<filename>options/extra_args_mtl.py
from .arguments import TrainOptions

class MTL_Options(TrainOptions):
    def initialize(self):
        TrainOptions.initialize(self)
        self.parser.add_argument('--mtl_method', default='gradreg')
        self.parser.add_argument('--tasks', nargs='+', default=['depth'], help='all tasks separated by a space')
        self.parser.add_argument('--outputs_nc', nargs='+', default=[1], type=int, help='all tasks separated by a space')
        self.parser.add_argument('--regression_loss', default='L1')
        self.parser.add_argument('--alpha', type=float, default=0.5, help='weight of losses for semantic and depth')
        
        # For toy dataset example
        self.parser.add_argument('--n_tasks', type=int, default=10)
        self.parser.add_argument('--sigma', type=float, default=100.0)
        self.parser.add_argument('--per_sample', action='store_true')
        # self.parser.add_argument('--optim', default='adam')
<filename>options/extra_args_mtl.py
from .arguments import TrainOptions

class MTL_Options(TrainOptions):
    def initialize(self):
        TrainOptions.initialize(self)
        self.parser.add_argument('--mtl_method', default='gradreg')
        self.parser.add_argument('--tasks', nargs='+', default=['depth'], help='all tasks separated by a space')
        self.parser.add_argument('--outputs_nc', nargs='+', default=[1], type=int, help='all tasks separated by a space')
        self.parser.add_argument('--regression_loss', default='L1')
        self.parser.add_argument('--alpha', type=float, default=0.5, help='weight of losses for semantic and depth')
        
        # For toy dataset example
        self.parser.add_argument('--n_tasks', type=int, default=10)
        self.parser.add_argument('--sigma', type=float, default=100.0)
        self.parser.add_argument('--per_sample', action='store_true')
        # self.parser.add_argument('--optim', default='adam')
# For toy dataset example # self.parser.add_argument('--optim', default='adam')