262 lines
9.4 KiB
Python
262 lines
9.4 KiB
Python
'''Neural style transfer with Keras.
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Run the script with:
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```
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python neural_style_transfer.py path_to_your_base_image.jpg path_to_your_reference.jpg prefix_for_results
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```
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e.g.:
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```
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python neural_style_transfer.py img/tuebingen.jpg img/starry_night.jpg results/my_result
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```
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It is preferable to run this script on GPU, for speed.
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Example result: https://twitter.com/fchollet/status/686631033085677568
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# Details
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Style transfer consists in generating an image
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with the same "content" as a base image, but with the
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"style" of a different picture (typically artistic).
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This is achieved through the optimization of a loss function
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that has 3 components: "style loss", "content loss",
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and "total variation loss":
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- The total variation loss imposes local spatial continuity between
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the pixels of the combination image, giving it visual coherence.
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- The style loss is where the deep learning keeps in --that one is defined
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using a deep convolutional neural network. Precisely, it consists in a sum of
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L2 distances between the Gram matrices of the representations of
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the base image and the style reference image, extracted from
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different layers of a convnet (trained on ImageNet). The general idea
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is to capture color/texture information at different spatial
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scales (fairly large scales --defined by the depth of the layer considered).
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- The content loss is a L2 distance between the features of the base
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image (extracted from a deep layer) and the features of the combination image,
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keeping the generated image close enough to the original one.
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# References
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- [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)
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'''
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from __future__ import print_function
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from keras.preprocessing.image import load_img, img_to_array
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from scipy.misc import imsave
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import numpy as np
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from scipy.optimize import fmin_l_bfgs_b
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import time
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import argparse
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from keras.applications import vgg16
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from keras import backend as K
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parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
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parser.add_argument('base_image_path', metavar='base', type=str,
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help='Path to the image to transform.')
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parser.add_argument('style_reference_image_path', metavar='ref', type=str,
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help='Path to the style reference image.')
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parser.add_argument('result_prefix', metavar='res_prefix', type=str,
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help='Prefix for the saved results.')
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args = parser.parse_args()
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base_image_path = args.base_image_path
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style_reference_image_path = args.style_reference_image_path
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result_prefix = args.result_prefix
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# these are the weights of the different loss components
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total_variation_weight = 1.
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style_weight = 1.
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content_weight = 0.025
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# dimensions of the generated picture.
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img_nrows = 400
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img_ncols = 400
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assert img_ncols == img_nrows, 'Due to the use of the Gram matrix, width and height must match.'
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# util function to open, resize and format pictures into appropriate tensors
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def preprocess_image(image_path):
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img = load_img(image_path, target_size=(img_nrows, img_ncols))
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img = img_to_array(img)
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img = np.expand_dims(img, axis=0)
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img = vgg16.preprocess_input(img)
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return img
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# util function to convert a tensor into a valid image
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def deprocess_image(x):
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if K.image_dim_ordering() == 'th':
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x = x.reshape((3, img_nrows, img_ncols))
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x = x.transpose((1, 2, 0))
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else:
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x = x.reshape((img_nrows, img_ncols, 3))
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# Remove zero-center by mean pixel
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x[:, :, 0] += 103.939
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x[:, :, 1] += 116.779
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x[:, :, 2] += 123.68
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# 'BGR'->'RGB'
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x = x[:, :, ::-1]
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x = np.clip(x, 0, 255).astype('uint8')
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return x
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# get tensor representations of our images
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base_image = K.variable(preprocess_image(base_image_path))
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style_reference_image = K.variable(preprocess_image(style_reference_image_path))
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# this will contain our generated image
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if K.image_dim_ordering() == 'th':
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combination_image = K.placeholder((1, 3, img_nrows, img_ncols))
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else:
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combination_image = K.placeholder((1, img_nrows, img_ncols, 3))
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# combine the 3 images into a single Keras tensor
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input_tensor = K.concatenate([base_image,
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style_reference_image,
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combination_image], axis=0)
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# build the VGG16 network with our 3 images as input
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# the model will be loaded with pre-trained ImageNet weights
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model = vgg16.VGG16(input_tensor=input_tensor,
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weights='imagenet', include_top=False)
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print('Model loaded.')
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# get the symbolic outputs of each "key" layer (we gave them unique names).
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outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
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# compute the neural style loss
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# first we need to define 4 util functions
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# the gram matrix of an image tensor (feature-wise outer product)
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def gram_matrix(x):
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assert K.ndim(x) == 3
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if K.image_dim_ordering() == 'th':
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features = K.batch_flatten(x)
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else:
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features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
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gram = K.dot(features, K.transpose(features))
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return gram
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# the "style loss" is designed to maintain
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# the style of the reference image in the generated image.
