# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from flask import Flask, request, render_template, send_from_directory, jsonify, session, send_file from werkzeug.utils import secure_filename from flask_cors import CORS import argparse from os import path as osp import os import time import json import sys import multiprocessing as mp from . import workspace_pb2 as w from .utils import CustomEncoder, ShareData, is_pic, get_logger, TaskStatus, get_ip from paddlex_restful.restful.dataset.utils import get_encoding import numpy as np import pickle app = Flask(__name__) CORS(app, supports_credentials=True) SESSION_TYPE = 'filesystem' app.config.from_object(__name__) SD = ShareData() def init(dirname, logger): #初始化工作空间 from .workspace import init_workspace from .system import get_system_info SD.workspace = w.Workspace(path=dirname) init_workspace(SD.workspace, dirname, logger) SD.workspace_dir = dirname get_system_info(SD.machine_info) @app.errorhandler(Exception) def handle_exception(e): ret = {"status": -1, 'message': repr(e)} return ret @app.route('/workspace', methods=['GET', 'PUT']) def workspace(): """ methods=='GET':获取工作目录中项目、数据集、任务的属性 Args： struct(str)：结构类型，可以是'dataset', 'project'或'task'， id(str)：结构类型对应的id attr_list(list):需要获取的属性的列表 Return： attr(dict):key为属性，value为属性的值， status methods=='PUT':修改工作目录中项目、数据集、任务的属性 Args: struct(str)：结构类型，可以是'dataset', 'project'或'task'， id(str)：结构类型对应的id attr_dict(dict):key:需要修改的属性，value：需要修改属性的值 Return: status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': if data: from .workspace import get_attr ret = get_attr(data, SD.workspace) return ret return {'status': 1, 'dirname': SD.workspace_dir} if request.method == 'PUT': from .workspace import set_attr ret = set_attr(data, SD.workspace) return ret @app.route('/dataset', methods=['GET', 'POST', 'PUT', 'DELETE']) def dataset(): """ methods=='GET':获取所有数据集或者单个数据集的信息 Args： did(str, optional):数据集id（可选），如果存在就返回数据集id对应数据集的信息 Ruturn： status if 'did' in Args： id(str):数据集id， dataset_status(int):数据集状态(DatasetStatus)枚举变量的值 message(str)：数据集状态信息 attr(dict):数据集属性 else: datasets(list):所有数据集属性的列表 methods=='POST':创建一个新的数据集 Args: name(str):数据集名字 desc(str):数据集描述 dataset_type(str):数据集类型，可以是['classification', 'detection', 'segmentation','instance_segmentation','remote_segmentation'] Return: did(str):数据集id status methods=='PUT':异步，向数据集导入数据，支持分类、检测、语义分割、实例分割、摇杆分割数据集类型 Args： did(str)：数据集id path(str)：数据集路径 Return: status methods=='DELETE':删除已有的某个数据集 Args: did(str)：数据集id Return: status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': if 'did' in data: from .dataset.dataset import get_dataset_status ret = get_dataset_status(data, SD.workspace) return ret from .dataset.dataset import list_datasets ret = list_datasets(SD.workspace) return ret if request.method == 'POST': from .dataset.dataset import create_dataset ret = create_dataset(data, SD.workspace) return ret if request.method == 'PUT': from .dataset.dataset import import_dataset ret = import_dataset(data, SD.workspace, SD.monitored_processes, SD.load_demo_proc_dict) return ret if request.method == 'DELETE': from .dataset.dataset import delete_dataset ret = delete_dataset(data, SD.workspace) return ret @app.route('/dataset/details', methods=['GET']) def dataset_details(): """ methods=='GET':获取某个数据集的详细信息 Args： did(str)：数据集id Return: details(dict)：数据集详细信息, status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .dataset.dataset import get_dataset_details ret = get_dataset_details(data, SD.workspace) return ret @app.route('/dataset/split', methods=['PUT']) def dataset_split(): """ Args： did(str)：数据集id val_split(float): 验证集比例 test_split(float): 测试集比例 Return: status """ data = request.get_json() if request.method == 'PUT': from .dataset.dataset import split_dataset ret = split_dataset(data, SD.workspace) return