import os import subprocess import sys import json import logging # 获取当前脚本所在目录的父目录（项目根目录） script_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(script_dir) # 配置日志 - 使用相对路径 log_file = os.path.join(project_root, 'environment_setup.log') logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.StreamHandler(), logging.FileHandler(log_file) ] ) def run_command(command, description=None, show_output=False): """运行命令并实时打印输出""" if description: logging.info(f"=== {description} ===") logging.info(f"执行: {command}") try: # 创建进程并实时获取输出 process = subprocess.Popen( ['/bin/bash', '-c', command], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, # 将stderr重定向到stdout universal_newlines=True, bufsize=1 ) # 收集所有输出 all_output = [] # 实时读取并显示输出 for line in process.stdout: line = line.rstrip() all_output.append(line) print(line) # 直接打印到控制台 # 等待进程结束 process.wait() # 检查返回码 if process.returncode == 0: logging.info(f"命令执行成功: {command}") return True, '\n'.join(all_output) else: logging.error(f"命令执行失败: {command}") return False, '\n'.join(all_output) except Exception as e: logging.error(f"命令执行异常: {command}") logging.error(f"异常信息: {str(e)}") return False, str(e) def setup_environment(): """执行环境配置步骤""" # 确保当前工作目录是项目根目录 os.chdir(project_root) # 检查环境检查结果是否存在 results_file = os.path.join(project_root, 'environment_check_results.json') try: with open(results_file, 'r') as f: check_data = json.load(f) dependencies_met = check_data.get("dependencies_met", False) check_results = check_data.get("check_results", []) except (FileNotFoundError, json.JSONDecodeError): logging.warning("未找到环境检查结果，请先运行 check_environment.py") dependencies_met = False check_results = [] results = [] # 记录检查结果 results.append({ "step": "环境依赖检查", "success": dependencies_met, "output": "环境依赖检查已完成", "details": check_results }) if not dependencies_met: logging.warning("环境检查未通过，但将继续配置环境，可能会遇到兼容性问题。") # 获取conda路径（提前获取，避免变量引用错误） success, conda_path_output = run_command("conda info --base", "获取conda基础路径") if success: conda_path = conda_path_output.strip() logging.info(f"Conda 基础路径: {conda_path}") else: logging.error("无法获取conda路径，环境配置失败") results.append({"step": "获取conda路径", "success": False, "output": "无法获取conda基础路径"}) return { "dependencies_check": { "passed": dependencies_met, "details": check_results }, "installation": { "total_steps": 1, "success_steps": 0, "results": results[1:] } } # 步骤0: 安装Vulkan success, output = run_command( "sudo apt install -y libvulkan1 mesa-vulkan-drivers vulkan-tools", "安装Vulkan" ) results.append({"step": "安装Vulkan", "success": success, "output": output}) # 步骤1: 检查是否已存在RoboTwin环境，如果有则直接激活，否则创建新环境 check_env_cmd = "conda env list | grep -w RoboTwin || echo ''" success, env_check_output = run_command( check_env_cmd, "检查是否存在RoboTwin环境" ) # 更严格地验证环境是否存在 if success and env_check_output.strip() and "RoboTwin" in env_check_output: # 进一步验证 - 尝试列出环境中的包 validate_cmd = "conda activate RoboTwin && conda list || echo 'fail'" success_validate, validate_output = run_command( f"source {conda_path}/etc/profile.d/conda.sh && {validate_cmd}", "验证RoboTwin环境" ) if success_validate and "fail" not in validate_output: logging.info("RoboTwin环境验证成功，将直接激活") results.append({"step": "检查conda环境", "success": True, "output": "RoboTwin环境已存在，验证成功"}) else: # 环境存在但无法激活，可能已损坏，重新创建 logging.warning("RoboTwin环境存在但无法激活，将重新创建") remove_cmd = "conda env remove -n RoboTwin -y" run_command(remove_cmd, "移除损坏的RoboTwin环境") create_cmd = "conda create -n RoboTwin python=3.8 -y" success, output = run_command(create_cmd, "创建conda环境") results.append({"step": "创建conda环境", "success": success, "output": output}) else: # 创建新的conda环境 logging.info("RoboTwin环境不存在，将创建新环境") success, output = run_command( "conda create -n RoboTwin python=3.8 -y", "创建conda环境" ) results.append({"step": "创建conda环境", "success": success, "output": output}) # 定义激活命令（conda_path已经在前面获取了） activate_command = f"source {conda_path}/etc/profile.d/conda.sh && conda activate RoboTwin && " # 步骤2: 安装基本依赖 logging.info("开始安装基本依赖包...") pip_install_cmd = activate_command + "pip install torch==2.4.1 torchvision sapien==3.0.0b1 scipy==1.10.1 