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hparams_registry.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
from domainbed.lib import misc
def _define_hparam(hparams, hparam_name, default_val, random_val_fn):
hparams[hparam_name] = (hparams, hparam_name, default_val, random_val_fn)
def _hparams(algorithm, dataset, random_seed):
"""
Global registry of hyperparams. Each entry is a (default, random) tuple.
New algorithms / networks / etc. should add entries here.
"""
SMALL_IMAGES = ['Debug28', 'RotatedMNIST', 'ColoredMNIST']
hparams = {}
def _hparam(name, default_val, random_val_fn):
"""Define a hyperparameter. random_val_fn takes a RandomState and
returns a random hyperparameter value."""
assert(name not in hparams)
random_state = np.random.RandomState(
misc.seed_hash(random_seed, name)
)
hparams[name] = (default_val, random_val_fn(random_state))
# Unconditional hparam definitions.
_hparam('data_augmentation', True, lambda r: True)
_hparam('resnet18', False, lambda r: False)
_hparam('resnet_dropout', 0., lambda r: r.choice([0., 0.1, 0.5]))
_hparam('class_balanced', False, lambda r: False)
# TODO: nonlinear classifiers disabled
_hparam('nonlinear_classifier', False,
lambda r: bool(r.choice([False, False])))
# Algorithm-specific hparam definitions. Each block of code below
# corresponds to exactly one algorithm.
if algorithm in ['DANN', 'CDANN']:
_hparam('lambda', 1.0, lambda r: 10**r.uniform(-2, 2))
_hparam('weight_decay_d', 0., lambda r: 10**r.uniform(-6, -2))
_hparam('d_steps_per_g_step', 1, lambda r: int(2**r.uniform(0, 3)))
_hparam('grad_penalty', 0., lambda r: 10**r.uniform(-2, 1))
_hparam('beta1', 0.5, lambda r: r.choice([0., 0.5]))
_hparam('mlp_width', 256, lambda r: int(2 ** r.uniform(6, 10)))
_hparam('mlp_depth', 3, lambda r: int(r.choice([3, 4, 5])))
_hparam('mlp_dropout', 0., lambda r: r.choice([0., 0.1, 0.5]))
elif algorithm == 'Fish':
_hparam('meta_lr', 0.5, lambda r:r.choice([0.05, 0.1, 0.5]))
elif algorithm == "RSC":
_hparam('rsc_f_drop_factor', 1/3, lambda r: r.uniform(0, 0.5))
_hparam('rsc_b_drop_factor', 1/3, lambda r: r.uniform(0, 0.5))
elif algorithm == "SagNet":
_hparam('sag_w_adv', 0.1, lambda r: 10**r.uniform(-2, 1))
elif algorithm == "IRM":
_hparam('irm_lambda', 1e2, lambda r: 10**r.uniform(-1, 5))
_hparam('irm_penalty_anneal_iters', 500,
lambda r: int(10**r.uniform(0, 4)))
elif algorithm == "Mixup":
_hparam('mixup_alpha', 0.2, lambda r: 10**r.uniform(-1, -1))
elif algorithm == "GroupDRO":
_hparam('groupdro_eta', 1e-2, lambda r: 10**r.uniform(-3, -1))
elif algorithm == "MMD" or algorithm == "CORAL":
_hparam('mmd_gamma', 1., lambda r: 10**r.uniform(-1, 1))
elif algorithm == "MLDG":
_hparam('mldg_beta', 1., lambda r: 10**r.uniform(-1, 1))
elif algorithm == "MTL":
_hparam('mtl_ema', .99, lambda r: r.choice([0.5, 0.9, 0.99, 1.]))
elif algorithm == "VREx":
_hparam('vrex_lambda', 1e1, lambda r: 10**r.uniform(-1, 5))
_hparam('vrex_penalty_anneal_iters', 500,
lambda r: int(10**r.uniform(0, 4)))
elif algorithm == "SD":
_hparam('sd_reg', 0.1, lambda r: 10**r.uniform(-5, -1))
elif algorithm == "ANDMask":
_hparam('tau', 1, lambda r: r.uniform(0.5, 1.))
elif algorithm == "IGA":
_hparam('penalty', 1000, lambda r: 10**r.uniform(1, 5))
elif algorithm == "SANDMask":
_hparam('tau', 1.0, lambda r: r.uniform(0.0, 1.))
