np.float32)
start_np[:, 0, Offsets.GO] = 0
start = tf.constant(start_np)
start = tf.tile(start, [tf.shape(model.probs)[0], 1, 1])
model.probs = tf.concat([start, model.probs], 1)
ones = tf.fill(tf.shape(model.lengths), 1)
lengths = tf.add(model.lengths, ones)
if model.crf is True:
indices, _ = tf.contrib.crf.crf_decode(model.probs, model.A,
lengths)
indices = indices[:, 1:]
list_of_labels = [''] * len(labels)
for label, idval in labels.items():
list_of_labels[idval] = label
class_tensor = tf.constant(list_of_labels)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
class_tensor)
classes = table.lookup(tf.to_int64(indices))
self._restore_checkpoint(sess, basename)
return model, indices, values
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
# FIXME: Somehow not writing this anymore
#if __version__ != _state['version']:
# logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
if not tf.executing_eagerly():
_state['sess'] = kwargs.pop('sess', create_session())
embeddings_info = _state.pop("embeddings")
with _state['sess'].graph.as_default():
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = read_json("{}.labels".format(basename))
# FIXME: referring to the `constraint_mask` in the base where its not mentioned isnt really clean
if _state.get('constraint_mask') is not None:
# Dummy constraint values that will be filled in by the check pointing
_state['constraint_mask'] = [np.zeros((len(labels), len(labels))) for _ in range(2)]
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.create_loss()
if kwargs.get('init', True):
model.sess.run(tf.compat.v1.global_variables_initializer())
model.saver = tf.compat.v1.train.Saver()
model.saver.restore(model.sess, basename)
else:
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
# FIXME: referring to the `constraint_mask` in the base where its not mentioned isnt really clean
if _state.get('constraint_mask') is not None:
# Dummy constraint values that will be filled in by the check pointing
_state['constraint_mask'] = [np.zeros((len(labels), len(labels))) for _ in range(2)]
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def load(cls, basename: str, **kwargs) -> 'ClassifierModelBase':
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
if not tf.executing_eagerly():
_state['sess'] = kwargs.pop('sess', create_session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
if kwargs.get('init', True):
model.sess.run(tf.compat.v1.global_variables_initializer())
model.saver = tf.compat.v1.train.Saver()
model.saver.restore(model.sess, basename)
else:
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json(basename + '.state')
if not tf.executing_eagerly():
_state['sess'] = kwargs.pop('sess', create_session())
embeddings_info = _state.pop("embeddings")
with _state['sess'].graph.as_default():
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
_state['model_type'] = kwargs.get('model_type', 'default')
model = cls.create(embeddings, **_state)
model._state = _state
do_init = kwargs.get('init', True)
if do_init:
init = tf.compat.v1.global_variables_initializer()
model.sess.run(init)
model.saver = tf.compat.v1.train.Saver()
model.saver.restore(model.sess, basename)
else:
_state = read_json(basename + '.state')
_state['model_type'] = kwargs.get('model_type', 'default')
embeddings = {}
embeddings_dict = _state.pop("embeddings")
for key, class_name in embeddings_dict.items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(class_name)
embeddings[key] = Constructor(key, **embed_args)
model = cls.create(embeddings, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def download(self):
if is_file_correct(self.embedding_file):
logger.info("embedding file location: {}".format(self.embedding_file))
return self.embedding_file
dcache_path = os.path.join(self.data_download_cache, DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if self.embedding_file in dcache and not self.cache_ignore:
download_loc = dcache[self.embedding_file]
logger.info("files for {} found in cache".format(self.embedding_file))
return self._get_embedding_file(download_loc, self.embedding_key)
else: # try to download the bundle and unzip
url = self.embedding_file
if not validate_url(url):
raise RuntimeError("can not download from the given url")
else:
cache_dir = self.data_download_cache
temp_file = web_downloader(url)
download_loc = extractor(filepath=temp_file, cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(mime_type(temp_file), None))
if self.sha1 is not None:
if os.path.split(download_loc)[-1] != self.sha1:
raise RuntimeError("The sha1 of the downloaded file does not match with the provided one")
dcache.update({url: download_loc})
write_json(dcache, os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return self._get_embedding_file(download_loc, self.embedding_key)
def download(self):
dload_bundle = self.dataset_desc.get("download", None)
if dload_bundle is not None: # download a zip/tar/tar.gz directory, look for train, dev test files inside that.
