Artificial Intelligence

Neural type switch with keen execution and Keras

28/06/2024
florenc85

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How would your summer time vacation’s images 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?

Type switch on photos just isn’t new, however obtained a lift when Gatys, Ecker, and Bethge(Gatys, Ecker, and Bethge 2015) confirmed how you can efficiently do it with deep studying.
The primary concept is easy: Create a hybrid that could be a tradeoff between the content material picture we need to manipulate, and a type picture we need to imitate, by optimizing for maximal resemblance to each on the identical time.

If you happen to’ve learn the chapter on neural type switch from Deep Studying with R, it’s possible you’ll acknowledge among the code snippets that comply with.
Nonetheless, there is a crucial distinction: This publish makes use of TensorFlow Keen Execution, permitting for an crucial manner of coding that makes it simple to map ideas to code.
Identical to earlier posts on keen execution on this weblog, it is a port of a Google Colaboratory pocket book that performs the identical job in Python.

As ordinary, 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 relies on the newest variations of a number of of the TensorFlow R packages. You’ll be able to set up these packages as follows:

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 type mannequin, Hokusai’s The Nice Wave off Kanagawa, which you’ll obtain from Wikimedia Commons:

style_path <- "The_Great_Wave_off_Kanagawa.jpg"

style_image <-  image_load(content_path, target_size = img_shape[1:2])
style_image %>% 
  image_to_array() %>%
  `/`(., 255) %>%
  as.raster() %>%
  plot()

We create a wrapper that hundreds and preprocesses the enter photos for us.
As we will likely be working with VGG19, a community that has been skilled on ImageNet, we have to rework our enter photos in the identical manner that was used coaching it. Later, we’ll apply the inverse transformation to our mixture picture earlier than displaying it.

load_and_preprocess_image <- perform(path) {
  img <- image_load(path, target_size = img_shape[1:2]) %>%
    image_to_array() %>%
    k_expand_dims(axis = 1) %>%
    imagenet_preprocess_input()
}

deprocess_image <- perform(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 type 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), so as to transfer it within the desired path.

We will likely be taken with two sorts of outputs from the community, similar to our two objectives.
Firstly, we need to hold the mix picture much like the content material picture, on a excessive degree. In a convnet, higher layers map to extra holistic ideas, so we’re selecting a layer excessive up within the graph to check outputs from the supply and the mix.

Secondly, the generated picture ought to “seem like” the type picture. Type corresponds to decrease degree options like texture, shapes, strokes… So to check the mix towards the type instance, we select a set of decrease degree conv blocks for comparability and combination 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 <- perform() {
  vgg <- application_vgg19(include_top = FALSE, weights = "imagenet")
  vgg$trainable <- FALSE
  style_outputs <- map(style_layers, perform(layer) vgg$get_layer(layer)$output)
  content_outputs <- map(content_layers, perform(layer) vgg$get_layer(layer)$output)
  model_outputs <- c(style_outputs, content_outputs)
  keras_model(vgg$enter, model_outputs)
}

Losses

When optimizing the enter picture, we’ll think about three varieties of losses. Firstly, the content material loss: How completely different is the mix picture from the supply? Right here, we’re utilizing the sum of the squared errors for comparability.

content_loss <- perform(content_image, goal) {
  k_sum(k_square(goal - content_image))
}

Our second concern is having the types match as carefully as potential. Type is usually operationalized because the Gram matrix of flattened characteristic maps in a layer. We thus assume that type is expounded to how maps in a layer correlate with different.

We due to this fact compute the Gram matrices of the layers we’re taken with (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 <- perform(x) {
  options <- k_batch_flatten(k_permute_dimensions(x, c(3, 1, 2)))
  gram <- k_dot(options, k_transpose(options))
  gram
}

style_loss <- perform(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 mix picture to look overly pixelated, thus we’re including in a regularization part, the whole variation within the picture:

total_variation_loss <- perform(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 how you can 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 <- 0.01

Get mannequin outputs for the content material and elegance photos

We’d like the mannequin’s output for the content material and elegance photos, however right here it suffices to do that simply as soon as.
We concatenate each photos alongside the batch dimension, cross that enter to the mannequin, and get again a listing of outputs, the place each ingredient of the checklist is a 4-d tensor. For the type picture, we’re within the type outputs at batch place 1, whereas for the content material picture, we’d like the content material output at batch place 2.

