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       "name": "Utilities",
       "headline": "Utilities",
       "description": "Neural network utilities in PyTorch C++ \u2014 parameter initialization, module cloning, padding layers, and vision utilities.",
       "url": "/api/nn/utilities.html",
       "articleBody": "Utilities# Additional utilities for building neural networks: parameter initialization, module cloning, type-erased containers, padding layers, and vision utilities. Parameter Initialization# The torch::nn::init namespace provides functions for initializing module parameters: #include \u003ctorch/nn/init.h\u003e // Xavier/Glorot initialization torch::nn::init::xavier_uniform_(linear-\u003eweight); torch::nn::init::xavier_normal_(linear-\u003eweight); // Kaiming/He initialization torch::nn::init::kaiming_uniform_(conv-\u003eweight, /*a=*/0, torch::kFanIn, torch::kReLU); torch::nn::init::kaiming_normal_(conv-\u003eweight); // Other initializations torch::nn::init::zeros_(linear-\u003ebias); torch::nn::init::ones_(bn-\u003eweight); torch::nn::init::constant_(linear-\u003ebias, 0.1); torch::nn::init::normal_(linear-\u003eweight, /*mean=*/0, /*std=*/0.01); torch::nn::init::uniform_(linear-\u003eweight, /*a=*/-0.1, /*b=*/0.1); torch::nn::init::orthogonal_(rnn-\u003eweight_hh); Cloneable# template\u003ctypename Derived\u003eclass Cloneable : public torch::nn::Module# The clone() method in the base Module class does not have knowledge of the concrete runtime type of its subclasses. Therefore, clone() must either be called from within the subclass, or from a base class that has knowledge of the concrete type. Cloneable uses the CRTP to gain knowledge of the subclass\u2019 static type and provide an implementation of the clone() method. We do not want to use this pattern in the base class, because then storing a module would always require templatizing it. Public Functions virtual void reset() = 0# reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules. inline virtual std::shared_ptr\u003cModule\u003e clone(const std::optional\u003cDevice\u003e \u0026device = std::nullopt) const override# Performs a recursive \u201cdeep copy\u201d of the Module, such that all parameters and submodules in the cloned module are different from those in the original module. explicit Module(std::string name)# Tells the base Module about the name of the submodule. Module()# Constructs the module without immediate knowledge of the submodule\u2019s name. The name of the submodule is inferred via RTTI (if possible) the first time .name() is invoked. Module(const Module\u0026) = default# Module(Module\u0026\u0026) noexcept = default# All torch::nn modules inherit from Cloneable, enabling deep copies: auto model = torch::nn::Linear(10, 5); auto model_copy = std::dynamic_pointer_cast\u003ctorch::nn::LinearImpl\u003e(model-\u003eclone()); AnyModule# AnyModule provides type-erased storage for any module, allowing you to store heterogeneous modules in a single container. class AnyModule# Stores a type erased Module. The PyTorch C++ API does not impose an interface on the signature of forward() in Module subclasses. This gives you complete freedom to design your forward() methods to your liking. However, this also means there is no unified base type you could store in order to call forward() polymorphically for any module. This is where the AnyModule comes in. Instead of inheritance, it relies on type erasure for polymorphism. An AnyModule can store any nn::Module subclass that provides a forward() method. This forward() may accept any types and return any type. Once stored in an AnyModule, you can invoke the underlying module\u2019s forward() by calling AnyModule::forward() with the arguments you would supply to the stored module (though see one important limitation below). Example: .. code-block:: cpp struct GenericTrainer { torch::nn::AnyModule module; void train(torch::Tensor input) { module.forward(input); } }; GenericTrainer trainer1{torch::nn::Linear(3, 4)}; GenericTrainer trainer2{torch::nn::Conv2d(3, 4, 2)}; As AnyModule erases the static type of the stored module (and its forward() method) to achieve polymorphism, type checking of arguments is moved