Artificial Intelligence

Neural model switch with keen execution and Keras

August 22, 2024
roosho

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?

Fashion switch on photographs is just not new, however bought a lift when Gatys, Ecker, and Bethge(Gatys, Ecker, and Bethge 2015) confirmed learn how to efficiently do it with deep studying.
The primary thought is easy: Create a hybrid that may be a tradeoff between the content material picture we wish to manipulate, and a model picture we wish to imitate, by optimizing for maximal resemblance to each on the similar time.

In the event you’ve learn the chapter on neural model switch from Deep Studying with R, you could acknowledge among the code snippets that comply with.
Nonetheless, there is a vital distinction: This put up makes use of TensorFlow Keen Execution, permitting for an crucial means of coding that makes it straightforward 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 process in Python.

As normal, please ensure you have the required package deal variations put in. And no want to repeat the snippets – you’ll discover the whole code among the many Keras examples.

Stipulations

The code on this put up is determined by the latest variations of a number of of the TensorFlow R packages. You possibly can 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 model 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 masses and preprocesses the enter photographs for us.
As we can be working with VGG19, a community that has been skilled on ImageNet, we have to remodel our enter photographs 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 <- 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 model 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 route.

We can be fascinated with two sorts of outputs from the community, similar to our two targets.
Firstly, we wish to hold the mixture picture much like the content material picture, on a excessive stage. 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 model picture. Fashion corresponds to decrease stage options like texture, shapes, strokes… So to check the mixture in opposition to the model instance, we select a set of decrease stage 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 <- 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 are going to contemplate three forms of losses. Firstly, the content material loss: How totally different is the mixture 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 kinds match as intently as attainable. Fashion is usually operationalized because the Gram matrix of flattened function maps in a layer. We thus assume that model 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 fascinated with (outlined above), for the supply picture in addition to the optimization candidate, and examine 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 mixture picture to look overly pixelated, thus we’re including in a regularization element, the full 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 learn how 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 <- 0.01

Get mannequin outputs for the content material and elegance photographs

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

Within the beneath feedback, please observe that the sizes of dimensions 2 and three will differ if you happen to’re loading photographs at a unique 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(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(perform(batch) batch[1, , , ])
    content_features <- 
      model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)] %>%
      map(perform(batch) batch[2, , , ])
    
    listing(style_features, content_features)
  }

Computing the losses

On each iteration, we have to cross the mixture picture via the mannequin, receive the model and content material outputs, and compute the losses. Once more, the code is extensively commented with tensor sizes for straightforward verification, however please needless to say the precise numbers presuppose you’re working with 128×128 photographs.

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)]
    
    # model 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 <- 
  perform(mannequin, loss_weights, init_image, gram_style_features, content_features) {
    with(tf$GradientTape() %as% tape, {
      scores <-
        compute_loss(mannequin,
                     loss_weights,
                     init_image,
                     gram_style_features,
                     content_features)
    })
    total_loss <- scores[[1]]
    listing(tape$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 is just not 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 can apply the calculated gradients to the picture variable (tf$practice$AdamOptimizer)

Beneath, we get the model options (of the model picture) and the content material function (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 guide, 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 offer a concise introduction to keen execution.
Nonetheless, you possibly can plug in one other optimization technique if you happen to wished, changing
optimizer$apply_gradients(listing(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(function) gram_matrix(function))
  
  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(
        "Whole loss: {k_cast_to_floatx(loss)},
        model 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), important = glue("Iteration {i}"))
        dev.off()
      }
    }
  }
  
  glue("Whole time: {Sys.time() - global_start} seconds") %>% print()
  listing(best_image, best_loss)
}

Able to run

Now, we’re prepared to begin 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 model switch, some fiddling round could also be wanted till you get the consequence you need. However as our instance exhibits, this doesn’t imply the code must be sophisticated. Moreover to being straightforward to understand, keen execution additionally permits you to add debugging output, and step via the code line-by-line to verify 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 Fashion.” CoRR abs/1508.06576. http://arxiv.org/abs/1508.06576.