小茗同学：二狗子家的小猫又来偷吃了，小花猫已经饿坏了，咋办呀？？？
怎么办？？？小宠物们识别要大学科研团队才能做，作为幼儿园的我怎么办？？？
小茗同学苦恼的挠了挠头，发出痛苦的啊啊啊声音。。。。。。
小茗同学查到了新闻报道《“猴脸识别技术”来了！》new.qq.com/omn/2021022…
别着急，幼儿园小茗同学我教你用飞桨的PaddleHub来实现小猫识别，不认识的小猫不准进。
1.数据采集
所有猫的视频均采集公开视频,通过视频截图来获得小猫的正脸照片，不用单独拍照了。
!unzip -q data/data71411/cat.zip
1.1 python调用openCV每隔1秒从视频中截取一张图片，编号后保存。
import cv2 import os for i in range(1,5): # 创建图片目录 print(i) mp4_file=str(i)+'.mp4' dir_path=os.path.join('dataset',str(i)) if not os.path.exists(dir_path): os.makedirs(dir_path) # 每秒存一次图片 vidcap = cv2.VideoCapture(mp4_file) success,image = vidcap.read() fps = int(vidcap.get(cv2.CAP_PROP_FPS)) count = 0 while success: if count % fps == 0: cv2.imwrite("{}/{}.jpg".format(dir_path, int(count / fps)), image) print('Process %dth seconds: ' % int(count / fps), success) success,image = vidcap.read() count += 1
1.2 生成图片进行处理
删除片尾等不正常图片
手动中...................................
import matplotlib.pyplot as plt %matplotlib inline import cv2 as cv import numpy as np # jupyter notebook显示 def visualize_images(): img = cv.imread('dataset/1/1.jpg') plt.imshow(img) plt.show() visualize_images()
1.3 数据集查看
4只不同的小猫
1.4 list生成
自定义的数据集，首先要生成图像列表，把自定的图像分为测试集和训练集，并带有标签。下面的程序可以单独运行，只要把一个大类的文件夹路径传进去就可以了,该程序会把里面的每个小类别都迭代,生成固定格式的列表.比如我们把人脸类别的根目录传进去./dataset。最后会在指定目录下面生成三个文件，readme.json、train.list和test.list.
import os import json # 设置要生成文件的路径 data_root_path = '/home/aistudio/dataset' # 所有类别的信息 class_detail = [] # 获取所有类别保存的文件夹名称，这里是['1', '2', '3','4'] class_dirs = os.listdir(data_root_path) # 类别标签 class_label = 0 # 获取总类别的名称 father_paths = data_root_path.split('/') #['', 'home', 'aistudio', 'dataset'] while True: if father_paths[father_paths.__len__() - 1] == '': del father_paths[father_paths.__len__() - 1] else: break father_path = father_paths[father_paths.__len__() - 1] # 把生产的数据列表都放在自己的总类别文件夹中 data_list_path = '/home/aistudio/%s/' % father_path # 如果不存在这个文件夹,就创建 isexist = os.path.exists(data_list_path) if not isexist: os.makedirs(data_list_path) # 清空原来的数据 with open(data_list_path + "test.txt", 'w') as f: pass with open(data_list_path + "trainer.txt", 'w') as f: pass # 总的图像数量 all_class_images = 0 # 读取每个类别 for class_dir in class_dirs: # 每个类别的信息 class_detail_list = {} test_sum = 0 trainer_sum = 0 # 统计每个类别有多少张图片 class_sum = 0 # 获取类别路径 path = data_root_path + "/" + class_dir # 获取所有图片 img_paths = os.listdir(path) for img_path in img_paths: # 遍历文件夹下的每个图片 name_path = path + '/' + img_path # 每张图片的路径 if class_sum % 10 == 0: # 每10张图片取一个做测试数据 test_sum += 1 #test_sum测试数据的数目 with open(data_list_path + "test.txt", 'a') as f: f.write(name_path + "\t%d" % class_label + "\n") #class_label 标签：0,1,2 else: trainer_sum += 1 #trainer_sum测试数据的数目 with open(data_list_path + "trainer.txt", 'a') as f: f.write(name_path + "\t%d" % class_label + "\n")#class_label 标签：0,1,2 class_sum += 1 #每类图片的数目 all_class_images += 1 #所有类图片的数目 # 说明的json文件的class_detail数据 class_detail_list['class_name'] = class_dir #类别名称，如jiangwen class_detail_list['class_label'] = class_label #类别标签，0,1,2 class_detail_list['class_test_images'] = test_sum #该类数据的测试集数目 class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目 class_detail.append(class_detail_list) class_label += 1 #class_label 标签：0,1,2 # 获取类别数量 all_class_sum = class_dirs.__len__() # 说明的json文件信息 readjson = {} readjson['all_class_name'] = father_path #文件父目录 readjson['all_class_sum'] = all_class_sum # readjson['all_class_images'] = all_class_images readjson['class_detail'] = class_detail jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': ')) with open(data_list_path + "readme.json",'w') as f: f.write(jsons) print ('生成数据列表完成！')
生成数据列表完成！
1.5 DataSet构造
import paddle import paddle.vision.transforms as T import numpy as np from PIL import Image class MiaoMiaoDataset(paddle.io.Dataset): """ 2类Bee数据集类的定义 """ def __init__(self,mode='train'): """ 初始化函数 """ self.data = [] with open('dataset/{}.txt'.format(mode)) as f: for line in f.readlines(): info = line.strip().split('\t') if len(info) > 0: self.data.append([info[0].strip(), info[1].strip()]) if mode == 'train': self.transforms = T.Compose([ T.Resize((224,224)), T.RandomHorizontalFlip(0.5), # 随机水平翻转 T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 图像归一化 ]) else: self.transforms = T.Compose([ T.Resize((224,224)), # 图像大小修改 # T.RandomCrop(IMAGE_SIZE), # 随机裁剪 T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 图像归一化 ]) def get_origin_data(self): return self.data def __getitem__(self, index): """ 根据索引获取单个样本 """ image_file, label = self.data[index] image = Image.open(image_file) if image.mode != 'RGB': image = image.convert('RGB') image = self.transforms(image) return image, np.array(label, dtype='int64') def __len__(self): """ 获取样本总数 """ return len(self.data)
train_dataset=MiaoMiaoDataset(mode='trainer') test_dataset=MiaoMiaoDataset(mode='test') print('train_data len: {}, test_data len:{}'.format(train_dataset.__len__(), test_dataset.__len__()))
train_data len: 45, test_data len:7
2.Model 定义 and 训练
目前，数据已经划分了train和test数据集，以及分类数量等。
下一步我们将定义model，再复习一下resnet50网络了。
import paddle from paddle import Model # 定义网络 network=paddle.vision.models.resnet50(num_classes=4, pretrained=True) model = paddle.Model(network) model.summary((-1, 3, 224 , 224))
100%|██████████| 151272/151272 [00:02<00:00, 72148.01it/s] ------------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =============================================================================== Conv2D-1 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408 BatchNorm2D-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 256 ReLU-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 0 MaxPool2D-1 [[1, 64, 112, 112]] [1, 64, 56, 56] 0 Conv2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096 BatchNorm2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-5 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-5 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 Conv2D-2 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-1 [[1, 64, 56, 56]] [1, 256, 56, 56] 0 Conv2D-6 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-6 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-8 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-8 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-9 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-9 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-4 