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Template Struct ConvFuncOptions

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Struct Documentation

template<size_t D>
struct torch::nn::functional::ConvFuncOptions

Options for a D-dimensional convolution functional.

Public Types

using padding_t = torch::nn::detail::conv_padding_t<D>

Public Functions

auto bias(const torch::Tensor &new_bias) -> decltype(*this)

optional bias of shape (out_channels). Default: None

auto bias(torch::Tensor &&new_bias) -> decltype(*this)
const torch::Tensor &bias() const noexcept
torch::Tensor &bias() noexcept
auto stride(const ExpandingArray<D> &new_stride) -> decltype(*this)

The stride of the convolving kernel.

For a D-dim convolution, must be a single number or a list of D numbers.

auto stride(ExpandingArray<D> &&new_stride) -> decltype(*this)
const ExpandingArray<D> &stride() const noexcept
ExpandingArray<D> &stride() noexcept
auto padding(const padding_t &new_padding) -> decltype(*this)

Implicit paddings on both sides of the input.

For a D-dim convolution, must be a single number or a list of D numbers.

auto padding(padding_t &&new_padding) -> decltype(*this)
const padding_t &padding() const noexcept
padding_t &padding() noexcept
decltype(auto) padding(std::initializer_list<int64_t> il)
auto dilation(const ExpandingArray<D> &new_dilation) -> decltype(*this)

The spacing between kernel elements.

For a D-dim convolution, must be a single number or a list of D numbers.

auto dilation(ExpandingArray<D> &&new_dilation) -> decltype(*this)
const ExpandingArray<D> &dilation() const noexcept
ExpandingArray<D> &dilation() noexcept
auto groups(const int64_t &new_groups) -> decltype(*this)

Split input into groups, in_channels should be divisible by the number of groups.

auto groups(int64_t &&new_groups) -> decltype(*this)
const int64_t &groups() const noexcept
int64_t &groups() noexcept

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