zoukankan html css js c++ java

Deeplab v3+中的骨干模型resnet（加入atrous)的源码解析，以及普通resnet整个结构的构建过程

加入带洞卷积的resnet结构的构建，以及普通resnet如何通过模块的组合来堆砌深层卷积网络。

第一段代码为deeplab v3+(pytorch版本）中的基本模型改进版resnet的构建过程，

第二段代码为model的全部结构图示，以文字的方式表示，forward过程并未显示其中

  1 import math
  2 import torch.nn as nn
  3 import torch.utils.model_zoo as model_zoo
  4 from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  5 
  6 class Bottleneck(nn.Module):
  7     # 此类为resnet的基本模块，在构建resnet不同层的时候，主要以模块的个数以及参数不同来区分。
  8     expansion = 4
  9 
 10     def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
 11         super(Bottleneck, self).__init__()
 12         self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
 13         self.bn1 = BatchNorm(planes)
 14         self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
 15                                dilation=dilation, padding=dilation, bias=False)
 16         self.bn2 = BatchNorm(planes)
 17         self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
 18         self.bn3 = BatchNorm(planes * 4)
 19         self.relu = nn.ReLU(inplace=True)
 20         self.downsample = downsample
 21         self.stride = stride
 22         self.dilation = dilation
 23 
 24     def forward(self, x):
 25         # 传播的过程，在这里设置残差的操作；
 26         residual = x
 27 
 28         out = self.conv1(x)
 29         out = self.bn1(out)
 30         out = self.relu(out)
 31 
 32         out = self.conv2(out)
 33         out = self.bn2(out)
 34         out = self.relu(out)
 35 
 36         out = self.conv3(out)
 37         out = self.bn3(out)
 38 
 39         if self.downsample is not None:
 40             residual = self.downsample(x)
 41 
 42         out += residual
 43         out = self.relu(out)
 44 
 45         return out
 46 
 47 class ResNet(nn.Module):
 48     # 加入atrous的resnet结构，获取不同的感受野以及上下文信息。
 49     def __init__(self, block, layers, output_stride, BatchNorm, pretrained=True):
 50         # 定义resnet的基本结构，通过前面的几层直接设计加上不同参数的bottleneck模块的组合构成；
 51         # layers参数，在创建resnet类对象的时候，赋予一个数组，在构建多层网络模块的时候调用。
 52         # block代表模块结构，在这里指的是bottleneck.
 53         self.inplanes = 64
 54         super(ResNet, self).__init__()
 55         blocks = [1, 2, 4]
 56         if output_stride == 16:
 57             strides = [1, 2, 2, 1]
 58             dilations = [1, 1, 1, 2]
 59         elif output_stride == 8:
 60             strides = [1, 2, 1, 1]
 61             dilations = [1, 1, 2, 4]
 62         else:
 63             raise NotImplementedError
 64 
 65         # Modules
 66         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
 67                                 bias=False)
 68         self.bn1 = BatchNorm(64)
 69         self.relu = nn.ReLU(inplace=True)
 70         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 71         # 下面通过调用make_layer函数来构造不同参数的bottleneck模块；
 72         self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm)
 73         self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm)
 74         self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm)
 75         self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
 76         # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
 77         self._init_weight()
 78 
 79         if pretrained:
 80             self._load_pretrained_model()
 81 
 82     def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
 83         # block传入的是bottleneck模块；
 84         # planes参数是改变卷积层参数的重要变量，在这里分别传入64.128.256.512，
 85         # 目的是为了给conv2d(in_channels,out_channels,kernel_size,stride,……)传递各种参数，
 86         
 87         # blocks是定义有几个参数相同的bottleneck模块，即在最下面的Layers参数【3.4.23.3】，在总模型结构图中可以清晰的看出。
 88         # dilation参数是为了设置带洞卷积（atrous），默认为1即普通卷积；
 89         downsample = None
