def get_default(self,product='mclient'): if not self.default: self.default = cf.DefaultConfig(product) self.default.run() return self.default
import torch import torchvision import numpy as np import fire import config import models import utils import os import cv2 import visdom import data import time import random random.seed(time.time()) import copy opts = config.DefaultConfig() def train(**kwargs): ''' para : opts:the para from your return: the train model ''' opts.parse_kwargs(**kwargs) print "train begin!" viz = utils.Visualizer(opts.env) #model our_model = getattr(models, opts.model)(opts) our_model.load_state_dict(
import config import pickle import os import datasetmaker import models import dict import time import utils import codecs import optims import metrics import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt opt = config.DefaultConfig() def plotlc(x, y, figname='learning_curve'): plt.plot(x, y) plt.title('learning curve') plt.xlabel('epoch') plt.ylabel('loss') # plt.show() plt.savefig(figname) def load_data(train): print("loading data...") with open(os.path.join(opt.root, 'data.pkl'), 'rb') as f: data = pickle.load(f)
def db_connect(): # 读取配置,链接数据库,返回session db_con = config.DefaultConfig().db_connect db_session = sessionmaker(bind=create_engine(db_con)) return db_session()
def db_oracle_connect(): db_con = config.DefaultConfig().db_connect print db_con db_engine = create_engine('oracle://' + db_con) db_session = sessionmaker(bind=db_engine) return db_session
from datetime import datetime from aiohttp import web from aiohttp.web import Request, Response, json_response from botbuilder.core import ( BotFrameworkAdapterSettings, TurnContext, BotFrameworkAdapter, ) from botbuilder.core.integration import aiohttp_error_middleware from botbuilder.schema import Activity, ActivityTypes from bot import MyBot import config CONFIG = config.DefaultConfig() # Create adapter. # See https://aka.ms/about-bot-adapter to learn more about how bots work. SETTINGS = BotFrameworkAdapterSettings(CONFIG.APP_ID, CONFIG.APP_PASSWORD) ADAPTER = BotFrameworkAdapter(SETTINGS) # Catch-all for errors. async def on_error(context: TurnContext, error: Exception): # This check writes out errors to console log .vs. app insights. # NOTE: In production environment, you should consider logging this to Azure # application insights. print(f"\n [on_turn_error] unhandled error: {error}", file=sys.stderr) traceback.print_exc()
default='default', choices=["default", "stream"], help="Use 'stream' mode for using camera and provide 'stream_url'") parser.add_argument( "--stream-url", default='local', dest='stream_url', help="enter stream URL otherwise it uses USB camera by default") parser.add_argument("--gen", default='unet', choices=['unet', 'resnet'], dest='generator', help="which generator to use unet by default") args = parser.parse_args() a = config.DefaultConfig(parsed_args=args) Examples = collections.namedtuple( "Examples", "paths, inputs, targets, count, steps_per_epoch") Model = collections.namedtuple( "Model", "outputs, predict_real, predict_fake, discrim_loss, discrim_grads_and_vars, gen_loss_GAN, gen_loss_L1, gen_grads_and_vars, train" ) def get_checkpoint(): if a.checkpoint is not None: return a.checkpoint return a.output_dir