Unsqueeze#

Unsqueeze - 13 #

Version

name: Unsqueeze (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

Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).

For example:: Given an input tensor (data) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].

The input axes should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes. Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in axes does not matter and can come in any order.

Inputs

data (heterogeneous) - T: Original tensor
axes (heterogeneous) - tensor(int64): List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

Outputs

expanded (heterogeneous) - T: Reshaped tensor with same data as input.

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.

Examples

_unsqueeze_one_axis

x = np.random.randn(3, 4, 5).astype(np.float32)

for i in range(x.ndim):
    axes = np.array([i]).astype(np.int64)
    node = onnx.helper.make_node(
        "Unsqueeze",
        inputs=["x", "axes"],
        outputs=["y"],
    )
    y = np.expand_dims(x, axis=i)

    expect(
        node,
        inputs=[x, axes],
        outputs=[y],
        name="test_unsqueeze_axis_" + str(i),
    )

_unsqueeze_two_axes

x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([1, 4]).astype(np.int64)

node = onnx.helper.make_node(
    "Unsqueeze",
    inputs=["x", "axes"],
    outputs=["y"],
)
y = np.expand_dims(x, axis=1)
y = np.expand_dims(y, axis=4)

expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes")

_unsqueeze_three_axes

x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([2, 4, 5]).astype(np.int64)

node = onnx.helper.make_node(
    "Unsqueeze",
    inputs=["x", "axes"],
    outputs=["y"],
)
y = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=4)
y = np.expand_dims(y, axis=5)

expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes")

_unsqueeze_unsorted_axes

x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([5, 4, 2]).astype(np.int64)

node = onnx.helper.make_node(
    "Unsqueeze",
    inputs=["x", "axes"],
    outputs=["y"],
)
y = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=4)
y = np.expand_dims(y, axis=5)

expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes")

_unsqueeze_negative_axes

node = onnx.helper.make_node(
    "Unsqueeze",
    inputs=["x", "axes"],
    outputs=["y"],
)
x = np.random.randn(1, 3, 1, 5).astype(np.float32)
axes = np.array([-2]).astype(np.int64)
y = np.expand_dims(x, axis=-2)
expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes")

Differences

`0`	`0`	`Insert single-dimensional entries to the shape of an input tensor (data).`	`Insert single-dimensional entries to the shape of an input tensor (data).`
`1`	`1`	`Takes one required argument axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).`	`Takes one required input axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).`
`2`	`2`
`3`	`3`	`For example:`	`For example:`
`4`	`4`	`Given an input tensor (data) of shape [3, 4, 5], then`	`Given an input tensor (data) of shape [3, 4, 5], then`
`5`	`5`	`Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].`	`Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].`
`6`	`6`
`7`	`7`	`The attribute axes should not contain any duplicate entries. It is an error if it contains duplicates.`	`The input axes should not contain any duplicate entries. It is an error if it contains duplicates.`
`8`	`8`	`The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes.`	`The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes.`
`9`	`9`	`Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1].`	`Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1].`
`10`	`10`	`The order of values in axes does not matter and can come in any order.`	`The order of values in axes does not matter and can come in any order.`
`11`	`11`
	`12`		`Inputs`
	`13`
`12`	`14`	`Attributes`	`* data (heterogeneous) - T:`
`13`
	`15`		`Original tensor`
`14`	`16`	`* axes (required):`	`* axes (heterogeneous) - tensor(int64):`
`15`	`17`	`List of integers indicating the dimensions to be inserted. Negative`	`List of integers indicating the dimensions to be inserted. Negative`
`16`	`18`	`value means counting dimensions from the back. Accepted range is`	`value means counting dimensions from the back. Accepted range is`
`17`	`19`	`[-r, r-1] where r = rank(expanded).`	`[-r, r-1] where r = rank(expanded).`
`18`	`20`
`19`		`Inputs`
`20`
`21`		`* data (heterogeneous) - T:`
`22`		`Original tensor`
`23`
`24`	`21`	`Outputs`	`Outputs`
`25`	`22`
`26`	`23`	`* expanded (heterogeneous) - T:`	`* expanded (heterogeneous) - T:`
`27`	`24`	`Reshaped tensor with same data as input.`	`Reshaped tensor with same data as input.`
`28`	`25`
`29`	`26`	`Type Constraints`	`Type Constraints`
`30`	`27`
`31`	`28`	`* T in (`	`* T in (`
	`29`		`tensor(bfloat16),`
`32`	`30`	`tensor(bool),`	`tensor(bool),`
`33`	`31`	`tensor(complex128),`	`tensor(complex128),`
`34`	`32`	`tensor(complex64),`	`tensor(complex64),`
`35`	`33`	`tensor(double),`	`tensor(double),`
`36`	`34`	`tensor(float),`	`tensor(float),`
`37`	`35`	`tensor(float16),`	`tensor(float16),`
`38`	`36`	`tensor(int16),`	`tensor(int16),`
`39`	`37`	`tensor(int32),`	`tensor(int32),`
`40`	`38`	`tensor(int64),`	`tensor(int64),`
`41`	`39`	`tensor(int8),`	`tensor(int8),`
`42`	`40`	`tensor(string),`	`tensor(string),`
`43`	`41`	`tensor(uint16),`	`tensor(uint16),`
`44`	`42`	`tensor(uint32),`	`tensor(uint32),`
`45`	`43`	`tensor(uint64),`	`tensor(uint64),`
`46`	`44`	`tensor(uint8)`	`tensor(uint8)`
`47`	`45`	`):`	`):`
`48`	`46`	`Constrain input and output types to all tensor types.`	`Constrain input and output types to all tensor types.`

