#Contact: Xiao Wang ([email protected], [email protected]) #Some codes adopted from https://github.com/facebookresearch/moco from ops.argparser import argparser from ops.Config_Envrionment import Config_Environment import torch.multiprocessing as mp from training.main_worker import main_worker def main(args): #config environment ngpus_per_node=Config_Environment(args) # call training main control function if args.multiprocessing_distributed==1: # Since we have ngpus_per_node processes per node, the total world_size # needs to be adjusted accordingly args.world_size = ngpus_per_node * args.world_size # Use torch.multiprocessing.spawn to launch distributed processes: the # main_worker process function mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) else: # Simply call main_worker function main_worker(args.gpu, ngpus_per_node, args) if __name__ == '__main__': #use_cuda = torch.cuda.is_available() #print("starting check cuda status",use_cuda) #if use_cuda: args,params=argparser() main(args)
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License V3 for more details.
#
# You should have received a copy of the GNU v3.0 General Public License
# along with this program. If not, see https://www.gnu.org/licenses/gpl-3.0.en.html.
import os
from ops.argparser import argparser
from ops.os_operation import mkdir
import shutil
if __name__ == "__main__":
params = argparser()
if params['mode'] == 0:
input_map = params['F']
input_map = os.path.abspath(input_map)
type = params['type']
choose = params['gpu']
os.environ["CUDA_VISIBLE_DEVICES"] = choose
if type == 0:
indicate = 'SIMU6'
elif type == 1:
indicate = 'SIMU10'
elif type == 2:
indicate = 'SIMU_MIX'
elif type == 3:
indicate = 'REAL'
else:
def main(args):
if args.choose is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.choose
print("Current we choose gpu:%s" % args.choose)
#config environment
ngpus_per_node = Config_Environment(args)
# call training main control function
if args.multiprocessing_distributed == 1:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker,
nprocs=ngpus_per_node,
args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(args.gpu, ngpus_per_node, args)
if __name__ == '__main__':
#use_cuda = torch.cuda.is_available()
#print("starting check cuda status",use_cuda)
#if use_cuda:
parser = argparser()
args = parser.parse_args()
main(args)