dcache_path = os.path.join(self.data_download_cache, DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if dload_bundle in dcache and \
is_dir_correct(dcache[dload_bundle], self.dataset_desc, self.data_download_cache, dload_bundle,
self.enc_dec) and not self.cache_ignore:
download_dir = dcache[dload_bundle]
logger.info("files for {} found in cache, not downloading".format(dload_bundle))
return {k: os.path.join(download_dir, self.dataset_desc[k]) for k in self.dataset_desc
if k.endswith("_file")}
else: # try to download the bundle and unzip
if not validate_url(dload_bundle):
raise RuntimeError("can not download from the given url")
else:
cache_dir = self.data_download_cache
temp_file = web_downloader(dload_bundle)
download_dir = extractor(filepath=temp_file, cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(mime_type(temp_file), None))
if "sha1" in self.dataset_desc:
if os.path.split(download_dir)[-1] != self.dataset_desc["sha1"]:
raise RuntimeError("The sha1 of the downloaded file does not match with the provided one")
dcache.update({dload_bundle: download_dir})
write_json(dcache, os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return {k: os.path.join(download_dir, self.dataset_desc[k]) for k in self.dataset_desc
if k.endswith("_file")}
else: # we have download links to every file or they exist
if not self.enc_dec:
return {k: SingleFileDownloader(self.dataset_desc[k], self.data_download_cache).download()
for k in self.dataset_desc if k.endswith("_file")}
else:
return {k: self.dataset_desc[k] for k in self.dataset_desc if k.endswith("_file")}
def load(cls, basename, **kwargs):
_state = read_json('{}.state'.format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
if 'predict' in kwargs:
_state['predict'] = kwargs['predict']
if 'beam' in kwargs:
_state['beam'] = kwargs['beam']
_state['sess'] = kwargs.get('sess', create_session())
with _state['sess'].graph.as_default():
src_embeddings_info = _state.pop('src_embeddings')
src_embeddings = reload_embeddings(src_embeddings_info,
basename)
for k in src_embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
tgt_embedding_info = _state.pop('tgt_embedding')
tgt_embedding = reload_embeddings(tgt_embedding_info,
basename)['tgt']
model = cls.create(src_embeddings, tgt_embedding, **_state)
model._state = _state
if kwargs.get('init', True):
model.sess.run(tf.compat.v1.global_variables_initializer())
model.saver = tf.compat.v1.train.Saver()
#reload_checkpoint(model.sess, basename, ['OptimizeLoss/'])
model.saver.restore(model.sess, basename)
return model
def load(cls, basename, **kwargs):
_state = read_json('{}.state'.format(basename))
if __version__ != _state['version']:
logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
if 'predict' in kwargs:
_state['predict'] = kwargs['predict']
if 'sampling' in kwargs:
_state['sampling'] = kwargs['sampling']
if 'sampling_temp' in kwargs:
_state['sampling_temp'] = kwargs['sampling_temp']
if 'beam' in kwargs:
_state['beam'] = kwargs['beam']
_state['sess'] = kwargs.get('sess', tf.Session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
model = cls.create(embeddings, **_state)
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def download(self):
file_loc = self.dataset_file
if is_file_correct(file_loc):
return file_loc
elif validate_url(
file_loc): # is it a web URL? check if exists in cache
url = file_loc
dcache_path = os.path.join(self.data_download_cache,
DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if url in dcache and is_file_correct(
dcache[url], self.data_download_cache,
url) and not self.cache_ignore:
print(
"file for {} found in cache, not downloading".format(url))
return dcache[url]
else: # download the file in the cache, update the json
cache_dir = self.data_download_cache
print("using {} as data/embeddings cache".format(cache_dir))
temp_file = web_downloader(url)
dload_file = extractor(filepath=temp_file,
cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(
mime_type(temp_file), None))
dcache.update({url: dload_file})
write_json(
dcache,
os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return dload_file
raise RuntimeError(
"the file [{}] is not in cache and can not be downloaded".format(
file_loc))
def load(cls, basename, **kwargs):
_state = read_json('{}.state'.format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
if 'predict' in kwargs:
_state['predict'] = kwargs['predict']
if 'sampling' in kwargs:
_state['sampling'] = kwargs['sampling']
if 'sampling_temp' in kwargs:
_state['sampling_temp'] = kwargs['sampling_temp']
if 'beam' in kwargs:
_state['beam'] = kwargs['beam']
_state['sess'] = kwargs.get('sess', tf.Session())
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
model = cls.create(embeddings, **_state)
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def download(self):
dload_bundle = self.dataset_desc.get("download", None)
if dload_bundle is not None: # download a zip/tar/tar.gz directory, look for train, dev test files inside that.
