keras/examples/deep_dream.py

'''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 preferrable 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 scipy.misc import imread, imresize, imsave
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import time
import argparse
import h5py
import os

from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, MaxPooling2D
from keras import backend as K

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': {'conv4_1': 0.05,
                              'conv4_2': 0.01,
                              'conv4_3': 0.01},
                 'continuity': 0.1,
                 'dream_l2': 0.8,
                 'jitter': 5},
    'dreamy': {'features': {'conv5_1': 0.05,
                            'conv5_2': 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 = imresize(imread(image_path), (img_width, img_height))
    img = img.transpose((2, 0, 1)).astype('float64')
    img = np.expand_dims(img, axis=0)
    return img

# util function to convert a tensor into a valid image
def deprocess_image(x):
    x = x.transpose((1, 2, 0))
    x = np.clip(x, 0, 255).astype('uint8')
    return x

# build the VGG16 network
model = Sequential()
model.add(ZeroPadding2D((1, 1), batch_input_shape=(1, 3, img_width, img_height)))
first_layer = model.layers[-1]
# this is a placeholder tensor that will contain our generated images
dream = first_layer.input

model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

# load the weights of the VGG16 networks
# (trained on ImageNet, won the ILSVRC competition in 2014)
# note: when there is a complete match between your model definition
# and your weight savefile, you can simply call model.load_weights(filename)
assert os.path.exists(weights_path), 'Model weights not found (see "weights_path" variable in script).'
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
    if k >= len(model.layers):
        # we don't look at the last (fully-connected) layers in the savefile
        break
    g = f['layer_{}'.format(k)]
    weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
    model.layers[k].set_weights(weights)
f.close()
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
    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
    loss -= coeff * K.sum(K.square(x[:, :, 2: shape[2]-2, 2: shape[3]-2])) / np.prod(shape[1:])

# add continuity loss (gives image local coherence, can result in an artful blur)
loss += settings['continuity'] * continuity_loss(dream) / (3 * img_width * img_height)
# 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)) / (3 * img_width * img_height)

# 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 type(grads) in {list, tuple}:
    outputs += grads
else:
    outputs.append(grads)

f_outputs = K.function([dream], outputs)
def eval_loss_and_grads(x):
    x = x.reshape((1, 3, img_width, img_height))
    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.grads_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((3, img_width, img_height)) - 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((3, img_width, img_height))
    x -= random_jitter
    img = deprocess_image(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))