shrimo

Small Transformer Model with GUI and Visualization

This project demonstrates a small Transformer model designed for natural language generation. It features a graphical user interface (GUI) using tkinter for user input and displays generated text as well as advanced real-time vector visualization using OpenCV.

Features

1. Small Transformer Model:
   • A lightweight Transformer model optimized for environments with limited resources (e.g., GPUs with small memory capacities).
   • Features include reduced embedding size, fewer Transformer layers, and fewer attention heads.

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import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random

# --- PARAMETERS ---
GRID_SIZE = 64
NUM_ORGANISMS = 20

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import math
import random

class SimpleTransformer:
    def __init__(self, d_model):
        """Initializes the transformer with a given embedding size (d_model)."""
        self.d_model = d_model
        self.W_q = [[random.uniform(-0.5, 0.5) for _ in range(d_model)] for _ in range(d_model)]
        self.W_k = [[random.uniform(-0.5, 0.5) for _ in range(d_model)] for _ in range(d_model)]
        self.W_v = [[random.uniform(-0.5, 0.5) for _ in range(d_model)] for _ in range(d_model)]

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import torch
import torch.nn as nn
import torch.optim as optim
import os

# Tokenization
def tokenize(sentence):
    return sentence.lower().split()

# Vocabulary

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#!/usr/bin/python3

"""
In computing, a shebang is the character sequence #!, 
consisting of the characters number sign (also known as sharp or 
hash) and exclamation mark (also known as bang), 
at the beginning of a script.
"""

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import cv2
import numpy as np

file = "image.jpg"
img = cv2.imread(file)

[
    cv2.circle(img, (np.int32(ppt)), 5, (0, 255, 0), 1)
    for ppt in [
        (int(points.pt[0]), int(points.pt[1]))

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#!/usr/bin/python3
# read, akaze and show

import cv2
import multiprocessing

# Function to read video frames
def read_video_stream(video_file, frame_queue):
    cap = cv2.VideoCapture(video_file)

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#!/usr/bin/python3
# Dividing the frame into tiles

import multiprocessing
import numpy as np
import cv2


class OpenCV_Multiprocessing:
    def __init__(self, features):

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class ReduceDraw:
    """
    Class for drawing many objects without loops
    """
    __slots__ = ['frame_tmp','color', 'size', 'font']
    def __init__(self, color = (0, 0, 255), size = 10):
        self.frame_tmp = None
        self.color = color
        self.size = size
        self.font = cv2.FONT_HERSHEY_SIMPLEX

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import cv2 
import numpy as np
from matplotlib import pyplot as plt


img = cv2.imread('image_big.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rows, colss = img.shape
crow, ccol = rows//2 , colss//2
print(crow, ccol)