# -*- coding: utf-8 -*-
"""
Neural Transfer with PyTorch
============================
**Author**: `Alexis Jacq `_
Introduction
------------
Welcome! This tutorial explains how to impletment the
`Neural-Style `__ algorithm developed
by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.
Neural what?
~~~~~~~~~~~~
The Neural-Style, or Neural-Transfer, is an algorithm that takes as
input a content-image (e.g. a tortle), a style-image (e.g. artistic
waves) and return the content of the content-image as if it was
'painted' using the artistic style of the style-image:
.. figure:: /_static/img/neural-style/neuralstyle.png
:alt: content1
How does it work?
~~~~~~~~~~~~~~~~~
The principle is simple: we define two distances, one for the content
(:math:`D_C`) and one for the style (:math:`D_S`). :math:`D_C` measures
how different the content is between two images, while :math:`D_S`
measures how different the style is between two images. Then, we take a
third image, the input, (e.g. a with noise), and we transform it in
order to both minimize its content-distance with the content-image and
its style-distance with the style-image.
OK. How does it work?
^^^^^^^^^^^^^^^^^^^^^
Well, going further requires some mathematics. Let :math:`C_{nn}` be a
pre-trained deep convolutional neural network and :math:`X` be any
image. :math:`C_{nn}(X)` is the network fed by :math:`X` (containing
feature maps at all layers). Let :math:`F_{XL} \in C_{nn}(X)` be the
feature maps at depth layer :math:`L`, all vectorized and concatenated
in one single vector. We simply define the content of :math:`X` at layer
:math:`L` by :math:`F_{XL}`. Then, if :math:`Y` is another image of same
the size than :math:`X`, we define the distance of content at layer
:math:`L` as follow:
.. math:: D_C^L(X,Y) = \|F_{XL} - F_{YL}\|^2 = \sum_i (F_{XL}(i) - F_{YL}(i))^2
Where :math:`F_{XL}(i)` is the :math:`i^{th}` element of :math:`F_{XL}`.
The style is a bit less trivial to define. Let :math:`F_{XL}^k` with
:math:`k \leq K` be the vectorized :math:`k^{th}` of the :math:`K`
feature maps at layer :math:`L`. The style :math:`G_{XL}` of :math:`X`
at layer :math:`L` is defined by the Gram produce of all vectorized
feature maps :math:`F_{XL}^k` with :math:`k \leq K`. In other words,
:math:`G_{XL}` is a :math:`K`\ x\ :math:`K` matrix and the element
:math:`G_{XL}(k,l)` at the :math:`k^{th}` line and :math:`l^{th}` column
of :math:`G_{XL}` is the vectorial produce between :math:`F_{XL}^k` and
:math:`F_{XL}^l` :
.. math::
G_{XL}(k,l) = \langle F_{XL}^k, F_{XL}^l\\rangle = \sum_i F_{XL}^k(i) . F_{XL}^l(i)
Where :math:`F_{XL}^k(i)` is the :math:`i^{th}` element of
:math:`F_{XL}^k`. We can see :math:`G_{XL}(k,l)` as a measure of the
correlation between feature maps :math:`k` and :math:`l`. In that way,
:math:`G_{XL}` represents the correlation matrix of feature maps of
:math:`X` at layer :math:`L`. Note that the size of :math:`G_{XL}` only
depends on the number of feature maps, not on the size of :math:`X`.
Then, if :math:`Y` is another image *of any size*, we define the
distance of style at layer :math:`L` as follow:
.. math::
D_S^L(X,Y) = \|G_{XL} - G_{YL}\|^2 = \sum_{k,l} (G_{XL}(k,l) - G_{YL}(k,l))^2
In order to minimize in one shot :math:`D_C(X,C)` between a variable
image :math:`X` and target content-image :math:`C` and :math:`D_S(X,S)`
between :math:`X` and target style-image :math:`S`, both computed at
several layers , we compute and sum the gradients (derivative with
respect to :math:`X`) of each distance at each wanted layer:
.. math::
\\nabla_{\textit{total}}(X,S,C) = \sum_{L_C} w_{CL_C}.\\nabla_{\textit{content}}^{L_C}(X,C) + \sum_{L_S} w_{SL_S}.\\nabla_{\textit{style}}^{L_S}(X,S)
Where :math:`L_C` and :math:`L_S` are respectivement the wanted layers
(arbitrary stated) of content and style and :math:`w_{CL_C}` and
:math:`w_{SL_S}` the weights (arbitrary stated) associated with the
style or the content at each wanted layer. Then, we run a gradient
descent over :math:`X`:
.. math:: X \leftarrow X - \\alpha \\nabla_{\textit{total}}(X,S,C)
Ok. That's enough with maths. If you want to go deeper (how to compute
the gradients) **we encourage you to read the original paper** by Leon
A. Gatys and AL, where everything is much better and much clearer
explained.
For our implementation in PyTorch, we already have everything
we need: indeed, with PyTorch, all the gradients are automatically and
dynamically computed for you (while you use functions from the library).
This is why the implementation of this algorithm becomes very
comfortable with PyTorch.