Within the under feedback, please observe that the sizes of dimensions 2 and three will differ in the event you’re loading photos at a special measurement.

get_feature_representations <-
  perform(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(checklist(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(perform(batch) batch[1, , , ])
    content_features <- 
      model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)] %>%
      map(perform(batch) batch[2, , , ])
    
    checklist(style_features, content_features)
  }

Computing the losses

On each iteration, we have to cross the mix picture by way of the mannequin, get hold of the type and content material outputs, and compute the losses. Once more, the code is extensively commented with tensor sizes for straightforward verification, however please remember that the precise numbers presuppose you’re working with 128×128 photos.

compute_loss <-
  perform(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)]
    
    # type loss
    weight_per_style_layer <- 1 / num_style_layers
    style_score <- 0
    # dim(style_zip[[5]][[1]]) == (512, 512)
    style_zip <- transpose(checklist(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(checklist(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)
    }
    
    # complete 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
    checklist(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 <- 
  perform(mannequin, loss_weights, init_image, gram_style_features, content_features) {
    with(tf$GradientTape() %as% tape, {
      scores <-
        compute_loss(mannequin,
                     loss_weights,
                     init_image,
                     gram_style_features,
                     content_features)
    })
    total_loss <- scores[[1]]
    checklist(tape$gradient(total_loss, init_image), scores)
  }

Coaching part

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 just isn’t VGG19 (that one we’re simply utilizing as a device), however a minimal setup of simply:

  • a Variable that 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$prepare$AdamOptimizer)

Beneath, we get the type options (of the type 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 an alternative, we’re not utilizing L-BFGS for optimization, however Adam, as our purpose right here is to offer a concise introduction to keen execution.
Nonetheless, you could possibly plug in one other optimization technique in the event you wished, changing
optimizer$apply_gradients(checklist(tuple(grads, init_image)))
by an algorithm of your alternative (and naturally, assigning the results of the optimization to the Variable holding the picture).

run_style_transfer <- perform(content_path, style_path) {
  mannequin <- get_model()
  stroll(mannequin$layers, perform(layer) layer$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, perform(characteristic) gram_matrix(characteristic))
  
  init_image <- load_and_process_image(content_path)
  init_image <- tf$contrib$keen$Variable(init_image, dtype = "float32")
  
  optimizer <- tf$prepare$AdamOptimizer(learning_rate = 1,
                                      beta1 = 0.99,
                                      epsilon = 1e-1)
  
  c(best_loss, best_image) %<-% checklist(Inf, NULL)
  loss_weights <- checklist(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$apply_gradients(checklist(tuple(grads, init_image)))
    clipped <- tf$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(
        "Whole loss: {k_cast_to_floatx(loss)},
        type loss: {k_cast_to_floatx(style_score)},
        content material loss: {k_cast_to_floatx(content_score)},
        complete 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), most important = glue("Iteration {i}"))
        dev.off()
      }
    }
  }
  
  glue("Whole time: {Sys.time() - global_start} seconds") %>% print()
  checklist(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 trying:

… positively extra inviting than had it been painted by Edvard Munch!

Conclusion

With neural type switch, some fiddling round could also be wanted till you get the end result you need. However as our instance exhibits, this doesn’t imply the code must be difficult. Moreover to being simple to know, keen execution additionally enables you to add debugging output, and step by way of the code line-by-line to test on tensor shapes.
Till subsequent time in our keen execution sequence!

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2015. “A Neural Algorithm of Creative Type.” CoRR abs/1508.06576. http://arxiv.org/abs/1508.06576.

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