utils_cv/tracking/references/fairmot/models/networks/dlav0.py [151:201]:
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        self.relu = nn.ReLU(inplace=True)
        self.residual = residual

    def forward(self, *x):
        children = x
        x = self.conv(torch.cat(x, 1))
        x = self.bn(x)
        if self.residual:
            x += children[0]
        x = self.relu(x)

        return x


class Tree(nn.Module):
    def __init__(self, levels, block, in_channels, out_channels, stride=1,
                 level_root=False, root_dim=0, root_kernel_size=1,
                 dilation=1, root_residual=False):
        super(Tree, self).__init__()
        if root_dim == 0:
            root_dim = 2 * out_channels
        if level_root:
            root_dim += in_channels
        if levels == 1:
            self.tree1 = block(in_channels, out_channels, stride,
                               dilation=dilation)
            self.tree2 = block(out_channels, out_channels, 1,
                               dilation=dilation)
        else:
            self.tree1 = Tree(levels - 1, block, in_channels, out_channels,
                              stride, root_dim=0,
                              root_kernel_size=root_kernel_size,
                              dilation=dilation, root_residual=root_residual)
            self.tree2 = Tree(levels - 1, block, out_channels, out_channels,
                              root_dim=root_dim + out_channels,
                              root_kernel_size=root_kernel_size,
                              dilation=dilation, root_residual=root_residual)
        if levels == 1:
            self.root = Root(root_dim, out_channels, root_kernel_size,
                             root_residual)
        self.level_root = level_root
        self.root_dim = root_dim
        self.downsample = None
        self.project = None
        self.levels = levels
        if stride > 1:
            self.downsample = nn.MaxPool2d(stride, stride=stride)
        if in_channels != out_channels:
            self.project = nn.Sequential(
                nn.Conv2d(in_channels, out_channels,
                          kernel_size=1, stride=1, bias=False),
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utils_cv/tracking/references/fairmot/models/networks/pose_dla_dcn.py [153:203]:
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        self.relu = nn.ReLU(inplace=True)
        self.residual = residual

    def forward(self, *x):
        children = x
        x = self.conv(torch.cat(x, 1))
        x = self.bn(x)
        if self.residual:
            x += children[0]
        x = self.relu(x)

        return x


class Tree(nn.Module):
    def __init__(self, levels, block, in_channels, out_channels, stride=1,
                 level_root=False, root_dim=0, root_kernel_size=1,
                 dilation=1, root_residual=False):
        super(Tree, self).__init__()
        if root_dim == 0:
            root_dim = 2 * out_channels
        if level_root:
            root_dim += in_channels
        if levels == 1:
            self.tree1 = block(in_channels, out_channels, stride,
                               dilation=dilation)
            self.tree2 = block(out_channels, out_channels, 1,
                               dilation=dilation)
        else:
            self.tree1 = Tree(levels - 1, block, in_channels, out_channels,
                              stride, root_dim=0,
                              root_kernel_size=root_kernel_size,
                              dilation=dilation, root_residual=root_residual)
            self.tree2 = Tree(levels - 1, block, out_channels, out_channels,
                              root_dim=root_dim + out_channels,
                              root_kernel_size=root_kernel_size,
                              dilation=dilation, root_residual=root_residual)
        if levels == 1:
            self.root = Root(root_dim, out_channels, root_kernel_size,
                             root_residual)
        self.level_root = level_root
        self.root_dim = root_dim
        self.downsample = None
        self.project = None
        self.levels = levels
        if stride > 1:
            self.downsample = nn.MaxPool2d(stride, stride=stride)
        if in_channels != out_channels:
            self.project = nn.Sequential(
                nn.Conv2d(in_channels, out_channels,
                          kernel_size=1, stride=1, bias=False),
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