Source code for

from typing import Iterator, List, Optional, Union
from collections import Counter
import logging
from operator import itemgetter
from random import choices, sample

import numpy as np

import torch
from import DistributedSampler
from import BatchSampler, Sampler

from import DatasetFromSampler
from catalyst.utils.misc import find_value_ids

[docs]class BalanceClassSampler(Sampler): """Allows you to create stratified sample on unbalanced classes. Args: labels: list of class label for each elem in the dataset mode: Strategy to balance classes. Must be one of [downsampling, upsampling] """
[docs] def __init__( self, labels: List[int], mode: Union[str, int] = "downsampling" ): """Sampler initialisation.""" 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_flag = self.samples_per_class > len(self.lbl2idx[key]) indices += np.random.choice( self.lbl2idx[key], self.samples_per_class, replace=replace_flag ).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 BalanceBatchSampler(Sampler): """ This kind of sampler can be used for both metric learning and classification task. Sampler with the given strategy for the C unique classes dataset: - Selection P of C classes for the 1st batch - Selection K instances for each class for the 1st batch - Selection P of C - P remaining classes for 2nd batch - Selection K instances for each class for the 2nd batch - ... The epoch ends when there are no classes left. So, the batch sise is P * K except the last one. Thus, in each epoch, all the classes will be selected once, but this does not mean that all the instances will be selected during the epoch. One of the purposes of this sampler is to be used for forming triplets and pos/neg pairs inside the batch. To guarante existance of these pairs in the batch, P and K should be > 1. (1) Behavior in corner cases: - If a class does not contain K instances, a choice will be made with repetition. - If C % P == 1 then one of the classes should be dropped otherwise statement (1) will not be met. This type of sampling can be found in the classical paper of Person Re-Id, where P equals 32 and K equals 4: `In Defense of the Triplet Loss for Person Re-Identification`_. Args: labels: list of classes labeles for each elem in the dataset p: number of classes in a batch, should be > 1 k: number of instances of each class in a batch, should be > 1 .. _In Defense of the Triplet Loss for Person Re-Identification: """
[docs] def __init__(self, labels: Union[List[int], np.ndarray], p: int, k: int): """Sampler initialisation.""" super().__init__(self) classes = set(labels) assert isinstance(p, int) and isinstance(k, int) assert (1 < p <= len(classes)) and (1 < k) assert all( n > 1 for n in Counter(labels).values() ), "Each class shoud contain at least 2 instances to fit (1)" self._labels = labels self._p = p self._k = k self._batch_size = self._p * self._k self._classes = classes # to satisfy statement (1) num_classes = len(self._classes) if num_classes % self._p == 1: self._num_epoch_classes = num_classes - 1 else: self._num_epoch_classes = num_classes
@property def batch_size(self) -> int: """ Returns: this value should be used in DataLoader as batch size """ return self._batch_size @property def batches_in_epoch(self) -> int: """ Returns: number of batches in an epoch """ return int(np.ceil(self._num_epoch_classes / self._p))
[docs] def __len__(self) -> int: """ Returns: number of samples in an epoch """ return self._num_epoch_classes * self._k
[docs] def __iter__(self) -> Iterator[int]: """ Returns: indeces for sampling dataset elems during an epoch """ inds = [] for cls_id in sample(self._classes, self._num_epoch_classes): all_cls_inds = find_value_ids(self._labels, cls_id) # we've checked in __init__ that this value must be > 1 num_samples_exists = len(all_cls_inds) if num_samples_exists < self._k: selected_inds = sample( all_cls_inds, k=num_samples_exists ) + choices(all_cls_inds, k=self._k - num_samples_exists) else: selected_inds = sample(all_cls_inds, k=self._k) inds.extend(selected_inds) return iter(inds)
