深度学习笔记--使用VGG16预训练模型_vgg16预训练权重

1--常用预训练模型下载

        参考常用预训练模型下载地址

        如果不想额外下载预训练模型，可通过以下代码设置自动下载对应的权重文件（下载速度可能较慢）：

vgg16 = models.vgg16(pretrained=True)
 
# 替换下面代码:
# vgg16 = models.vgg16()
# weights = torch.load('./vgg16-397923af.pth')
# vgg16.load_state_dict(weights)

2--使用VGG16预训练模型

from torchvision import models
import cv2
from torchvision import transforms
import torch
from PIL import Image
import numpy as np
 
# 初始化模型
device = 0
vgg16 = models.vgg16().to(device)
weights = torch.load('./vgg16-397923af.pth')
vgg16.load_state_dict(weights)
 
# 前处理
normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406], # imagenet dataset的均值
                                 std = [0.229, 0.224, 0.225])
                                 
tran = transforms.Compose([transforms.Resize((224,224)),
                            transforms.ToTensor(),
                            transforms.Normalize(mean = [0.485, 0.456, 0.406],
                            std = [0.229, 0.224, 0.225])])
 
if __name__ == "__main__":
    # 读取图片
    img = cv2.imread('./test1.jpg')
 
    # 前处理
    img = Image.fromarray(np.uint8(img)).convert('RGB') # [3, 224, 224]
    img = tran(img)
    img.unsqueeze_(dim=0) # [1, 3, 224, 224]
 
    # 推理
    output = vgg16(img.to(device)) # [1, 1000]
 
    # 后处理
    output = output.data[0] # [1000]
    output = output.cpu().detach().numpy()
    print(output.shape)