_hparam('k', 1e+1, lambda r: 10**r.uniform(-3, 5))
elif algorithm == "Fishr":
_hparam('lambda', 1000., lambda r: 10**r.uniform(1., 4.))
_hparam('penalty_anneal_iters', 1500, lambda r: int(r.uniform(0., 5000.)))
_hparam('ema', 0.95, lambda r: r.uniform(0.90, 0.99))
elif algorithm == "TRM":
_hparam('cos_lambda', 1e-4, lambda r: 10 ** r.uniform(-5, 0))
_hparam('iters', 200, lambda r: int(10 ** r.uniform(0, 4)))
_hparam('groupdro_eta', 1e-2, lambda r: 10 ** r.uniform(-3, -1))
elif algorithm == "IB_ERM":
_hparam('ib_lambda', 1e2, lambda r: 10**r.uniform(-1, 5))
_hparam('ib_penalty_anneal_iters', 500,
lambda r: int(10**r.uniform(0, 4)))
elif algorithm == "IB_IRM":
_hparam('irm_lambda', 1e2, lambda r: 10**r.uniform(-1, 5))
_hparam('irm_penalty_anneal_iters', 500,
lambda r: int(10**r.uniform(0, 4)))
_hparam('ib_lambda', 1e2, lambda r: 10**r.uniform(-1, 5))
_hparam('ib_penalty_anneal_iters', 500,
lambda r: int(10**r.uniform(0, 4)))
elif algorithm == "CAD" or algorithm == "CondCAD":
_hparam('lmbda', 1e-1, lambda r: r.choice([1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2]))
_hparam('temperature', 0.1, lambda r: r.choice([0.05, 0.1]))
_hparam('is_normalized', False, lambda r: False)
_hparam('is_project', False, lambda r: False)
_hparam('is_flipped', True, lambda r: True)
# Dataset-and-algorithm-specific hparam definitions. Each block of code
# below corresponds to exactly one hparam. Avoid nested conditionals.
if dataset in SMALL_IMAGES:
_hparam('lr', 1e-3, lambda r: 10**r.uniform(-4.5, -2.5))
else:
_hparam('lr', 5e-5, lambda r: 10**r.uniform(-5, -3.5))
if dataset in SMALL_IMAGES:
_hparam('weight_decay', 0., lambda r: 0.)
else:
_hparam('weight_decay', 0., lambda r: 10**r.uniform(-6, -2))
if dataset in SMALL_IMAGES:
_hparam('batch_size', 64, lambda r: int(2**r.uniform(3, 9)))
elif algorithm == 'ARM':
_hparam('batch_size', 8, lambda r: 8)
elif dataset == 'DomainNet':
_hparam('batch_size', 32, lambda r: int(2**r.uniform(3, 5)))
else:
_hparam('batch_size', 32, lambda r: int(2**r.uniform(3, 5.5)))
if algorithm in ['DANN', 'CDANN'] and dataset in SMALL_IMAGES:
_hparam('lr_g', 1e-3, lambda r: 10**r.uniform(-4.5, -2.5))
elif algorithm in ['DANN', 'CDANN']:
_hparam('lr_g', 5e-5, lambda r: 10**r.uniform(-5, -3.5))
if algorithm in ['DANN', 'CDANN'] and dataset in SMALL_IMAGES:
_hparam('lr_d', 1e-3, lambda r: 10**r.uniform(-4.5, -2.5))
elif algorithm in ['DANN', 'CDANN']:
_hparam('lr_d', 5e-5, lambda r: 10**r.uniform(-5, -3.5))
if algorithm in ['DANN', 'CDANN'] and dataset in SMALL_IMAGES:
_hparam('weight_decay_g', 0., lambda r: 0.)
elif algorithm in ['DANN', 'CDANN']:
_hparam('weight_decay_g', 0., lambda r: 10**r.uniform(-6, -2))
return hparams
def default_hparams(algorithm, dataset):
return {a: b for a, (b, c) in _hparams(algorithm, dataset, 0).items()}
def random_hparams(algorithm, dataset, seed):
return {a: c for a, (b, c) in _hparams(algorithm, dataset, seed).items()}