# tensorplay.functional.quantize_per_channel

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.quantize_per_channel.html

```python
tensorplay.functional.quantize_per_channel(input, scales, zero_points, axis, dtype) → Tensor
```

Converts a float tensor to a per-channel quantized tensor with given scales and zero points.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – float tensor to quantize

- scales ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – float 1D tensor of scales to use, size should match input.size(axis)

- zero_points ([int](https://docs.python.org/3/builtins/functions.html#int)) – integer 1D tensor of offset to use, size should match input.size(axis)

- axis ([int](https://docs.python.org/3/builtins/functions.html#int)) – dimension on which apply per-channel quantization

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType)) – the desired data type of returned tensor. Has to be one of the quantized dtypes: tensorplay.quint8, tensorplay.qint8, tensorplay.qint32

Returns:

A newly quantized tensor

Return type:

[Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)

Example:

```
>>> x = tensorplay.tensor([[-1.0, 0.0], [1.0, 2.0]])
>>> tensorplay.quantize_per_channel(x, tensorplay.tensor([0.1, 0.01]), tensorplay.tensor([10, 0]), 0, tensorplay.quint8)
tensor([[-1.,  0.],
        [ 1.,  2.]], size=(2, 2), dtype=tensorplay.quint8,
       quantization_scheme=tensorplay.per_channel_affine,
       scale=tensor([0.1000, 0.0100], dtype=tensorplay.float64),
       zero_point=tensor([10,  0]), axis=0)
>>> tensorplay.quantize_per_channel(x, tensorplay.tensor([0.1, 0.01]), tensorplay.tensor([10, 0]), 0, tensorplay.quint8).int_repr()
tensor([[  0,  10],
        [100, 200]], dtype=tensorplay.uint8)
```