TWIT in C Works

Status update....

TWIT now has a .c file that can be called from Python to do the core iteration of TWIT.

Performance

The performance is now at 2 ms. per TWIT of 255x255x3 to 100 x 200 x 3 with preclear of destionation to 0.0, of the dog1.jpg image.
I tested it with a loop of 10,000 iterations with a cached twit definition.
Generating the twit parameters is very fast too.

Before in pure python it was many seconds for one transfer.


                                      Original, and TWIT stretched and Weight Fade.


The code in Python

Available on GitHub at https://github.com/RMKeene/twit

Next step..... Multithread!

import numpy as np
import twit
import twitc
from PIL import Image
import matplotlib.pyplot as plt
 
def scale_image():
    """
    Test code and example to scale an image using Twit.
    """
 
    im = Image.open("dog1.jpg")
    plt.imshow(im, cmap='Greys_r')
    plt.show()
    pass
    np_im = np.array(im)
 
    # 225 x 225 x 3
    print("Tensor shape: " + str(np_im.shape))
 
    dest = np.zeros((100, 200, 3))
    twit.tensor_transfer(np_im, dest, preclear=False, weight_range=(0.0, 1.0), weight_axis=1)
    new_im = Image.fromarray(dest.astype(np.uint8))
 
    plt.imshow(new_im, cmap='Greys_r')
    plt.show()
    pass
 
def scale_image_C_code():
    """
    Test code and example to scale an image using Twit.
    """
 
    im = Image.open(r"K:\twit\dog1.jpg")
    plt.imshow(im, cmap='Greys_r')
    plt.show()
    pass
    np_im = np.array(im, dtype=np.float64)
 
    # 225 x 225 x 3
    print("Tensor shape: " + str(np_im.shape))
 
    dest = np.zeros((100, 200, 3), dtype=np.float64)
 
    twt = twitc.compute_twit_multi_dimension(3, 
            np.array([0, np_im.shape[0] - 1, 0, 99, 0, np_im.shape[1] - 1, 0, 199, 
                      0, np_im.shape[2] - 1, 0, 2], dtype=np.int64),  
            np.array([0.0, 1.0, 0.0, 1.0, 1.0, 1.0], dtype=np.float64))
    N = 1
    pc = 1
    print("Start apply twit loop %d iterations with preclear %d" % (N, pc))
    for i in range(N):
        twitc.apply_twit(twt, np_im, dest, 1)
    print("End apply twit loop")
    new_im = Image.fromarray(dest.astype(np.uint8))
 
    plt.imshow(new_im, cmap='Greys_r')
    plt.show()
    pass
 
 
if __name__ == '__main__':
    print("Scale Image with twit sample program.")
    scale_image_C_code()

Comments

Post a Comment

Popular posts from this blog

UE4 Asset Directory Listing Blueprint

Image
So I needed to be able to, in game, get listing of asset directories and assets in a folder style.  The purpose is to let me make on-the-fly menu selections of something like 10000 assets in game and do it fast. Turns out in UE4.25 you can do all of this in blueprints. The low level problem is that assets get packaged into .pak files when you ship the game.  Regular C++ style file and directory operations will not find any assets.  Instead there is the Asset Manager. Assets are bundled as assets and are not in a directory structure in the pak file.  The path to an asset is in effect just a string.  So the over all flow is... Load all the assets under one or more asset directories.  When the game is packaged what was something like Content/DefaultObjects/Trees becomes /Game/DefaultObjects/Trees. When you want to ask "What sub directories and assets are in /Game/DefaultObject/Trees you call my blueprint node.  It returns a result structure wit...
Read more

Subsumption and Neural Hiding - The 'Object Oriented Paradigm' of Artificial Intelligence

In computer programming Object Oriented Programming (OOP) gave programmers a neat way to compartmentalize programs, rather than having a mess of disorganized code and data.  One can neatly make an object, treat it as a black-box and reduce the cognitive load on the programmer.  Nifty. In reactive neural networks where one is taking world inputs and generating response outputs Subsumption vastly simplifies the work of designing the neural processing. From dictionary.com: verb (used with object),   sub·sumed,   sub·sum·ing. to  consider  or  include  (an  idea,   term,   proposition,   etc.)  as  part  of a  more   comprehensive   one. to  bring  (a  case,   instance,   etc.)   under  a  rule. to  take  up  into  a  more   inclusive   classification. Subsumption in the context of neural networks is where some ...
Read more

Self Cycling Asynchronous Neural Systems

Current neural network systems have a general paradigm of being given some input, and then producing a resulting output. A true general AI should act like a biological system and be self-sequencing. The neural system should just free-run, responding to inputs and creating outputs and learning. One method I am experimenting with in Cognate and NuTank is having a hard-wired set of concept maps (a N-dimensional set of neurons used to embody a concept, e.g. foveolar edge angles.) that take inputs from the external world and produce hard-wired reactions as outputs to control a real or simulated robot. Such a system also uses subsumption to aid in much easier design. This is the Concept Map System (CMS) for the purposes of this post. For now lets ignore the idea that the hard-wired concept map system might also be adapting and evolving over time simulating neroplasticity. One then has a large 3D array of neurons that are in acting as a pattern matcher on the entire CMS.  What this ...
Read more