In this tutorial, we will walk through the steps to perform Neural Style Transfer using TensorFlow.
Step 1: Set up the Environment
Make sure you have TensorFlow and other required dependencies installed. You can install TensorFlow using pip:
pip install tensorflow
Step 2: Load the Content and Style Images
To begin with, select a content image and a style image that will be used for the transfer. You can use any images you like. Load these images using the tf.image module:
import tensorflow as tf
content_path = "path_to_content_image.jpg"
style_path = "path_to_style_image.jpg"
content_image = tf.io.read_file(content_path)
content_image = tf.image.decode_image(content_image, channels=3)
content_image = tf.image.convert_image_dtype(content_image, tf.float32)
style_image = tf.io.read_file(style_path)
style_image = tf.image.decode_image(style_image, channels=3)
style_image = tf.image.convert_image_dtype(style_image, tf.float32)
Step 3: Preprocess the Images
Next, preprocess the images before feeding them into the NST model. This involves resizing the images and normalizing the pixel values. The tf.image module provides convenient functions for this:
from tensorflow.keras.applications.vgg19 import preprocess_input
def preprocess(image):
image = tf.image.resize(image, (512, 512))
image = preprocess_input(image)
return image
preprocessed_content = preprocess(content_image)
preprocessed_style = preprocess(style_image)
Step 4: Load a Pretrained VGG19 Model
NST typically utilizes a pretrained convolutional neural network (CNN) model, such as VGG19, to extract both content and style features from the images. Load the VGG19 model using the tensorflow.keras.applications.vgg19 module:
from tensorflow.keras.applications.vgg19 import VGG19
model = VGG19(include_top=False, weights="imagenet")
model.trainable = False
Step 5: Define the Content and Style Layers
To extract the content and style features, select specific layers from the VGG19 model. The choice of layers depends on the desired level of content and style representation. Typically, lower-level layers capture content, while higher-level layers capture style. You can experiment with different layers to achieve different effects:
content_layers = ["block5_conv2"]
style_layers = ["block1_conv1", "block2_conv1", "block3_conv1", "block4_conv1", "block5_conv1"]
Step 6: Build the NST Model
Now, build the Neural Style Transfer model by defining a custom model that outputs the content and style features for the given layers using the tensorflow.keras.models.Model class:
from tensorflow.keras.models import Model
def style_transfer_model(content_layers, style_layers):
outputs = [model.get_layer(name).output for name in content_layers + style_layers]
model = Model(inputs=model.input, outputs=outputs)
return model
nst_model = style_transfer_model(content_layers, style_layers)
Step 7: Compute the Content and Style Loss
To train the NST model, define the loss functions for both content and style. The content loss is calculated using the mean squared error (MSE) between the content features of the base image and the generated image. The style loss is calculated using the Gram matrix, which measures the correlation between feature maps. TensorFlow provides functions to calculate both losses:
def content_loss(content_features, generated_features):
return tf.reduce_mean(tf.square(content_features - generated_features))
def style_loss(style_features, generated_features):
gram_style = gram_matrix(style_features)
gram_generated = gram_matrix(generated_features)
return tf.reduce_mean(tf.square(gram_style - gram_generated))
def gram_matrix(features):
shape = tf.shape(features)
reshaped = tf.reshape(features, (shape[0], -1))
gram = tf.matmul(reshaped, reshaped, transpose_a=True)
return gram / tf.cast(shape[1]*shape[2], tf.float32)
Step 8: Define the Total Loss
The total loss is a combination of the content loss and style loss, weighted by hyperparameters alpha and beta, respectively:
alpha = 1e3
beta = 1e-2
def total_loss(content_features, style_features, generated_features):
content = content_loss(content_features, generated_features)
style = style_loss(style_features, generated_features)
total = alpha * content + beta * style
return total
Step 9: Perform Neural Style Transfer
Finally, it’s time to perform the neural style transfer. Initialize the generated image as a copy of the content image, and use an optimization algorithm, such as gradient descent, to iteratively minimize the loss:
generated_image = tf.Variable(preprocessed_content, dtype=tf.float32)
optimizer = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)
@tf.function()
def train_step():
with tf.GradientTape() as tape:
content_features = model(preprocess(content_image))
style_features = model(preprocess(style_image))
generated_features = model(generated_image)
loss = total_loss(content_features, style_features, generated_features)
gradients = tape.gradient(loss, generated_image)
optimizer.apply_gradients([(gradients, generated_image)])
epochs = 10
for i in range(epochs):
train_step()
stylized_image = generated_image.numpy()
Congratulations! You have successfully implemented Neural Style Transfer using TensorFlow in Python. Feel free to experiment with different content and style images, as well as hyperparameters, to create unique visual combinations. Happy styling!