（原）堆叠hourglass网络

转载请注明出处：

https://www.cnblogs.com/darkknightzh/p/11486185.html

论文：

https://arxiv.org/abs/1603.06937

官方torch代码（没具体看）：

https://github.com/princeton-vl/pose-hg-demo

第三方pytorch代码（位于models/StackedHourGlass.py）：

https://github.com/Naman-ntc/Pytorch-Human-Pose-Estimation

1. 简介

该论文利用多尺度特征来识别姿态，如下图所示，每个子网络称为hourglass Network，是一个沙漏型的结构，多个这种结构堆叠起来，称作stacked hourglass。堆叠的方式，方便每个模块在整个图像上重新估计姿态和特征。如下图所示，输入图像通过全卷积网络fcn后，得到特征，而后通过多个堆叠的hourglass，得到最终的热图。

Hourglass如下图所示。其中每个方块均为下下图的残差模块。

Hourglass采用了中间监督（Intermediate Supervision）。每个hourglass均会有热图（蓝色）。训练阶段，将这些热图和真实热图计算损失MSE，并求和，得到损失；推断阶段，使用的是最后一个hourglass的热图。

2. stacked hourglass

堆叠hourglass结构如下图所示（nChannels=256,nStack=2,nModules=2,numReductions=4, nJoints=17）：

代码如下：

class StackedHourGlass(nn.Module):
    """docstring for StackedHourGlass"""
    def __init__(self, nChannels, nStack, nModules, numReductions, nJoints):
        super(StackedHourGlass, self).__init__()
        self.nChannels = nChannels
        self.nStack = nStack
        self.nModules = nModules
        self.numReductions = numReductions
        self.nJoints = nJoints

        self.start = M.BnReluConv(3, 64, kernelSize = 7, stride = 2, padding = 3)  # BN+ReLU+conv

        self.res1 = M.Residual(64, 128) # 输入和输出不等，输入通过1*1conv结果和3*(BN+ReLU+conv)求和
        self.mp = nn.MaxPool2d(2, 2)
        self.res2 = M.Residual(128, 128) # 输入和输出相等，为x+3*(BN+ReLU+conv)
        self.res3 = M.Residual(128, self.nChannels) # 输入和输出相等，为x+3*(BN+ReLU+conv)；否则输入通过1*1conv结果和3*(BN+ReLU+conv)求和。

        _hourglass, _Residual, _lin1, _chantojoints, _lin2, _jointstochan = [],[],[],[],[],[]

        for _ in range(self.nStack):  # 堆叠个数
            _hourglass.append(Hourglass(self.nChannels, self.numReductions, self.nModules))
            _ResidualModules = []
            for _ in range(self.nModules):
                _ResidualModules.append(M.Residual(self.nChannels, self.nChannels))   # 输入和输出相等，为x+3*(BN+ReLU+conv)
            _ResidualModules = nn.Sequential(*_ResidualModules)
            _Residual.append(_ResidualModules)   # self.nModules 个 3*(BN+ReLU+conv)
            _lin1.append(M.BnReluConv(self.nChannels, self.nChannels))       # BN+ReLU+conv
            _chantojoints.append(nn.Conv2d(self.nChannels, self.nJoints,1))  # 1*1 conv，维度变换
            _lin2.append(nn.Conv2d(self.nChannels, self.nChannels,1))        # 1*1 conv，维度不变
            _jointstochan.append(nn.Conv2d(self.nJoints,self.nChannels,1))   # 1*1 conv，维度变换

        self.hourglass = nn.ModuleList(_hourglass)
        self.Residual = nn.ModuleList(_Residual)
        self.lin1 = nn.ModuleList(_lin1)
        self.chantojoints = nn.ModuleList(_chantojoints)
        self.lin2 = nn.ModuleList(_lin2)
        self.jointstochan = nn.ModuleList(_jointstochan)

    def forward(self, x):
        x = self.start(x)
        x = self.res1(x)
        x = self.mp(x)
        x = self.res2(x)
        x = self.res3(x)
        out = []

        for i in range(self.nStack):
            x1 = self.hourglass[i](x)
            x1 = self.Residual[i](x1)
            x1 = self.lin1[i](x1)
            out.append(self.chantojoints[i](x1))
            x1 = self.lin2[i](x1)
            x = x + x1 + self.jointstochan[i](out[i])   # 特征求和

        return (out)

3. hourglass

hourglass在numReductions>1时，递归调用自己，结构如下：

代码如下：

class Hourglass(nn.Module):
    """docstring for Hourglass"""
    def __init__(self, nChannels = 256, numReductions = 4, nModules = 2, poolKernel = (2,2), poolStride = (2,2), upSampleKernel = 2):
        super(Hourglass, self).__init__()
        self.numReductions = numReductions
        self.nModules = nModules
        self.nChannels = nChannels
        self.poolKernel = poolKernel
        self.poolStride = poolStride
        self.upSampleKernel = upSampleKernel

        """For the skip connection, a residual module (or sequence of residuaql modules)  """
        _skip = []
        for _ in range(self.nModules):
            _skip.append(M.Residual(self.nChannels, self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
        self.skip = nn.Sequential(*_skip)

        """First pooling to go to smaller dimension then pass input through
        Residual Module or sequence of Modules then  and subsequent cases:
            either pass through Hourglass of numReductions-1 or pass through M.Residual Module or sequence of Modules """
        self.mp = nn.MaxPool2d(self.poolKernel, self.poolStride)

        _afterpool = []
        for _ in range(self.nModules):
            _afterpool.append(M.Residual(self.nChannels, self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
        self.afterpool = nn.Sequential(*_afterpool)

        if (numReductions > 1):
            self.hg = Hourglass(self.nChannels, self.numReductions-1, self.nModules, self.poolKernel, self.poolStride)  # 嵌套调用本身
        else:
            _num1res = []
            for _ in range(self.nModules):
                _num1res.append(M.Residual(self.nChannels,self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
            self.num1res = nn.Sequential(*_num1res)  # doesnt seem that important ?