to runtime. That is, passing an argument with an incorrect type to an AnyModule will compile, but throw an exception at runtime: .. code-block:: cpp torch::nn::AnyModule module(torch::nn::Linear(3, 4)); // Linear takes a tensor as input, but we are passing an integer. // This will compile, but throw a torch::Error exception at runtime. module.forward(123); .. attention:: One noteworthy limitation of AnyModule is that its forward() method does not support implicit conversion of argument types. For example, if the stored module\u2019s forward() method accepts a float and you call any_module.forward(3.4) (where 3.4 is a double), this will throw an exception. The return type of the AnyModule\u2019s forward() method is controlled via the first template argument to AnyModule::forward(). It defaults to torch::Tensor. To change it, you can write any_module.forward\u003cint\u003e(), for example. .. code-block:: cpp torch::nn::AnyModule module(torch::nn::Linear(3, 4)); auto output = module.forward(torch::ones({2, 3})); struct IntModule { int forward(int x) { return x; } }; torch::nn::AnyModule module(IntModule{}); int output = module.forward(5); The only other method an AnyModule provides access to on the stored module is clone(). However, you may acquire a handle on the module via .ptr(), which returns a shared_ptr\u003cnn::Module\u003e. Further, if you know the concrete type of the stored module, you can get a concrete handle to it using .get\u003cT\u003e() where T is the concrete module type. .. code-block:: cpp torch::nn::AnyModule module(torch::nn::Linear(3, 4)); std::shared_ptrnn::Module ptr = module.ptr(); torch::nn::Linear linear(module.gettorch::nn::Linear()); Public Functions AnyModule() = default# A default-constructed AnyModule is in an empty state. template\u003ctypename ModuleType\u003eexplicit AnyModule(std::shared_ptr\u003cModuleType\u003e module)# Constructs an AnyModule from a shared_ptr to concrete module object. template\u003ctypename ModuleType, typename = torch::detail::enable_if_module_t\u003cModuleType\u003e\u003eexplicit AnyModule(ModuleType \u0026\u0026module)# Constructs an AnyModule from a concrete module object. template\u003ctypename ModuleType\u003eexplicit AnyModule(const ModuleHolder\u003cModuleType\u003e \u0026module_holder)# Constructs an AnyModule from a module holder. AnyModule(AnyModule\u0026\u0026) = default# Move construction and assignment is allowed, and follows the default behavior of move for std::unique_ptr. AnyModule \u0026operator=(AnyModule\u0026\u0026) = default# inline AnyModule(const AnyModule \u0026other)# Creates a shallow copy of an AnyModule. inline AnyModule \u0026operator=(const AnyModule \u0026other)# inline AnyModule clone(std::optional\u003cDevice\u003e device = std::nullopt) const# Creates a deep copy of an AnyModule if it contains a module, else an empty AnyModule if it is empty. template\u003ctypename ModuleType\u003eAnyModule \u0026operator=(std::shared_ptr\u003cModuleType\u003e module)# Assigns a module to the AnyModule (to circumvent the explicit constructor). template\u003ctypename ...ArgumentTypes\u003eAnyValue any_forward(ArgumentTypes\u0026\u0026... arguments)# Invokes forward() on the contained module with the given arguments, and returns the return value as an AnyValue. Use this method when chaining AnyModules in a loop. template\u003ctypename ReturnType = torch::Tensor, typename ...ArgumentTypes\u003eReturnType forward(ArgumentTypes\u0026\u0026... arguments)# Invokes forward() on the contained module with the given arguments, and casts the returned AnyValue to the supplied ReturnType (which defaults to torch::Tensor). template\u003ctypename T, typename = torch::detail::enable_if_module_t\u003cT\u003e\u003eT \u0026get()# Attempts to cast the underlying module to the given module type. Throws an exception if the types do not match. template\u003ctypename T, typename = torch::detail::enable_if_module_t\u003cT\u003e\u003econst T \u0026get() const# Attempts to cast the underlying module to the given module type. Throws an exception if the types do not match. template\u003ctypename T, typename ContainedType = typename T::ContainedType\u003eT get() const# Returns the contained module in a