堆叠hourglass网络

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转载最新推荐文章于 2026-10-12 15:12:00 发布 1.7k 阅读 AI 写同款· GEO 优化 ›

Hourglass Network 沙漏网络（pose estimation姿态估计）在ECCV2016《Stacked Hourglass Networks for Human Pose Estimation》这篇paper中提出是一个姿态估计（pose estimation）的网络结构姿态估计（Pose Estimation）是 CV 领域一个非常重要的方向，而Stacked Hourglass Networks 级联漏斗网络的提出就是为了提升姿态估计的效果，但是其中的经典思想可以扩展到其他方向，比如目标识别方向，代表网络是 CornerNet（预测目标的左上... 阅读详情

本文目录

目录

1. 简介

2. stacked hourglass

3. hourglass

4. 上采样myUpsample

5. 残差模块

6. conv

7. ConvBlock

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：

 1 class StackedHourGlass(nn.Module):
 2     """docstring for StackedHourGlass"""
 3     def __init__(self, nChannels, nStack, nModules, numReductions, nJoints):
 4         super(StackedHourGlass, self).__init__()
 5         self.nChannels = nChannels
 6         self.nStack = nStack
 7         self.nModules = nModules
 8         self.numReductions = numReductions
 9         self.nJoints = nJoints
10 
11         self.start = M.BnReluConv(3, 64, kernelSize = 7, stride = 2, padding = 3)  # BN+ReLU+conv
12 
13         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
14         self.mp = nn.MaxPool2d(2, 2)
15         self.res2 = M.Residual(128, 128) # 输入和输出相等，为x+3*(BN+ReLU+conv)
16         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。
17 
18         _hourglass, _Residual, _lin1, _chantojoints, _lin2, _jointstochan = [],[],[],[],[],[]
19 
20         for _ in range(self.nStack):  # 堆叠个数
21             _hourglass.append(Hourglass(self.nChannels, self.numReductions, self.nModules))
22             _ResidualModules = []
23             for _ in range(self.nModules):
24                 _ResidualModules.append(M.Residual(self.nChannels, self.nChannels))   # 输入和输出相等，为x+3*(BN+ReLU+conv)
25             _ResidualModules = nn.Sequential(*_ResidualModules)
26             _Residual.append(_ResidualModules)   # self.nModules 个 3*(BN+ReLU+conv)
27             _lin1.append(M.BnReluConv(self.nChannels, self.nChannels))       # BN+ReLU+conv
28             _chantojoints.append(nn.Conv2d(self.nChannels, self.nJoints,1))  # 1*1 conv，维度变换
29             _lin2.append(nn.Conv2d(self.nChannels, self.nChannels,1))        # 1*1 conv，维度不变
30             _jointstochan.append(nn.Conv2d(self.nJoints,self.nChannels,1))   # 1*1 conv，维度变换
31 
32         self.hourglass = nn.ModuleList(_hourglass)
33         self.Residual = nn.ModuleList(_Residual)
34         self.lin1 = nn.ModuleList(_lin1)
35         self.chantojoints = nn.ModuleList(_chantojoints)
36         self.lin2 = nn.ModuleList(_lin2)
37         self.jointstochan = nn.ModuleList(_jointstochan)
38 
39     def forward(self, x):
40         x = self.start(x)
41         x = self.res1(x)
42         x = self.mp(x)
43         x = self.res2(x)
44         x = self.res3(x)
45         out = []
46 
47         for i in range(self.nStack):
48             x1 = self.hourglass[i](x)
49             x1 = self.Residual[i](x1)
50             x1 = self.lin1[i](x1)
51             out.append(self.chantojoints[i](x1))
52             x1 = self.lin2[i](x1)
53             x = x + x1 + self.jointstochan[i](out[i])   # 特征求和
54 
55         return (out)

