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deep_dream_demo.py
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# boilerplate code
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
#from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
#!wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip && unzip inception5h.zip
model_fn = 'tensorflow_inception_graph.pb'
# creating TensorFlow session and loading the model
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':t_preprocessed})
layers = [op.name for op in graph.get_operations() if op.type=='Conv2D' and 'import/' in op.name]
feature_nums = [int(graph.get_tensor_by_name(name+':0').get_shape()[-1]) for name in layers]
print('Number of layers', len(layers))
print('Total number of feature channels:', sum(feature_nums))
layer = 'mixed4d_3x3_bottleneck_pre_relu'
channel = 139 # picking some feature channel to visualize
# start with a gray image with a little noise
img_noise = np.random.uniform(size=(224, 224, 3)) + 100.0
def showarray(a, fname, fmt='jpeg'):
a = np.uint8(np.clip(a, 0, 1) * 255)
#f = BytesIO()
PIL.Image.fromarray(a).save(fname, fmt)
#display(Image(data=f.getvalue()))
def visstd(a, s=0.1):
'''Normalize the image range for visualization'''
return (a - a.mean()) / max(a.std(), 1e-4) * s + 0.5
def T(layer):
'''Helper for getting layer output tensor'''
return graph.get_tensor_by_name("import/%s:0" % layer)
def render_naive(t_obj, img0=img_noise, iter_n=20, step=1.0):
t_score = tf.reduce_mean(t_obj) # defining the optimization objective. This is mean of a given channel in a tensor layer defined by t_obj
# we want to maaximize this objective
t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
img = img0.copy()
showarray(visstd(img),'./results/result_0')
act_obj = sess.run(t_obj, {t_input: img_noise})
print('objective tensor size', act_obj.shape)
for i in range(iter_n):
g, score = sess.run([t_grad, t_score], {t_input: img})
# normalizing the gradient, so the same step size should work
g /= g.std() + 1e-8 # for different layers and networks
img += g * step
print(i, ' ', score)
fname='./results/result_'+str(i)
showarray(visstd(img),fname)
# clear_output()
showarray(visstd(img),'./results/result_final')
#render_naive(T(layer)[:, :, :, channel])
#Multiscale image generation
# We are going to apply gradient ascent on multiple scales.
# Details formed on smaller scale will be upscaled and augmented with additional details on the next scale.
#With multiscale image generation it may be tempting to set the number of octaves to some high value to produce wallpaper-sized images.
# Storing network activations and backprop values will quickly run out of GPU memory in this case. There is a simple trick to avoid this:
# split the image into smaller tiles and compute each tile gradient independently.
#Applying random shifts to the image before every iteration helps avoid tile seams and improves the overall image quality.
print('**** Multiscale ****')
def tffunc(*argtypes):
'''Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
'''
placeholders = list(map(tf.placeholder, argtypes))
def wrap(f):
out = f(*placeholders)
def wrapper(*args, **kw):
return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))
return wrapper
return wrap
# Helper function that uses TF to resize an image
def resize(img, size):
img = tf.expand_dims(img, 0)
return tf.image.resize_bilinear(img, size)[0,:,:,:]
resize = tffunc(np.float32, np.int32)(resize)
def calc_grad_tiled(img, t_grad, t_score, t_obj, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
sz = tile_size
#print('tile size', tile_size)
h, w = img.shape[:2]
sx, sy = np.random.randint(sz, size=2)
img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
grad = np.zeros_like(img)
y=0
x=0
sub = img_shift[y:y + sz, x:x + sz]
act_obj = sess.run(t_obj, {t_input: sub})
print('objective tensor size', act_obj.shape)
for y in range(0, max(h-sz//2, sz),sz):
for x in range(0, max(w-sz//2, sz),sz):
sub = img_shift[y:y+sz,x:x+sz]
g, score = sess.run([t_grad,t_score ], {t_input: sub})
#score = sess.run(t_score, {input: sub})
grad[y:y+sz,x:x+sz] = g
#print('x:',x,'y:',y)
#print('score: ', score)
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
def render_multiscale(t_obj, img0=img_noise, iter_n=10, step=1.0, octave_n=3, octave_scale=1.4):
t_score = tf.reduce_mean(t_obj) # defining the optimization objective
t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
img = img0.copy()
for octave in range(octave_n):
if octave > 0:
hw = np.float32(img.shape[:2]) * octave_scale
img = resize(img, np.int32(hw))
for i in range(iter_n):
g = calc_grad_tiled(img, t_grad, t_score, t_obj)
# normalizing the gradient, so the same step size should work
g /= g.std() + 1e-8 # for different layers and networks
img += g * step
print('o: ' ,octave,'i: ',i, 'size:', g.shape, end=' ')
# clear_output()
fname = './results/multi_scale_result_' + str(i)+ '_'+str(octave)
showarray(visstd(img), fname)
#render_multiscale(T(layer)[:, :, :, channel])
#************************************************************
# deap dream
#**************************************************************
print('**** deep dream ****')
def render_deepdream(t_obj, img0=img_noise,
iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
print('calculating score and grad functions...')
t_score = tf.reduce_mean(t_obj) # defining the optimization objective
t_grad = tf.gradients(t_score, t_input)[0] # behold the power of automatic differentiation!
print('done!')
# split the image into a number of octaves
print('splitting the image into octaves...')
img = img0
octaves = []
for i in range(octave_n - 1):
print('octave i:', i)
hw = img.shape[:2]
print('calculate lo',i)
lo = resize(img, np.int32(np.float32(hw) / octave_scale))
print('calculate hi', i)
hi = img - resize(lo, hw)
img = lo
octaves.append(hi)
print('save hi and lo results', i)
#fname = './results/deepdream_octaves_hi' + str(i)
#showarray(visstd(hi), fname)
#fname = './results/deepdream_octaves_lo' + str(i)
#showarray(visstd(lo), fname)
print('done!')
print('generate details octave by octave...')
# generate details octave by octave
for octave in range(octave_n):
print('octave:',octave)
if octave > 0:
print('resize image:', octave)
hi = octaves[-octave]
img = resize(img, hi.shape[:2]) + hi
for i in range(iter_n):
print('calculate grad iter:', i, 'octave:', octave)
g = calc_grad_tiled(img, t_grad,t_score, t_obj)
img += g * (step / (np.abs(g).mean() + 1e-7))
# clear_output()
#if (i%4 ==1):
fname = './results/deep_dream_result_oct_'+str(octave)+'_iter_' + str(i)
showarray(img / 255.0, fname)
print('done!')
#img0 = PIL.Image.open('./source_images/pilatus800.jpg')
#img0 = PIL.Image.open('mmd_22000_01_9000.png')
img0 = PIL.Image.open('./source_images/0016_9000.png')
img0 = np.float32(img0)
#showarray(img0/255.0)
#render_deepdream(tf.square(T('mixed4c')), img0)
print('image read')
#render_deepdream(T(layer)[:,:,:,139], img0, iter_n=20, step=1.5, octave_n=4)
#render_deepdream(tf.square(T('mixed4c')), img0, iter_n=20, octave_n=10)
render_deepdream(T(layer)[:,:,:,139], img0, iter_n=20, octave_n=19)
#render_deepdream(T(layer)[:,:,:,65]+T(layer)[:,:,:,139], img0, iter_n=20, octave_n=12)