def convert(self,line):
feature = convert_single_example(0, line, self.label2id, FLAGS.max_seq_length, self.tokenizer, 'xxxx')
input_ids = np.reshape(feature.input_ids, [batch_size, FLAGS.max_seq_length])
input_mask = np.reshape(feature.input_mask, [batch_size, FLAGS.max_seq_length])
segment_ids = np.reshape(feature.segment_ids, [batch_size, FLAGS.max_seq_length])
laebls_ids = np.reshape(feature.label_ids, [batch_size, FLAGS.max_seq_length])
return input_ids, input_mask, segment_ids, laebls_ids
def pre_inference(payload, signature, metadata):
input_example = run_classifier.InputExample(guid="",
text_a=payload["review"],
label=0)
input_feature = run_classifier.convert_single_example(
0, input_example, [0, 1], 128, tokenizer)
return {"input_ids": [input_feature.input_ids]}
def main():
MAX_SEQ_LENGTH, LABELS_LIST, VOCAB_FILE_PATH = get_config("cn")
# get model from the server
channel = grpc.insecure_channel(server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
# get the sentences of input
sentences = globals.request.form.to_dict()
# convert single sentence to feature
tokenizer = tokenization.FullTokenizer(
vocab_file=VOCAB_FILE_PATH, do_lower_case=True)
# Construct the request to tensorflow serving
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
request.model_spec.signature_name = 'serving_default'
results = {}
for key, sentence in sentences.items():
example = run_classifier.InputExample(
guid="test-0", text_a=tokenization.convert_to_unicode(sentence), text_b=None, label=LABELS_LIST[0])
feature = run_classifier.convert_single_example(
0, example, LABELS_LIST, MAX_SEQ_LENGTH, tokenizer)
# get the input of model
input_ids = np.reshape([feature.input_ids], (1, MAX_SEQ_LENGTH))
input_mask = np.reshape([feature.input_mask], (1, MAX_SEQ_LENGTH))
segment_ids = np.reshape([feature.segment_ids], (MAX_SEQ_LENGTH))
label_ids = [feature.label_id]
# package the input into request, Note the format of the input(follow the model)
request.inputs['input_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(input_ids, shape=[1, MAX_SEQ_LENGTH], dtype=tf.int32))
request.inputs['input_mask'].CopyFrom(
tf.contrib.util.make_tensor_proto(input_mask, shape=[1, MAX_SEQ_LENGTH], dtype=tf.int32))
request.inputs['label_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(label_ids, shape=[1], dtype=tf.int32))
request.inputs['segment_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(segment_ids, shape=[1, MAX_SEQ_LENGTH], dtype=tf.int32))
# do predict
result = stub.Predict(request, 10.0) # 10 secs timeout
# parse the result
probabilities_tensor_proto = result.outputs["probabilities"]
probabilities = list(probabilities_tensor_proto.float_val)
probabilities_np = np.array(probabilities)
top3_index_np = probabilities_np.argsort()[-3:][::-1]
probabilities_top3 = probabilities_np[top3_index_np]
label_top3 = np.array(LABELS_LIST)[top3_index_np]
# shape = tf.TensorShape(probabilities_tensor_proto.tensor_shape)
# probabilities = np.array(probabilities_tensor_proto.float_val).reshape(
# shape.as_list())
result_list = []
for index in range(3):
result_list.append(
{"label": label_top3[index], "score": str(probabilities_top3[index])})
results[key] = result_list
return Response(json.dumps(results), mimetype='application/json')
def predict(self, payload):
input_example = run_classifier.InputExample(guid="", text_a=payload["review"], label=0)
input_feature = run_classifier.convert_single_example(
0, input_example, [0, 1], 128, self._tokenizer
)
model_input = {"input_ids": [input_feature.input_ids]}
prediction = self.client.predict(model_input)
return labels[prediction["labels"][0]]
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer):
"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" %
(ex_index, len(examples)))
feature = run_classifier.convert_single_example(
10, example, label_list, max_seq_length, tokenizer)
features.append(feature)
return features
def generateInferenceRequest(sentence: str):
req = {}
req['signature_name'] = "serving_default"
req['inputs'] = {}
example = run_classifier.InputExample(
guid="test-0",
text_a=tokenization.convert_to_unicode(sentence),
text_b=None,
label=LABELS_LIST[0])
feature = run_classifier.convert_single_example(0, example, LABELS_LIST,
MAX_SEQ_LENGTH, TOKENIZER)
req['inputs']['input_ids'] = feature.input_ids
req['inputs']['input_mask'] = feature.input_mask
req['inputs']['segment_ids'] = feature.segment_ids
req['inputs']['label_ids'] = feature.label_id
req = json.dumps(req)
return req
def convert_line(line, label_list, max_seq_length, tokenizer):
"""Function to convert a line that should be predicted into BERT
input features.
