Enum TensorFormat 

#[repr(i32)]
pub enum TensorFormat {
    kLINEAR = 0,
    kCHW2 = 1,
    kHWC8 = 2,
    kCHW4 = 3,
    kCHW16 = 4,
    kCHW32 = 5,
    kDHWC8 = 6,
    kCDHW32 = 7,
    kHWC = 8,
    kDLA_LINEAR = 9,
    kDLA_HWC4 = 10,
    kHWC16 = 11,
    kDHWC = 12,
}

! ! \enum TensorFormat ! ! \brief Format of the input/output tensors. ! ! This enum is used by both plugins and network I/O tensors. ! ! \see IPluginV2::supportsFormat(), safe::ICudaEngine::getBindingFormat() ! ! Many of the formats are vector-major or vector-minor. These formats specify ! a vector dimension and scalars per vector. ! For example, suppose that the tensor has has dimensions [M,N,C,H,W], ! the vector dimension is C and there are V scalars per vector. ! ! * A vector-major format splits the vectorized dimension into two axes in the ! memory layout. The vectorized dimension is replaced by an axis of length ceil(C/V) ! and a new dimension of length V is appended. For the example tensor, the memory layout ! is equivalent to an array with dimensions [M][N][ceil(C/V)][H][W][V]. ! Tensor coordinate (m,n,c,h,w) maps to array location [m][n][c/V][h][w][c%V]. ! ! * A vector-minor format moves the vectorized dimension to become the last axis ! in the memory layout. For the example tensor, the memory layout is equivalent to an ! array with dimensions [M][N][H][W][ceil(C/V)*V]. Tensor coordinate (m,n,c,h,w) maps ! array location subscript [m][n][h][w][c]. ! ! In interfaces that refer to “components per element”, that’s the value of V above. ! ! For more information about data formats, see the topic “Data Format Description” located in the ! TensorRT Developer Guide. ! https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/advanced.html#i-o-formats !

Variants

kLINEAR = 0

! Memory layout is similar to an array in C or C++. ! The stride of each dimension is the product of the dimensions after it. ! The last dimension has unit stride. ! ! This format supports all TensorRT types. ! For DLA usage, the tensor sizes are limited to C,H,W in the range [1,8192].

kCHW2 = 1

! Vector-major format with two scalars per vector. ! Vector dimension is third to last. ! ! This format requires FP16 or BF16 and at least three dimensions.

kHWC8 = 2

! Vector-minor format with eight scalars per vector. ! Vector dimension is third to last. ! This format requires FP16 or BF16 and at least three dimensions.

kCHW4 = 3

! Vector-major format with four scalars per vector. ! Vector dimension is third to last. ! ! This format requires INT8 and at least three dimensions. ! For INT8, the length of the vector dimension must be a build-time constant. ! ! Deprecated usage: ! ! If running on the DLA, this format can be used for acceleration ! with the caveat that C must be less than or equal to 4. ! If used as DLA input and the build option kGPU_FALLBACK is not specified, ! it needs to meet line stride requirement of DLA format. Column stride in ! bytes must be a multiple of 64 on Orin.

kCHW16 = 4

! Vector-major format with 16 scalars per vector. ! Vector dimension is third to last. ! ! This format is only supported by DLA and requires FP16 and at least three dimensions. ! This format maps to the native feature format for FP16, ! and the tensor sizes are limited to C,H,W in the range [1,8192].

kCHW32 = 5

! Vector-major format with 32 scalars per vector. ! Vector dimension is third to last. ! ! This format requires INT8, FP32, or FP16 and at least three dimensions. ! ! For DLA usage, this format maps to the native feature format for INT8, ! and the tensor sizes are limited to C,H,W in the range [1,8192].

kDHWC8 = 6

! Vector-minor format with eight scalars per vector. ! Vector dimension is fourth to last. ! ! This format requires FP16 or BF16 and at least four dimensions.

kCDHW32 = 7

! Vector-major format with 32 scalars per vector. ! Vector dimension is fourth to last. ! ! This format requires FP16 or INT8 and at least four dimensions.

kHWC = 8

! Vector-minor format where channel dimension is third to last and unpadded. ! ! This format requires either FP32 or UINT8 and at least three dimensions.

kDLA_LINEAR = 9

! DLA planar format. For a tensor with dimension {N, C, H, W}, the W axis ! always has unit stride. The stride for stepping along the H axis is ! rounded up to 64 bytes. ! ! The memory layout is equivalent to a C array with dimensions ! [N][C][H][roundUp(W, 64/elementSize)] where elementSize is ! 2 for FP16 and 1 for Int8, with the tensor coordinates (n, c, h, w) ! mapping to array subscript [n][c][h][w].

kDLA_HWC4 = 10

! DLA image format. For a tensor with dimension {N, C, H, W} the C axis ! always has unit stride. The stride for stepping along the H axis is rounded up ! to 64 bytes on Orin. C can only be 1, 3 or 4. ! If C == 1, it will map to grayscale format. ! If C == 3 or C == 4, it will map to color image format. And if C == 3, ! the stride for stepping along the W axis needs to be padded to 4 in elements. ! ! When C is {1, 3, 4}, then C’ is {1, 4, 4} respectively, ! the memory layout is equivalent to a C array with dimensions ! [N][H][roundUp(W, 64/C’/elementSize)][C’] on Orin ! where elementSize is 2 for FP16 ! and 1 for Int8. The tensor coordinates (n, c, h, w) mapping to array ! subscript [n][h][w][c].

kHWC16 = 11

! Vector-minor format with 16 scalars per vector. ! Vector dimension is third to last. ! ! This requires FP16, INT8 or FP8 and at least three dimensions.

kDHWC = 12

! Vector-minor format with one scalar per vector. ! Vector dimension is fourth to last. ! ! This format requires FP32 and at least four dimensions.

Trait Implementations

impl Clone for TensorFormat

fn clone(&self) -> TensorFormat

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for TensorFormat

impl Debug for TensorFormat

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Eq for TensorFormat

impl From<TensorFormat> for TensorFormat

fn from(value: TensorFormat) -> Self

Converts to this type from the input type.

impl Hash for TensorFormat

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl Into<TensorFormat> for TensorFormat

fn into(self) -> TensorFormat

Converts this type into the (usually inferred) input type.

impl Ord for TensorFormat

fn cmp(&self, other: &TensorFormat) -> Ordering

This method returns an Ordering between self and other.

fn max(self, other: Self) -> Self

where Self: Sized,

Compares and returns the maximum of two values.

fn min(self, other: Self) -> Self

where Self: Sized,

Compares and returns the minimum of two values.

fn clamp(self, min: Self, max: Self) -> Self

where Self: Sized,

Restrict a value to a certain interval.

impl PartialEq for TensorFormat

fn eq(&self, other: &TensorFormat) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PartialOrd for TensorFormat

fn partial_cmp(&self, other: &TensorFormat) -> Option<Ordering>

This method returns an ordering between self and other values if one exists.

fn lt(&self, other: &Rhs) -> bool

Tests less than (for self and other) and is used by the < operator.

fn le(&self, other: &Rhs) -> bool

Tests less than or equal to (for self and other) and is used by the <= operator.

fn gt(&self, other: &Rhs) -> bool

Tests greater than (for self and other) and is used by the > operator.

fn ge(&self, other: &Rhs) -> bool

Tests greater than or equal to (for self and other) and is used by the >= operator.

impl StructuralPartialEq for TensorFormat