dcache_path = os.path.join(self.data_download_cache, DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if dload_bundle in dcache and \
is_dir_correct(dcache[dload_bundle], self.dataset_desc, self.data_download_cache, dload_bundle,
self.enc_dec) and not self.cache_ignore:
download_dir = dcache[dload_bundle]
logger.info("files for {} found in cache, not downloading".format(dload_bundle))
return {k: os.path.join(download_dir, self.dataset_desc[k]) for k in self.dataset_desc
if k.endswith("_file")}
else: # try to download the bundle and unzip
if not validate_url(dload_bundle):
raise RuntimeError("can not download from the given url")
else:
cache_dir = self.data_download_cache
temp_file = web_downloader(dload_bundle)
download_dir = extractor(filepath=temp_file, cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(mime_type(temp_file), None))
if "sha1" in self.dataset_desc:
if os.path.split(download_dir)[-1] != self.dataset_desc["sha1"]:
raise RuntimeError("The sha1 of the downloaded file does not match with the provided one")
dcache.update({dload_bundle: download_dir})
write_json(dcache, os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return {k: os.path.join(download_dir, self.dataset_desc[k]) for k in self.dataset_desc
if k.endswith("_file")}
else: # we have download links to every file or they exist
if not self.enc_dec:
return {k: SingleFileDownloader(self.dataset_desc[k], self.data_download_cache).download()
for k in self.dataset_desc if k.endswith("_file") and self.dataset_desc[k]}
else:
return {k: self.dataset_desc[k] for k in self.dataset_desc if k.endswith("_file")}
def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
import onnxruntime as ort
# can delegate
if os.path.isdir(bundle):
directory = bundle
# Try and unzip if its a zip file
else:
directory = unzip_files(bundle)
model_basename = find_model_basename(directory)
# model_basename = model_basename.replace(".pyt", "")
model_name = f"{model_basename}.onnx"
vocabs = load_vocabs(directory)
vectorizers = load_vectorizers(directory)
# Currently nothing to do here
labels = read_json(model_basename + '.labels')
model = ort.InferenceSession(model_name)
return cls(vocabs, vectorizers, model, labels)
def download(self):
if is_file_correct(self.embedding_file):
logger.info("embedding file location: {}".format(self.embedding_file))
return self.embedding_file
dcache_path = os.path.join(self.data_download_cache, DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if self.embedding_file in dcache and not self.cache_ignore:
download_loc = dcache[self.embedding_file]
logger.info("files for {} found in cache".format(self.embedding_file))
return self._get_embedding_file(download_loc, self.embedding_key)
else: # try to download the bundle and unzip
url = self.embedding_file
if not validate_url(url):
raise RuntimeError("can not download from the given url")
else:
cache_dir = self.data_download_cache
temp_file = web_downloader(url)
download_loc = extractor(filepath=temp_file, cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(mime_type(temp_file), None))
if self.sha1 is not None:
if os.path.split(download_loc)[-1] != self.sha1:
raise RuntimeError("The sha1 of the downloaded file does not match with the provided one")
dcache.update({url: download_loc})
write_json(dcache, os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return self._get_embedding_file(download_loc, self.embedding_key)
def _create_remote_model(directory, backend, remote, name, task_name,
signature_name, beam, **kwargs):
"""Reads the necessary information from the remote bundle to instatiate
a client for a remote model.
:directory the location of the exported model bundle
:remote a url endpoint to hit
:name the model name, as defined in tf-serving's model.config
:signature_name the signature to use.
:beam used for s2s and found in the kwargs. We default this and pass it in.
