Return ResNet Unit symbol for building ResNet
    Parameters
    ----------
    data : str
        Input data
    num_filter : int
        Number of output channels
    bnf : int
        Bottle neck channels factor with regard to num_filter
    stride : tuple
        Stride used in convolution
    dim_match : Boolean
        True means channel number between input and output is the same, otherwise means differ
    name : str
        Base name of the operators
    workspace : int
        Workspace used in convolution operator

def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, num_group=32, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNet Unit symbol for building ResNet
    Parameters
    ----------
    data : str
        Input data
    num_filter : int
        Number of output channels
    bnf : int
        Bottle neck channels factor with regard to num_filter
    stride : tuple
        Stride used in convolution
    dim_match : Boolean
        True means channel number between input and output is the same, otherwise means differ
    name : str
        Base name of the operators
    workspace : int
        Workspace used in convolution operator
    """
    if bottle_neck:
        # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper

        conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0),
                                      no_bias=True, workspace=workspace, name=name + '_conv1')
        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')


        conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.5), num_group=num_group, kernel=(3,3), stride=stride, pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv2')
        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
        act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')


        conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
                                   workspace=workspace, name=name + '_conv3')
        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')

        if dim_match:
            shortcut = data
        else:
            shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_sc')
            shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')

        if memonger:
            shortcut._set_attr(mirror_stage='True')
        eltwise =  bn3 + shortcut
        return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')
    else:

        conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv1')
        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')


        conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv2')
        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')

        if dim_match:
            shortcut = data
        else:
            shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_sc')
            shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')

        if memonger:
            shortcut._set_attr(mirror_stage='True')
        eltwise = bn2 + shortcut
        return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')