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
