def test(cfg, model):
torch.cuda.empty_cache()
dataset_name = cfg.DATASET.TEST
model_dir = os.path.join(cfg.MODEL_DIR, cfg.MODEL.NAME)
output_folder = os.path.join(model_dir, "inference", dataset_name)
os.makedirs(output_folder, exist_ok=True)
data_loader_val = make_data_loader(cfg, is_train=False)
inference(
cfg,
model,
data_loader_val,
device=cfg.MODEL.DEVICE,
output_folder=output_folder,
)
def main():
parser = argparse.ArgumentParser(description="ReID Baseline Inference")
parser.add_argument(
"--config_file",
default="/home/lab3/bi/0716/Veri/ai_city/configs/submit.yml",
help="path to config file",
type=str)
parser.add_argument("opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
mkdir(output_dir)
logger = setup_logger("reid_baseline", output_dir, 0)
logger.info("Using {} GPUS".format(num_gpus))
logger.info(args)
if args.config_file != "":
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))
if cfg.MODEL.DEVICE == "cuda":
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
cudnn.benchmark = True
train_loader, val_loader, num_query, num_classes, dataset = make_data_loader(
cfg)
model = build_model(cfg, num_classes)
model.load_param(cfg.TEST.WEIGHT)
inference(cfg, model, val_loader, num_query, dataset)
def main():
parser = argparse.ArgumentParser(description="Dense Correspondence")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# cfg.freeze()
model = build_matching_model(cfg)
model.to(cfg.MODEL.DEVICE)
model = torch.nn.DataParallel(model)
model_dir = os.path.join(cfg.MODEL_DIR, cfg.MODEL.NAME)
checkpointer = Checkpointer(cfg, model, save_dir=model_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
dataset_name = cfg.DATASET.TEST
output_folder = os.path.join(model_dir, "inference", dataset_name)
os.makedirs(output_folder, exist_ok=True)
data_loader_val = make_data_loader(cfg, is_train=False)
inference(
cfg,
model,
data_loader_val,
device=cfg.MODEL.DEVICE,
output_folder=output_folder,
)
def main():
parser = argparse.ArgumentParser(description="ReID Baseline Inference")
parser.add_argument(
"--config_file", default="./configs/debug.yml", help="path to config file", type=str
)
parser.add_argument("opts", help="Modify config options using the command-line", default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
mkdir(output_dir)
logger = setup_logger("reid_baseline", output_dir, 0)
logger.info("Using {} GPUS".format(num_gpus))
logger.info(args)
if args.config_file != "":
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))
if cfg.MODEL.DEVICE == "cuda":
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
cudnn.benchmark = True
train_loader, val_loader, num_query, num_classes, dataset = make_data_loader(cfg)
model = build_model(cfg, num_classes)
model.load_param(cfg.TEST.WEIGHT)
indices_np = inference(cfg, model, val_loader, num_query, dataset)
## read meta information
dataset = AICity20(cfg.DATASETS.ROOT_DIR)
# write_result(indices_np, os.path.dirname(cfg.TEST.WEIGHT), topk=100)
write_result_with_track(indices_np, os.path.dirname(cfg.TEST.WEIGHT), dataset.test_tracks,'329')