mindspore.nn.probability.bnn_layers.ConvReparam

class mindspore.nn.probability.bnn_layers.ConvReparam(in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_prior_fn=NormalPrior, weight_posterior_fn=normal_post_fn, bias_prior_fn=NormalPrior, bias_posterior_fn=normal_post_fn)[source]

Convolutional variational layers with Reparameterization.

For more details, refer to the paper Auto-Encoding Variational Bayes.

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]]) – The data type is an integer or a tuple of 2 integers. The kernel size specifies the height and width of the 2D convolution window. a single integer stands for the value is for both height and width of the kernel. With the kernel_size being a tuple of 2 integers, the first value is for the height and the other is the width of the kernel.

  • stride (Union[int, tuple[int]]) – The distance of kernel moving, an integer number represents that the height and width of movement are both strides, or a tuple of two integers numbers represents that height and width of movement respectively. Default: 1.

  • pad_mode (str) –

    Specifies the padding mode. The optional values are “same”, “valid”, and “pad”. Default: “same”.

    • same: Adopts the way of completion. Output height and width will be the same as the input. The total number of padding will be calculated for in horizontal and vertical directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the last extra padding will be done from the bottom and the right side. If this mode is set, padding must be 0.

    • valid: Adopts the way of discarding. The possible largest height and width of the output will be returned without padding. Extra pixels will be discarded. If this mode is set, padding must be 0.

    • pad: Implicit paddings on both sides of the input. The number of padding will be padded to the input Tensor borders. padding must be greater than or equal to 0.

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

  • dilation (Union[int, tuple[int]]) – The data type is an integer or a tuple of 2 integers. This parameter specifies the dilation rate of the dilated convolution. If set to be \(k > 1\), there will be \(k - 1\) pixels skipped for each sampling location. Its value must be greater or equal to 1 and bounded by the height and width of the input. Default: 1.

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

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

  • weight_prior_fn (Cell) – The prior distribution for weight. It must return a mindspore distribution instance. Default: NormalPrior. (which creates an instance of standard normal distribution). The current version only supports normal distribution.

  • weight_posterior_fn (function) – The posterior distribution for sampling weight. It must be a function handle which returns a mindspore distribution instance. Default: normal_post_fn. The current version only supports normal distribution.

  • bias_prior_fn (Cell) – The prior distribution for bias vector. It must return a mindspore distribution. Default: NormalPrior(which creates an instance of standard normal distribution). The current version only supports normal distribution.

  • bias_posterior_fn (function) – The posterior distribution for sampling bias vector. It must be a function handle which returns a mindspore distribution instance. Default: normal_post_fn. The current version only supports normal distribution.

Inputs:

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

Outputs:

Tensor, with the shape being \((N, C_{out}, H_{out}, W_{out})\).

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.nn.probability import bnn_layers
>>> net = bnn_layers.ConvReparam(120, 240, 4, has_bias=False)
>>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
>>> output = net(input).shape
>>> print(output)
(1, 240, 1024, 640)
apply_variational_weight(inputs)[source]

Calculate weight.