行人重试别的无监督训练过程，在backbone中为了使源域和目标域的BN训练参数区分开，不相互影响，使用了下面的代码：
import torch import torch.nn as nn # Domain-specific BatchNorm class DSBN2d(nn.Module): def __init__(self, planes): super(DSBN2d, self).__init__() self.num_features = planes self.BN_S = nn.BatchNorm2d(planes) self.BN_T = nn.BatchNorm2d(planes) def forward(self, x): if (not self.training): return self.BN_T(x) bs = x.size(0) assert (bs%2==0) split = torch.split(x, int(bs/2), 0) out1 = self.BN_S(split[0].contiguous()) out2 = self.BN_T(split[1].contiguous()) out = torch.cat((out1, out2), 0) return out class DSBN1d(nn.Module): def __init__(self, planes): super(DSBN1d, self).__init__() self.num_features = planes self.BN_S = nn.BatchNorm1d(planes) self.BN_T = nn.BatchNorm1d(planes) def forward(self, x): if (not self.training): return self.BN_T(x) bs = x.size(0) assert (bs%2==0) split = torch.split(x, int(bs/2), 0) out1 = self.BN_S(split[0].contiguous()) out2 = self.BN_T(split[1].contiguous()) out = torch.cat((out1, out2), 0) return out def convert_dsbn(model): for _, (child_name, child) in enumerate(model.named_children()): assert(not next(model.parameters()).is_cuda) if isinstance(child, nn.BatchNorm2d): m = DSBN2d(child.num_features) m.BN_S.load_state_dict(child.state_dict()) m.BN_T.load_state_dict(child.state_dict()) setattr(model, child_name, m) elif isinstance(child, nn.BatchNorm1d): m = DSBN1d(child.num_features) m.BN_S.load_state_dict(child.state_dict()) m.BN_T.load_state_dict(child.state_dict()) setattr(model, child_name, m) else: convert_dsbn(child) def convert_bn(model, use_target=True): for _, (child_name, child) in enumerate(model.named_children()): assert(not next(model.parameters()).is_cuda) if isinstance(child, DSBN2d): m = nn.BatchNorm2d(child.num_features) if use_target: m.load_state_dict(child.BN_T.state_dict()) else: m.load_state_dict(child.BN_S.state_dict()) setattr(model, child_name, m) elif isinstance(child, DSBN1d): m = nn.BatchNorm1d(child.num_features) if use_target: m.load_state_dict(child.BN_T.state_dict()) else: m.load_state_dict(child.BN_S.state_dict()) setattr(model, child_name, m) else: convert_bn(child, use_target=use_target)在前向过程中，将源域和目标域数据经过不同的BN层，这样相对于不加区分的来说，有了很大的提升。在测试阶段，我们也可以将目标域的数据只通过目标域对应的BN层，但是实验结果发现，这样的性能和将测试数据统一送入网络不加区分源域还是目标域的BN层，结果上没什么区别。
