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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import cv2\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from importlib.util import find_spec\n",
+ "if find_spec(\"text_recognizer\") is None:\n",
+ " import sys\n",
+ " sys.path.append('..')\n",
+ "\n",
+ "from text_recognizer.networks.transformer.attention import MultiHeadAttention"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temp_mha = MultiHeadAttention(hidden_dim=512, num_heads=8)\n",
+ "def print_out(Q, K, V):\n",
+ " temp_out, temp_attn = temp_mha.scaled_dot_product_attention(Q, K, V)\n",
+ " print('Attention weights are:', temp_attn.squeeze())\n",
+ " print('Output is:', temp_out.squeeze())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_K = torch.tensor(\n",
+ " [[10, 0, 0],\n",
+ " [ 0,10, 0],\n",
+ " [ 0, 0,10],\n",
+ " [ 0, 0,10]]\n",
+ ").float()[None,None]\n",
+ "\n",
+ "test_V = torch.tensor(\n",
+ " [[ 1,0,0],\n",
+ " [ 10,0,0],\n",
+ " [ 100,5,0],\n",
+ " [1000,6,0]]\n",
+ ").float()[None,None]\n",
+ "\n",
+ "test_Q = torch.tensor(\n",
+ " [[0, 10, 0]]\n",
+ ").float()[None,None]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26])\n",
+ "Output is: tensor([1.0000e+01, 9.2766e-25, 0.0000e+00])\n"
+ ]
+ }
+ ],
+ "source": [
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Attends to the second element, as it should!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01])\n",
+ "Output is: tensor([550.0000, 5.5000, 0.0000])\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_Q = torch.tensor([[0, 0, 10]]).float()[None,None]\n",
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Focuses equally on the third and fourth key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([[4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01],\n",
+ " [8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26],\n",
+ " [5.0000e-01, 5.0000e-01, 4.2166e-26, 4.2166e-26]])\n",
+ "Output is: tensor([[5.5000e+02, 5.5000e+00, 0.0000e+00],\n",
+ " [1.0000e+01, 9.2766e-25, 0.0000e+00],\n",
+ " [5.5000e+00, 4.6383e-25, 0.0000e+00]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_Q = torch.tensor(\n",
+ " [[0, 0, 10], [0, 10, 0], [10, 10, 0]]\n",
+ ").float()[None,None]\n",
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}