def __init__(self):
""" Initializes the extension """
super(DockerExtension, self).__init__()
self.docker_client = docker.from_env()
self.subscribe(KeywordQueryEvent, KeywordQueryEventListener())
self.subscribe(ItemEnterEvent, ItemEnterEventListener())
parser = ArgumentParser()
parser.add_argument('-c', '--c', action='store', dest='container_id')
parser.add_argument('-a',
'--a',
action='store_true',
default=False,
dest='all_containers')
parser.add_argument('-i',
'--i',
action='store_true',
default=False,
dest='info')
self.arg_parser = parser
self.list_containers_view = ListContainersView(self)
self.container_details_view = ContainerDetailsView(self)
self.info_view = InfoView(self)
self.utils_view = UtilsView(self)
Notify.init("DockerExtension")
def parse_args(args):
parser = ArgumentParser()
parser.add_argument('tests', nargs='+')
parser.add_argument('-d', '--duration', type=int, default=200,
help='Graph over duration hours (default 200)')
parser.add_argument('-s', '--smoothing', type=int, default=12,
help='Rolling average hours (defaults to 12)')
return parser.parse_args(args)
def parse_args(args):
parser = ArgumentParser()
parser.add_argument('words', nargs='+')
parser.add_argument('-r',
'--release',
type=str,
default='approved',
help='The name or version of the release')
return parser.parse_args(args)
def parse_args(args):
parser = ArgumentParser()
parser.add_argument('msg', nargs='*', default='')
parser.add_argument('-m',
'--mood',
type=str,
default='default',
help='PixieBoots mood')
return parser.parse_known_args(args)
def parse_args():
parser = ArgumentParser(
description=make_description("download the images"))
parser.add_images_source_dir_argument()
parser.add_dataset_dir_argument()
parser.add_argument(
"--no-dataset",
action="store_true",
help="If given, do not download the dataset (~13GB).",
)
return parser.parse_args()
def make_parser():
parser = ArgumentParser(description=make_description("training"))
parser.add_argument(
"style",
type=str,
nargs="*",
help=
("Style images for which the training is performed successively. If "
"relative path, the image is searched in IMAGES_SOURCE_DIR. Can also be a "
"valid key from the built-in images. Defaults to all built-in style images."
),
)
parser.add_images_source_dir_argument()
parser.add_models_dir_argument()
parser.add_dataset_dir_argument()
parser.add_impl_params_and_instance_norm_arguments()
parser.add_device_argument()
return parser
import argparse
import numpy as np
import tensorflow as tf
from queue import Queue
from core.Classifier import *
from core.efficientnet.utils import *
from utils.Utils import *
from utils.Teacher import *
from utils.ArgumentParser import *
from utils.Tensorflow_Utils import *
args = ArgumentParser().parse_args()
args['warmup_iteration'] = int(args['max_iteration'] *
0.05) # warmup iteration = 5%
model_name = '{}-{}-EfficientNet-{}'.format(args['experimenter'], get_today(),
args['option'])
if not args['multi_scale']:
width_coeff, depth_coeff, resolution, dropout_rate = efficientnet.efficientnet_params(
'efficientnet-{}'.format(args['option']))
args['max_image_size'] = resolution
num_gpu = len(args['use_gpu'].split(','))
os.environ["CUDA_VISIBLE_DEVICES"] = args['use_gpu']
args['batch_size'] = args['batch_size_per_gpu'] * num_gpu