前言
本文介紹怎樣執行Neural-Style演算法。Neural-Style或者叫做Neural-Transfer，將一個內容影象和一個風格影象作為輸入，返回一個按照所選擇的風格影象加工的內容影象。
原理是非常簡單的：我們定義兩個距離，一個用於內容（Dc），另一個用於（Ds）。Dc測量兩個影象的內容有多像，Ds測量兩個影象的風格有多像。然後我們採用一個新影象（例如一個噪聲影象），對它進行變化，同時最小化與內容影象的距離和與風格影象的距離。

PyTorch中的實現
Packages

from __future__ import print_function

import torch
import
 
 torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim

from PIL import Image
import matplotlib.pyplot as plt

import torchvision.transforms as transforms
import torchvision.models as models

import copy

Cuda

use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor
 
 if use_cuda else torch.FloatTensor

Load images
將下面兩個影象picasso.jpg和dancing.jpg下載下來，放到images資料夾中。Images資料夾要在程式執行的當前目錄。

# desired size of the output image
imsize = 512 if use_cuda else 128  # use small size if no gpu

loader = transforms.Compose([
    transforms.Scale(imsize),  # scale imported image
 

    transforms.ToTensor()])  # transform it into a torch tensor


def image_loader(image_name):
    image = Image.open(image_name)
    image = Variable(loader(image))
    # fake batch dimension required to fit network's input dimensions
    image = image.unsqueeze(0)
    return image


style_img = image_loader("images/picasso.jpg").type(dtype)
content_img = image_loader("images/dancing.jpg").type(dtype)

assert style_img.size() == content_img.size(), \
    "we need to import style and content images of the same size"

Display images

unloader = transforms.ToPILImage()  # reconvert into PIL image

plt.ion()

def imshow(tensor, title=None):
    image = tensor.clone().cpu()  # we clone the tensor to not do changes on it
    image = image.view(3, imsize, imsize)  # remove the fake batch dimension
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001) # pause a bit so that plots are updated


plt.figure()
imshow(style_img.data, title='Style Image')

plt.figure()
imshow(content_img.data, title='Content Image')

執行效果：

Content loss

class ContentLoss(nn.Module):

    def __init__(self, target, weight):
        super(ContentLoss, self).__init__()
        # we 'detach' the target content from the tree used
        self.target = target.detach() * weight
        # to dynamically compute the gradient: this is a stated value,
        # not a variable. Otherwise the forward method of the criterion
        # will throw an error.
        self.weight = weight
        self.criterion = nn.MSELoss()

    def forward(self, input):
        self.loss = self.criterion(input * self.weight, self.target)
        self.output = input
        return self.output

    def backward(self, retain_graph=True):
        self.loss.backward(retain_graph=retain_graph)
        return self.loss

Style loss

class GramMatrix(nn.Module):

    def forward(self, input):
        a, b, c, d = input.size()  # a=batch size(=1)
        # b=number of feature maps
        # (c,d)=dimensions of a f. map (N=c*d)

        features = input.view(a * b, c * d)  # resise F_XL into \hat F_XL

        G = torch.mm(features, features.t())  # compute the gram product

        # we 'normalize' the values of the gram matrix
        # by dividing by the number of element in each feature maps.
        return G.div(a * b * c * d)

class StyleLoss(nn.Module):

    def __init__(self, target, weight):
        super(StyleLoss, self).__init__()
        self.target = target.detach() * weight
        self.weight = weight
        self.gram = GramMatrix()
        self.criterion = nn.MSELoss()

    def forward(self, input):
        self.output = input.clone()
        self.G = self.gram(input)
        self.G.mul_(self.weight)
        self.loss = self.criterion(self.G, self.target)
        return self.output

    def backward(self, retain_graph=True):
        self.loss.backward(retain_graph=retain_graph)
        return self.loss

