How to generate dynamic number of branches?
My code use a list to store the branches, but got

RuntimeError: Caught RuntimeError in replica 1 on device 1.


RuntimeError: Expected tensor for argument #1 ‘input’ to have the same device as tensor for argument #2 ‘weight’; but device 1 does not equal 0 (while checking arguments for cudnn_convolution)

I think it is because the weights for branches are not copied to other GPUs.
class ResNetONE(nn.Module):
    def __init__(self, depth, num_classes=1000, num_branches=3, block_name='BasicBlock'):
        super(ResNetONE, self).__init__()
        # Model type specifies number of layers for CIFAR-10 model
        if block_name.lower() == 'basicblock':
            assert(depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202'
            n = (depth - 2) // 6
            block = BasicBlock
        elif block_name.lower() == 'bottleneck':
            assert (depth == 2) % 9 == 0, 'When use bottleneck, depth should be 9n + 2, e.g. 20, 29, 47, 56, 110, 1199'
            n = (depth - 2) // 9
            block = Bottleneck
        else:
            raise ValueError('block_name shinterval_sumould be Basicblock or Bottleneck')

        self.inplanes = 16
        self.num_branches = num_branches
        self.num_classes = num_classes

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, n)
        self.layer2 = self._make_layer(block, 32, n, stride=2)
        self.layer3 = self._make_layer(block, 64, n, stride=2)
        self.avgpool = nn.AvgPool2d(8)
        self.fc = nn.Linear(64 * block.expansion, self.num_classes)
        self.branches = self._make_branches(self.layer3, self.avgpool)
        self.gate = self._make_gate()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2./n))
                # "normal_" is defined by torch._C._TensorBase
                # https://pytorch.org/docs/stable/tensors.html#torch.Tensor.normal_
                # https://zhuanlan.zhihu.com/p/100937718
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def _make_branches(self, *layers):
        branches = []
        for i in range(self.num_branches):
            branch = nn.Sequential(*layers)
            branches.append(branch)
        return branches