def main():
"""
Main function
"""
log = logging.getLogger(__name__)
log.info("Starting AutoTV")
usage = "%prog -c FILE [--debug]"
# the version of Auto TV.
version = "0.1"
parser = optparse.OptionParser(usage=usage, version=version)
parser.add_option(
"-c", "--config",
dest="config_file",
help="the config file(e.g. autotv.cfg)",
metavar="FILE")
parser.add_option(
"--debug",
action="store_true",
dest="verbose",
default=False,
help="print more info")
options, _ = parser.parse_args()
# setup logging
if options.verbose:
# set logging to debug
utils.setup_logging(logging.DEBUG)
else:
utils.setup_logging(logging.INFO)
log.debug("Input arguments: {%s}".format(options))
# the config file is mandatory
if not options.config_file:
parser.error("Missing mandatory argument: '-c/--config'")
log.info('Starting Auto TV...')
# read the config
cfg = Config(options.config_file)
# cfg_options = cfg.cfg_options
# Start TV Parsing
tvp = TVParser(cfg)
if options.verbose:
tvp.store_debug_info()
copy_tvs = tvp.get_unstored_tv_contents()
tvp.transfer_tv_contents(copy_tvs)
# Start Movie Parsing
movieparser = MovieParser(cfg)
if options.verbose:
movieparser.store_debug_info()
log.info("exit!")
def main():
setup_logging('REPROSERVER-RUNNER')
# SQL database
global SQLSession
engine, SQLSession = database.connect()
# AMQP
tasks = TaskQueues()
# Object storage
global object_store
object_store = get_object_store()
logging.info("Ready, listening for requests")
tasks.consume_run_tasks(run_request)
from flask import Flask
from flask_restful import Api
from common.utils import setup_logging
from resources.fruit import Fruit
from resources.fruits import Fruits
from resources.nested_fruits import NestedFruits
app = Flask(__name__)
app.secret_key = "dev"
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db'
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
api = Api(app)
api.add_resource(Fruits, "/api/v1/fruits")
api.add_resource(Fruit, "/api/v1/fruit/<int:_id>")
api.add_resource(NestedFruits, "/api/v1/fruit/nest")
if __name__ == "__main__":
@app.before_first_request
def create_tables():
db.create_all()
from db import db
setup_logging()
db.init_app(app)
app.run(port=5000, host="0.0.0.0")
if _args.aicrowd_challenge:
from aicrowd import utils_pytorch as pyu, aicrowd_helpers
# Export the representation extractor
path_to_saved = pyu.export_model(pyu.RepresentationExtractor(model.model.encoder, 'mean'),
input_shape=(1, model.num_channels, model.image_size, model.image_size))
logging.info(f'A copy of the model saved in {path_to_saved}')
if on_aicrowd_server:
# AICrowd will handle the evaluation
aicrowd_helpers.register_progress(1.0)
aicrowd_helpers.submit()
else:
# Run evaluation locally
# The local_evaluation is implemented by aicrowd in the global namespace, so importing it suffices.
# todo: implement a modular version of local_evaluation
# noinspection PyUnresolvedReferences
from aicrowd import local_evaluation
if __name__ == "__main__":
_args = get_args(sys.argv[1:])
setup_logging(_args.verbose)
initialize_seeds(_args.seed)
# set the environment variables for dataset directory and name, and check if the root dataset directory exists.
set_environment_variables(_args.dset_dir, _args.dset_name)
assert os.path.exists(os.environ.get('DISENTANGLEMENT_LIB_DATA', '')), \
'Root dataset directory does not exist at: \"{}\"'.format(_args.dset_dir)
main(_args)
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from common.utils import setup_logging # noqa
setup_logging('REPROSERVER-WEB')
from web.main import main # noqa
main()