old_style_transfer copy.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
from matplotlib import pyplot as plt

from module import Normalization, ContentLoss, StyleLoss

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

imsize = 512 if torch.cuda.is_available() else 128  # use small size if no gpu
loader = transforms.Compose([
    transforms.Resize(imsize),  # scale imported image
    transforms.ToTensor()])  # transform it into a torch tensor

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


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

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

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

plt.ion()
def imshow(tensor, title=None):
    image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
    image = image.squeeze(0)      # 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


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

CONTENT_LAYER_DEFAULT = ['conv_4']
STYLE_LAYER_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_LAYER_DEFAULT, style_layers=STYLE_LAYER_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)

    # trim off the layers after the last content and style losses
    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=300, 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)

    # we want to optimize the input and not the model parameters
    input_img.requires_grad_(True)
    # We also put the model in evaluation mode, so that specific layers
    # such as dropout or batch normalization layers behave correctly.
    model.eval()
    model.requires_grad_(False)

    optimizer = get_input_optimizer(input_img)

    print('Optimizing..')
    run = [0]
    while run[0] <= num_steps:
        def closure():
            # correct the values of updated input image
            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)
    with torch.no_grad():
        input_img.data.clamp_(0, 1)
    return input_img

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)

input_img = content_img.clone()

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

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

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

# save
output = output.cpu().clone()
output = output.squeeze(0)
output = unloader(output)
output.save("./data/images/output.jpg")