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import numpy as np
from collections import Counter
from torch.utils.data import Subset
from sklearn.model_selection import train_test_split
def __balance_val_split(dataset, val_split=0.):
targets = np.array(dataset.targets)
train_indices, val_indices = train_test_split(
np.arange(targets.shape[0]),
test_size=val_split,
stratify=targets
)
train_dataset = Subset(dataset, indices=train_indices)
val_dataset = Subset(dataset, indices=val_indices)
return train_dataset, val_dataset
def __split_of_train_sequence(subset: Subset, train_split=1.0):
if train_split == 1:
return subset
targets = np.array([subset.dataset.targets[i] for i in subset.indices])
train_indices, _ = train_test_split(
np.arange(targets.shape[0]),
test_size=1 - train_split,
stratify=targets
)
train_dataset = Subset(subset.dataset, indices=[subset.indices[i] for i in train_indices])
return train_dataset
def __log_class_statistics(subset: Subset):
train_classes = [subset.dataset.targets[i] for i in subset.indices]
print(dict(Counter(train_classes)))
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