diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..bba3bc6e04882c1cfbeea0b9644abba7a69942a9 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,20 @@ +FROM python:3.10-slim + +# 安装系统依赖 +RUN apt-get update && apt-get install -y \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# 安装 PyTorch（CPU 版本即可，避免太大） +RUN pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cpu + +# 安装其他依赖 +COPY requirements.txt . +RUN pip install -r requirements.txt + +# 拷贝代码 +COPY . /app +WORKDIR /app + +# 入口 +CMD ["python", "app.py"] diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000000000000000000000000000000000000..c851c896d357caa7fd94c67079fe2a0c3ecbd823 --- /dev/null +++ b/Readme.md @@ -0,0 +1,161 @@ +# DeepCubeA: 基于启发式搜索的魔方求解器复现 + +![GitHub repo size](https://img.shields.io/github/repo-size/xiaofeng218/DeepcubeA) +![Python](https://img.shields.io/badge/python-3.10%2B-blue) +![PyTorch](https://img.shields.io/badge/pytorch-2.0%2B-orange) + +## 项目概述 + +本项目是 [DeepCubeA](https://cse.sc.edu/~foresta/assets/files/SolvingTheRubiksCubeWithDeepReinforcementLearningAndSearch_Final.pdf) 方法的复现，训练使用 PyTorch Lightning 框架。该方法结合深度强化学习和搜索算法来解决魔方问题。原始论文展示了如何通过结合神经网络和搜索技术来解决复杂的组合优化问题，如魔方。 + +## 安装指南 + +### 训练环境 + +- Python 3.10.16 +- PyTorch 2.5.1 +- CUDA (可选，用于加速训练) + +### 安装步骤 + +1. 克隆仓库： + + ```bash + git clone https://github.com/xiaofeng218/DeepcubeA.git + cd DeepcubeA + ``` + +2. 创建环境并安装依赖项： + + ```bash + conda create -n deepcubea python=3.10.16 + conda activate deepcubea + conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.1 -c pytorch -c nvidia + pip install -r requirements.txt + ``` + +## 使用方法 + +### 训练模型 + +```bash +python train.py +``` + +### 求解魔方 + +下载训练好的 [final_model_K_30.pth](https://drive.google.com/file/d/1jdmdoXkkJb7sNq6oy-iudtnVIgQXDsLl/view?usp=drive_link) 模型，将其放在checkpoint文件夹下。 + +注意修改 `config.py` 中的 model_path 为 `checkpoint/final_model_K_30.pth` + +#### 1. 推理 + +随机打乱魔方并求解： + +```bash +python inference.py +``` + +指定初始状态求解，可选 action 为 `U, R, F, D, L, B, U_inv, R_inv, F_inv, D_inv, L_inv, B_inv`，多个动作之间用空格分隔。 + +```bash +python inference.py --actions "U R F D L_inv B_inv" +``` + +运行`inference.py`脚本后，会生成一个HTML文件 `rubiks_solution.html`，用于可视化求解过程。 + +#### 2. 网页应用 + +运行 `app.py` 启动网页应用： + +```bash +python app.py +``` + +在浏览器中打开 `http://localhost:5000` 即可访问网页应用。 + +### 配置参数说明 + +主要配置参数 (在config.py中定义)： + +- `--batch_size`: 训练批次大小 (默认: 10000) +- `--num_workers`: 数据加载线程数 (默认: 16) +- `--K`: 最大打乱次数 (默认: 30) +- `--max_epochs`: 最大训练轮数 (默认: 100) +- `--learning_rate`: 学习率 (默认: 1e-3) +- `--convergence_threshold`: 收敛阈值 (默认: 0.05) +- `--compile`: 是否编译加速模型 (默认: True) +- `--model_path`: 模型路径 (默认: `checkpoint/final_model_K_30.pth`) +- `--actions`: 初始状态动作 (默认: `""`) + +## 实现细节 + +详细的实现方法和算法说明请参阅 [Implement.md](Implement.md) 文件，包括： + +- 魔方状态表示 +- 动作表示 +- 深度近似值迭代算法 +- 训练伪代码 +- BWAS搜索算法 +- 神经网络架构 + +## 结果展示 + +### 训练结果 + +不同K值模型收敛（损失小于0.05）所需的epoch数（`1000 step/epoch`）： + ![k_convergence_epochs](assets/k_convergence_epochs.png) + +可以看到，模型收敛所需的训练epoch数随K的增加呈现出指数级增加的趋势，考虑到复现成本，在 K>15 之后，我们并未再 +让模型训练到收敛（即损失小于0.05），而是限定最大epoch为20. + +### 测试结果（K=30训练获得的最终收敛模型） + +#### 模型在不同打乱次数下状态输入的cost-to-go预测值统计（平均值，最大值） + + ![model_output](assets/model_output_vs_shuffles.png) + +#### 测试样例：打乱100步的魔方，求解结果及所需时间 + +由于我们的模型并未严格按照原文中设置的收敛域进行训练，因此模型能力会一定程度上弱于原文中的描述，下面是一个魔方求解案例： + +| 指标 | 值 | +| --- | --- | +| 打乱步数 | 100 | +| 解决方案路径长度 | 23 | +| 求解时间 | 7.6645 秒 | +| 解决方案路径 | `['D_inv', 'R', 'U_inv', 'F', 'L_inv', 'R', 'B_inv', 'L_inv', 'U_inv', 'F', 'B_inv', 'D_inv', 'L_inv', 'F_inv', 'R', 'F', 'L_inv', 'F_inv', 'R_inv', 'D_inv', 'B', 'U']` | + +[查看魔方还原过程](https://xiaofeng218.github.io/DeepcubeA/assets/rubiks_solution.html) + +#### 性能分析 + +构建了200组打乱1000-10000次的魔方作为测试集，使用A-star算法进行求解： + +> - 总测试数：200 +> - 测试硬件：NVIDIA A100 +> - 超参数：N=1000，$lambda=0.6$ +> - a-star搜索最大迭代次数：200 +> - 成功求解数：191 +> - 成功率：95.50% +> - 测试集平均打乱次数：5446.30 +> - 平均解长度：22.30 +> - 平均求解时间：13.10秒 +> - 最大解长度：25 + +## 引用 + +如果您在研究中使用了本项目的代码，请引用原始论文： + +```bibtex +@article{agostinelli2019solving, + title={Solving the Rubik’s cube with deep reinforcement learning and search}, + author={Agostinelli, Forest and McAleer, Stephen and Shmakov, Alexander and Baldi, Pierre}, + journal={Nature Machine Intelligence}, + volume={1}, + number={8}, + pages={356--363}, + year={2019}, + publisher={Nature Publishing Group UK London} +} +``` diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..d77bbe00a23b80c4a7fd6bf69e577f2573cd6d84 --- /dev/null +++ b/app.py @@ -0,0 +1,131 @@ +import flask +from flask import request, jsonify +import torch +import numpy as np +import os +from config import Config +from model.DNN import DNN +from model.Cube import Cube, TARGET_STATE +from solver_utils import * + +# 初始化Flask应用 +app = flask.Flask(__name__, static_folder=None) +app.config['JSON_AS_ASCII'] = False +app.config['DEBUG'] = True + +# 加载配置 +config = Config() +args = config.parse_args() +# 设置默认模型路径 +model_path = args.model_path + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# 加载模型和创建Cube对象 +model = load_model(model_path, device) +cube = Cube() + +# 初始化状态接口 +@app.route('/initState', methods=['POST']) +def init_state(): + # 初始状态设置为目标状态 + initial_state = TARGET_STATE.copy() + + # 生成旋转索引和状态映射 + rotateIdxs_old = {} + rotateIdxs_new = {} + for move_name in cube.moves.keys(): + # 使用Cube类中的实际移动映射 + move_mapping = cube.moves[move_name] + # 构建old到new的映射 + rotateIdxs_old[move_name] = move_mapping.tolist() + rotateIdxs_new[move_name] = list(range(54)) + + # 定义状态到特征提取和反向的映射 + # 这里假设状态和特征提取使用相同的顺序 + stateToFE = list(range(54)) + FEToState = list(range(54)) + legalMoves = list(cube.moves.keys()) + + response = { + 'state': initial_state.tolist(), + 'rotateIdxs_old': rotateIdxs_old, + 'rotateIdxs_new': rotateIdxs_new, + 'stateToFE': stateToFE, + 'FEToState': FEToState, + 'legalMoves': legalMoves + } + + return jsonify(response) + +# 求解魔方接口 +@app.route('/solve', methods=['POST']) +def solve(): + try: + data = request.json + if not data or 'state' not in data: + return jsonify({'error': '请求参数错误，缺少state字段'}), 400 + + state = np.array(data['state']) + if state.shape != (54,): + return jsonify({'error': 'state参数格式错误，应为长度为54的数组'}), 400 + + print("开始求解魔方...") + action_path, solution_state_path = a_star_search(state, model, cube) + + if action_path is None: + return jsonify({'error': '未能找到解决方案'}), 404 + + # 生成反向动作路径 + solveMoves_rev = [] + for action in action_path: + rev_action = action[:] + # 反转动作方向 + if "inv" in rev_action: + rev_action = rev_action[0] + else: + rev_action += "_inv" + solveMoves_rev.append(rev_action) + + print(action_path) + print(solveMoves_rev) + + response = { + 'moves': [action for action in action_path], + 'moves_rev': solveMoves_rev, + 'solve_text': action_path + } + + return jsonify(response) + except Exception as e: + print(f"求解过程中发生错误: {str(e)}") + return jsonify({'error': f'服务器内部错误: {str(e)}'}), 500 + +# 静态文件服务 +@app.route('/static/') +def send_static(path): + print("Serving static file:", path) + return flask.send_from_directory('web/deepcube.igb.uci.edu/static', path) + +# 主页 +@app.route('/') +def home(): + return flask.send_from_directory('web/deepcube.igb.uci.edu', 'index.html') + +# 处理缺失的heapq模块 +import heapq + +if __name__ == '__main__': + # 确保checkpoint目录存在 + if not os.path.exists('checkpoint'): + os.makedirs('checkpoint') + print("创建checkpoint目录，请将模型文件放入该目录") + + # 检查模型文件是否存在 + if not os.path.exists(model_path): + print(f"警告：未找到模型文件 {model_path}") + print("请确保模型文件存在于checkpoint目录中") + + # 启动服务器 + # 修改为仅监听本地主机 + app.run(host='127.0.0.1', port=5000) \ No newline at end of file diff --git a/checkpoint/final_model_K_30.pth b/checkpoint/final_model_K_30.pth new file mode 120000 index 0000000000000000000000000000000000000000..cc1e518fe69baefe05a681ae948a5dfe35665829 --- /dev/null +++ b/checkpoint/final_model_K_30.pth @@ -0,0 +1 @@ +/data/hanxiaofeng/deepcube/logs/20250818_2124/converged_checkpoints/final_model_K_30.pth \ No newline at end of file diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..5e810b852ef6dd68540df3d0b34141289c78cd60 --- /dev/null +++ b/config.py @@ -0,0 +1,42 @@ +import argparse + +from numpy import False_ + +class Config: + def __init__(self): + self.parser = argparse.ArgumentParser(description='PyTorch Lightning Training Config') + + # 数据配置 + self.parser.add_argument('--data_dir', type=str, default='./data', help='数据存储目录') + self.parser.add_argument('--batch_size', type=int, default=10000, help='批次大小 (根据Readme设置为10000)') + self.parser.add_argument('--num_workers', type=int, default=16, help='数据加载线程数') + self.parser.add_argument('--K', type=int, default=30, help='最大打乱次数 (对于魔方设置为30)') + self.parser.add_argument('--num_val_samples', type=int, default=10000 * 100, help='每个epoch样本数') + self.parser.add_argument('--num_train_samples', type=int, default=10000 * 1000, help='每个epoch样本数') + + # 训练配置 + self.parser.add_argument('--max_epochs', type=int, default=20, help='最大训练轮数') + self.parser.add_argument('--learning_rate', type=float, default=2e-4, help='学习率') + self.parser.add_argument('--weight_decay', type=float, default=0, help='权重衰减 (根据Readme不使用正则化)') + self.parser.add_argument('--devices', type=str, default="2", help="Devices to use: 'cpu', 'auto', '0', '1', '0,1', etc.") + self.parser.add_argument('--convergence_threshold', type=float, default=0.05, help='收敛阈值 (根据Readme设置为0.05)') + self.parser.add_argument('--chunk_size', type=int, default=10000 * 12, help='分块大小 (用于模型预测时的分块处理)') + self.parser.add_argument('--compile', type=bool, default=True, help='是否编译模型') + + # 其他配置 + self.parser.add_argument('--log_dir', type=str, default='./logs', help='日志存储目录') + self.parser.add_argument('--checkpoint_dir', type=str, default='checkpoints', help='模型 checkpoint 存储目录') + self.parser.add_argument('--converged_checkpoint_dir', type=str, default='converged_checkpoints', help='收敛模型 checkpoint 存储目录') + self.parser.add_argument('--seed', type=int, default=42, help='随机种子') + + # inference + self.parser.add_argument('--model_path', type=str, default='checkpoint/final_model_K_30.pth', help='模型路径') + self.parser.add_argument('--actions', type=str, default=None, help='指定的魔方动作序列，用空格分隔，如 "U R F D L B"') + + def parse_args(self): + return self.parser.parse_args() + +if __name__ == '__main__': + config = Config() + args = config.parse_args() + print(args) \ No newline at end of file diff --git a/dataset/__pycache__/dataloader.cpython-310.pyc b/dataset/__pycache__/dataloader.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6cffd35279945fc35913ce80973f6492d5d142e1 Binary files /dev/null and b/dataset/__pycache__/dataloader.cpython-310.pyc differ diff --git a/dataset/dataloader.py b/dataset/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..003a206d5dadfd9288c37e59a63607b5356401d4 --- /dev/null +++ b/dataset/dataloader.py @@ -0,0 +1,102 @@ +import os +import torch +import numpy as np +from torch.utils.data import DataLoader, Dataset, random_split +from pytorch_lightning import LightningDataModule +from model.Cube import Cube, TARGET_STATE + +class RubikDataset(Dataset): + def __init__(self, config, num_samples, is_train=True): + super().__init__() + self.config = config + self.num_samples = num_samples + self.is_train = is_train + self.cube = Cube() + self.K = config.K # 最大打乱次数 + self.all_actions = list(self.cube.moves.keys()) + + def __len__(self): + return self.num_samples + + def get_neighbors(self, state): + """ + 获取给定状态的所有邻居状态 + 参数: + state: 当前魔方状态，np.array + 返回: + 所有邻居状态的列表 + """ + return self.cube.get_neibor_state(state) + + def __getitem__(self, idx): + # 随机选择打乱次数 i ∈ [1, K]，其中50%概率为K，50%概率从[1, K-1]中均匀选择 + if np.random.random() < 0.5 and self.is_train: # 训练时提高K次打乱的概率，加速收敛 + i = self.K + else: + i = np.random.randint(1, self.K+1) + + # 从初始状态开始，随机应用 i 次动作 + state = TARGET_STATE.copy() + # 采样i个随机动作： + actions = np.random.choice(self.all_actions, size=i, replace=True) + + for action in actions: + state = self.cube.apply_action(state, action) + + # 获取所有邻居状态 + neighbor_states = self.get_neighbors(state.copy()) + + # 返回包装成dict的数据 + return { + 'state': state, # 54 + 'steps': i, + 'neighbors': neighbor_states # 12, 54 + } + +class RubikDataModule(LightningDataModule): + def __init__(self, config): + super().