How to use the torchfcn.models.fcn8s.FCN8s function in torchfcn

def __init__(self, n_class=21):
        super(FCN8s, self).__init__()
        # conv1
        self.conv1_1 = nn.Conv2d(3, 64, 3, padding=100)
        self.relu1_1 = nn.ReLU(inplace=True)
        self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
        self.relu1_2 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)  # 1/2

        # conv2
        self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
        self.relu2_1 = nn.ReLU(inplace=True)
        self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
        self.relu2_2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)  # 1/4

        # conv3
        self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)

def copy_params_from_fcn16s(self, fcn16s):
        for name, l1 in fcn16s.named_children():
            try:
                l2 = getattr(self, name)
                l2.weight  # skip ReLU / Dropout
            except Exception:
                continue
            assert l1.weight.size() == l2.weight.size()
            l2.weight.data.copy_(l1.weight.data)
            if l1.bias is not None:
                assert l1.bias.size() == l2.bias.size()
                l2.bias.data.copy_(l1.bias.data)


class FCN8sAtOnce(FCN8s):

    pretrained_model = \
        osp.expanduser('~/data/models/pytorch/fcn8s-atonce_from_caffe.pth')

    @classmethod
    def download(cls):
        return fcn.data.cached_download(
            url='http://drive.google.com/uc?id=0B9P1L--7Wd2vblE1VUIxV1o2d2M',
            path=cls.pretrained_model,
            md5='bfed4437e941fef58932891217fe6464',
        )

    def forward(self, x):
        h = x
        h = self.relu1_1(self.conv1_1(h))
        h = self.relu1_2(self.conv1_2(h))