python 将图像分类模型转换为分层模型

zqdjd7g9 于 2023-06-28 发布在 Python

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我使用ResNet50从图像中提取特征。我如何删除网络的分类头，并且还因为我需要拆分网络以具有中间特征，所以我如何将网络转换为可以访问中间网络架构的分层形式，如下所示：

import torch
from torchvision.models import resnet50, ResNet50_Weights
import torch.nn as nn

class model(nn.Module):
    def __init__(self, pretrained=True):
        super(model, self).__init__()

        self.featureExtractor = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)

def forward(self, x):   
        
      x1= self.featureExtractor_1(x)     #(number of feature maps:256)
      x2= self.featureExtractor_2(x1)    # (number of feature maps:512)
      x3= self.featureExtractor_3(x2)    # (number of feature maps:1024)
      x4= self.featureExtractor_4(x3)    # (number of feature maps:2048)

 return x1, x2, x3, x4

虽然我知道如何使用hook方法提取网络的中间特征，但我不知道如何将网络划分为这样的层次结构？
你知道吗？

python

来源：https://stackoverflow.com/questions/76564741/transforming-an-image-classification-model-into-a-hierarchical-model

1条答案

按热度按时间

smtd7mpg1#

您可以执行以下操作：

import torch
from torchvision.models import resnet50, ResNet50_Weights
import torch.nn as nn

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()

        self.model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
        self.block1 = nn.Sequential(*list(self.model.children())[:5])
        self.block2 = nn.Sequential(*list(self.model.children())[5:6])
        self.block3 = nn.Sequential(*list(self.model.children())[6:7])
        self.block4 = nn.Sequential(*list(self.model.children())[7:8])

    def forward(self, x):   
        
        x1 = self.block1(x)    
        x2 = self.block2(x1)
        x3 = self.block3(x2)
        x4 = self.block4(x3)

        return(x1, x2, x3, x4)

x = torch.randn(1, 3, 256, 256)
model = model()
x1, x2, x3, x4 = model(x)

print(x1.shape, x2.shape, x3.shape, x4.shape)

其给出：

torch.Size([1, 256, 64, 64]) torch.Size([1, 512, 32, 32]) torch.Size([1, 1024, 16, 16]) torch.Size([1, 2048, 8, 8])

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python 将图像分类模型转换为分层模型

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