:returns a RemoteModel
"""
from baseline.remote import create_remote
assets = read_json(os.path.join(directory, 'model.assets'))
model_name = assets['metadata']['exported_model']
preproc = assets['metadata'].get('preproc',
kwargs.get('preproc', 'client'))
labels = read_json(os.path.join(directory, model_name) + '.labels')
lengths_key = assets.get('lengths_key', None)
inputs = assets.get('inputs', [])
return_labels = bool(assets['metadata']['return_labels'])
version = kwargs.get('version')
if backend not in {'tf', 'onnx'}:
raise ValueError(
f"Unsupported backend {backend} for remote Services")
import_user_module('baseline.{}.remote'.format(backend))
exp_type = kwargs.get('remote_type')
if exp_type is None:
exp_type = 'http' if remote.startswith('http') else 'grpc'
exp_type = '{}-preproc'.format(
exp_type) if preproc == 'server' else exp_type
exp_type = f'{exp_type}-{task_name}'
model = create_remote(
exp_type,
remote=remote,
name=name,
signature=signature_name,
labels=labels,
lengths_key=lengths_key,
inputs=inputs,
beam=beam,
return_labels=return_labels,
version=version,
)
return model, preproc
def init_embeddings(self, embeddings_map, basename):
embeddings = dict()
for key, class_name in embeddings_map:
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(class_name)
embeddings[key] = Constructor(key, **embed_args)
return embeddings
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
_state['sess'] = kwargs.pop('sess', create_session())
embeddings_info = _state.pop("embeddings")
with _state['sess'].graph.as_default():
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = read_json("{}.labels".format(basename))
if _state.get('constraint') is not None:
# Dummy constraint values that will be filled in by the check pointing
_state['constraint'] = [
tf.zeros((len(labels), len(labels))) for _ in range(2)
]
if 'lengths' in kwargs:
_state['lengths'] = kwargs['lengths']
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.create_loss()
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def __init__(self, name, embed_file=None, known_vocab=None, **kwargs):
super(ELMoEmbeddings, self).__init__(name=name)
# options file
self.weight_file = embed_file
self.dsz = kwargs['dsz']
elmo_config = embed_file.replace('weights.hdf5', 'options.json')
self.model = BidirectionalLanguageModel(elmo_config, self.weight_file)
self.vocab = UnicodeCharsVocabulary(known_vocab)
elmo_config = read_json(elmo_config)
assert self.dsz == 2 * int(elmo_config['lstm']['projection_dim'])
def reload_embeddings(embeddings_dict, basename):
embeddings = {}
for key, cls in embeddings_dict.items():
embed_args = read_json('{}-{}-md.json'.format(basename, key))
module = embed_args.pop('module')
name = embed_args.pop('name', None)
assert name is None or name == key
mod = import_user_module(module)
Constructor = getattr(mod, cls)
embeddings[key] = Constructor(key, **embed_args)
return embeddings
def reload_embeddings(embeddings_dict, basename):
embeddings = {}
for key, cls in embeddings_dict.items():
embed_args = read_json('{}-{}-md.json'.format(basename, key))
module = embed_args.pop('module')
name = embed_args.pop('name', None)
assert name is None or name == key
mod = import_user_module(module)
Constructor = getattr(mod, cls)
embeddings[key] = Constructor(key, **embed_args)
return embeddings
def __init__(self, name, embed_file=None, known_vocab=None, **kwargs):
super(ELMoEmbeddings, self).__init__(name=name, **kwargs)
# options file
self.weight_file = embed_file
self.dsz = kwargs['dsz']
elmo_config = embed_file.replace('weights.hdf5', 'options.json')
self.model = BidirectionalLanguageModel(elmo_config, self.weight_file)
self.known_vocab = known_vocab
self.vocab = UnicodeCharsVocabulary(known_vocab)
elmo_config = read_json(elmo_config)
assert self.dsz == 2*int(elmo_config['lstm']['projection_dim'])
def load(cls, basename, **kwargs):
state = read_json(basename + '.state')
if 'predict' in kwargs:
state['predict'] = kwargs['predict']
if 'beam' in kwargs:
state['beam'] = kwargs['beam']
state['sess'] = kwargs.get('sess', tf.Session())
state['model_type'] = kwargs.get('model_type', 'default')
with open(basename + '.saver') as fsv:
saver_def = tf.train.SaverDef()
text_format.Merge(fsv.read(), saver_def)
src_embeddings = dict()
src_embeddings_dict = state.pop('src_embeddings')
for key, class_name in src_embeddings_dict.items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(class_name)
