ONE - On-device Neural Engine
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Pads a tensor with constant value. More...
#include <Nodes.h>
Public Member Functions | |
Node * | input (void) const |
void | input (Node *node) |
Node * | constant (void) const |
void | constant (Node *node) |
const PaddingND * | padding (void) const |
PaddingND * | padding (void) |
Public Member Functions inherited from loco::CanonicalNodeDef< CanonicalOpcode::TensorConstantPad, FixedArity< 2 >::Mixin > | |
virtual | ~CanonicalNodeDef ()=default |
uint32_t | opnum (void) const final |
CanonicalOpcode | opcode (void) const final |
Public Member Functions inherited from loco::CanonicalNode | |
virtual | ~CanonicalNode ()=default |
const Dialect * | dialect (void) const final |
Return "Dialect" identifier that this node belongs to. | |
template<typename T > | |
T | accept (CanonicalNodeVisitorBase< T > *) const |
template<typename T > | |
T | accept (CanonicalNodeMutableVisitorBase< T > *) |
Public Member Functions inherited from loco::Node | |
Node ()=default | |
Node (const Node &)=delete | |
Node (Node &&)=delete | |
virtual | ~Node () |
Graph * | graph (void) |
const Graph * | graph (void) const |
virtual uint32_t | arity (void) const =0 |
Return the number of arguments. | |
virtual Node * | arg (uint32_t N) const =0 |
Access N-th argument node. | |
virtual void | drop (void)=0 |
Drop all the reference of arguments. | |
Public Member Functions inherited from loco::AnnotatedItem< NodeAnnotation > | |
AnnotatedItem ()=default | |
virtual | ~AnnotatedItem ()=default |
const T * | annot (void) const |
Retrieve a stored annotation of type T. | |
void | annot (std::unique_ptr< T > &&p) |
Attach or remove a new annotation of type T. | |
Pads a tensor with constant value.
Pads a input tensor according to the padding with constant value.
The dimension of each axis n of the output is output.dim(n) = padding.front(n) + input.dim(n) + padding.back(n)
For example, input tensor of shape [1, 2] with
padding.front(0) = 1; padding.back(0) = 2;
padding.front(1) = 3; padding.back(1) = 4;
will be a output tensor of shape [padding.front(0) + 1 + padding.back(0), padding.front(1) + 2 + padding.back(1)] = [4,9].
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