def datasets(self, params: FitEvalParams):
from thexp import DatasetBuilder
from data.constant import norm_val
from data.transforms import ToNormTensor, Weak, Strong
mean, std = norm_val.get(params.dataset, [None, None])
weak = Weak(mean, std)
dataset_fn = datasets.datasets[params.dataset]
train_x, train_y = dataset_fn(True)
train_y = np.array(train_y)
from data.noisy import symmetric_noisy
train_x, train_y = train_x[:params.train_size], train_y[:params.train_size]
part_size = params.train_size // 2
noisy_x1, noisy_true_y1 = train_x[:part_size], train_y[:part_size]
noisy_x2, noisy_true_y2 = train_x[part_size:], train_y[part_size:]
noisy_ratio = 0.9
noisy_y1 = symmetric_noisy(noisy_true_y1, noisy_ratio, n_classes=params.n_classes)
noisy_y2 = symmetric_noisy(noisy_true_y2, noisy_ratio, n_classes=params.n_classes)
self.logger.info('noisy dataset ratio: ',
(noisy_true_y1 == noisy_y1).mean(),
(noisy_true_y2 == noisy_y2).mean())
noisy_set1 = (
DatasetBuilder(noisy_x1, noisy_true_y1)
.add_labels(noisy_y2, source_name='noisy_y')
.toggle_id()
.add_x(transform=weak)
.add_y()
.add_y(source='noisy_y')
)
noisy_set2 = (
DatasetBuilder(noisy_x2, noisy_true_y2)
.add_labels(noisy_y2, source_name='noisy_y')
.toggle_id()
.add_x(transform=weak)
.add_y()
.add_y(source='noisy_y')
)
self.noisy_loader1 = noisy_set1.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.noisy_loader2 = noisy_set2.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.eval_state = 1 - int(params.eval_mode == 'clean')
self.toggle_dataset((self.eval_state % 2) == 0)
self.to(self.device)
def datasets(self, params: DivideMixParams):
from data.dataxy import datasets
dataset_fn = datasets[params.dataset]
test_x, test_y = dataset_fn(False)
train_x, train_y = dataset_fn(True)
mean, std = norm_val.get(params.dataset, [None, None])
toTensor = ToNormTensor(mean, std)
weak = Weak(mean, std)
strong = Strong(mean, std)
if params.noisy_type == 'asymmetric':
from data.noisy import asymmetric_noisy
noisy_y = asymmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
elif params.noisy_type == 'symmetric':
from data.noisy import symmetric_noisy
noisy_y = symmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
else:
assert False, params.noisy_type
self.train_set_pack = [train_x, np.array(train_y), noisy_y]
self.logger.info('noisy acc = {}'.format((train_y == noisy_y).mean()))
train_set = (DatasetBuilder(train_x, train_y).add_labels(
noisy_y, 'noisy_y').toggle_id().add_x(transform=weak).add_x(
transform=strong).add_y().add_y(source='noisy_y'))
train_dataloader = train_set.DataLoader(batch_size=params.batch_size *
2,
num_workers=params.num_workers,
shuffle=True)
from thexp import DataBundler
self.eval_train_dataloader = (DataBundler().add(
DatasetBuilder(train_x, noisy_y).toggle_id().add_x(
transform=toTensor).add_y().DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers // 2,
shuffle=False)).to(self.device))
test_dataloader = (DatasetBuilder(
test_x, test_y).add_x(transform=toTensor).add_y().DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers // 2,
shuffle=False))
self.regist_databundler(train=train_dataloader, test=test_dataloader)
self.to(self.device)
def datasets(self, params: NoisyParams):
self.rnd.mark('kk')
params.noisy_type = params.default('symmetric', True)
params.noisy_ratio = params.default(0.2, True)
from data.constant import norm_val
mean, std = norm_val.get(params.dataset, [None, None])
from data.transforms import ToNormTensor
toTensor = ToNormTensor(mean, std)
from data.transforms import Weak
weak = Weak(mean, std)
from data.transforms import Strong
dataset_fn = datasets.datasets[params.dataset]
train_x, train_y = dataset_fn(True)
train_y = np.array(train_y)
from thexp import DatasetBuilder
from data.noisy import symmetric_noisy
noisy_y = symmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
