def RunModel(saved_model_dir, signature_def_key, tag, text, ngrams_list=None):
saved_model = reader.read_saved_model(saved_model_dir)
meta_graph = None
for meta_graph_def in saved_model.meta_graphs:
if tag in meta_graph_def.meta_info_def.tags:
meta_graph = meta_graph_def
break
if meta_graph_def is None:
raise ValueError("Cannot find saved_model with tag" + tag)
signature_def = signature_def_utils.get_signature_def_by_key(
meta_graph, signature_def_key)
text = text_utils.TokenizeText(text)
ngrams = None
if ngrams_list is not None:
ngrams_list = text_utils.ParseNgramsOpts(ngrams_list)
ngrams = text_utils.GenerateNgrams(text, ngrams_list)
example = inputs.BuildTextExample(text, ngrams=ngrams)
example = example.SerializeToString()
inputs_feed_dict = {
signature_def.inputs["inputs"].name: [example],
}
if signature_def_key == "proba":
output_key = "scores"
elif signature_def_key == "embedding":
output_key = "outputs"
else:
raise ValueError("Unrecognised signature_def %s" % (signature_def_key))
output_tensor = signature_def.outputs[output_key].name
with tf.Session() as sess:
loader.load(sess, [tag], saved_model_dir)
outputs = sess.run(output_tensor, feed_dict=inputs_feed_dict)
return outputs
def Request(text):
example = inputs.BuildTextExample(text_utils.TokenizeText(text))
request = classification_pb2.ClassificationRequest()
request.model_spec.name = 'default'
request.model_spec.signature_name = FLAGS.signature_def
request.input.example_list.examples.extend([example])
return request
def Request(text, ngrams):
text = text_utils.TokenizeText(text)
ngrams = None
if ngrams is not None:
ngrams_list = text_utils.ParseNgramsOpts(ngrams)
ngrams = text_utils.GenerateNgrams(text, ngrams_list)
example = inputs.BuildTextExample(text, ngrams=ngrams)
request = classification_pb2.ClassificationRequest()
request.model_spec.name = 'default'
request.model_spec.signature_name = 'proba'
request.input.example_list.examples.extend([example])
return request
def WriteExamples(examples, outputfile, num_shards):
"""Write examles in TFRecord format.
Args:
examples: list of feature dicts.
{'text': [words], 'label': [labels]}
outputfile: full pathname of output file
"""
shard = 0
num_per_shard = len(examples) / num_shards + 1
for n, example in enumerate(examples):
if n % num_per_shard == 0:
shard += 1
writer = tf.python_io.TFRecordWriter(outputfile + '-%d-of-%d' % \
(shard, num_shards))
record = inputs.BuildTextExample(example["text"], example["label"])
writer.write(record.SerializeToString())