How would your summer season vacation’s photographs look had Edvard Munch painted them? (Maybe it’s higher to not know).
Let’s take a extra comforting instance: How would a pleasant, summarly river panorama look if painted by Katsushika Hokusai?
Model switch on pictures shouldn’t be new, however received a lift when Gatys, Ecker, and Bethge(Gatys, Ecker, and Bethge 2015) confirmed the right way to efficiently do it with deep studying.
The principle concept is easy: Create a hybrid that could be a tradeoff between the content material picture we wish to manipulate, and a fashion picture we wish to imitate, by optimizing for maximal resemblance to each on the similar time.
For those who’ve learn the chapter on neural fashion switch from Deep Studying with R, you might acknowledge a few of the code snippets that observe.
Nevertheless, there is a vital distinction: This publish makes use of TensorFlow Keen Execution, permitting for an crucial means of coding that makes it straightforward to map ideas to code.
Similar to earlier posts on keen execution on this weblog, it is a port of a Google Colaboratory pocket book that performs the identical process in Python.
As traditional, please be sure you have the required bundle variations put in. And no want to repeat the snippets – you’ll discover the entire code among the many Keras examples.
Conditions
The code on this publish is dependent upon the newest variations of a number of of the TensorFlow R packages. You possibly can set up these packages as follows:
set up.packages(c("tensorflow", "keras", "tfdatasets"))You also needs to make sure that you’re operating the very newest model of TensorFlow (v1.10), which you’ll be able to set up like so:
library(tensorflow)
install_tensorflow()There are extra necessities for utilizing TensorFlow keen execution. First, we have to name tfe_enable_eager_execution() proper at the start of this system. Second, we have to use the implementation of Keras included in TensorFlow, somewhat than the bottom Keras implementation.
Conditions behind us, let’s get began!
Enter pictures
Right here is our content material picture – exchange by a picture of your personal:
# You probably have sufficient reminiscence in your GPU, no have to load the pictures
# at such small measurement.
# That is the dimensions I discovered working for a 4G GPU.
img_shape <- c(128, 128, 3)
content_path <- "isar.jpg"
content_image <- image_load(content_path, target_size = img_shape[1:2])
content_image %>%
image_to_array() %>%
`/`(., 255) %>%
as.raster() %>%
plot()
And right here’s the fashion mannequin, Hokusai’s The Nice Wave off Kanagawa, which you’ll be able to obtain from Wikimedia Commons:
We create a wrapper that hundreds and preprocesses the enter pictures for us.
As we will likely be working with VGG19, a community that has been educated on ImageNet, we have to rework our enter pictures in the identical means that was used coaching it. Later, we’ll apply the inverse transformation to our mixture picture earlier than displaying it.
load_and_preprocess_image <- operate(path) {
img <- image_load(path, target_size = img_shape[1:2]) %>%
image_to_array() %>%
k_expand_dims(axis = 1) %>%
imagenet_preprocess_input()
}
deprocess_image <- operate(x) {
x <- x[1, , ,]
# Take away zero-center by imply pixel
x[, , 1] <- x[, , 1] + 103.939
x[, , 2] <- x[, , 2] + 116.779
x[, , 3] <- x[, , 3] + 123.68
# 'BGR'->'RGB'
x <- x[, , c(3, 2, 1)]
x[x > 255] <- 255
x[x < 0] <- 0
x[] <- as.integer(x) / 255
x
}Setting the scene
We’re going to use a neural community, however we gained’t be coaching it. Neural fashion switch is a bit unusual in that we don’t optimize the community’s weights, however again propagate the loss to the enter layer (the picture), to be able to transfer it within the desired path.
We will likely be fascinated about two sorts of outputs from the community, equivalent to our two objectives.
Firstly, we wish to hold the mixture picture much like the content material picture, on a excessive degree. In a convnet, higher layers map to extra holistic ideas, so we’re choosing a layer excessive up within the graph to check outputs from the supply and the mixture.
Secondly, the generated picture ought to “appear to be” the fashion picture. Model corresponds to decrease degree options like texture, shapes, strokes… So to check the mixture towards the fashion instance, we select a set of decrease degree conv blocks for comparability and mixture the outcomes.
content_layers <- c("block5_conv2")
style_layers <- c("block1_conv1",
"block2_conv1",
"block3_conv1",
"block4_conv1",
"block5_conv1")
num_content_layers <- size(content_layers)
num_style_layers <- size(style_layers)
get_model <- operate() {
vgg <- application_vgg19(include_top = FALSE, weights = "imagenet")
vgg$trainable <- FALSE
style_outputs <- map(style_layers, operate(layer) vgg$get_layer(layer)$output)
content_outputs <- map(content_layers, operate(layer) vgg$get_layer(layer)$output)
model_outputs <- c(style_outputs, content_outputs)
keras_model(vgg$enter, model_outputs)
}Losses
When optimizing the enter picture, we are going to contemplate three varieties of losses. Firstly, the content material loss: How completely different is the mixture picture from the supply? Right here, we’re utilizing the sum of the squared errors for comparability.
content_loss <- operate(content_image, goal) {
k_sum(k_square(goal - content_image))
}Our second concern is having the types match as intently as potential. Model is usually operationalized because the Gram matrix of flattened characteristic maps in a layer. We thus assume that fashion is expounded to how maps in a layer correlate with different.
