def __init__(self, n_feats, scale, rgb_range, res_scale, n_colors, conv=common.default_conv):
    super(SEAN, self).__init__()
    kernel_size = 3
    act = nn.ReLU(True)
    rgb_mean = (0.4488, 0.4371, 0.404)
    rgb_std = (1.0, 1.0, 1.0)
    self.sub_mean = common.MeanShift(rgb_range, rgb_mean, rgb_std)
    m_LFF = [LFF(scale, n_colors, n_feats=n_feats)]
    m_Edge = [Edge_Net(scale, n_colors, n_feats=n_feats)]
    m_Fushion = [conv(6, n_feats, kernel_size=1)]
    m_Net = [Net(scale, res_scale, n_feats=n_feats)]
    self.add_mean = common.MeanShift(rgb_range, rgb_mean, rgb_std, 1)
    self.lff = nn.Sequential(*m_LFF)
    self.edge = nn.Sequential(*m_Edge)
    self.fushion = nn.Sequential(*m_Fushion)
    self.net = nn.Sequential(*m_Net)
def __init__(self, n_feats, scale, rgb_range, res_scale, n_colors, conv=common.default_conv):
    super(SEAN, self).__init__()
    kernel_size = 3
    act = nn.ReLU(True)
    rgb_mean = (0.4488, 0.4371, 0.404)
    rgb_std = (1.0, 1.0, 1.0)
    self.sub_mean = common.MeanShift(rgb_range, rgb_mean, rgb_std)
    m_LFF = [LFF(scale, n_colors, n_feats=n_feats)]
    m_Edge = [Edge_Net(scale, n_colors, n_feats=n_feats)]
    m_Fushion = [conv(6, n_feats, kernel_size=1)]
    m_Net = [Net(scale, res_scale, n_feats=n_feats)]
    self.add_mean = common.MeanShift(rgb_range, rgb_mean, rgb_std, 1)
    self.lff = nn.Sequential(*m_LFF)
    self.edge = nn.Sequential(*m_Edge)
    self.fushion = nn.Sequential(*m_Fushion)
    self.net = nn.Sequential(*m_Net)