handle_distributed(args_parser,
os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
abs_data_dir = os.path.expanduser(configer.get('data', 'data_dir'))
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
data_dir = configer.get('data', 'data_dir')
if isinstance(data_dir, str):
data_dir = [data_dir]
abs_data_dir = [os.path.expanduser(x) for x in data_dir]
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'))
cudnn.benchmark = False
if configer.get('gpu') is not None and not configer.get('distributed',
default=False):
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(gpu_id) for gpu_id in configer.get('gpu'))
if configer.get('network', 'norm_type') is None:
configer.update('network.norm_type', 'batchnorm')
if torch.cuda.device_count() <= 1 or configer.get('distributed',
default=False):
configer.update('network.gather', True)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add('project_dir', project_dir)
if configer.get('phase') == 'test':
from tools.data_generator import DataGenerator
DataGenerator.gen_toyset(project_dir)
for source in range(configer.get('data', 'num_data_sources')):
configer.update('data.src{}_label_path'.format(source),
os.path.join(project_dir, 'toyset/label.txt'))
configer.update('logging.logfile_level', None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'),
dist_rank=configer.get('local_rank'))