Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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Community
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city_receiveCash(m_ck)
else:
count_ck, count_u = [], []
if not ccfxj_help:
print("您未配置助力的账号，\n助力账号名称：可填用户名 或 pin的值不要; \nenv 设置 export ccfxj_help=\"Curtinlv&用户2\" 多账号&分隔\n本次退出。")
sys.exit(0)
for ckname in ccfxj_help:
try:
ckNum = userNameList.index(ckname)
except Exception as e:
try:
ckNum = userNameList.index(unquote(ckname))
except:
print(f"请检查被助力账号【{ckname}】名称是否正确？提示：助力名字可填pt_pin的值、也可以填账号名。")
continue
userName = userNameList[ckNum]
try:
invid, poolMoney, cityCodeList, roundNumList = getInviteId(cookiesList[ckNum])
except:
print(f"账号异常【{ckname}】，无法获取助力码，请手动分享~")
continue
msg(f"### 本次助力车头：{userName}")
count_ck.append(cookiesList[ckNum])
count_u.append(ckname)
z_cookiesList, z_userNameList = delckValue(z_cookiesList, z_userNameList)
for ck,user in zip(z_cookiesList,z_userNameList):
if userName == user:
continue
zhuli(ck, invid, user)
city_receiveCash(cookiesList[ckNum])
msg("城城分现金当前余额：")
msg("*"*40)
if ccfxj_isOrder == "true":
for ck,user in zip(cookiesList,userNameList):
invid, poolMoney, cityCodeList, roundNumList = getInviteId(ck)
msg(f"用户[{user}]\t待提现{poolMoney}")
else:
for ck,user in zip(count_ck,count_u):
invid, poolMoney, cityCodeList, roundNumList = getInviteId(ck)
msg(f"用户[{user}]\t待提现{poolMoney}")
msg("*" * 40)
msg("\n***************\n城城分现金入口：\n25:/￥81H1VBRi2hU6z%☆")
if isNotice == "true":
send(scriptName, msg_info)
if __name__ == '__main__':
start()
# <FILESEP>
''' SSMA: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
Copyright (C) 2018 Abhinav Valada, Rohit Mohan and Wolfram Burgard
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.'''
import argparse
import datetime
import importlib
import os
import numpy as np
import tensorflow as tf
import yaml
from dataset.helper import *
PARSER = argparse.ArgumentParser()
PARSER.add_argument('-c', '--config', default='config/cityscapes_test.config')
def test_func(config):
os.environ['CUDA_VISIBLE_DEVICES'] = config['gpu_id']
module = importlib.import_module('models.' + config['model'])
model_func = getattr(module, config['model'])
data_list, iterator = get_test_data(config)
model = model_func(num_classes=config['num_classes'], training=False)
images_pl = tf.placeholder(tf.float32, [None, config['height'], config['width'], 3])
images1_pl = tf.placeholder(tf.float32, [None, config['height'], config['width'], 3])
model.build_graph(images_pl, images1_pl)
config1 = tf.ConfigProto()
config1.gpu_options.allow_growth = True
sess = tf.Session(config=config1)
sess.run(tf.global_variables_initializer())
import_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
print 'total_variables_loaded:', len(import_variables)
saver = tf.train.Saver(import_variables)
saver.restore(sess, config['checkpoint'])
sess.run(iterator.initializer)
step = 0
total_num = 0
output_matrix = np.zeros([config['num_classes'], 3])
while 1:
try:
img, label, img1 = sess.run([data_list[0], data_list[1], data_list[2]])
feed_dict = {images_pl : img, images1_pl : img1}
probabilities = sess.run([model.softmax], feed_dict=feed_dict)
prediction = np.argmax(probabilities[0], 3)
gt = np.argmax(label, 3)
prediction[gt == 0] = 0