pytorch实现用Resnet提取特征并保存为txt文件的方法

# -*- coding: utf-8 -*- import os.path import torchimport torch.nn as nnfrom torchvision import models, transformsfrom torch.autograd import Variable  import numpy as npfrom PIL import Image  features_dir = './features' img_path = "hymenoptera_data/train/ants/0013035.jpg"file_name = img_path.split('/')[-1]feature_path = os.path.join(features_dir, file_name + '.txt')  transform1 = transforms.Compose([    transforms.Scale(256),    transforms.CenterCrop(224),    transforms.ToTensor()  ]) img = Image.open(img_path)img1 = transform1(img) #resnet18 = models.resnet18(pretrained = True)resnet50_feature_extractor = models.resnet50(pretrained = True)resnet50_feature_extractor.fc = nn.Linear(2048, 2048)torch.nn.init.eye(resnet50_feature_extractor.fc.weight) for param in resnet50_feature_extractor.parameters():  param.requires_grad = False#resnet152 = models.resnet152(pretrained = True)#densenet201 = models.densenet201(pretrained = True) x = Variable(torch.unsqueeze(img1, dim=0).float(), requires_grad=False)#y1 = resnet18(x)y = resnet50_feature_extractor(x)y = y.data.numpy()np.savetxt(feature_path, y, delimiter=',')#y3 = resnet152(x)#y4 = densenet201(x) y_ = np.loadtxt(feature_path, delimiter=',').reshape(1, 2048)

# -*- coding: utf-8 -*-import os, torch, globimport numpy as npfrom torch.autograd import Variablefrom PIL import Image from torchvision import models, transformsimport torch.nn as nnimport shutildata_dir = './hymenoptera_data'features_dir = './features'shutil.copytree(data_dir, os.path.join(features_dir, data_dir[2:]))  def extractor(img_path, saved_path, net, use_gpu):  transform = transforms.Compose([      transforms.Scale(256),      transforms.CenterCrop(224),      transforms.ToTensor()  ]  )    img = Image.open(img_path)  img = transform(img)      x = Variable(torch.unsqueeze(img, dim=0).float(), requires_grad=False)  if use_gpu:    x = x.cuda()    net = net.cuda()  y = net(x).cpu()  y = y.data.numpy()  np.savetxt(saved_path, y, delimiter=',')  if __name__ == '__main__':  extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']      files_list = []  sub_dirs = [x[0] for x in os.walk(data_dir) ]  sub_dirs = sub_dirs[1:]  for sub_dir in sub_dirs:    for extention in extensions:      file_glob = os.path.join(sub_dir, '*.' + extention)      files_list.extend(glob.glob(file_glob))      resnet50_feature_extractor = models.resnet50(pretrained = True)  resnet50_feature_extractor.fc = nn.Linear(2048, 2048)  torch.nn.init.eye(resnet50_feature_extractor.fc.weight)  for param in resnet50_feature_extractor.parameters():    param.requires_grad = False        use_gpu = torch.cuda.is_available()   for x_path in files_list:    print(x_path)    fx_path = os.path.join(features_dir, x_path[2:] + '.txt')    extractor(x_path, fx_path, resnet50_feature_extractor, use_gpu)

    resnet = models.resnet152(pretrained=True)    modules = list(resnet.children())[:-1]   # delete the last fc layer.    convnet = nn.Sequential(*modules)

resnet = models.resnet152(pretrained=True)del resnet.fc