Unsqueeze - 11 #

Version

name: Unsqueeze (GitHub)
domain: main
since_version: 11
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 11.

Summary

Insert single-dimensional entries to the shape of an input tensor (data). Takes one required argument axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).

For example:: Given an input tensor (data) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].

The attribute axes should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes. Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in axes does not matter and can come in any order.

Attributes

axes (required): List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

Inputs

data (heterogeneous) - T: Original tensor

Outputs

expanded (heterogeneous) - T: Reshaped tensor with same data as input.

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.

Differences

`0`	`0`	`Insert single-dimensional entries to the shape of a tensor.`	`Insert single-dimensional entries to the shape of an input tensor (data).`
`1`	`1`	`Takes one required argument axes, a list of dimensions that will be inserted.`	`Takes one required argument axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).`
	`2`
`2`	`3`	`Dimension indices in axes are as seen in the output tensor. For example:`	`For example:`
`3`	`4`	`Given a tensor such that tensor with shape [3, 4, 5], then`	`Given an input tensor (data) of shape [3, 4, 5], then`
	`5`		`Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].`
	`6`
	`7`		`The attribute axes should not contain any duplicate entries. It is an error if it contains duplicates.`
`4`	`8`	`Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]`	`The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes.`
	`9`		`Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1].`
	`10`		`The order of values in axes does not matter and can come in any order.`
`5`	`11`
`6`	`12`	`Attributes`	`Attributes`
`7`	`13`
`8`	`14`	`* axes (required):`	`* axes (required):`
`9`	`15`	`List of non-negative integers, indicate the dimensions to be`	`List of integers indicating the dimensions to be inserted. Negative`
`10`		`inserted`
	`16`		`value means counting dimensions from the back. Accepted range is`
	`17`		`[-r, r-1] where r = rank(expanded).`
`11`	`18`
`12`	`19`	`Inputs`	`Inputs`
`13`	`20`
`14`	`21`	`* data (heterogeneous) - T:`	`* data (heterogeneous) - T:`
`15`	`22`	`Original tensor`	`Original tensor`
`16`	`23`
`17`	`24`	`Outputs`	`Outputs`
`18`	`25`
`19`	`26`	`* expanded (heterogeneous) - T:`	`* expanded (heterogeneous) - T:`
`20`	`27`	`Reshaped tensor with same data as input.`	`Reshaped tensor with same data as input.`
`21`	`28`
`22`	`29`	`Type Constraints`	`Type Constraints`
`23`	`30`
`24`	`31`	`* T in (`	`* T in (`
`25`	`32`	`tensor(bool),`	`tensor(bool),`
`26`	`33`	`tensor(complex128),`	`tensor(complex128),`
`27`	`34`	`tensor(complex64),`	`tensor(complex64),`
`28`	`35`	`tensor(double),`	`tensor(double),`
`29`	`36`	`tensor(float),`	`tensor(float),`
`30`	`37`	`tensor(float16),`	`tensor(float16),`
`31`	`38`	`tensor(int16),`	`tensor(int16),`
`32`	`39`	`tensor(int32),`	`tensor(int32),`
`33`	`40`	`tensor(int64),`	`tensor(int64),`
`34`	`41`	`tensor(int8),`	`tensor(int8),`
`35`	`42`	`tensor(string),`	`tensor(string),`
`36`	`43`	`tensor(uint16),`	`tensor(uint16),`
`37`	`44`	`tensor(uint32),`	`tensor(uint32),`
`38`	`45`	`tensor(uint64),`	`tensor(uint64),`
`39`	`46`	`tensor(uint8)`	`tensor(uint8)`
`40`	`47`	`):`	`):`
`41`	`48`	`Constrain input and output types to all tensor types.`	`Constrain input and output types to all tensor types.`

Unsqueeze - 1 #

Version

name: Unsqueeze (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

Insert single-dimensional entries to the shape of a tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor. For example:

Given a tensor such that tensor with shape [3, 4, 5], then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]

Attributes

axes (required): List of non-negative integers, indicate the dimensions to be inserted

Inputs

data (heterogeneous) - T: Original tensor

Outputs

expanded (heterogeneous) - T: Reshaped tensor with same data as input.

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.

Unsqueeze#

Unsqueeze - 13#

Unsqueeze - 11#

Unsqueeze - 1#

Unsqueeze - 13 #

Unsqueeze - 11 #

Unsqueeze - 1 #