def start():
# 连接MongoDB，查询tokens，根据contractAddress到etherscan查询最新数据
client = MongoCluster().connect()
db = client.get_database('gse-transaction')
collection = db.get_collection('mrout_6000001-6001000')
# collection.insert_one()
# 连接HDFS读取文件
from pyhdfs import HdfsClient
client2 = HdfsClient(hosts='%s,50070' % hdfs_ip, max_tries=10)
# 返回这个用户的根目录
print client2.get_home_directory()
# 返回可用的namenode节点
print client2.get_active_namenode()
# 返回指定目录下的所有文件
print client2.listdir("/user/leon/mrout_3_6000001-6001000/")
# 读某个文件
client2.mkdirs("/user/leon")
inputfile = client2.open('/user/leon/mrout_3_6000001-6001000/part-00000')
# 查看文件内容
for r in inputfile:
line = str(r).encode('utf-8') # open后是二进制,str()转换为字符串并转码
print(line)
def inference(image_path,model_path,img_w,img_h,channel,out_result,net,**kwargs):
client = HdfsClient(hosts='172.16.18.112,172.16.18.114', user_name='hadoop')
active_namenode = client.get_active_namenode()
HDFS_HOSTS = "hdfs://" + active_namenode.split(":")[0] + ":" + '9000'
# img = Image.open(image_path)
# image_test = np.asarray(np.resize(img, (1, img_w, img_h, channel)))
image_path = HDFS_HOSTS + image_path + '/data/1.jpg'
image = tf.read_file(image_path)
image = tf.image.decode_jpeg(image, 1)
image = tf.image.resize_image_with_crop_or_pad(image, img_w, img_h)
image = tf.image.per_image_standardization(image)
# image = np.asarray(np.resize(image, (1, 32,32,1)))
image = tf.reshape(image, [1, img_w, img_h, channel])
sess = tf.InteractiveSession()
image_test = sess.run(image)
hdfs_model_path = HDFS_HOSTS + model_path + '/model/'
tf.reset_default_graph() # 清除默认图形堆栈并重置全局默认图形。
with tf.Session()as sess:
meta_graph_def = tf.saved_model.loader.load(sess, [tag_constants.SERVING], hdfs_model_path)
signature = meta_graph_def.signature_def # 从meta_graph_def取出SignatureDef对象
images_placeholder = signature['test_signature'].inputs['input_x'].name # 从SignatureDef对象中找出具体的输入输出张量
embeddinigs = signature['test_signature'].outputs['outputs'].name
output = sess.run(embeddinigs, feed_dict={images_placeholder: image_test})
output = sess.run(tf.nn.softmax(output))
y = tf.argmax(output,axis=1)
y_pred = sess.run(y)
print(y_pred)
import tensorflow as tf
import tensorflow as tf
from pyhdfs import HdfsClient
# 端口配置
cfg_http_port = '50070'
cfg_rpc_port = '9000'
user_name = 'hadoop'
hosts = '172.16.18.112,172.16.18.114'
# 数据集接口url
url1 = 'http://test.cop.com/vbap3/dsc/dataSetDefs/updateByOther'
url2 = 'http://test.cop.com/vbap3/dsc/dataSetDefs/uploadFields'
# hdfs配置
client = HdfsClient(hosts=hosts, user_name=user_name)
active_namenode = client.get_active_namenode()
HDFS_HOSTS = "hdfs://" + active_namenode.split(":")[0] + ":" + cfg_rpc_port
client1 = HdfsClient(hosts=hosts, user_name=user_name)
active_namenode = client.get_active_namenode()
HDFS_HOSTS1 = "hdfs://" + active_namenode.split(":")[0] + ":" + cfg_http_port
