# ClearML - Example of Pytorch and matplotlib integration and reporting
#
"""
Neural Transfer Using PyTorch
=============================
**Author**: `Alexis Jacq <https://alexis-jacq.github.io>`_
**Edited by**: `Winston Herring <https://github.com/winston6>`_
Introduction
------------
This tutorial explains how to implement the `Neural-Style algorithm <https://arxiv.org/abs/1508.06576>`__
developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.
Neural-Style, or Neural-Transfer, allows you to take an image and
reproduce it with a new artistic style. The algorithm takes three images,
an input image, a content-image, and a style-image, and changes the input
to resemble the content of the content-image and the artistic style of the style-image.
.. figure:: /_static/img/neural-style/neuralstyle.png
:alt: content1
"""
######################################################################
# Underlying Principle
# --------------------
#
# 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, and
# transform it to minimize both its content-distance with the
# content-image and its style-distance with the style-image. Now we can
# import the necessary packages and begin the neural transfer.
#
# Importing Packages and Selecting a Device
# -----------------------------------------
# Below is a list of the packages needed to implement the neural transfer.
#
# - ``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`` (transform PIL images into tensors)
# - ``torchvision.models`` (train or load pre-trained models)
# - ``copy`` (to deep copy the models; system package)
from __future__ import print_function
import os
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
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init(project_name='examples', task_name='PyTorch with Matplotlib example', task_type=Task.TaskTypes.testing)
######################################################################
# Next, we need to choose which device to run the network on and import the
# content and style images. Running the neural transfer algorithm on large
# images takes longer and will go much faster when running on a GPU. We can
# use ``torch.cuda.is_available()`` to detect if there is a GPU available.
# Next, we set the ``torch.device`` for use throughout the tutorial. Also the ``.to(device)``
# method is used to move tensors or modules to a desired device.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
######################################################################
# Loading the Images
# ------------------
#
# Now we will import the style and content images. The original PIL images have values between 0 and 255, but when
# transformed into torch tensors, their values are converted to be between
# 0 and 1. The images also need to be resized to have the same dimensions.
# An important detail to note is that neural networks from the
# torch library are trained with tensor values ranging from 0 to 1. If you
# try to feed the networks with 0 to 255 tensor images, then the activated
# feature maps will be unable sense the intended content and style.
# However, pre-trained networks from the Caffe library are trained with 0
# to 255 tensor images.
#
#
# .. Note::
# Here are links to download the images required to run the tutorial:
# `picasso.jpg <https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg>`__ and
# `dancing.jpg <https://pytorch.org/tutorials/_static/img/neural-style/dancing.jpg>`__.
# Download these two images and add them to a directory
# with name ``images`` in your current working directory.
# 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(os.path.join("..", "..", "reporting", "data_samples", "picasso.jpg"))
content_img = image_loader(os.path.join("..", "..", "reporting", "data_samples", "dancing.jpg"))
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
######################################################################
# Now, let's create a function that displays an image by reconverting a
# copy of it to PIL format and displaying the copy using
# ``plt.imshow``. We will try displaying the content and style images
# to ensure they were imported correctly.
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')
