“华为云杯”2019人工智能创新应用大赛：版本一customize_service.py文件

傷城~ 2023-06-24 08:26 42阅读 0赞

import ast
    import numpy as np
    from PIL import Image
    from collections import OrderedDict
    from model_service.pytorch_model_service import PTServingBaseService
    import torch
    from torchvision import transforms
    
    input_size = 380
    test_transforms = transforms.Compose([
        transforms.Resize(input_size),
        transforms.CenterCrop(input_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    
    
    class garbage_classify_service(PTServingBaseService):
        def __init__(self, model_name, model_path):
            print('model_name: ', model_name)
            print('model_path: ', model_path)
            # these three parameters are no need to modify
            self.model_name = model_name
            # self.model_path = model_path
            # self.signature_key = 'predict_images'
            # add the input and output key of your pb model here,
            # these keys are defined when you save a pb file
            self.input_key_1 = 'input_img'
            map_location = torch.device('cpu')
            self.model = torch.load(model_path, map_location=map_location)
            self.model.eval()
            self.idx_to_cls = {'0': 0, '1': 1, '2': 10, '3': 11, '4': 12, '5': 13, '6': 14, '7': 15, '8': 16, '9': 17,
                          '10': 18, '11': 19, '12': 2, '13': 20, '14': 21, '15': 22, '16': 23, '17': 24, '18': 25,
                          '19': 26, '20': 27, '21': 28, '22': 29, '23': 3, '24': 30, '25': 31, '26': 32, '27': 33,
                          '28': 34, '29': 35, '30': 36, '31': 37, '32': 38, '33': 39, '34': 4, '35': 40, '36': 41,
                          '37': 42, '38': 43, '39': 44, '40': 45, '41': 46, '42': 47, '43': 48, '44': 49, '45': 5,
                          '46': 50, '47': 51, '48': 52, '49': 53, '50': 6, '51': 7, '52': 8, '53': 9}
            self.label_id_name_dict = \
                {
                    "0": "工艺品/仿唐三彩",
                    "1": "工艺品/仿宋木叶盏",
                    "2": "工艺品/布贴绣",
                    "3": "工艺品/景泰蓝",
                    "4": "工艺品/木马勺脸谱",
                    "5": "工艺品/柳编",
                    "6": "工艺品/葡萄花鸟纹银香囊",
                    "7": "工艺品/西安剪纸",
                    "8": "工艺品/陕历博唐妞系列",
                    "9": "景点/关中书院",
                    "10": "景点/兵马俑",
                    "11": "景点/南五台",
                    "12": "景点/大兴善寺",
                    "13": "景点/大观楼",
                    "14": "景点/大雁塔",
                    "15": "景点/小雁塔",
                    "16": "景点/未央宫城墙遗址",
                    "17": "景点/水陆庵壁塑",
                    "18": "景点/汉长安城遗址",
                    "19": "景点/西安城墙",
                    "20": "景点/钟楼",
                    "21": "景点/长安华严寺",
                    "22": "景点/阿房宫遗址",
                    "23": "民俗/唢呐",
                    "24": "民俗/皮影",
                    "25": "特产/临潼火晶柿子",
                    "26": "特产/山茱萸",
                    "27": "特产/玉器",
                    "28": "特产/阎良甜瓜",
                    "29": "特产/陕北红小豆",
                    "30": "特产/高陵冬枣",
                    "31": "美食/八宝玫瑰镜糕",
                    "32": "美食/凉皮",
                    "33": "美食/凉鱼",
                    "34": "美食/德懋恭水晶饼",
                    "35": "美食/搅团",
                    "36": "美食/枸杞炖银耳",
                    "37": "美食/柿子饼",
                    "38": "美食/浆水面",
                    "39": "美食/灌汤包",
                    "40": "美食/烧肘子",
                    "41": "美食/石子饼",
                    "42": "美食/神仙粉",
                    "43": "美食/粉汤羊血",
                    "44": "美食/羊肉泡馍",
                    "45": "美食/肉夹馍",
                    "46": "美食/荞面饸饹",
                    "47": "美食/菠菜面",
                    "48": "美食/蜂蜜凉粽子",
                    "49": "美食/蜜饯张口酥饺",
                    "50": "美食/西安油茶",
                    "51": "美食/贵妃鸡翅",
                    "52": "美食/醪糟",
                    "53": "美食/金线油塔"
                }
    
        def _preprocess(self, data):
            preprocessed_data = {}
            for k, v in data.items():
                for file_name, file_content in v.items():
                    img = Image.open(file_content)
                    img = test_transforms(img)
                    preprocessed_data[k] = img
            return preprocessed_data
    
        def _inference(self, data):
            """
            model inference function
            Here are a inference example of resnet, if you use another model, please modify this function
            """
            img = data[self.input_key_1]
            img = img[np.newaxis, :, :, :]  # the input tensor shape of resnet is [?, 224, 224, 3]
            # pred_score = self.sess.run([self.output_score], feed_dict={self.input_images: img})
    
            pred_score = self.model(img)
            pred_score = pred_score.detach().numpy()
            if pred_score is not None:
                pred_label = np.argmax(pred_score, axis=1)[0]
                pred_label = self.idx_to_cls[str(int(pred_label))]
                result = {'result': self.label_id_name_dict[str(pred_label)]}
    
            else:
                result = {'result': 'predict score is None'}
    
    
            return result
    
    
        def _postprocess(self, data):
            return data