pytorch 可视化feature map的示例代码

import torchfrom torch.autograd import Variableimport torch.nn as nnimport picklefrom sys import pathpath.append('/residual model path')import residual_modelfrom residual_model import Residual_Modelmodel = 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,out3def 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model3.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, out2with 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 modelprint('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]])