def setup(args):
"""
Create configs and setup logger from arguments and the given config file.
"""
cfg = get_shapenet_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# register dataset
data_dir, splits_file = register_shapenet(cfg.DATASETS.NAME)
cfg.DATASETS.DATA_DIR = data_dir
cfg.DATASETS.SPLITS_FILE = splits_file
##
cfg.PRETRAINED_MODEL = args.trained_model_from_Pix_2_Vox
cfg.PRETRAINED_MODEL2 = args.trained_model_from_Mesh_RCNN
##
# if data was copied the data dir has changed
if args.copy_data:
cfg.DATASETS.DATA_DIR = args.data_dir
cfg.freeze()
colorful_logging = not args.no_color
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
os.makedirs(output_dir, exist_ok=True)
comm.synchronize()
logger = setup_logger(
output_dir, color=colorful_logging, name="shapenet", distributed_rank=comm.get_rank()
)
logger.info(
"Using {} GPUs per machine. Rank of current process: {}".format(
args.num_gpus, comm.get_rank()
)
)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
logger.info(
"Loaded config file {}:\n{}".format(args.config_file, open(args.config_file, "r").read())
)
logger.info("Running with full config:\n{}".format(cfg))
if comm.is_main_process() and output_dir:
path = os.path.join(output_dir, "config.yaml")
with open(path, "w") as f:
f.write(cfg.dump())
logger.info("Full config saved to {}".format(os.path.abspath(path)))
return cfg
parser.add_argument("--output", help="A directory to save output visualizations")
parser.add_argument("--checkpoint",help="A path to a checkpoint file")
parser.add_argument("--saved_weights_dir", help="Path to saved weights to eval from")
parser.add_argument("--split", default='val', help='train_eval or val split')
return parser
if __name__ == "__main__":
args = get_parser().parse_args()
logger = setup_logger(name="demo")
logger.info("Arguments: " + str(args))
cfg = setup_cfg(args)
data_dir, splits_file = register_shapenet(cfg.DATASETS.NAME)
cfg.DATASETS.DATA_DIR = data_dir
cfg.DATASETS.SPLITS_FILE = splits_file
split = args.split
data_loader = build_data_loader(cfg, 'MeshVoxMulti', split, multigpu=False, num_workers=8)
print('Steps in dataloader: {}'.format(len(data_loader)))
data_dir = args.data_dir
saved_weights_dir = args.saved_weights_dir
saved_weights = sorted(glob.glob(saved_weights_dir+'/*.pth'))
#wandb.init(project='MeshRCNN', config=cfg, name='eval_'+split+'_'+saved_weights_dir)
for checkpoint_lp_model in saved_weights:
running_correct = 0
running_total = 0
