nvinfer1::IDequantizeLayer Class Reference

A Dequantize layer in a network definition. More...

#include <NvInfer.h>

Inheritance diagram for nvinfer1::IDequantizeLayer:

Public Member Functions
int32_t	getAxis () const noexcept
	Get the quantization axis. More...
void	setAxis (int32_t axis) noexcept
	Set the quantization axis. More...
Public Member Functions inherited from nvinfer1::ILayer
LayerType	getType () const noexcept
	Return the type of a layer. More...
void	setName (const char *name) noexcept
	Set the name of a layer. More...
const char *	getName () const noexcept
	Return the name of a layer. More...
int32_t	getNbInputs () const noexcept
	Get the number of inputs of a layer.
ITensor *	getInput (int32_t index) const noexcept
	Get the layer input corresponding to the given index. More...
int32_t	getNbOutputs () const noexcept
	Get the number of outputs of a layer.
ITensor *	getOutput (int32_t index) const noexcept
	Get the layer output corresponding to the given index. More...
void	setInput (int32_t index, ITensor &tensor) noexcept
	Replace an input of this layer with a specific tensor. More...
void	setPrecision (DataType dataType) noexcept
	Set the computational precision of this layer. More...
DataType	getPrecision () const noexcept
	get the computational precision of this layer More...
bool	precisionIsSet () const noexcept
	whether the computational precision has been set for this layer More...
void	resetPrecision () noexcept
	reset the computational precision for this layer More...
void	setOutputType (int32_t index, DataType dataType) noexcept
	Set the output type of this layer. More...
DataType	getOutputType (int32_t index) const noexcept
	get the output type of this layer More...
bool	outputTypeIsSet (int32_t index) const noexcept
	whether the output type has been set for this layer More...
void	resetOutputType (int32_t index) noexcept
	reset the output type for this layer More...

Protected Attributes
apiv::VDequantizeLayer *	mImpl
Protected Attributes inherited from nvinfer1::ILayer
apiv::VLayer *	mLayer

Additional Inherited Members
Protected Member Functions inherited from nvinfer1::INoCopy
	INoCopy (const INoCopy &other)=delete
INoCopy &	operator= (const INoCopy &other)=delete
	INoCopy (INoCopy &&other)=delete
INoCopy &	operator= (INoCopy &&other)=delete

Detailed Description

A Dequantize layer in a network definition.

This layer accepts a signed 8-bit integer input tensor, and uses the configured scale and zeroPt inputs to dequantize the input according to: output = (input - zeroPt) * scale

The first input (index 0) is the tensor to be quantized. The second (index 1) and third (index 2) are the scale and zero point respectively. Each of scale and zeroPt must be either a scalar, or a 1D tensor.

The zeroPt tensor is optional, and if not set, will be assumed to be zero. Its data type must be DataType::kINT8. zeroPt must only contain zero-valued coefficients, because only symmetric quantization is supported. The scale value must be either a scalar for per-tensor quantization, or a 1D tensor for per-channel quantization. All scale coefficients must have positive values. The size of the 1-D scale tensor must match the size of the quantization axis. The size of the scale must match the size of the zeroPt.

The subgraph which terminates with the scale tensor must be a build-time constant. The same restrictions apply to the zeroPt. The output type, if constrained, must be constrained to DataType::kINT8. The input type, if constrained, must be constrained to DataType::kFLOAT (FP16 input is not supported). The output size is the same as the input size. The quantization axis is in reference to the input tensor's dimensions.

IDequantizeLayer only supports DataType::kINT8 precision and will default to this precision during instantiation. IDequantizeLayer only supports DataType::kFLOAT output.

As an example of the operation of this layer, imagine a 4D NCHW activation input which can be quantized using a single scale coefficient (referred to as per-tensor quantization): For each n in N: For each c in C: For each h in H: For each w in W: output[n,c,h,w] = (input[n,c,h,w] - zeroPt) * scale

Per-channel dequantization is supported only for input that is rooted at an IConstantLayer (i.e. weights). Activations cannot be quantized per-channel. As an example of per-channel operation, imagine a 4D KCRS weights input and K (dimension 0) as the quantization axis. The scale is an array of coefficients, which is the same size as the quantization axis. For each k in K: For each c in C: For each r in R: For each s in S: output[k,c,r,s] = (input[k,c,r,s] - zeroPt[k]) * scale[k]

Note

Only symmetric quantization is supported.

Currently the only allowed build-time constant scale and \zeroPt subgraphs are:

1. Constant -> Quantize
2. Constant -> Cast -> Quantize

Warning

Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.

Member Function Documentation

getAxis()

int32_t nvinfer1::IDequantizeLayer::getAxis ( ) const

inlinenoexcept

Get the quantization axis.

Returns

axis parameter set by setAxis(). The return value is the index of the quantization axis in the input tensor's dimensions. A value of -1 indicates per-tensor quantization. The default value is -1.

setAxis()

void nvinfer1::IDequantizeLayer::setAxis ( int32_t  axis )

inlinenoexcept

Set the quantization axis.

Set the index of the quantization axis (with reference to the input tensor's dimensions). The axis must be a valid axis if the scale tensor has more than one coefficient. The axis value will be ignored if the scale tensor has exactly one coefficient (per-tensor quantization).

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The documentation for this class was generated from the following file:

• NvInfer.h