nn::ModuleHolder subclass if possible (i.e. if T has a constructor for the underlying module type). inline std::shared_ptr\u003cModule\u003e ptr() const# Returns a std::shared_ptr whose dynamic type is that of the underlying module. template\u003ctypename T, typename = torch::detail::enable_if_module_t\u003cT\u003e\u003estd::shared_ptr\u003cT\u003e ptr() const# Like ptr(), but casts the pointer to the given type. inline const std::type_info \u0026type_info() const# Returns the type_info object of the contained value. inline bool is_empty() const noexcept# Returns true if the AnyModule does not contain a module. Example: torch::nn::AnyModule any_module(torch::nn::Linear(10, 5)); auto output = any_module.forward(input); Functional# Wraps a function or callable as a module, useful for inserting arbitrary functions into a Sequential container. class FunctionalImpl : public torch::nn::Cloneable\u003cFunctionalImpl\u003e# Wraps a function in a Module. The Functional module allows wrapping an arbitrary function or function object in an nn::Module. This is primarily handy for usage in Sequential. .. code-block:: cpp Sequential sequential( Linear(3, 4), Functional(torch::relu), BatchNorm1d(3), Functional(torch::elu, /*alpha=*\u0026zwj;/1)); While a Functional module only accepts a single Tensor as input, it is possible for the wrapped function to accept further arguments. However, these have to be bound at construction time. For example, if you want to wrap torch::leaky_relu, which accepts a slope scalar as its second argument, with a particular value for its slope in a Functional module, you could write .. code-block:: cpp Functional(torch::leaky_relu, /slope=\u200d/0.5) The value of 0.5 is then stored within the Functional object and supplied to the function call at invocation time. Note that such bound values are evaluated eagerly and stored a single time. See the documentation of std::bind for more information on the semantics of argument binding. .. attention:: After passing any bound arguments, the function must accept a single tensor and return a single tensor. Note that Functional overloads the call operator (operator()) such that you can invoke it with my_func(...). Public Types using Function = std::function\u003cTensor(Tensor)\u003e# Public Functions explicit FunctionalImpl(Function function)# Constructs a Functional from a function object. template\u003ctypename SomeFunction, typename ...Args, typename = std::enable_if_t\u003c(sizeof...(Args) \u003e 0)\u003e\u003einline explicit FunctionalImpl(SomeFunction original_function, Args\u0026\u0026... args)# virtual void reset() override# reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules. virtual void pretty_print(std::ostream \u0026stream) const override# Pretty prints the Functional module into the given stream. Tensor forward(Tensor input)# Forwards the input tensor to the underlying (bound) function object. Tensor operator()(Tensor input)# Calls forward(input). virtual bool is_serializable() const override# Returns whether the Module is serializable. ModuleHolder# template\u003ctypename Contained\u003eclass ModuleHolder : private torch::detail::ModuleHolderIndicator# A ModuleHolder is essentially a wrapper around std::shared_ptr\u003cM\u003e where M is an nn::Module subclass, with convenient constructors defined for the kind of constructions we want to allow for our modules. Public Types using ContainedType = Contained# Public Functions inline ModuleHolder()# Default constructs the contained module if it has a default constructor, else produces a static error. NOTE: This uses the behavior of template classes in C++ that constructors (or any methods) are only compiled when actually used. inline ModuleHolder(std::nullptr_t)# Constructs the ModuleHolder with an empty contained value. Access to the underlying module is not permitted and will throw an exception, until a value is assigned. template\u003ctypename Head, typename ...Tail, typename = std::enable_if_t\u003c!(torch::detail::is_module_holder_of\u003cHead, ContainedType\u003e::value \u0026\u0026 (sizeof...