Mean#

Mean - 13#

Version

  • name: Mean (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

Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

Between 1 and 2147483647 inputs.

  • data_0 (variadic, heterogeneous) - T: List of tensors for mean.

Outputs

  • mean (heterogeneous) - T: Output tensor.

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Examples

default

data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([2, 3, 4]).astype(np.float32)
node = onnx.helper.make_node(
    'Mean',
    inputs=['data_0', 'data_1', 'data_2'],
    outputs=['result'],
)
expect(node, inputs=[data_0, data_1, data_2], outputs=[result],
       name='test_mean_example')

node = onnx.helper.make_node(
    'Mean',
    inputs=['data_0'],
    outputs=['result'],
)
expect(node, inputs=[data_0], outputs=[data_0],
       name='test_mean_one_input')

result = np.divide(np.add(data_0, data_1), 2.)
node = onnx.helper.make_node(
    'Mean',
    inputs=['data_0', 'data_1'],
    outputs=['result'],
)
expect(node, inputs=[data_0, data_1], outputs=[result],
       name='test_mean_two_inputs')

Differences

00Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).
11All inputs and outputs must have the same data type.All inputs and outputs must have the same data type.
22This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX _.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX _.
33
44**Inputs****Inputs**
55
66Between 1 and 2147483647 inputs.Between 1 and 2147483647 inputs.
77
88* **data_0** (variadic, heterogeneous) - **T**:* **data_0** (variadic, heterogeneous) - **T**:
99 List of tensors for mean. List of tensors for mean.
1010
1111**Outputs****Outputs**
1212
1313* **mean** (heterogeneous) - **T**:* **mean** (heterogeneous) - **T**:
1414 Output tensor. Output tensor.
1515
1616**Type Constraints****Type Constraints**
1717
1818* **T** in (* **T** in (
19 tensor(bfloat16),
1920 tensor(double), tensor(double),
2021 tensor(float), tensor(float),
2122 tensor(float16) tensor(float16)
2223 ): ):
2324 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

Mean - 8#

Version

  • name: Mean (GitHub)

  • domain: main

  • since_version: 8

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 8.

Summary

Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

Between 1 and 2147483647 inputs.

  • data_0 (variadic, heterogeneous) - T: List of tensors for mean.

Outputs

  • mean (heterogeneous) - T: Output tensor.

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

0Element-wise mean of each of the input tensors (with Numpy-style broadcasting support).
1All inputs and outputs must have the same data type.
02Element-wise mean of each of the input tensors. All inputs and outputs mustThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX <https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md>_.
1have the same shape and data type.
23
34**Inputs****Inputs**
45
56Between 1 and 2147483647 inputs.Between 1 and 2147483647 inputs.
67
78* **data_0** (variadic, heterogeneous) - **T**:* **data_0** (variadic, heterogeneous) - **T**:
89 List of tensors for Mean. List of tensors for mean.
910
1011**Outputs****Outputs**
1112
1213* **mean** (heterogeneous) - **T**:* **mean** (heterogeneous) - **T**:
1314 Output tensor. Same dimension as inputs. Output tensor.
1415
1516**Type Constraints****Type Constraints**
1617
1718* **T** in (* **T** in (
1819 tensor(double), tensor(double),
1920 tensor(float), tensor(float),
2021 tensor(float16) tensor(float16)
2122 ): ):
2223 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

Mean - 6#

Version

  • name: Mean (GitHub)

  • domain: main

  • since_version: 6

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 6.

Summary

Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type.

Inputs

Between 1 and 2147483647 inputs.

  • data_0 (variadic, heterogeneous) - T: List of tensors for Mean.

Outputs

  • mean (heterogeneous) - T: Output tensor. Same dimension as inputs.

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

00Element-wise mean of each of the input tensors. All inputs and outputs mustElement-wise mean of each of the input tensors. All inputs and outputs must
11have the same shape and data type.have the same shape and data type.
22
3**Attributes**
4
5* **consumed_inputs**:
6 legacy optimization attribute.
7
83**Inputs****Inputs**
94
105Between 1 and 2147483647 inputs.Between 1 and 2147483647 inputs.
116
127* **data_0** (variadic, heterogeneous) - **T**:* **data_0** (variadic, heterogeneous) - **T**:
138 List of tensors for Mean. List of tensors for Mean.
149
1510**Outputs****Outputs**
1611
1712* **mean** (heterogeneous) - **T**:* **mean** (heterogeneous) - **T**:
1813 Output tensor. Same dimension as inputs. Output tensor. Same dimension as inputs.
1914
2015**Type Constraints****Type Constraints**
2116
2217* **T** in (* **T** in (
2318 tensor(double), tensor(double),
2419 tensor(float), tensor(float),
2520 tensor(float16) tensor(float16)
2621 ): ):
2722 Constrain input and output types to float tensors. Constrain input and output types to float tensors.

Mean - 1#

Version

  • name: Mean (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

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

Summary

Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type.

Attributes

  • consumed_inputs: legacy optimization attribute.

Inputs

Between 1 and 2147483647 inputs.

  • data_0 (variadic, heterogeneous) - T: List of tensors for Mean.

Outputs

  • mean (heterogeneous) - T: Output tensor. Same dimension as inputs.

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.