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 #下载预训练模型vgg19 vgg = models.vgg19(pretrained=True) vgg = vgg.to(device) print(vgg) modulelist = list(vgg.features.modules()) def prod(image, layer, iterations, lr): input = preprocess(image).unsqueeze(0) input=input.to(device).requires_grad_(True) vgg.zero_grad() for i in range(iterations): out = input for j in range(layer): out = modulelist[j+1](out) #以特征值的L2为损失值 loss = out.norm() loss.backward() #使梯度增大 with torch.no_grad(): input += lr * input.grad input = input.squeeze() #交互维度 input.transpose_(0,1) input.transpose_(1,2) #使数据限制在[0,1]之间 input = np.clip(deprocess(input).detach().cpu().numpy(), 0, 1) im = Image.fromarray(np.uint8(input*255)) return im def deep_dream_vgg(image, layer, iterations, lr, octave_scale=2, num_octaves=20): if num_octaves>0: image1 = image.filter(ImageFilter.GaussianBlur(2)) if(image1.size[0]/octave_scale < 1 or image1.size[1]/octave_scale<1): size = image1.size else: size = (int(image1.size[0]/octave_scale), int(image1.size[1]/octave_scale)) #缩小图片 image1 = image1.resize(size,Image.ANTIALIAS) image1 = deep_dream_vgg(image1, layer, iterations, lr, octave_scale, num_octaves-1) size = (image.size[0], image.size[1]) #放大图片 image1 = image1.resize(size,Image.ANTIALIAS) image = ImageChops.blend(image, image1, 0.6) img_result = prod(image, layer, iterations, lr) img_result = img_result.resize(image.size) plt.imshow(img_result) return img_result night_sky = load_image('data/starry_night.jpg') #使用VGG19的第32层 night_sky_32 = deep_dream_vgg(night_sky, 32, 6, 0.2) #使用VGG19的第8层 night_sky_8 = deep_dream_vgg(night_sky, 8, 6, 0.2) #使用VGG19的第4层 night_sky_4 = deep_dream_vgg(night_sky, 4, 6, 0.2)
