Transpose

Transpose - 24

Version

  • name: Transpose (GitHub)

  • domain: main

  • since_version: 24

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 24.

Summary

Returns a transpose of the input tensor. (Similar to numpy.transpose). The optional attribute perm must be a permutation of the dimensions of the input tensor. Axis i of the output tensor corresponds to the axis perm[i] of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute perm is omitted, its default value is (n-1, ..., 0), where n is the rank of the input tensor.

Attributes

  • perm - INTS :

    A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.

Inputs

  • data (heterogeneous) - T:

    An input tensor.

Outputs

  • transposed (heterogeneous) - T:

    Transposed output.

Type Constraints

  • T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float4e2m1), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(float8e8m0), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all tensor types.

Transpose - 23

Version

  • name: Transpose (GitHub)

  • domain: main

  • since_version: 23

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 23.

Summary

Returns a transpose of the input tensor. (Similar to numpy.transpose). The optional attribute perm must be a permutation of the dimensions of the input tensor. Axis i of the output tensor corresponds to the axis perm[i] of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute perm is omitted, its default value is (n-1, ..., 0), where n is the rank of the input tensor.

Attributes

  • perm - INTS :

    A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.

Inputs

  • data (heterogeneous) - T:

    An input tensor.

Outputs

  • transposed (heterogeneous) - T:

    Transposed output.

Type Constraints

  • T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float4e2m1), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all tensor types.

Transpose - 21

Version

  • name: Transpose (GitHub)

  • domain: main

  • since_version: 21

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 21.

Summary

Returns a transpose of the input tensor. (Similar to numpy.transpose). The optional attribute perm must be a permutation of the dimensions of the input tensor. Axis i of the output tensor corresponds to the axis perm[i] of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute perm is omitted, its default value is (n-1, ..., 0), where n is the rank of the input tensor.

Attributes

  • perm - INTS :

    A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.

Inputs

  • data (heterogeneous) - T:

    An input tensor.

Outputs

  • transposed (heterogeneous) - T:

    Transposed output.

Type Constraints

  • T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all tensor types.

Transpose - 13

Version

  • name: Transpose (GitHub)

  • domain: main

  • since_version: 13

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 13.

Summary

Returns a transpose of the input tensor. (Similar to numpy.transpose). The optional attribute perm must be a permutation of the dimensions of the input tensor. Axis i of the output tensor corresponds to the axis perm[i] of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute perm is omitted, its default value is (n-1, ..., 0), where n is the rank of the input tensor.

Attributes

  • perm - INTS :

    A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.

Inputs

  • data (heterogeneous) - T:

    An input tensor.

Outputs

  • transposed (heterogeneous) - T:

    Transposed output.

Type Constraints

  • T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all tensor types.

Transpose - 1

Version

  • name: Transpose (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 1.

Summary

Returns a transpose of the input tensor. (Similar to numpy.transpose). The optional attribute perm must be a permutation of the dimensions of the input tensor. Axis i of the output tensor corresponds to the axis perm[i] of the input tensor. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). When perm=(1, 2, 0), given an input tensor of shape (1, 2, 3), the output shape will be (2, 3, 1). If the attribute perm is omitted, its default value is (n-1, ..., 0), where n is the rank of the input tensor.

Attributes

  • perm - INTS :

    A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.

Inputs

  • data (heterogeneous) - T:

    An input tensor.

Outputs

  • transposed (heterogeneous) - T:

    Transposed output.

Type Constraints

  • T in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to all tensor types.