PyTorch implementation
----------------------
If you are not sure to understand all the mathematics above, you will
probably get it by implementing it. If you are discovering PyTorch, we
recommend you to first read this :doc:`Introduction to
PyTorch `.
Packages
~~~~~~~~
We will have recourse to the following packages:
- ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for
neural networks with PyTorch)
- ``torch.optim`` (efficient gradient descents)
- ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display
images)
- ``torchvision.transforms`` (treat PIL images and transform into torch
tensors)
- ``torchvision.models`` (train or load pre-trained models)
- ``copy`` (to deep copy the models; system package)
"""
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
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
# ~~~~
#
# If you have a GPU on your computer, it is preferable to run the
# algorithm on it, especially if you want to try larger networks (like
# VGG). For this, we have ``torch.cuda.is_available()`` that returns
# ``True`` if you computer has an available GPU. Then, we can set the
# ``torch.device`` that will be used in this script. Then, we will use
# the method ``.to(device)`` that a tensor or a module to the desired
# device. When we want to move back this tensor or module to the
# CPU (e.g. to use numpy), we can use the ``.cpu()`` method.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
######################################################################
# Load images
# ~~~~~~~~~~~
#
# In order to simplify the implementation, let's start by importing a
# style and a content image of the same dimentions. We then scale them to
# the desired output image size (128 or 512 in the example, depending on gpu
# availablity) and transform them into torch tensors, ready to feed
# a neural network:
#
# .. Note::
# Here are links to download the images required to run the tutorial:
# `picasso.jpg `__ and
# `dancing.jpg `__.
# Download these two images and add them to a directory
# with name ``images``
# desired size of the output image
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("images/picasso.jpg")
content_img = image_loader("images/dancing.jpg")
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
######################################################################
# Imported PIL images has values between 0 and 255. Transformed into torch
# tensors, their values are between 0 and 1. This is an important detail:
# neural networks from torch library are trained with 0-1 tensor image. If
# you try to feed the networks with 0-255 tensor images the activated
# feature maps will have no sense. This is not the case with pre-trained
# networks from the Caffe library: they are trained with 0-255 tensor
# images.
#
# Display images
# ~~~~~~~~~~~~~~
#
# We will use ``plt.imshow`` to display images. So we need to first
# reconvert them into PIL images:
#
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
plt.figure()
imshow(style_img, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image')
######################################################################
# Content loss
# ~~~~~~~~~~~~
#
# The content loss is a function that takes as input the feature maps
# :math:`F_{XL}` at a layer :math:`L` in a network fed by :math:`X` and
# return the weigthed content distance :math:`w_{CL}.D_C^L(X,C)` between
# this image and the content image. Hence, the weight :math:`w_{CL}` and
# the target content :math:`F_{CL}` are parameters of the function. We
# implement this function as a torch module with a constructor that takes
# these parameters as input. The distance :math:`\|F_{XL} - F_{YL}\|^2` is
# the Mean Square Error between the two sets of feature maps, that can be
# computed using a criterion ``nn.MSELoss`` stated as a third parameter.
#
# We will add our content losses at each desired layer as additive modules
# of the neural network. That way, each time we will feed the network with
# an input image :math:`X`, all the content losses will be computed at the
# desired layers and, thanks to autograd, all the gradients will be
# computed. For that, we just need to make the ``forward`` method of our
# module returning the input: the module becomes a ''transparent layer''
# of the neural network. The computed loss is saved as a parameter of the
# module.
#
# Finally, we define a fake ``backward`` method, that just call the
# backward method of ``nn.MSELoss`` in order to reconstruct the gradient.
# This method returns the computed loss: this will be useful when running
# the gradient descent in order to display the evolution of style and
# content losses.
#
class ContentLoss(nn.Module):
def __init__(self, target,):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
# 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.target = target.detach()
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
######################################################################
# .. Note::
# **Important detail**: this module, although it is named ``ContentLoss``,
# is not a true PyTorch Loss function. If you want to define your content
# loss as a PyTorch Loss, you have to create a PyTorch autograd Function
# and to recompute/implement the gradient by the hand in the ``backward``
# method.
#
# Style loss
# ~~~~~~~~~~
#
# For the style loss, we need first to define a module that compute the
# gram produce :math:`G_{XL}` given the feature maps :math:`F_{XL}` of the
# neural network fed by :math:`X`, at layer :math:`L`. Let
# :math:`\hat{F}_{XL}` be the re-shaped version of :math:`F_{XL}` into a
# :math:`K`\ x\ :math:`N` matrix, where :math:`K` is the number of feature
# maps at layer :math:`L` and :math:`N` the lenght of any vectorized
# feature map :math:`F_{XL}^k`. The :math:`k^{th}` line of
# :math:`\hat{F}_{XL}` is :math:`F_{XL}^k`. We let you check that
# :math:`\hat{F}_{XL} \cdot \hat{F}_{XL}^T = G_{XL}`. Given that, it
# becomes easy to implement our module:
#
def gram_matrix(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)