"""
label = tokenization.convert_to_unicode("0") # Mock label
text_a = tokenization.convert_to_unicode(line)
example = rc.InputExample(guid=0,
text_a=text_a,
text_b=None,
label=label)
feature = rc.convert_single_example(0, example, label_list,
max_seq_length, tokenizer)
input_ids = np.reshape([feature.input_ids], (1, max_seq_length))
input_mask = np.reshape([feature.input_mask], (1, max_seq_length))
segment_ids = np.reshape([feature.segment_ids], (max_seq_length))
label_ids = [feature.label_id]
return input_ids, input_mask, segment_ids, label_ids
def convert_tfrecord_for_bert(filenames, input_data_path, output_data_path,
bert_tfhub_url, text_key, label_key,
max_seq_length):
"""Converts input TFRecords into the format expected by the BERT model."""
tokenizer = create_tokenizer_from_hub_module(bert_tfhub_url)
for filename in filenames:
print('Working on {}...'.format(filename))
in_filepath = '{}{}'.format(input_data_path, filename)
#TODO: Check if file exists, if not write new file
#TODO: Have the filename reflect the max_sequence_length and path reflect model
out_filepath = '{}{}'.format(output_data_path, filename)
record_iterator = tf.python_io.tf_record_iterator(path=in_filepath)
writer = tf.python_io.TFRecordWriter(out_filepath)
for ex_index, string_record in enumerate(record_iterator):
example = tf.train.Example()
example.ParseFromString(string_record)
text = example.features.feature[text_key].bytes_list.value[0]
label = example.features.feature[label_key].float_list.value[0]
label = round(label)
ex = run_classifier.InputExample(
guid=None, # Globally unique ID for bookkeeping
text_a=text,
text_b=None,
label=label)
label_list = [0, 1]
feature = run_classifier.convert_single_example(
ex_index, ex, label_list, max_seq_length, tokenizer)
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(
feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
print('... Done!')
def main():
credentials = grpc.ssl_channel_credentials(
root_certificates=ROOT_CERT.encode())
channel = grpc.secure_channel(
'{}:{}'.format(MODEL_SERVER_HOST, MODEL_SERVER_PORT), credentials)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
# get the sentence of input
sentence = str(globals.request.headers.getlist('Text')[0])
# convert single sentence to feature
tokenizer = tokenization.FullTokenizer(vocab_file=VOCAB_FILE_PATH,
do_lower_case=True)
example = run_classifier.InputExample(
guid="test-0",
text_a=tokenization.convert_to_unicode(sentence),
text_b=None,
label=LABELS_LIST[0])
feature = run_classifier.convert_single_example(0, example, LABELS_LIST,
MAX_SEQ_LENGTH, tokenizer)
# get the input of model
input_ids = np.reshape([feature.input_ids], (1, MAX_SEQ_LENGTH))
input_mask = np.reshape([feature.input_mask], (1, MAX_SEQ_LENGTH))
segment_ids = np.reshape([feature.segment_ids], (MAX_SEQ_LENGTH))
label_ids = [feature.label_id]
# Construct the request to tensorflow serving
request = predict_pb2.PredictRequest()
request.model_spec.name = MODEL_NAME
request.model_spec.signature_name = 'serving_default'
# package the input into request, Note the format of the input(follow the model)
request.inputs['input_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(input_ids,
shape=[1, MAX_SEQ_LENGTH],
dtype=tf.int32))
request.inputs['input_mask'].CopyFrom(
tf.contrib.util.make_tensor_proto(input_mask,
shape=[1, MAX_SEQ_LENGTH],
dtype=tf.int32))
request.inputs['label_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(label_ids, shape=[1],
dtype=tf.int32))
request.inputs['segment_ids'].CopyFrom(
tf.contrib.util.make_tensor_proto(segment_ids,
shape=[1, MAX_SEQ_LENGTH],
dtype=tf.int32))
# do predict
result = stub.Predict(request, 100,
metadata=metadata_transformer()) # 120 secs timeout
# parse the result
probabilities_tensor_proto = result.outputs["probabilities"]
probabilities = list(probabilities_tensor_proto.float_val)
probabilities_np = np.array(probabilities)
top3_index_np = probabilities_np.argsort()[-3:][::-1]
probabilities_top3 = probabilities_np[top3_index_np]
label_top3 = np.array(LABELS_LIST)[top3_index_np]
# shape = tf.TensorShape(probabilities_tensor_proto.tensor_shape)
# probabilities = np.array(probabilities_tensor_proto.float_val).reshape(
# shape.as_list())
result_list = []
for index in range(3):
result_list.append({
"label": label_top3[index],
"score": str(probabilities_top3[index])
})
output_json = {"predictions": [{"results": result_list}]}
return Response(json.dumps(output_json), mimetype='application/json')
