Lucid local development notebook

This notebook allows you to use your development version of the lucid codebase. It is meant to only be used during development of lucid, not when using it.

Instead, see "usage_example.ipynb" in this folder for an example of how to use lucid in a notebook.

Setup

Add local package to search path



In [5]:

    
import os
import sys

module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
    sys.path.append(module_path)

Now we should be able to import it directly, just like if we installed it using pip:

import lucid

However, we will use autoreload so we can quickly iterate on local code changes:

Enable autoreload



In [6]:

    
%load_ext autoreload



In [7]:

    
%aimport lucid
%aimport -tensorflow
%aimport -numpy
%aimport -sklearn



In [8]:

    
%aimport









    



Modules to reload:
lucid

Modules to skip:
numpy sklearn tensorflow

Let's check that we're actually seeing the local version, not a package installed in site-packages. Th next cell should show the path at which you cloned the lucid repo, not a system path:



In [9]:

    
module_path = lucid.__path__[0]
print("Lucid was loaded from {}.".format(module_path))
assert os.path.abspath("..") in module_path
del module_path









    



Lucid was loaded from /Users/ludwigschubert/Code/deepviz/lucid.

Example usage



In [6]:

    
import numpy as np



In [7]:

    
image = np.random.normal(loc=.5, scale=.1, size=(200,200))



In [15]:

    
# this is how you set log levels globally:

import logging
logging.getLogger('lucid').setLevel(logging.DEBUG)

# or per module:
# logging.getLogger('lucid.misc.io').setLevel(logging.INFO)



In [16]:

    
%autoreload

from lucid.misc.io import load, save, show

image = load('../tests/fixtures/noise.jpeg')

print(image.dtype)
show(image)









    



DEBUG:lucid.misc.io.loading:Using inferred loader 'img' due to passed file extension '.png'.
float64
DEBUG:lucid.misc.io.showing:Show is assuming rank 2 or 3 tensor to be an image.
DEBUG:lucid.misc.io.serialize_array:Converting inexact array by scaling by 255.00.



In [10]:

    
%autoreload

array = load("../tests/fixtures/noise.jpeg")
print(array.dtype, array.shape)
show(array, domain=None)
array









    



(dtype('float64'), (5, 10))






    











    Out[10]:





array([[ 70., 180., 110., 104.,  66., 147., 100., 130., 112., 116.],
       [112., 165., 154., 106., 175., 129., 152., 111., 144., 167.],
       [135., 139.,  73., 133., 139.,  89., 120., 132.,  98., 115.],
       [106., 142.,  79.,  93., 132., 161., 175., 137., 139., 118.],
       [200., 129., 115., 130., 125., 116.,  95., 109., 117., 179.]])



In [11]:

    
show(load("https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png"))

Optvis Usage



In [12]:

    
from lucid.optvis import objectives, param, transform, render
from lucid.modelzoo.vision_models import InceptionV1



In [13]:

    
model = InceptionV1()
model.load_graphdef()



In [14]:

    
_ = render.render_vis(model, "mixed3b_pre_relu:470", thresholds=(32, 256, 1024))









    



32 416.72797






    











    



256 713.85126






    











    



1024 765.27924



In [12]:

    
import os
repr(os.urandom(16))









    Out[12]:





"'\\x0f\\xb8\\xaa\\xc7\\xaf\\x9b\\xff\\xd5i\\xe3Z\\xed:IC\\t'"



In [12]:

    
import numpy as np
from lucid.misc.channel_reducer import ChannelReducer



In [98]:

    
array = np.zeros((100,100,10), dtype=np.float32)
for d in range(array.shape[-1]):
    array[:,:,d] = np.eye(100,100)



In [104]:

    
array += 0.1 * np.random.uniform(size=array.shape)



In [106]:

    
reducer = ChannelReducer(3, reduction_alg='PCA')



In [107]:

    
reducer.fit(array)









    Out[107]:





PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)



In [108]:

    
reducer._reducer.components_









    Out[108]:





array([[ 0.31404045,  0.3159232 ,  0.31751755,  0.31607628,  0.31686693,
         0.31543642,  0.31630793,  0.31591657,  0.3158789 ,  0.31829447],
       [-0.16002858,  0.50028497, -0.12999336,  0.5603771 , -0.16221319,
         0.17761908, -0.4573163 , -0.34593767,  0.06547735, -0.04716743],
       [-0.4286834 ,  0.11218928, -0.51907194,  0.2326237 , -0.03332246,
         0.12981284,  0.4749903 ,  0.30447853, -0.36753035,  0.09344025]],
      dtype=float32)



In [109]:

    
reduced = reducer.transform(array)



In [110]:

    
show(np.dsplit(reduced, reduced.shape[-1]))



In [105]:

    
from lucid.misc.io import load, save, show

show(np.dsplit(array, array.shape[-1]))