(Tail) == 0))\u003e\u003einline explicit ModuleHolder(Head \u0026\u0026head, Tail\u0026\u0026... tail)# Constructs the ModuleHolder with a contained module, forwarding all arguments to its constructor. inline ModuleHolder(std::shared_ptr\u003cContained\u003e module)# Constructs the ModuleHolder from a pointer to the contained type. Example: Linear(std::make_shared\u003cLinearImpl\u003e(...)). inline explicit operator bool() const noexcept# Returns true if the ModuleHolder contains a module, or false if it is nullptr. inline Contained *operator-\u003e()# Forwards to the contained module. inline const Contained *operator-\u003e() const# Forwards to the contained module. inline Contained \u0026operator*()# Returns a reference to the contained module. inline const Contained \u0026operator*() const# Returns a const reference to the contained module. inline const std::shared_ptr\u003cContained\u003e \u0026ptr() const# Returns a shared pointer to the underlying module. inline Contained *get()# Returns a pointer to the underlying module. inline const Contained *get() const# Returns a const pointer to the underlying module. template\u003ctypename ...Args\u003einline auto operator()(Args\u0026\u0026... args) -\u003e torch::detail::return_type_of_forward_t\u003cContained, Args...\u003e# Calls the forward() method of the contained module. template\u003ctypename Arg\u003einline auto operator[](Arg \u0026\u0026arg)# Forwards to the subscript operator of the contained module. NOTE: std::forward is qualified to prevent VS2017 emitting error C2872: \u2018std\u2019: ambiguous symbol inline bool is_empty() const noexcept# Returns true if the ModuleHolder does not contain a module. CosineSimilarity# class CosineSimilarity : public torch::nn::ModuleHolder\u003cCosineSimilarityImpl\u003e# A ModuleHolder subclass for CosineSimilarityImpl. See the documentation for CosineSimilarityImpl class to learn what methods it provides, and examples of how to use CosineSimilarity with torch::nn::CosineSimilarityOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = CosineSimilarityImpl# PairwiseDistance# class PairwiseDistance : public torch::nn::ModuleHolder\u003cPairwiseDistanceImpl\u003e# A ModuleHolder subclass for PairwiseDistanceImpl. See the documentation for PairwiseDistanceImpl class to learn what methods it provides, and examples of how to use PairwiseDistance with torch::nn::PairwiseDistanceOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = PairwiseDistanceImpl# PackedSequence# class torch::nn::utils::rnn::PackedSequence# Holds the data and list of batch_sizes of a packed sequence. All RNN modules accept packed sequences as inputs. const Tensor \u0026data() const# Returns the packed tensor containing all sequence elements. const Tensor \u0026batch_sizes() const# Returns a 1D tensor of batch sizes at each time step. const Tensor \u0026sorted_indices() const# Returns indices used to sort sequences by length (descending). const Tensor \u0026unsorted_indices() const# Returns indices to restore the original sequence order. PackedSequence to(torch::Device device) const# Moves the packed sequence to the specified device. See also: torch::nn::utils::rnn::pack_padded_sequence and torch::nn::utils::rnn::pad_packed_sequence. Padding Layers# ReflectionPad1d / ReflectionPad2d / ReflectionPad3d# class ReflectionPad1d : public torch::nn::ModuleHolder\u003cReflectionPad1dImpl\u003e# A ModuleHolder subclass for ReflectionPad1dImpl. See the documentation for ReflectionPad1dImpl class to learn what methods it provides, and examples of how to use ReflectionPad1d with torch::nn::ReflectionPad1dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReflectionPad1dImpl# class ReflectionPad2d : public torch::nn::ModuleHolder\u003cReflectionPad2dImpl\u003e# A ModuleHolder subclass for ReflectionPad2dImpl. See the documentation for ReflectionPad2dImpl class to learn what methods it provides, and examples of how to use ReflectionPad2d with torch::nn::ReflectionPad2dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReflectionPad2dImpl# class ReflectionPad3d : public torch::nn::ModuleHolder\u003cReflectionPad3dImpl\u003e# A ModuleHolder subclass for ReflectionPad3dImpl. See the documentation for ReflectionPad3dImpl class to learn what methods it provides, and examples of how to use ReflectionPad3d with