You could create a method to load your weights.
I created a small example for you.
You could definitely write the code in a more compact way, so this should be a starter only. 
class SSD(nn.Module):
    def __init__(self, init_weights=True):
        super(SSD, self).__init__()

        # ===================================[ G_1 ]================================== #
        self.g1 = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
                                nn.ReLU(inplace=True),
                                nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
                                nn.ReLU(inplace=True))

    def forward(self, x):
        x = self.g1(x)
        return x


vgg = vgg16(pretrained=False)
model = SSD()

model.g1[0].weight.data.copy_(vgg.features[0].weight.data)
model.g1[0].bias.data.copy_(vgg.features[0].bias.data)
model.g1[2].weight.data.copy_(vgg.features[2].weight.data)
model.g1[2].bias.data.copy_(vgg.features[2].bias.data)