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"""This is a slightly modified version of https://github.com/ufoym/imbalanced-dataset-sampler."""
from typing import Callable, List, Optional
import pandas as pd
import torch
import torch.utils.data
class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):
"""Samples elements randomly from a given list of indices for imbalanced dataset.
Arguments:
indices: a list of indices
num_samples: number of samples to draw
callback_get_label: a callback-like function which takes one argument - the dataset
"""
def __init__(
self,
dataset,
labels: Optional[List] = None,
indices: Optional[List] = None,
num_samples: Optional[int] = None,
callback_get_label: Optional[Callable] = None,
):
# if indices is not provided, all elements in the dataset will be considered
self.indices = list(range(len(dataset))) if indices is None else indices
# define custom callback
self.callback_get_label = callback_get_label
# if num_samples is not provided, draw `len(indices)` samples in each iteration
self.num_samples = len(self.indices) if num_samples is None else num_samples
# distribution of classes in the dataset
df = pd.DataFrame()
df["label"] = self._get_labels(dataset) if labels is None else labels
df.index = self.indices
df = df.sort_index()
label_to_count = df["label"].value_counts()
weights = 1.0 / label_to_count[df["label"]]
self.weights = torch.DoubleTensor(weights.to_list())
def _get_labels(self, dataset):
if self.callback_get_label:
return self.callback_get_label(dataset)
elif isinstance(dataset, torch.utils.data.TensorDataset):
return dataset.tensors[1]
elif isinstance(dataset, torch.utils.data.Subset):
return dataset.dataset.imgs[:][1]
elif isinstance(dataset, torch.utils.data.Dataset):
return dataset.get_labels()
else:
raise NotImplementedError
def __iter__(self):
return (
self.indices[i]
for i in torch.multinomial(self.weights, self.num_samples, replacement=True)
)
def __len__(self):
return self.num_samples
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