omok_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting ipywidgets\n",
      "  Downloading ipywidgets-8.1.5-py3-none-any.whl.metadata (2.3 kB)\n",
      "Requirement already satisfied: comm>=0.1.3 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipywidgets) (0.2.2)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipywidgets) (8.12.2)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipywidgets) (5.14.3)\n",
      "Collecting widgetsnbextension~=4.0.12 (from ipywidgets)\n",
      "  Downloading widgetsnbextension-4.0.13-py3-none-any.whl.metadata (1.6 kB)\n",
      "Collecting jupyterlab-widgets~=3.0.12 (from ipywidgets)\n",
      "  Downloading jupyterlab_widgets-3.0.13-py3-none-any.whl.metadata (4.1 kB)\n",
      "Requirement already satisfied: backcall in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (0.2.0)\n",
      "Requirement already satisfied: decorator in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n",
      "Requirement already satisfied: matplotlib-inline in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n",
      "Requirement already satisfied: pickleshare in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.48)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n",
      "Requirement already satisfied: stack-data in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.2)\n",
      "Requirement already satisfied: typing-extensions in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (4.11.0)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets) (0.2.13)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.1.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (3.0.0)\n",
      "Requirement already satisfied: pure-eval in /home/aikusrv01/anaconda3/envs/omok/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.3)\n",
      "Downloading ipywidgets-8.1.5-py3-none-any.whl (139 kB)\n",
      "Downloading jupyterlab_widgets-3.0.13-py3-none-any.whl (214 kB)\n",
      "Downloading widgetsnbextension-4.0.13-py3-none-any.whl (2.3 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-8.1.5 jupyterlab-widgets-3.0.13 widgetsnbextension-4.0.13\n"
     ]
    }
   ],
   "source": [
    "!pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fed02a4a20704001982f2d8a5a42e06d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=7, description='x', max=14), IntSlider(value=7, description='y', max=14)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "from IPython.display import display, clear_output\n",
    "from game import Game\n",
    "from agent import Agent, User\n",
    "from model1 import Residual_CNN\n",
    "import config\n",
    "import torch\n",
    "\n",
    "# 게임 초기화\n",
    "game = Game()\n",
    "board_size = int(game.state_size ** 0.5)  # 보드 크기 계산\n",
    "game.reset()  # 초기화\n",
    "\n",
    "# 이미 학습된 모델 로드\n",
    "model_path = \"/home/aikusrv01/omok/torch_omok_test/DeepReinforcementLearning/models/gomokucurr_0025.pt\"  # 학습된 모델 파일 경로\n",
    "\n",
    "ai_nn = Residual_CNN(\n",
    "    config.REG_CONST,\n",
    "    config.LEARNING_RATE,\n",
    "    game.input_shape,\n",
    "    game.action_size,\n",
    "    config.HIDDEN_CNN_LAYERS,\n",
    ")\n",
    "ai_nn.load_state_dict(torch.load(model_path, map_location=torch.device(\"cpu\")))\n",
    "ai_nn.eval()  # 평가 모드 설정\n",
    "\n",
    "# AI Agent 초기화\n",
    "ai = Agent(\"AI\", game.state_size, game.action_size, config.MCTS_SIMS, config.CPUCT, ai_nn, device=None)\n",
    "\n",
    "# 보드 초기화\n",
    "board = np.zeros((board_size, board_size), dtype=int)\n",
    "\n",
    "# 승리 체크 함수\n",
    "def check_winner():\n",
    "    if game.gameState.isEndGame == 1:\n",
    "        winner = \"User (Black)\" if game.gameState.playerTurn == -1 else \"AI (White)\"\n",
    "        return winner\n",
    "    return None\n",
    "\n",
    "# 보드 시각화 함수\n",
    "def render_board():\n",
    "    clear_output(wait=True)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.imshow(np.zeros((board_size, board_size)), cmap=\"gray\", extent=(0, board_size, 0, board_size))\n",
    "    plt.xticks(range(board_size))\n",
    "    plt.yticks(range(board_size))\n",
    "    plt.grid(color=\"black\", linestyle=\"-\", linewidth=1)\n",
    "\n",
    "    for i in range(board_size):\n",
    "        for j in range(board_size):\n",
    "            if board[i, j] == 1:  # Black stone\n",
    "                plt.plot(j + 0.5, board_size - i - 0.5, 'ko', markersize=20)\n",
    "            elif board[i, j] == -1:  # White stone\n",
    "                plt.plot(j + 0.5, board_size - i - 0.5, 'wo', markersize=20)\n",
    "\n",
    "    plt.show() \n",
    "\n",
    "# 유저의 턴 처리 함수\n",
    "def user_turn(row, col):\n",
    "    global board\n",
    "    action = row * board_size + col\n",
    "\n",
    "    if game.gameState.board[action] != 0:\n",
    "        print(\"Cell is already occupied. Choose another cell.\")\n",
    "        return\n",
    "\n",
    "    # 유저가 돌을 놓음\n",
    "    game.step(action)\n",
    "    board[row, col] = 1\n",
    "\n",
    "    winner = check_winner()\n",
    "    if winner:\n",
    "        render_board()\n",
    "        print(f\"Game Over! {winner} wins!\")\n",
    "        return\n",
    "\n",
    "    # AI의 턴\n",
    "    ai_action, _, _, _ = ai.act(game.gameState, tau=0)\n",
    "    ai_row, ai_col = divmod(ai_action, board_size)\n",
    "    game.step(ai_action)\n",
    "    board[ai_row, ai_col] = -1\n",
    "\n",
    "    winner = check_winner()\n",
    "    render_board()\n",
    "    if winner:\n",
    "        print(f\"Game Over! {winner} wins!\")\n",
    "\n",
    "# 인터랙티브 UI 설정\n",
    "def interactive_game(row, col):\n",
    "    user_turn(row, col)\n",
    "\n",
    "# 위젯 생성\n",
    "interact(\n",
    "    interactive_game,\n",
    "    row=(0, board_size - 1, 1),\n",
    "    col=(0, board_size - 1, 1)\n",
    ")\n",
    "\n",
    "render_board()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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