提交 6691abb7 编写于 作者: W wangjiawei04

fix web service

上级 2c516c8d
......@@ -20,7 +20,7 @@ from paddle_serving_server import OpMaker, OpSeqMaker, Server
from paddle_serving_client import Client
from contextlib import closing
import socket
import numpy as np
from paddle_serving_server import pipeline
from paddle_serving_server.pipeline import Op
......@@ -64,8 +64,8 @@ class WebService(object):
f = open(client_config, 'r')
model_conf = google.protobuf.text_format.Merge(
str(f.read()), model_conf)
self.feed_names = [var.alias_name for var in model_conf.feed_var]
self.fetch_names = [var.alias_name for var in model_conf.fetch_var]
self.feed_vars = {var.name: var for var in model_conf.feed_var}
self.fetch_vars = {var.name: var for var in model_conf.fetch_var}
def _launch_rpc_service(self):
op_maker = OpMaker()
......@@ -201,6 +201,15 @@ class WebService(object):
def preprocess(self, feed=[], fetch=[]):
print("This API will be deprecated later. Please do not use it")
is_batch = True
feed_dict = {}
for var_name in self.feed_vars.keys():
feed_dict[var_name] = []
for feed_ins in feed:
for key in feed_ins:
feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
feed = {}
for key in feed_dict:
feed[key] = np.concatenate(feed_dict[key], axis=0)
return feed, fetch, is_batch
def postprocess(self, feed=[], fetch=[], fetch_map=None):
......
......@@ -70,8 +70,8 @@ class WebService(object):
f = open(client_config, 'r')
model_conf = google.protobuf.text_format.Merge(
str(f.read()), model_conf)
self.feed_names = [var.alias_name for var in model_conf.feed_var]
self.fetch_names = [var.alias_name for var in model_conf.fetch_var]
self.feed_vars = {var.name: var for var in model_conf.feed_var}
self.fetch_vars = {var.name: var for var in model_conf.fetch_var}
def set_gpus(self, gpus):
print("This API will be deprecated later. Please do not use it")
......@@ -278,6 +278,15 @@ class WebService(object):
def preprocess(self, feed=[], fetch=[]):
print("This API will be deprecated later. Please do not use it")
is_batch = True
feed_dict = {}
for var_name in self.feed_vars.keys():
feed_dict[var_name] = []
for feed_ins in feed:
for key in feed_ins:
feed_dict[key].append(np.array(feed_ins[key]).reshape(list(self.feed_vars[key].shape))[np.newaxis,:])
feed = {}
for key in feed_dict:
feed[key] = np.concatenate(feed_dict[key], axis=0)
return feed, fetch, is_batch
def postprocess(self, feed=[], fetch=[], fetch_map=None):
......
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