加载Pytorch中的预训练模型及部分结构的导入
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torchvision.model
model子包中包含了用于处理不同任务的经典模型的定义，包括：图像分类、像素级语义分割、对象检测、实例分割、人员关键点检测和视频分类。
图像分类：
语义分割：
对象检测、实例分割和人员关键点检测：
视频分类：
- ResNet 3D
- ResNet Mixed Convolution
- ResNet (2+1)D
模型的导入
你可以通过调用构造函数来构造一个带有随机权重的模型（需要重新训练）:
import torchvision.models as models resnet18 = models.resnet18() alexnet = models.alexnet() vgg16 = models.vgg16() squeezenet = models.squeezenet1_0() densenet = models.densenet161() inception = models.inception_v3() googlenet = models.googlenet() shufflenet = models.shufflenet_v2_x1_0() mobilenet = models.mobilenet_v2() resnext50_32x4d = models.resnext50_32x4d() wide_resnet50_2 = models.wide_resnet50_2() mnasnet = models.mnasnet1_0()
导入预训练好的模型：
我们使用PyTorch torch.utils.model_zoo提供预训练的模型。这些可以通过传递pretrained=True来构造:
import torchvision.models as models resnet18 = models.resnet18(pretrained=True) alexnet = models.alexnet(pretrained=True) squeezenet = models.squeezenet1_0(pretrained=True) vgg16 = models.vgg16(pretrained=True) densenet = models.densenet161(pretrained=True) inception = models.inception_v3(pretrained=True) googlenet = models.googlenet(pretrained=True) shufflenet = models.shufflenet_v2_x1_0(pretrained=True) mobilenet = models.mobilenet_v2(pretrained=True) resnext50_32x4d = models.resnext50_32x4d(pretrained=True) wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) mnasnet = models.mnasnet1_0(pretrained=True)
导入预训练模型的部分结构
有时候，我们想利用一些经典模型的架构作为特征提取器，仅想导入部分结构，而不是整个模型。
以densenet121为例，例如仅利用denseblock1及之前的结构+Transition
首先检查densenet121的结构：
import torchvision.models as torch_models dense = torch_models.densenet121(pretrained=True) dense.features
输出：
1 Sequential( 2 (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) 3 (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 4 (relu0): ReLU(inplace=True) 5 (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) 6 (denseblock1): _DenseBlock( 7 (denselayer1): _DenseLayer( 8 (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 9 (relu1): ReLU(inplace=True) 10 (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 11 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 12 (relu2): ReLU(inplace=True) 13 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 14 ) 15 (denselayer2): _DenseLayer( 16 (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 17 (relu1): ReLU(inplace=True) 18 (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 19 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 20 (relu2): ReLU(inplace=True) 21 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 22 ) 23 (denselayer3): _DenseLayer( 24 (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 25 (relu1): ReLU(inplace=True) 26 (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 27 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 28 (relu2): ReLU(inplace=True) 29 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 30 ) 31 (denselayer4): _DenseLayer( 32 (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 33 (relu1): ReLU(inplace=True) 34 (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 35 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 36 (relu2): ReLU(inplace=True) 37 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 38 ) 39 (denselayer5): _DenseLayer( 40 (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 41 (relu1): ReLU(inplace=True) 42 (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 43 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 44 (relu2): ReLU(inplace=True) 45 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 46 ) 47 (denselayer6): _DenseLayer( 48 (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 49 (relu1): ReLU(inplace=True) 50 (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 51 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 52 (relu2): ReLU(inplace=True) 53 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 54 ) 55 ) 56 (transition1): _Transition( 57 (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 58 (relu): ReLU(inplace=True) 59 (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 60 (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) 61 ) 62 (denseblock2): _DenseBlock( 63 (denselayer1): _DenseLayer( 64 (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 65 (relu1): ReLU(inplace=True) 66 (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 67 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 68 (relu2): ReLU(inplace=True) 69 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 70 ) 71 (denselayer2): _DenseLayer( 72 (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 73 (relu1): ReLU(inplace=True) 74 (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 75 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 76 (relu2): ReLU(inplace=True) 77 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 78 ) 79 (denselayer3): _DenseLayer( 80 (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 81 (relu1): ReLU(inplace=True) 82 (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 83 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 84 (relu2): ReLU(inplace=True) 85 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 86 ) 87 (denselayer4): _DenseLayer( 88 (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 89 (relu1): ReLU(inplace=True) 90 (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 91 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 92 (relu2): ReLU(inplace=True) 93 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 94 ) 95 (denselayer5): _DenseLayer( 96 (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 97 (relu1): ReLU(inplace=True) 98 (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 99 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 100 (relu2): ReLU(inplace=True) 101 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 102 ) 103 (denselayer6): _DenseLayer( 104 (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 105 (relu1): ReLU(inplace=True) 106 (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 107 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 108 (relu2): ReLU(inplace=True) 109 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 110 ) 111 (denselayer7): _DenseLayer( 112 (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 113 (relu1): ReLU(inplace=True) 114 (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 115 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 116 (relu2): ReLU(inplace=True) 