Core

In this file we define the basic building blocks before implementing the whole project.

We automatically select the device on which to run our experiments.

We also set a basic set of hyper parameters. These hypermeters can later be replaced by other values.

Loading the target image

path = '../images/emoji_u1f98e.png'

# load image using PILL
img = Image.open(path).resize((TARGET_SIZE, TARGET_SIZE))

# Convert the image to numpy array 
img = np.array(img)
img = img.astype(np.float32) / 255.0

# Display the image
plt.imshow(img)
plt.show()

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load_image

 load_image (path)

Load the image specified by path and return a torch.tensor version of the image with shape B, C, H, W, already on the default device

img_tensor = load_image(path)

Implementing the filters

filters

tensor([[[ 0.,  0.,  0.],
         [ 0.,  1.,  0.],
         [ 0.,  0.,  0.]],

        [[-1.,  0.,  1.],
         [-2.,  0.,  2.],
         [-1.,  0.,  1.]],

        [[-1., -2., -1.],
         [ 0.,  0.,  0.],
         [ 1.,  2.,  1.]]], device='cuda:0')

We also need a function that will apply these filters per channel on an input image.

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perchannel_conv

 perchannel_conv (x, filters)

Let’s test the results of applying the filters to the original image

filtered_images = perchannel_conv(img_tensor, filters)

# We apply 3 filter seperately on the 4 channels of the image
# resulting in a total of 12 images.
# To visualize them we will use a 4x3 grid (channelsxfilters)
plt.figure(figsize=(6,8))
for i in range(12):
    plt.subplot(4, 3, i+1)
    plt.imshow(filtered_images[0, i].detach().cpu(), cmap='gray')
    plt.axis('off')

Create the CA model

First, we’ll create a function to detect alive cells. A cell is considered alive if it has an alpha value greater than 0.1 or if any of its neighbors does.

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alive

 alive (x, threshold=0.1)

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CAModel

 CAModel (channel_n, update_rate=0.5)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Testing the Model…

# Instantiate the model
ca = CAModel(16).to(def_device)

# Create a dummy input
seed = torch.rand(1, 16, TARGET_SIZE, TARGET_SIZE).to(def_device)

# Activate the model
res = ca(seed)

res.shape

torch.Size([1, 16, 40, 40])

Display Animation

Later in the experiment, we’ll require a tool for displaying animations. This will be used for visualizing the life and the expansion of the cellular automaton.

# create a dummy series of images
images = []
for i in range(20):
    images.append(np.random.rand(40, 40, 4))

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display_animation

 display_animation (images)

	Details
images	A list containing the frames of the animation. Each frame should be of shape [H, W, 4]

display_animation(images)

Once Loop Reflect

Create animation from the CAModel

We’ll also incorporate the capability for the model to generate an animation depicting the cellular automaton’s evolution across multiple steps.

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show_doc

 show_doc (sym, renderer=None, name:Optional[str]=None, title_level:int=3)

Show signature and docstring for sym

	Type	Default	Details
sym			Symbol to document
renderer	NoneType	None	Optional renderer (defaults to markdown)
name	str | None	None	Optionally override displayed name of sym
title_level	int	3	Heading level to use for symbol name

images = ca.grow_animation(seed, 20)

display_animation(images)

Once Loop Reflect