def caricature(img, model, layer, n_steps=512, cossim_pow=0.0, verbose=True):
if isinstance(layer, str):
layers = [layer]
elif isinstance(layer, (tuple, list)):
layers = layer
else:
raise TypeError("layer must be str, tuple or list")
with tf.Graph().as_default(), tf.Session() as sess:
img = resize(img, model.image_shape[:2])
objective = objectives.Objective.sum([
1.0 * dot_compare(layer, cossim_pow=cossim_pow, batch=i+1)
for i, layer in enumerate(layers)
])
t_input = tf.placeholder(tf.float32, img.shape)
param_f = param.image(img.shape[0], decorrelate=True, fft=True, alpha=False, batch=len(layers))
param_f = tf.concat([t_input[None], param_f], 0)
transforms = transform.standard_transforms + [transform.crop_or_pad_to(*model.image_shape[:2])]
T = render.make_vis_T(model, objective, param_f, transforms=transforms)
loss, vis_op, t_image = T("loss"), T("vis_op"), T("input")
tf.global_variables_initializer().run()
for i in range(n_steps): _ = sess.run([vis_op], {t_input: img})
result = t_image.eval(feed_dict={t_input: img})
if verbose:
lucid.misc.io.showing.images(result[1:], layers)
return result
def test_resize_noop(image):
"""FYI: this essentially tests the `if original_size == target_size: return image`
part of `resize()`'s implementation. Without it, machine precision makes the
following assert no longer true, but I thought it was an imoprtant property to have.
"""
resized = resize(image, image.shape[-3:-1])
assert np.all(resized == image)
def image_activations(model, image, layer_names=None):
if layer_names is None:
layer_names = [
layer["name"] for layer in model.layers if "conv" in layer.tags
]
resized_image = resize(image, model.image_shape[:2])
with tf.Graph().as_default() as graph, tf.Session() as sess:
image_t = tf.placeholder_with_default(resized_image,
shape=model.image_shape)
model.import_graph(image_t, scope="import")
layer_ts = {}
for layer_name in layer_names:
name = layer_name if layer_name.endswith(
":0") else layer_name + ":0"
layer_t = graph.get_tensor_by_name("import/{}".format(name))[0]
layer_ts[layer_name] = layer_t
activations = sess.run(layer_ts)
return activations
def test_resize(image):
size = (3, 3)
resized = resize(image, size)
assert resized.shape[-3:-1] == size