Arbitrary number of channels parametrization

This notebook uses the new param.image parametrization that takes any number of channels.



In [1]:

    
import numpy as np
import tensorflow as tf

import lucid.modelzoo.vision_models as models
from lucid.misc.io import show
import lucid.optvis.objectives as objectives
import lucid.optvis.param as param
import lucid.optvis.render as render
import lucid.optvis.transform as transform



In [2]:

    
model = models.InceptionV1()
model.load_graphdef()

Testing params

The following params are introduced to test the new param.imag parametrization by going back to three channels for the existing modelzoo models



In [3]:

    
def arbitrary_channels_to_rgb(*args, channels=None, **kwargs):
    channels = channels or 10
    full_im = param.image(*args, channels=channels, **kwargs)
    r = tf.reduce_mean(full_im[...,:channels//3]**2, axis=-1)
    g = tf.reduce_mean(full_im[...,channels//3:2*channels//3]**2, axis=-1)
    b = tf.reduce_mean(full_im[...,2*channels//3:]**2, axis=-1)
    return tf.stack([r,g,b], axis=-1)

def grayscale_image_to_rgb(*args, **kwargs):
    """Takes same arguments as image"""
    output = param.image(*args, channels=1, **kwargs)
    return tf.tile(output, (1,1,1,3))

Arbitrary channels parametrization

param.arbitrary_channels calls param.image and then reduces the arbitrary number of channels to 3 for visualizing with modelzoo models.



In [9]:

    
_ = render.render_vis(model, "mixed4a_pre_relu:476", param_f=lambda:arbitrary_channels_to_rgb(128, channels=10))









    



512 1162.5448

Grayscale parametrization

param.grayscale_image creates param.image with a single channel and then tiles them 3 times for visualizing with modelzoo models.



In [5]:

    
_ = render.render_vis(model, "mixed4a_pre_relu:476", param_f=lambda:grayscale_image_to_rgb(128))









    



512 771.40924

Testing different objectives

Different objectives applied to both parametrizations.



In [6]:

    
_ = render.render_vis(model, objectives.deepdream("mixed4a_pre_relu"), param_f=lambda:arbitrary_channels_to_rgb(128, channels=10))
_ = render.render_vis(model, objectives.channel("mixed4a_pre_relu", 360), param_f=lambda:arbitrary_channels_to_rgb(128, channels=10))
_ = render.render_vis(model, objectives.neuron("mixed4a_pre_relu", 476), param_f=lambda:arbitrary_channels_to_rgb(128, channels=10))









    



512 176003.55






    












    



512 997.41125






    












    



512 [1250.0197]



In [8]:

    
_ = render.render_vis(model, objectives.deepdream("mixed4a_pre_relu"), param_f=lambda:grayscale_image_to_rgb(128))
_ = render.render_vis(model, objectives.channel("mixed4a_pre_relu", 360), param_f=lambda:grayscale_image_to_rgb(128))
_ = render.render_vis(model, objectives.neuron("mixed4a_pre_relu", 476), param_f=lambda:grayscale_image_to_rgb(128))









    



512 142696.16






    












    



512 912.55396






    












    



512 [1286.3795]