mplib==0.1.1 gymnasium==0.29.1 trimesh==4.4.3 open3d==0.18.0 imageio==2.34.2 pydantic zarr openai huggingface_hub==0.25.0" success, output = run_command( pip_install_cmd, "安装基本依赖", show_output=True ) results.append({"step": "安装基本依赖", "success": success, "output": output}) # 步骤3: 安装pytorch3d logging.info("开始安装pytorch3d...") pytorch3d_cmd = activate_command + "cd third_party/pytorch3d_simplified && pip install -e . && cd ../.." success, output = run_command( pytorch3d_cmd, "安装pytorch3d", show_output=True ) results.append({"step": "安装pytorch3d", "success": success, "output": output}) # 步骤4: 检查资源文件是否已存在，如果已存在则跳过下载 check_assets_cmd = "ls -la aloha_urdf/ main_models/ 2>/dev/null || echo '资源文件不存在'" success, assets_check = run_command( check_assets_cmd, "检查资源文件是否已存在" ) if success and "资源文件不存在" not in assets_check and "aloha_urdf" in assets_check and "main_models" in assets_check: logging.info("资源文件已存在，跳过下载步骤") results.append({"step": "下载资源", "success": True, "output": "资源文件已存在，跳过下载步骤"}) else: # 需要下载资源 logging.info("开始下载资源文件...") # 根据当前 git 分支决定解压哪个 zip 文件 branch_cmd = "$(git rev-parse --abbrev-ref HEAD)" unzip_cmd = f'if [ "{branch_cmd}" = "main" ]; then unzip -o aloha_urdf.zip && unzip -o main_models.zip; else unzip -o aloha_urdf.zip && unzip -o gpt_models.zip; fi' download_cmd = activate_command + f"python ./script/download_asset.py && {unzip_cmd}" success, output = run_command( download_cmd, "下载资源" ) results.append({"step": "下载资源", "success": success, "output": output}) # 步骤5: 修改 mplib 库代码 # 查找 mplib 位置 logging.info("开始修改 mplib 库代码...") mplib_locate_cmd = activate_command + "pip show mplib | grep Location | awk '{print $2}'" success, mplib_path = run_command(mplib_locate_cmd, "查找mplib位置") if success and mplib_path: mplib_path = mplib_path.strip() planner_path = os.path.join(mplib_path, "mplib", "planner.py") logging.info(f"mplib planner.py 路径: {planner_path}") if os.path.exists(planner_path): # 首先检查文件内容，看是否已经修改过 check_content_cmd = f"cat {planner_path}" success, file_content = run_command(check_content_cmd, "检查mplib代码内容", show_output=False) # 步骤5.1: 检查并移除 convex=True convex_exists = "convex=True" in file_content if convex_exists: logging.info("正在移除 convex=True 参数...") sed_convex_cmd = f"sed -i 's/convex=True,/# convex=True,/' {planner_path}" success, output = run_command( sed_convex_cmd, "修改mplib库代码 - 移除convex=True" ) results.append({"step": "修改mplib - 移除convex=True", "success": success, "output": output}) else: logging.info("mplib中的convex=True已经被移除，跳过此步骤") results.append({"step": "修改mplib - 移除convex=True", "success": True, "output": "已经被移除，无需修改"}) # 步骤5.2: 检查并移除 or collide collide_exists = "or collide or not within_joint_limit" in file_content if collide_exists: logging.info("正在移除 or collide 条件...") sed_collide_cmd = f"sed -i 's/or collide or not within_joint_limit/or not within_joint_limit/' {planner_path}" success, output = run_command( sed_collide_cmd, "修改mplib库代码 - 移除 or collide" ) results.append({"step": "修改mplib - 移除 or collide", "success": success, "output": output}) else: logging.info("mplib中的'or collide'已经被移除，跳过此步骤") results.append({"step": "修改mplib - 移除 or collide", "success": True, "output": "已经被移除，无需修改"}) else: logging.error(f"mplib planner.py 文件不存在: {planner_path}") results.append({"step": "查找mplib文件", "success": False, "output": f"文件不存在: {planner_path}"}) else: logging.error("无法找到mplib安装路径") results.append({"step": "查找mplib路径", "success": False, "output": "无法找到mplib安装路径"}) # 总结安装结果 logging.info("\n=== 环境配置总结 ===") success_count = sum(1 for result in results[1:] if result["success"]) # 不包括依赖检查 total_steps = len(results) - 1 # 不包括依赖检查 logging.info(f"总步骤: {total_steps}, 成功: {success_count}, 失败: {total_steps - success_count}") for result in results[1:]: # 不包括依赖检查 status = "✓" if result["success"] else "✗" logging.info(f"{status} {result['step']}") return { "dependencies_check": { "passed": dependencies_met, "details": check_results }, "installation": { "total_steps": total_steps, "success_steps": success_count, "results": results[1:] # 不包括依赖检查 } } if __name__ == "__main__": setup_environment()