clean_mask = (train_y == noisy_y)
noisy_mask = np.logical_not(clean_mask)
noisy_mask = np.where(noisy_mask)[0]
nmask_a = noisy_mask[:len(noisy_mask) // 2]
nmask_b = noisy_mask[len(noisy_mask) // 2:]
clean_x, clean_y = train_x[clean_mask], noisy_y[clean_mask]
clean_true_y = train_y[clean_mask]
raw_x, raw_true_y = train_x[nmask_a], train_y[nmask_a]
raw_y = noisy_y[nmask_a]
change_x, change_true_y, change_y = train_x[nmask_b], train_y[
nmask_b], noisy_y[nmask_b]
first_x, first_y, first_true_y = (
clean_x + raw_x,
np.concatenate([clean_y, raw_y]),
np.concatenate([clean_true_y, raw_true_y]),
)
second_x, second_y, second_true_y = (
clean_x + change_x,
np.concatenate([clean_y, change_y]),
np.concatenate([clean_true_y, change_true_y]),
)
first_set = (DatasetBuilder(first_x, first_true_y).add_labels(
first_y, 'noisy_y').toggle_id().add_x(
transform=weak).add_y().add_y(source='noisy_y'))
second_set = (DatasetBuilder(second_x, second_true_y).add_labels(
second_y, 'noisy_y').toggle_id().add_x(
transform=weak).add_y().add_y(source='noisy_y'))
self.first_dataloader = first_set.DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.second_dataloader = second_set.DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.second_dataloader = second_set.DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.second = False
self.regist_databundler(train=self.first_dataloader)
self.cur_set = 0
self.to(self.device)
def datasets(self, params: NoisyParams):
params.noisy_type = params.default('symmetric', True)
params.noisy_ratio = params.default(0.2, True)
import numpy as np
dataset_fn = datasets[params.dataset]
test_x, test_y = dataset_fn(False)
train_x, train_y = dataset_fn(True)
# train_ids, query_ids = splits.train_val_split(train_y, val_size=params.val_size)
query_ids, train_ids, eval_ids = splits.semi_split(
train_y,
params.query_size // params.n_classes,
val_size=params.val_size,
repeat_sup=False)
# train_ids = train_ids[:3000]
self.train_size = len(train_ids)
train_x, query_x, eval_x = train_x[train_ids], train_x[
query_ids], train_x[eval_ids]
train_y, query_y, eval_y = train_y[train_ids], train_y[
query_ids], train_y[eval_ids]
mean, std = norm_val.get(params.dataset, [None, None])
toTensor = ToNormTensor(mean, std)
weak = Weak(mean, std)
strong = Strong(mean, std)
if params.noisy_type == 'asymmetric':
from data.noisy import asymmetric_noisy
noisy_y = asymmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
elif params.noisy_type == 'symmetric':
from data.noisy import symmetric_noisy
noisy_y = symmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
else:
assert False
self.logger.info('noisy acc = {}'.format((train_y == noisy_y).mean()))
self.rnd.shuffle()
self.logger.info(len(train_y), len(train_x), len(noisy_y))
train_set = (DatasetBuilder(train_x, train_y).add_labels(
noisy_y, 'noisy_y').toggle_id().add_x(transform=strong))
params.K = params.default(0, True)
for _ in range(params.K):
train_set.add_x(transform=weak)
train_set = (train_set.add_y().add_y(source='noisy_y'))
if params.distributed:
from torch.utils.data.distributed import DistributedSampler
sampler = DistributedSampler(train_set, num_replicas=4)
self.sampler_a = sampler
else:
sampler = None
train_set = train_set.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
sampler=sampler,
shuffle=not params.distributed)
query_set = (DatasetBuilder(query_x,
query_y).add_x(transform=strong).add_y())
if params.distributed:
from torch.utils.data.distributed import DistributedSampler
sampler = DistributedSampler(train_set, num_replicas=4)
self.sampler_b = sampler
else:
sampler = None
query_set = query_set.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
sampler=sampler,
shuffle=not params.distributed)
val_dataloader = (
DatasetBuilder(eval_x, eval_y).add_x(transform=toTensor).add_y().
