def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict_images'
image, label = test_data_set.next_batch(1)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0],
shape=[1, image[0].size]))
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
def main(_):
# 参数校验
# if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
# print('Usage: mnist_saved_model.py [--training_iteration=x] '
# '[--model_version=y] export_dir')
# sys.exit(-1)
# if FLAGS.training_iteration <= 0:
# print('Please specify a positive value for training iteration.')
# sys.exit(-1)
# if FLAGS.model_version <= 0:
# print('Please specify a positive value for version number.')
# sys.exit(-1)
# Train model
print('Training model...')
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32), }
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
tf.constant([str(i) for i in range(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print('training accuracy %g' % sess.run(
accuracy, feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
}))
print('Done training!')
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
# export_path_base = sys.argv[-1]
export_path_base = "/Users/xingoo/PycharmProjects/ml-in-action/实践-tensorflow/01-官方文档-学习和使用ML/save_model"
export_path = os.path.join(tf.compat.as_bytes(export_path_base), tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
# 配置导出地址，创建SaveModel
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
# 创建TensorInfo，包含type,shape,name
classification_inputs = tf.saved_model.utils.build_tensor_info(serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
# 分类签名：算法类型+输入+输出（概率和名字）
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
# 预测签名：输入的x和输出的y
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
# 构建图和变量的信息：
"""
sess 会话
tags 标签，默认提供serving、train、eval、gpu、tpu
signature_def_map 签名
main_op 初始化？
strip_default_attrs strp?
"""
# TODO predict_images和serving_default的区别
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
main_op=tf.tables_initializer(),
strip_default_attrs=True)
# 保存
builder.save()
print('Done exporting!')
import numpy as np
import sys
import threading
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
tf.app.flags.DEFINE_integer('concurrency', 1,
'maximum number of concurrent inference requests')
tf.app.flags.DEFINE_integer('num_tests', 100, 'Number of test images')
tf.app.flags.DEFINE_string('server', 'localhost:8500',
'PredictionService host:port')
tf.app.flags.DEFINE_string('work_dir', './tmp', 'Working directory. ')
FLAGS = tf.app.flags.FLAGS
test_data_set = mnist_input_data.read_data_sets(FLAGS.work_dir).test
channel = grpc.insecure_channel(FLAGS.server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
class _ResultCounter(object):
"""Counter for the prediction results."""
def __init__(self, num_tests, concurrency):
self._num_tests = num_tests
self._concurrency = concurrency
self._error = 0
self._done = 0
self._active = 0
self._condition = threading.Condition()
def inc_error(self):
import requests
import tensorflow as tf
import basic.mnist_input_data as mnist_input_data
headers = {"content-type": "application/json"}
# json_response = requests.post('http://localhost:8501/v1/models/half_plus_two:predict',
# data='{"instances": [1.0, 2.0, 5.0]}',
# headers=headers)
# print(json_response.text)
url = 'http://localhost:8501/v1/models/mnist:predict'
# json_response = requests.post(url,
# data='{"instances": [1.0, 2.0, 5.0]}',
# headers=headers)
# print(json_response.text)
test_data_set = mnist_input_data.read_data_sets('./tmp').test
print(type(test_data_set))
for _ in range(10):
image, label = test_data_set.next_batch(1)
print(tf.contrib.util.make_tensor_proto(image[0], shape=[1,
image[0].size]))
json_response = requests.post(url,
data='{"inputs": [image[0]]}',
headers=headers)
print(json_response.text)
# channel = grpc.insecure_channel(hostport)
# stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
# result_counter = _ResultCounter(num_tests, concurrency)