Load the neural network

cnn = models.vgg19(pretrained=True).features

# move it to the GPU if possible:
if use_cuda:
    cnn = cnn.cuda()

# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']


def get_style_model_and_losses(cnn, style_img, content_img,
                               style_weight=1000, content_weight=1,
                               content_layers=content_layers_default,
                               style_layers=style_layers_default):
    cnn = copy.deepcopy(cnn)

    # just in order to have an iterable access to or list of content/syle
    # losses
    content_losses = []
    style_losses = []

    model = nn.Sequential()  # the new Sequential module network
    gram = GramMatrix()  # we need a gram module in order to compute style targets

    # move these modules to the GPU if possible:
    if use_cuda:
        model = model.cuda()
        gram = gram.cuda()

    i = 1
    for layer in list(cnn):
        if isinstance(layer, nn.Conv2d):
            name = "conv_" + str(i)
            model.add_module(name, layer)

            if name in content_layers:
                # add content loss:
                target = model(content_img).clone()
                content_loss = ContentLoss(target, content_weight)
                model.add_module("content_loss_" + str(i), content_loss)
                content_losses.append(content_loss)

            if name in style_layers:
                # add style loss:
                target_feature = model(style_img).clone()
                target_feature_gram = gram(target_feature)
                style_loss = StyleLoss(target_feature_gram, style_weight)
                model.add_module("style_loss_" + str(i), style_loss)
                style_losses.append(style_loss)

        if isinstance(layer, nn.ReLU):
            name = "relu_" + str(i)
            model.add_module(name, layer)

            if name in content_layers:
                # add content loss:
                target = model(content_img).clone()
                content_loss = ContentLoss(target, content_weight)
                model.add_module("content_loss_" + str(i), content_loss)
                content_losses.append(content_loss)

            if name in style_layers:
                # add style loss:
                target_feature = model(style_img).clone()
                target_feature_gram = gram(target_feature)
                style_loss = StyleLoss(target_feature_gram, style_weight)
                model.add_module("style_loss_" + str(i), style_loss)
                style_losses.append(style_loss)

            i += 1

        if isinstance(layer, nn.MaxPool2d):
            name = "pool_" + str(i)
            model.add_module(name, layer)  # ***

    return model, style_losses, content_losses

Input image

input_img = content_img.clone()
# if you want to use a white noise instead uncomment the below line:
# input_img = Variable(torch.randn(content_img.data.size())).type(dtype)

# add the original input image to the figure:
plt.figure()
imshow(input_img.data, title='Input Image')

執行效果：

Gradient descent

def get_input_param_optimizer(input_img):
    # this line to show that input is a parameter that requires a gradient
    input_param = nn.Parameter(input_img.data)
    optimizer = optim.LBFGS([input_param])
    return input_param, optimizer

定義演算法並執行

def run_style_transfer(cnn, content_img, style_img, input_img, num_steps=300,
                       style_weight=1000, content_weight=1):
    """Run the style transfer."""
    print('Building the style transfer model..')
    model, style_losses, content_losses = get_style_model_and_losses(cnn,
        style_img, content_img, style_weight, content_weight)
    input_param, optimizer = get_input_param_optimizer(input_img)

    print('Optimizing..')
    run = [0]
    while run[0] <= num_steps:

        def closure():
            # correct the values of updated input image
            input_param.data.clamp_(0, 1)

            optimizer.zero_grad()
            model(input_param)
            style_score = 0
            content_score = 0

            for sl in style_losses:
                style_score += sl.backward()
            for cl in content_losses:
                content_score += cl.backward()

            run[0] += 1
            if run[0] % 50 == 0:
                print("run {}:".format(run))
                print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                    style_score.data[0], content_score.data[0]))
                print()

            return style_score + content_score

        optimizer.step(closure)

    # a last correction...
    input_param.data.clamp_(0, 1)

    return input_param.data

output = run_style_transfer(cnn, content_img, style_img, input_img)

plt.figure()
imshow(output, title='Output Image')

# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()

執行結果：