src_embeddings[key] = Constructor(key, **embed_args)
tgt_class_name = state.pop('tgt_embedding')
md = read_json('{}-tgt-md.json'.format(basename))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(tgt_class_name)
tgt_embedding = Constructor('tgt', **embed_args)
model = cls.create(src_embeddings, tgt_embedding, **state)
for prop in ls_props(model):
if prop in state:
setattr(model, prop, state[prop])
do_init = kwargs.get('init', True)
if do_init:
init = tf.global_variables_initializer()
model.sess.run(init)
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def __init__(self, name, **kwargs):
super(TransformerLMEmbeddings, self).__init__(name)
self.vocab = read_json(kwargs.get('vocab_file'))
self.cls_index = self.vocab['[CLS]']
self.vsz = len(self.vocab)
layers = int(kwargs.get('layers', 8))
num_heads = int(kwargs.get('num_heads', 10))
pdrop = kwargs.get('dropout', 0.1)
self.d_model = int(kwargs.get('dsz', kwargs.get('d_model', 410)))
d_ff = int(kwargs.get('d_ff', 2100))
x_embedding = PositionalLookupTableEmbeddings('pos', vsz=self.vsz, dsz=self.d_model)
self.init_embed({'x': x_embedding})
self.transformer = TransformerEncoderStack(num_heads, d_model=self.d_model, pdrop=pdrop, scale=True, layers=layers, d_ff=d_ff)
def _create_model(self, sess, basename):
# Read the labels
labels = read_json(basename + '.labels')
# Get the parameters from MEAD
model_params = self.task.config_params["model"]
model_params["sess"] = sess
# Read the state file
state = read_json(basename + '.state')
# Re-create the embeddings sub-graph
embeddings = dict()
for key, class_name in state['embeddings'].items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
Constructor = eval(class_name)
embeddings[key] = Constructor(key, **embed_args)
# Instantiate a graph
model = baseline.model.create_model_for(self.task.task_name(),
embeddings, labels,
**model_params)
# Set the properties of the model from the state file
for prop in ls_props(model):
if prop in state:
setattr(model, prop, state[prop])
# Append to the graph for class output
values, indices = tf.nn.top_k(model.probs, len(labels))
class_tensor = tf.constant(model.labels)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
class_tensor)
classes = table.lookup(tf.to_int64(indices))
# Restore the checkpoint
self._restore_checkpoint(sess, basename)
return model, classes, values
def _create_remote_model(directory, backend, remote, name, signature_name, beam, **kwargs):
"""Reads the necessary information from the remote bundle to instatiate
a client for a remote model.
:directory the location of the exported model bundle
:remote a url endpoint to hit
:name the model name, as defined in tf-serving's model.config
:signature_name the signature to use.
:beam used for s2s and found in the kwargs. We default this and pass it in.
:returns a RemoteModel
"""
from baseline.remote import create_remote
assets = read_json(os.path.join(directory, 'model.assets'))
model_name = assets['metadata']['exported_model']
preproc = assets['metadata'].get('preproc', kwargs.get('preproc', 'client'))
labels = read_json(os.path.join(directory, model_name) + '.labels')
lengths_key = assets.get('lengths_key', None)
inputs = assets.get('inputs', [])
return_labels = bool(assets['metadata']['return_labels'])
version = kwargs.get('version')
if backend not in {'tf', 'pytorch'}:
raise ValueError("only Tensorflow and Pytorch are currently supported for remote Services")
import_user_module('baseline.{}.remote'.format(backend))
exp_type = 'http' if remote.startswith('http') else 'grpc'
exp_type = '{}-preproc'.format(exp_type) if preproc == 'server' else exp_type
model = create_remote(
exp_type,
remote=remote, name=name,
signature=signature_name,
labels=labels,
lengths_key=lengths_key,
inputs=inputs,
beam=beam,
return_labels=return_labels,
version=version,
)
return model, preproc
def __init__(self, name, **kwargs):
from baseline.pytorch.embeddings import PositionalCharConvEmbeddings
X_CHAR_EMBEDDINGS = {
"dsz":
16,
"wsz":
128,
"embed_type":
"positional-char-conv",
"keep_unused":
True,
"cfiltsz": [[1, 32], [2, 32], [3, 64], [4, 128], [5, 256],
[6, 512], [7, 1024]],
"gating":
"highway",
"num_gates":
2,
"projsz":
512
}
super(TransformerLMEmbeddings, self).__init__(name)
self.vocab = read_json(kwargs.get('vocab_file'), strict=True)
self.cls_index = self.vocab['[CLS]']
self.vsz = len(self.vocab)
