Source code for catalyst.data.sampler

from typing import Iterator, List, Optional
from operator import itemgetter

import numpy as np

import torch
from torch.utils.data import DistributedSampler
from torch.utils.data.sampler import BatchSampler, Sampler

from catalyst.data import DatasetFromSampler


class BalanceClassSampler(Sampler):
    """Abstraction over data sampler.

    Allows you to create stratified sample on unbalanced classes.
    """

    def __init__(self, labels: List[int], mode: str = "downsampling"):
        """
        Args:
            labels (List[int]): list of class label
                for each elem in the dataset
            mode (str): Strategy to balance classes.
                Must be one of [downsampling, upsampling]
        """
        super().__init__(labels)

        labels = np.array(labels)
        samples_per_class = {
            label: (labels == label).sum() for label in set(labels)
        }

        self.lbl2idx = {
            label: np.arange(len(labels))[labels == label].tolist()
            for label in set(labels)
        }

        if isinstance(mode, str):
            assert mode in ["downsampling", "upsampling"]

        if isinstance(mode, int) or mode == "upsampling":
            samples_per_class = (
                mode
                if isinstance(mode, int)
                else max(samples_per_class.values())
            )
        else:
            samples_per_class = min(samples_per_class.values())

        self.labels = labels
        self.samples_per_class = samples_per_class
        self.length = self.samples_per_class * len(set(labels))

    def __iter__(self) -> Iterator[int]:
        """
        Yields:
            indices of stratified sample
        """
        indices = []
        for key in sorted(self.lbl2idx):
            replace_ = self.samples_per_class > len(self.lbl2idx[key])
            indices += np.random.choice(
                self.lbl2idx[key], self.samples_per_class, replace=replace_
            ).tolist()
        assert len(indices) == self.length
        np.random.shuffle(indices)

        return iter(indices)

    def __len__(self) -> int:
        """
        Returns:
             length of result sample
        """
        return self.length


class MiniEpochSampler(Sampler):
    """
    Sampler iterates mini epochs from the dataset used by ``mini_epoch_len``.

    Example:
        >>> MiniEpochSampler(len(dataset), mini_epoch_len=100)
        >>> MiniEpochSampler(len(dataset), mini_epoch_len=100,
        >>>     drop_last=True)
        >>> MiniEpochSampler(len(dataset), mini_epoch_len=100,
        >>>     shuffle="per_epoch")
    """

    def __init__(
        self,
        data_len: int,
        mini_epoch_len: int,
        drop_last: bool = False,
        shuffle: str = None,
    ):
        """
        Args:
            data_len (int): Size of the dataset
            mini_epoch_len (int): Num samples from the dataset used in one
                mini epoch.
            drop_last (bool): If ``True``, sampler will drop the last batches
                if its size would be less than ``batches_per_epoch``
            shuffle (str): one of  ``"always"``, ``"real_epoch"``, or `None``.
                The sampler will shuffle indices
                > "per_mini_epoch" - every mini epoch (every ``__iter__`` call)
                > "per_epoch" -- every real epoch
                > None -- don't shuffle
        """
        super().__init__(None)

        self.data_len = int(data_len)
        self.mini_epoch_len = int(mini_epoch_len)

        self.steps = int(data_len / self.mini_epoch_len)
        self.state_i = 0

        has_reminder = data_len - self.steps * mini_epoch_len > 0
        if self.steps == 0:
            self.divider = 1
        elif has_reminder and not drop_last:
            self.divider = self.steps + 1
        else:
            self.divider = self.steps

        self._indices = np.arange(self.data_len)
        self.indices = self._indices
        self.end_pointer = max(self.data_len, self.mini_epoch_len)

        if not (shuffle is None or shuffle in ["per_mini_epoch", "per_epoch"]):
            raise ValueError(
                f"Shuffle must be one of ['per_mini_epoch', 'per_epoch']. "
                f"Got {shuffle}"
            )
        self.shuffle_type = shuffle

    def shuffle(self) -> None:
        """@TODO: Docs. Contribution is welcome."""
        if self.shuffle_type == "per_mini_epoch" or (
            self.shuffle_type == "per_epoch" and self.state_i == 0
        ):
            if self.data_len >= self.mini_epoch_len:
                self.indices = self._indices
                np.random.shuffle(self.indices)
            else:
                self.indices = np.random.choice(
                    self._indices, self.mini_epoch_len, replace=True
                )