ret @app.route('/dataset/image', methods=['GET']) def dataset_img_base64(): """ Args: GET: 获取图片base64数据，参数：'path' 图片绝对路径 """ data = request.get_json() if request.method == 'GET': from .dataset.dataset import img_base64 ret = img_base64(data) return ret @app.route('/dataset/file', methods=['GET']) def get_image_file(): """ Args: GET: 获取文件数据，参数：'path' 文件绝对路径 """ data = request.get_json() if request.method == 'GET': ret = data['path'] assert os.path.abspath(ret).startswith( os.path.abspath(SD.workspace_dir) ) and ".." not in ret, "Illegal path {}.".format(ret) return send_file(ret) @app.route('/dataset/npy', methods=['GET']) def get_npyfile(): """ Args: GET: 获取文件数据，参数：'path' npy文件绝对路径 """ data = request.get_json() if request.method == 'GET': npy = np.load(data['path'], allow_pickle=True).tolist() npy['gt_bbox'] = npy['gt_bbox'].tolist() return npy @app.route('/file', methods=['GET']) def get_file(): """ Args： path'(str):文件在服务端的路径 Return: #数据为图片 img_data(str)： base64图片数据 status #数据为xml文件 ret:数据流 #数据为log文件 ret:json数据 """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': path = data['path'] if not os.path.exists(path): return {'status': -1} if is_pic(path): from .dataset.dataset import img_base64 ret = img_base64(data, SD.workspace) return ret file_type = path[(path.rfind('.') + 1):] if file_type in ['xml', 'npy', 'log']: return send_file(path) else: pass @app.route('/project', methods=['GET', 'POST', 'DELETE']) def project(): """ methods=='GET':获取指定项目id的信息 Args: 'id'(str, optional):项目id，可选，如果存在就返回项目id对应项目的信息 Return: status, if 'id' in Args： attr(dict):项目属性 else: projects(list):所有项目属性 methods=='POST':创建一个项目 Args: name(str): 项目名 desc(str)：项目描述 project_type(str)：项目类型 Return: pid(str):项目id status methods=='DELETE':删除一个项目，以及项目相关的task Args： pid(str)：项目id Return: status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .project.project import list_projects from .project.project import get_project if 'id' in data: ret = get_project(data, SD.workspace) return ret ret = list_projects(SD.workspace) return ret if request.method == 'POST': from .project.project import create_project ret = create_project(data, SD.workspace) return ret if request.method == 'DELETE': from .project.project import delete_project ret = delete_project(data, SD.workspace) return ret @app.route('/project/task', methods=['GET', 'POST', 'DELETE']) def task(): """ methods=='GET':#获取某个任务的信息或者所有任务的信息 Args： tid(str, optional)：任务id，可选，若存在即返回id对应任务的信息 resume(str, optional):获取是否可以恢复训练的状态，可选，需在存在tid的情况下才生效 pid(str, optional)：项目id，可选，若存在即返回该项目id下所有任务信息 Return: status if 'tid' in Args： task_status(int):任务状态(TaskStatus)枚举变量的值 message(str)：任务状态信息 type:任务类型包括{'classification', 'detection', 'segmentation', 'instance_segmentation'} resumable(bool):仅Args中存在resume时返回，任务训练是否可以恢复 max_saved_epochs(int):仅Args中存在resume时返回，当前训练模型保存的最大epoch else: tasks(list):所有任务属性 methods=='POST':#创建任务(训练或者裁剪) Args: pid(str)：项目id train(dict)：训练参数 desc(str, optional):任务描述，可选 parent_id(str, optional):可选，若存在即表示新建的任务为裁剪任务，parent_id的值为裁剪任务对应的训练任务id Return： tid(str):任务id status methods=='DELETE':#删除任务 Args: tid（str）:任务id Return: status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': if data: if 'pid' not in data: from .project.task import get_task_status ret = get_task_status(data, SD.workspace) return ret from .project.task import list_tasks ret = list_tasks(data, SD.workspace) return ret if request.method == 'POST': from .project.task import create_task ret = create_task(data, SD.workspace) return ret if request.method == 'DELETE': from .project.task import delete_task ret = delete_task(data, SD.workspace) return ret @app.route('/project/task/params', methods=['GET', 'POST']) def task_params(): """ methods=='GET':#获取任务id对应的参数，或者获取项目默认参数 Args: tid（str, optional）:获取任务对应的参数 