本文主要是介绍【python 走进pytotch】pytorch实现用Resnet提取特征，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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而且非常风趣幽默，像看小说一样！觉得太牛了，所以分享给大家。点这里可以跳转到教程。人工智能教程
准备一张图片，pytorch可以方便地实现用预训练的网络提取特征。
下面我们用pytorch提取图片采用预训练网络resnet50，提取图片特征。
# -*- 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
import warnings
warnings.filterwarnings("ignore")features_dir = 'F:/img_spam/test/features/'img_path = "F:/img_spam/test/10064004487036357500320010026498.jpg"
file_name = img_path.split('/')[-1]
feature_path = os.path.join(features_dir, file_name + '.txt')print(feature_path)transform1 = transforms.Compose([transforms.Scale(256),transforms.CenterCrop(224),transforms.ToTensor()]
)img = Image.open(img_path)
img1 = transform1(img)print(img1)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 = Falsex = Variable(torch.unsqueeze(img1, dim=0).float(), requires_grad=False)print(x)
y = resnet50_feature_extractor(x)
y = y.data.numpy()
print(y)np.savetxt(feature_path, y, delimiter=',')
y_ = np.loadtxt(feature_path, delimiter=',').reshape(1, 2048)print(y_)
运行结果：
E:\laidefa\python.exe C:/Users/xiaohu/PycharmProjects/深度学习/pytorch实战/resNet提取图片特征.py
F:/img_spam/test/features/10064004487036357500320010026498.jpg.txt
tensor([[[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],...,[ 0.9059, 0.9059, 0.8902, ..., 0.9020, 0.9569, 0.9412],[ 0.9255, 0.9098, 0.9255, ..., 0.8667, 0.9294, 0.9216],[ 0.9059, 0.9098, 0.9059, ..., 0.9216, 0.9529, 0.9412]],[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],...,[ 0.3412, 0.1137, 0.3255, ..., 0.3608, 0.5176, 0.1569],[ 0.4667, 0.2824, 0.4314, ..., 0.3020, 0.5216, 0.1490],[ 0.3137, 0.0667, 0.2863, ..., 0.2510, 0.3020, 0.0784]],[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],...,[ 0.0627, 0.0275, 0.0627, ..., 0.0980, 0.1294, 0.0471],[ 0.1098, 0.0941, 0.1255, ..., 0.0627, 0.1098, 0.0275],[ 0.0588, 0.0353, 0.0667, ..., 0.0941, 0.0902, 0.0392]]])
tensor([[[[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],[ 0.9961, 0.9961, 0.9961, ..., 0.9961, 0.9961, 0.9961],...,[ 0.9059, 0.9059, 0.8902, ..., 0.9020, 0.9569, 0.9412],[ 0.9255, 0.9098, 0.9255, ..., 0.8667, 0.9294, 0.9216],[ 0.9059, 0.9098, 0.9059, ..., 0.9216, 0.9529, 0.9412]],[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],...,[ 0.3412, 0.1137, 0.3255, ..., 0.3608, 0.5176, 0.1569],[ 0.4667, 0.2824, 0.4314, ..., 0.3020, 0.5216, 0.1490],[ 0.3137, 0.0667, 0.2863, ..., 0.2510, 0.3020, 0.0784]],[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],...,[ 0.0627, 0.0275, 0.0627, ..., 0.0980, 0.1294, 0.0471],[ 0.1098, 0.0941, 0.1255, ..., 0.0627, 0.1098, 0.0275],[ 0.0588, 0.0353, 0.0667, ..., 0.0941, 0.0902, 0.0392]]]])
[[0.36967766 0.5629435 0.49159744 ... 0.33528978 0.42739153 0.3224204 ]]
[[0.36967766 0.56294352 0.49159744 ... 0.33528978 0.42739153 0.32242039]]Process finished with exit code 0这篇关于【python 走进pytotch】pytorch实现用Resnet提取特征的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
