【Pytorch】 Méthode d'information sur le modèle de réseau de sortie rapide

Tout d'abord, installeztorchsummary

pip isntall torchsummary

Ce qui suit est le code de démonstration, qui (3, 100, 100)est le nombre de canaux, la hauteur et la largeur d'une seule image.

from torchsummary import summary
import torchvision
import torch

def model_info(model):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    backbone = model.to(device)
    summary(backbone, (3, 100, 100))

model = torchvision.models.vgg16(pretrained=False)
model_info(model)

Le résultat de sortie est le suivant, 13 Conv2d (couche convolutionnelle), 3 linéaires (couche entièrement connectée), un total de 16 couches, vgg16 est très bien

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 100, 100]           1,792
              ReLU-2         [-1, 64, 100, 100]               0
            Conv2d-3         [-1, 64, 100, 100]          36,928
              ReLU-4         [-1, 64, 100, 100]               0
         MaxPool2d-5           [-1, 64, 50, 50]               0
            Conv2d-6          [-1, 128, 50, 50]          73,856
              ReLU-7          [-1, 128, 50, 50]               0
            Conv2d-8          [-1, 128, 50, 50]         147,584
              ReLU-9          [-1, 128, 50, 50]               0
        MaxPool2d-10          [-1, 128, 25, 25]               0
           Conv2d-11          [-1, 256, 25, 25]         295,168
             ReLU-12          [-1, 256, 25, 25]               0
           Conv2d-13          [-1, 256, 25, 25]         590,080
             ReLU-14          [-1, 256, 25, 25]               0
           Conv2d-15          [-1, 256, 25, 25]         590,080
             ReLU-16          [-1, 256, 25, 25]               0
        MaxPool2d-17          [-1, 256, 12, 12]               0
           Conv2d-18          [-1, 512, 12, 12]       1,180,160
             ReLU-19          [-1, 512, 12, 12]               0
           Conv2d-20          [-1, 512, 12, 12]       2,359,808
             ReLU-21          [-1, 512, 12, 12]               0
           Conv2d-22          [-1, 512, 12, 12]       2,359,808
             ReLU-23          [-1, 512, 12, 12]               0
        MaxPool2d-24            [-1, 512, 6, 6]               0
           Conv2d-25            [-1, 512, 6, 6]       2,359,808
             ReLU-26            [-1, 512, 6, 6]               0
           Conv2d-27            [-1, 512, 6, 6]       2,359,808
             ReLU-28            [-1, 512, 6, 6]               0
           Conv2d-29            [-1, 512, 6, 6]       2,359,808
             ReLU-30            [-1, 512, 6, 6]               0
        MaxPool2d-31            [-1, 512, 3, 3]               0
AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0
           Linear-33                 [-1, 4096]     102,764,544
             ReLU-34                 [-1, 4096]               0
          Dropout-35                 [-1, 4096]               0
           Linear-36                 [-1, 4096]      16,781,312
             ReLU-37                 [-1, 4096]               0
          Dropout-38                 [-1, 4096]               0
           Linear-39                 [-1, 1000]       4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.11
Forward/backward pass size (MB): 43.51
Params size (MB): 527.79
Estimated Total Size (MB): 571.42
----------------------------------------------------------------