We subsequently compute the Gram matrices of the layers we’re fascinated about (outlined above), for the supply picture in addition to the optimization candidate, and evaluate them, once more utilizing the sum of squared errors.
gram_matrix <- operate(x) {
options <- k_batch_flatten(k_permute_dimensions(x, c(3, 1, 2)))
gram <- k_dot(options, k_transpose(options))
gram
}
style_loss <- operate(gram_target, mixture) {
gram_comb <- gram_matrix(mixture)
k_sum(k_square(gram_target - gram_comb)) /
(4 * (img_shape[3] ^ 2) * (img_shape[1] * img_shape[2]) ^ 2)
}Thirdly, we don’t need the mixture picture to look overly pixelated, thus we’re including in a regularization part, the overall variation within the picture:
total_variation_loss <- operate(picture) {
y_ij <- picture[1:(img_shape[1] - 1L), 1:(img_shape[2] - 1L),]
y_i1j <- picture[2:(img_shape[1]), 1:(img_shape[2] - 1L),]
y_ij1 <- picture[1:(img_shape[1] - 1L), 2:(img_shape[2]),]
a <- k_square(y_ij - y_i1j)
b <- k_square(y_ij - y_ij1)
k_sum(k_pow(a + b, 1.25))
}The difficult factor is the right way to mix these losses. We’ve reached acceptable outcomes with the next weightings, however be happy to mess around as you see match:
content_weight <- 100
style_weight <- 0.8
total_variation_weight <- 0.01Get mannequin outputs for the content material and elegance pictures
We want the mannequin’s output for the content material and elegance pictures, however right here it suffices to do that simply as soon as.
We concatenate each pictures alongside the batch dimension, go that enter to the mannequin, and get again a listing of outputs, the place each component of the listing is a 4-d tensor. For the fashion picture, we’re within the fashion outputs at batch place 1, whereas for the content material picture, we want the content material output at batch place 2.
Within the under feedback, please word that the sizes of dimensions 2 and three will differ when you’re loading pictures at a distinct measurement.
get_feature_representations <-
operate(mannequin, content_path, style_path) {
# dim == (1, 128, 128, 3)
style_image <-
load_and_process_image(style_path) %>% k_cast("float32")
# dim == (1, 128, 128, 3)
content_image <-
load_and_process_image(content_path) %>% k_cast("float32")
# dim == (2, 128, 128, 3)
stack_images <- k_concatenate(listing(style_image, content_image), axis = 1)
# size(model_outputs) == 6
# dim(model_outputs[[1]]) = (2, 128, 128, 64)
# dim(model_outputs[[6]]) = (2, 8, 8, 512)
model_outputs <- mannequin(stack_images)
style_features <-
model_outputs[1:num_style_layers] %>%
map(operate(batch) batch[1, , , ])
content_features <-
model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)] %>%
map(operate(batch) batch[2, , , ])
listing(style_features, content_features)
}Computing the losses
On each iteration, we have to go the mixture picture via the mannequin, get hold of the fashion and content material outputs, and compute the losses. Once more, the code is extensively commented with tensor sizes for simple verification, however please understand that the precise numbers presuppose you’re working with 128×128 pictures.
compute_loss <-
operate(mannequin, loss_weights, init_image, gram_style_features, content_features) {
c(style_weight, content_weight) %<-% loss_weights
model_outputs <- mannequin(init_image)
style_output_features <- model_outputs[1:num_style_layers]
content_output_features <-
model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)]
# fashion loss
weight_per_style_layer <- 1 / num_style_layers
style_score <- 0
# dim(style_zip[[5]][[1]]) == (512, 512)
style_zip <- transpose(listing(gram_style_features, style_output_features))
for (l in 1:size(style_zip)) {
# for l == 1:
# dim(target_style) == (64, 64)
# dim(comb_style) == (1, 128, 128, 64)
c(target_style, comb_style) %<-% style_zip[[l]]
style_score <- style_score + weight_per_style_layer *
style_loss(target_style, comb_style[1, , , ])
}
# content material loss
weight_per_content_layer <- 1 / num_content_layers
content_score <- 0
content_zip <- transpose(listing(content_features, content_output_features))
for (l in 1:size(content_zip)) {
# dim(comb_content) == (1, 8, 8, 512)
# dim(target_content) == (8, 8, 512)
c(target_content, comb_content) %<-% content_zip[[l]]
content_score <- content_score + weight_per_content_layer *
content_loss(comb_content[1, , , ], target_content)
}
# whole variation loss
variation_loss <- total_variation_loss(init_image[1, , ,])
style_score <- style_score * style_weight
content_score <- content_score * content_weight
variation_score <- variation_loss * total_variation_weight
loss <- style_score + content_score + variation_score
listing(loss, style_score, content_score, variation_score)
}Computing the gradients
As quickly as now we have the losses, acquiring the gradients of the general loss with respect to the enter picture is only a matter of calling tape$gradient on the GradientTape. Observe that the nested name to compute_loss, and thus the decision of the mannequin on our mixture picture, occurs contained in the GradientTape context.