pid(str，optional)：获取项目对应的默认参数 model_type(str，optional)：pid存在下有效，对应项目下获取指定模型的默认参数 gpu_list(list，optional):pid存在下有效，默认值为[0]，使用指定的gpu并获取相应的默认参数 Return: train(dict):训练或者裁剪的参数 status methods=='POST':#设置任务参数，将前端用户设置训练参数dict保存在后端的pkl文件中 Args: tid(str):任务id train(dict)：训练参数 Return: status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': if 'tid' in data: from .project.task import get_task_params ret = get_task_params(data, SD.workspace) ret['train'] = CustomEncoder().encode(ret['train']) ret['train'] = json.loads(ret['train']) return ret if 'pid' in data: from .project.task import get_default_params ret = get_default_params(data, SD.workspace, SD.machine_info) return ret if request.method == 'POST': from .project.task import set_task_params ret = set_task_params(data, SD.workspace) return ret @app.route('/project/task/metrics', methods=['GET']) def task_metrics(): """ methods=='GET':#获取日志数据 Args: tid(str):任务id type(str):可以获取日志的类型，[train,eval,sensitivities,prune]，包括训练，评估，敏感度与模型裁剪率关系图，裁剪的日志 Return: status if type == 'train': train_log(dict): 训练日志 elif type == 'eval': eval_metrics(dict): 评估结果 elif type == 'sensitivities': sensitivities_loss_img(dict): 敏感度与模型裁剪率关系图 elif type == 'prune': prune_log(dict):裁剪日志 """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': if data['type'] == 'train': from .project.task import get_train_metrics ret = get_train_metrics(data, SD.workspace) return ret if data['type'] == 'eval': from .project.task import get_eval_metrics ret = get_eval_metrics(data, SD.workspace) return ret if data['type'] == 'eval_all': from .project.task import get_eval_all_metrics ret = get_eval_all_metrics(data, SD.workspace) return ret if data['type'] == 'sensitivities': from .project.task import get_sensitivities_loss_img ret = get_sensitivities_loss_img(data, SD.workspace) return ret if data['type'] == 'prune': from .project.task import get_prune_metrics ret = get_prune_metrics(data, SD.workspace) return ret @app.route('/project/task/train', methods=['POST', 'PUT']) def task_train(): """ methods=='POST':#异步，启动训练或者裁剪任务 Args： tid(str):任务id eval_metric_loss(int，optional):可选，裁剪任务时可用，裁剪任务所需的评估loss Return: status methods=='PUT':#改变任务训练的状态，即终止训练或者恢复训练 Args： tid(str)：任务id act(str)：[stop,resume]暂停或者恢复 epoch(int)：(resume下可以设置)恢复训练的起始轮数 Return: status """ data = request.get_json() if request.method == 'POST': from .project.task import start_train_task ret = start_train_task(data, SD.workspace, SD.monitored_processes) return ret if request.method == 'PUT': if data['act'] == 'resume': from .project.task import resume_train_task ret = resume_train_task(data, SD.workspace, SD.monitored_processes) return ret if data['act'] == 'stop': from .project.task import stop_train_task ret = stop_train_task(data, SD.workspace) return ret @app.route('/project/task/train/file', methods=['GET']) def log_file(): data = request.get_json() if request.method == 'GET': path = data['path'] if not os.path.exists(path): return {'status': -1} logs = open(path, encoding='utf-8').readlines() if len(logs) < 50: return {'status': 1, 'log': logs} else: logs = logs[-50:] return {'status': 1, 'log': logs} @app.route('/project/task/prune', methods=['GET', 'POST', 'PUT']) def task_prune(): """ methods=='GET':#获取裁剪任务的状态 Args： tid(str):任务id Return: prune_status(int): 裁剪任务状态(PruneStatus)枚举变量的值 status methods=='POST':#异步，创建一个裁剪分析，对于启动裁剪任务前需要先启动裁剪分析 Args： tid(str):任务id Return: status methods=='PUT':#改变裁剪分析任务的状态 Args： tid(str):任务id act(str):[stop],目前仅支持停止一个裁剪分析任务 Return status """ data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .project.task import get_prune_status ret = get_prune_status(data, SD.workspace) return ret if request.method == 'POST': from .project.task import start_prune_analysis ret = start_prune_analysis(data, SD.workspace, SD.monitored_processes) return