def __init__(self, custom_parser=None):
self.version = __version__
logger.info("PyTorch Version {}, Furnace Version {}".format(
torch.__version__, self.version))
self.state = State()
self.devices = None
self.distributed = False
if custom_parser is None:
self.parser = argparse.ArgumentParser()
else:
assert isinstance(custom_parser, argparse.ArgumentParser)
self.parser = custom_parser
self.inject_default_parser()
self.args = self.parser.parse_args()
self.continue_state_object = self.args.continue_fpath
if 'WORLD_SIZE' in os.environ:
self.distributed = int(os.environ['WORLD_SIZE']) >= 1
if self.distributed:
self.local_rank = self.args.local_rank
self.world_size = int(os.environ['WORLD_SIZE'])
torch.cuda.set_device(self.local_rank)
os.environ['MASTER_PORT'] = self.args.port
dist.init_process_group(backend="nccl", init_method='env://')
self.devices = [i for i in range(self.world_size)]
else:
self.devices = parse_devices(self.args.devices)
def __init__(self, custom_parser=None):
self.state = State()
self.devices = None
self.distributed = False
if custom_parser is None:
self.parser = argparse.ArgumentParser()
else:
assert isinstance(custom_parser, argparse.ArgumentParser)
self.parser = custom_parser
self.inject_default_parser()
self.args = self.parser.parse_args()
self.continue_state_object = self.args.continue_fpath
if 'WORLD_SIZE' in os.environ:
self.distributed = int(os.environ['WORLD_SIZE']) > 1
if self.distributed:
self.local_rank = self.args.local_rank
self.world_size = int(os.environ['WORLD_SIZE'])
torch.cuda.set_device(self.local_rank)
dist.init_process_group(backend="nccl", init_method='env://')
self.devices = [i for i in range(self.world_size)]
else:
self.devices = parse_devices(self.args.devices)
def __init__(self, custom_parser=None):
self.version = __version__
logger.info("PyTorch Version {}, Furnace Version {}".format(
torch.__version__, self.version))
self.state = State()
self.devices = None
self.distributed = False
if custom_parser is None:
self.parser = argparse.ArgumentParser()
else:
assert isinstance(custom_parser, argparse.ArgumentParser)
self.parser = custom_parser
self.inject_default_parser()
self.args = self.parser.parse_args()
self.continue_state_object = self.args.continue_fpath
if 'WORLD_SIZE' in os.environ:
self.distributed = int(os.environ['WORLD_SIZE']) > 1
self.devices = parse_devices(self.args.devices)
return result_line
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--epochs', default='last', type=str)
parser.add_argument('-d', '--devices', default='1', type=str)
parser.add_argument('-v', '--verbose', default=False, action='store_true')
parser.add_argument('--show_image',
'-s',
default=False,
action='store_true')
parser.add_argument('--save_path', '-p', default=None)
args = parser.parse_args()
all_dev = parse_devices(args.devices)
mp_ctx = mp.get_context('spawn')
network = CPNet(config.num_classes, criterion=None)
data_setting = {
'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source
}
dataset = ADE(data_setting, 'val', None)
with torch.no_grad():
segmentor = SegEvaluator(dataset, config.num_classes,
config.image_mean, config.image_std, network,
config.eval_scale_array, config.eval_flip,