    def __iter__(self) -> Iterator[int]:
        """@TODO: Docs. Contribution is welcome."""
        self.state_i = self.state_i % self.divider
        self.shuffle()

        start = self.state_i * self.mini_epoch_len
        stop = (
            self.end_pointer
            if (self.state_i == self.steps)
            else (self.state_i + 1) * self.mini_epoch_len
        )
        indices = self.indices[start:stop].tolist()

        self.state_i += 1
        return iter(indices)

    def __len__(self) -> int:
        """
        Returns:
            int: length of the mini-epoch
        """
        return self.mini_epoch_len


class DynamicLenBatchSampler(BatchSampler):
    """
    A dynamic batch length data sampler.
    Should be used with `catalyst.utils.trim_tensors`.

    Adapted from "Dynamic minibatch trimming to improve BERT training speed"
    https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/94779

    Args:
        sampler (torch.utils.data.Sampler): Base sampler.
        batch_size (int): Size of minibatch.
        drop_last (bool): If ``True``, the sampler will drop the last batch
        if its size would be less than ``batch_size``.

    Usage example:

        >>> from torch.utils import data
        >>> from catalyst.data import DynamicLenBatchSampler
        >>> from catalyst import utils

        >>> dataset = data.TensorDataset(
        >>>     input_ids, input_mask, segment_ids, labels
        >>> )

        >>> sampler_ = data.RandomSampler(dataset)
        >>> sampler = DynamicLenBatchSampler(
        >>>     sampler_, batch_size=16, drop_last=False
        >>> )
        >>> loader = data.DataLoader(dataset, batch_sampler=sampler)

        >>> for batch in loader:
        >>>     tensors = utils.trim_tensors(batch)
        >>>     b_input_ids, b_input_mask, b_segment_ids, b_labels = \
        >>>         tuple(t.to(device) for t in tensors)
    """

    def __iter__(self):
        """
        Iteration over BatchSampler.
        """
        buckets = [[]] * 100
        yielded = 0

        for idx in self.sampler:
            count_zeros = torch.sum(self.sampler.data_source[idx][0] == 0)
            count_zeros = int(count_zeros / 64)
            if len(buckets[count_zeros]) == 0:
                buckets[count_zeros] = []

            buckets[count_zeros].append(idx)

            if len(buckets[count_zeros]) == self.batch_size:
                batch = list(buckets[count_zeros])
                yield batch
                yielded += 1
                buckets[count_zeros] = []

        batch = []
        leftover = [idx for bucket in buckets for idx in bucket]

        for idx in leftover:
            batch.append(idx)
            if len(batch) == self.batch_size:
                yielded += 1
                yield batch
                batch = []

        if len(batch) > 0 and not self.drop_last:
            yielded += 1
            yield batch

        assert len(self) == yielded, (
            "produced an inccorect number of batches. "
            "expected %i, but yielded %i" % (len(self), yielded)
        )


class DistributedSamplerWrapper(DistributedSampler):
    """
    Wrapper over `Sampler` for distributed training.
    Allows you to use any sampler in distributed mode.

    It is especially useful in conjunction with
    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
    process can pass a DistributedSamplerWrapper instance as a DataLoader
    sampler, and load a subset of subsampled data of the original dataset
    that is exclusive to it.

    .. note::
        Sampler is assumed to be of constant size.
    """

    def __init__(
        self,
        sampler,
        num_replicas: Optional[int] = None,
        rank: Optional[int] = None,
        shuffle: bool = True,
    ):
        """
        Args:
            sampler: Sampler used for subsampling
            num_replicas (int, optional): Number of processes participating in
                distributed training
            rank (int, optional): Rank of the current process
                within ``num_replicas``
            shuffle (bool, optional): If true (default),
                sampler will shuffle the indices
        """
        super(DistributedSamplerWrapper, self).__init__(
            DatasetFromSampler(sampler),
            num_replicas=num_replicas,
            rank=rank,
            shuffle=shuffle,
        )
        self.sampler = sampler

    def __iter__(self):
        """@TODO: Docs. Contribution is welcome."""
        self.dataset = DatasetFromSampler(self.sampler)
        indexes_of_indexes = super().__iter__()
        subsampler_indexes = self.dataset
        return iter(itemgetter(*indexes_of_indexes)(subsampler_indexes))


__all__ = [
    "BalanceClassSampler",
    "MiniEpochSampler",
    "DistributedSamplerWrapper",
    "DynamicLenBatchSampler",
]