 90         if stride != 1 or self.inplanes != planes * block.expansion:
 91             downsample = nn.Sequential(
 92                 nn.Conv2d(self.inplanes, planes * block.expansion,
 93                           kernel_size=1, stride=stride, bias=False),
 94                 BatchNorm(planes * block.expansion),
 95             )
 96 
 97         layers = []
 98         layers.append(block(self.inplanes, planes, stride, dilation, downsample, BatchNorm))
 99         self.inplanes = planes * block.expansion
100         for i in range(1, blocks):
101             layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm))
102 
103         return nn.Sequential(*layers)
104 
105     def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
106         downsample = None
107         if stride != 1 or self.inplanes != planes * block.expansion:
108             downsample = nn.Sequential(
109                 nn.Conv2d(self.inplanes, planes * block.expansion,
110                           kernel_size=1, stride=stride, bias=False),
111                 BatchNorm(planes * block.expansion),
112             )
113 
114         layers = []
115         layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
116                             downsample=downsample, BatchNorm=BatchNorm))
117         self.inplanes = planes * block.expansion
118         for i in range(1, len(blocks)):
119             layers.append(block(self.inplanes, planes, stride=1,
120                                 dilation=blocks[i]*dilation, BatchNorm=BatchNorm))
121 
122         return nn.Sequential(*layers)
123 
124     def forward(self, input):
125         x = self.conv1(input)
126         x = self.bn1(x)
127         x = self.relu(x)
128         x = self.maxpool(x)
129 
130         x = self.layer1(x)
131         low_level_feat = x
132         x = self.layer2(x)
133         x = self.layer3(x)
134         x = self.layer4(x)
135         return x, low_level_feat
136 
137     def _init_weight(self):
138         for m in self.modules():
139             if isinstance(m, nn.Conv2d):
140                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
141                 m.weight.data.normal_(0, math.sqrt(2. / n))
142             elif isinstance(m, SynchronizedBatchNorm2d):
143                 m.weight.data.fill_(1)
144                 m.bias.data.zero_()
145             elif isinstance(m, nn.BatchNorm2d):
146                 m.weight.data.fill_(1)
147                 m.bias.data.zero_()
148 
149     def _load_pretrained_model(self):
150         # pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth')
151         pretrain_dict = torch.load('/home/huihua/NewDisk/resnet50-19c8e357.pth')
152         # 直接加载下载好模型预训练的参数，不用再次下载
153         model_dict = {}
154         state_dict = self.state_dict()
155         for k, v in pretrain_dict.items():
156             if k in state_dict:
157                 model_dict[k] = v
158         state_dict.update(model_dict)
159         self.load_state_dict(state_dict)
160 
161 def ResNet101(output_stride, BatchNorm, pretrained=True):
162     """Constructs a ResNet-101 model.
163     Args:
164         pretrained (bool): If True, returns a model pre-trained on ImageNet
165     """
166     model = ResNet(Bottleneck, [3, 4, 23, 3], output_stride, BatchNorm, pretrained=pretrained)
167     # 【3,4,23,3】代表make_layer中的block(bottleneck)的块数。resnet源代码中以此来确定resnet50或者101以及更深的。
168     return model
169 
170 if __name__ == "__main__":
171     import torch
172     model = ResNet101(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=8)
173     print(model) #打印模型结构，方便观察如何构造，以及各个参数的含义。
174     input = torch.rand(1, 3, 512, 512)
175     output, low_level_feat = model(input)
176     print(output.size())
177     print(low_level_feat.size())

