Spaces:
Running
on
T4
Running
on
T4
File size: 4,107 Bytes
d4b77ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
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
|