接觸pytorch一天，發(fā)現(xiàn)pytorch上手的確比TensorFlow更快?？梢愿奖愕貙?shí)現(xiàn)用預(yù)訓(xùn)練的網(wǎng)絡(luò)提特征。

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以下是提取一張jpg圖像的特征的程序：

# -*- 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)

名稱欄目：pytorch實(shí)現(xiàn)用Resnet提取特征并保存為txt文件的方法-創(chuàng)新互聯(lián)
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