打印出的model结构如下：

  1 /home/huihua/anaconda3/bin/python /home/huihua/PycharmProjects/untitled/pytorch-deeplab-xception-master/modeling/backbone/resnet.py
  2 ResNet(
  3   (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  4   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  5   (relu): ReLU(inplace)
  6   (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  7   (layer1): Sequential(
  8     (0): Bottleneck(
  9       (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
 10       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 11       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 12       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 13       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 14       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 15       (relu): ReLU(inplace)
 16       (downsample): Sequential(
 17         (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 18         (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 19       )
 20     )
 21     (1): Bottleneck(
 22       (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
 23       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 24       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 25       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 26       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 27       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 28       (relu): ReLU(inplace)
 29     )
 30     (2): Bottleneck(
 31       (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
 32       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 33       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 34       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 35       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 36       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 37       (relu): ReLU(inplace)
 38     )
 39   )
 40   (layer2): Sequential(
 41     (0): Bottleneck(
 42       (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
 43       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 44       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
 45       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 46       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
 47       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 48       (relu): ReLU(inplace)
 49       (downsample): Sequential(
 50         (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
 51         (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 52       )
 53     )
 54     (1): Bottleneck(
 55       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
 56       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 57       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 58       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 59       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
 60       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 61       (relu): ReLU(inplace)
 62     )
 63     (2): Bottleneck(
 64       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
 65       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 66       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 67       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 68       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
 69       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 70       (relu): ReLU(inplace)
 71     )
 72     (3): Bottleneck(
 73       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
 74       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 75       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
 76       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 77       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
 78       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 79       (relu): ReLU(inplace)
 80     )
 81   )
 82   (layer3): Sequential(
 83     (0): Bottleneck(
 84       (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 85       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 86       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
 87       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 88       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
 89       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 90       (relu): ReLU(inplace)
 91       (downsample): Sequential(
 92         (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
 93         (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 94       )
 95     )
 96     (1): Bottleneck(
 97       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
 98       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 99       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
100       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
101       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
102       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
103       (relu): ReLU(inplace)
104     )
105     (2): Bottleneck(
106       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
107       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
108       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
109       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
110       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
111       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
112       (relu): ReLU(inplace)
113     )
114     (3): Bottleneck(
115       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
116       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
117       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
118       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
119       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
120       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
121       (relu): ReLU(inplace)
122     )
123     (4): Bottleneck(
124       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
125       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
126       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
127       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
128       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
129       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
130       (relu): ReLU(inplace)
131     )
132     (5): Bottleneck(
133       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
134       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
135       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
136       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
137       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
138       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
139       (relu): ReLU(inplace)
140     )
141     (6): Bottleneck(
142       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
143       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
144       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
145       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
146       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
147       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
148       (relu): ReLU(inplace)
149     )
150     (7): Bottleneck(
151       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
152       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
153       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
154       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
155       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
156       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
157       (relu): ReLU(inplace)
158     )
159     (8): Bottleneck(
160       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
161       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
162       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
163       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
164       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
165       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
166       (relu): ReLU(inplace)
167     )
168     (9): Bottleneck(
169       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
170       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
171       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
172       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
173       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
174       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
175       (relu): ReLU(inplace)
176     )
177     (10): Bottleneck(
178       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
179       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
180       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
181       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
182       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
183       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
184       (relu): ReLU(inplace)
185     )
186     (11): Bottleneck(
187       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
188       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
189       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
190       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
191       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
192       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
193       (relu): ReLU(inplace)
194     )
195     (12): Bottleneck(
196       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
197       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
198       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
199       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
200       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
201       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
202       (relu): ReLU(inplace)
203     )
204     (13): Bottleneck(
205       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
206       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
207       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
208       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
209       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
210       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
211       (relu): ReLU(inplace)
212     )
213     (14): Bottleneck(
214       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
215       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
216       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
217       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
218       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
219       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
220       (relu): ReLU(inplace)
221     )
222     (15): Bottleneck(
223       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
224       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
225       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
226       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
227       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
228       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
229       (relu): ReLU(inplace)
230     )
231     (16): Bottleneck(
232       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
233       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
234       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
235       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
236       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
237       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
238       (relu): ReLU(inplace)
239     )
240     (17): Bottleneck(
241       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
242       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
243       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
244       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
245       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
246       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
247       (relu): ReLU(inplace)
248     )
249     (18): Bottleneck(
250       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
251       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
252       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
253       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
254       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
255       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
256       (relu): ReLU(inplace)
257     )
258     (19): Bottleneck(
259       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
260       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
261       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
262       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
263       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
264       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
265       (relu): ReLU(inplace)
266     )
267     (20): Bottleneck(
268       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
269       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
270       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
271       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
272       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
273       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
274       (relu): ReLU(inplace)
275     )
276     (21): Bottleneck(
277       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
278       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
279       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
280       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
281       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
282       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
283       (relu): ReLU(inplace)
284     )
285     (22): Bottleneck(
286       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
287       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
288       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
289       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
290       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
291       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
292       (relu): ReLU(inplace)
293     )
294   )
295   (layer4): Sequential(
296     (0): Bottleneck(
297       (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
298       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
299       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
300       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
301       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
302       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
303       (relu): ReLU(inplace)
304       (downsample): Sequential(
305         (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
306         (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
307       )
308     )
309     (1): Bottleneck(
310       (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
311       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
312       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8), bias=False)
313       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
314       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
315       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
316       (relu): ReLU(inplace)
317     )
318     (2): Bottleneck(
319       (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
320       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
321       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16), bias=False)
322       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
323       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
324       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
325       (relu): ReLU(inplace)
326     )
327   )
328 )
329 torch.Size([1, 2048, 64, 64])
330 torch.Size([1, 256, 128, 128])
331 
332 Process finished with exit code 0

查看全文

相关阅读:
寒假特训——搜索——H
寒假特训——I
寒假训练——搜索 K
three.js 加载STL文件
 three.js 加载3DS 404 文件找不到
 C# 请求数据方式1
学习一个简单的业务处理
 ABP 05 创建Model 以及相应的增删改查
 ABP 04 用户的创建
 ABP 00 常用知识

原文地址：https://www.cnblogs.com/ywheunji/p/10608791.html