大橙子网站建设,新征程启航
为企业提供网站建设、域名注册、服务器等服务
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。
我们提供的服务有:成都做网站、网站设计、微信公众号开发、网站优化、网站认证、东港ssl等。为近1000家企事业单位解决了网站和推广的问题。提供周到的售前咨询和贴心的售后服务,是有科学管理、有技术的东港网站制作公司以下是提取一张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)