DataLoader(
batch_size=params.batch_size,
shuffle=
False, # do not shuffle # no shuffle for probe, so a batch is class balanced.(?)
num_workers=params.num_workers))
train_dataloader = DataBundler().add(train_set).cycle(
query_set).zip_mode()
test_dataloader = (DatasetBuilder(
test_x, test_y).add_x(transform=toTensor).add_y().DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
shuffle=True))
self.regist_databundler(train=train_dataloader,
eval=val_dataloader,
test=test_dataloader)
self.to(self.device)
def datasets(self, params: NoisyParams):
self.rnd.mark('fix_noisy')
params.noisy_type = params.default('symmetric', True)
params.noisy_ratio = params.default(0.2, True)
dataset_fn = datasets[params.dataset]
test_x, test_y = dataset_fn(False)
train_x, train_y = dataset_fn(True)
train_ids, val_ids = splits.train_val_split(train_y, val_size=5000)
train_x, val_x = train_x[train_ids], train_x[val_ids]
train_y, val_y = train_y[train_ids], train_y[val_ids]
mean, std = norm_val.get(params.dataset, [None, None])
toTensor = ToNormTensor(mean, std)
weak = Weak(mean, std)
strong = Strong(mean, std)
if params.noisy_type == 'asymmetric':
from data.noisy import asymmetric_noisy
noisy_y = asymmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
elif params.noisy_type == 'symmetric':
from data.noisy import symmetric_noisy
noisy_y = symmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
else:
assert False
clean_mask = train_y == noisy_y
self.logger.info('noisy acc = {}'.format((train_y == noisy_y).mean()))
self.rnd.shuffle()
train_set = (DatasetBuilder(train_x, train_y).add_labels(
noisy_y, 'noisy_y').toggle_id().add_x(transform=weak).add_x(
transform=strong).add_y().add_y(source='noisy_y'))
from thextra.noisy_sampler import NoisySampler
sampler = None
if params.order_sampler:
sampler = NoisySampler(train_set, clean_mask)
self.train_size = len(train_set)
train_dataloader = train_set.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
sampler=sampler,
shuffle=True)
val_dataloader = (DatasetBuilder(
val_x, val_y).add_x(transform=toTensor).add_y().DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
shuffle=True))
test_dataloader = (DatasetBuilder(
test_x, test_y).add_x(transform=toTensor).add_y().DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
shuffle=True))
self.regist_databundler(train=train_dataloader,
eval=val_dataloader,
test=test_dataloader)
self.to(self.device)
def datasets(self, params: EvalParams):
params.noisy_type = params.default('symmetric', True)
params.noisy_ratio = params.default(0.2, True)
from data.constant import norm_val
mean, std = norm_val.get(params.dataset, [None, None])
from data.transforms import ToNormTensor
toTensor = ToNormTensor(mean, std)
from data.transforms import Weak
weak = Weak(mean, std)
from data.transforms import Strong
dataset_fn = datasets.datasets[params.dataset]
train_x, train_y = dataset_fn(True)
train_y = np.array(train_y)
from thexp import DatasetBuilder
from data.noisy import symmetric_noisy
noisy_y = symmetric_noisy(train_y,
params.noisy_ratio,
n_classes=params.n_classes)
clean_mask = (train_y == noisy_y)
noisy_mask = np.logical_not(clean_mask)
if params.eval_mode in ['full', 'same_epoch', 'same_acc']:
first_x, first_y = train_x[clean_mask], noisy_y[clean_mask]
first_true_y = train_y[clean_mask]
elif params.eval_mode in ['mix', 'raw', 'direct']:
first_x, first_y = train_x, noisy_y
first_true_y = train_y
else:
assert False
second_x, second_true_y = train_x[noisy_mask], train_y[noisy_mask]
second_y = noisy_y[noisy_mask]
self.logger.info('noisy acc = {}'.format(
(first_true_y == first_y).mean()))
self.logger.info('noisy acc = {}'.format(
(second_true_y == second_y).mean()))
self.rnd.shuffle()
first_set = (DatasetBuilder(first_x, first_true_y).add_labels(
first_y, 'noisy_y').toggle_id().add_x(
transform=weak).add_y().add_y(source='noisy_y'))
noisy_set = (DatasetBuilder(second_x, second_true_y).add_labels(
second_y, 'noisy_y').toggle_id().add_x(
transform=weak).add_y().add_y(source='noisy_y'))
first_dataloader = first_set.DataLoader(batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.second_dataloader = noisy_set.DataLoader(
batch_size=params.batch_size,
num_workers=params.num_workers,
drop_last=True,
shuffle=True)
self.second = False
self.regist_databundler(train=first_dataloader)
self.to(self.device)
if params.eval_mode == 'direct':
self.change_dataset()