torch::nn::ReflectionPad3dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReflectionPad3dImpl# ReplicationPad1d / ReplicationPad2d / ReplicationPad3d# class ReplicationPad1d : public torch::nn::ModuleHolder\u003cReplicationPad1dImpl\u003e# A ModuleHolder subclass for ReplicationPad1dImpl. See the documentation for ReplicationPad1dImpl class to learn what methods it provides, and examples of how to use ReplicationPad1d with torch::nn::ReplicationPad1dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReplicationPad1dImpl# class ReplicationPad2d : public torch::nn::ModuleHolder\u003cReplicationPad2dImpl\u003e# A ModuleHolder subclass for ReplicationPad2dImpl. See the documentation for ReplicationPad2dImpl class to learn what methods it provides, and examples of how to use ReplicationPad2d with torch::nn::ReplicationPad2dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReplicationPad2dImpl# class ReplicationPad3d : public torch::nn::ModuleHolder\u003cReplicationPad3dImpl\u003e# A ModuleHolder subclass for ReplicationPad3dImpl. See the documentation for ReplicationPad3dImpl class to learn what methods it provides, and examples of how to use ReplicationPad3d with torch::nn::ReplicationPad3dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ReplicationPad3dImpl# ZeroPad1d / ZeroPad2d / ZeroPad3d# class ZeroPad1d : public torch::nn::ModuleHolder\u003cZeroPad1dImpl\u003e# A ModuleHolder subclass for ZeroPad1dImpl. See the documentation for ZeroPad1dImpl class to learn what methods it provides, and examples of how to use ZeroPad1d with torch::nn::ZeroPad1dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ZeroPad1dImpl# class ZeroPad2d : public torch::nn::ModuleHolder\u003cZeroPad2dImpl\u003e# A ModuleHolder subclass for ZeroPad2dImpl. See the documentation for ZeroPad2dImpl class to learn what methods it provides, and examples of how to use ZeroPad2d with torch::nn::ZeroPad2dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ZeroPad2dImpl# class ZeroPad3d : public torch::nn::ModuleHolder\u003cZeroPad3dImpl\u003e# A ModuleHolder subclass for ZeroPad3dImpl. See the documentation for ZeroPad3dImpl class to learn what methods it provides, and examples of how to use ZeroPad3d with torch::nn::ZeroPad3dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ZeroPad3dImpl# ConstantPad1d / ConstantPad2d / ConstantPad3d# class ConstantPad1d : public torch::nn::ModuleHolder\u003cConstantPad1dImpl\u003e# A ModuleHolder subclass for ConstantPad1dImpl. See the documentation for ConstantPad1dImpl class to learn what methods it provides, and examples of how to use ConstantPad1d with torch::nn::ConstantPad1dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ConstantPad1dImpl# class ConstantPad2d : public torch::nn::ModuleHolder\u003cConstantPad2dImpl\u003e# A ModuleHolder subclass for ConstantPad2dImpl. See the documentation for ConstantPad2dImpl class to learn what methods it provides, and examples of how to use ConstantPad2d with torch::nn::ConstantPad2dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ConstantPad2dImpl# class ConstantPad3d : public torch::nn::ModuleHolder\u003cConstantPad3dImpl\u003e# A ModuleHolder subclass for ConstantPad3dImpl. See the documentation for ConstantPad3dImpl class to learn what methods it provides, and examples of how to use ConstantPad3d with torch::nn::ConstantPad3dOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = ConstantPad3dImpl# Vision Layers# PixelShuffle# class PixelShuffle : public torch::nn::ModuleHolder\u003cPixelShuffleImpl\u003e# A ModuleHolder subclass for PixelShuffleImpl. See the documentation for PixelShuffleImpl class to learn what methods it provides, and examples of how to use PixelShuffle with torch::nn::PixelShuffleOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = PixelShuffleImpl# struct PixelShuffleOptions# Options for the PixelShuffle module. Example: PixelShuffle model(PixelShuffleOptions(5)); Public Functions inline PixelShuffleOptions(int64_t upscale_factor)# inline auto upscale_factor(const int64_t \u0026new_upscale_factor) -\u003e decltype(*this)# Factor to increase spatial resolution by. inline auto upscale_factor(int64_t \u0026\u0026new_upscale_factor) -\u003e decltype(*this)# inline const