Convolution Layers

Convolutional layers apply learnable filters to input data, extracting local features through sliding window operations. They are fundamental to CNNs for image, audio, and sequential data processing.

• Conv1d/2d/3d: Standard convolution for 1D sequences, 2D images, or 3D volumes

• ConvTranspose1d/2d/3d: Transposed convolution (deconvolution) for upsampling

Key parameters:

• in_channels: Number of input channels

• out_channels: Number of output channels (number of filters)

• kernel_size: Size of the convolving kernel

• stride: Stride of the convolution (default: 1)

• padding: Zero-padding added to input (default: 0)

• dilation: Spacing between kernel elements (default: 1)

• groups: Number of blocked connections (default: 1, use in_channels for depthwise)

Conv1d

Applies 1D convolution over an input signal composed of several input planes.

class Conv1d : public torch::nn::ModuleHolder<Conv1dImpl>

A ModuleHolder subclass for Conv1dImpl.

See the documentation for Conv1dImpl class to learn what methods it provides, and examples of how to use Conv1d with torch::nn::Conv1dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = Conv1dImpl

class Conv1dImpl : public torch::nn::ConvNdImpl<1, Conv1dImpl>

Applies convolution over a 1-D input.

See https://pytorch.org/docs/main/nn.html#torch.nn.Conv1d to learn about the exact behavior of this module.

See the documentation for torch::nn::Conv1dOptions class to learn what constructor arguments are supported for this module.

Example:

Conv1d model(Conv1dOptions(3, 2, 3).stride(1).bias(false));

Public Functions

inline Conv1dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<1> kernel_size)

explicit Conv1dImpl(Conv1dOptions options_)

Tensor forward(const Tensor &input)

Conv2d

Applies 2D convolution over an input image. The most commonly used layer for image processing tasks.

class Conv2d : public torch::nn::ModuleHolder<Conv2dImpl>

A ModuleHolder subclass for Conv2dImpl.

See the documentation for Conv2dImpl class to learn what methods it provides, and examples of how to use Conv2d with torch::nn::Conv2dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = Conv2dImpl

class Conv2dImpl : public torch::nn::ConvNdImpl<2, Conv2dImpl>

Applies convolution over a 2-D input.

See https://pytorch.org/docs/main/nn.html#torch.nn.Conv2d to learn about the exact behavior of this module.

See the documentation for torch::nn::Conv2dOptions class to learn what constructor arguments are supported for this module.

Example:

Conv2d model(Conv2dOptions(3, 2, 3).stride(1).bias(false));

Public Functions

inline Conv2dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<2> kernel_size)

explicit Conv2dImpl(Conv2dOptions options_)

Tensor forward(const Tensor &input)

Example:

// Create Conv2d: 3 input channels, 64 output channels, 3x3 kernel
auto conv = torch::nn::Conv2d(
    torch::nn::Conv2dOptions(3, 64, 3)
        .stride(1)
        .padding(1)
        .bias(true));

auto output = conv->forward(input);  // input: [N, 3, H, W]

Conv3d

Applies 3D convolution over an input volume (e.g., video frames or 3D medical images).

class Conv3d : public torch::nn::ModuleHolder<Conv3dImpl>

A ModuleHolder subclass for Conv3dImpl.

See the documentation for Conv3dImpl class to learn what methods it provides, and examples of how to use Conv3d with torch::nn::Conv3dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = Conv3dImpl

class Conv3dImpl : public torch::nn::ConvNdImpl<3, Conv3dImpl>

Applies convolution over a 3-D input.

See https://pytorch.org/docs/main/nn.html#torch.nn.Conv3d to learn about the exact behavior of this module.

See the documentation for torch::nn::Conv3dOptions class to learn what constructor arguments are supported for this module.

Example:

Conv3d model(Conv3dOptions(3, 2, 3).stride(1).bias(false));

Public Functions

inline Conv3dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<3> kernel_size)

explicit Conv3dImpl(Conv3dOptions options_)

Tensor forward(const Tensor &input)

ConvTranspose1d

Applies 1D transposed convolution (fractionally-strided convolution) for upsampling.

class ConvTranspose1d : public torch::nn::ModuleHolder<ConvTranspose1dImpl>

A ModuleHolder subclass for ConvTranspose1dImpl.

