def __init__(self, scale, n_colors, conv=common.default_conv, n_feats=64):
    super(Edge_Net, self).__init__()
    kernel_size = 3
    act = nn.ReLU(True)
    n_blocks = 5
    self.n_blocks = n_blocks
    modules_head = [conv(3, n_feats, kernel_size)]
    modules_body = nn.ModuleList()
    for i in range(n_blocks):
        modules_body.append(MSRB())
    modules_tail = [nn.Conv2d((n_feats * (self.n_blocks + 1)), n_feats, 1, padding=0, stride=1), conv(n_feats, n_feats, kernel_size), common.Upsampler(conv, scale, n_feats, act=False), conv(n_feats, n_colors, kernel_size)]
    self.Edge_Net_head = nn.Sequential(*modules_head)
    self.Edge_Net_body = nn.Sequential(*modules_body)
    self.Edge_Net_tail = nn.Sequential(*modules_tail)
def __init__(self, scale, n_colors, conv=common.default_conv, n_feats=64):
    super(Edge_Net, self).__init__()
    kernel_size = 3
    act = nn.ReLU(True)
    n_blocks = 5
    self.n_blocks = n_blocks
    modules_head = [conv(3, n_feats, kernel_size)]
    modules_body = nn.ModuleList()
    for i in range(n_blocks):
        modules_body.append(MSRB())
    modules_tail = [nn.Conv2d((n_feats * (self.n_blocks + 1)), n_feats, 1, padding=0, stride=1), conv(n_feats, n_feats, kernel_size), common.Upsampler(conv, scale, n_feats, act=False), conv(n_feats, n_colors, kernel_size)]
    self.Edge_Net_head = nn.Sequential(*modules_head)
    self.Edge_Net_body = nn.Sequential(*modules_body)
    self.Edge_Net_tail = nn.Sequential(*modules_tail)