        """ Now another M.Residual Module or sequence of M.Residual Modules  """
        _lowres = []
        for _ in range(self.nModules):
            _lowres.append(M.Residual(self.nChannels,self.nChannels))   # 输入和输出相等，为x+3*(BN+ReLU+conv)
        self.lowres = nn.Sequential(*_lowres)

        """ Upsampling Layer (Can we change this??????) As per Newell's paper upsamping recommended  """
        self.up = myUpsample()#nn.Upsample(scale_factor = self.upSampleKernel)   # 将高和宽扩充为原来2倍，实现上采样


    def forward(self, x):
        out1 = x
        out1 = self.skip(out1)          # 输入和输出相等，为x+3*(BN+ReLU+conv)
        out2 = x
        out2 = self.mp(out2)            # 降维
        out2 = self.afterpool(out2)     # 输入和输出相等，为x+3*(BN+ReLU+conv)
        if self.numReductions>1:
            out2 = self.hg(out2)        # 嵌套调用本身
        else:
            out2 = self.num1res(out2)   # 输入和输出相等，为x+3*(BN+ReLU+conv)
        out2 = self.lowres(out2)        # 输入和输出相等，为x+3*(BN+ReLU+conv)
        out2 = self.up(out2)            # 升维

        return out2 + out1              # 求和

4. 上采样myUpsample

上采样代码如下：

class myUpsample(nn.Module):
    def __init__(self):
        super(myUpsample, self).__init__()
        pass
    def forward(self, x):   # 将高和宽扩充为原来2倍，实现上采样
        return x[:, :, :, None, :, None].expand(-1, -1, -1, 2, -1, 2).reshape(x.size(0), x.size(1), x.size(2)*2, x.size(3)*2)

其中x为(N)(C)(H)(W)的矩阵，x[:, :, :, None, :, None]为(N)(C)(H)(1)(W)(1)的矩阵，expand之后变成(N)(C)(H)(2)(W)(2)的矩阵，最终reshape之后变成(N)(C)(2H) (2W)的矩阵，实现了将1个像素水平和垂直方向各扩充2倍，变成4个像素（4个像素值相同），完成了上采样。

5. 残差模块

残差模块结构如下：

代码如下：

class Residual(nn.Module):
        """docstring for Residual"""  # 输入和输出相等，为x+3*(BN+ReLU+conv)；否则输入通过1*1conv结果和3*(BN+ReLU+conv)求和
        def __init__(self, inChannels, outChannels):
                super(Residual, self).__init__()
                self.inChannels = inChannels
                self.outChannels = outChannels
                self.cb = ConvBlock(inChannels, outChannels)      # 3 * (BN+ReLU+conv) 其中第一组降维，第二组不变，第三组升维
                self.skip = SkipLayer(inChannels, outChannels)    # 输入和输出通道相等，则输出=输入，否则为1*1 conv

        def forward(self, x):
                out = 0
                out = out + self.cb(x)
                out = out + self.skip(x)
                return out

其中skiplayer代码如下：

class SkipLayer(nn.Module):
        """docstring for SkipLayer"""  # 输入和输出通道相等，则输出=输入，否则为1*1 conv
        def __init__(self, inChannels, outChannels):
                super(SkipLayer, self).__init__()
                self.inChannels = inChannels
                self.outChannels = outChannels
                if (self.inChannels == self.outChannels):
                        self.conv = None
                else:
                        self.conv = nn.Conv2d(self.inChannels, self.outChannels, 1)

        def forward(self, x):
                if self.conv is not None:
                        x = self.conv(x)
                return x

6. conv

class BnReluConv(nn.Module):
        """docstring for BnReluConv"""    # BN+ReLU+conv
        def __init__(self, inChannels, outChannels, kernelSize = 1, stride = 1, padding = 0):
                super(BnReluConv, self).__init__()
                self.inChannels = inChannels
                self.outChannels = outChannels
                self.kernelSize = kernelSize
                self.stride = stride
                self.padding = padding

                self.bn = nn.BatchNorm2d(self.inChannels)
                self.conv = nn.Conv2d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)
                self.relu = nn.ReLU()

        def forward(self, x):
                x = self.bn(x)
                x = self.relu(x)
                x = self.conv(x)
                return x

7. ConvBlock

class ConvBlock(nn.Module):
        """docstring for ConvBlock"""  # 3 * (BN+ReLU+conv) 其中第一组降维，第二组不变，第三组升维
        def __init__(self, inChannels, outChannels):
                super(ConvBlock, self).__init__()
                self.inChannels = inChannels
                self.outChannels = outChannels
                self.outChannelsby2 = outChannels//2

                self.cbr1 = BnReluConv(self.inChannels, self.outChannelsby2, 1, 1, 0)        # BN+ReLU+conv
                self.cbr2 = BnReluConv(self.outChannelsby2, self.outChannelsby2, 3, 1, 1)    # BN+ReLU+conv
                self.cbr3 = BnReluConv(self.outChannelsby2, self.outChannels, 1, 1, 0)       # BN+ReLU+conv

        def forward(self, x):
                x = self.cbr1(x)
                x = self.cbr2(x)
                x = self.cbr3(x)
                return x