image_output_2.png image_output_1.png
from PIL import Image

import matplotlib.pyplot as plt

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

import torchvision.models as models

import torchvision.transforms as transforms

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)
style_img = image_loader('./style.jpg')
content_img = image_loader('./content.jpg')
assert style_img.size() == content_img.size(), 'we need to import style and content images of the same size'

unloader = transforms.ToPILImage()
plt.ion()
def imshow(tensor, title=None):
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = unloader(image)
    plt.pause(0.001)

class ContentLoss(nn.Module):
    def __init__(self, target):
        super(ContentLoss, self).__init__()
        self.target = target.detach()
    def forward(self, input):
        self.loss = F.mse_loss(input, self.target)
        return input

def gram_matrix(input):
    a, b, c, d = input.size()
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G.div(a * b * c * d)

class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()
    def forward(self, input):
        G = gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input

cnn = models.vgg19(pretrained=True).features.to(device).eval()

cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = torch.tensor(std).view(-1, 1, 1)
    def forward(self, img):
        return (img - self.mean) / self.std

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, normalization_mean, normalization_std, style_img, content_img, content_layers=content_layers_default, style_layers=style_layers_default):
    normalization = Normalization(normalization_mean, normalization_std).to(device)
    content_losses = []
    style_losses = []
    model = nn.Sequential(normalization)
    i = 0
    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = 'conv_{}'.format(i)
        elif isinstance(layer, nn.ReLU):
            name = 'relu_{}'.format(i)
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = 'pool_{}'.format(i)
        elif isinstance(layer, nn.BatchNorm2d):
            name = 'bn_{}'.format(i)
        else:
            raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
        model.add_module(name, layer)
        if name in content_layers:
            target = model(content_img).detach()
            content_loss = ContentLoss(target)
            model.add_module('content_loss_{}'.format(i), content_loss)
            content_losses.append(content_loss)
        if name in style_layers:
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            model.add_module('style_loss_{}'.format(i), style_loss)
            style_losses.append(style_loss)
    for i in range(len(model) - 1, -1, -1):
        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
            break
    model = model[:i + 1]
    return (model, style_losses, content_losses)

input_img = content_img.clone()

def get_input_optimizer(input_img):
    optimizer = optim.LBFGS([input_img])
    return optimizer

def run_style_transfer(cnn, normalization_mean, normalization_std, content_img, style_img, input_img, num_steps=300, style_weight=1000000, content_weight=1):
    """Run the style transfer."""
    model, style_losses, content_losses = get_style_model_and_losses(cnn, normalization_mean, normalization_std, style_img, content_img)
    input_img.requires_grad_(True)
    model.requires_grad_(False)
    optimizer = get_input_optimizer(input_img)
    run = [0]
    while run[0] <= num_steps:
        def closure():
            with torch.no_grad():
                input_img.clamp_(0, 1)
            optimizer.zero_grad()
            model(input_img)
            style_score = 0
            content_score = 0
            for sl in style_losses:
                style_score += sl.loss
            for cl in content_losses:
                content_score += cl.loss
            style_score *= style_weight
            content_score *= content_weight
            loss = style_score + content_score
            loss.backward()
            run[0] += 1
            return style_score + content_score
        optimizer.step(closure)
    with torch.no_grad():
        input_img.clamp_(0, 1)
    return input_img

output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std, content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
plt.ioff()
plt.show()