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605f784 | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | from types import SimpleNamespace
import torch
import math
from wilds.common.utils import get_counts
from algorithms.ERM import ERM
from algorithms.AFN import AFN
from algorithms.DANN import DANN
from algorithms.groupDRO import GroupDRO
from algorithms.deepCORAL import DeepCORAL
from algorithms.IRM import IRM
from algorithms.fixmatch import FixMatch
from algorithms.pseudolabel import PseudoLabel
from algorithms.noisy_student import NoisyStudent
from configs.supported import algo_log_metrics, losses
from losses import initialize_loss
def initialize_algorithm(config, datasets, train_grouper, unlabeled_dataset=None):
train_dataset = datasets['train']['dataset']
train_loader = datasets['train']['loader']
d_out = infer_d_out(train_dataset, config)
# Other config
n_train_steps = math.ceil(len(train_loader)/config.gradient_accumulation_steps) * config.n_epochs
loss = initialize_loss(config.loss_function, config)
metric = algo_log_metrics[config.algo_log_metric]
if config.algorithm == 'ERM':
algorithm = ERM(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps)
elif config.algorithm == 'groupDRO':
train_g = train_grouper.metadata_to_group(train_dataset.metadata_array)
is_group_in_train = get_counts(train_g, train_grouper.n_groups) > 0
algorithm = GroupDRO(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
is_group_in_train=is_group_in_train)
elif config.algorithm == 'deepCORAL':
algorithm = DeepCORAL(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps)
elif config.algorithm == 'IRM':
algorithm = IRM(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps)
elif config.algorithm == 'DANN':
if unlabeled_dataset is not None:
unlabeled_dataset = unlabeled_dataset['dataset']
metadata_array = torch.cat(
[train_dataset.metadata_array, unlabeled_dataset.metadata_array]
)
else:
metadata_array = train_dataset.metadata_array
groups = train_grouper.metadata_to_group(metadata_array)
group_counts = get_counts(groups, train_grouper.n_groups)
group_ids_to_domains = group_counts.tolist()
domain_idx = 0
for i, count in enumerate(group_ids_to_domains):
if count > 0:
group_ids_to_domains[i] = domain_idx
domain_idx += 1
group_ids_to_domains = torch.tensor(group_ids_to_domains, dtype=torch.long)
algorithm = DANN(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
n_domains = domain_idx,
group_ids_to_domains=group_ids_to_domains,
)
elif config.algorithm == 'AFN':
algorithm = AFN(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps
)
elif config.algorithm == 'FixMatch':
algorithm = FixMatch(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps)
elif config.algorithm == 'PseudoLabel':
algorithm = PseudoLabel(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps)
elif config.algorithm == 'NoisyStudent':
if config.soft_pseudolabels:
unlabeled_loss = initialize_loss("cross_entropy_logits", config)
else:
unlabeled_loss = loss
algorithm = NoisyStudent(
config=config,
d_out=d_out,
grouper=train_grouper,
loss=loss,
unlabeled_loss=unlabeled_loss,
metric=metric,
n_train_steps=n_train_steps)
else:
raise ValueError(f"Algorithm {config.algorithm} not recognized")
return algorithm
def infer_d_out(train_dataset, config):
# Configure the final layer of the networks used
# The code below are defaults. Edit this if you need special config for your model.
if train_dataset.is_classification:
if train_dataset.y_size == 1:
# For single-task classification, we have one output per class
d_out = train_dataset.n_classes
elif train_dataset.y_size is None:
d_out = train_dataset.n_classes
elif (train_dataset.y_size > 1) and (train_dataset.n_classes == 2):
# For multi-task binary classification (each output is the logit for each binary class)
d_out = train_dataset.y_size
else:
raise RuntimeError('d_out not defined.')
elif train_dataset.is_detection:
# For detection, d_out is the number of classes
d_out = train_dataset.n_classes
if config.algorithm in ['deepCORAL', 'IRM']:
raise ValueError(f'{config.algorithm} is not currently supported for detection datasets.')
else:
# For regression, we have one output per target dimension
d_out = train_dataset.y_size
return d_out
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