Hardmax#

Hardmax - 13#

Version

• name: Hardmax (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

The operator computes the hardmax values for the given input:

Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise

The “axis” attribute indicates the dimension along which Hardmax will be performed. The output tensor has the same shape and contains the Hardmax values of the corresponding input.

Attributes

• axis:

Describes the dimension Hardmax will be performed on. Negative

value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Default value is -1.

Inputs

• input (heterogeneous) - T: The input tensor of rank >= axis.

Outputs

• output (heterogeneous) - T: The output values with the same shape as the input tensor.

Type Constraints

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

Examples

default

node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
)

x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2],
[0, 1, 2, 3]]).astype(np.float32)
# expect result:
# [[1. 0. 0. 0.]
# [0. 1. 0. 0.]
# [0. 0. 1. 0.]
# [0. 0. 0. 1.]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_example')

# For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
x = np.array([[3, 3, 3, 1]]).astype(np.float32)
# expect result:
# [[1, 0, 0, 0]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_one_hot')


_hardmax_axis

x = np.random.randn(3, 4, 5).astype(np.float32)
node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
axis=0,
)
y = hardmax(x, axis=0)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_axis_0')

node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
axis=1,
)
y = hardmax(x, axis=1)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_axis_1')

node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
axis=2,
)
y = hardmax(x, axis=2)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_axis_2')

node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
axis=-1,
)
y = hardmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_negative_axis')

# default axis is -1
node = onnx.helper.make_node(
'Hardmax',
inputs=['x'],
outputs=['y'],
)
expect(node, inputs=[x], outputs=[y],
name='test_hardmax_default_axis')


Differences

 0 The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch 1 0  of the given input. The operator computes the hardmax values for the given input: 2 1 3 The input does not need to explicitly be a 2D vector; rather, it will be 4 2 coerced into one. For an arbitrary n-dimensional tensor  Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise 5 input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is 3 6 4 the axis provided, then input will be coerced into a 2-dimensional tensor with The "axis" attribute indicates the dimension along which Hardmax 7 dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default 8 case where axis=1, this means the input tensor will be coerced into a 2D tensor 9 of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. 10 In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. 11 Each of these dimensions must be matched correctly, or else the operator 12 5 will throw errors. The output tensor has the same shape will be performed. The output tensor has the same shape 13 6 and contains the hardmax values of the corresponding input. and contains the Hardmax values of the corresponding input. 14 7 15 8 **Attributes** **Attributes** 16 9 17 10 * **axis**: * **axis**: 18 11  Describes the axis of the inputs when coerced to 2D; defaults to one  Describes the dimension Hardmax will be performed on. Negative 19  because the 0th axis most likely describes the batch_size. Negative 20 12  value means counting dimensions from the back. Accepted range is  value means counting dimensions from the back. Accepted range is 21 13  [-r, r-1] where r = rank(input). Default value is 1.  [-r, r-1] where r = rank(input). Default value is -1. 22 14 23 15 **Inputs** **Inputs** 24 16 25 17 * **input** (heterogeneous) - **T**: * **input** (heterogeneous) - **T**: 26 18  The input tensor that's coerced into a 2D matrix of size (NxD) as  The input tensor of rank >= axis. 27  described above. 28 19 29 20 **Outputs** **Outputs** 30 21 31 22 * **output** (heterogeneous) - **T**: * **output** (heterogeneous) - **T**: 32 23  The output values with the same shape as input tensor (the original  The output values with the same shape as the input tensor. 33  size without coercion). 34 24 35 25 **Type Constraints** **Type Constraints** 36 26 37 27 * **T** in ( * **T** in ( 28  tensor(bfloat16), 38 29  tensor(double),  tensor(double), 39 30  tensor(float),  tensor(float), 40 31  tensor(float16)  tensor(float16) 41 32  ):  ): 42 33  Constrain input and output types to float tensors.  Constrain input and output types to float tensors.

