CNN网络结构-ResNext

CNN网络结构-ResNext

背景

2016年，KaiMing将AlexNet中的group convolution引进了ResNet中，以获得更少的参数，在ImageNet上刷出了排名第二的好成绩。

101-layer的ResNeXt可以达到ResNet的精确度，在complexity却只有后者的一半。

结构

ResNext的group convolution结构如下：

 

网络参数：

• ResNeXt 与 ResNet 在相同参数个数情况下，训练时前者错误率更低，但下降速度差不多
• 相同参数情况下，增加 cardinality 比增加卷几个数更加有效
• 101 层的 ResNeXt 比 200 层的 ResNet 更好
• 几种 sota 的模型，ResNeXt 准确率最高

实现

以下是Resnext 34的mxnet实现，略修改简化。

相比Resnet最大的改动就是residual_unit中的conv2加上了num_group=32的参数，作用是将输入数据切割成num_group个partitions，然后在每个partition上使用卷积操作，再将卷积的结果连接起来。

'''
Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
Original author Wei Wu

Implemented the following paper:
Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He. "Aggregated Residual Transformations for Deep Neural Network"
'''
import mxnet as mx

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 : tupe
        Stride used in convolution
    dim_match : Boolen
        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')

def resnext(units=[3, 4, 6, 3], num_stages=4, filter_list=[64, 64, 128, 256, 512], num_classes=1000, num_group=32, bottle_neck=False, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNeXt symbol of
    Parameters
    ----------
    units : list
        Number of units in each stage
    num_stages : int
        Number of stage
    filter_list : list
        Channel size of each stage
    num_classes : int
        Ouput size of symbol
    num_groupes: int
    Number of conv groups
    dataset : str
        Dataset type, only cifar10 and imagenet supports
    workspace : int
        Workspace used in convolution operator
    """
    num_unit = len(units)
    assert(num_unit == num_stages)
    data = mx.sym.Variable(name='data')
    data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
   
    body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
                              no_bias=True, name="conv0", workspace=workspace)
    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
    body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
    body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')

    for i in range(num_stages):
        body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
                             name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, num_group=num_group,
                             bn_mom=bn_mom, workspace=workspace, memonger=memonger)
        for j in range(units[i]-1):
            body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
                                 bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger)

    pool1 = mx.symbol.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
    flat = mx.symbol.Flatten(data=pool1)
    fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
    return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')