com.ms.internal.nhwc - QLinearConvTranspose#

QLinearConvTranspose - 1#

Version

  • name: QLinearConvTranspose (GitHub)

  • domain: com.ms.internal.nhwc

  • since_version: 1

  • function:

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 1 of domain com.ms.internal.nhwc.

Summary

Attributes

  • auto_pad - STRING : auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET

  • dilations - INTS : dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.

  • group - INT : number of groups input channels and output channels are divided into.

  • kernel_shape - INTS : The shape of the convolution kernel. If not present, should be inferred from input W.

  • output_padding - INTS : Additional elements added to the side with higher coordinate indices in the output. Each padding value in “output_padding” must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn’t directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If “output_shape” is explicitly provided, “output_padding” does not contribute additional size to “output_shape” but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.

  • output_shape - INTS : The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads

  • pads - INTS : Padding for the beginning and ending along each spatial axis

  • strides - INTS : Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.

Inputs

Between 8 and 9 inputs.

  • x (heterogeneous) - T1:

  • x_scale (heterogeneous) - tensor(float):

  • x_zero_point (heterogeneous) - T1:

  • w (heterogeneous) - T2:

  • w_scale (heterogeneous) - tensor(float):

  • w_zero_point (heterogeneous) - T2:

  • y_scale (heterogeneous) - tensor(float):

  • y_zero_point (heterogeneous) - T3:

  • B (optional, heterogeneous) - T4:

Outputs

  • y (heterogeneous) - T3:

Type Constraints

  • T1 in ( tensor(int8), tensor(uint8) ): Constrain input type to 8-bit integer tensor.

  • T2 in ( tensor(int8), tensor(uint8) ): Constrain filter type to 8-bit integer tensor.

  • T3 in ( tensor(int8), tensor(uint8) ): Constrain output type to 8-bit integer tensor.

  • T4 in ( tensor(int32) ): Constrain bias type to 32-bit integer tensor.

Examples