Struct ITensor 

pub struct ITensor { /* private fields */ }

! ! \class ITensor ! ! \brief A tensor in a network definition. ! ! To remove a tensor from a network definition, use INetworkDefinition::removeTensor(). ! ! When using the DLA, the cumulative size of all Tensors that are not marked as Network Input or Output tensors, ! must be less than 1GB in size to fit into a single subgraph. If the build option kGPU_FALLBACK is specified, then ! multiple subgraphs can be created, with each subgraph limited to less than 1GB of internal tensors data. ! ! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ! ABI. !

Implementations

impl ITensor

pub unsafe fn setName(self: Pin<&mut ITensor>, name: *const c_char)

! ! \brief Set the tensor name. ! ! For a network input, the name is assigned by the application. For tensors which are layer outputs, ! a default name is assigned consisting of the layer name followed by the index of the output in brackets. ! Each input and output tensor must have a unique name. ! ! This method copies the name string. ! ! \param name The name. ! ! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. ! ! \see getName() !

pub fn getName(self: &ITensor) -> *const c_char

! ! \brief Get the tensor name. ! ! \return The name as a null-terminated C-style string. ! ! \see setName() !

pub fn setDimensions(self: Pin<&mut ITensor>, dimensions: &Dims64)

! ! \brief Set the dimensions of a tensor. ! ! For a network input, the dimensions are assigned by the application. For a network output, the dimensions are ! computed based on the layer parameters and the inputs to the layer. If a tensor size or a parameter is modified ! in the network, the dimensions of all dependent tensors will be recomputed. ! ! This call is only legal for network input tensors, since the dimensions of layer output tensors are inferred ! based on layer inputs and parameters. ! ! \param dimensions The dimensions of the tensor. ! ! \see getDimensions() !

pub fn getDimensions(self: &ITensor) -> Dims64

! ! \brief Get the dimensions of a tensor. ! ! \return The dimensions of the tensor. ! ! \warning getDimensions() returns a -1 for dimensions that are derived from a wildcard dimension. ! ! \see setDimensions() !

pub fn setType(self: Pin<&mut ITensor>, type_: DataType)

! ! \brief Set the data type of a tensor. ! ! \param type The data type of the tensor when the type is not inferred. ! ! For strongly typed networks, this method should be used only for network inputs, ! since the types of all other tensors are inferred. Setting the type of a network ! output is tolerated if the type equals the inferred type, otherwise an error occurs ! and the type is not updated. ! ! For weakly typed networks, this method can be used for network outputs too, but ! the type merely has to be implicitly convertible from the inferred type to the ! specified type. In this case it does not matter whether the type is set first ! or the tensor is marked as an output first (via INetworkDefinition::markOutput ! or INetworkDefinition::markOutputForShapes). ! ! However, marking it first has two advantages: ! ! * It avoids warnings that the tensor is not yet a network I/O tensor. ! * It causes method getType() to return the type that was set instead of the inferred type. ! ! \see getType() ! ! \note This function does more than just set the type, so t.setType(t.getType()) is not necessarily a no-op, ! particularly for input and output tensors! ! ! \note Repeated consecutive applications of t.setType(t.getType()) ! would be idempotent, provided the state of the ITensor isn’t changed between calls. ! ! Unsupported in TensorRT-RTX and deprecated in 1.0. Use strong typing instead. !

pub fn getType(self: &ITensor) -> DataType

! ! \brief Get the data type of a tensor. ! ! \return The data type of the tensor. ! ! The type is the type set by setType if the tensor is a network input or output. ! Otherwise the type is the inferred type. ! ! \see setType() !

pub fn isNetworkInput(self: &ITensor) -> bool

! ! \brief Whether the tensor is a network input. !

pub fn isNetworkOutput(self: &ITensor) -> bool

! ! \brief Whether the tensor is a network output. !

pub fn setAllowedFormats(self: Pin<&mut ITensor>, formats: u32)

! ! \brief Set allowed formats for an input or output tensor. By default all formats are allowed. ! Shape tensors (for which isShapeTensor() returns true) may only have row-major linear format. ! ! When running network on DLA and the build option kGPU_FALLBACK is not specified, if DLA format(kCHW4 with Int8, ! kCHW4 with FP16, kCHW16 with FP16, kCHW32 with Int8) is set, the input format is treated as native DLA format ! with line stride requirement. Input/output binding with these format should have correct layout during ! inference. ! ! Tensor formats are determined at build time by TensorRT for tensors not marked as input or output. ! ! \param formats A bitmask of TensorFormat values that are supported for this tensor. ! ! \see ITensor::getAllowedFormats() ! ! \see TensorFormats !

