return performance_dict
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='running experiments on multimodal datasets.')
parser.add_argument('-config',
action='store',
dest='config_file',
help='please enter configuration file.',
default='config/run.ini')
args = parser.parse_args()
params = Params()
params.parse_config(args.config_file)
params.config_file = args.config_file
mode = 'run'
if 'mode' in params.__dict__:
mode = params.mode
set_seed(params)
params.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
if mode == 'run':
results = []
reader = setup(params)
reader.read(params)
print(params.output_dim_emo)
params.reader = reader
if params.train_type == "joint":
# results["mean_iou"] = IOULoss().forward(groundtruth, mesh_silhouettes).detach().cpu().numpy().tolist()
# results["mean_dice"] = DiceCoeffLoss().forward(groundtruth, mesh_silhouettes)
manager.set_pred_results(results)
manager.close()
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO,
format="%(levelname)s: %(message)s")
args = get_args()
args_dict = vars(args)
params = Params()
params.config_file = args_dict['config_file']
params.__post_init__()
params._set_with_dict(args_dict)
params.ransac_iou_threshold = args_dict['ransac_iou_threshold']
# Set the device
dev_num = params.gpu_num
os.environ["CUDA_VISIBLE_DEVICES"] = dev_num
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
device = torch.device(f"cuda" if torch.cuda.is_available() else "cpu")
logging.info(f"Using {device} as computation device")
if device == f"cuda":
torch.cuda.set_device()
logging.info(f"Using {device} as computation device")
params.device = device
params.logger = logging