def main():
"""Entry."""
# init distributed
global is_root_rank
if FLAGS.use_distributed:
udist.init_dist()
FLAGS.batch_size = udist.get_world_size() * FLAGS.per_gpu_batch_size
FLAGS._loader_batch_size = FLAGS.per_gpu_batch_size
if FLAGS.bn_calibration:
FLAGS._loader_batch_size_calib = FLAGS.bn_calibration_per_gpu_batch_size
FLAGS.data_loader_workers = round(FLAGS.data_loader_workers /
udist.get_local_size())
is_root_rank = udist.is_master()
else:
count = torch.cuda.device_count()
FLAGS.batch_size = count * FLAGS.per_gpu_batch_size
FLAGS._loader_batch_size = FLAGS.batch_size
if FLAGS.bn_calibration:
FLAGS._loader_batch_size_calib = FLAGS.bn_calibration_per_gpu_batch_size * count
is_root_rank = True
FLAGS.lr = FLAGS.base_lr * (FLAGS.batch_size / FLAGS.base_total_batch)
# NOTE: don't drop last batch, thus must use ceil, otherwise learning rate
# will be negative
FLAGS._steps_per_epoch = int(np.ceil(NUM_IMAGENET_TRAIN /
FLAGS.batch_size))
if is_root_rank:
FLAGS.log_dir = '{}/{}'.format(FLAGS.log_dir,
time.strftime("%Y%m%d-%H%M%S"))
create_exp_dir(
FLAGS.log_dir,
FLAGS.config_path,
blacklist_dirs=[
'exp',
'.git',
'pretrained',
'tmp',
'deprecated',
'bak',
],
)
setup_logging(FLAGS.log_dir)
for k, v in _ENV_EXPAND.items():
logging.info('Env var expand: {} to {}'.format(k, v))
logging.info(FLAGS)
set_random_seed(FLAGS.get('random_seed', 0))
with SummaryWriterManager():
train_val_test()
def main():
"""Entry."""
FLAGS.test_only = True
mc.setup_distributed()
if udist.is_master():
FLAGS.log_dir = '{}/{}'.format(FLAGS.log_dir,
time.strftime("%Y%m%d-%H%M%S-eval"))
setup_logging(FLAGS.log_dir)
for k, v in _ENV_EXPAND.items():
logging.info('Env var expand: {} to {}'.format(k, v))
logging.info(FLAGS)
set_random_seed(FLAGS.get('random_seed', 0))
with mc.SummaryWriterManager():
val()
def main():
"""Entry."""
NUM_IMAGENET_TRAIN = 1281167
if FLAGS.dataset == 'cityscapes':
NUM_IMAGENET_TRAIN = 2975
elif FLAGS.dataset == 'ade20k':
NUM_IMAGENET_TRAIN = 20210
elif FLAGS.dataset == 'coco':
NUM_IMAGENET_TRAIN = 149813
mc.setup_distributed(NUM_IMAGENET_TRAIN)
if FLAGS.net_params and FLAGS.model_kwparams.task == 'segmentation':
tag, input_channels, block1, block2, block3, block4, last_channel = FLAGS.net_params.split(
'-')
input_channels = [int(item) for item in input_channels.split('_')]
block1 = [int(item) for item in block1.split('_')]
block2 = [int(item) for item in block2.split('_')]
block3 = [int(item) for item in block3.split('_')]
block4 = [int(item) for item in block4.split('_')]
last_channel = int(last_channel)
inverted_residual_setting = []
for item in [block1, block2, block3, block4]:
for _ in range(item[0]):
inverted_residual_setting.append([
item[1], item[2:-int(len(item) / 2 - 1)],
item[-int(len(item) / 2 - 1):]
])
FLAGS.model_kwparams.input_channel = input_channels
FLAGS.model_kwparams.inverted_residual_setting = inverted_residual_setting
FLAGS.model_kwparams.last_channel = last_channel
if udist.is_master():
FLAGS.log_dir = '{}/{}'.format(FLAGS.log_dir,
time.strftime("%Y%m%d-%H%M%S"))
# yapf: disable
create_exp_dir(FLAGS.log_dir, FLAGS.config_path, blacklist_dirs=[
'exp', '.git', 'pretrained', 'tmp', 'deprecated', 'bak', 'output'])
# yapf: enable
setup_logging(FLAGS.log_dir)
for k, v in _ENV_EXPAND.items():
logging.info('Env var expand: {} to {}'.format(k, v))
logging.info(FLAGS)
set_random_seed(FLAGS.get('random_seed', 0))
with mc.SummaryWriterManager():
train_val_test()
def main():
"""Entry."""
NUM_IMAGENET_TRAIN = 1281167
mc.setup_distributed(NUM_IMAGENET_TRAIN)
if udist.is_master():
FLAGS.log_dir = '{}/{}'.format(FLAGS.log_dir,
time.strftime("%Y%m%d-%H%M%S"))
# yapf: disable
create_exp_dir(FLAGS.log_dir, FLAGS.config_path, blacklist_dirs=[
'exp', '.git', 'pretrained', 'tmp', 'deprecated', 'bak'])
# yapf: enable
setup_logging(FLAGS.log_dir)
for k, v in _ENV_EXPAND.items():
logging.info('Env var expand: {} to {}'.format(k, v))
logging.info(FLAGS)
set_random_seed(FLAGS.get('random_seed', 0))
with mc.SummaryWriterManager():
train_val_test()
def main():
"""main method"""
parser = argparse.ArgumentParser(
description="Get your total worth of coins across exchanges")
parser.add_argument("-f",
default="settings/api_keys.yml",
help="File path for api keys")
args = parser.parse_args()
# load the logging configuration
setup_logging()
logger = logging.getLogger(__name__)
# mute request log
logging.getLogger("requests").setLevel(logging.WARNING)
# load the api keys
with open(args.f, 'r') as ymlfile:
api_keys = yaml.load(ymlfile)
# init total wallet
total_value = 0.
# init cur_address to store already processed eth addresses
cur_address = []
for exchange, auth in api_keys.items():
if exchange == "bittrex":
# instantiate Bittrex class
client = bittrex.Bittrex(auth['key'], auth['secret'])
# get wallet value
wallet = client.get_wallet()
value = client.get_wallet_value(wallet)
logger.info("%s worth(USD):$%s" % (exchange, value))
total_value = total_value + value
elif exchange == "bitfinex":
# instantiate Bitfinex class
client = bitfinex.Bitfinex(auth['key'], auth['secret'])
# get wallet value
wallet = client.get_wallet()
value = client.get_wallet_value(wallet)
logger.info("%s worth(USD):$%s" % (exchange, value))
total_value = total_value + value
elif exchange == "coinbase":
# instantiate Coinbase class
client = coinbase.Coinbase(auth['key'], auth['secret'])
# get wallet value
wallet = client.get_wallet()
value = client.get_wallet_value(wallet)
logger.info("%s worth(USD):$%s" % (exchange, value))
total_value = total_value + value
elif exchange == "etherscan":
# process each eth address
for acc in auth['account']:
if acc not in cur_address:
# save acc to cur_address
cur_address.append(acc)
# instantiate Etherscan class
client = etherscan.Etherscan(auth['key'], acc)
# get wallet value
wallet = client.get_wallet()
value = client.get_wallet_value(wallet)
logger.info("Ethereum address %s worth(USD):$%s" %
(acc['address'], value))
total_value = total_value + value
logger.info("Total crpyto worth(usd):$%s" % total_value)