import base64 import tensorflow as tf import requests #encoding=utf8 import requests import numpy as np import tensorflow.compat.v1 as tf import time tf.disable_v2_behavior() np.set_printoptions(threshold=np.inf) np.set_printoptions(precision=3) from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2_grpc import grpc tf.app.flags.DEFINE_string('server', 'localhost:8502', 'PredictionService host:port') FLAGS = tf.app.flags.FLAGS def prediction(): options = [('grpc.max_send_message_length', 1000 * 1024 * 1024), ('grpc.max_receive_message_length', 1000 * 1024 * 1024)] channel = grpc.insecure_channel(FLAGS.server, options = options) stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) request = predict_pb2.PredictRequest() request.model_spec.name = 'wide_deep' #对应上图第一个方框 request.model_spec.signature_name = 'predict' #对应上图第二个方框with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f: for _ in range(1): with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f: count = 0 examples = [] for line in f: splits = line.split('|') features = { 'ITEM_CATEGORY_card_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[0].encode()] * 2)), 'USER_CATEGORY_device_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[2].encode()] * 2)), 'USER_CATEGORY_os': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[3].encode()] * 2)), 'USER_CATEGORY_user_city_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[4].encode()] * 2)), 'USER_MULTI_CATEGORY_second_solutions': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[6].split(','))) * 2)), 'USER_MULTI_CATEGORY_second_demands': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[7].split(','))) * 2)), 'USER_MULTI_CATEGORY_second_positions': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[8].split(','))) * 2)), 'USER_MULTI_CATEGORY_projects': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[9].split(','))) * 2)), 'ITEM_NUMERIC_click_count_sum': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[10])] * 2)), 'ITEM_NUMERIC_click_count_avg': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[11])] * 2)), 'ITEM_NUMERIC_click_count_stddev': tf.train.Feature( float_list=tf.train.FloatList(value=[float(splits[12])])), 'ITEM_NUMERIC_exp_count_sum': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[13])] * 2)), 'ITEM_NUMERIC_exp_count_avg': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[14])] * 2)), 'ITEM_NUMERIC_exp_count_stddev': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[15])] * 2)), 'ITEM_NUMERIC_discount': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[16])] * 2)), 'ITEM_NUMERIC_case_count': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[17])] * 2)), 'ITEM_NUMERIC_sales_count': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[18])] * 2)), 'ITEM_CATEGORY_service_type': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[19].encode()] * 2)), 'ITEM_CATEGORY_merchant_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[20].encode()] * 2)), 'ITEM_CATEGORY_doctor_type': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[21].encode()] * 2)), 'ITEM_CATEGORY_doctor_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[22].encode()] * 2)), 'ITEM_CATEGORY_doctor_famous': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[23].encode()] * 2)), 'ITEM_CATEGORY_hospital_id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[24].encode()] * 2)), 'ITEM_CATEGORY_hospital_city_tag_id': tf.train.Feature( bytes_list=tf.train.BytesList(value=[splits[25].encode()] * 2)), 'ITEM_CATEGORY_hospital_type': tf.train.Feature(bytes_list=tf.train.BytesList(value=[splits[26].encode()] * 2)), 'ITEM_CATEGORY_hospital_is_high_quality': tf.train.Feature( bytes_list=tf.train.BytesList(value=[splits[27].encode()] * 2)), 'ITEM_MULTI_CATEGORY_second_demands': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[28].split(','))) * 2)), 'ITEM_MULTI_CATEGORY_second_solutions': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[29].split(','))) * 2)), 'ITEM_MULTI_CATEGORY_second_positions': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[30].split(','))) * 2)), 'ITEM_MULTI_CATEGORY_projects': tf.train.Feature( bytes_list=tf.train.BytesList(value=list(map(lambda s: s.encode(), splits[31].split(','))) * 2)), 'ITEM_NUMERIC_sku_price': tf.train.Feature(float_list=tf.train.FloatList(value=[float(splits[32])] * 2)), # 'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[int(splits[5])] * 2)), } # print(features) # print(splits[32]) tf_features = tf.train.Features(feature=features) tf_example = tf.train.Example(features=tf_features) # print(tf_example) tf_serialized = tf_example.SerializeToString() examples.append(tf_serialized) count += 1 if count == 1000: break start = time.time() # request.inputs['examples'].CopyFrom(tf.make_tensor_proto(examples)) # in对应上图第三个方框，为模型的输入Name # print(examples) tensor_proto = tf.make_tensor_proto(examples) print(time.time() - start) request.inputs['examples'].CopyFrom(tensor_proto) # in对应上图第三个方框，为模型的输入Name result_future = stub.Predict.future(request, 10.0) # 10 secs timeout result = result_future.result() # print(result) print(time.time() - start) if __name__ == "__main__": prediction()