__init__() + self.config = config + self.batch_size = config.batch_size + self.num_workers = config.num_workers + self.num_train_samples = config.num_train_samples + self.num_val_samples = config.num_val_samples + + def prepare_data(self): + # 不需要下载数据，数据集是自动生成的 + pass + + def setup(self, stage=None): + # 创建训练、验证数据集 + self.train_dataset = RubikDataset( + self.config, self.num_train_samples, is_train=True + ) + self.val_dataset = RubikDataset( + self.config, self.num_val_samples, is_train=False + ) + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + worker_init_fn=self._worker_init_fn + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + worker_init_fn=self._worker_init_fn + ) + + def _worker_init_fn(self, worker_id): + # 获取 worker 的初始种子（会随 epoch 变化） + worker_seed = (self.config.seed + worker_id + torch.initial_seed()) % 2**32 + + # 设置 numpy、torch、python random 的种子 + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) diff --git a/inference.py b/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc8dac3c19636f65166161bc8c1f7ae1c963154 --- /dev/null +++ b/inference.py @@ -0,0 +1,113 @@ +import torch +import numpy as np +import heapq +import os +from config import Config +from model.DNN import DNN +from model.Cube import Cube, TARGET_STATE +from vis import generate_rubiks_html +import time + +from solver_utils import * + +def generate_html(initial_state, solution_path): + # 颜色映射 (0=白, 1=红, 2=绿, 3=黄, 4=橙, 5=蓝) + COLOR_MAP = { + 0: "white", + 1: "red", + 2: "green", + 3: "yellow", + 4: "orange", + 5: "blue" + } + + # 面顺序和索引范围 + FACE_ORDER = { + 'U': list(range(0, 9)), # 顶面 + 'R': list(range(9, 18)), # 右面 + 'F': list(range(18, 27)), # 前面 + 'D': list(range(27, 36)), # 底面 + 'L': list(range(36, 45)), # 左面 + 'B': list(range(45, 54)) # 后面 + } + + # 将初始状态转换为generate_rubiks_html需要的格式 + initial_state_dict = {} + for face, indices in FACE_ORDER.items(): + initial_state_dict[face] = [COLOR_MAP[initial_state[i]] for i in indices] + + # 生成每一步的状态 + moves = [] + for state in solution_path: + move_state = {} + for face, indices in FACE_ORDER.items(): + move_state[face] = [COLOR_MAP[state[i]] for i in indices] + moves.append(move_state) + + # 调用generate_rubiks_html生成网页 + output_file = "rubiks_solution.html" + generate_rubiks_html(initial_state_dict, FACE_ORDER, moves, output_file) + print(f"已生成解决方案网页: {output_file}") + + +def main(): + # 加载配置 + config = Config() + args = config.parse_args() + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # 加载模型 + model = load_model(args.model_path, device=device) + + # 创建Cube对象 + cube = Cube() + + # 初始状态为目标状态 + initial_state = TARGET_STATE.copy() + state_path = [] + + if args.actions: + # 使用用户指定的动作序列 + print(f"使用指定的动作序列: {args.actions}") + shuffle_actions = args.actions.split() + # 验证动作是否有效 + valid_actions = set(cube.moves.keys()) + invalid_actions = [action for action in shuffle_actions if action not in valid_actions] + if invalid_actions: + raise ValueError(f"无效的动作: {invalid_actions}，有效的动作是: {valid_actions}") + else: + # 随机生成动作序列 + print("随机打乱魔方100次...") + all_actions = list(cube.moves.keys()) + shuffle_actions = np.random.choice(all_actions, size=100, replace=True) + + # 应用动作序列 + for action in shuffle_actions: + initial_state = cube.apply_action(initial_state, action) + state_path.append(initial_state.copy()) + + print("开始A*搜索...") + + # 执行A*搜索 + start_time = time.time() + action_path, solution_state_path = a_star_search(initial_state, model, cube) + end_time = time.time() + solving_time = end_time - start_time + + # 保存为可视化结果 + if solution_state_path: + # 合并打乱路径和解决方案路径以展示完整过程 + # full_state_path = state_path # + solution_state_path[1:] + generate_html(initial_state.copy(), solution_state_path) + # generate_html(initial_state, solution_state_path[1:]) + print(f"找到解决方案，路径长度: {len(solution_state_path)}") + print(f"求解时间: {solving_time:.4f} 秒") # 打印求解时间 + print("打乱路径:", shuffle_actions) + if action_path: + print("解决方案路径:", action_path) + else: + print("未找到解决方案") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/model/Cube.py b/model/Cube.py new file mode 100644 index 0000000000000000000000000000000000000000..8369adc23a00f19c05328ab3c122105ca92673f3 --- /dev/null +++ b/model/Cube.py @@ -0,0 +1,179 @@ +import numpy as np + +# Target State +# 用整数表示颜色（0=白, 1=红, 2=绿, 3=黄, 4=橙, 5=蓝） +TARGET_STATE = np.array([ + 0,0,0, 0,0,0, 0,0,0, # U 面 + 1,1,1, 1,1,1, 1,1,1, # R 面 + 2,2,2, 2,2,2, 2,2,2, # F 面 + 3,3,3, 3,3,3, 3,3,3, # D 面 + 4,4,4, 4,4,4, 4,4,4, # L 面 + 5,5,5, 5,5,5, 5,5,5 # B 面 +], dtype=np.int32) + +TARGET_STATE_ONE_HOT = np.eye(6)[TARGET_STATE] + +# Function to print state as a cube unfolded diagram +def print_cube_state(state, title=None): + print(title) + # U face + print(" " * 6 + " ".join(map(str, state[0:3]))) + print(" " * 6 + " ".join(map(str, state[3:6]))) + print(" " * 6 + " ".join(map(str, state[6:9]))) + # L, F, R, B faces + for i in range(3): + print(" ".join(map(str, state[36+i*3:39+i*3])) + " " + + " ".join(map(str, state[18+i*3:21+i*3])) + " " + + " ".join(map(str, state[9+i*3:12+i*3])) + " " + + " ".join(map(str, state[51+i*3:48+i*3]))) + # D face + print(" " * 6 + " ".join(map(str, state[27:30]))) + print(" " * 6 + " ".join(map(str, state[30:33]))) + print(" " * 6 + " ".join(map(str, state[33:36]))) + print() + +def invert_mapping(mapping): + """生成逆映射""" + inv = np.empty(len(mapping), dtype=int) + inv[mapping] = np.arange(len(mapping)) + return inv + +def make_moves(): + """ + 生成 3x3x3 魔方的贴纸索引映射（逆时针 + 顺时针） + 贴纸编号顺序： + U: 0-8, R: 9-17, F: 18-26, + D: 27-35, L: 36-44, B: 51-47 + """ + + moves = {} + + def cycle(mapping, positions): + """按循环位置更新映射""" + temp = mapping.copy() + for cycle_pos in positions: + cycle = np.append(cycle_pos, cycle_pos[0]) + mapping[cycle[:-1]] = temp[cycle[1:]] + + # 初始化基础状态（映射到自身） + identity = np.arange(54) + + # 定义每个面的顺时针旋转 + face_cycles = { + 'U': [ + # 上面自身旋转 + [2, 8, 6, 0], [5, 7, 3, 1], + # 侧面环 + [20, 9, 53, 36], [19, 10, 52, 37], [18, 11, 51, 38] + ], + 'D': [ + [29, 35, 33, 27], [28, 32, 34, 30], + [24, 44, 45, 17], [25, 43, 46, 16], [26, 42, 47, 15] + ], + 'F': [ + [18, 24, 26, 20], [19, 21, 25, 23], + [17, 2, 36, 33], [14, 1, 39, 34], [11, 0, 42, 35] + ], + 'B': [ + [51, 45, 47, 53], [52, 48, 46, 50], + [9, 29, 44, 6], [12, 28, 41, 7], [15, 27, 38, 8] + ], + 'L': [ + [36, 38, 44, 42], [37, 41, 43, 39], + [33, 18, 6, 47], [30, 21, 3, 50], [27, 