def setup_arch_flags(cls):
super().setup_arch_flags()
flags.DEFINE_integer('max_epoch',
default=200,
help='number of max_epoch')
flags.DEFINE_multi_integer('milestones',
default=[
10, 20, 30, 40, 50, 60, 70, 80, 90, 100,
110, 120, 140, 160
],
help='miletones for lr_decay')
flags.DEFINE_float('gamma', default=0.85, help='gamma for lr_decay')
flags.DEFINE_string('device', default='cpu', help='cpu or cuda?')
flags.DEFINE_integer('printfreq',
default=5,
help='how many output for an epoch')
flags.DEFINE_integer('num_admm_innerloop',
default=2,
help='how many output for an epoch')
flags.DEFINE_integer('num_workers',
default=1,
help='how many output for an epoch')
flags.DEFINE_integer('batch_size',
default=1,
help='how many output for an epoch')
flags.DEFINE_boolean('vis_during_training',
default=False,
help='matplotlib plot image during training')
flags.DEFINE_boolean('group',
default=False,
help='group patient to perform 3D dice')
def setup_arch_flags(cls):
""" Setup the arch_hparams """
flags.DEFINE_float('weight_decay',
default=0,
help='decay of learning rate schedule')
flags.DEFINE_float('lr', default=0.001, help='learning rate')
flags.DEFINE_boolean('amsgrad', default=False, help='amsgrad')
flags.DEFINE_integer('optim_inner_loop_num',
default=5,
help='optim_inner_loop_num')
flags.DEFINE_string('arch', default='enet', help='arch_name')
flags.DEFINE_integer('num_classes', default=2, help='num of classes')
flags.DEFINE_string('method', default='admm_gc_size', help='arch_name')
flags.DEFINE_boolean('ignore_negative',
default=False,
help='ignore negative examples in the training')
def setup_arch_flags(cls):
super().setup_arch_flags()
flags.DEFINE_boolean(
'individual_size_constraint',
default=True,
help='Individual size constraint for each input image')
flags.DEFINE_float('eps', default=0.001, help='Individual size eps')
flags.DEFINE_integer(
'global_upbound',
default=2000,
help='global upper bound if individual_size_constraint is False')
flags.DEFINE_integer(
'global_lowbound',
default=20,
help='global lower bound if individual_size_constraint is False')
SizeConstraint.setup_arch_flag()
def setup_arch_flag(cls):
flags.DEFINE_float('reg_eps',
default=0.25,
help='eps for inequality method')
flags.DEFINE_float('reg_p_p', default=1, help='penalty for p_positive')
flags.DEFINE_float('reg_p_n', default=1, help='penalty for p_negative')
flags.DEFINE_float('reg_lamda',
default=1,
help='lamda for unary term and pairwise term')
flags.DEFINE_float('reg_sigma',
default=0.02,
help='smoothness term for pairwise term')
flags.DEFINE_integer('reg_dilation_level',
default=10,
help='dilation for the weak mask')
flags.DEFINE_integer('reg_kernelsize',
default=5,
help='dilation for the weak mask')
def setup_arch_flags(cls):
super().setup_arch_flags()
flags.DEFINE_float(
'lamda',
default=0.01,
help='balance between the unary and the neighor term')
flags.DEFINE_float('sigma',
default=0.001,
help='Smooth the neigh term')
flags.DEFINE_integer('kernelsize',
default=5,
help='kernelsize of the gc')
flags.DEFINE_integer(
'dilation_level',
default=6,
help='iterations to execute the dilation operation')
flags.DEFINE_integer('stop_dilation_epoch',
default=70,
help='stop dilation operation at this epoch')
def setup_arch_flags(cls):
super().setup_arch_flags()
RegConstraint.setup_arch_flag()
flags.DEFINE_integer('stop_dilation_epoch',
default=100,
help='stop dilation')