cppn-abstract-run.py

from pyxll import xl_func, xl_app
import matplotlib.pyplot as plt
import numpy as np
import torch


@xl_func
def nn_Run(net, image_name, scale=1, offset=-0.5, seed=None):
    """Run the neural network with random weights"""

    # Set the seed for the RNG
    seed = int(seed) if seed is not None else torch.random.initial_seed()
    torch.manual_seed(seed)

    # Initialize the weights
    def init_weights(m):
        if isinstance(m, nn.Linear):
            nn.init.normal_(m.weight)

    net.apply(init_weights)

    # Find the Excel image
    xl = xl_app()
    sheet = xl.Caller.Worksheet
    image = sheet.Pictures(image_name)

    # Get the image size in pixels
    size_x, size_y = get_image_size(image)

    # Create the inputs
    inputs = np.zeros((size_y, size_x, 2))
    for x in np.arange(0, size_x, 1):
        for y in np.arange(0, size_y, 1):
            scaled_x = scale * ((float(x) / size_x) + offset)
            scaled_y = scale * ((float(y) / size_y) + offset)
            inputs[y][x] = np.array([scaled_x, scaled_y])

    inputs = inputs.reshape(size_x * size_y, 2)

    # Compute the results
    result = net(torch.tensor(inputs).type(torch.FloatTensor)).detach().numpy()
    result = result.reshape((size_y, size_x, 3))

    # Create a temporary file to write the result to
    file = create_temporary_file(suffix=".png")

    # Write the image to the file
    plt.imsave(file, result)
    file.flush()

    # Replace the old image with the new one
    new_image = sheet.Shapes.AddPicture(Filename=file.name,
                                        LinkToFile=0,  # msoFalse
                                        SaveWithDocument=-1,  # msoTrue
                                        Left=image.Left,
                                        Top=image.Top,
                                        Width=image.Width,
                                        Height=image.Height)

    image_name = image.Name
    image.Delete()
    new_image.Name = image_name

    return f"[{new_image.Name}]"