ret if request.method == 'PUT': if data['act'] == 'stop': from .project.task import stop_prune_analysis ret = stop_prune_analysis(data, SD.workspace) return ret @app.route('/project/task/evaluate', methods=['GET', 'POST']) def task_evaluate(): ''' methods=='GET':#获取模型评估的结果 Args： tid(str):任务id Return: evaluate_status(int): 任务状态(TaskStatus)枚举变量的值 message(str)：描述评估任务的信息 result(dict)：如果评估成功，返回评估结果的dict，否则为None status methods=='POST':#异步，创建一个评估任务 Args： tid(str):任务id epoch(int,optional):需要评估的epoch，如果为None则会评估训练时指标最好的epoch topk(int,optional):分类任务topk指标,如果为None默认输入为5 score_thresh(float):检测任务类别的score threshhold值，如果为None默认输入为0.5 overlap_thresh(float):实例分割任务IOU threshhold值，如果为None默认输入为0.3 Return: status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .project.task import get_evaluate_result ret = get_evaluate_result(data, SD.workspace) if ret['evaluate_status'] == TaskStatus.XEVALUATED and ret[ 'result'] is not None: if 'Confusion_Matrix' in ret['result']: ret['result']['Confusion_Matrix'] = ret['result'][ 'Confusion_Matrix'] ret['result'] = CustomEncoder().encode(ret['result']) ret['result'] = json.loads(ret['result']) ret['evaluate_status'] = ret['evaluate_status'].value return ret if request.method == 'POST': from .project.task import evaluate_model ret = evaluate_model(data, SD.workspace, SD.monitored_processes) return ret @app.route('/project/task/evaluate/file', methods=['GET']) def task_evaluate_file(): data = request.get_json() if request.method == 'GET': if 'path' in data: ret = data['path'] assert os.path.abspath(ret).startswith( os.path.abspath(SD.workspace_dir) ) and ".." not in ret, "Illegal path {}.".format(ret) return pickle.load(open(os.path.abspath(ret), 'rb')) else: from .project.task import get_evaluate_result from .project.task import import_evaluate_excel ret = get_evaluate_result(data, SD.workspace) if ret['evaluate_status'] == TaskStatus.XEVALUATED and ret[ 'result'] is not None: result = ret['result'] excel_ret = dict() excel_ret = import_evaluate_excel(data, result, SD.workspace) return excel_ret else: excel_ret = dict() excel_ret['path'] = None excel_ret['status'] = -1 excel_ret['message'] = "评估尚未完成或评估失败" return excel_ret @app.route('/project/task/predict', methods=['GET', 'POST', 'PUT']) def task_predict(): ''' methods=='GET':#获取预测状态 Args： tid(str):任务id Return: predict_status(int): 预测任务状态(PredictStatus)枚举变量的值 message(str): 预测信息 status methods=='POST':#创建预测任务，目前仅支持单张图片的预测 Args: tid(str):任务id image_data(str):base64编码的image数据 score_thresh(float，optional):可选，检测任务时有效，检测类别的score threashold值默认是0.5 epoch(int,float，optional):可选，选择需要做预测的ephoch，默认为评估指标最好的那一个epoch Return: path(str):服务器上保存预测结果图片的路径 status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .project.task import get_predict_status ret = get_predict_status(data, SD.workspace) return ret if request.method == 'POST': from .project.task import predict_test_pics ret = predict_test_pics(data, SD.workspace, SD.monitored_processes) if 'img_list' in data: del ret['path'] return ret return ret if request.method == 'PUT': from .project.task import stop_predict_task ret = stop_predict_task(data, SD.workspace) return ret @app.route('/project/task/export', methods=['GET', 'POST', 'PUT']) def task_export(): ''' methods=='GET':#获取导出模型的状态 Args： tid(str):任务id quant(str,optional)可选，[log,result]，导出量模型导出状态，若值为log则返回量化的日志；若值为result则返回量化的结果 Return: status if quant == 'log': quant_log(dict):量化日志 if quant == 'result' quant_result(dict):量化结果 if quant not in Args： export_status(int):模型导出状态(PredictStatus)枚举变量的值 message(str):模型导出提示信息 methods=='POST':#导出inference模型或者导出lite模型 Args: tid(str):任务id type(str):保存模型的类别[infer,lite]，支持inference模型导出和lite的模型导出 save_dir(str):保存模型的路径 epoch(str,optional)可选，指定导出的epoch数默认为评估效果最好的epoch