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import argparse |
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def str2bool(v): |
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return v.lower() in ("true", "1") |
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arg_lists = [] |
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parser = argparse.ArgumentParser() |
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def add_argument_group(name): |
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arg = parser.add_argument_group(name) |
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arg_lists.append(arg) |
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return arg |
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net_arg = add_argument_group("Network") |
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net_arg.add_argument( |
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"--model_name", type=str, default="SGM", help="" "model for training" |
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) |
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net_arg.add_argument( |
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"--config_path", |
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type=str, |
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default="configs/sgm.yaml", |
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help="" "config path for model", |
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) |
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data_arg = add_argument_group("Data") |
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data_arg.add_argument( |
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"--rawdata_path", type=str, default="rawdata", help="" "path for rawdata" |
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) |
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data_arg.add_argument( |
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"--dataset_path", type=str, default="dataset", help="" "path for dataset" |
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) |
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data_arg.add_argument( |
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"--desc_path", type=str, default="desc", help="" "path for descriptor(kpt) dir" |
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) |
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data_arg.add_argument( |
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"--num_kpt", type=int, default=1000, help="" "number of kpt for training" |
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) |
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data_arg.add_argument( |
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"--input_normalize", |
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type=str, |
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default="img", |
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help="" "normalize type for input kpt, img or intrinsic", |
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) |
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data_arg.add_argument( |
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"--data_aug", |
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type=str2bool, |
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default=True, |
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help="" "apply kpt coordinate homography augmentation", |
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) |
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data_arg.add_argument( |
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"--desc_suffix", type=str, default="suffix", help="" "desc file suffix" |
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) |
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loss_arg = add_argument_group("loss") |
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loss_arg.add_argument("--momentum", type=float, default=0.9, help="" "momentum") |
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loss_arg.add_argument( |
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"--seed_loss_weight", |
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type=float, |
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default=250, |
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help="" "confidence loss weight for sgm", |
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) |
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loss_arg.add_argument( |
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"--mid_loss_weight", type=float, default=1, help="" "midseeding loss weight for sgm" |
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) |
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loss_arg.add_argument( |
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"--inlier_th", |
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type=float, |
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default=5e-3, |
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help="" "inlier threshold for epipolar distance (for sgm and visualization)", |
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) |
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train_arg = add_argument_group("Train") |
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train_arg.add_argument("--train_lr", type=float, default=1e-4, help="" "learning rate") |
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train_arg.add_argument("--train_batch_size", type=int, default=16, help="" "batch size") |
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train_arg.add_argument( |
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"--gpu_id", type=str, default="0", help="id(s) for CUDA_VISIBLE_DEVICES" |
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) |
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train_arg.add_argument( |
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"--train_iter", type=int, default=1000000, help="" "training iterations to perform" |
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) |
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train_arg.add_argument("--log_base", type=str, default="./log/", help="" "log path") |
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train_arg.add_argument( |
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"--val_intv", type=int, default=20000, help="" "validation interval" |
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) |
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train_arg.add_argument( |
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"--save_intv", type=int, default=1000, help="" "summary interval" |
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) |
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train_arg.add_argument("--log_intv", type=int, default=100, help="" "log interval") |
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train_arg.add_argument( |
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"--decay_rate", type=float, default=0.999996, help="" "lr decay rate" |
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) |
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train_arg.add_argument( |
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"--decay_iter", type=float, default=300000, help="" "lr decay iter" |
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) |
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train_arg.add_argument( |
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"--local_rank", type=int, default=0, help="" "local rank for ddp" |
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) |
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train_arg.add_argument( |
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"--train_vis_folder", |
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type=str, |
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default=".", |
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help="" "visualization folder during training", |
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) |
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vis_arg = add_argument_group("Visualization") |
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vis_arg.add_argument( |
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"--tqdm_width", type=int, default=79, help="" "width of the tqdm bar" |
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) |
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def get_config(): |
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config, unparsed = parser.parse_known_args() |
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return config, unparsed |
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def print_usage(): |
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parser.print_usage() |
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