Python深度学习基于PyTorch加载VGG19使用方法 (Chapter 12 实战生产式模型 Section 12.1 DeepDream模型 )

VGG16及VGG19预训练模型可以到本人上传资源去下载。 

import torch
import torch.nn as nn
from torchvision import models
from torchvision import transforms, utils
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from PIL import Image, ImageFilter, ImageChops
 
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dtype = torch.float
 
 
def load_image(path):
    image = Image.open(path)
    plt.imshow(image)
    plt.title("Image loaded successfully")
    return image
 
normalise = transforms.Normalize(
    mean=[0.485, 0.456, 0.406],
    std=[0.229, 0.224, 0.225]
    )
 
preprocess = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    normalise
    ])
 
def deprocess(image):
    images=image * torch.tensor([0.229, 0.224, 0.225],device=device)  + torch.tensor([0.485, 0.456, 0.406],device=device)
    return  images
 
 
#加载已经放到本地的VGG19载预训练模型，可与下面这边