layers = int(kwargs.get('layers', 18))
num_heads = int(kwargs.get('num_heads', 10))
pdrop = kwargs.get('dropout', 0.1)
self.d_model = int(kwargs.get('dsz', kwargs.get('d_model', 410)))
d_ff = int(kwargs.get('d_ff', 2100))
x_embedding = PositionalCharConvEmbeddings('pcc',
vsz=self.vsz,
**X_CHAR_EMBEDDINGS)
self.dsz = self.init_embed({'x': x_embedding})
self.proj_to_dsz = pytorch_linear(
self.dsz, self.d_model) if self.dsz != self.d_model else _identity
self.init_embed({'x': x_embedding})
self.transformer = TransformerEncoderStack(num_heads,
d_model=self.d_model,
pdrop=pdrop,
scale=True,
layers=layers,
d_ff=d_ff)
self.mlm = kwargs.get('mlm', False)
pooling = kwargs.get('pooling', 'cls')
if pooling == 'max':
self.pooling_op = _max_pool
elif pooling == 'mean':
self.pooling_op = _mean_pool
else:
self.pooling_op = self._cls_pool
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
_state['sess'] = kwargs.pop('sess', tf.Session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def load(cls, basename, **kwargs):
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
_state['sess'] = kwargs.pop('sess', tf.Session())
embeddings_info = _state.pop("embeddings")
with _state['sess'].graph.as_default():
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
labels = read_json("{}.labels".format(basename))
if _state.get('constraint') is not None:
# Dummy constraint values that will be filled in by the check pointing
_state['constraint'] = [tf.zeros((len(labels), len(labels))) for _ in range(2)]
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.create_loss()
if kwargs.get('init', True):
model.sess.run(tf.global_variables_initializer())
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def load(cls, basename, **kwargs):
K.clear_session()
model = cls()
model.impl = keras.models.load_model(basename)
state = read_json(basename + '.state')
for prop in ls_props(model):
if prop in state:
setattr(model, prop, state[prop])
inputs = dict({(v.name[:v.name.find(':')], v) for v in model.impl.inputs})
model.embeddings = dict()
for key, class_name in state['embeddings'].items():
md = read_json('{}-{}-md.json'.format(basename, key))
embed_args = dict({'vsz': md['vsz'], 'dsz': md['dsz']})
embed_args[key] = inputs[key]
Constructor = eval(class_name)
model.embeddings[key] = Constructor(key, **embed_args)
##model.lengths_key = state.get('lengths_key')
with open(basename + '.labels', 'r') as f:
model.labels = json.load(f)
return model
def load_model_for(activity, filename, **kwargs):
# Sniff state to see if we need to import things
state = read_json('{}.state'.format(filename))
# There won't be a module for pytorch (there is no state file to load).
if 'module' in state:
import_user_module(state['module'])
# Allow user to override model type (for back compat with old api), backoff
# to the model type in the state file or to default.
# TODO: Currently in pytorch all models are always reloaded with the load
# classmethod with a default model class. This is fine given how simple pyt
# loading is but it could cause problems if a model has a custom load
model_type = kwargs.get('model_type', state.get('model_type', 'default'))
creator_fn = BASELINE_LOADERS[activity][model_type]
logger.info('Calling model %s', creator_fn)
return creator_fn(filename, **kwargs)
def load_model_for(activity, filename, **kwargs):
# Sniff state to see if we need to import things
state = read_json('{}.state'.format(filename))
# There won't be a module for pytorch (there is no state file to load).
if 'module' in state:
import_user_module(state['module'])
# Allow user to override model type (for back compat with old api), backoff
# to the model type in the state file or to default.
# TODO: Currently in pytorch all models are always reloaded with the load
# classmethod with a default model class. This is fine given how simple pyt
# loading is but it could cause problems if a model has a custom load
model_type = kwargs.get('type', kwargs.get('model_type', state.get('type', state.get('model_type', 'default'))))
creator_fn = BASELINE_LOADERS[activity][model_type]
logger.info('Calling model %s', creator_fn)
return creator_fn(filename, **kwargs)
def load(cls, basename: str, **kwargs) -> 'DependencyParserModelBase':
"""Reload the model from a graph file and a checkpoint
The model that is loaded is independent of the pooling and stacking layers, making this class reusable
by sub-classes.
