def __init__(self):
settings.init_settings()
time.sleep(5)
self.drivers = settings.get_drivers()
self.case_list = settings.get_case_list()
self.drivers_users = generate_users(self.drivers)
self.lock = Lock()
def run_migrations_offline():
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
# postgresql://postgres:postgres@localhost/oracles_vision
# url = context_config.get_main_option("sqlalchemy.url")
init_settings()
url = 'postgresql://%s:%s@%s/%s' % (config.get(
'DB', 'user'), config.get('DB', 'password'), config.get(
'DB', 'host'), config.get('DB', 'database'))
context.configure(url=url,
target_metadata=target_metadata,
literal_binds=True)
with context.begin_transaction():
context.run_migrations()
def __init__(self):
gedit.Plugin.__init__(self)
self._instances = {}
self._data_dir = self.get_data_dir()
settings.init_settings(self._data_dir)
def __init__(self):
settings.init_settings()
time.sleep(5)
self.drivers = settings.get_drivers()
self.case_list = settings.get_case_list()
self.login_users = settings.get_user_table()
self.sign_users = settings.get_email_sign_table()
# self.lock = Lock()
self.driver_queue = Queue()
self.thread_pool = ThreadPoolExecutor(max_workers=len(self.drivers))
# Train the Style Transfer Net from __future__ import print_function import numpy as np import tensorflow as tf import os import settings data_set = "imagenet" #"imagenet" data_set_name = "imagenet_shallowest" settings.init_settings(data_set_name, suffix="_attack") logger = settings.logger from imagenetmod.interface import build_imagenet_model, imagenet, restore_parameter from style_transfer_net import StyleTransferNet, StyleTransferNet_adv from utils import get_train_images from cifar10_class import Model import cifar10_input from PIL import Image from adaptive_instance_norm import normalize STYLE_LAYERS = settings.config["STYLE_LAYERS"] # (height, width, color_channels) TRAINING_IMAGE_SHAPE = settings.config["IMAGE_SHAPE"] EPOCHS = 4 EPSILON = 1e-5 BATCH_SIZE = settings.config["BATCH_SIZE"] if data_set == "cifar10":
import os, sys, torch
import numpy as np
from torch.autograd import Variable
from dataset import *
import settings, log
import torch.nn as nn, torch.utils.data as data
import torch.optim as optim
from utils import *
from tqdm import tqdm
from model import resnet6
force_new_model = True
pretrained_model = None
# init the settings
settings.init_settings(force_new_model, pretrained_model)
# init the log
log.init_logger(tensorboard=False)
def show_images(img):
show_image(img[:, :, 0])
show_image(img[:, :, 1])
def train(model, train_data, criterion, optimizer, epoch):
total_train_images = len(train_data)
# print(total_train_images)
if opt["useGPU"]:
model = model.cuda()
from settings import Settings, init_settings
from word import *
app_ver = 'wordman test 1.0.1'
###### 프로그램 초기화 및 설정 시작 ######
setup.init()
logging.info(app_ver)
config = load_config()
settings = init_settings(config['db'])
words = init_words(config['db'])
examples = init_examples(config['db'])
synonyms = init_synonyms(config['db'])
antonyms = init_antonyms(config['db'])
my_key = settings.get_or_input('secret_key', 'your secret key')
logging.debug('secret key = ' + my_key)
my_port = settings.get_or_input('http_port', 'your http port', type(0))
logging.debug('http port = ' + my_port)
# Start HTTP Server
app = Flask(__name__, template_folder = './themes')
Reggie(app)
# Train the Style Transfer Net
from __future__ import print_function
import numpy as np
import tensorflow as tf
import os
import settings
data_set = "imagenet" #cifar10" # "imagenet"
settings.init_settings("imagenet_shallow")
from adaptive_instance_norm import normalize
from PIL import Image
import cifar10_input
from cifar10_class import Model
from utils import get_train_images
from style_transfer_net import StyleTransferNet, StyleTransferNet_adv
from imagenetmod.interface import build_imagenet_model, imagenet, restore_parameter
STYLE_LAYERS = settings.config["STYLE_LAYERS"]
# (height, width, color_channels)
TRAINING_IMAGE_SHAPE = settings.config["IMAGE_SHAPE"]
EPOCHS = 4
EPSILON = 1e-5
BATCH_SIZE = settings.config["BATCH_SIZE"]
if data_set == "cifar10":
LEARNING_RATE = 1e-1
LR_DECAY_RATE = 1e-4 # 5e-5
import os, sys, torch
import numpy as np
from torch.autograd import Variable
from dataset import *
import settings, log
import torch.nn as nn, torch.utils.data as data
import torch.optim as optim
from utils import *
from tqdm import tqdm
from model import resnet6
import scipy.io as sio
pretrained_model = "../scratch/sysu_mm01/deepzeropadding-14May2019-125214_deep-zero-padding/deep_zero_model#156.pth"
# init the settings
settings.init_settings(False, pretrained_model)
# init the log
log.init_logger(tensorboard=False, prepend_text="test_")
def get_max_test_id(test_ids):
int_test_ids = [int(ID) for ID in test_ids]
return np.max(int_test_ids)
def prepare_empty_matfile_config(max_test_id):
cam_features = np.empty(max_test_id, dtype=object)
for i in range(len(cam_features)):
cam_features[i] = []
return cam_features
'''
Created on 2018年7月20日
@author: cloud
'''
from helper import elements
import time, logging
import settings
import unittest
import cases
from cases import match
from concurrent.futures import ThreadPoolExecutor, wait
from threading import Thread
logger = logging.getLogger()
settings.init_settings()
drivers = settings.get_drivers()
time.sleep(3)
driver = drivers[0]
data = {}
# elements.GenderFilterMatchButton(driver).click()
# time.sleep(2)
# driver.find_elements_by_android_uiautomator("new UiSelector().text(\"Purchase\")")
# a=driver.find_element_by_id("com.videochat.livu:id/view_title").find_element_by_xpath("android.widget.TextView[@text='Purchase']")
# print(a)
# print(elements.RechargeButton(driver).element.text)
# elements.RechargeButton(driver).click()
# driver.back()
# driver.back()
# driver.back()
# elements.MatchHistoryButton(driver).click()