.. _op_ai_onnx_HardSwish:

HardSwish
=========

- **Domain**: ``ai.onnx``
- **Since version**: 22

HardSwish takes one input data (Tensor ) and produces one output data (Tensor ) where
the HardSwish function, y = x \* max(0, min(1, alpha \* x + beta)) = x \* HardSigmoid (x),
where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.

**Inputs**

- **X** (*T*): Input tensor

**Outputs**

- **Y** (*T*): Output tensor

**Type Constraints**

- **T**: Constrain input and output types to float tensors.
  Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).

Examples
--------

**test_cc_hardswish**

.. code-block:: text

    Node:
      HardSwish(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float32
        [[-4., -1.,  0.],
         [ 1.,  2.,  4.]]

    Outputs:
      Y: shape=(2, 3), dtype=float32
        [[-0.       , -0.3333333,  0.       ],
         [ 0.6666667,  1.6666667,  4.       ]]

Differences with previous version (14)
--------------------------------------

**SchemaDiff**: ``HardSwish`` (domain ``'ai.onnx'``)

* old version: 14
* new version: 22
* breaking: no

**Type constraints:**

* changed 'T': added types: ['tensor(bfloat16)']

Version History
---------------

- :doc:`Version 14 <HardSwish-14>`