######################################################################
# Loss Functions
# --------------
# Content Loss
# ~~~~~~~~~~~~
#
# The content loss is a function that represents a weighted version of the
# content distance for an individual layer. The function takes the feature
# maps :math:`F_{XL}` of a layer :math:`L` in a network processing input :math:`X` and returns the
# weighted content distance :math:`w_{CL}.D_C^L(X,C)` between the image :math:`X` and the
# content image :math:`C`. The feature maps of the content image(:math:`F_{CL}`) must be
# known by the function in order to calculate the content distance. We
# implement this function as a torch module with a constructor that takes
# :math:`F_{CL}` as an input. The distance :math:`\|F_{XL} - F_{CL}\|^2` is the mean square error
# between the two sets of feature maps, and can be computed using ``nn.MSELoss``.
#
# We will add this content loss module directly after the convolution
# layer(s) that are being used to compute the content distance. This way
# each time the network is fed an input image the content losses will be
# computed at the desired layers and because of auto grad, all the
# gradients will be computed. Now, in order to make the content loss layer
# transparent we must define a ``forward`` method that computes the content
# loss and then returns the layer's input. The computed loss is saved as a
# parameter of the module.
#
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**: although this module is named ``ContentLoss``, it
# is not a true PyTorch Loss function. If you want to define your content
# loss as a PyTorch Loss function, you have to create a PyTorch autograd function
# to recompute/implement the gradient manually in the ``backward``
# method.
######################################################################
# Style Loss
# ~~~~~~~~~~
#
# The style loss module is implemented similarly to the content loss
# module. It will act as a transparent layer in a
# network that computes the style loss of that layer. In order to
# calculate the style loss, we need to compute the gram matrix :math:`G_{XL}`. A gram
# matrix is the result of multiplying a given matrix by its transposed
# matrix. In this application the given matrix is a reshaped version of
# the feature maps :math:`F_{XL}` of a layer :math:`L`. :math:`F_{XL}` is reshaped to form :math:`\hat{F}_{XL}`, a :math:`K`\ x\ :math:`N`
# matrix, where :math:`K` is the number of feature maps at layer :math:`L` and :math:`N` is the
# length of any vectorized feature map :math:`F_{XL}^k`. For example, the first line
# of :math:`\hat{F}_{XL}` corresponds to the first vectorized feature map :math:`F_{XL}^1`.
#
# Finally, the gram matrix must be normalized by dividing each element by
# the total number of elements in the matrix. This normalization is to
# counteract the fact that :math:`\hat{F}_{XL}` matrices with a large :math:`N` dimension yield
# larger values in the Gram matrix. These larger values will cause the
# first layers (before pooling layers) to have a larger impact during the
# gradient descent. Style features tend to be in the deeper layers of the
# network so this normalization step is crucial.
#
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)
######################################################################
# Now the style loss module looks almost exactly like the content loss
# module. The style distance is also computed using the mean square
# error between :math:`G_{XL}` and :math:`G_{SL}`.
#
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
######################################################################
# Importing the Model
# -------------------
#
# Now we need to import a pre-trained neural network. We will use a 19
# layer VGG network like the one used in the paper.
#
# PyTorch's implementation of VGG is a module divided into two child
# ``Sequential`` modules: ``features`` (containing convolution and pooling layers),
# and ``classifier`` (containing fully connected layers). We will use the
# ``features`` module because we need the output of the individual
# convolution layers to measure content and style loss. Some layers have
# different behavior during training than evaluation, so we must set the
# network to evaluation mode using ``.eval()``.
#
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 it 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. We need to add our
# content loss and style loss layers immediately after the convolution
# layer they are detecting. To do this we must create a new ``Sequential``
# module that has content loss and style loss modules correctly inserted.
#
# 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
######################################################################
# Next, we select the input image. You can use a copy of the content image
# or white noise.
#
input_img = content_img.clone()
# if you want to use 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 <https://discuss.pytorch.org/t/pytorch-tutorial-for-neural-transfert-of-artistic-style/336/20?u=alexis-jacq>`__, 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 will create a PyTorch L-BFGS optimizer ``optim.LBFGS`` and pass
# our image to it as the tensor to optimize.
#
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
######################################################################
# Finally, we must define a function that performs the neural transfer. For
# each iteration of the networks, it is fed an updated input and computes
# new losses. We will run the ``backward`` methods of each loss module to
# dynamicaly compute their gradients. The optimizer requires a "closure"
# function, which reevaluates the modul and returns the loss.
#
# We still have one final constraint to address. The network may try to
# optimize the input with values that exceed the 0 to 1 tensor range for
# the image. We can address this by correcting the input values to be
# between 0 to 1 each time the network is run.
#
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
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)
# a last correction...
input_img.data.clamp_(0, 1)
return input_img
######################################################################
# Finally, we can 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()