## （原）堆叠hourglass网络

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

https://arxiv.org/abs/1603.06937

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

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

# 1. 简介

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

 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)
View Code

# 3. hourglass

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

 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
View Code

# 4. 上采样myUpsample

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)
View Code

# 5. 残差模块

 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
View Code

 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
View Code

# 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
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
View Code

# 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
View Code

posted on 2019-09-08 14:46  darkknightzh  阅读(4992)  评论(0编辑  收藏  举报