117 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 118 ) 119 (denselayer8): _DenseLayer( 120 (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 121 (relu1): ReLU(inplace=True) 122 (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 123 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 124 (relu2): ReLU(inplace=True) 125 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 126 ) 127 (denselayer9): _DenseLayer( 128 (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 129 (relu1): ReLU(inplace=True) 130 (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 131 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 132 (relu2): ReLU(inplace=True) 133 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 134 ) 135 (denselayer10): _DenseLayer( 136 (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 137 (relu1): ReLU(inplace=True) 138 (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 139 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 140 (relu2): ReLU(inplace=True) 141 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 142 ) 143 (denselayer11): _DenseLayer( 144 (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 145 (relu1): ReLU(inplace=True) 146 (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 147 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 148 (relu2): ReLU(inplace=True) 149 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 150 ) 151 (denselayer12): _DenseLayer( 152 (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 153 (relu1): ReLU(inplace=True) 154 (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 155 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 156 (relu2): ReLU(inplace=True) 157 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 158 ) 159 ) 160 (transition2): _Transition( 161 (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 162 (relu): ReLU(inplace=True) 163 (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) 164 (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) 165 ) 166 (denseblock3): _DenseBlock( 167 (denselayer1): _DenseLayer( 168 (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 169 (relu1): ReLU(inplace=True) 170 (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 171 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 172 (relu2): ReLU(inplace=True) 173 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 174 ) 175 (denselayer2): _DenseLayer( 176 (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 177 (relu1): ReLU(inplace=True) 178 (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 179 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 180 (relu2): ReLU(inplace=True) 181 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 182 ) 183 (denselayer3): _DenseLayer( 184 (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 185 (relu1): ReLU(inplace=True) 186 (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 187 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 188 (relu2): ReLU(inplace=True) 189 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 190 ) 191 (denselayer4): _DenseLayer( 192 (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 193 (relu1): ReLU(inplace=True) 194 (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 195 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 196 (relu2): ReLU(inplace=True) 197 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 198 ) 199 (denselayer5): _DenseLayer( 200 (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 201 (relu1): ReLU(inplace=True) 202 (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 203 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 204 (relu2): ReLU(inplace=True) 205 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 206 ) 207 (denselayer6): _DenseLayer( 208 (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 209 (relu1): ReLU(inplace=True) 210 (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 211 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 212 (relu2): ReLU(inplace=True) 213 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 214 ) 215 (denselayer7): _DenseLayer( 216 (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 217 (relu1): ReLU(inplace=True) 218 (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 219 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 220 (relu2): ReLU(inplace=True) 221 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 222 ) 223 (denselayer8): _DenseLayer( 224 (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 225 (relu1): ReLU(inplace=True) 226 (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 227 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 228 (relu2): ReLU(inplace=True) 229 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 230 ) 231 (denselayer9): _DenseLayer( 232 (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 233 (relu1): ReLU(inplace=True) 234 (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 235 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 236 (relu2): ReLU(inplace=True) 237 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 238 ) 239 (denselayer10): _DenseLayer( 240 (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 241 (relu1): ReLU(inplace=True) 242 (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 243 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 244 (relu2): ReLU(inplace=True) 245 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 246 ) 247 (denselayer11): _DenseLayer( 248 (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 249 (relu1): ReLU(inplace=True) 250 (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 251 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 252 (relu2): ReLU(inplace=True) 253 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 254 ) 255 (denselayer12): _DenseLayer( 256 (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 257 (relu1): ReLU(inplace=True) 258 (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 259 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 260 (relu2): ReLU(inplace=True) 261 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 262 ) 263 (denselayer13): _DenseLayer( 264 (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 265 (relu1): ReLU(inplace=True) 266 (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 267 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 268 (relu2): ReLU(inplace=True) 269 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 