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# It is based on the gram matrices (which capture style) of
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# feature maps from the style reference image
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# and from the generated image
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def style_loss(style, combination):
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assert K.ndim(style) == 3
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assert K.ndim(combination) == 3
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S = gram_matrix(style)
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C = gram_matrix(combination)
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channels = 3
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size = img_nrows * img_ncols
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return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))
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# an auxiliary loss function
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# designed to maintain the "content" of the
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# base image in the generated image
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def content_loss(base, combination):
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return K.sum(K.square(combination - base))
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# the 3rd loss function, total variation loss,
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# designed to keep the generated image locally coherent
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def total_variation_loss(x):
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assert K.ndim(x) == 4
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if K.image_dim_ordering() == 'th':
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a = K.square(x[:, :, :img_nrows-1, :img_ncols-1] - x[:, :, 1:, :img_ncols-1])
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b = K.square(x[:, :, :img_nrows-1, :img_ncols-1] - x[:, :, :img_nrows-1, 1:])
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else:
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a = K.square(x[:, :img_nrows-1, :img_ncols-1, :] - x[:, 1:, :img_ncols-1, :])
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b = K.square(x[:, :img_nrows-1, :img_ncols-1, :] - x[:, :img_nrows-1, 1:, :])
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return K.sum(K.pow(a + b, 1.25))
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# combine these loss functions into a single scalar
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loss = K.variable(0.)
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layer_features = outputs_dict['block4_conv2']
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base_image_features = layer_features[0, :, :, :]
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combination_features = layer_features[2, :, :, :]
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loss += content_weight * content_loss(base_image_features,
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combination_features)
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feature_layers = ['block1_conv1', 'block2_conv1',
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'block3_conv1', 'block4_conv1',
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'block5_conv1']
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for layer_name in feature_layers:
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layer_features = outputs_dict[layer_name]
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style_reference_features = layer_features[1, :, :, :]
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combination_features = layer_features[2, :, :, :]
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sl = style_loss(style_reference_features, combination_features)
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loss += (style_weight / len(feature_layers)) * sl
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loss += total_variation_weight * total_variation_loss(combination_image)
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# get the gradients of the generated image wrt the loss
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grads = K.gradients(loss, combination_image)
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outputs = [loss]
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if type(grads) in {list, tuple}:
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outputs += grads
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else:
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outputs.append(grads)
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f_outputs = K.function([combination_image], outputs)
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def eval_loss_and_grads(x):
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if K.image_dim_ordering() == 'th':
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x = x.reshape((1, 3, img_nrows, img_ncols))
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else:
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x = x.reshape((1, img_nrows, img_ncols, 3))
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outs = f_outputs([x])
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loss_value = outs[0]
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if len(outs[1:]) == 1:
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grad_values = outs[1].flatten().astype('float64')
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else:
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grad_values = np.array(outs[1:]).flatten().astype('float64')
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return loss_value, grad_values
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# this Evaluator class makes it possible
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# to compute loss and gradients in one pass
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# while retrieving them via two separate functions,
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# "loss" and "grads". This is done because scipy.optimize
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# requires separate functions for loss and gradients,
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# but computing them separately would be inefficient.
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class Evaluator(object):
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def __init__(self):
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self.loss_value = None
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self.grads_values = None
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def loss(self, x):
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assert self.loss_value is None
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loss_value, grad_values = eval_loss_and_grads(x)
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self.loss_value = loss_value
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self.grad_values = grad_values
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return self.loss_value
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def grads(self, x):
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assert self.loss_value is not None
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grad_values = np.copy(self.grad_values)
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self.loss_value = None
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self.grad_values = None
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return grad_values
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evaluator = Evaluator()
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# run scipy-based optimization (L-BFGS) over the pixels of the generated image
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# so as to minimize the neural style loss
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if K.image_dim_ordering() == 'th':
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x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.
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else:
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x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.
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for i in range(10):
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print('Start of iteration', i)
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start_time = time.time()
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x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
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fprime=evaluator.grads, maxfun=20)
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print('Current loss value:', min_val)
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# save current generated image
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img = deprocess_image(x.copy())
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fname = result_prefix + '_at_iteration_%d.png' % i
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imsave(fname, img)
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end_time = time.time()
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print('Image saved as', fname)
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print('Iteration %d completed in %ds' % (i, end_time - start_time))
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