pytorch 可视化feature map的示例代码

Z时代
2024-01-10
分类：综合

之前做的一些项目中涉及到feature map 可视化的问题，一个层中feature map的数量往往就是当前层out_channels的值，我们可以通过以下代码可视化自己网络中某层的feature map，个人感觉可视化feature map对调参还是很有用的。

不多说了，直接看代码：


import torch
from torch.autograd import Variable
import torch.nn as nn
import pickle
from sys import path
path.append('/residual model path')
import residual_model
from residual_model import Residual_Model
model = Residual_Model()
model.load_state_dict(torch.load('./model.pkl'))
class myNet(nn.Module):
  def __init__(self,pretrained_model,layers):
    super(myNet,self).__init__()
    self.net1 = nn.Sequential(*list(pretrained_model.children())[:layers[0]])
    self.net2 = nn.Sequential(*list(pretrained_model.children())[:layers[1]])
    self.net3 = nn.Sequential(*list(pretrained_model.children())[:layers[2]])
  def forward(self,x):
    out1 = self.net1(x)
    out2 = self.net(out1)
    out3 = self.net(out2)
    return out1,out2,out3
def get_features(pretrained_model, x, layers = [3, 4, 9]): ## get_features 其实很简单
'''
1.首先import model 
2.将weights load 进model
3.熟悉model的每一层的位置，提前知道要输出feature map的网络层是处于网络的那一层
4.直接将test_x输入网络，*list(model.chidren())是用来提取网络的每一层的结构的。net1 = nn.Sequential(*list(pretrained_model.children())[:layers[0]]) ,就是第三层前的所有层。
'''
  net1 = nn.Sequential(*list(pretrained_model.children())[:layers[0]]) 
#  print net1 
  out1 = net1(x) 
  net2 = nn.Sequential(*list(pretrained_model.children())[layers[0]:layers[1]]) 
#  print net2 
  out2 = net2(out1) 
  #net3 = nn.Sequential(*list(pretrained_model.children())[layers[1]:layers[2]]) 
  #out3 = net3(out2) 
  return out1, out2
with open('test.pickle','rb') as f:
  data = pickle.load(f)
x = data['test_mains'][0]
x = Variable(torch.from_numpy(x)).view(1,1,128,1) ## test_x必须为Varibable
#x = Variable(torch.randn(1,1,128,1))
if torch.cuda.is_available():
  x = x.cuda() # 如果模型的训练是用cuda加速的话，输入的变量也必须是cuda加速的，两个必须是对应的，网络的参数weight都是用cuda加速的，不然会报错
  model = model.cuda()
output1,output2 = get_features(model,x)## model是训练好的model,前面已经import 进来了Residual model
print('output1.shape:',output1.shape)
print('output2.shape:',output2.shape)
#print('output3.shape:',output3.shape)
output_1 = torch.squeeze(output2,dim = 0)
output_1_arr = output_1.data.cpu().numpy() # 得到的cuda加速的输出不能直接转变成numpy格式的，当时根据报错的信息首先将变量转换为cpu的，然后转换为numpy的格式
output_1_arr = output_1_arr.reshape([output_1_arr.shape[0],output_1_arr.shape[1]])