neural_style.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from PIL import Image

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

from io import BytesIO
import requests


def download(url):
    response = requests.get(url)
    binary_data = response.content

    temp_file = BytesIO()
    temp_file.write(binary_data)
    temp_file.seek(0)

    return temp_file


def image_loader(image_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    imsize = 512 if torch.cuda.is_available() else 128

    loader = transforms.Compose([
        transforms.Resize((imsize, imsize)),
        transforms.ToTensor()])

    image = download(image_path)
    image = Image.open(image).convert('RGB')
    image = loader(image)
    image = image.unsqueeze(0)

    return image.to(device, torch.float)


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 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


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


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


# VGG19 ver.
content_layers_default = ['conv_3']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']

# resnet50 ver.
# content_layers_default = ['conv_1']
# 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):

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

    cnn = copy.deepcopy(cnn)

    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)
        elif isinstance(layer, nn.Linear):
            name = 'fc_{}'.format(i)
        elif isinstance(layer, nn.Sequential):
            name = 'sq_{}'.format(i)
        elif isinstance(layer, nn.AdaptiveAvgPool2d):
            name = 'adap_{}'.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


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

    return optimizer


def run_style_transfer(cnn, normalization_mean, normalization_std,
                       content_img, style_img, input_img, num_steps=350,
                       style_weight=1000000, content_weight=1):

    print('Building the style transfer model..')
    model, style_losses, content_losses = get_style_model_and_losses(cnn,
                                                                     normalization_mean, normalization_std, style_img,
                                                                     content_img)
    optimizer = get_input_optimizer(input_img)

    print('Optimizing..')

    run = [0]
    while run[0] <= num_steps:

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

            return style_score + content_score

        optimizer.step(closure)

    input_img.data.clamp_(0, 1)

    return input_img


def set_neural_style(style_image, content_image):

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

    # VGG19 ver.
    cnn = models.vgg19(pretrained=True).features.to(device).eval()

    # resnet50 ver.
    # cnn = models.resnet50(pretrained=True).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)

    style_img = image_loader(style_image)
    content_img = image_loader(content_image)

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

    input_img = content_img.clone()

    return cnn, cnn_normalization_mean, cnn_normalization_std, style_img, content_img, input_img