int64_t \u0026upscale_factor() const noexcept# inline int64_t \u0026upscale_factor() noexcept# PixelUnshuffle# class PixelUnshuffle : public torch::nn::ModuleHolder\u003cPixelUnshuffleImpl\u003e# A ModuleHolder subclass for PixelUnshuffleImpl. See the documentation for PixelUnshuffleImpl class to learn what methods it provides, and examples of how to use PixelUnshuffle with torch::nn::PixelUnshuffleOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = PixelUnshuffleImpl# struct PixelUnshuffleOptions# Options for the PixelUnshuffle module. Example: PixelUnshuffle model(PixelUnshuffleOptions(5)); Public Functions inline PixelUnshuffleOptions(int64_t downscale_factor)# inline auto downscale_factor(const int64_t \u0026new_downscale_factor) -\u003e decltype(*this)# Factor to decrease spatial resolution by. inline auto downscale_factor(int64_t \u0026\u0026new_downscale_factor) -\u003e decltype(*this)# inline const int64_t \u0026downscale_factor() const noexcept# inline int64_t \u0026downscale_factor() noexcept# Upsample# class Upsample : public torch::nn::ModuleHolder\u003cUpsampleImpl\u003e# A ModuleHolder subclass for UpsampleImpl. See the documentation for UpsampleImpl class to learn what methods it provides, and examples of how to use Upsample with torch::nn::UpsampleOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = UpsampleImpl# struct UpsampleOptions# Options for the Upsample module. Example: Upsample model(UpsampleOptions().scale_factor(std::vector\u003cdouble\u003e({3})).mode(torch::kLinear).align_corners(false)); Public Functions inline auto size(const std::optional\u003cstd::vector\u003cint64_t\u003e\u003e \u0026new_size) -\u003e decltype(*this)# output spatial sizes. inline auto size(std::optional\u003cstd::vector\u003cint64_t\u003e\u003e \u0026\u0026new_size) -\u003e decltype(*this)# inline const std::optional\u003cstd::vector\u003cint64_t\u003e\u003e \u0026size() const noexcept# inline std::optional\u003cstd::vector\u003cint64_t\u003e\u003e \u0026size() noexcept# inline auto scale_factor(const std::optional\u003cstd::vector\u003cdouble\u003e\u003e \u0026new_scale_factor) -\u003e decltype(*this)# multiplier for spatial size. inline auto scale_factor(std::optional\u003cstd::vector\u003cdouble\u003e\u003e \u0026\u0026new_scale_factor) -\u003e decltype(*this)# inline const std::optional\u003cstd::vector\u003cdouble\u003e\u003e \u0026scale_factor() const noexcept# inline std::optional\u003cstd::vector\u003cdouble\u003e\u003e \u0026scale_factor() noexcept# inline auto mode(const mode_t \u0026new_mode) -\u003e decltype(*this)# inline auto mode(mode_t \u0026\u0026new_mode) -\u003e decltype(*this)# inline const mode_t \u0026mode() const noexcept# inline mode_t \u0026mode() noexcept# inline auto align_corners(const std::optional\u003cbool\u003e \u0026new_align_corners) -\u003e decltype(*this)# if \u201cTrue\u201d, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect when :attr:mode is \u201clinear\u201d, \u201cbilinear\u201d, \u201cbicubic\u201d, or \u201ctrilinear\u201d. Default: \u201cFalse\u201d inline auto align_corners(std::optional\u003cbool\u003e \u0026\u0026new_align_corners) -\u003e decltype(*this)# inline const std::optional\u003cbool\u003e \u0026align_corners() const noexcept# inline std::optional\u003cbool\u003e \u0026align_corners() noexcept# Fold / Unfold# class Fold : public torch::nn::ModuleHolder\u003cFoldImpl\u003e# A ModuleHolder subclass for FoldImpl. See the documentation for FoldImpl class to learn what methods it provides, and examples of how to use Fold with torch::nn::FoldOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = FoldImpl# struct FoldOptions# Options for the Fold module. Example: Fold model(FoldOptions({8, 8}, {3, 3}).dilation(2).padding({2, 1}).stride(2)); Public Functions inline FoldOptions(ExpandingArray\u003c2\u003e output_size, ExpandingArray\u003c2\u003e kernel_size)# inline auto output_size(const ExpandingArray\u003c2\u003e \u0026new_output_size) -\u003e decltype(*this)# describes the spatial shape of the large containing tensor of the sliding local blocks. It is useful to resolve the ambiguity when multiple input shapes map to same number of sliding blocks, e.g., with stride \u003e 0. inline