使用ResNet18输出每个模块单个特征图

import cv2
import time
import os
import matplotlib.pyplot as plt
import torch
from torch import nn
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np

savepath='./picture' #设置输出特征图的路径
if not os.path.exists(savepath):
    os.mkdir(savepath)

def draw_features(width,height,x,savename):
    tic=time.time()
    fig = plt.figure(figsize=(16, 16))
    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)
    for i in range(width*height):
        plt.subplot(height,width, i + 1)
        plt.axis('off')
        # plt.tight_layout()
        img = x[0, i, :, :]
        pmin = np.min(img)
        pmax = np.max(img)
        img = (img - pmin) / (pmax - pmin + 0.000001)
        plt.imshow(img, cmap='gray')
        print("{}/{}".format(i,width*height))
    fig.savefig(savename, dpi=100)
    fig.clf()
    plt.close()
    print("time:{}".format(time.time()-tic))

class ft_net(nn.Module):

    def __init__(self):
        super(ft_net, self).__init__()
        model_ft = models.resnet18(pretrained=True)
        self.model = model_ft

    def forward(self, x):
        if True: # draw features or not
            x = self.model.conv1(x)
            draw_features(1,1,x.cpu().numpy(),"{}/conv1.png".format(savepath))

            x = self.model.bn1(x)
            x = self.model.relu(x)
            x = self.model.maxpool(x)
          
            x = self.model.layer1(x)
            draw_features(1, 1, x.cpu().numpy(), "{}/layer1.png".format(savepath))

            x = self.model.layer2(x)
            draw_features(1, 1, x.cpu().numpy(), "{}/layer2.png".format(savepath))

            x = self.model.layer3(x)
            draw_features(1, 1, x.cpu().numpy(), "{}/layer3.png".format(savepath))

            x = self.model.layer4(x)
            draw_features(1, 1, x.cpu().numpy()[:, 0:1024, :, :], "{}/layer4.png".format(savepath))
         
            x = self.model.avgpool(x)
            plt.clf()
            plt.close()

            x = x.view(x.size(0), -1)
            x = self.model.fc(x)
            # plt.plot(np.linspace(1, 1000, 1000), x.cpu().numpy()[0, :])
            # plt.savefig("{}/f10_fc.png".format(savepath))
            plt.clf()
            plt.close()
        else :
            x = self.model.conv1(x)
            x = self.model.bn1(x)
            x = self.model.relu(x)
            x = self.model.maxpool(x)
            x = self.model.layer1(x)
            x = self.model.layer2(x)
            x = self.model.layer3(x)
            x = self.model.layer4(x)
            x = self.model.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.model.fc(x)

        return x

model=ft_net().cuda()

model.eval()
img=cv2.imread('02.jpg') #读入原图片
img=cv2.resize(img,(224,224));
img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
img=transform(img).cuda()
img=img.unsqueeze(0)
with torch.no_grad():
    start=time.time()
    out=model(img)
    print("total time:{}".format(time.time()-start))
    result=out.cpu().numpy()
    # ind=np.argmax(out.cpu().numpy())
    ind=np.argsort(result,axis=1)
    for i in range(5):
        print("predict:top {} = cls {} : score {}".format(i+1,ind[0,1000-i-1],result[0,1000-i-1]))
    print("done")

ResNet18网络结构：

输出结果为：

参考：
https://www.jianshu.com/p/fcdbf942fe82
https://zhuanlan.zhihu.com/p/163577599