stage = [[3, 3, 3, 1, 1], [7, 7, 7, 7, 7, 1, 1]]
branches_cfg = [[[128, 128, 128, 512, 38], [128, 128, 128, 512, 19]],
    [[128, 128, 128, 128, 128, 128, 38], [128, 128, 128, 128, 128, 128, 19]]]

# used for add two branches as well as adapt to certain stage
def add_extra(i, branches_cfg, stage):
 """
 only add CNN of brancdes S & L in stage Ti at the end of net
 :param in_channels:the input channels & out
 :param stage: size of filter
 :param branches_cfg: channels of image
 :return:list of layers
 """
 in_channels = i
 layers = []
 for k in range(len(stage)):
  padding = stage[k] // 2
  conv2d = nn.Conv2d(in_channels, branches_cfg[k], kernel_size=stage[k], padding=padding)
  layers += [conv2d, nn.ReLU(inplace=True)]
  in_channels = branches_cfg[k]
 return layers

conf_bra_list = []
paf_bra_list = []

# param for branch network
in_channels = 128

for i in range(all_stage):
 if i > 0:
  branches = branches_cfg[1]
  conv_sz = stage[1]
 else:
  branches = branches_cfg[0]
  conv_sz = stage[0]

 conf_bra_list.append(nn.Sequential(*add_extra(in_channels, branches[0], conv_sz)))
 paf_bra_list.append(nn.Sequential(*add_extra(in_channels, branches[1], conv_sz)))
 in_channels = 185

# to list
self.conf_bra = nn.ModuleList(conf_bra_list)
self.paf_bra = nn.ModuleList(paf_bra_list)

out_0 = x
# the base transform
for k in range(len(self.vgg)):
 out_0 = self.vgg[k](out_0)

# local name space
name = locals()
confs = []
pafs = []
outs = []

length = len(self.conf_bra)
for i in range(length):
 name['conf_%s' % (i + 1)] = self.conf_bra[i](name['out_%s' % i])
 name['paf_%s' % (i + 1)] = self.paf_bra[i](name['out_%s' % i])
 name['out_%s' % (i + 1)] = torch.cat([name['conf_%s' % (i + 1)], name['paf_%s' % (i + 1)], out_0], 1)
 confs.append('conf_%s' % (i + 1))
 pafs.append('paf_%s' % (i + 1))
 outs.append('out_%s' % (i + 1))