See the documentation for ConvTranspose1dImpl class to learn what methods it provides, and examples of how to use ConvTranspose1d with torch::nn::ConvTranspose1dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = ConvTranspose1dImpl

class ConvTranspose1dImpl : public torch::nn::ConvTransposeNdImpl<1, ConvTranspose1dImpl>

Applies the ConvTranspose1d function.

See https://pytorch.org/docs/main/nn.html#torch.nn.ConvTranspose1d to learn about the exact behavior of this module.

See the documentation for torch::nn::ConvTranspose1dOptions class to learn what constructor arguments are supported for this module.

Example:

ConvTranspose1d model(ConvTranspose1dOptions(3, 2,
3).stride(1).bias(false));

Public Functions

inline ConvTranspose1dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<1> kernel_size)

explicit ConvTranspose1dImpl(ConvTranspose1dOptions options_)

Tensor forward(const Tensor &input, const std::optional<at::IntArrayRef> &output_size = std::nullopt)

Friends

friend struct torch::nn::AnyModuleHolder

ConvTranspose2d

Applies 2D transposed convolution for upsampling. Commonly used in decoder networks and generative models.

class ConvTranspose2d : public torch::nn::ModuleHolder<ConvTranspose2dImpl>

A ModuleHolder subclass for ConvTranspose2dImpl.

See the documentation for ConvTranspose2dImpl class to learn what methods it provides, and examples of how to use ConvTranspose2d with torch::nn::ConvTranspose2dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = ConvTranspose2dImpl

class ConvTranspose2dImpl : public torch::nn::ConvTransposeNdImpl<2, ConvTranspose2dImpl>

Applies the ConvTranspose2d function.

See https://pytorch.org/docs/main/nn.html#torch.nn.ConvTranspose2d to learn about the exact behavior of this module.

See the documentation for torch::nn::ConvTranspose2dOptions class to learn what constructor arguments are supported for this module.

Example:

ConvTranspose2d model(ConvTranspose2dOptions(3, 2,
3).stride(1).bias(false));

Public Functions

inline ConvTranspose2dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<2> kernel_size)

explicit ConvTranspose2dImpl(ConvTranspose2dOptions options_)

Tensor forward(const Tensor &input, const std::optional<at::IntArrayRef> &output_size = std::nullopt)

Friends

friend struct torch::nn::AnyModuleHolder

Example:

// Create ConvTranspose2d for upsampling
auto conv_transpose = torch::nn::ConvTranspose2d(
    torch::nn::ConvTranspose2dOptions(64, 32, 4)
        .stride(2)
        .padding(1));

ConvTranspose3d

Applies 3D transposed convolution for upsampling volumetric data.

class ConvTranspose3d : public torch::nn::ModuleHolder<ConvTranspose3dImpl>

A ModuleHolder subclass for ConvTranspose3dImpl.

See the documentation for ConvTranspose3dImpl class to learn what methods it provides, and examples of how to use ConvTranspose3d with torch::nn::ConvTranspose3dOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = ConvTranspose3dImpl

class ConvTranspose3dImpl : public torch::nn::ConvTransposeNdImpl<3, ConvTranspose3dImpl>

Applies the ConvTranspose3d function.

See https://pytorch.org/docs/main/nn.html#torch.nn.ConvTranspose3d to learn about the exact behavior of this module.

See the documentation for torch::nn::ConvTranspose3dOptions class to learn what constructor arguments are supported for this module.

Example:

ConvTranspose3d model(ConvTranspose3dOptions(2, 2,
2).stride(1).bias(false));

Public Functions

inline ConvTranspose3dImpl(int64_t input_channels, int64_t output_channels, ExpandingArray<3> kernel_size)

explicit ConvTranspose3dImpl(ConvTranspose3dOptions options_)

Tensor forward(const Tensor &input, const std::optional<at::IntArrayRef> &output_size = std::nullopt)

Friends

friend struct torch::nn::AnyModuleHolder