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MicrosoftDequantizeLinear

Description

The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. The dequantization formula is y = (x – x_zero_point) * x_scale. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per ‘axis’).

 

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

x (heterogeneous) – T1 : object, N-D quantized Input tensor to be de-quantized.
x_scale (heterogeneous) – T2 : object, scale for input ‘x’. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional, heterogeneous) – T1 : object, zero point for input ‘x’. Shape must match x_scale. It’s optional. Zero point is 0 when it’s not specified.

 Parameters : cluster,

axis : integer, the axis along which same quantization parameters are applied. It’s optional.If it’s not specified, it means per-tensor quantization and input ‘x_scale’ and ‘x_zero_point’ must be scalars.If it’s specified, it means per ‘axis’ quantization and input ‘x_scale’ and ‘x_zero_point’ must be 1-D tensors.
Default value “0”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.

Output parameters

 

y (heterogeneous) – T2 : object, N-D full precision output tensor. It has same shape as input ‘x’.

Type Constraints

T1 in (tensor(int8), tensor(uint8), tensor(int16), tensor(uint16), tensor(int32),
tensor(int4), tensor(uint4)) : Constrain ‘x’ and ‘x_zero_point’ to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.

T2 in (tensor(float), tensor(float16)) : Constrain ‘y’, ‘x_scale’ to float tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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