我已经开始使用下面显示的 export_textsum.py 文件导出 textsum 模型，当我使用下面的 textsumclient.py 文件连接时，我收到错误：
回溯（最后一次调用）：文件“textsum_client.py”，第 90 行，在 tf.app.run() 文件“/usr/local/lib/python2.7/site-packages/tensorflow/python/platform/app .py”，第 48 行，运行中py"，第 72 行，在 do_singleDecode 结果 = stub.Predict(request, 5.0) # 5 seconds File "/usr/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py"，第 324 行，通话中 self._request_serializer，self._response_deserializer）文件“/usr/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py”，第 210 行，在 _blocking_unary_unary 中引发 _abortion_error(rpc_error_call) grpc.framework.interfaces。 face.face.AbortionError: AbortionError(code=StatusCode.INVALID_ARGUMENT, details="输入大小与签名不匹配")
我相信这可能与在我的 export_textsum 文件中构建 tf_example 有关，但老实说，到目前为止我还没有运气弄清楚这一点。任何有更多经验的人都知道我在这里做错了什么？如果有任何想法可以帮助我缩小这里发生的确切范围，我愿意接受任何建议。谢谢。
textsumclient.py
from __future__ import print_function
import sys
import threading
# This is a placeholder for a Google-internal import.
from grpc.beta import implementations
import numpy
import tensorflow as tf
from datetime import datetime
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
#from tensorflow_serving.example import mnist_input_data
tf.app.flags.DEFINE_integer('concurrency', 1,
'maximum number of concurrent inference requests')
tf.app.flags.DEFINE_integer('num_tests', 10, 'Number of test images')
tf.app.flags.DEFINE_string('server', '172.17.0.2:9000', 'PredictionService host:port')
tf.app.flags.DEFINE_string('work_dir', '/tmp', 'Working directory. ')
FLAGS = tf.app.flags.FLAGS
def do_singleDecode(hostport, work_dir, concurrency, num_tests):
#Connect to server
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
#Prepare our request object
request = predict_pb2.PredictRequest()
request.model_spec.name = 'textsum_model'
request.model_spec.signature_name = 'predict'
#Make some test data
test_data_set = ['This is a test','This is a sample']
#Lets test her out
now = datetime.now()
article, abstract = test_data_set
#***** POPULATE REQUEST INPUTS *****
request.inputs['article'].CopyFrom(
tf.contrib.util.make_tensor_proto(test_data_set[0], shape=[len(test_data_set[0])]))
request.inputs['abstract'].CopyFrom(
tf.contrib.util.make_tensor_proto(test_data_set[1], shape=[len(test_data_set[1])]))
result = stub.Predict(request, 5.0) # 5 seconds
waiting = datetime.now() - now
return result, waiting.microseconds
def main(_):
if not FLAGS.server:
print('please specify server host:port')
return
result, waiting = do_singleDecode(FLAGS.server, FLAGS.work_dir,
FLAGS.concurrency, FLAGS.num_tests)
print('\nTextsum result: %s%%' % result)
print('Waiting time is: ', waiting, 'microseconds.')
if __name__ == '__main__':
tf.app.run()
export_textsum.py
decode_mdl_hps = hps
# Only need to restore the 1st step and reuse it since
# we keep and feed in state for each step's output.
decode_mdl_hps = hps._replace(dec_timesteps=1)
model = seq2seq_attention_model.Seq2SeqAttentionModel(
decode_mdl_hps, vocab, num_gpus=FLAGS.num_gpus)
decoder = seq2seq_attention_decode.BSDecoder(model, batcher, hps, vocab)
serialized_output = tf.placeholder(tf.string, name='tf_output')
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {
'article': tf.FixedLenFeature(shape=[1], dtype=tf.string),
'abstract': tf.FixedLenFeature(shape=[1], dtype=tf.string),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement = True)
with tf.Session(config = config) as sess:
# Restore variables from training checkpoints.
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
print('Successfully loaded model from %s at step=%s.' %
(ckpt.model_checkpoint_path, global_step))
else:
print('No checkpoint file found at %s' % FLAGS.checkpoint_dir)
return
# ************** EXPORT MODEL ***************
export_path = os.path.join(FLAGS.export_dir,str(FLAGS.export_version))
print('Exporting trained model to %s' % export_path)
#-------------------------------------------
tensor_info_inputs = tf.saved_model.utils.build_tensor_info(serialized_tf_example)
tensor_info_outputs = tf.saved_model.utils.build_tensor_info(serialized_output)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={ tf.saved_model.signature_constants.PREDICT_INPUTS: tensor_info_inputs},
outputs={tf.saved_model.signature_constants.PREDICT_OUTPUTS:tensor_info_outputs},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
))
#----------------------------------
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder = saved_model_builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict':prediction_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print('Successfully exported model to %s' % export_path)
except:
traceback.print_exc()
pass
def main(_):
Export()
if __name__ == "__main__":
tf.app.run()