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