3. hourglass

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

代码如下：

1 class Hourglass(nn.Module):
 2     """docstring for Hourglass"""
 3     def __init__(self, nChannels = 256, numReductions = 4, nModules = 2, poolKernel = (2,2), poolStride = (2,2), upSampleKernel = 2):
 4         super(Hourglass, self).__init__()
 5         self.numReductions = numReductions
 6         self.nModules = nModules
 7         self.nChannels = nChannels
 8         self.poolKernel = poolKernel
 9         self.poolStride = poolStride
10         self.upSampleKernel = upSampleKernel
11 
12         """For the skip connection, a residual module (or sequence of residuaql modules)  """
13         _skip = []
14         for _ in range(self.nModules):
15             _skip.append(M.Residual(self.nChannels, self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
16         self.skip = nn.Sequential(*_skip)
17 
18         """First pooling to go to smaller dimension then pass input through
19         Residual Module or sequence of Modules then  and subsequent cases:
20             either pass through Hourglass of numReductions-1 or pass through M.Residual Module or sequence of Modules """
21         self.mp = nn.MaxPool2d(self.poolKernel, self.poolStride)
22 
23         _afterpool = []
24         for _ in range(self.nModules):
25             _afterpool.append(M.Residual(self.nChannels, self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
26         self.afterpool = nn.Sequential(*_afterpool)
27 
28         if (numReductions > 1):
29             self.hg = Hourglass(self.nChannels, self.numReductions-1, self.nModules, self.poolKernel, self.poolStride)  # 嵌套调用本身
30         else:
31             _num1res = []
32             for _ in range(self.nModules):
33                 _num1res.append(M.Residual(self.nChannels,self.nChannels))  # 输入和输出相等，为x+3*(BN+ReLU+conv)
34             self.num1res = nn.Sequential(*_num1res)  # doesnt seem that important ?
35 
36         """ Now another M.Residual Module or sequence of M.Residual Modules  """
37         _lowres = []
38         for _ in range(self.nModules):
39             _lowres.append(M.Residual(self.nChannels,self.nChannels))   # 输入和输出相等，为x+3*(BN+ReLU+conv)
40         self.lowres = nn.Sequential(*_lowres)
41 
42         """ Upsampling Layer (Can we change this??????) As per Newell's paper upsamping recommended  """
43         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
44 
45 
46     def forward(self, x):
47         out1 = x
48         out1 = self.skip(out1)          # 输入和输出相等，为x+3*(BN+ReLU+conv)
49         out2 = x
50         out2 = self.mp(out2)            # 降维
51         out2 = self.afterpool(out2)     # 输入和输出相等，为x+3*(BN+ReLU+conv)
52         if self.numReductions>1:
53             out2 = self.hg(out2)        # 嵌套调用本身
54         else:
55             out2 = self.num1res(out2)   # 输入和输出相等，为x+3*(BN+ReLU+conv)
56         out2 = self.lowres(out2)        # 输入和输出相等，为x+3*(BN+ReLU+conv)
57         out2 = self.up(out2)            # 升维
58 
59         return out2 + out1              # 求和

4. 上采样myUpsample

上采样代码如下：

1 class myUpsample(nn.Module):
2     def __init__(self):
3         super(myUpsample, self).__init__()
4         pass
5     def forward(self, x):   # 将高和宽扩充为原来2倍，实现上采样
6         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：

1 class Residual(nn.Module):
 2         """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
 3         def __init__(self, inChannels, outChannels):
 4                 super(Residual, self).__init__()
 5                 self.inChannels = inChannels
 6                 self.outChannels = outChannels
 7                 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
 8                 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
 9 
10         def forward(self, x):
11                 out = 0
12                 out = out + self.cb(x)
13                 out = out + self.skip(x)
14                 return out

其中skiplayer代码如下：

 1 class SkipLayer(nn.Module):
 2         """docstring for SkipLayer"""  # 输入和输出通道相等，则输出=输入，否则为1*1 conv
 3         def __init__(self, inChannels, outChannels):
 4                 super(SkipLayer, self).__init__()
 5                 self.inChannels = inChannels
 6                 self.outChannels = outChannels
 7                 if (self.inChannels == self.outChannels):
 8                         self.conv = None
 9                 else:
10                         self.conv = nn.Conv2d(self.inChannels, self.outChannels, 1)
11 
12         def forward(self, x):
13                 if self.conv is not None:
14                         x = self.conv(x)
15                 return x

6. conv

 1 class BnReluConv(nn.Module):
 2         """docstring for BnReluConv"""    # BN+ReLU+conv
 3         def __init__(self, inChannels, outChannels, kernelSize = 1, stride = 1, padding = 0):
 4                 super(BnReluConv, self).__init__()
 5                 self.inChannels = inChannels
 6                 self.outChannels = outChannels
 7                 self.kernelSize = kernelSize
 8                 self.stride = stride
 9                 self.padding = padding
10 
11                 self.bn = nn.BatchNorm2d(self.inChannels)
12                 self.conv = nn.Conv2d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)
13                 self.relu = nn.ReLU()
14 
15         def forward(self, x):
16                 x = self.bn(x)
17                 x = self.relu(x)
18                 x = self.conv(x)
19                 return x

7. ConvBlock

 1 class ConvBlock(nn.Module):
 2         """docstring for ConvBlock"""  # 3 * (BN+ReLU+conv) 其中第一组降维，第二组不变，第三组升维
 3         def __init__(self, inChannels, outChannels):
 4                 super(ConvBlock, self).__init__()
 5                 self.inChannels = inChannels
 6                 self.outChannels = outChannels
 7                 self.outChannelsby2 = outChannels//2
 8 
 9                 self.cbr1 = BnReluConv(self.inChannels, self.outChannelsby2, 1, 1, 0)        # BN+ReLU+conv
10                 self.cbr2 = BnReluConv(self.outChannelsby2, self.outChannelsby2, 3, 1, 1)    # BN+ReLU+conv
11                 self.cbr3 = BnReluConv(self.outChannelsby2, self.outChannels, 1, 1, 0)       # BN+ReLU+conv
12 
13         def forward(self, x):
14                 x = self.cbr1(x)
15                 x = self.cbr2(x)
16                 x = self.cbr3(x)
17                 return x

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