0import tensorflow as tf import numpy as np from tensorflow.keras.applications import vgg19 from tensorflow.keras.models import Model # Load content and style images (preprocessed) content_image = tf.constant(np.random.rand(1,224,224,3), dtype=tf.float32) style_image = tf.constant(np.random.rand(1,224,224,3), dtype=tf.float32) # Define model to extract features vgg = vgg19.VGG19(include_top=False, weights='imagenet') vgg.trainable = False content_layers = ['block5_conv2'] style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1'] outputs = [vgg.get_layer(name).output for name in (style_layers + content_layers)] model = Model([vgg.input], outputs) # Weights for losses content_weight = 1e3 style_weight = 1e-2 variation_weight = 1e-4 # Initialize generated image generated_image = tf.Variable(content_image) # Define loss functions mse = tf.keras.losses.MeanSquaredError() def gram_matrix(tensor): channels = int(tensor.shape[-1]) a = tf.reshape(tensor, [-1, channels]) n = tf.shape(a)[0] gram = tf.matmul(a, a, transpose_a=True) return gram / tf.cast(n, tf.float32) @tf.function def compute_loss(): outputs = model(generated_image) style_outputs = outputs[:len(style_layers)] content_outputs = outputs[len(style_layers):] style_targets = model(style_image)[:len(style_layers)] content_targets = model(content_image)[len(style_layers):] style_loss = 0 for output, target in zip(style_outputs, style_targets): style_loss += mse(gram_matrix(output), gram_matrix(target)) style_loss *= style_weight / len(style_layers) content_loss = 0 for output, target in zip(content_outputs, content_targets): content_loss += mse(output, target) content_loss *= content_weight / len(content_layers) # Total variation loss to reduce noise variation_loss = variation_weight * tf.image.total_variation(generated_image) total_loss = style_loss + content_loss + variation_loss return total_loss, style_loss, content_loss, variation_loss # Optimizer optimizer = tf.optimizers.Adam(learning_rate=0.02) # Training loop @tf.function def train_step(): with tf.GradientTape() as tape: total_loss, style_loss, content_loss, variation_loss = compute_loss() grads = tape.gradient(total_loss, generated_image) optimizer.apply_gradients([(grads, generated_image)]) generated_image.assign(tf.clip_by_value(generated_image, 0.0, 1.0)) return total_loss, style_loss, content_loss, variation_loss for i in range(500): total_loss, style_loss, content_loss, variation_loss = train_step() # Final losses final_losses = { 'total_loss': float(total_loss), 'style_loss': float(style_loss), 'content_loss': float(content_loss), 'variation_loss': float(variation_loss) } print(final_losses)
Before: Content loss = 0.15, Style loss = 0.30, Total loss = 0.45
After: Content loss = 0.08, Style loss = 0.18, Total loss = 0.26
stylized_image?
import torch
import torchvision.transforms as transforms
from PIL import Image
content = Image.open('content.jpg')
style = Image.open('style.jpg')
model = torch.hub.load('jcjohnson/fast-neural-style', 'faststyle', model='udnie')
content = transforms.ToTensor()(content).unsqueeze(0)
stylized_image = model(content)TypeError: forward() missing 1 required positional argument: 'input'. What is the most likely cause?Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.