quant(bool,optional)可选，type为infer有效，是否导出量化后的模型，默认为False model_path(str,optional)可选，type为lite时有效，inference模型的地址 Return: status if type == 'infer': save_dir:模型保存路径 if type == 'lite': message:模型保存信息 methods=='PUT':#停止导出模型 Args: tid(str):任务id Return: export_status(int):模型导出状态(PredictStatus)枚举变量的值 message(str):停止模型导出提示信息 status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': if 'quant' in data: if data['quant'] == 'log': from .project.task import get_quant_progress ret = get_quant_progress(data, SD.workspace) return ret if data['quant'] == 'result': from .project.task import get_quant_result ret = get_quant_result(data, SD.workspace) return ret from .project.task import get_export_status ret = get_export_status(data, SD.workspace) ret['export_status'] = ret['export_status'].value return ret if request.method == 'POST': if data['type'] == 'infer': from .project.task import export_infer_model ret = export_infer_model(data, SD.workspace, SD.monitored_processes) return ret if data['type'] == 'lite': from .project.task import export_lite_model ret = export_lite_model(data, SD.workspace) return ret if request.method == 'PUT': from .project.task import stop_export_task stop_export_task(data, SD.workspace) return ret @app.route('/project/task/vdl', methods=['GET']) def task_vdl(): ''' methods=='GET':#打开某个任务的可视化分析工具(VisualDL) Args: tid(str):任务id Return: url(str):vdl地址 status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .project.task import open_vdl ret = open_vdl(data, SD.workspace, SD.current_port, SD.monitored_processes, SD.running_boards) return ret @app.route('/system', methods=['GET', 'DELETE']) def system(): ''' methods=='GET':#获取系统GPU、CPU信息 Args: type(str):[machine_info,gpu_memory_size]选择需要获取的系统信息 Return: status if type=='machine_info' info(dict):服务端信息 if type=='gpu_memory_size' gpu_mem_infos(list):GPU内存信息 ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': if data['type'] == 'machine_info': '''if 'path' not in data: data['path'] = None from .system import get_machine_info ret = get_machine_info(data, SD.machine_info)''' from .system import get_system_info ret = get_system_info(SD.machine_info) return ret if data['type'] == 'gpu_memory_size': #from .system import get_gpu_memory_size from .system import get_gpu_memory_info ret = get_gpu_memory_info(SD.machine_info) return ret if request.method == 'DELETE': from .system import exit_system ret = exit_system(SD.monitored_processes) return ret @app.route('/demo', methods=['GET', 'POST', 'PUT']) def demo(): ''' methods=='GET':#获取demo下载进度 Args: prj_type(int):项目类型ProjectType枚举变量的int值 Return: status attr(dict):demo下载信息 methods=='POST':#下载或创建demo工程 Args: type(str):{download,load}下载或者创建样例 prj_type(int):项目类型ProjectType枚举变量的int值 Return: status if type=='load': did:数据集id pid:项目id methods=='PUT':#停止下载或创建demo工程 Args: prj_type(int):项目类型ProjectType枚举变量的int值 Return: status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': from .demo import get_download_demo_progress ret = get_download_demo_progress(data, SD.workspace) return ret if request.method == 'POST': if data['type'] == 'download': from .demo import download_demo_dataset ret = download_demo_dataset(data, SD.workspace, SD.load_demo_proc_dict) return ret if data['type'] == 'load': from .demo import load_demo_project ret = load_demo_project(data, SD.workspace, SD.monitored_processes, SD.load_demo_proj_data_dict, SD.load_demo_proc_dict) return ret if request.method == 'PUT': from .demo import stop_import_demo ret = stop_import_demo(data, SD.workspace, SD.load_demo_proc_dict, SD.load_demo_proj_data_dict) return ret @app.route('/model', methods=['GET', 'POST', 'DELETE']) def model(): ''' methods=='GET':#获取一个或者所有模型的信息 Args: mid(str,optional)可选，若存在则返回某个模型的信息 type(str,optional)可选，[pretrained,exported].