这时我轻轻地闭上了眼睛，我好像来到童话世界，好像在和小鸟讨论秋天的美景，好像在和小草拍秋天的照片。农民伯伯在田野里收获了庄稼，果农们在果园里收获了果子，我们在学校里收获快乐、收获知识、收获成长。
接触pytorch一天，发现pytorch上手的确比TensorFlow更快。可以更方便地实现用预训练的网络提特征。
以下是提取一张jpg图像的特征的程序：
# -*- coding: utf-8 -*-
import os.path
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.autograd import Variable
import numpy as np
from 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)
以下是提取一个文件夹下所有jpg、jpeg图像的程序：
# -*- coding: utf-8 -*-
import os, torch, glob
import numpy as np
from torch.autograd import Variable
from PIL import Image
from torchvision import models, transforms
import torch.nn as nn
import shutil
data_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)
另外最近发现一个很简单的提取不含FC层的网络的方法：
resnet = models.resnet152(pretrained=True)
modules = list(resnet.children())[:-1] # delete the last fc layer.
convnet = nn.Sequential(*modules)
另一种更简单的方法：
resnet = models.resnet152(pretrained=True) del resnet.fc
以上这篇pytorch实现用Resnet提取特征并保存为txt文件的方法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。
