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":
emo, act = run(params)
save_performance(params, emo, "joint_emotion")
save_performance(params, act, "joint_act")
else:
performance_dict = run(params)
save_performance(params, performance_dict, params.train_type)
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
try:
exper_path = os.path.join(params.exper_dir, params.name)
os.makedirs(exper_path, exist_ok=True)
with open(os.path.join(exper_path, 'config.json'), 'w') as file:
file.write(json.dumps(params.as_dict()))
if params.is_real_data:
unet = UNetDynamic.load(params)
unet = nn.DataParallel(unet).to(device)
logging.info(unet)
logging.info(
f"Loaded UNet Model from {params.model_cpt}- Starting training"
)
pred_evimo(unet, params, device)