auto output_size(ExpandingArray\u003c2\u003e \u0026\u0026new_output_size) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026output_size() const noexcept# inline ExpandingArray\u003c2\u003e \u0026output_size() noexcept# inline auto kernel_size(const ExpandingArray\u003c2\u003e \u0026new_kernel_size) -\u003e decltype(*this)# the size of the sliding blocks inline auto kernel_size(ExpandingArray\u003c2\u003e \u0026\u0026new_kernel_size) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026kernel_size() const noexcept# inline ExpandingArray\u003c2\u003e \u0026kernel_size() noexcept# inline auto dilation(const ExpandingArray\u003c2\u003e \u0026new_dilation) -\u003e decltype(*this)# controls the spacing between the kernel points; also known as the \u00e0 trous algorithm. inline auto dilation(ExpandingArray\u003c2\u003e \u0026\u0026new_dilation) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026dilation() const noexcept# inline ExpandingArray\u003c2\u003e \u0026dilation() noexcept# inline auto padding(const ExpandingArray\u003c2\u003e \u0026new_padding) -\u003e decltype(*this)# controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension before reshaping. inline auto padding(ExpandingArray\u003c2\u003e \u0026\u0026new_padding) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026padding() const noexcept# inline ExpandingArray\u003c2\u003e \u0026padding() noexcept# inline auto stride(const ExpandingArray\u003c2\u003e \u0026new_stride) -\u003e decltype(*this)# controls the stride for the sliding blocks. inline auto stride(ExpandingArray\u003c2\u003e \u0026\u0026new_stride) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026stride() const noexcept# inline ExpandingArray\u003c2\u003e \u0026stride() noexcept# class Unfold : public torch::nn::ModuleHolder\u003cUnfoldImpl\u003e# A ModuleHolder subclass for UnfoldImpl. See the documentation for UnfoldImpl class to learn what methods it provides, and examples of how to use Unfold with torch::nn::UnfoldOptions. See the documentation for ModuleHolder to learn about PyTorch\u2019s module storage semantics. Public Types using Impl = UnfoldImpl# struct UnfoldOptions# Options for the Unfold module. Example: Unfold model(UnfoldOptions({2, 4}).dilation(2).padding({2, 1}).stride(2)); Public Functions inline UnfoldOptions(ExpandingArray\u003c2\u003e kernel_size)# inline auto kernel_size(const ExpandingArray\u003c2\u003e \u0026new_kernel_size) -\u003e decltype(*this)# the size of the sliding blocks inline auto kernel_size(ExpandingArray\u003c2\u003e \u0026\u0026new_kernel_size) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026kernel_size() const noexcept# inline ExpandingArray\u003c2\u003e \u0026kernel_size() noexcept# inline auto dilation(const ExpandingArray\u003c2\u003e \u0026new_dilation) -\u003e decltype(*this)# controls the spacing between the kernel points; also known as the \u00e0 trous algorithm. inline auto dilation(ExpandingArray\u003c2\u003e \u0026\u0026new_dilation) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026dilation() const noexcept# inline ExpandingArray\u003c2\u003e \u0026dilation() noexcept# inline auto padding(const ExpandingArray\u003c2\u003e \u0026new_padding) -\u003e decltype(*this)# controls the amount of implicit zero-paddings on both sides for padding number of points for each dimension before reshaping. inline auto padding(ExpandingArray\u003c2\u003e \u0026\u0026new_padding) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026padding() const noexcept# inline ExpandingArray\u003c2\u003e \u0026padding() noexcept# inline auto stride(const ExpandingArray\u003c2\u003e \u0026new_stride) -\u003e decltype(*this)# controls the stride for the sliding blocks. inline auto stride(ExpandingArray\u003c2\u003e \u0026\u0026new_stride) -\u003e decltype(*this)# inline const ExpandingArray\u003c2\u003e \u0026stride() const noexcept# inline ExpandingArray\u003c2\u003e \u0026stride() noexcept#",
       "author": {
         "@type": "Organization",
         "name": "PyTorch Contributors",
         "url": "https://pytorch.org"
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       "datePublished": "2023-01-01T00:00:00Z",
       "dateModified": "2023-01-01T00:00:00Z"
     }