Pytorch 查看模型网络结构
安装torchsummary包
sudo pip3 install torchsummary下面以查看vgg19为例：
代码如下：
import torchvision.models as models
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vgg = models.vgg19().to(device)
summary(vgg, (3, 224, 224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
Conv2d-17 [-1, 256, 56, 56] 590,080
ReLU-18 [-1, 256, 56, 56] 0
MaxPool2d-19 [-1, 256, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 1,180,160
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
Conv2d-24 [-1, 512, 28, 28] 2,359,808
ReLU-25 [-1, 512, 28, 28] 0
Conv2d-26 [-1, 512, 28, 28] 2,359,808
ReLU-27 [-1, 512, 28, 28] 0
MaxPool2d-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
Conv2d-31 [-1, 512, 14, 14] 2,359,808
ReLU-32 [-1, 512, 14, 14] 0
Conv2d-33 [-1, 512, 14, 14] 2,359,808
ReLU-34 [-1, 512, 14, 14] 0
Conv2d-35 [-1, 512, 14, 14] 2,359,808
ReLU-36 [-1, 512, 14, 14] 0
MaxPool2d-37 [-1, 512, 7, 7] 0
AdaptiveAvgPool2d-38 [-1, 512, 7, 7] 0
Linear-39 [-1, 4096] 102,764,544
ReLU-40 [-1, 4096] 0
Dropout-41 [-1, 4096] 0
Linear-42 [-1, 4096] 16,781,312
ReLU-43 [-1, 4096] 0
Dropout-44 [-1, 4096] 0
Linear-45 [-1, 1000] 4,097,000
================================================================
Total params: 143,667,240
Trainable params: 143,667,240
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 238.69
Params size (MB): 548.05
Estimated Total Size (MB): 787.31
----------------------------------------------------------------
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