主要是借助tensorboardX中的writer.add_image和torchvision.utils中的make_grid来生成的。
主要代码：

  extract_result[i]=extract_result[i].permute(1,0,2,3).cpu()   #1,c,h,w--->c,1,h,wextract_result[i]=make_grid(extract_result[i])   #concat the images writer.add_image('step'+str(i),extract_result[i])  #add image to tensorboard 

对于要提取的特征层，由于模型不同可能不太好提取，建议直接在模型的forward的函数里面改，加入一个标志位，使forward根据不同的情况输出不同的类型参考代码：

def forward(self,x,vs_feature=False):if vs_feature:features=[]#encode partx = self.resnet.conv1(x)x = self.resnet.bn1(x)x = self.resnet.relu(x)x = self.resnet.maxpool(x)x1 = self.resnet.layer1(x)      #x1 :b,64,88,88x2 = self.resnet.layer2(x1)     #x2 :b,128,44,44          x3 = self.resnet.layer3(x2)     #x3 :b,256,22,22x4 = self.resnet.layer4(x3)     #x4 :b,512,11,11 x1_rb=self.RFB_1(x1)   #x1_rb :b,32,88,88x2_rb=self.RFB_2(x2)   #x2_rb :b,64,44,44x3_rb=self.RFB_3(x3)   #x3_rb :b,128,22,22x4_rb=self.RFB_4(x4)   #x1_rb :b,256,11,11# decoding + concat path   U-net partd4=self.up_4(x4_rb)      #d4 :b,128,22,22d4 = torch.cat((x3_rb,d4),dim=1)  #d4 :b,256,22,22d4=self.up_conv4(d4) #d4 :b,128,22,22d3=self.up_3(d4)      #d3 :b,64,44,44d3 = torch.cat((x2_rb,d3),dim=1)  #d3 :b,128,44,44d3=self.up_conv3(d3) #d3 :b,64,44,44d2=self.up_2(d3)      #d2 :b,32,88,88d2 = torch.cat((x1_rb,d2),dim=1)  #d2 :b,64,88,88d2=self.up_conv2(d2) #d2 :b,32,88,88d1_output=self.output_conv_1(d2)  #d1_output:b,1,88,88d2_output=self.output_conv_2(d3)  #d1_output:b,1,44,44d3_output=self.output_conv_3(d4)  #d1_output:b,1,22,22d1_output=F.upsample(d1_output,scale_factor=4, mode='bilinear', align_corners=True)# d2_output=F.upsample(d2_output,scale_factor=8, mode='bilinear', align_corners=True)# d3_output=F.upsample(d3_output,scale_factor=16, mode='bilinear', align_corners=True)if vs_feature:features.extend([x1,x2,x3,x4,x1_rb,x2_rb,x3_rb,x4_rb,d4,d3,d2,d1_output,d2_output,d3_output])return d1_output.sigmoid(),features    #return features which you want to visualizationreturn d1_output.sigmoid(),d2_output.sigmoid(),d3_output.sigmoid()  

完整的借助tensorboardX可视化的代码：

import os
import torch
import torch.nn as nn
from my_model import Scotasap_model
import tensorboardX as tbX
import argparse
import cv2
from torchvision import transforms
from torchvision.utils import make_grid
from PIL import Image 
from scipy import miscparser = argparse.ArgumentParser()
parser.add_argument('--batch_size',type=int,default=2,help='batch_size')   
parser.add_argument('--epoch',type=int,default=10,help='训练总轮数')
parser.add_argument('--train_dir',type=str,default='./train_dataset/',help='训练集')
parser.add_argument('--test_dir',type=str,default='./test_dataset/',help='测试集')
parser.add_argument('--lr',type=float,default='0.0001',help='学习率')
parser.add_argument('--model_dir',type=str,default='./trained_model/',help='模型存放位置')
parser.add_argument('--size',type=int,default=352,help='图像处理大小')
parser.add_argument('--max_length',type=int,default=90,help='最长序列的大小,不足这个长度全部补齐')
parser.add_argument('--process_image_size',type=int,default=60,help='经过神经网络处理后的图像的大小')
parser.add_argument('--grad_clip',type=float,default=5,help='梯度限幅')
parser.add_argument('--total_step',type=int,default=300,help='一个epoch中训练的步数')
parser.add_argument('--image_save_dir',type=str,default='./result_image_save/',help='照片结果储存路径')
parser.add_argument('--lr_decay_rate',type=float,default=0.3,help='学习率衰减率')
parser.add_argument('--lr_decay_epoch',type=int,default=2,help='多少个epcoh不变开始改变学习率')
parser.add_argument('--save_epoch',type=int,default=5,help='每训练几个epoch储存一下模型')
parser.add_argument('--checkpoint_savedir',type=str,default='./checkpoint/',help='储存模型checkpoint')
parser.add_argument('--channels',type=int,default=32,help='the main numbers of channel in the model')
args = parser.parse_args()device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')writer=tbX.SummaryWriter('runs/exp2')
model=Scotasap_model(args).to(device)
model.load_state_dict(torch.load(os.path.join(args.model_dir,'parnet_best_test_value.pth')))pic_dir='COD10K-CAM-1-Aquatic-1-BatFish-6.jpg'
image=cv2.imread(pic_dir,cv2.IMREAD_COLOR)
image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)image_process=transforms.Compose([transforms.Resize(size=(args.size,args.size)),transforms.ToTensor(),transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))   #归一化图像，可选])
image=image_process(image).unsqueeze(dim=0)   #1,c,h,w
print(image.shape)
image=image.to(device)model.eval()
with torch.no_grad():pred,extract_result = model(image,vs_feature=True)pred = (pred - pred.min()) / (pred.max() - pred.min() + 1e-8)pred = pred.squeeze().cpu().numpy()for i in range(len(extract_result)):extract_result[i]=extract_result[i].permute(1,0,2,3).cpu()   #1,c,h,w--->c,1,h,wextract_result[i]=make_grid(extract_result[i])# print(extract_result[i].shape)writer.add_image('step'+str(i),extract_result[i])misc.imsave('pred-'+pic_dir,pred)