/
hparams_registry.py
133 lines (98 loc) · 5.11 KB
/
hparams_registry.py
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import numpy as np
from utils 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.
"""
IMAGE_DATASETS = ['MIMIC', 'CheXpert', 'NIH', 'PadChest']
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('resnet18', False, lambda r: False)
# nonlinear classifiers disabled
_hparam('nonlinear_classifier', False, lambda r: bool(r.choice([False, False])))
if algorithm in ['ReSample', 'CRT']:
_hparam('group_balanced', True, lambda r: True)
else:
_hparam('group_balanced', False, lambda r: False)
if algorithm in ['ReSampleAttr']:
_hparam('attr_balanced', True, lambda r: True)
else:
_hparam('attr_balanced', False, lambda r: False)
# Algorithm-specific hparam definitions
# Each block of code below corresponds to one algorithm
if algorithm == 'CBLoss':
_hparam('beta', 0.9999, lambda r: 1 - 10**r.uniform(-5, -2))
elif algorithm == 'Focal':
_hparam('gamma', 1, lambda r: 0.5 * 10**r.uniform(0, 1))
elif algorithm == 'LDAM':
_hparam('max_m', 0.5, lambda r: 10**r.uniform(-1, -0.1))
_hparam('scale', 30., lambda r: r.choice([10., 30.]))
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 "Mixup" in algorithm:
_hparam('mixup_alpha', 0.2, lambda r: 10**r.uniform(-1, 1))
elif "GroupDRO" in algorithm:
_hparam('groupdro_eta', 1e-2, lambda r: 10**r.uniform(-3, -1))
elif algorithm in ["MMD", "CORAL"]:
_hparam('mmd_gamma', 1., lambda r: 10**r.uniform(-1, 1))
elif 'DANN' in algorithm:
_hparam('lambda', 1.0, lambda r: 10**r.uniform(-2, 2))
_hparam('weight_decay_d', 0., lambda r: 10**r.uniform(-6, -2))
_hparam('weight_decay_g', 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('mlp_width', 256, lambda r: int(2**r.uniform(7, 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 == 'CVaRDRO':
_hparam('joint_dro_alpha', 0.1, lambda r: 10**r.uniform(-2, 0))
elif algorithm == 'JTT':
_hparam('first_stage_step_frac', 0.5, lambda r: r.uniform(0.2, 0.8))
_hparam('jtt_lambda', 10, lambda r: 10**r.uniform(0, 2.5))
elif algorithm == 'LISA':
_hparam('LISA_alpha', 2., lambda r: 10**r.uniform(-1, 1))
_hparam('LISA_p_sel', 0.5, lambda r: r.uniform(0, 1))
_hparam('LISA_mixup_method', 'mixup', lambda r: r.choice(['mixup', 'cutmix']))
elif algorithm == 'DFR':
_hparam('dfr_reg', .1, lambda r: 10**r.uniform(-2, 0.5))
elif algorithm == 'MA':
_hparam('ma_start_iter', 1000, lambda r: int(1000 * r.choice(range(1, 6))))
elif algorithm == 'SAM':
_hparam('sam_rho', 0.05, lambda r: r.choice([0.01, 0.02, 0.05, 0.1]))
elif algorithm == 'SWA':
_hparam('swa_start', 500, lambda r: int(100 * r.uniform(5, 10)))
_hparam('swa_lr', 5e-5, lambda r: 10**r.uniform(-4.5, -4))
_hparam('swa_anneal_steps', 500, lambda r: int(100 * r.uniform(5, 10)))
_hparam('swa_update_steps', 500, lambda r: int(100 * r.uniform(5, 10)))
elif algorithm == 'SWAD':
_hparam('swad_n_converge', 3, lambda r: r.randint(2, 8))
_hparam('swad_n_tolerance', 6, lambda r: r.randint(4, 16))
_hparam('swad_tolerance_ratio', 0.3, lambda r: r.uniform(0.2, 0.4))
# Dataset-and-algorithm-specific hparam definitions
# Each block of code below corresponds to exactly one hparam. Avoid nested conditionals
_hparam('pretrained', True, lambda r: True)
_hparam('optimizer', 'adam', lambda r: 'adam')
_hparam('last_layer_dropout', 0., lambda r: 0.)
_hparam('lr', 1e-3, lambda r: 10**r.uniform(-4, -2))
_hparam('weight_decay', 1e-4, lambda r: 10**r.uniform(-6, -3))
_hparam('batch_size', 64, lambda r: 64)
if 'DANN' in algorithm:
_hparam('lr_g', 1e-3, lambda r: 10**r.uniform(-4, -2))
_hparam('lr_d', 1e-3, lambda r: 10**r.uniform(-4, -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()}