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309f586 Jan 11, 2017
@fchollet @Rompei @kemaswill @jakelee8 @ftence
221 lines (181 sloc) 7.17 KB
'''Deep Dreaming in Keras.
Run the script with:
```
python deep_dream.py path_to_your_base_image.jpg prefix_for_results
```
e.g.:
```
python deep_dream.py img/mypic.jpg results/dream
```
It is preferable to run this script on GPU, for speed.
If running on CPU, prefer the TensorFlow backend (much faster).
Example results: http://i.imgur.com/FX6ROg9.jpg
'''
from __future__ import print_function
from keras.preprocessing.image import load_img, img_to_array
import numpy as np
from scipy.misc import imsave
from scipy.optimize import fmin_l_bfgs_b
import time
import argparse
from keras.applications import vgg16
from keras import backend as K
from keras.layers import Input
parser = argparse.ArgumentParser(description='Deep Dreams with Keras.')
parser.add_argument('base_image_path', metavar='base', type=str,
help='Path to the image to transform.')
parser.add_argument('result_prefix', metavar='res_prefix', type=str,
help='Prefix for the saved results.')
args = parser.parse_args()
base_image_path = args.base_image_path
result_prefix = args.result_prefix
# dimensions of the generated picture.
img_width = 600
img_height = 600
# path to the model weights file.
weights_path = 'vgg16_weights.h5'
# some settings we found interesting
saved_settings = {
'bad_trip': {'features': {'block4_conv1': 0.05,
'block4_conv2': 0.01,
'block4_conv3': 0.01},
'continuity': 0.1,
'dream_l2': 0.8,
'jitter': 5},
'dreamy': {'features': {'block5_conv1': 0.05,
'block5_conv2': 0.02},
'continuity': 0.1,
'dream_l2': 0.02,
'jitter': 0},
}
# the settings we will use in this experiment
settings = saved_settings['dreamy']
# util function to open, resize and format pictures into appropriate tensors
def preprocess_image(image_path):
img = load_img(image_path, target_size=(img_width, img_height))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = vgg16.preprocess_input(img)
return img
# util function to convert a tensor into a valid image
def deprocess_image(x):
if K.image_dim_ordering() == 'th':
x = x.reshape((3, img_width, img_height))
x = x.transpose((1, 2, 0))
else:
x = x.reshape((img_width, img_height, 3))
# Remove zero-center by mean pixel
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
# 'BGR'->'RGB'
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
if K.image_dim_ordering() == 'th':
img_size = (3, img_width, img_height)
else:
img_size = (img_width, img_height, 3)
# this will contain our generated image
dream = Input(batch_shape=(1,) + img_size)
# build the VGG16 network with our placeholder
# the model will be loaded with pre-trained ImageNet weights
model = vgg16.VGG16(input_tensor=dream,
weights='imagenet', include_top=False)
print('Model loaded.')
# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers])
# continuity loss util function
def continuity_loss(x):
assert K.ndim(x) == 4
if K.image_dim_ordering() == 'th':
a = K.square(x[:, :, :img_width - 1, :img_height - 1] -
x[:, :, 1:, :img_height - 1])
b = K.square(x[:, :, :img_width - 1, :img_height - 1] -
x[:, :, :img_width - 1, 1:])
else:
a = K.square(x[:, :img_width - 1, :img_height - 1, :] -
x[:, 1:, :img_height - 1, :])
b = K.square(x[:, :img_width - 1, :img_height - 1, :] -
x[:, :img_width - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
# define the loss
loss = K.variable(0.)
for layer_name in settings['features']:
# add the L2 norm of the features of a layer to the loss
assert layer_name in layer_dict.keys(), 'Layer ' + layer_name + ' not found in model.'
coeff = settings['features'][layer_name]
x = layer_dict[layer_name].output
shape = layer_dict[layer_name].output_shape
# we avoid border artifacts by only involving non-border pixels in the loss
if K.image_dim_ordering() == 'th':
loss -= coeff * K.sum(K.square(x[:, :, 2: shape[2] - 2, 2: shape[3] - 2])) / np.prod(shape[1:])
else:
loss -= coeff * K.sum(K.square(x[:, 2: shape[1] - 2, 2: shape[2] - 2, :])) / np.prod(shape[1:])
# add continuity loss (gives image local coherence, can result in an artful blur)
loss += settings['continuity'] * continuity_loss(dream) / np.prod(img_size)
# add image L2 norm to loss (prevents pixels from taking very high values, makes image darker)
loss += settings['dream_l2'] * K.sum(K.square(dream)) / np.prod(img_size)
# feel free to further modify the loss as you see fit, to achieve new effects...
# compute the gradients of the dream wrt the loss
grads = K.gradients(loss, dream)
outputs = [loss]
if isinstance(grads, (list, tuple)):
outputs += grads
else:
outputs.append(grads)
f_outputs = K.function([dream], outputs)
def eval_loss_and_grads(x):
x = x.reshape((1,) + img_size)
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
grad_values = outs[1].flatten().astype('float64')
else:
grad_values = np.array(outs[1:]).flatten().astype('float64')
return loss_value, grad_values
# this Evaluator class makes it possible
# to compute loss and gradients in one pass
# while retrieving them via two separate functions,
# "loss" and "grads". This is done because scipy.optimize
# requires separate functions for loss and gradients,
# but computing them separately would be inefficient.
class Evaluator(object):
def __init__(self):
self.loss_value = None
self.grad_values = None
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = eval_loss_and_grads(x)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values
evaluator = Evaluator()
# run scipy-based optimization (L-BFGS) over the pixels of the generated image
# so as to minimize the loss
x = preprocess_image(base_image_path)
for i in range(5):
print('Start of iteration', i)
start_time = time.time()
# add a random jitter to the initial image. This will be reverted at decoding time
random_jitter = (settings['jitter'] * 2) * (np.random.random(img_size) - 0.5)
x += random_jitter
# run L-BFGS for 7 steps
x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
fprime=evaluator.grads, maxfun=7)
print('Current loss value:', min_val)
# decode the dream and save it
x = x.reshape(img_size)
x -= random_jitter
img = deprocess_image(np.copy(x))
fname = result_prefix + '_at_iteration_%d.png' % i
imsave(fname, img)
end_time = time.time()
print('Image saved as', fname)
print('Iteration %d completed in %ds' % (i, end_time - start_time))