def main():
''' parse config file '''
parser = argparse.ArgumentParser(
description="Graph Reasoning Machine for Visual Question Answering")
parser.add_argument("--config-file",
default="configs/baseline_res101.yaml")
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--session", type=int, default=0)
parser.add_argument("--resume", type=int, default=0)
parser.add_argument("--batchsize", type=int, default=0)
parser.add_argument("--inference", action='store_true')
parser.add_argument("--produce", action='store_true')
parser.add_argument("--instance", type=int, default=-1)
parser.add_argument("--use_freq_prior", action='store_true')
parser.add_argument("--visualize", action='store_true')
parser.add_argument("--algorithm", type=str, default='sg_baseline')
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.resume = args.resume
cfg.instance = args.instance
cfg.inference = args.inference
cfg.MODEL.USE_FREQ_PRIOR = args.use_freq_prior
cfg.MODEL.ALGORITHM = args.algorithm
if args.batchsize > 0:
cfg.DATASET.TRAIN_BATCH_SIZE = args.batchsize
if args.session > 0:
cfg.MODEL.SESSION = str(args.session)
# cfg.freeze()
if not os.path.exists("logs") and get_rank() == 0:
os.mkdir("logs")
logger = setup_logger("scene_graph_generation",
"logs",
get_rank(),
filename="{}_{}.txt".format(args.algorithm,
get_timestamp()))
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
output_config_path = os.path.join("logs", 'config.yml')
logger.info("Saving config into: {}".format(output_config_path))
save_config(cfg, output_config_path)
if args.produce:
produce(cfg, args)
elif not args.inference:
model = train(cfg, args)
else:
test(cfg, args)
gt_triplets, gt_triplet_boxes, _ = _triplet(gt_pred_labels, gt_relations,
gt_classes, gt_boxes,
gt_predicate_scores,
gt_class_scores)
return gt_triplets, gt_triplet_boxes
if __name__ == '__main__':
info = json.load(
open(os.path.join(cfg.DATASET.PATH, "VG-SGG-dicts.json"), 'r'))
itola = info['idx_to_label']
itopred = info['idx_to_predicate']
meters = MetricLogger(delimiter=" ")
data_loader = build_data_loader(cfg)
end = time.time()
logger = setup_logger("scene_graph_generation", "logs", get_rank())
output_config_path = os.path.join("logs", 'config.yml')
logger.info("Saving config into: {}".format(output_config_path))
logger = logging.getLogger("scene_graph_generation")
logger.info("Start training")
max_iter = len(data_loader)
result_dic: {str: int} = defaultdict(int)
all_images = 0
with open('browse_data.txt', 'w') as f:
for i, data in enumerate(data_loader):
data_time = time.time() - end
imgs, target, idx = data
all_images += len(imgs.tensors)