24, 0, 53] + ], + 'R': [ + [17, 15, 9, 11], [16, 12, 10, 14], + [45, 8, 20, 35], [48, 5, 23, 32], [51, 2, 26, 29] + ] + } + + # 生成顺时针和逆时针映射 + for face, cycles in face_cycles.items(): + mapping = identity.copy() + cycle(mapping, cycles) + moves[face] = mapping + moves[face + "_inv"] = invert_mapping(mapping) + + return moves + +class Cube: + def __init__(self): + # 初始化移动映射 + self.moves = make_moves() + + def apply_action(self, state, action): + """ + 根据输入的action和state得到新的state + 参数: + state: 当前魔方状态，np.array + action: 要执行的动作，如 'U', 'R_inv', 等 + 返回: + 新的魔方状态 + """ + if action not in self.moves.keys(): + raise ValueError(f'不支持的动作: {action}') + return state[self.moves[action]] + + def get_neibor_state(self, state): + """ + 获取当前state的所有邻居状态 + 参数: + state: 当前魔方状态，np.array + 返回: + 所有邻居状态的列表，np.array + """ + neibor_states = [] + for action in self.moves.keys(): + neibor_states.append(self.apply_action(state, action)) + return np.stack(neibor_states, axis=0) + + def is_solved(self, state): + """ + 判断当前state是否是魔方被还原后的state + 参数: + state: 当前魔方状态，np.array + 返回: + 是否还原的布尔值 + """ + return np.array_equal(state, TARGET_STATE) + + def view_state(self, state): + pass + + + +if __name__ == "__main__": + # Initialize the Cube object + cube = Cube() + + # Get the initial solved state + initial_state = np.arange(54) + + # Define the rotation action + action = 'F' + # Print the initial state + print_cube_state(initial_state, "Initial Cube State:") + + # Print the applied action + print(f"Applied action: {action}") + + # Apply the rotation action + new_state = cube.apply_action(initial_state, action) + + # Print the new state + print_cube_state(new_state, "Cube State after Rotation:") + + action = 'U' + + new_state = cube.apply_action(new_state, action) + + # Print the applied action + print(f"Applied action: {action}") + print_cube_state(new_state, "Cube State after Rotation:") + + + + + + diff --git a/model/DNN.py b/model/DNN.py new file mode 100644 index 0000000000000000000000000000000000000000..cff3ac932b662f5acf7122ca70da12649f6c9634 --- /dev/null +++ b/model/DNN.py @@ -0,0 +1,65 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.Cube import TARGET_STATE_ONE_HOT + +class ResidualBlock(nn.Module): + def __init__(self, input_dim, hidden_dim): + super(ResidualBlock, self).__init__() + self.fc1 = nn.Linear(input_dim, hidden_dim) + self.bn1 = nn.BatchNorm1d(hidden_dim) + self.fc2 = nn.Linear(hidden_dim, hidden_dim) + self.bn2 = nn.BatchNorm1d(hidden_dim) + + def forward(self, x): + residual = x + out = F.relu(self.bn1(self.fc1(x))) + out = self.bn2(self.fc2(out)) + out += residual + out = F.relu(out) + return out + +class DNN(nn.Module): + def __init__(self, input_dim, num_residual_blocks=4): + super(DNN, self).__init__() + + # 前两个隐藏层 + self.fc1 = nn.Linear(input_dim, 5000) + self.bn1 = nn.BatchNorm1d(5000) + self.fc2 = nn.Linear(5000, 1000) + self.bn2 = nn.BatchNorm1d(1000) + + # 残差块 + self.residual_blocks = nn.ModuleList() + for _ in range(num_residual_blocks): + self.residual_blocks.append(ResidualBlock(1000, 1000)) + + # 输出层 + self.output_layer = nn.Linear(1000, 1) + + def forward(self, x): + # 前两个隐藏层 + x = F.relu(self.bn1(self.fc1(x))) + x = F.relu(self.bn2(self.fc2(x))) + + # 残差块 + for block in self.residual_blocks: + x = block(x) + + # 输出层 + x = self.output_layer(x) + + return x # * self.K + +# 示例用法 +if __name__ == '__main__': + # 假设输入维度为54*6=324（根据Readme中提到的魔方状态表示） + input_dim = 324 + model = DNN(input_dim, num_residual_blocks=4) + print(model) + + # 测试前向传播 + test_input = torch.randn(10, input_dim) + output = model(test_input) + print(f'Input shape: {test_input.shape}') + print(f'Output shape: {output.shape}') \ No newline at end of file diff --git a/model/DeepcubeA_module.py b/model/DeepcubeA_module.py new file mode 100644 index 0000000000000000000000000000000000000000..151ed741e76aa58bd8597248d6f6d131e016e81d --- /dev/null +++ b/model/DeepcubeA_module.py @@ -0,0 +1,186 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from pytorch_lightning import LightningModule +from model.DNN import DNN +from model.Cube import TARGET_STATE_ONE_HOT + + +class RelativeMSELoss(nn.Module): + def forward(self, pred, target): + return torch.mean(((pred - target) / (target + 1e-8)) ** 2) + +# 判断A中是否有和b相同的张量 +def row_allclose_mask(A, b, rtol=1e-4, atol=1e-6): + # 计算逐元素误差 + diff = torch.abs(A - b) # (B, D) + tol = atol + rtol * torch.abs(b) # (D,), 广播自动扩展到 (B, D) + + # 满足误差条件的元素掩码 + mask_elements = diff <= tol # (B, D), bool + + # 判断每行是否所有元素都满足条件 + mask_rows = mask_elements.all(dim=1) # (B,) + + return mask_rows + + +class DeepcubeA(LightningModule): + def __init__(self, config): + super().__init__() + self.config = config + self.learning_rate = config.learning_rate + self.weight_decay = config.weight_decay + self.convergence_threshold = config.convergence_threshold + self.chunk_size = config.chunk_size + self.converged_checkpoint_dir = config.converged_checkpoint_dir + self.compile = config.compile + + # 输入维度（54个贴纸，每个有6种可能的颜色，使用one-hot编码） + self.input_dim = 54 * 6 + + self.model_theta = DNN(self.input_dim, num_residual_blocks=4) # 训练模型 + self.model_theta_e = DNN(self.input_dim, num_residual_blocks=4).eval() # 监督模型 + + self.target_state = torch.tensor(TARGET_STATE_ONE_HOT, dtype=torch.float32).reshape(1, -1) + + if self.compile: + self.model_theta = torch.compile(self.model_theta) + self.model_theta_e = torch.compile(self.model_theta_e) + + self.K = 1 + + # 损失函数 + self.criterion = nn.MSELoss() + + # 保存超参数 + self.save_hyperparameters(config) + + def transfer_batch_to_tensor(self, batch): + """ + 批量将batch中的数据转移到tensor并移动到正确的设备上 + 参数: + batch: 输入的batch数据 + 返回: + 处理后的batch字典，包含tensor格式的数据 + """ + batch_dict = {} + for key, value in batch.items(): + if isinstance(value, torch.Tensor): + batch_dict[key] = value.to(self.device) + else: + batch_dict[key] = torch.tensor(value, device=self.device) + return batch_dict + + def forward(self, x): + return self.model_theta(x) + + def model_step(self, batch): + # 从batch中获取状态和邻居 + batch_dict = self.transfer_batch_to_tensor(batch) + states = batch_dict['state'] + neighbor_states = batch_dict['neighbors'] + + B, N, D = neighbor_states.shape + + states = F.one_hot(states.long(), num_classes=6).float().view(B, -1) + neighbor_states = F.one_hot(neighbor_states.long(), num_classes=6).float().view(B*N, -1) + + # 分块预测以避免显存不足 + num_chunks = (B * N + self.chunk_size - 1) // self.chunk_size + chunked_neighbors = torch.chunk(neighbor_states, num_chunks, dim=0) + + with torch.no_grad(): + neighbor_costs = [] + for chunk in chunked_neighbors: + mask = row_allclose_mask(chunk, self.target_state.to(chunk.device)) + cost = self.model_theta_e(chunk) + cost[mask] = 0.0 + neighbor_costs.append(cost) + + # 聚合结果 + neighbor_costs = torch.cat(neighbor_costs, dim=0) + neighbor_costs = neighbor_costs.view(B, N) + + # 计算min[J_theta_e(A(x_i, a)) + 1] + min_neighbor_cost = neighbor_costs.abs().min(dim=1)[0] + 1 + + # 使用model_theta预测当前状态的cost + current_cost = self.model_theta(states) + + # 总是计算损失 + loss = self.criterion(current_cost.squeeze(), min_neighbor_cost) + return loss, current_cost + + def training_step(self, batch, batch_idx): + # 调用model_step获取损失 + loss, _ = self.model_step(batch) + + # 记录指标 + self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True) + + return loss + + def on_validation_epoch_end(self): + # 获取验证损失 + val_loss = self.trainer.callback_metrics.get('val_loss') + + if val_loss is not None and val_loss < self.convergence_threshold: + self.log('converged', True) + + # 保存模型参数到专门的收敛模型目录 + import os + os.makedirs(self.converged_checkpoint_dir, exist_ok=True) + checkpoint_path = os.path.join(self.converged_checkpoint_dir, f"converged_model_K_{self.K}.pth") + torch.save(self.model_theta.state_dict(), checkpoint_path) + print(f'模型已保存到 {checkpoint_path}') + + # 如果收敛，更新model_theta_e + self.model_theta_e.load_state_dict(self.model_theta.state_dict()) + + # 原文中没有找到上一轮训练的模型下一轮是否要继承参数，这里选择完全继承上一轮的参数，因为从头训练开销太大 + # self.model_theta = DNN(self.input_dim, num_residual_blocks=4) + # if self.compile: + # self.model_theta = torch.compile(self.model_theta) + + # 停止训练 + self.trainer.should_stop = True + + def on_train_end(self): + # 检查训练是否正常结束（非early stopping） + # 只有当训练不是因为converged而停止时，才执行保存操作 + if not self.trainer.callback_metrics.get('converged', False): + # 获取最后一个epoch的验证损失 + val_loss = self.trainer.callback_metrics.get('val_loss') + + # 保存模型参数到专门的收敛模型目录 + import os + os.makedirs(self.converged_checkpoint_dir, exist_ok=True) + checkpoint_path = os.path.join(self.converged_checkpoint_dir, f"final_model_K_{self.K}.pth") + torch.save(self.model_theta.state_dict(), checkpoint_path) + print(f'训练结束，模型已保存到 {checkpoint_path}') + + # 更新model_theta_e + self.model_theta_e.load_state_dict(self.model_theta.state_dict()) + + def validation_step(self, batch, batch_idx): + # 计算验证损失 + loss, current_cost = self.model_step(batch) + self.log('val_loss', loss, on_epoch=True, prog_bar=True) + self.log('val_cost', current_cost.mean(), on_epoch=True) + return loss + + def configure_optimizers(self): + optimizer = optim.AdamW( + self.model_theta.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay + ) + + return {'optimizer': optimizer} + + def load_state_dict_theta_e(self, checkpoint_path): + state_dict = torch.load(checkpoint_path) + self.model_theta_e.load_state_dict(state_dict) + self.model_theta_e.zero_output = False \ No newline at end of file diff --git a/model/__pycache__/Cube.cpython-310.pyc b/model/__pycache__/Cube.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e1af5102fc38e2e4655f314e37f004229a569ab3 Binary files /dev/null and b/model/__pycache__/Cube.cpython-310.pyc differ diff --git a/model/__pycache__/DNN.cpython-310.pyc b/model/__pycache__/DNN.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cdeb792ec44321eae9ddcc36a65be809ded8e20a Binary files /dev/null and b/model/__pycache__/DNN.cpython-310.pyc differ diff --git a/model/__pycache__/DeepcubeA_module.cpython-310.pyc b/model/__pycache__/DeepcubeA_module.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..de644c6b3157ddf5e9c4f9a73f3150960176c6f0 Binary files /dev/null and b/model/__pycache__/DeepcubeA_module.cpython-310.pyc differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..71fa003cbae1a58665386779c32ee307b03bb236 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +pytorch-lightning==2.5.1 +numpy==1.26.4 +matplotlib==3.10.3 +flask==3.1.1 + + + diff --git a/solver_utils.py b/solver_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..57cce5341eb97eced1e328db1c5d8833c1337b55 --- /dev/null +++ b/solver_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import heapq +import os +from model.Cube import TARGET_STATE +from model.DNN import DNN + +# 设置矩阵乘法精度，与train.py保持一致 +torch.set_float32_matmul_precision('medium') + + +def load_model(model_path, device): + """ + 加载预训练模型 + + 参数: + model_path: 模型权重文件路径 + device: 运行设备 (cpu 或 cuda) + use_half_precision: 是否使用半精度浮点数进行推理 + """ + input_dim = 54 * 6 # 魔方54个贴纸，每个6种颜色，使用one-hot编码 + model = DNN(input_dim, num_residual_blocks=4) + + try: + checkpoint = torch.load(model_path, map_location=device, weights_only=True) + # 处理模型权重，移除_orig_mod.前缀 + model_weights = {k.replace('_orig_mod.', ''): v for k, v in checkpoint.items() if k.startswith('_orig_mod.')} + if not model_weights: + model_weights = checkpoint + model.load_state_dict(model_weights) + except Exception as e: + print(f"加载模型失败: {str(e)}") + raise + + model = model.to(device) + + # 启用推理模式 + model.eval() + + # 可选：使用TorchScript优化模型执行 + try: + # 准备一个示例输入以进行追踪 + example_input = torch.randn(1, input_dim).to(device) + model = torch.jit.trace(model, example_input) + print("已使用TorchScript优化模型") + except Exception as e: + print(f"TorchScript优化失败: {str(e)}") + pass + + return model + + +def state_to_one_hot(state): + """ + 将魔方状态转换为one-hot编码 + """ + one_hot = np.zeros(54 * 6) + for i, color in enumerate(state): + one_hot[i * 6 + color] = 1 + return one_hot + + +def h(state, model): + """ + 启发函数，使用模型预测当前状态到目标状态的距离 + """ + with torch.no_grad(): + if len(state.shape) == 2: + prediction = model(state) + return prediction.squeeze().tolist() + else: + state = state.unsqueeze(0) + prediction = model(state) + return prediction.item() + + +def a_star_search(initial_state, model, cube, lam=0.6, max_iterations=200, N=1000): + """ + A*搜索算法求解魔方 + """ + # 检查初始状态是否为目标状态 + if np.array_equal(initial_state, TARGET_STATE): + return [], [initial_state] + + open_set = [] + closed_set = set() + + initial_state_tensor = torch.tensor(initial_state, device=next(model.parameters()).device).long() + initial_state_tensor = torch.nn.functional.one_hot(initial_state_tensor, num_classes=6).float().view(-1) + g_score = {tuple(initial_state): 0} + h_score = {tuple(initial_state): h(initial_state_tensor, model)} + f_score = {tuple(initial_state): lam * g_score[tuple(initial_state)] + h_score[tuple(initial_state)]} + + heapq.heappush(open_set, (f_score[tuple(initial_state)], tuple(initial_state))) + + came_from = {} + iterations = 0 + + while open_set and iterations < max_iterations: + iterations += 1 + #print(f"当前迭代: {iterations}, 开放集大小: {len(open_set)}") + + current_states = [] + for _ in range(min(N, len(open_set))): + _, state_tuple = heapq.heappop(open_set) + current_states.append(state_tuple) + + # 收集所有邻居状态 + neighbor_states = [] + + for current_state_tuple in current_states: + current_state = np.array(current_state_tuple) + + if np.array_equal(current_state, TARGET_STATE): + action_path = [] + state_path = [current_state] + while current_state_tuple in came_from: + current_state_tuple, action = came_from[current_state_tuple] + action_path.append(action) + state_path.append(current_state_tuple) + return action_path[::-1], state_path[::-1] + + if current_state_tuple in closed_set: + continue + + closed_set.add(current_state_tuple) + + for action in cube.moves.keys(): + next_state = cube.apply_action(current_state, action) + next_state_tuple = tuple(next_state) + + tentative_g_score = g_score[current_state_tuple] + 1 + + if next_state_tuple in closed_set: + if tentative_g_score < g_score.get(next_state_tuple, float('inf')): + closed_set.remove(next_state_tuple) + else: + continue + + if next_state_tuple not in g_score or tentative_g_score < g_score[next_state_tuple]: + came_from[next_state_tuple] = (current_state_tuple, action) + g_score[next_state_tuple] = tentative_g_score + neighbor_states.append(next_state) + + if neighbor_states: + neighbor_states = np.stack(neighbor_states) + neighbor_states = np.unique(neighbor_states, axis=0) + neighbor_states_tensor = torch.tensor(neighbor_states, device=next(model.parameters()).device).long() + neighbor_states_tensor = torch.nn.functional.one_hot(neighbor_states_tensor, num_classes=6).float().view(-1, 324) + + neighbor_h_scores = h(neighbor_states_tensor, model) + for i, state in enumerate(neighbor_states): + state_tuple = tuple(state) + h_score[state_tuple] = neighbor_h_scores[i] + f_score[state_tuple] = lam * g_score[state_tuple] + neighbor_h_scores[i] + heapq.heappush(open_set, (f_score[state_tuple], state_tuple)) + + return None, None # 未找到解决方案 \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..53e959070b12845a9949b275e5ef5d97b9548616 --- /dev/null +++ b/train.py @@ -0,0 +1,115 @@ +import os +import torch +import random +import numpy as np +from pytorch_lightning import Trainer, seed_everything +from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor, model_checkpoint +from pytorch_lightning.loggers import TensorBoardLogger +from config import Config +from dataset.dataloader import RubikDataModule +from model.DeepcubeA_module import DeepcubeA +import datetime + +torch.set_float32_matmul_precision('medium') + +def main(): + # 解析配置 + config = Config() + args = config.parse_args() + + # 设置随机种子 + seed_everything(args.seed, workers=True) + + args.log_dir = os.path.join(args.log_dir, datetime.datetime.now().strftime("%Y%m%d_%H%M")) + args.checkpoint_dir = os.path.join(args.log_dir, args.checkpoint_dir) + args.converged_checkpoint_dir = os.path.join(args.log_dir, args.converged_checkpoint_dir) + + # 设置 accelerator & devices + if args.devices.lower() == "cpu": + accelerator = "cpu" + devices = 1 # CPU 默认只用一个进程 + elif args.devices.lower() == "auto": + accelerator = "gpu" if torch.cuda.is_available() else "cpu" + devices = "auto" + else: + # 用户指定了 GPU id(s) + accelerator = "gpu" + if "," in args.devices: + devices = [int(x) for x in args.devices.split(",")] + else: + devices = [int(args.devices)] + + # 创建必要的目录 + os.makedirs(args.log_dir, exist_ok=True) + os.makedirs(args.checkpoint_dir, exist_ok=True) + os.makedirs(args.converged_checkpoint_dir, exist_ok=True) + + # 初始化模型（只初始化一次，后续复用） + model = DeepcubeA(args) + + # 设置初始K值和最大K值 + initial_K = 16 + max_K = args.K # 可以根据需要调整 + + model_e_checkpoint = "logs/20250818_1819/converged_checkpoints/final_model_K_14.pth" + model.model_theta_e.load_state_dict(torch.load(model_e_checkpoint)) + model_checkpoint = "logs/20250818_1819/converged_checkpoints/final_model_K_15.pth" + model.model_theta.load_state_dict(torch.load(model_checkpoint)) + + for K in range(initial_K, max_K + 1): + print(f'\n--- 开始训练 K={K} ---') + + # 更新模型的K值 + model.K = K + + # 创建新的数据集配置 + args.K = K # 设置当前K值 + + # 初始化新的数据模块 + data_module = RubikDataModule(args) + + # # 设置回调函数，暂时不添加这个，因为好像没什么用 + # checkpoint_callback = ModelCheckpoint( + # dirpath=args.checkpoint_dir, + # filename=f'K_{K}_'+'{epoch}-{val_loss:.2f}', + # save_top_k=3, + # monitor='val_loss', + # mode='min' + # ) + + early_stopping_callback = EarlyStopping( + monitor='val_loss', + patience=5, + mode='min', + ) + + # lr_monitor = LearningRateMonitor(logging_interval='epoch') + + # # 设置日志记录器（每个K值使用不同的日志目录） + # logger = TensorBoardLogger( + # save_dir=args.log_dir, + # name=f'train_logs_K_{K}' + # ) + + # 初始化新的训练器，默认每个epoch验证一次，即5000步 + trainer = Trainer( + max_epochs=args.max_epochs, + accelerator=accelerator, + precision="16-mixed", # 启用混合精度 + devices=devices, + logger=False, + callbacks=[early_stopping_callback], + deterministic=True, + enable_progress_bar=True, + enable_checkpointing=True + ) + + print(trainer.log_every_n_steps) + + # 训练模型 + trainer.fit(model, datamodule=data_module) + + print(f'--- 完成训练 K={K} ---\n') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/vis.py b/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..37036713c283b62590f35b24cd99253f11fa65a2 --- /dev/null +++ b/vis.py @@ -0,0 +1,550 @@ +# rubiks_color_change.py +# 运行: python rubiks_color_change.py +# 会生成 rubiks_color_change.html + +import json +import model.Cube as Cube + +def generate_rubiks_html(initial_state, indince, moves, output_file="rubiks_move.html"): + """ + initial_state: dict, {face_name: [9个颜色字符串]} + face_name: U, D, F, B, L, R + 颜色字符串可用 "white","yellow","red","orange","blue","green" + moves: list, 每步是一个新的状态 (即9个颜色变化后的完整魔方) + """ + + html_template = f""" + + + + +Rubik's Cube Color Change Animation + + + + + + + + +""" + with open(output_file, "w", encoding="utf-8") as f: + f.write(html_template) + print(f"已生成 {output_file} ，用浏览器打开即可。") + + +# ===== 测试示例 ===== +if __name__ == "__main__": + initial_state = { + "U": ["white"] * 9, + "D": ["yellow"] * 9, + "F": ["red"] * 9, + "B": ["orange"] * 9, + "L": ["blue"] * 9, + "R": ["green"] * 9 + } + + # 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+.sticker { + width: 33.3%; + height: 33.3%; + background-color: #000000; + border: 2px solid black; + float: left; + + display: flex; + align-items: center; + justify-content: center; + font-size: 14px; + color: white; +} + +#cube .front { + transform: rotateY( 0deg) translateZ(100px); +} + +#cube .back { + transform: rotateX( 180deg) rotateY(180deg) translateZ(-100px); +} + +#cube .right { + transform: rotateY( -90deg) translateZ(-100px); +} + +#cube .left { + transform: rotateY( 90deg) translateZ(-100px); +} + +#cube .top { + transform: rotateX( 90deg) translateZ(100px); +} + +#cube .bottom { + transform: rotateX( -90deg) translateZ(100px); +} + +#cube { + /*transform: translateZ( -100px);*/ +} + +button { + border: 1px solid #0066cc; + background-color: #0099cc; + color: #ffffff; + padding: 5px 10px; +} + +button:hover { + border: 1px solid #0099cc; + background-color: #00aacc; + color: #ffffff; + padding: 5px 10px; +} + +button:disabled, +button[disabled]{ + border: 1px solid #999999; + background-color: #cccccc; + color: #666666; +} diff --git a/web/deepcube.igb.uci.edu/static/main.js b/web/deepcube.igb.uci.edu/static/main.js new file mode 100644 index 0000000000000000000000000000000000000000..522a3d9c759e8e93e58b6155e9419e73363865df --- /dev/null +++ b/web/deepcube.igb.uci.edu/static/main.js @@ -0,0 +1,429 @@ +var state = []; +var rotateIdxs_old = null; +var rotateIdxs_new = null; +var stateToFE = null; +var FEToState = null; +var legalMoves = null; + +var solveStartState = []; +var solveMoves = []; +var solveMoves_rev = []; +var solveIdx = null; +var solution_text = null; + +// var faceNames = ["top", "bottom", "left", "right", "back", "front"]; +var faceNames = ["top", "right", "front", "bottom", "left", "back"]; +var colorMap = { + 0: "#ffffff", // 白色 + 1: "#ff0000", // 红色 + 2: "#00cc00", // 绿色 + 3: "#ffff00", // 黄色 + 4: "#ff9900", // 橙色 + 5: "#0000ff" // 蓝色 +}; +var lastMouseX = 0, + lastMouseY = 0; +var rotX = -30, + rotY = -30; + +var moves = [] + +var initState = [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 2, + 3, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 4, + 5, 5, 5, 5, 5, 5, 5, 5, 5 +]; + +// 定义 idx 对换映射表 +const idxSwapMap = { + 6: 0, + 0: 6, + 7: 1, + 1: 7, + 8: 2, + 2: 8, + 27: 33, + 33: 27, + 28: 34, + 34: 28, + 29: 35, + 35: 29 +}; + +function mapIndex(idx) { + return (idx in idxSwapMap) ? idxSwapMap[idx] : idx; +} + +function reOrderArray(arr,indecies) { + var temp = [] + for(var i = 0; i < indecies.length; i++) { + var index = indecies[i] + temp.push(arr[index]) + } + + return temp; +} + +/* + Rand int between min (inclusive) and max (exclusive) +*/ +function randInt(min, max) { + return Math.floor(Math.random() * (max - min)) + min; +} + +function clearCube() { + for (i = 0; i < faceNames.length; i++) { + var myNode = document.getElementById(faceNames[i]); + while (myNode.firstChild) { + myNode.removeChild(myNode.firstChild); + } + } +} + +function restoreCube() { + setStickerColors(initState) +} + +// function setStickerColors(newState) { +// state = newState +// clearCube() +// idx = 0 +// for (i = 0; i < faceNames.length; i++) { +// for (j = 0; j < 9; j++) { +// var iDiv = document.createElement('div'); +// iDiv.className = 'sticker'; +// // 修正颜色索引获取方式 +// iDiv.style["background-color"] = colorMap[newState[idx]] +// document.getElementById(faceNames[i]).appendChild(iDiv); +// idx = idx + 1 +// } +// } +// } + +function setStickerColors(newState) { + state = newState; + clearCube(); + idx = 0; + for (i = 0; i < faceNames.length; i++) { + for (j = 0; j < 9; j++) { + var iDiv = document.createElement('div'); + iDiv.className = 'sticker'; + + swaped_idx = mapIndex(idx) + + // 设置颜色 + iDiv.style["background-color"] = colorMap[newState[swaped_idx]]; + + // 在 sticker 上显示数字（idx） + iDiv.textContent = swaped_idx; // 显示在小方块里面 + iDiv.style.color = "black"; // 文字颜色 + iDiv.style.fontSize = "14px"; // 字体大小 + iDiv.style.textAlign = "center"; // 居中 + iDiv.style.lineHeight = "33.3%"; // 垂直居中 + + document.getElementById(faceNames[i]).appendChild(iDiv); + + idx = idx + 1; + } + } +} + +function buttonPressed(ev) { + var face = '' + var direction = '' + + if (ev.shiftKey) { + direction = '_inv' + } + if (ev.which == 85 || ev.which == 117) { + face='U' + } else if (ev.which == 68 || ev.which == 100) { + face = 'D' + } else if (ev.which == 76 || ev.which == 108) { + face = 'L' + } else if (ev.which == 82 || ev.which == 114) { + face = 'R' + } else if (ev.which == 66 || ev.which == 98) { + face = 'B' + } else if (ev.which == 70 || ev.which == 102) { + face = 'F' + } + if (face != '') { + clearSoln(); + moves.push(face + direction); + nextState(); + } +} + + +function enableScroll() { + document.getElementById("first_state").disabled=false; + document.getElementById("prev_state").disabled=false; + document.getElementById("next_state").disabled=false; + document.getElementById("last_state").disabled=false; +} + +function disableScroll() { + document.getElementById("first_state").blur(); //so keyboard input can work without having to click away from disabled button + document.getElementById("prev_state").blur(); + document.getElementById("next_state").blur(); + document.getElementById("last_state").blur(); + + document.getElementById("first_state").disabled=true; + document.getElementById("prev_state").disabled=true; + document.getElementById("next_state").disabled=true; + document.getElementById("last_state").disabled=true; +} + +/* + Clears