def worker_init_reset_seed(worker_id):
seed_all_rng(np.random.randint(2**31) + worker_id)
def main():
parser = argparse.ArgumentParser(description="wetectron training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
parser.add_argument(
"--use-tensorboard",
dest="use_tensorboard",
help="Use tensorboardX logger (Requires tensorboardX installed)",
action="store_true",
)
args = parser.parse_args()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
update_iters()
cfg.freeze()
# make sure each worker has a different, yet deterministic seed if specified
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + get_rank())
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("wetectron", output_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml')
logger.info("Saving config into: {}".format(output_config_path))
# save overloaded model config in the output directory
save_config(cfg, output_config_path)
if cfg.DB.METHOD == "concrete":
model = train_cdb(
cfg=cfg,
local_rank=args.local_rank,
distributed=args.distributed,
use_tensorboard=args.use_tensorboard
)
else:
model = train(
cfg=cfg,
local_rank=args.local_rank,
distributed=args.distributed,
use_tensorboard=args.use_tensorboard
)
if not args.skip_test:
run_test(cfg, model, args.distributed)
def main():
parser = argparse.ArgumentParser(description="wetectron training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"--data_path",
type=str,
help="path to dataset training",
)
parser.add_argument(
"--proposal_path",
type=str,
help="path to proposal training",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
parser.add_argument(
"--use-tensorboard",
dest="use_tensorboard",
help="Use tensorboardX logger (Requires tensorboardX installed)",
action="store_true",
)
#################### AD CODE ##############
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
# begin: The rows tha
#run = Run.get_context()
#ws = run.experiment.workspace
#datastore = ws.get_default_datastore()
#dataset = Dataset.get_by_name(workspace=ws, name='datasets/voc')
#proposal = Dataset.get_by_name(workspace=ws, name= 'datasets/proposal')
#dataset = Dataset.File.from_files(path=(datastore, 'datasets/voc'))
#proposal = Dataset.File.from_files(path=(datastore, 'datasets/proposal'))
#dataset = dataset.as_named_input('inputdata').as_mount()
#proposal = proposal.as_named_input('inputp').as_mount()
print('Proposals path', args.proposal_path)
print('Dataset path', args.data_path)
#print('DATA SET FILES', os.listdir((os.path.join(args.data_path), 'datasets/voc')))
print('PROPOSALS SET FILES', os.listdir(args.proposal_path))
#cfg.DATASETS.TRAIN = (args.data_path, args.data_path)
#p_train_path = glob.glob(os.path.join(proposal.path_on_compute, '**/SS-voc_2007_train-boxes.pkl'), recursive=True)[0]
#p_val_path = glob.glob(os.path.join(proposal.path_on_compute, '**/SS-voc_2007_val-boxes.pkl'), recursive=True)[0]
#cfg.PROPOSAL_FILES.TRAIN = (args.proposal_path+'/SS-voc_2007_train-boxes.pkl', args.proposal_path+'/SS-voc_2007_val-boxes.pkl')
# End of added row
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
update_iters()
######## ADD ##############
cfg.PATH_DATA_TRAIN = args.data_path
cfg.PROPOSAL_FILES.TRAIN = (
os.path.join(args.proposal_path, 'SS-voc_2007_train-boxes.pkl'),
os.path.join(args.proposal_path, 'SS-voc_2007_val-boxes.pkl'),
)
############################
cfg.freeze()
# make sure each worker has a different, yet deterministic seed if specified
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + get_rank())
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("wetectron", output_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml')
logger.info("Saving config into: {}".format(output_config_path))
# save overloaded model config in the output directory
save_config(cfg, output_config_path)
print('CFG_DIRE', cfg.PATH_DATA_TRAIN)
if cfg.DB.METHOD == "concrete":
model = train_cdb(cfg=cfg,
local_rank=args.local_rank,
distributed=args.distributed,
use_tensorboard=args.use_tensorboard)
else:
model = train(cfg=cfg,
local_rank=args.local_rank,
distributed=args.distributed,
use_tensorboard=args.use_tensorboard)
if not args.skip_test:
run_test(cfg, model, args.distributed)