######################################################################
# The longer is the feature maps dimension :math:`N`, the bigger are the
# values of the Gram matrix. Therefore, if we don't normalize by :math:`N`,
# the loss computed at the first layers (before pooling layers) will have
# much more importance during the gradient descent. We dont want that,
# since the most interesting style features are in the deepest layers!
#
# Then, the style loss module is implemented exactly the same way than the
# content loss module, but it compares the difference in Gram matrices of target
# and 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
######################################################################
# Load the neural network
# ~~~~~~~~~~~~~~~~~~~~~~~
#
# Now, we have to import a pre-trained neural network. As in the paper, we
# are going to use a pretrained VGG network with 19 layers (VGG19).
#
# PyTorch's implementation of VGG is a module divided in two child
# ``Sequential`` modules: ``features`` (containing convolution and pooling
# layers) and ``classifier`` (containing fully connected layers). We are
# just interested by ``features``:
# Some layers have different behavior in training and in evaluation. Since we
# are using it as a feature extractor. We will use ``.eval()`` to set the
# network in evaluation mode.
#
cnn = models.vgg19(pretrained=True).features.to(device).eval()
######################################################################
# Additionally, VGG networks are trained on images with each channel normalized
# by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. We will use them
# to normalize the image before sending into the network.
#
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)
# create a module to normalize input image so we can easily put it in a
# nn.Sequential
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
# .view the mean and std to make them [C x 1 x 1] so that they can
# directly work with image Tensor of shape [B x C x H x W].
# B is batch size. C is number of channels. H is height and W is width.
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
# normalize img
return (img - self.mean) / self.std
######################################################################
# A ``Sequential`` module contains an ordered list of child modules. For
# instance, ``vgg19.features`` contains a sequence (Conv2d, ReLU,
# MaxPool2d, Conv2d, ReLU...) aligned in the right order of depth. As we
# said in *Content loss* section, we wand to add our style and content
# loss modules as additive 'transparent' layers in our network, at desired
# depths. For that, we construct a new ``Sequential`` module, in which we
# are going to add modules from ``vgg19`` and our loss modules in the
# right order:
#
# 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, normalization_mean, normalization_std,
style_img, content_img,
content_layers=content_layers_default,
style_layers=style_layers_default):
cnn = copy.deepcopy(cnn)
# normalization module
normalization = Normalization(normalization_mean, normalization_std).to(device)
# just in order to have an iterable access to or list of content/syle
# losses
content_losses = []
style_losses = []
# assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
# to put in modules that are supposed to be activated sequentially
model = nn.Sequential(normalization)
i = 0 # increment every time we see a conv
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)
# The in-place version doesn't play very nicely with the ContentLoss
# and StyleLoss we insert below. So we replace with out-of-place
# ones here.
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:
# add content loss:
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:
# add style loss:
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)
# now we 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
######################################################################
# .. Note::
# In the paper they recommend to change max pooling layers into
# average pooling. With AlexNet, that is a small network compared to VGG19
# used in the paper, we are not going to see any difference of quality in
# the result. However, you can use these lines instead if you want to do
# this substitution:
#
# ::
#
# # avgpool = nn.AvgPool2d(kernel_size=layer.kernel_size,
# # stride=layer.stride, padding = layer.padding)
# # model.add_module(name,avgpool)
######################################################################
# Input image
# ~~~~~~~~~~~
#
# Again, in order to simplify the code, we take an image of the same
# dimensions than content and style images. This image can be a white
# noise, or it can also be a copy of the content-image.
#
input_img = content_img.clone()
# if you want to use a white noise instead uncomment the below line:
# input_img = torch.randn(content_img.data.size(), device=device)
# add the original input image to the figure:
plt.figure()
imshow(input_img, title='Input Image')
######################################################################
# Gradient descent
# ~~~~~~~~~~~~~~~~
#
# As Leon Gatys, the author of the algorithm, suggested
# `here `__,
# we will use L-BFGS algorithm to run our gradient descent. Unlike
# training a network, we want to train the input image in order to
# minimise the content/style losses. We would like to simply create a
# PyTorch L-BFGS optimizer ``optim.LBFGS``, passing our image as the
# Tensor to optimize. We use ``.requires_grad_()`` to make sure that this
# image requires gradient.
#
def get_input_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
optimizer = optim.LBFGS([input_img.requires_grad_()])
return optimizer
######################################################################
# **Last step**: the loop of gradient descent. At each step, we must feed
# the network with the updated input in order to compute the new losses,
# we must run the ``backward`` methods of each loss to dynamically compute
# their gradients and perform the step of gradient descent. The optimizer
# requires as argument a "closure": a function that reevaluates the model
# and returns the loss.
#
# However, there's a small catch. The optimized image may take its values
# between :math:`-\infty` and :math:`+\infty` instead of staying between 0
# and 1. In other words, the image might be well optimized and have absurd
# values. In fact, we must perform an optimization under constraints in
# order to keep having right vaues into our input image. There is a simple
# solution: at each step, to correct the image to maintain its values into
# the 0-1 interval.
#
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."""
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():
# 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).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)
# a last correction...
input_img.data.clamp_(0, 1)
return input_img
######################################################################
# Finally, run the algorithm
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()