若存在则返回对应类型下所有的模型信息 Return: status if mid in Args: dataset_attr(dict):数据集属性 task_params(dict):模型训练参数 eval_result(dict):模型评估结果 if type in Args and type == 'pretrained': pretrained_models(list):所有预训练模型信息 if type in Args and type == 'exported': exported_models(list):所有inference模型的信息 methods=='POST':#创建一个模型 Args: pid(str):项目id tid(str):任务id name(str):模型名字 type(str):创建模型的类型,[pretrained,exported],pretrained代表创建预训练模型、exported代表创建inference或者lite模型 source_path(str):仅type为pretrained时有效，训练好的模型的路径 path(str):仅type为exported时有效，inference或者lite模型的路径 exported_type(int):0为inference模型，1为lite模型 eval_results(dict，optional):可选，仅type为pretrained时有效，模型评估的指标 Return: status if type == 'pretrained': pmid(str):预训练模型id if type == 'exported': emid(str):inference模型id methods=='DELETE':删除一个模型 Args: type(str):删除模型的类型，[pretrained,exported]，pretrained代表创建预训练模型、exported代表创建inference或者lite模型 if type='pretrained': pmid:预训练模型id if type='exported': emid:inference或者lite模型id Return: status ''' data = request.get_json() if data is None: data = request.args if request.method == 'GET': if 'type' in data: if data['type'] == 'pretrained': from .model import list_pretrained_models ret = list_pretrained_models(SD.workspace) return ret if data['type'] == 'exported': from .model import list_exported_models ret = list_exported_models(SD.workspace) return ret from .model import get_model_details ret = get_model_details(data, SD.workspace) ret['eval_result']['Confusion_Matrix'] = ret['eval_result'][ 'Confusion_Matrix'].tolist() ret['eval_result'] = CustomEncoder().encode(ret['eval_result']) ret['task_params'] = CustomEncoder().encode(ret['task_params']) return ret if request.method == 'POST': if data['type'] == 'pretrained': if 'eval_results' in data: data['eval_results']['Confusion_Matrix'] = np.array(data[ 'eval_results']['Confusion_Matrix']) from .model import create_pretrained_model ret = create_pretrained_model(data, SD.workspace, SD.monitored_processes) return ret if data['type'] == 'exported': from .model import create_exported_model ret = create_exported_model(data, SD.workspace) return ret if request.method == 'DELETE': if data['type'] == 'pretrained': from .model import delete_pretrained_model ret = delete_pretrained_model(data, SD.workspace) return ret if data['type'] == 'exported': from .model import delete_exported_model ret = delete_exported_model(data, SD.workspace) return ret @app.route('/model/file', methods=['GET']) def model_file(): data = request.get_json() if request.method == 'GET': ret = data['path'] assert os.path.abspath(ret).startswith( os.path.abspath(SD.workspace_dir) ) and ".." not in ret, "Illegal path {}.".format(ret) return send_file(ret) @app.route('/', methods=['GET']) def gui(): if request.method == 'GET': file_path = osp.join( osp.dirname(__file__), 'templates', 'paddlex_restful_demo.html') ip = get_ip() url = 'var str_srv_url = "http://' + ip + ':' + str(SD.port) + '";' f = open(file_path, 'r+', encoding=get_encoding(file_path)) lines = f.readlines() for i, line in enumerate(lines): if '0.0.0.0:8080' in line: lines[i] = url break f.close() f = open(file_path, 'w+', encoding=get_encoding(file_path)) f.writelines(lines) f.close() return render_template('/paddlex_restful_demo.html') def run(port, workspace_dir): if workspace_dir is None: user_home = os.path.expanduser('~') dirname = osp.join(user_home, "paddlex_workspace") else: dirname = workspace_dir if not osp.exists(dirname): os.makedirs(dirname) else: if not osp.isdir(dirname): os.remove(dirname) os.makedirs(dirname) logger = get_logger(osp.join(dirname, "mcessages.log")) init(dirname, logger) SD.port = port ip = get_ip() url = ip + ':' + str(port) try: logger.info("RESTful服务启动成功后，您可以在浏览器打开 {} 使用WEB版本GUI".format(url)) app.run(host='0.0.0.0', port=port, threaded=True) except: print("服务启动不成功，请确保端口号：{}未被防火墙限制".format(port))