模型部署与在线体验

import paddle
import paddle.fluid as fluid
import numpy
import math
import sys
BATCH_SIZE = 20
train_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.uci_housing.train(), buf_size=500),
        batch_size=BATCH_SIZE)
test_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.uci_housing.test(), buf_size=500),
        batch_size=BATCH_SIZE)
params_dirname = "model2"
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
main_program = fluid.default_main_program()
startup_program = fluid.default_startup_program()
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_loss)
#clone a test_program
test_program = main_program.clone(for_test=True)
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
num_epochs = 100
# For training test cost
def train_test(executor, program, reader, feeder, fetch_list):
    accumulated = 1 * [0]
    count = 0
    for data_test in reader():
        outs = executor.run(program=program,
                            feed=feeder.feed(data_test),
                            fetch_list=fetch_list)
        accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
        count += 1
    return [x_d / count for x_d in accumulated]
params_dirname = "fit_a_line.inference.model"
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
naive_exe = fluid.Executor(place)
naive_exe.run(startup_program)
step = 0
exe_test = fluid.Executor(place)
# main train loop.
for pass_id in range(num_epochs):
    for data_train in train_reader():
        avg_loss_value, = exe.run(main_program,
                                  feed=feeder.feed(data_train),
                                  fetch_list=[avg_loss])
        if step % 10 == 0:  # record a train cost every 10 batches
            print (step, avg_loss_value[0])
        if step % 100 == 0:  # record a test cost every 100 batches
            test_metics = train_test(executor=exe_test,
                                     program=test_program,
                                     reader=test_reader,
                                     fetch_list=[avg_loss.name],
                                     feeder=feeder)
            print (step, test_metics[0])
            # If the accuracy is good enough, we can stop the training.
            if test_metics[0] < 10.0:
                break
        step += 1
        if math.isnan(float(avg_loss_value[0])):
            sys.exit("got NaN loss, training failed.")
    if params_dirname is not None:
        # We can save the trained parameters for the inferences later
        fluid.io.save_inference_model(params_dirname, ['x'],
                                      [y_predict], exe)

def reader_infer(data_args):
  	"""
  	reader_infer 输入参数转换器方法
  	:param data_args: 接口传入的数据，以k-v形式
  	:return [[]], feeder
  	"""
  	#构造内容
  	pass

def output(results, data_args):
  	"""
  	output 输出参数转换器方法
  	:param results 模型预测结果
  	:param data_args: 接口传入的数据，以k-v形式
 	 	:return array 需要能被json_encode的数据格式
 	 	"""
  	#构造内容
  	pass

# -*- coding: utf-8 -*-
"""
input hook
@Author 友军的奸细
@Time 2021-08-30 14:00:12
"""
import paddle.fluid as fluid
def reader_infer(data_args):
   '''
   reader inter
   :param data_args: 接口请求参数
   :return [[]], feeder
   '''
   def reader():
       # x <type 'string'> default value:'' 
       arg_x = data_args['price'].split(',')
       # todo
       feed_x = fluid.layers.data(name='x',shape=[13],dtype='float32')
       feeder = fluid.DataFeeder(place=fluid.CPUPlace(),feed_list=[feed_x])
       return [[arg_x]], feeder
   return reader

# -*- coding: utf-8 -*-
def output(results, data_args):
   """
   模型评测结果输出转换
   :param results
   :param data_args 请求参数
   :return dict
   """
   # todo
   return {'y':results.tolist()[0]}

import json
import traceback
import urllib
from urllib import request,error
from urllib.request import urlopen
formdata = {"price":'0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332'}
header = {"Content-Type": "application/json; charset=utf-8"}
url = "https://aistudio.baidu.com/serving/online/xxx?apiKey=xxxxxxxxxx"
data = bytes(json.dumps(formdata), 'utf8')
try:
    request = request.Request(url, data, header)
    response=urllib.request.urlopen(request).read().decode("utf-8")
    print(response)
except error.HTTPError as e:
    print("The server couldn't fulfill the request")
    print(e.code)
    print(e.read())
except error.URLError as e:
    print("Failed to reach the server")
    print(e.reason)
except:
    traceback.print_exc()