:param basename: The base directory to load from
:param kwargs: See below
:Keyword Arguments:
* *sess* -- An optional tensorflow session. If not passed, a new session is
created
:return: A restored model
"""
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
logger.warning(
"Loaded model is from baseline version %s, running version is %s",
_state['version'], __version__)
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
# If there is a kwarg that is the same name as an embedding object that
# is taken to be the input of that layer. This allows for passing in
# subgraphs like from a tf.split (for data parallel) or preprocessing
# graphs that convert text to indices
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
# TODO: convert labels into just another vocab and pass number of labels to models.
labels = read_json("{}.labels".format(basename))
model = cls.create(embeddings, labels, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def load(cls, basename, **kwargs):
_state = read_json("{}.state".format(basename))
if __version__ != _state['version']:
bl_logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
_state['sess'] = kwargs.pop('sess', create_session())
with _state['sess'].graph.as_default():
embeddings_info = _state.pop('embeddings')
embeddings = reload_embeddings(embeddings_info, basename)
for k in embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
model = cls.create(embeddings, init=kwargs.get('init', True), **_state)
model._state = _state
model.saver = tf.train.Saver()
model.saver.restore(model.sess, basename)
return model
def _create_model(self, sess, basename, **kwargs):
model = load_tagger_model(basename, sess=sess, **kwargs)
softmax_output = tf.nn.softmax(model.probs)
values, _ = tf.nn.top_k(softmax_output, 1)
indices = model.best
if self.return_labels:
labels = read_json(basename + '.labels')
list_of_labels = [''] * len(labels)
for label, idval in labels.items():
list_of_labels[idval] = label
class_tensor = tf.constant(list_of_labels)
table = tf.contrib.lookup.index_to_string_table_from_tensor(class_tensor)
classes = table.lookup(tf.to_int64(indices))
return model, classes, values
else:
return model, indices, values
def _create_model(self, sess, basename, **kwargs):
model = load_tagger_model(basename, sess=sess, **kwargs)
softmax_output = tf.nn.softmax(model.probs)
values, _ = tf.nn.top_k(softmax_output, 1)
indices = model.best
if self.return_labels:
labels = read_json(basename + '.labels')
list_of_labels = [''] * len(labels)
for label, idval in labels.items():
list_of_labels[idval] = label
class_tensor = tf.constant(list_of_labels)
table = tf.contrib.lookup.index_to_string_table_from_tensor(class_tensor)
classes = table.lookup(tf.to_int64(indices))
return model, classes, values
else:
return model, indices, values
def __init__(self, name, embed_file=None, known_vocab=None, **kwargs):
super().__init__(trainable=True, name=name, dtype=tf.float32, **kwargs)
# options file
self.weight_file = embed_file
if 'options' not in kwargs:
print('Warning: old style configuration')
elmo_config = embed_file.replace('weights.hdf5', 'options.json')
elmo_config = read_json(elmo_config)
else:
elmo_config = kwargs['options']
print(elmo_config)
self.dsz = kwargs.get('dsz', 2*int(elmo_config['lstm']['projection_dim']))
self.model = BidirectionalLanguageModel(elmo_config, self.weight_file)
self.known_vocab = known_vocab
self.vocab = UnicodeCharsVocabulary(known_vocab)
assert self.dsz == 2*int(elmo_config['lstm']['projection_dim'])
def __init__(self, logger_file, mead_config):
super(Task, self).__init__()
self.config_params = None
self.ExporterType = None
self.mead_config = mead_config
if os.path.exists(mead_config):
mead_settings = read_json(mead_config)
else:
mead_settings = {}
if 'datacache' not in mead_settings:
self.data_download_cache = os.path.expanduser("~/.bl-data")
mead_settings['datacache'] = self.data_download_cache
write_json(mead_settings, mead_config)
else:
self.data_download_cache = os.path.expanduser(mead_settings['datacache'])
print("using {} as data/embeddings cache".format(self.data_download_cache))
self._configure_logger(logger_file)
def read_cred(config_file):
dbtype = None
dbhost = None
dbport = None
user = None
passwd = None
try:
j = read_json(config_file, strict=True)
dbtype = j.get('dbtype')
dbhost = j.get('dbhost')
dbport = j.get('dbport')
user = j.get('user')
passwd = j.get('passwd')
except IOError:
pass
return dbtype, dbhost, dbport, user, passwd
def read_cred(config_file):
dbtype = None
dbhost = None
dbport = None
user = None
passwd = None
try:
j = read_json(config_file, strict=True)
