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mindspore.nn.Conv2dBnFoldQuantOneConv

class mindspore.nn.Conv2dBnFoldQuantOneConv(in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, eps=1e-05, momentum=0.997, has_bias=False, weight_init='normal', bias_init='zeros', beta_init='zeros', gamma_init='ones', mean_init='zeros', var_init='ones', fake=True, quant_config=quant_config_default, quant_dtype=QuantDtype.INT8)

2D convolution which use the convolution layer statistics once to calculate Batch Normalization operation folded construct.

This part is a more detailed overview of Conv2d operation. For more details about Quantization, please refer to the implementation of class of FakeQuantWithMinMaxObserver, FakeQuantWithMinMaxObserver.

$$\begin{array}{r}\begin{array}{c}{w}_q=quant(\frac{w}{\sqrt{va{r}_G+ϵ}}\ast \gamma )\\ b=\frac{-{\mu }_G}{\sqrt{va{r}_G+ϵ}}\ast \gamma +\beta \\ y=w_q\times x+b\end{array}\end{array}$$

where $quant$ is the continuous execution of quant and dequant, you can refer to the implementation of subclass of FakeQuantWithMinMaxObserver, mindspore.nn.FakeQuantWithMinMaxObserver. mu _{G} and var_{G} represent the global mean and variance respectively.

Parameters

• in_channels (int) – The number of input channel $C_{in}$.

• out_channels (int) – The number of output channel $C_{out}$.

• kernel_size (Union[int, tuple[int]]) – Specifies the height and width of the 2D convolution window.

• stride (Union[int, tuple[int]]) – Specifies stride for all spatial dimensions with the same value. Default: 1.

• pad_mode (str) – Specifies padding mode. The optional values are “same”, “valid”, “pad”. Default: “same”.

• padding (Union[int, tuple[int]]) – Implicit paddings on both sides of the x. Default: 0.

• dilation (Union[int, tuple[int]]) – Specifies the dilation rate to use for dilated convolution. Default: 1.

• group (int) – Splits filter into groups, in_ channels and out_channels must be divisible by the number of groups. Default: 1.

• eps (float) – Parameters for Batch Normalization. Default: 1e-5.

• momentum (float) – Parameters for Batch Normalization op. Default: 0.997.

• has_bias (bool) – Specifies whether the layer uses a bias vector, which is temporarily invalid. Default: False.

• weight_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the convolution kernel. Default: ‘normal’.

• bias_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the bias vector. Default: ‘zeros’.

• beta_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the beta vector. Default: ‘zeros’.

• gamma_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the gamma vector. Default: ‘ones’.

• mean_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the mean vector. Default: ‘zeros’.

• var_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the variance vector. Default: ‘ones’.

• fake (bool) – Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMaxObserver. Default: True.

• quant_config (QuantConfig) – Configures the types of quant observer and quant settings of weight and activation. Note that, QuantConfig is a special namedtuple, which is designed for quantization and can be generated by mindspore.compression.quant.create_quant_config() method. Default: QuantConfig with both items set to default FakeQuantWithMinMaxObserver.

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

Inputs:

• x (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 or group is not an int.

• TypeError – If kernel_size, stride, padding or dilation is neither an int nor a tuple.

• TypeError – If has_bias or fake is not a bool.

• TypeError – If data_format is not a string.

• ValueError – If in_channels, out_channels, kernel_size, stride or dilation is less than 1.

• ValueError – If padding is less than 0.

• ValueError – If pad_mode is not one of ‘same’, ‘valid’, ‘pad’.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore
>>> from mindspore.compression import quant
>>> from mindspore import Tensor
>>> qconfig = quant.create_quant_config()
>>> conv2d_bnfold = nn.Conv2dBnFoldQuantOneConv(1, 1, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
...                                             weight_init="ones", quant_config=qconfig)
>>> x = Tensor(np.array([[[[1, 0, 3], [1, 4, 7], [2, 5, 2]]]]), mindspore.float32)
>>> result = conv2d_bnfold(x)
>>> print(result)
[[[[5.9296875 13.8359375]
   [11.859375 17.78125]]]]

extend_repr()

Display instance object as string.