270 ) 271 (denselayer14): _DenseLayer( 272 (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 273 (relu1): ReLU(inplace=True) 274 (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 275 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 276 (relu2): ReLU(inplace=True) 277 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 278 ) 279 (denselayer15): _DenseLayer( 280 (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 281 (relu1): ReLU(inplace=True) 282 (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 283 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 284 (relu2): ReLU(inplace=True) 285 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 286 ) 287 (denselayer16): _DenseLayer( 288 (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 289 (relu1): ReLU(inplace=True) 290 (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 291 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 292 (relu2): ReLU(inplace=True) 293 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 294 ) 295 (denselayer17): _DenseLayer( 296 (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 297 (relu1): ReLU(inplace=True) 298 (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 299 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 300 (relu2): ReLU(inplace=True) 301 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 302 ) 303 (denselayer18): _DenseLayer( 304 (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 305 (relu1): ReLU(inplace=True) 306 (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 307 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 308 (relu2): ReLU(inplace=True) 309 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 310 ) 311 (denselayer19): _DenseLayer( 312 (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 313 (relu1): ReLU(inplace=True) 314 (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 315 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 316 (relu2): ReLU(inplace=True) 317 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 318 ) 319 (denselayer20): _DenseLayer( 320 (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 321 (relu1): ReLU(inplace=True) 322 (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 323 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 324 (relu2): ReLU(inplace=True) 325 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 326 ) 327 (denselayer21): _DenseLayer( 328 (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 329 (relu1): ReLU(inplace=True) 330 (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 331 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 332 (relu2): ReLU(inplace=True) 333 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 334 ) 335 (denselayer22): _DenseLayer( 336 (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 337 (relu1): ReLU(inplace=True) 338 (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 339 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 340 (relu2): ReLU(inplace=True) 341 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 342 ) 343 (denselayer23): _DenseLayer( 344 (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 345 (relu1): ReLU(inplace=True) 346 (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 347 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 348 (relu2): ReLU(inplace=True) 349 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 350 ) 351 (denselayer24): _DenseLayer( 352 (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 353 (relu1): ReLU(inplace=True) 354 (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 355 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 356 (relu2): ReLU(inplace=True) 357 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 358 ) 359 ) 360 (transition3): _Transition( 361 (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 362 (relu): ReLU(inplace=True) 363 (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) 364 (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) 365 ) 366 (denseblock4): _DenseBlock( 367 (denselayer1): _DenseLayer( 368 (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 369 (relu1): ReLU(inplace=True) 370 (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 371 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 372 (relu2): ReLU(inplace=True) 373 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 374 ) 375 (denselayer2): _DenseLayer( 376 (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 377 (relu1): ReLU(inplace=True) 378 (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 379 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 380 (relu2): ReLU(inplace=True) 381 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 382 ) 383 (denselayer3): _DenseLayer( 384 (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 385 (relu1): ReLU(inplace=True) 386 (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 387 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 388 (relu2): ReLU(inplace=True) 389 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 390 ) 391 (denselayer4): _DenseLayer( 392 (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 393 (relu1): ReLU(inplace=True) 394 (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 395 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 396 (relu2): ReLU(inplace=True) 397 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 398 ) 399 (denselayer5): _DenseLayer( 400 (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 401 (relu1): ReLU(inplace=True) 402 (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 403 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 404 (relu2): ReLU(inplace=True) 405 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 406 ) 407 (denselayer6): _DenseLayer( 408 (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 409 (relu1): ReLU(inplace=True) 410 (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 411 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 412 (relu2): ReLU(inplace=True) 413 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 414 ) 415 (denselayer7): _DenseLayer( 416 (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 417 (relu1): ReLU(inplace=True) 418 (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 419 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 420 (relu2): ReLU(inplace=True) 421 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 422 ) 423 (denselayer8): _DenseLayer( 424 (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 425 (relu1): ReLU(inplace=True) 426 (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 427 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 428 (relu2): ReLU(inplace=True) 429 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 430 ) 431 (denselayer9): _DenseLayer( 432 (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 433 (relu1): ReLU(inplace=True) 434 (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 435 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 436 (relu2): ReLU(inplace=True) 437 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 438 ) 439 (denselayer10): _DenseLayer( 440 (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 441 (relu1): ReLU(inplace=True) 442 (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 443 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 444 (relu2): ReLU(inplace=True) 445 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 446 ) 447 (denselayer11): _DenseLayer( 448 (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 449 (relu1): ReLU(inplace=True) 450 (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 451 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 452 (relu2): ReLU(inplace=True) 453 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 454 ) 455 (denselayer12): _DenseLayer( 456 (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 457 (relu1): ReLU(inplace=True) 458 (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 459 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 460 (relu2): ReLU(inplace=True) 461 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 462 ) 463 (denselayer13): _DenseLayer( 464 (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 465 (relu1): ReLU(inplace=True) 466 (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 467 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 468 (relu2): ReLU(inplace=True) 469 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 470 ) 471 (denselayer14): _DenseLayer( 472 (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 473 (relu1): ReLU(inplace=True) 474 (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 475 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 476 (relu2): ReLU(inplace=True) 477 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 478 ) 479 (denselayer15): _DenseLayer( 480 (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 481 (relu1): ReLU(inplace=True) 482 (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 483 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 484 (relu2): ReLU(inplace=True) 485 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 486 ) 487 (denselayer16): _DenseLayer( 488 (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 489 (relu1): ReLU(inplace=True) 490 (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 491 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 492 (relu2): ReLU(inplace=True) 493 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 494 ) 495 ) 496 (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 497 )
关于densenet的Pytorch实现源码解析参考【pytorch torchvision源码解读系列—5】DenseNet，关于Densenet参考DenseNet。
检查densenet的结构：
1 dense.features[0] 2 3 Out[10]: Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) 4 5 dense.features[1] 6 Out[11]: BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 7 8 dense.features[2] 9 Out[12]: ReLU(inplace=True) 10 11 dense.features[3] 12 Out[13]: MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) 13 14 dense.features[4] 15 Out[14]: 16 _DenseBlock( 17 (denselayer1): _DenseLayer( 18 (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 19 (relu1): ReLU(inplace=True) 20 (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 21 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 22 (relu2): ReLU(inplace=True) 23 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 24 ) 25 (denselayer2): _DenseLayer( 26 (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 27 (relu1): ReLU(inplace=True) 28 (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 29 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 30 (relu2): ReLU(inplace=True) 31 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 32 ) 33 (denselayer3): _DenseLayer( 34 (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 35 (relu1): ReLU(inplace=True) 36 (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 37 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 38 (relu2): ReLU(inplace=True) 39 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 40 ) 41 (denselayer4): _DenseLayer( 42 (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 43 (relu1): ReLU(inplace=True) 44 (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 45 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 46 (relu2): ReLU(inplace=True) 47 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 48 ) 49 (denselayer5): _DenseLayer( 50 (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 51 (relu1): ReLU(inplace=True) 52 (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 53 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 54 (relu2): ReLU(inplace=True) 55 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 56 ) 57 (denselayer6): _DenseLayer( 58 (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 59 (relu1): ReLU(inplace=True) 60 (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 61 (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 62 (relu2): ReLU(inplace=True) 63 (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 64 ) 65 ) 66 67 68 dense.features[5] 69 Out[15]: 70 _Transition( 71 (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 72 (relu): ReLU(inplace=True) 73 (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) 74 (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) 75 )
调用部分结构即可，例如：
1 class mydensenet121(nn.Module): # [N, 3, 224, 224] --> [N, 128, 56, 56] 2 def __init__(self): 3 super(mydensenet121, self).__init__() 4 densenet_pretrained = torch_models.densenet121(pretrained=True) 5 self.features = densenet_pretrained.features[:5] # 预处理层+denseblock1(denselayer1:6) 6 self.bn_relu_1x1conv = densenet_pretrained.features[5][:3] # 这里报错？ 7 8 9 def forward(self, x): 10 out = self.features(x) 11 out = self.bn_relu_1x1conv(out) 12 return out
1 TypeError: __init__() missing 1 required positional argument: ‘num_output_features‘
但是分别调用是没问题的，不知道为啥？
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