[docs]class DynamicBalanceClassSampler(Sampler): """ This kind of sampler can be used for classification tasks with significant class imbalance. The idea of this sampler that we start with the original class distribution and gradually move to uniform class distribution like with downsampling. Let's define :math: D_i = #C_i/ #C_min where :math: #C_i is a size of class i and :math: #C_min is a size of the rarest class, so :math: D_i define class distribution. Also define :math: g(n_epoch) is a exponential scheduler. On each epoch current :math: D_i calculated as :math: current D_i = D_i ^ g(n_epoch), after this data samples according this distribution. Notes: In the end of the training, epochs will contain only min_size_class * n_classes examples. So, possible it will not necessary to do validation on each epoch. For this reason use ControlFlowCallback. Examples: >>> import torch >>> import numpy as np >>> from import DynamicBalanceClassSampler >>> from torch.utils import data >>> features = torch.Tensor(np.random.random((200, 100))) >>> labels = np.random.randint(0, 4, size=(200,)) >>> sampler = DynamicBalanceClassSampler(labels) >>> labels = torch.LongTensor(labels) >>> dataset = data.TensorDataset(features, labels) >>> loader = data.dataloader.DataLoader(dataset, batch_size=8) >>> for batch in loader: >>> b_features, b_labels = batch Sampler was inspired by """
[docs] def __init__( self, labels: List[Union[int, str]], exp_lambda: float = 0.9, start_epoch: int = 0, max_d: Optional[int] = None, mode: Union[str, int] = "downsampling", ignore_warning: bool = False, ): """ Args: labels: list of labels for each elem in the dataset exp_lambda: exponent figure for schedule start_epoch: start epoch number, can be useful for multi-stage experiments max_d: if not None, limit on the difference between the most frequent and the rarest classes, heuristic mode: number of samples per class in the end of training. Must be "downsampling" or number. Before change it, make sure that you understand how does it work ignore_warning: ignore warning about min class size """ assert isinstance(start_epoch, int) assert 0 < exp_lambda < 1, "exp_lambda must be in (0, 1)" super().__init__(labels) self.exp_lambda = exp_lambda if max_d is None: max_d = np.inf self.max_d = max_d self.epoch = start_epoch labels = np.array(labels) samples_per_class = Counter(labels) self.min_class_size = min(samples_per_class.values()) if self.min_class_size < 100 and not ignore_warning: logger = logging.getLogger(__name__) logger.warning( f"the smallest class contains only" f" {self.min_class_size} examples. At the end of" f" training, epochs will contain only" f" {self.min_class_size * len(samples_per_class)}" f" examples" ) self.original_d = { key: value / self.min_class_size for key, value in samples_per_class.items() } self.label2idxes = { label: np.arange(len(labels))[labels == label].tolist() for label in set(labels) } if isinstance(mode, int): self.min_class_size = mode else: assert mode == "downsampling" self.labels = labels self._update()
def _update(self) -> None: """ Update d coefficients Returns: None """ current_d = { key: min(value ** self._exp_scheduler(), self.max_d) for key, value in self.original_d.items() } samples_per_classes = { key: int(value * self.min_class_size) for key, value in current_d.items() } self.samples_per_classes = samples_per_classes self.length = np.sum(list(samples_per_classes.values())) self.epoch += 1 def _exp_scheduler(self) -> float: return self.exp_lambda ** self.epoch
[docs] def __iter__(self) -> Iterator[int]: """ Yields: indices of stratified sample """ indices = [] for key in sorted(self.label2idxes): samples_per_class = self.samples_per_classes[key] replace_flag = samples_per_class > len(self.label2idxes[key]) indices += np.random.choice( self.label2idxes[key], samples_per_class, replace=replace_flag ).tolist() assert len(indices) == self.length np.random.shuffle(indices) self._update() 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``. Args: data_len: Size of the dataset mini_epoch_len: Num samples from the dataset used in one mini epoch. drop_last: If ``True``, sampler will drop the last batches if its size would be less than ``batches_per_epoch`` shuffle: 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 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, ): """Sampler initialisation.""" 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( "Shuffle must be one of ['per_mini_epoch', 'per_epoch']. " + f"Got {shuffle}" ) self.shuffle_type = shuffle
[docs] def shuffle(self) -> None: """Shuffle sampler indices.""" 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]: """Iterate over sampler. Returns: python iterator """ 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`_. Args: sampler: Base sampler. batch_size: Size of minibatch. drop_last: 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 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( for t in tensors) .. _`Dynamic minibatch trimming to improve BERT training speed`: """
[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 = [idx2 for bucket in buckets for idx2 in bucket] for idx3 in leftover: batch.append(idx3) 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 `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", "BalanceBatchSampler", "DistributedSamplerWrapper", "DynamicBalanceClassSampler", "DynamicLenBatchSampler", "MiniEpochSampler", ]