該模塊融合了4種不同金字塔尺度的特征，第一行紅色是最粗糙的特征–全局池化生成單個bin輸出，后面三行是不同尺度的池化特征。

為了保證全局特征的權(quán)重，如果金字塔共有N個級別，則在每個級別后使用1×1 1×11×1的卷積將對于級別通道降為原本的1/N。再通過雙線性插值獲得未池化前的大小，最終concat到一起。

 1 import torch
 2 import torch.nn.functional as F
 3 from torch import nn
 4 from torchvision import models
 5 
 6 from utils import initialize_weights
 7 from utils.misc import Conv2dDeformable
 8 from .config import res101_path
 9 
10 //金字塔模塊,將從前面卷積結(jié)構(gòu)提取的特征分別進(jìn)行不同的池化操作,得到不同感受野以及全局語境信息(或者叫做不同層級的信息)
11 class _PyramidPoolingModule(nn.Module):
12     def __init__(self, in_dim, reduction_dim, setting):
13         super(_PyramidPoolingModule, self).__init__()
14         self.features = []
15         for s in setting:      //對應(yīng)不同的池化操作,單個bin,多個bin
16             self.features.append(nn.Sequential(
17                 nn.AdaptiveAvgPool2d(s),
18                 nn.Conv2d(in_dim, reduction_dim, kernel_size=1, bias=False),
19                 nn.BatchNorm2d(reduction_dim, momentum=.95),
20                 nn.ReLU(inplace=True)
21             ))
22         self.features = nn.ModuleList(self.features)
23 
24     def forward(self, x):
25         x_size = x.size()
26         out = [x]
27         for f in self.features:
28             out.append(F.upsample(f(x), x_size[2:], mode='bilinear'))
29         out = torch.cat(out, 1)
30         return out
31 
32 //整個pspnet網(wǎng)絡(luò)的結(jié)構(gòu)
33 class PSPNet(nn.Module):
34     def __init__(self, num_classes, pretrained=True, use_aux=True):
35         super(PSPNet, self).__init__()
36         self.use_aux = use_aux
37         resnet = models.resnet101()     //采用resnet101作為骨干模型,提取特征
38         if pretrained:
39             resnet.load_state_dict(torch.load(res101_path))
40         self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool)
41         self.layer1, self.layer2, self.layer3, self.layer4 = resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4
42 　　　　//設(shè)置帶洞卷積的參數(shù)(dilation),以及卷積的參數(shù)
43         for n, m in self.layer3.named_modules():
44             if 'conv2' in n:
45                 m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
46             elif 'downsample.0' in n:
47                 m.stride = (1, 1)
48         for n, m in self.layer4.named_modules():
49             if 'conv2' in n:
50                 m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)
51             elif 'downsample.0' in n:
52                 m.stride = (1, 1)
53 　　　　//加入ppm模塊,以及最后的連接層(卷積)
54         self.ppm = _PyramidPoolingModule(2048, 512, (1, 2, 3, 6))
55         self.final = nn.Sequential(
56             nn.Conv2d(4096, 512, kernel_size=3, padding=1, bias=False),
57             nn.BatchNorm2d(512, momentum=.95),
58             nn.ReLU(inplace=True),
59             nn.Dropout(0.1),
60             nn.Conv2d(512, num_classes, kernel_size=1)
61         )
62 
63         if use_aux:
64             self.aux_logits = nn.Conv2d(1024, num_classes, kernel_size=1)
65             initialize_weights(self.aux_logits)
66 # 初始化權(quán)重
67         initialize_weights(self.ppm, self.final)
68 
69     def forward(self, x):
70         x_size = x.size()
71         x = self.layer0(x)
72         x = self.layer1(x)
73         x = self.layer2(x)
74         x = self.layer3(x)
75         if self.training and self.use_aux:
76             aux = self.aux_logits(x)
77         x = self.layer4(x)
78         x = self.ppm(x)
79         x = self.final(x)
80         if self.training and self.use_aux:
81             return F.upsample(x, x_size[2:], mode='bilinear'), F.upsample(aux, x_size[2:], mode='bilinear')
82         return F.upsample(x, x_size[2:], mode='bilinear')

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