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如何提取神经网络中间层特征向量

article 2025/3/12 11:45:15

方法一钩子（Hook）函数

举个实例来讲解，假设搭建的神经网络结构为一个简单的CNN，代码见下：

class CNN(nn.Module): #n=101   (n+2p-f)/s+1
    def __init__(self):
        super(CNN, self).__init__()
        self.cnn=nn.Sequential(
            #Layer1
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=0), #n=97
            nn.BatchNorm2d(16),
            nn.ReLU(),
            # nn.Dropout2d(0.1),
            nn.MaxPool2d(kernel_size=2, stride=2), #48

            #Layer2
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=0), #44
            nn.BatchNorm2d(32),
            nn.ReLU(),
            # nn.Dropout2d(0.1),
            nn.MaxPool2d(kernel_size=2, stride=2), #22

            #Layer3
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=1, padding=0), #19
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),  # 9

            #Layer4
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=1, padding=0),  # 6
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2) #3
        )

        self.linear=nn.Sequential(
            nn.Linear(3*3*128,256),
            nn.ReLU(),
            nn.Linear(256,4)
        )

    def forward(self, x):
        x=self.cnn(x)
        x = x.view(x.shape[0], -1) 
        x=self.linear(x)

        return x

我们如果想要提取出全连接层中第一个线性层输出的特征向量，即linear中nn.Linear(3*3*128,256)输出的维度为256的特征向量，那么我们可以这样做：

# 定义hook函数
fmap_black0={}
def forward_hook0(model,inp,outp):
    fmap_black0['input']=inp
    fmap_black0['output'] = outp

model.linear[0].register_forward_hook(forward_hook0)  # linear[0]代表运行linear中第一行的结果（每一行代表一层），特征向量即从网络中第一个FC输出的维度为128的向量

feature = tensor_list_train.append(fmap_black0["output"])

这样就可以将所需特征向量提取出来啦。

但是，该方法适用于结构较简单的神将网络，如果是比较复杂的网络，比如主干网络中还嵌套了其他网络（如下代码，只是举个例子，可以看到网络内嵌套了Feature_Net、TransformerModel等网络），但又想提取出嵌套网络中某一层的特征，就只能望洋兴叹了，因此下面提供方法二。

class TFFusion_Net_double(nn.Module):
    def __init__(self, feature_dim=128, d_model=256, img_size=16, patch_size=16, embed_dim=128, depth=4, num_heads=4, output_dim=4):
        super(TFFusion_Net_double, self).__init__()

        self.feature_model = Feature_Net(feature_dim=feature_dim)
        self.attention = TransformerModel(d_model=d_model)
        self.xcit_model = XCiT(img_size=img_size,
                               patch_size=patch_size, embed_dim=embed_dim, depth=depth, num_heads=num_heads, mlp_ratio=4, 
                               qkv_bias=True,norm_layer=partial(nn.LayerNorm, eps=1e-6), eta=1.0, tokens_norm=False)
        self.Dense = nn.Sequential(
            nn.Linear(embed_dim*4*4, 1024),
            nn.Mish(),
            nn.Linear(1024, 128),
            nn.Mish(),
            nn.Linear(128, output_dim)
        )
    
    def forward(self, x, y):
        x, y = self.feature_model(x), self.feature_model(y) # (64,128)
        mix = torch.cat((x, y), dim=1) # (64,256)
        mix = torch.unsqueeze(mix, dim=1) # (64,1,256)
        mix = self.attention(mix)
        mix = mix.view(x.shape[0], 1, 16, 16)
        mix = mix.expand(-1, 3, -1, -1)
        mix = self.xcit_model(mix)
        mix = mix[0]
        mix = mix.contiguous().view(mix.shape[0], -1)
        mix = self.Dense(mix)

        return mix

方法二 torchextractor

Github开源地址：antoinebrl/torchextractor: Feature extraction made simple with torchextractor

Github中的讲解也很清楚，这里依然举一个实例来讲解：

import torchextractor as tx # PyTorch Intermediate Feature Extraction

if __name__ == '__main__':

    # 下载网络模型
    model = CNN_Net(output_dim=4)
    model_weight = r'XXXXXXXX.pth'
    model.load_state_dict(torch.load(model_weight))
    model.to(device)
    print(tx.list_module_names(model)) # 打印出网络结构
    '''
    ['', 'cnn', 'cnn.0', 'cnn.1', 'cnn.2', 'cnn.3', 'cnn.4', 'cnn.5', 'cnn.6', 'cnn.7', 'cnn.8', 'cnn.9', 'cnn.10', 'cnn.11', 
    'fc', 'fc.0', 'fc.1', 'fc.2']
    '''
    model = tx.Extractor(model, "fc.1") # 想要得到全连接层中第1层的输出
    model.eval()
    
    # 下面的inputs可从使用的数据集中选取一个样本，根据实际数据集来获取
    _, feature = model(inputs.float())
    feature = feature['fc.1']
    feature = feature.view(feature.shape[0],-1)[0]
    feature = feature.cpu().detach().numpy()
    # 保存特征向量
    scipy.io.savemat('XXXXXX.mat', {'data':feature})

比较复杂，含有嵌套网络的结构也可以打印出来，如：

'''
    ['', 'feature_model', 'feature_model.cnn', 'feature_model.cnn.0', 'feature_model.cnn.1', 
    'feature_model.cnn.2', 'feature_model.cnn.3', 'feature_model.cnn.4', 'feature_model.cnn.5', 
    'feature_model.cnn.6', 'feature_model.cnn.7', 'feature_model.cnn.8', 'feature_model.cnn.9', 
    'feature_model.cnn.10', 'feature_model.cnn.11', 'feature_model.fc', 'timexer_model', 
    'timexer_model.self_attn', 'timexer_model.self_attn.self_attn', 
    'timexer_model.self_attn.self_attn.out_proj', 'timexer_model.self_attn.linear1', 
    'timexer_model.self_attn.dropout', 'timexer_model.self_attn.linear2', 
    'timexer_model.self_attn.norm1', 'timexer_model.self_attn.norm2', 
    'timexer_model.self_attn.dropout1', 'timexer_model.self_attn.dropout2', 
    'timexer_model.cross_attn', 'timexer_model.cross_attn.self_attn', 
    'timexer_model.cross_attn.self_attn.out_proj', 'timexer_model.cross_attn.linear1', 
    'timexer_model.cross_attn.dropout', 'timexer_model.cross_attn.linear2', 
    'timexer_model.cross_attn.norm1', 'timexer_model.cross_attn.norm2', 
    'timexer_model.cross_attn.dropout1', 'timexer_model.cross_attn.dropout2']
'''

需要提取哪一层的输出特征，直接按照上述实例将"fc.1"替换即可。

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