导语 ：使用pytorch搭好网络之后，没有显式地初始化网络参数，是否可以直接训练网络呢？
_ConvNd
class _ConvNd(Module):__constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias','padding_mode', 'output_padding', 'in_channels','out_channels', 'kernel_size']def __init__(self, in_channels, out_channels, kernel_size, stride,padding, dilation, transposed, output_padding,groups, bias, padding_mode):super(_ConvNd, self).__init__()... ... # 此处略去几十行代码self.reset_parameters() # 核心代码def reset_parameters(self):init.kaiming_uniform_(self.weight, a=math.sqrt(5))if self.bias is not None:fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)bound = 1 / math.sqrt(fan_in)init.uniform_(self.bias, -bound, bound)
Remark :
- class Convkd(_ConvNd), where k = {1, 2, 3}.
- class ConvTransposekd(_ConvTransposeMixin, _ConvNd), where k = {1, 2, 3}.
_BatchNorm
class _NormBase(Module):"""Common base of _InstanceNorm and _BatchNorm"""_version = 2__constants__ = ['track_running_stats', 'momentum', 'eps', 'weight', 'bias','running_mean', 'running_var', 'num_batches_tracked','num_features', 'affine']def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,track_running_stats=True):super(_NormBase, self).__init__()... ... # 此处略去几十行代码self.reset_parameters() # 核心代码def reset_running_stats(self):if self.track_running_stats:self.running_mean.zero_()self.running_var.fill_(1)self.num_batches_tracked.zero_()def reset_parameters(self):self.reset_running_stats()if self.affine:init.ones_(self.weight)init.zeros_(self.bias)
class _BatchNorm(_NormBase):def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,track_running_stats=True):super(_BatchNorm, self).__init__(num_features, eps, momentum, affine, track_running_stats)
Remark : class BatchNormkd(_BatchNorm), where k = {1, 2, 3}.
Linear
class Linear(Module):__constants__ = ['bias', 'in_features', 'out_features']def __init__(self, in_features, out_features, bias=True):super(Linear, self).__init__()... ... # 此处略去几十行代码self.reset_parameters() # 核心代码def reset_parameters(self):init.kaiming_uniform_(self.weight, a=math.sqrt(5))if self.bias is not None:fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)bound = 1 / math.sqrt(fan_in)init.uniform_(self.bias, -bound, bound)
结语 ：使用Pytorch搭建网络后，如果不显式地指定网络参数的初始化方式，模型则采用默认的初始化方式！