def process(t):
command, queue = t
gpu_id = queue.get()
try:
command += "--device_id " + str(gpu_id)
finally:
queue.put(gpu_id)
if __name__ == "__main__":
if 'PROGRAMFILES' in os.environ.keys():
nvidia_smi_path = os.path.join(os.environ['PROGRAMFILES'],
'NVIDIA Corporation', 'NVSMI')
if nvidia_smi_path not in os.environ['PATH']:
os.environ['PATH'] = os.environ['PATH'] + ";" + nvidia_smi_path
gpus = len(nvgpu.available_gpus(max_used_percent=30.0))
args, unknown = parse_arguments(sys.argv)
buildings_root = os.path.join(args.m3d, "v1", "scans")
print("Working on M3D buildings @ %s" % buildings_root)
rendered_trajectories = []
if args.rendered_trajectories is not None and os.path.exists(
args.rendered_trajectories):
data = pyexcel.get_sheet(file_name=args.rendered_trajectories)
rendered_trajectories.extend(data.column_at(0))
commands = []
building_hashes = {}
for building_folder in os.listdir(buildings_root):
building_hash = os.path.join(buildings_root, building_folder,
building_folder, "matterport_mesh")
for root, dirs, files in os.walk(building_hash, topdown=False):
obj = next((s for s in files if 'obj' in s), None)
CLASSES = ['bed', 'chair', 'table', 'sofa', 'bookcase']
COMBINED_CLASSES = {'desk': 'table'}
IGNORE_IMAGES_PATH = os.path.abspath('./skip_image_paths.txt')
# Root directory of the project
ROOT_DIR = os.path.abspath('./')
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
try:
print('Try to set gpu ressources ...')
import nvgpu
available_gpus = nvgpu.available_gpus()
if type(available_gpus) is list and len(available_gpus) > 0:
os.environ["CUDA_VISIBLE_DEVICES"] = available_gpus[0]
print('Using GPU ', available_gpus[0])
else:
print('No free gpu found, try later..')
exit()
except Exception as e:
print(e)
pass
class SunConfig(Config):
logging.basicConfig(level=logging.DEBUG,
format='%(levelname)s: %(asctime)s %(filename)s'
' [%(funcName)s:%(lineno)d][%(process)d] %(message)s',
datefmt='%m-%d %H:%M:%S',
filename=None,
filemode='a')
if __name__ == "__main__":
from argparse import ArgumentParser
try:
arg_parser = ArgumentParser(
description="print available_gpu id, using nvgpu")
arg_parser.add_argument("-b",
"--best",
default=None,
type=int,
help="output best N")
args = arg_parser.parse_args()
if args.best is not None:
gpus = sorted(nvgpu.gpu_info(),
key=lambda x: (x['mem_used'], x['index']))
ids = [x['index'] for x in gpus]
print(','.join(ids[:args.best]))
else:
print(','.join(nvgpu.available_gpus()))
except Exception as e:
traceback.print_exc()
exit(-1)
'{asctime} - pid: {process} - threadid: {thread} - func: {funcName} - {levelname}: {message}',
style='{')
logging_file_handler = logging.FileHandler(LOGGER_OUTPUT_FILE)
logging_file_handler.setFormatter(logging_formatter)
LOGGER.addHandler(logging_file_handler)
LOGGER.addHandler(LOGGER_STREAM_HANDLER)
return
_initialize_logger()
###########
# Globals #
###########
HYPERPARAMETER_SEARCH_IS_DISTRIBUTED = True
CPU_COUNT = mp.cpu_count()
pandarallel.initialize(nb_workers=CPU_COUNT, progress_bar=False, verbose=0)
GPU_IDS = eager_map(int, nvgpu.available_gpus())
DEFAULT_GPU = GPU_IDS[0]
NUM_WORKERS = 0 if HYPERPARAMETER_SEARCH_IS_DISTRIBUTED else 2
if not os.path.isdir('./checkpoints'):
os.makedirs('./checkpoints')
if __name__ == '__main__':
print('This module contains global initializations and global values.')