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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))) | |