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Data

Data subpackage has data preprocessers and dataloader abstractions.

Dataset

DatasetFromSampler

class catalyst.data.dataset.DatasetFromSampler(sampler: torch.utils.data.sampler.Sampler)[source]

Bases: torch.utils.data.dataset.Dataset

Dataset to create indexes from Sampler.

Parameters

sampler – PyTorch sampler

SelfSupervisedDatasetWrapper

class catalyst.data.dataset.SelfSupervisedDatasetWrapper(dataset: torch.utils.data.dataset.Dataset, transforms: Callable = None, transform_left: Callable = None, transform_right: Callable = None, transform_original: Callable = None, is_target: bool = True)[source]

Bases: torch.utils.data.dataset.Dataset

The Self Supervised Dataset.

The class implemets contrastive logic (see Figure 2 from A Simple Framework for Contrastive Learning of Visual Representations.)

Parameters

  • dataset – original dataset for augmentation

  • transforms – transforms which will be applied to original batch to get both left and right output batch.

  • transform_left – transform only for left batch

  • transform_right – transform only for right batch

  • transform_original – transforms which will be applied to save original in batch

  • is_target – the flag for selection does dataset return (sample, target) or only sample

Example:

import torchvision
from torchvision.datasets import CIFAR10

from catalyst.data.dataset import SelfSupervisedDatasetWrapper

transforms = torchvision.transforms.Compose(
    [
        torchvision.transforms.RandomResizedCrop(32),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(
            [0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]
        ),
        torchvision.transforms.ColorJitter(0.8, 0.8, 0.8, 0.2),
    ]
)

cifar_dataset = CIFAR10(root="./data", download=True, transform=None)
cifar_contrastive = SelfSupervisedDatasetWrapper(
    cifar_dataset,
    transforms=transforms
)

for transformed_sample, aug_1, aug_2 in cifar_contrastive:
    pass

Loader

BatchLimitLoaderWrapper

class catalyst.data.loader.BatchLimitLoaderWrapper(loader: torch.utils.data.dataloader.DataLoader, num_batches: Union[int, float])[source]

Loader wrapper. Limits number of batches used per each iteration.

For example, if you have some loader and want to use only first 5 bathes:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.data.loader import BatchLimitLoaderWrapper

num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loader = BatchLimitLoaderWrapper(loader, num_batches=5)

or if you would like to use only some portion of Dataloader (we use 30% in the example below):

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.data.loader import BatchLimitLoaderWrapper

num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loader = BatchLimitLoaderWrapper(loader, num_batches=0.3)

Note

Generally speaking, this wrapper could be used with any iterator-like object. No DataLoader-specific code used.

BatchPrefetchLoaderWrapper

class catalyst.data.loader.BatchPrefetchLoaderWrapper(loader: torch.utils.data.dataloader.DataLoader, num_prefetches: int = None)[source]

Loader wrapper. Prefetches specified number of batches on the GPU.

Base usage:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.data import BatchPrefetchLoaderWrapper

num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loader = BatchPrefetchLoaderWrapper(loader)

Minimal working example:

import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from catalyst import dl, metrics
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.data import BatchPrefetchLoaderWrapper

class CustomRunner(dl.Runner):

    def handle_batch(self, batch):
        # model train/valid step
        x, y = batch
        y_hat = self.model(x.view(x.size(0), -1))

        loss = F.cross_entropy(y_hat, y)
        accuracy01 = metrics.accuracy(y_hat, y, topk=(1, ))
        self.batch_metrics.update(
            {"loss": loss, "accuracy01": accuracy01}
        )

        if self.is_train_loader:
            self.engine.backward(loss)
            self.optimizer.step()
            self.optimizer.zero_grad()

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

batch_size=32
loaders = {
    "train": DataLoader(
        MNIST(
            os.getcwd(),
            train=True,
            download=True,
            transform=ToTensor()
        ),
        batch_size=batch_size),
    "valid": DataLoader(
        MNIST(
            os.getcwd(),
            train=False,
            download=True,
            transform=ToTensor()
        ),
        batch_size=batch_size),
}
loaders = {
    k: BatchPrefetchLoaderWrapper(v) for k, v in loaders.items()
}

runner = CustomRunner()
# model training
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logs",
    num_epochs=5,
    verbose=True,
    load_best_on_end=True,
)

Samplers

BalanceClassSampler

class catalyst.data.sampler.BalanceClassSampler(labels: List[int], mode: Union[str, int] = 'downsampling')[source]

Allows you to create stratified sample on unbalanced classes.

Parameters

  • 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:

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,
)

BatchBalanceClassSampler

class catalyst.data.sampler.BatchBalanceClassSampler(labels: Union[List[int], numpy.ndarray], num_classes: int, num_samples: int, num_batches: int = None)[source]

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.

Parameters

  • 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))

Python API examples:

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,
)

DistributedSamplerWrapper

class catalyst.data.sampler.DistributedSamplerWrapper(sampler, num_replicas: Optional[int] = None, rank: Optional[int] = None, shuffle: bool = True)[source]

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.

DynamicBalanceClassSampler

class catalyst.data.sampler.DynamicBalanceClassSampler(labels: List[Union[str, int]], exp_lambda: float = 0.9, start_epoch: int = 0, max_d: Optional[int] = None, mode: Union[str, int] = 'downsampling', ignore_warning: bool = False)[source]

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

MiniEpochSampler

class catalyst.data.sampler.MiniEpochSampler(data_len: int, mini_epoch_len: int, drop_last: bool = False, shuffle: str = None)[source]

Sampler iterates mini epochs from the dataset used by mini_epoch_len.

Parameters

  • 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")