pub fn getAllowedFormats(self: &ITensor) -> u32

! ! \brief Get a bitmask of TensorFormat values that the tensor supports. ! For a shape tensor, only row-major linear format is allowed. ! ! \return The value specified by setAllowedFormats or all possible formats. ! ! \see ITensor::setAllowedFormats() !

pub fn isShapeTensor(self: &ITensor) -> bool

! ! \brief Whether the tensor is a shape tensor. ! ! A shape tensor is a tensor that is related to shape calculations. ! It must have type Int32, Int64, Bool, or Float, and its shape must be determinable at build time. ! Furthermore, it must be needed as a shape tensor, either marked as a network shape ! output via markOutputForShapes(), or as a layer input that is required to be a shape ! tensor, such as the second input to IShuffleLayer. Some layers are “polymorphic” in ! this respect. For example, the inputs to IElementWiseLayer must be shape tensors ! if the output is a shape tensor. ! ! The TensorRT Developer Guide gives the formal rules for what tensors are shape tensors. ! ! The result of isShapeTensor() is reliable only when network construction is complete. ! For example, if a partially built network sums two tensors T1 and T2 to create ! tensor T3, and none are yet needed as shape tensors, isShapeTensor() returns false ! for all three tensors. Setting the second input of IShuffleLayer to be T3 would ! cause all three tensors to be shape tensors, because IShuffleLayer requires that its ! second optional input be a shape tensor, and IElementWiseLayer is “polymorphic”. ! ! It is possible for a tensor to be both a shape tensor and an execution tensor. ! ! \return True if tensor is a shape tensor, false otherwise. ! ! \see INetworkDefinition::markOutputForShapes() !

pub fn isExecutionTensor(self: &ITensor) -> bool

! ! \brief Whether the tensor is an execution tensor. ! ! Tensors are usually execution tensors. The exceptions are tensors used ! solely for shape calculations or whose contents are not needed to compute the outputs. ! ! The result of isExecutionTensor() is reliable only when network construction is complete. ! For example, if a partially built network has no path from a tensor to a network output, ! isExecutionTensor() returns false. Completing the path would cause it to become true. ! ! ! A tensor with isShapeTensor() == false and isExecutionTensor() == false ! can still show up as an input to the engine if its dimensions are required. ! In that case, only its dimensions need to be set at runtime and a nullptr ! can be passed instead of a pointer to its contents. !

pub unsafe fn setDimensionName( self: Pin<&mut ITensor>, index: i32, name: *const c_char, )

! ! \brief Name a dimension of an input tensor. ! ! Associate a runtime dimension of an input tensor with a symbolic name. ! Dimensions with the same non-empty name must be equal at runtime. ! Knowing this equality for runtime dimensions may help the TensorRT optimizer. ! Both runtime and build-time dimensions can be named. ! ! For example, setDimensionName(0, “n”) associates the symbolic name “n” with the leading dimension. ! ! This method copies the name string. ! If the function is called again, with the same index, it will overwrite the previous name. ! If nullptr is passed as name, it will clear the name of the dimension. ! ! \param index index of the dimension ! \param name of the dimension, as a pointer to a null-terminated character sequence. ! ! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. ! ! \see getDimensionName() !

pub fn getDimensionName(self: &ITensor, index: i32) -> *const c_char

! ! \brief Get the name of an input dimension. ! ! \param index index of the dimension ! ! \return The name of the input dimension, or nullptr if the dimension has no name. ! The name is a pointer to a null-terminated character sequence. ! ! \see setDimensionName() !

Trait Implementations

impl ExternType for ITensor

type Id = (n, v, i, n, f, e, r, _1, (), I, T, e, n, s, o, r)

A type-level representation of the type’s C++ namespace and type name.

type Kind = Opaque

Either cxx::kind::Opaque or cxx::kind::Trivial.

impl MakeCppStorage for ITensor

unsafe fn allocate_uninitialized_cpp_storage() -> *mut ITensor

Allocates heap space for this type in C++ and return a pointer to that space, but do not initialize that space (i.e. do not yet call a constructor).

unsafe fn free_uninitialized_cpp_storage(arg0: *mut ITensor)

Frees a C++ allocation which has not yet had a constructor called.