獲取Pytorch中間某一層權重或者特征

問題:訓練好的網絡模型想知道中間某一層的權重或者看看中間某一層的特征,如何處理呢?

1.獲取某一層權重,並保存到excel中;

以resnet18為例說明:

import torch
import pandas as pd
import numpy as np
import torchvision.models as models

resnet18 = models.resnet18(pretrained=True)

parm={}
for name,parameters in resnet18.named_parameters():
    print(name,':',parameters.size())
    parm[name]=parameters.detach().numpy()

　　

上述代碼將每個模塊參數存入parm字典中,parameters.detach().numpy()將tensor類型變量轉換成numpy array形式,方便后續存儲到表格中.輸出為:

conv1.weight : torch.Size([64, 3, 7, 7])
bn1.weight : torch.Size([64])
bn1.bias : torch.Size([64])
layer1.0.conv1.weight : torch.Size([64, 64, 3, 3])
layer1.0.bn1.weight : torch.Size([64])
layer1.0.bn1.bias : torch.Size([64])
layer1.0.conv2.weight : torch.Size([64, 64, 3, 3])
layer1.0.bn2.weight : torch.Size([64])
layer1.0.bn2.bias : torch.Size([64])
layer1.1.conv1.weight : torch.Size([64, 64, 3, 3])
layer1.1.bn1.weight : torch.Size([64])
layer1.1.bn1.bias : torch.Size([64])
layer1.1.conv2.weight : torch.Size([64, 64, 3, 3])
layer1.1.bn2.weight : torch.Size([64])
layer1.1.bn2.bias : torch.Size([64])
layer2.0.conv1.weight : torch.Size([128, 64, 3, 3])
layer2.0.bn1.weight : torch.Size([128])
layer2.0.bn1.bias : torch.Size([128])
layer2.0.conv2.weight : torch.Size([128, 128, 3, 3])
layer2.0.bn2.weight : torch.Size([128])
layer2.0.bn2.bias : torch.Size([128])
layer2.0.downsample.0.weight : torch.Size([128, 64, 1, 1])
layer2.0.downsample.1.weight : torch.Size([128])
layer2.0.downsample.1.bias : torch.Size([128])
layer2.1.conv1.weight : torch.Size([128, 128, 3, 3])
layer2.1.bn1.weight : torch.Size([128])
layer2.1.bn1.bias : torch.Size([128])
layer2.1.conv2.weight : torch.Size([128, 128, 3, 3])
layer2.1.bn2.weight : torch.Size([128])
layer2.1.bn2.bias : torch.Size([128])
layer3.0.conv1.weight : torch.Size([256, 128, 3, 3])
layer3.0.bn1.weight : torch.Size([256])
layer3.0.bn1.bias : torch.Size([256])
layer3.0.conv2.weight : torch.Size([256, 256, 3, 3])
layer3.0.bn2.weight : torch.Size([256])
layer3.0.bn2.bias : torch.Size([256])
layer3.0.downsample.0.weight : torch.Size([256, 128, 1, 1])
layer3.0.downsample.1.weight : torch.Size([256])
layer3.0.downsample.1.bias : torch.Size([256])
layer3.1.conv1.weight : torch.Size([256, 256, 3, 3])
layer3.1.bn1.weight : torch.Size([256])
layer3.1.bn1.bias : torch.Size([256])
layer3.1.conv2.weight : torch.Size([256, 256, 3, 3])
layer3.1.bn2.weight : torch.Size([256])
layer3.1.bn2.bias : torch.Size([256])
layer4.0.conv1.weight : torch.Size([512, 256, 3, 3])
layer4.0.bn1.weight : torch.Size([512])
layer4.0.bn1.bias : torch.Size([512])
layer4.0.conv2.weight : torch.Size([512, 512, 3, 3])
layer4.0.bn2.weight : torch.Size([512])
layer4.0.bn2.bias : torch.Size([512])
layer4.0.downsample.0.weight : torch.Size([512, 256, 1, 1])
layer4.0.downsample.1.weight : torch.Size([512])
layer4.0.downsample.1.bias : torch.Size([512])
layer4.1.conv1.weight : torch.Size([512, 512, 3, 3])
layer4.1.bn1.weight : torch.Size([512])
layer4.1.bn1.bias : torch.Size([512])
layer4.1.conv2.weight : torch.Size([512, 512, 3, 3])
layer4.1.bn2.weight : torch.Size([512])
layer4.1.bn2.bias : torch.Size([512])
fc.weight : torch.Size([1000, 512])
fc.bias : torch.Size([1000])

　　

parm['layer1.0.conv1.weight'][0,0,:,:]

　　

輸出為:

array([[ 0.05759342, -0.09511436, -0.02027232],
       [-0.07455588, -0.799308  , -0.21283598],
       [ 0.06557069, -0.09653367, -0.01211061]], dtype=float32)

　　

利用如下函數將某一層的所有參數保存到表格中,數據維持卷積核特征大小,如3*3的卷積保存后還是3x3的.

def parm_to_excel(excel_name,key_name,parm):
    with pd.ExcelWriter(excel_name) as writer:
        [output_num,input_num,filter_size,_]=parm[key_name].size()
        for i in range(output_num):
            for j in range(input_num):
                data=pd.DataFrame(parm[key_name][i,j,:,:].detach().numpy())
                #print(data)
                data.to_excel(writer,index=False,header=True,startrow=i*(filter_size+1),startcol=j*filter_size)

　　

由於權重矩陣中有很多的值非常小,取出固定大小的值,並將全部權重寫入excel

counter=1
with pd.ExcelWriter('test1.xlsx') as writer:
    for key in parm_resnet50.keys():
        data=parm_resnet50[key].reshape(-1,1)
        data=data[data>0.001]
        
        data=pd.DataFrame(data,columns=[key])
        data.to_excel(writer,index=False,startcol=counter)
        counter+=1

　　

2.獲取中間某一層的特性

重寫一個函數,將需要輸出的層輸出即可.

 

def resnet_cifar(net,input_data):
    x = net.conv1(input_data)
    x = net.bn1(x)
    x = F.relu(x)
    x = net.layer1(x)
    x = net.layer2(x)
    x = net.layer3(x)
    x = net.layer4[0].conv1(x)  #這樣就提取了layer4第一塊的第一個卷積層的輸出
    x=x.view(x.shape[0],-1)
    return x

model = models.resnet18()
x = resnet_cifar(model,input_data)

　　

原文：https://blog.csdn.net/happyday_d/article/details/88974361