solution as well as disables scroll +*/ +function clearSoln() { + solveIdx = 0; + solveStartState = []; + solveMoves = []; + solveMoves_rev = []; + solution_text = null; + document.getElementById("solution_text").innerHTML = "Solution:"; + disableScroll(); +} + +function setSolnText(setColor=true) { + solution_text_mod = JSON.parse(JSON.stringify(solution_text)) + if (solveIdx >= 0) { + if (setColor == true) { + solution_text_mod[solveIdx] = solution_text_mod[solveIdx].bold().fontcolor("blue") + } else { + solution_text_mod[solveIdx] = solution_text_mod[solveIdx] + } + } + document.getElementById("solution_text").innerHTML = "Solution: "+ solution_text_mod.join(" "); +} + +function enableInput() { + document.getElementById("scramble").disabled=false; + document.getElementById("solve").disabled=false; + document.getElementById("clear").disabled=false; + $(document).on("keypress", buttonPressed); +} + +function disableInput() { + document.getElementById("scramble").disabled=true; + document.getElementById("solve").disabled=true; + $(document).off("keypress", buttonPressed); +} + +function nextState(moveTimeout=0) { + if (moves.length > 0) { + disableInput(); + disableScroll(); + move = moves.shift() // get Move + + // 添加安全检查 + if (!rotateIdxs_new || !rotateIdxs_new[move]) { + console.error('Invalid move or rotateIdxs_new not initialized:', move); + enableInput(); + return; + } + + //convert to python representation + state_rep = reOrderArray(state,FEToState) + newState_rep = JSON.parse(JSON.stringify(state_rep)) + + //swap stickers + for (var i = 0; i < rotateIdxs_new[move].length; i++) { + newState_rep[rotateIdxs_new[move][i]] = state_rep[rotateIdxs_old[move][i]] + } + + // Change move highlight + if (moveTimeout != 0){ //check if nextState is used for first_state click, prev_state,etc. + solveIdx++ + setSolnText(setColor=true) + } + + //convert back to HTML representation + newState = reOrderArray(newState_rep,stateToFE) + + //set new state + setStickerColors(newState) + + //Call again if there are more moves + if (moves.length > 0) { + setTimeout(function(){nextState(moveTimeout)}, moveTimeout); + } else { + enableInput(); + if (solveMoves.length > 0) { + enableScroll(); + setSolnText(); + } + } + } else { + enableInput(); + if (solveMoves.length > 0) { + enableScroll(); + setSolnText(); + } + } +} + +function scrambleCube() { + disableInput(); + clearSoln(); + + numMoves = randInt(100,200); + for (var i = 0; i < numMoves; i++) { + moves.push(legalMoves[randInt(0,legalMoves.length)]); + } + + nextState(0); +} + +function solveCube() { + disableInput(); + clearSoln(); + document.getElementById("solution_text").innerHTML = "SOLVING..." + $.ajax({ + url: '/solve', + data: JSON.stringify({"state": state}), + type: 'POST', + contentType: 'application/json', + dataType: 'json', + // timeout: 5000, + success: function(response) { + if (response.error) { + // 处理业务逻辑错误 + document.getElementById("solution_text").innerHTML = "Error: " + response.error; + enableInput(); + } else { + // 正常处理成功响应 + solveStartState = JSON.parse(JSON.stringify(state)) + solveMoves = response["moves"]; + solveMoves_rev = response["moves_rev"]; + solution_text = response["solve_text"]; + solution_text.push("SOLVED!") + setSolnText(true); + + moves = JSON.parse(JSON.stringify(solveMoves)) + + setTimeout(function(){nextState(500)}, 500); + } + }, + error: function(xhr, status, error) { + // 处理HTTP请求错误 + console.log("AJAX Error:", status, error); + var errorMessage = "请求失败，请重试"; + if (status === "timeout") { + errorMessage = "请求超时，请重试"; + } else if (xhr.status === 404) { + errorMessage = "未找到解决方案"; + } else if (xhr.status === 500) { + errorMessage = "服务器内部错误，请稍后再试"; + } else if (xhr.status === 400) { + errorMessage = "请求参数错误"; + } + document.getElementById("solution_text").innerHTML = errorMessage; + enableInput(); + } + }); +} + +$( document ).ready($(function() { + disableInput(); + clearSoln(); + $.ajax({ + url: '/initState', + data: {}, + type: 'POST', + dataType: 'json', + success: function(response) { + setStickerColors(response["state"]); + rotateIdxs_old = response["rotateIdxs_old"]; + rotateIdxs_new = response["rotateIdxs_new"]; + stateToFE = response["stateToFE"]; + FEToState = response["FEToState"]; + legalMoves = response["legalMoves"] + enableInput(); + }, + error: function(error) { + console.log(error); + }, + }); + + $("#cube").css("transform", "translateZ( -100px) rotateX( " + rotX + "deg) rotateY(" + rotY + "deg)"); //Initial orientation + + $('#scramble').click(function() { + scrambleCube() + }); + + $('#solve').click(function() { + solveCube() + }); + + $('#clear').click(function() { + restoreCube() + }) + + $('#first_state').click(function() { + if (solveIdx > 0) { + moves = solveMoves_rev.slice(0, solveIdx).reverse(); + solveIdx = 0; + nextState(); + } + }); + + $('#prev_state').click(function() { + if (solveIdx > 0) { + solveIdx = solveIdx - 1 + moves.push(solveMoves_rev[solveIdx]) + nextState() + } + }); + + $('#next_state').click(function() { + if (solveIdx < solveMoves.length) { + moves.push(solveMoves[solveIdx]) + solveIdx = solveIdx + 1 + nextState() + } + }); + + $('#last_state').click(function() { + if (solveIdx < solveMoves.length) { + moves = solveMoves.slice(solveIdx, solveMoves.length); + solveIdx = solveMoves.length + nextState(); + } + }); + + $('#cube_div').on("mousedown", function(ev) { + lastMouseX = ev.clientX; + lastMouseY = ev.clientY; + $('#cube_div').on("mousemove", mouseMoved); + }); + $('#cube_div').on("mouseup", function() { + $('#cube_div').off("mousemove", mouseMoved); + }); + $('#cube_div').on("mouseleave", function() { + $('#cube_div').off("mousemove", mouseMoved); + }); + + console.log( "ready!" ); +})); + + +function mouseMoved(ev) { + var deltaX = ev.pageX - lastMouseX; + var deltaY = ev.pageY - lastMouseY; + + lastMouseX = ev.pageX; + lastMouseY = ev.pageY; + + rotY += deltaX * 0.2; + rotX -= deltaY * 0.5; + + $("#cube").css("transform", "translateZ( -100px) rotateX( " + rotX + "deg) rotateY(" + rotY + "deg)"); +} +