from typing import Iterator, List, Optional, Union from collections import Counter import logging from operator import itemgetter import random import numpy as np from torch.utils.data import DistributedSampler from torch.utils.data.sampler import Sampler LOGGER = logging.getLogger(__name__) from torch.utils.data import Dataset, Sampler class DatasetFromSampler(Dataset): """Dataset to create indexes from `Sampler`. Args: sampler: PyTorch sampler """ def __init__(self, sampler: Sampler): """Initialisation for DatasetFromSampler.""" self.sampler = sampler self.sampler_list = None def __getitem__(self, index: int): """Gets element of the dataset. Args: index: index of the element in the dataset Returns: Single element by index """ if self.sampler_list is None: self.sampler_list = list(self.sampler) return self.sampler_list[index] def __len__(self) -> int: """ Returns: int: length of the dataset """ return len(self.sampler) 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] Python API examples: .. code-block:: python import os from torch import nn, optim from torch.utils.data import DataLoader from catalyst import dl from catalyst.data import ToTensor, BalanceClassSampler from catalyst.contrib.datasets import MNIST train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()) train_labels = train_data.targets.cpu().numpy().tolist() train_sampler = BalanceClassSampler(train_labels, mode=5000) valid_data = MNIST(os.getcwd(), train=False) loaders = { "train": DataLoader(train_data, sampler=train_sampler, batch_size=32), "valid": DataLoader(valid_data, batch_size=32), } model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10)) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.02) runner = dl.SupervisedRunner() # model training runner.train( model=model, criterion=criterion, optimizer=optimizer, loaders=loaders, num_epochs=1, logdir="./logs", valid_loader="valid", valid_metric="loss", minimize_valid_metric=True, verbose=True, ) """ 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)) def __iter__(self) -> Iterator[int]: """ Returns: iterator of 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) def __len__(self) -> int: """ Returns: length of result sample """ return self.length class BatchBalanceClassSampler(Sampler): """ This kind of sampler can be used for both metric learning and classification task. BatchSampler with the given strategy for the C unique classes dataset: - Selection `num_classes` of C classes for each batch - Selection `num_samples` instances for each class in the batch The epoch ends after `num_batches`. So, the batch sise is `num_classes` * `num_samples`. 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, `num_classes` and `num_samples` should be > 1. (1) This type of sampling can be found in the classical paper of Person Re-Id, where P (`num_classes`) equals 32 and K (`num_samples`) equals 4: `In Defense of the Triplet Loss for Person Re-Identification`_. Args: labels: list of classes labeles for each elem in the dataset num_classes: number of classes in a batch, should be > 1 num_samples: number of instances of each class in a batch, should be > 1 num_batches: number of batches in epoch (default = len(labels) // (num_classes * num_samples)) .. _In Defense of the Triplet Loss for Person Re-Identification: https://arxiv.org/abs/1703.07737 Python API examples: .. code-block:: python import os from torch import nn, optim from torch.utils.data import DataLoader from catalyst import dl from catalyst.data import ToTensor, BatchBalanceClassSampler from catalyst.contrib.datasets import MNIST train_data = MNIST(os.getcwd(), train=True, download=True) train_labels = train_data.targets.cpu().numpy().tolist() train_sampler = BatchBalanceClassSampler( train_labels, num_classes=10, num_samples=4) valid_data = MNIST(os.getcwd(), train=False) loaders = { "train": DataLoader(train_data, batch_sampler=train_sampler), "valid": DataLoader(valid_data, batch_size=32), } model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10)) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.02) runner = dl.SupervisedRunner() # model training runner.train( model=model, criterion=criterion, optimizer=optimizer, loaders=loaders, num_epochs=1, logdir="./logs", valid_loader="valid", valid_metric="loss", minimize_valid_metric=True, verbose=True, ) """ def __init__( self, labels: Union[List[int], np.ndarray], num_classes: int, num_samples: int, num_batches: int = None, ): """Sampler initialisation.""" super().__init__(labels) classes = set(labels) assert isinstance(num_classes, int) and isinstance(num_samples, int) assert (1 < num_classes <= len(classes)) and (1 < num_samples) assert all( n > 1 for n in Counter(labels).values() ), "Each class shoud contain at least 2 instances to fit (1)" labels = np.array(labels) self._labels = list(set(labels.tolist())) self._num_classes = num_classes self._num_samples = num_samples self._batch_size = self._num_classes * self._num_samples self._num_batches = num_batches or len(labels) // self._batch_size self.lbl2idx = { label: np.arange(len(labels))[labels == label].tolist() for label in set(labels) } @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 self._num_batches def __len__(self) -> int: """ Returns: number of samples in an epoch """ return self._num_batches # * self._batch_size def __iter__(self) -> Iterator[int]: """ Returns: indeces for sampling dataset elems during an epoch """ indices = [] for _ in range(self._num_batches): batch_indices = [] classes_for_batch = random.sample(self._labels, self._num_classes) while self._num_classes != len(set(classes_for_batch)): classes_for_batch = random.sample(self._labels, self._num_classes) for cls_id in classes_for_batch: replace_flag = self._num_samples > len(self.lbl2idx[cls_id]) batch_indices += np.random.choice( self.lbl2idx[cls_id], self._num_samples, replace=replace_flag ).tolist() indices.append(batch_indices) return iter(indices) 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 catalyst.data 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 https://arxiv.org/abs/1901.06783 """ 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.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.""" 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 def __iter__(self) -> Iterator[int]: """ Returns: iterator of 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) 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``. 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") """ 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 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 ) 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) def __len__(self) -> int: """ Returns: int: length of the mini-epoch """ return self.mini_epoch_len 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. """ 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) -> Iterator[int]: """Iterate over sampler. Returns: python iterator """ self.dataset = DatasetFromSampler(self.sampler) indexes_of_indexes = super().__iter__() subsampler_indexes = self.dataset return iter(itemgetter(*indexes_of_indexes)(subsampler_indexes)) __all__ = [ "BalanceClassSampler", "BatchBalanceClassSampler", "DistributedSamplerWrapper", "DynamicBalanceClassSampler", "MiniEpochSampler", ]