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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


[docs]class BalanceClassSampler(Sampler): """Abstraction over data sampler. Allows you to create stratified sample on unbalanced classes. """
[docs] 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))
[docs] 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)
[docs] def __len__(self) -> int: """ Returns: length of result sample """ return self.length
[docs]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") """
[docs] 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
[docs] 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 )
[docs] 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)
[docs] def __len__(self) -> int: """ Returns: int: length of the mini-epoch """ return self.mini_epoch_len
[docs]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) """
[docs] 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) )
[docs]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. """
[docs] 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
[docs] 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", ]