mindspore.nn.Conv2dBnFoldQuant

class mindspore.nn.Conv2dBnFoldQuant(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, freeze_bn=100000)[source]

2D convolution with 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.

\[ \begin{align}\begin{aligned}y = x\times w+ b\\w_{q}=quant(\frac{w}{\sqrt{Var[y]+\epsilon}}*\gamma )\\y_{out}= w_{q}\times x+\frac{b-E[y]}{\sqrt{Var[y]+\epsilon}}*\gamma +\beta\end{aligned}\end{align} \]

where \(quant\) is the continuous execution of quant and dequant. Two convolution and Batch Normalization operation are used here, the purpose of the first convolution and Batch Normalization is to count the mean E[y] and variance Var[y] of current batch output for quantization.

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. 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.

  • freeze_bn (int) – The quantization freeze Batch Normalization op is according to the global step. Default: 100000.

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.

  • 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’.

  • ValueError – If device_target in context is neither Ascend nor GPU.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore
>>> from mindspore.compression import quant
>>> from mindspore import Tensor
>>> qconfig = quant.create_quant_config()
>>> conv2d_bnfold = nn.Conv2dBnFoldQuant(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()[source]

Display instance object as string.