14. Neural Transfer
14.1. Input images
[1]:
%matplotlib inline
import numpy as np
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
np.random.seed(37)
torch.manual_seed(37)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def get_device():
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def get_image_size():
imsize = 512 if torch.cuda.is_available() else 128
return imsize
def get_loader():
image_size = get_image_size()
loader = transforms.Compose([
transforms.Resize((image_size, image_size)),
transforms.ToTensor()])
return loader
def get_unloader():
unloader = transforms.ToPILImage()
return unloader
def image_loader(image_name):
device = get_device()
image = Image.open(image_name)
# fake batch dimension required to fit network's input dimensions
loader = get_loader()
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
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
unloader = get_unloader()
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001)
style_img = image_loader("./styles/picasso-01.jpg")
content_img = image_loader("./styles/dancing.jpg")
input_img = content_img.clone()
assert style_img.size() == content_img.size(), \
f'size mismatch, style {style_img.size()}, content {content_img.size()}'
plt.ion()
plt.figure()
imshow(input_img, title='Input Image')
plt.figure()
imshow(style_img, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image')
14.2. Loss functions
14.2.1. Content loss
[2]:
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
14.2.2. Style loss
[3]:
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)
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
14.3. Model
[4]:
device = get_device()
cnn = models.vgg19(pretrained=True).features.to(device).eval()
14.4. Normalization
[5]:
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
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)
14.5. Loss
[6]:
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
14.6. Optimizer
[7]:
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
14.7. Transfer
[8]:
import warnings
from collections import namedtuple
RESULTS = namedtuple('RESULTS', 'run style content')
results = []
def run_style_transfer(cnn, normalization_mean, normalization_std,
content_img, style_img, input_img, num_steps=600,
style_weight=1000000, content_weight=1):
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)
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
results.append(RESULTS(run[0], style_score.item(), content_score.item()))
if run[0] % 10 == 0:
s_score = style_score.item()
c_score = content_score.item()
print(f'[{run[0]}/{num_steps}] Style Loss {s_score:.4f}, Content Loss {c_score}')
return style_score + content_score
optimizer.step(closure)
# a last correction...
input_img.data.clamp_(0, 1)
return input_img
with warnings.catch_warnings():
warnings.simplefilter('ignore')
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
content_img, style_img, input_img)
[10/600] Style Loss 5683.9438, Content Loss 28.085737228393555
[20/600] Style Loss 1513.7101, Content Loss 32.92472839355469
[30/600] Style Loss 839.8792, Content Loss 33.72367477416992
[40/600] Style Loss 495.5242, Content Loss 33.512489318847656
[50/600] Style Loss 356.6638, Content Loss 33.42991256713867
[60/600] Style Loss 269.5034, Content Loss 33.4761962890625
[70/600] Style Loss 216.9316, Content Loss 33.47385787963867
[80/600] Style Loss 163.2883, Content Loss 33.37232208251953
[90/600] Style Loss 135.9556, Content Loss 33.303672790527344
[100/600] Style Loss 109.1580, Content Loss 33.22367477416992
[110/600] Style Loss 91.8495, Content Loss 33.01668930053711
[120/600] Style Loss 79.5773, Content Loss 32.93888854980469
[130/600] Style Loss 68.3634, Content Loss 32.801029205322266
[140/600] Style Loss 57.8147, Content Loss 32.63578796386719
[150/600] Style Loss 49.9333, Content Loss 32.42307662963867
[160/600] Style Loss 44.3781, Content Loss 32.22199630737305
[170/600] Style Loss 38.0064, Content Loss 32.04217529296875
[180/600] Style Loss 34.1079, Content Loss 31.90805435180664
[190/600] Style Loss 37.2641, Content Loss 31.57277488708496
[200/600] Style Loss 27.3096, Content Loss 31.456771850585938
[210/600] Style Loss 24.5254, Content Loss 31.218570709228516
[220/600] Style Loss 22.1505, Content Loss 30.969505310058594
[230/600] Style Loss 20.1192, Content Loss 30.759197235107422
[240/600] Style Loss 18.0730, Content Loss 30.493648529052734
[250/600] Style Loss 16.4035, Content Loss 30.251787185668945
[260/600] Style Loss 15.0762, Content Loss 29.994197845458984
[270/600] Style Loss 13.7020, Content Loss 29.754240036010742
[280/600] Style Loss 12.5971, Content Loss 29.49911117553711
[290/600] Style Loss 11.6450, Content Loss 29.278596878051758
[300/600] Style Loss 10.8885, Content Loss 29.027385711669922
[310/600] Style Loss 10.3600, Content Loss 28.788755416870117
[320/600] Style Loss 9.4420, Content Loss 28.623498916625977
[330/600] Style Loss 8.8160, Content Loss 28.381839752197266
[340/600] Style Loss 8.2037, Content Loss 28.1502628326416
[350/600] Style Loss 7.7651, Content Loss 27.919567108154297
[360/600] Style Loss 7.2097, Content Loss 27.767620086669922
[370/600] Style Loss 6.7861, Content Loss 27.577590942382812
[380/600] Style Loss 6.4192, Content Loss 27.3933162689209
[390/600] Style Loss 6.0287, Content Loss 27.208782196044922
[400/600] Style Loss 5.7178, Content Loss 27.02840805053711
[410/600] Style Loss 5.4190, Content Loss 26.81221580505371
[420/600] Style Loss 5.0295, Content Loss 26.66666030883789
[430/600] Style Loss 4.7181, Content Loss 26.474632263183594
[440/600] Style Loss 4.4720, Content Loss 26.35723876953125
[450/600] Style Loss 4.2306, Content Loss 26.198747634887695
[460/600] Style Loss 4.0172, Content Loss 26.03799819946289
[470/600] Style Loss 3.7802, Content Loss 25.899450302124023
[480/600] Style Loss 4.3728, Content Loss 25.672344207763672
[490/600] Style Loss 3.3955, Content Loss 25.647024154663086
[500/600] Style Loss 3.2563, Content Loss 25.517745971679688
[510/600] Style Loss 3.1030, Content Loss 25.393512725830078
[520/600] Style Loss 3.0081, Content Loss 25.26365852355957
[530/600] Style Loss 2.8389, Content Loss 25.16840362548828
[540/600] Style Loss 2.7011, Content Loss 25.061481475830078
[550/600] Style Loss 2.5967, Content Loss 24.921926498413086
[560/600] Style Loss 2.4828, Content Loss 24.828998565673828
[570/600] Style Loss 2.3790, Content Loss 24.728757858276367
[580/600] Style Loss 2.2858, Content Loss 24.615867614746094
[590/600] Style Loss 2.2063, Content Loss 24.523954391479492
[600/600] Style Loss 2.1230, Content Loss 24.429014205932617
[610/600] Style Loss 2.0673, Content Loss 24.335756301879883
[620/600] Style Loss 1.9885, Content Loss 24.26019287109375
14.8. Results
[9]:
x = [r.run for r in results]
y1 = [r.style for r in results]
y2 = [r.content for r in results]
fig, ax1 = plt.subplots(figsize=(10, 5))
color = 'tab:red'
ax1.plot(x, y1, color=color)
ax1.set_ylabel('Style Loss', color=color)
ax1.tick_params(axis='y', labelcolor=color)
color = 'tab:blue'
ax2 = ax1.twinx()
ax2.plot(x, y2, color=color)
ax2.set_ylabel('Content Loss', color=color)
ax2.tick_params(axis='y', labelcolor=color)
14.9. Visualize
[10]:
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