compute_grads <-
operate(mannequin, loss_weights, init_image, gram_style_features, content_features) {
with(tf$GradientTape() %as% tape, {
scores <-
compute_loss(mannequin,
loss_weights,
init_image,
gram_style_features,
content_features)
})
total_loss <- scores[[1]]
listing(tape$gradient(total_loss, init_image), scores)
}Coaching section
Now it’s time to coach! Whereas the pure continuation of this sentence would have been “… the mannequin,” the mannequin we’re coaching right here shouldn’t be VGG19 (that one we’re simply utilizing as a instrument), however a minimal setup of simply:
- a
Variablethat holds our to-be-optimized picture - the loss features we outlined above
- an optimizer that may apply the calculated gradients to the picture variable (
tf$practice$AdamOptimizer)
Beneath, we get the fashion options (of the fashion picture) and the content material characteristic (of the content material picture) simply as soon as, then iterate over the optimization course of, saving the output each 100 iterations.
In distinction to the unique article and the Deep Studying with R e book, however following the Google pocket book as a substitute, we’re not utilizing L-BFGS for optimization, however Adam, as our objective right here is to supply a concise introduction to keen execution.
Nevertheless, you may plug in one other optimization methodology when you needed, changing
optimizer$apply_gradients(listing(tuple(grads, init_image)))
by an algorithm of your selection (and naturally, assigning the results of the optimization to the Variable holding the picture).
run_style_transfer <- operate(content_path, style_path) {
mannequin <- get_model()
stroll(mannequin$layers, operate(layer) layer$trainable = FALSE)
c(style_features, content_features) %<-%
get_feature_representations(mannequin, content_path, style_path)
# dim(gram_style_features[[1]]) == (64, 64)
gram_style_features <- map(style_features, operate(characteristic) gram_matrix(characteristic))
init_image <- load_and_process_image(content_path)
init_image <- tf$contrib$keen$Variable(init_image, dtype = "float32")
optimizer <- tf$practice$AdamOptimizer(learning_rate = 1,
beta1 = 0.99,
epsilon = 1e-1)
c(best_loss, best_image) %<-% listing(Inf, NULL)
loss_weights <- listing(style_weight, content_weight)
start_time <- Sys.time()
global_start <- Sys.time()
norm_means <- c(103.939, 116.779, 123.68)
min_vals <- -norm_means
max_vals <- 255 - norm_means
for (i in seq_len(num_iterations)) {
# dim(grads) == (1, 128, 128, 3)
c(grads, all_losses) %<-% compute_grads(mannequin,
loss_weights,
init_image,
gram_style_features,
content_features)
c(loss, style_score, content_score, variation_score) %<-% all_losses
optimizer$apply_gradients(listing(tuple(grads, init_image)))
clipped <- tf$clip_by_value(init_image, min_vals, max_vals)
init_image$assign(clipped)
end_time <- Sys.time()
if (k_cast_to_floatx(loss) < best_loss) {
best_loss <- k_cast_to_floatx(loss)
best_image <- init_image
}
if (i %% 50 == 0) {
glue("Iteration: {i}") %>% print()
glue(
"Complete loss: {k_cast_to_floatx(loss)},
fashion loss: {k_cast_to_floatx(style_score)},
content material loss: {k_cast_to_floatx(content_score)},
whole variation loss: {k_cast_to_floatx(variation_score)},
time for 1 iteration: {(Sys.time() - start_time) %>% spherical(2)}"
) %>% print()
if (i %% 100 == 0) {
png(paste0("style_epoch_", i, ".png"))
plot_image <- best_image$numpy()
plot_image <- deprocess_image(plot_image)
plot(as.raster(plot_image), foremost = glue("Iteration {i}"))
dev.off()
}
}
}
glue("Complete time: {Sys.time() - global_start} seconds") %>% print()
listing(best_image, best_loss)
}Able to run
Now, we’re prepared to start out the method:
c(best_image, best_loss) %<-% run_style_transfer(content_path, style_path)In our case, outcomes didn’t change a lot after ~ iteration 1000, and that is how our river panorama was wanting:
… undoubtedly extra inviting than had it been painted by Edvard Munch!
Conclusion
With neural fashion switch, some fiddling round could also be wanted till you get the consequence you need. However as our instance reveals, this doesn’t imply the code needs to be difficult. Moreover to being straightforward to understand, keen execution additionally permits you to add debugging output, and step via the code line-by-line to test on tensor shapes.
Till subsequent time in our keen execution sequence!