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]])
以上这篇pytorch 可视化feature map的示例代码就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持亿速云。
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