目录
一. 安装
二. 调用
三. 参数设置
四. 使用示例
一. 安装
Anaconda prompt 中激活虚拟环境
输入pip install torchsummary进行安装
pip install torchsummary
二. 调用
from torchsummary import summary可以成功调用就是安装成功
三. 参数设置
(model, input_size, batch_size=-1, device="cuda")- model：pytorch 模型
- input_size：模型输入size，形状为 C，H ，W
- batch_size：batch_size，默认为 -1，在展示模型每层输出的形状时显示的 batch_size
- device：“cuda"或者"cpu”，默认‘cuda’，如果用cpu，记得更改
四. 使用示例
from torchsummary import summary
import torch
from import resnet18 # 以 resnet18 为例
input_shape = [512, 512] # 设置输入大小
device = ('cuda' if .is_available() else 'cpu') # 选择是否使用GPU
model = resnet18().to(device) # 实例化网络
summary(model, (3, input_shape[0], input_shape[1]))输出结果：
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 256, 256] 9,408
BatchNorm2d-2 [-1, 64, 256, 256] 128
ReLU-3 [-1, 64, 256, 256] 0
MaxPool2d-4 [-1, 64, 128, 128] 0
Conv2d-5 [-1, 64, 128, 128] 36,864
BatchNorm2d-6 [-1, 64, 128, 128] 128
ReLU-7 [-1, 64, 128, 128] 0
Conv2d-8 [-1, 64, 128, 128] 36,864
BatchNorm2d-9 [-1, 64, 128, 128] 128
ReLU-10 [-1, 64, 128, 128] 0
BasicBlock-11 [-1, 64, 128, 128] 0
Conv2d-12 [-1, 64, 128, 128] 36,864
BatchNorm2d-13 [-1, 64, 128, 128] 128
ReLU-14 [-1, 64, 128, 128] 0
Conv2d-15 [-1, 64, 128, 128] 36,864
BatchNorm2d-16 [-1, 64, 128, 128] 128
ReLU-17 [-1, 64, 128, 128] 0
BasicBlock-18 [-1, 64, 128, 128] 0
Conv2d-19 [-1, 128, 64, 64] 73,728
BatchNorm2d-20 [-1, 128, 64, 64] 256
ReLU-21 [-1, 128, 64, 64] 0
Conv2d-22 [-1, 128, 64, 64] 147,456
BatchNorm2d-23 [-1, 128, 64, 64] 256
Conv2d-24 [-1, 128, 64, 64] 8,192
BatchNorm2d-25 [-1, 128, 64, 64] 256
ReLU-26 [-1, 128, 64, 64] 0
BasicBlock-27 [-1, 128, 64, 64] 0
Conv2d-28 [-1, 128, 64, 64] 147,456
BatchNorm2d-29 [-1, 128, 64, 64] 256
ReLU-30 [-1, 128, 64, 64] 0
Conv2d-31 [-1, 128, 64, 64] 147,456
BatchNorm2d-32 [-1, 128, 64, 64] 256
ReLU-33 [-1, 128, 64, 64] 0
BasicBlock-34 [-1, 128, 64, 64] 0
Conv2d-35 [-1, 256, 32, 32] 294,912
BatchNorm2d-36 [-1, 256, 32, 32] 512
ReLU-37 [-1, 256, 32, 32] 0
Conv2d-38 [-1, 256, 32, 32] 589,824
BatchNorm2d-39 [-1, 256, 32, 32] 512
Conv2d-40 [-1, 256, 32, 32] 32,768
BatchNorm2d-41 [-1, 256, 32, 32] 512
ReLU-42 [-1, 256, 32, 32] 0
BasicBlock-43 [-1, 256, 32, 32] 0
Conv2d-44 [-1, 256, 32, 32] 589,824
BatchNorm2d-45 [-1, 256, 32, 32] 512
ReLU-46 [-1, 256, 32, 32] 0
Conv2d-47 [-1, 256, 32, 32] 589,824
BatchNorm2d-48 [-1, 256, 32, 32] 512
ReLU-49 [-1, 256, 32, 32] 0
BasicBlock-50 [-1, 256, 32, 32] 0
Conv2d-51 [-1, 512, 16, 16] 1,179,648
BatchNorm2d-52 [-1, 512, 16, 16] 1,024
ReLU-53 [-1, 512, 16, 16] 0
Conv2d-54 [-1, 512, 16, 16] 2,359,296
BatchNorm2d-55 [-1, 512, 16, 16] 1,024
Conv2d-56 [-1, 512, 16, 16] 131,072
BatchNorm2d-57 [-1, 512, 16, 16] 1,024
ReLU-58 [-1, 512, 16, 16] 0
BasicBlock-59 [-1, 512, 16, 16] 0
Conv2d-60 [-1, 512, 16, 16] 2,359,296
BatchNorm2d-61 [-1, 512, 16, 16] 1,024
ReLU-62 [-1, 512, 16, 16] 0
Conv2d-63 [-1, 512, 16, 16] 2,359,296
BatchNorm2d-64 [-1, 512, 16, 16] 1,024
ReLU-65 [-1, 512, 16, 16] 0
BasicBlock-66 [-1, 512, 16, 16] 0
AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0
Linear-68 [-1, 1000] 513,000
================================================================
Total params: 11,689,512
Trainable params: 11,689,512
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 3.00
Forward/backward pass size (MB): 328.01
Params size (MB): 44.59
Estimated Total Size (MB): 375.60
----------------------------------------------------------------
Process finished with exit code 0
如图，torchsummary 可以查看网络的顺序结构，显示每一层的类型、out shape和参数量； 还有网络参数量，网络模型大小； fp/bp 一次需要的内存大小等信息。