dbtype = j.get('dbtype')
dbhost = j.get('dbhost')
dbport = j.get('dbport')
user = j.get('user')
passwd = j.get('passwd')
except IOError:
pass
return dbtype, dbhost, dbport, user, passwd
def download(self):
file_loc = self.dataset_file
if is_file_correct(file_loc):
return file_loc
elif validate_url(file_loc): # is it a web URL? check if exists in cache
url = file_loc
dcache_path = os.path.join(self.data_download_cache, DATA_CACHE_CONF)
dcache = read_json(dcache_path)
if url in dcache and is_file_correct(dcache[url], self.data_download_cache, url) and not self.cache_ignore:
logger.info("file for {} found in cache, not downloading".format(url))
return dcache[url]
else: # download the file in the cache, update the json
cache_dir = self.data_download_cache
logger.info("using {} as data/embeddings cache".format(cache_dir))
temp_file = web_downloader(url)
dload_file = extractor(filepath=temp_file, cache_dir=cache_dir,
extractor_func=Downloader.ZIPD.get(mime_type(temp_file), None))
dcache.update({url: dload_file})
write_json(dcache, os.path.join(self.data_download_cache, DATA_CACHE_CONF))
return dload_file
raise RuntimeError("the file [{}] is not in cache and can not be downloaded".format(file_loc))
def init_decode(self, **kwargs):
label_vocab = read_json(kwargs["label_vocab"])
label_trans = np.load(kwargs["label_trans"])
trans = np.zeros((len(self.labels), len(self.labels)))
for src, src_idx in self.labels.items():
if src not in label_vocab:
continue
for tgt, tgt_idx in self.labels.items():
if tgt not in label_vocab:
continue
trans[src_idx, tgt_idx] = label_trans[label_vocab[src],
label_vocab[tgt]]
name = kwargs.get("decode_name")
self.constraint_mask = kwargs.get("constraint_mask").unsqueeze(0)
return PremadeTransitionsTagger(len(self.labels),
self.constraint_mask,
transitions=trans)
def load(cls, basename, **kwargs):
_state = read_json('{}.state'.format(basename))
if __version__ != _state['version']:
logger.warning("Loaded model is from baseline version %s, running version is %s", _state['version'], __version__)
if 'predict' in kwargs:
_state['predict'] = kwargs['predict']
if 'beam' in kwargs:
_state['beam'] = kwargs['beam']
src_embeddings_info = _state.pop('src_embeddings')
src_embeddings = reload_embeddings(src_embeddings_info, basename)
for k in src_embeddings_info:
if k in kwargs:
_state[k] = kwargs[k]
tgt_embedding_info = _state.pop('tgt_embedding')
tgt_embedding = reload_embeddings(tgt_embedding_info, basename)['tgt']
model = cls.create(src_embeddings, tgt_embedding, **_state)
model._state = _state
model.load_weights(f"{basename}.wgt")
return model
def peek_lengths_key(model_file, field_map):
"""Check if there is lengths key to use when finding the length. (defaults to tokens)"""
peek_state = read_json(model_file + ".state")
lengths = peek_state.get('lengths_key', 'tokens')
lengths = lengths.replace('_lengths', '')
return field_map.get(lengths, lengths)
def update_cache(key, data_download_cache):
dcache = read_json(os.path.join(data_download_cache, DATA_CACHE_CONF))
if key not in dcache:
return
del dcache[key]
write_json(dcache, os.path.join(data_download_cache, DATA_CACHE_CONF))
def test_read_json_strict():
with pytest.raises(IOError):
read_json(os.path.join('not_there.json'), strict=True)
def test_read_json_given_default():
gold_default = 'default'
data = read_json(os.path.join(data_loc, 'not_there.json'), gold_default)
assert data == gold_default
def test_read_json_default_value():
gold_default = {}
data = read_json(os.path.join(data_loc, 'not_there.json'))
assert data == gold_default
def test_read_json(gold_data):
data = read_json(os.path.join(data_loc, 'test_json.json'))
assert data == gold_data
import os
import argparse
from baseline.utils import read_json
from mead.utils import index_by_label, convert_path
from mead.downloader import EmbeddingDownloader, DataDownloader
parser = argparse.ArgumentParser(description="Download all data and embeddings.")
parser.add_argument("--cache", default="~/.bl-data", type=os.path.expanduser, help="Location of the data cache")
parser.add_argument('--datasets', help='json library of dataset labels', default='config/datasets.json', type=convert_path)
parser.add_argument('--embeddings', help='json library of embeddings', default='config/embeddings.json', type=convert_path)
args = parser.parse_args()
datasets = read_json(args.datasets)
datasets = index_by_label(datasets)
for name, d in datasets.items():
print(name)
try:
DataDownloader(d, args.cache).download()
except Exception as e:
print(e)
emb = read_json(args.embeddings)
emb = index_by_label(emb)
for name, e in emb.items():
print(name)
try:
