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Transforms

Transforms apply preprocessing to data samples, such as normalization or augmentation. They can be chained using the .map() method on datasets.

Transform (Base Class)

The base class for all transforms. Subclass this to create custom transforms.

template<typename Input, typename Output> class Transform : public torch::data::transforms::BatchTransform<std::vector, std::vector>

A transformation of individual input examples to individual output examples.

Just like a Dataset is a BatchDataset, a Transform is a BatchTransform that can operate on the level of individual examples rather than entire batches. The batch-level transform is implemented (by default) in terms of the example-level transform, though this can be customized.

Public Types

using InputType = Input

using OutputType = Output

Public Functions

virtual OutputType apply(InputType input) = 0

Applies the transformation to the given input.

inline virtual std::vector apply_batch(std::vector input_batch) override

Applies the transformation over the entire input_batch.

BatchTransform (Base Class)

Base class for transforms that operate on entire batches.

template<typename InputBatch, typename OutputBatch> class BatchTransform

A transformation of a batch to a new batch.

Subclassed by torch::data::transforms::Transform< Example< Tensor, Tensor >, Example< Tensor, Tensor > >, torch::data::transforms::Transform< Input, Input >, torch::data::transforms::Stack< Example<> >, torch::data::transforms::Stack< TensorExample >

Public Types

using InputBatchType = InputBatch

using OutputBatchType = OutputBatch

Public Functions

virtual ~BatchTransform() = default

virtual OutputBatch apply_batch(InputBatch input_batch) = 0

Applies the transformation to the given input_batch.

TensorTransform

Base class for transforms that operate on tensors specifically.

template class TensorTransform : public torch::data::transforms::Transform<Example<Tensor, Tensor>, Example<Tensor, Tensor>>

A Transform that is specialized for the typical [Example](datasets.html#PyTorchstructtorch_1_1data_1_1_example)<Tensor, Tensor> combination.

It exposes a single operator() interface hook (for subclasses), and calls this function on input [Example](datasets.html#PyTorchstructtorch_1_1data_1_1_example) objects.

Public Types

using E = Example<Tensor, Target>

Public Functions

virtual Tensor operator()(Tensor input) = 0

Transforms a single input tensor to an output tensor.

inline virtual OutputType apply(InputType input) override

Implementation of Transform::apply that calls operator().

Normalize

Normalizes tensors with a given mean and standard deviation.

template struct Normalize : public torch::data::transforms::TensorTransform<Tensor>

Normalizes input tensors by subtracting the supplied mean and dividing by the given standard deviation.

Public Functions

inline Normalize(ArrayRef mean, ArrayRef stddev)

Constructs a Normalize transform.

The mean and standard deviation can be anything that is broadcastable over the input tensors (like single scalars).

inline virtual torch::Tensor operator()(Tensor input) override

Transforms a single input tensor to an output tensor.

Public Members

torch::Tensor mean

torch::Tensor stddev

Stack

Stacks a batch of tensors into a single tensor.

template<typename T = Example<>> struct Stack

Example:

auto dataset = torch::data::datasets::MNIST("./data")
 .map(torch::data::transforms::Normalize<>(0.5, 0.5))
 .map(torch::data::transforms::Stack<>());

Lambda

template<typename Input, typename Output = Input> class Lambda : public torch::data::transforms::Transform<Input, Input>

Public Types

using FunctionType = std::function<Output(Input)>

Public Functions

inline explicit Lambda(FunctionType function)

Constructs the Lambda from the given function object.

inline virtual OutputType apply(InputType input) override

Applies the user-provided function object to the input.

TensorLambda

template class TensorLambda : public torch::data::transforms::TensorTransform<Tensor>

A Lambda specialized for the typical [Example](datasets.html#PyTorchstructtorch_1_1data_1_1_example)<Tensor, Tensor> input type.

Public Types

using FunctionType = std::function<Tensor(Tensor)>

Public Functions

inline explicit TensorLambda(FunctionType function)

Creates a TensorLambda from the given function.

inline virtual Tensor operator()(Tensor input) override

Applies the user-provided functor to the input tensor.

BatchLambda

template<typename Input, typename Output = Input> class BatchLambda : public torch::data::transforms::BatchTransform<Input, Input>

A BatchTransform that applies a user-provided functor to a batch.

Public Types

using FunctionType = std::function<OutputBatchType(InputBatchType)>

Public Functions

inline explicit BatchLambda(FunctionType function)

Constructs the BatchLambda from the given function object.

inline virtual OutputBatchType apply_batch(InputBatchType input_batch) override

Applies the user-provided function object to the input_batch.

Chaining Transforms

Transforms can be chained together using .map():

auto dataset = torch::data::datasets::MNIST("./data")
 .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
 .map(torch::data::transforms::Stack<>());