mindspore.nn.DenseQuant

class mindspore.nn.DenseQuant(in_channels, out_channels, weight_init='normal', bias_init='zeros', has_bias=True, activation=None, quant_config=quant_config_default, quant_dtype=QuantDtype.INT8)[source]

The fully connected layer with fake quantized operation.

This part is a more detailed overview of Dense operation. For more detials about Quantilization, please refer to mindspore.nn.FakeQuantWithMinMaxObserver.

Parameters

  • in_channels (int) – The dimension of the input space.

  • out_channels (int) – The dimension of the output space.

  • weight_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable weight_init parameter. The dtype is same as input. The values of str refer to the function initializer. Default: ‘normal’.

  • bias_init (Union[Tensor, str, Initializer, numbers.Number]) – The trainable bias_init parameter. The dtype is same as input. The values of str refer to the function initializer. Default: ‘zeros’.

  • has_bias (bool) – Specifies whether the layer uses a bias vector. Default: True.

  • activation (Union[str, Cell, Primitive]) – The regularization function applied to the output of the layer, eg. ‘relu’. Default: None.

  • quant_config (QuantConfig) – Configures the oberser types and quant settings of weight and activation. Can be generated by compression.quant.create_quant_config method. Default: both set to default FakeQuantWithMinMaxObserver.

  • quant_dtype (QuantDtype) – Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

Inputs:

  • input (Tensor) - Tensor of shape \((N, C_{in}, H_{in}, W_{in})\).

Outputs:

Tensor of shape \((N, C_{out}, H_{out}, W_{out})\).

Raises

  • TypeError – If in_channels, out_channels is not an int.

  • TypeError – If has_bias is not a bool.

  • ValueError – If in_channels or out_channels is less than 1.

Supported Platforms:

Ascend GPU

Examples

>>> qconfig = compression.quant.create_quant_config()
>>> dense_quant = nn.DenseQuant(3, 6, quant_config=qconfig)
>>> input = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
>>> result = dense_quant(input)
>>> output = result.shape
>>> print(output)
(2, 6)
construct(x)[source]

Use operators to construct the Dense layer.

extend_repr()[source]

A pretty print for Dense layer.