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| import argparse | |
| def args_parser(): | |
| parser = argparse.ArgumentParser(description="General top layer trainer") | |
| parser.add_argument("--opt", type=str, default="config/train.yaml", help="Path to optional configuration file") | |
| parser.add_argument('--model', type=str, default='2d_net_appendEdges', | |
| help='Model block name, in the `model` directory') | |
| parser.add_argument('--name', type=str, default='flowFusion', help='Experiment name') | |
| parser.add_argument('--outputdir', type=str, default='/myData/ret/experiments', help='Output dir to save results') | |
| parser.add_argument('--datadir', type=str, default='/myData/', metavar='PATH') | |
| parser.add_argument('--datasetName_train', type=str, default='train_dataset_single_edge', | |
| help='The file name of the train dataset, in `data` directory') | |
| parser.add_argument('--dataMode', type=str, default='resize', choices=['resize', 'crop']) | |
| parser.add_argument('--network', type=str, default='flow_single_appendEdges', | |
| help='The network file which defines the training process, in the `network` directory') | |
| parser.add_argument('--PASSMASK', type=int, default=1, | |
| help='1 -> concat the mask with the corrupted optical flows to fill the flow') | |
| parser.add_argument('--L1M', type=float, default=1, help='The weight of L1 loss in masked area') | |
| parser.add_argument('--sm', type=float, default=1, help='The loss weight of smooth loss') | |
| parser.add_argument('--sm2', type=float, default=1, help='The loss weight of second order smooth loss') | |
| # model related parameters | |
| parser.add_argument('--use_bias', type=int, default=1, help='If 1, use bias in the convolution blocks') | |
| parser.add_argument('--norm', type=int, default=0, help='If 1, normalize the weights of layers (SN by deault)') | |
| parser.add_argument('--init_weights', type=int, default=1, | |
| help='If 1, initialize the network with kaiming parameter set.') | |
| parser.add_argument('--cnum', type=int, default=48, help='Initial channel of encoder') | |
| parser.add_argument('--finetune', type=int, default=0, help='Whether to fine tune trained models') | |
| # parser.add_argument('--checkPoint', type=str, default='', help='checkpoint path for continue training') | |
| parser.add_argument('--gen_state', type=str, default='', help='checkpoint of the gen state for continuous training') | |
| parser.add_argument('--opt_state', type=str, default='', help='checkpoint of the opt state for continuous training') | |
| parser.add_argument('--resBlocks', type=int, default=1, help='The number of resblocks in the modulation subnetwork') | |
| parser.add_argument('--edge_residuals', type=int, default=4, help='The number of resblocks in edge branch') | |
| parser.add_argument('--conv_type', type=str, choices=['vanilla', 'gated', 'partial'], default='vanilla', | |
| help='Which kind of conv to use') | |
| parser.add_argument('--edge_loss', type=float, default=1, help='Loss weight for edge loss') | |
| parser.add_argument('--in_channel', type=int, default=3, help='The input channel of the defined network') | |
| parser.add_argument('--record_iter', type=int, default=16, help='How many iters to print an item of log') | |
| parser.add_argument('--num_flows', type=int, default=1) | |
| parser.add_argument('--sample', type=str, default='seq', choices=['random', 'seq']) | |
| parser.add_argument('--use_edges', type=int, default=0) | |
| parser.add_argument('--flow_interval', type=int, default=1) | |
| parser.add_argument('--use_residual', type=int, default=1, help='Whether to use residual for the P3D network') | |
| parser.add_argument('--gc', type=int, default=0, help='Use gradient clip to stabilize training') | |
| parser.add_argument('--rescale', type=int, default=1, | |
| help='Whether to rescale the results to the original flow range for calculating losses?') | |
| parser.add_argument('--ternary', type=float, default=0.01) | |
| parser.add_argument('--use_valid', action='store_true') | |
| args = parser.parse_args() | |
| return args | |