Pytorch 学习（6）：Pytorch中的torch.nn Convolution Layers 卷积层参数初始化
class Conv1d(_ConvNd):
......
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _single(kernel_size)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
super(Conv1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _single(0), groups, bias)
def forward(self, input):
return F.conv1d(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)参数初始化调用 _ntuple方法：
import collections
from itertools import repeat
def _ntuple(n):
def parse(x):
if isinstance(x, collections.Iterable):
