BasicSepConv 这里面的卷积是深度可分离卷积吗？

[getr1ch]

class BasicSepConv(nn.Module):

def __init__(self, in_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
    super(BasicSepConv, self).__init__()
    self.out_channels = in_planes
    self.conv = nn.Conv2d(in_planes, in_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups = in_planes, bias=bias) #这是深度可分离卷积吗 用keras 的depthwise代替可以吗？
    self.bn = nn.BatchNorm2d(in_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None
    self.relu = nn.ReLU(inplace=True) if relu else None

def forward(self, x):
    x = self.conv(x)
    if self.bn is not None:
        x = self.bn(x)
    if self.relu is not None:
        x = self.relu(x)
    return x