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- 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, 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
- 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)
- 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 = sess.run(t_grad, {t_input:sub})
- grad[y:y+sz,x:x+sz] = g
- 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)
- # normalizing the gradient, so the same step size should work
- g /= g.std()+1e-8 # for different layers and networks
- img += g*step
- print('.', end = ' ')
- clear_output()
- showarray(visstd(img))
- render_multiscale(T(layer)[:,:,:,channel])
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