Hardmax - 11#

Version

• name: Hardmax (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

The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch

of the given input.

The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input in [a_0, a_1, …, a_{k-1}, a_k, …, a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * … * a_{k-1}, a_k * … * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * … * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * … * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the hardmax values of the corresponding input.

Attributes

• axis: Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Default value is 1.

Inputs

• input (heterogeneous) - T: The input tensor that’s coerced into a 2D matrix of size (NxD) as described above.

Outputs

• output (heterogeneous) - T: The output values with the same shape as input tensor (the original size without coercion).

Type Constraints

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

Differences

 0 0 The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch 1 1  of the given input. The input is a 2-D tensor (Tensor) of size  of the given input. 2 (batch_size x input_feature_dimensions). The output tensor has the same shape 3 and contains the hardmax values of the corresponding input. 4 2 5 3 Input does not need to explicitly be a 2D vector; rather, it will be The input does not need to explicitly be a 2D vector; rather, it will be 6 4 coerced into one. For an arbitrary n-dimensional tensor coerced into one. For an arbitrary n-dimensional tensor 7 5 input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is 8 6 the axis provided, then input will be coerced into a 2-dimensional tensor with the axis provided, then input will be coerced into a 2-dimensional tensor with 9 7 dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default 10 8 case where axis=1, this means the input tensor will be coerced into a 2D tensor case where axis=1, this means the input tensor will be coerced into a 2D tensor 11 9 of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. 12 10 In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. 13 11 Each of these dimensions must be matched correctly, or else the operator Each of these dimensions must be matched correctly, or else the operator 14 12 will throw errors. will throw errors. The output tensor has the same shape 13 and contains the hardmax values of the corresponding input. 15 14 16 15 **Attributes** **Attributes** 17 16 18 17 * **axis**: * **axis**: 19 18  Describes the axis of the inputs when coerced to 2D; defaults to one  Describes the axis of the inputs when coerced to 2D; defaults to one 20 19  because the 0th axis most likely describes the batch_size Default value is 1.  because the 0th axis most likely describes the batch_size. Negative 20  value means counting dimensions from the back. Accepted range is 21  [-r, r-1] where r = rank(input). Default value is 1. 21 22 22 23 **Inputs** **Inputs** 23 24 24 25 * **input** (heterogeneous) - **T**: * **input** (heterogeneous) - **T**: 25 26  The input tensor that's coerced into a 2D matrix of size (NxD) as  The input tensor that's coerced into a 2D matrix of size (NxD) as 26 27  described above.  described above. 27 28 28 29 **Outputs** **Outputs** 29 30 30 31 * **output** (heterogeneous) - **T**: * **output** (heterogeneous) - **T**: 31 32  The output values with the same shape as input tensor (the original  The output values with the same shape as input tensor (the original 32 33  size without coercion).  size without coercion). 33 34 34 35 **Type Constraints** **Type Constraints** 35 36 36 37 * **T** in ( * **T** in ( 37 38  tensor(double),  tensor(double), 38 39  tensor(float),  tensor(float), 39 40  tensor(float16)  tensor(float16) 40 41  ):  ): 41 42  Constrain input and output types to float tensors.  Constrain input and output types to float tensors.

Hardmax - 1#

Version

• name: Hardmax (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

The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch

of the given input. The input is a 2-D tensor (Tensor<float>) of size

(batch_size x input_feature_dimensions). The output tensor has the same shape and contains the hardmax values of the corresponding input.

Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input in [a_0, a_1, …, a_{k-1}, a_k, …, a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * … * a_{k-1}, a_k * … * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * … * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * … * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors.

Attributes

• axis: Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Default value is 1.

Inputs

• input (heterogeneous) - T: The input tensor that’s coerced into a 2D matrix of size (NxD) as described above.

Outputs

• output (heterogeneous) - T: The output values with the same shape as input tensor (the original size without coercion).

Type Constraints

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