分类目录：《深入浅出Pytorch函数》总目录
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torch.nn.init模块中的所有函数都用于初始化神经网络参数，因此它们都在torc.no_grad()模式下运行，autograd不会将其考虑在内。

该函数用$\text{Dirac}\delta$ 函数来填充3-5维输入张量或变量,在卷积层尽可能多的保存输入通道特征。

语法

torch.nn.init.dirac_(tensor, groups=1)

参数

• tensor：[Tensor] 一个3~5维张量torch.Tensor
• groups：[int] conv层中的组数，默认值为1

返回值

一个torch.Tensor且参数tensor也会更新

实例

w = torch.empty(3, 16, 5, 5)
nn.init.dirac_(w)
w = torch.empty(3, 24, 5, 5)
nn.init.dirac_(w, 3)

函数实现

def dirac_(tensor, groups=1):
    r"""Fills the {3, 4, 5}-dimensional input `Tensor` with the Dirac
    delta function. Preserves the identity of the inputs in `Convolutional`
    layers, where as many input channels are preserved as possible. In case
    of groups>1, each group of channels preserves identity

    Args:
        tensor: a {3, 4, 5}-dimensional `torch.Tensor`
        groups (int, optional): number of groups in the conv layer (default: 1)
    Examples:
        >>> w = torch.empty(3, 16, 5, 5)
        >>> nn.init.dirac_(w)
        >>> w = torch.empty(3, 24, 5, 5)
        >>> nn.init.dirac_(w, 3)
    """
    dimensions = tensor.ndimension()
    if dimensions not in [3, 4, 5]:
        raise ValueError("Only tensors with 3, 4, or 5 dimensions are supported")

    sizes = tensor.size()

    if sizes[0] % groups != 0:
        raise ValueError('dim 0 must be divisible by groups')

    out_chans_per_grp = sizes[0] // groups
    min_dim = min(out_chans_per_grp, sizes[1])

    with torch.no_grad():
        tensor.zero_()

        for g in range(groups):
            for d in range(min_dim):
                if dimensions == 3:  # Temporal convolution
                    tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2] = 1
                elif dimensions == 4:  # Spatial convolution
                    tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2,
                           tensor.size(3) // 2] = 1
                else:  # Volumetric convolution
                    tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2,
                           tensor.size(3) // 2, tensor.size(4) // 2] = 1
    return tensor