# !/usr/bin/env python # -*- coding:utf-8 -*- """ Copyright 2020 Tianshu AI Platform. 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. ============================================================= """ import os import sched import logging import time import json import common.util.algorithm.of_cnn_resnet as of_cnn_resnet import numpy as np from abc import ABC from program.abstract.algorithm import Algorithm schedule = sched.scheduler(time.time, time.sleep) base_path = "/nfs/" delayId = "" class Imagenet(Algorithm, ABC): @staticmethod def _init(): of_cnn_resnet.init_resnet() logging.info('env init finished') def __init__(self): pass def execute(task): return Imagenet.process(task) def process(task_dict): """Imagenet task method. Args: task_dict: imagenet task details. key: imagenet task key. """ id_list = [] image_path_list = [] annotation_path_list = [] for file in task_dict["files"]: id_list.append(file["id"]) image_path = base_path + file["url"] image_path_list.append(image_path) annotation_url = image_path.replace("origin/", "annotation/") annotation_path_list.append(os.path.splitext(annotation_url)[0]) isExists = os.path.exists(os.path.dirname(annotation_url)) if not isExists: try: os.makedirs(os.path.dirname(annotation_url)) except Exception as exception: logging.error(exception) label_list = task_dict["labels"] image_num = len(image_path_list) annotations = [] for inds in range(len(image_path_list)): temp = {} temp['id'] = id_list[inds] score, ca_id = of_cnn_resnet.resnet_inf(image_path_list[inds]) temp['annotation'] = [{'category_id': int(ca_id), 'score': np.float(score)}] temp['annotation'] = json.dumps(temp['annotation']) annotations.append(temp) with open(annotation_path_list[inds], 'w') as w: w.write(temp['annotation']) finish_data = {"annotations": annotations, "reTaskId": task_dict["reTaskId"]} return finish_data, True