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-11 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-11 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-13 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768 BatchNorm2D-13 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-14 [[1, 128, 56, 56]] [1, 128, 28, 28] 147,456 BatchNorm2D-14 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-15 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-15 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 Conv2D-12 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072 BatchNorm2D-12 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-4 [[1, 256, 56, 56]] [1, 512, 28, 28] 0 Conv2D-16 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-16 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-18 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-18 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-19 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-19 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-21 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-21 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-22 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-22 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-8 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-24 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-24 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-26 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072 BatchNorm2D-26 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-9 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-27 [[1, 256, 28, 28]] [1, 256, 14, 14] 589,824 BatchNorm2D-27 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-28 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 Conv2D-25 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288 BatchNorm2D-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-8 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0 Conv2D-29 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-29 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-10 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-30 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-31 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-9 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-32 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-32 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-11 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-33 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-34 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-10 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-35 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-35 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-12 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-36 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-37 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-11 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-38 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-38 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-40 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-12 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-41 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-41 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-14 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-43 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-45 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288 BatchNorm2D-45 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-46 [[1, 512, 14, 14]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-46 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-47 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-47 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 Conv2D-44 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152 BatchNorm2D-44 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-14 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0 Conv2D-48 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-48 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-49 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-50 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-15 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-51 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-51 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-17 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-52 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-53 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-53 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-16 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 AdaptiveAvgPool2D-1 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0 Linear-1 [[1, 2048]] [1, 4] 8,196 =============================================================================== Total params: 23,569,348 Trainable params: 23,463,108 Non-trainable params: 106,240 ------------------------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 261.48 Params size (MB): 89.91 Estimated Total Size (MB): 351.96 ------------------------------------------------------------------------------- /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1263: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [2048, 1000], but the expected shape is [2048, 4]. warnings.warn(("Skip loading for {}. ".format(key) + str(err))) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1263: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [4]. warnings.warn(("Skip loading for {}. ".format(key) + str(err))) {'total_params': 23569348, 'trainable_params': 23463108}
# 模型训练配置 model.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.000005,parameters=model.parameters()),# 优化器 loss=paddle.nn.CrossEntropyLoss(), # 损失函数 metrics=paddle.metric.Accuracy()) # 评估指标 # 训练可视化VisualDL工具的回调函数 visualdl = paddle.callbacks.VisualDL(log_dir='visualdl_log')
# 启动模型全流程训练 model.fit(train_dataset, # 训练数据集 # test_dataset, # 评估数据集 epochs=20, # 总的训练轮次 batch_size=256, # 批次计算的样本量大小 shuffle=True, # 是否打乱样本集 verbose=1, # 日志展示格式 save_dir='./chk_points/', # 分阶段的训练模型存储路径 callbacks=[visualdl]) # 回调函数使用
model.save('model_save')
3、Model 评估和测试
# plot the evaluate model.evaluate(test_dataset,verbose=1)
Eval begin... The loss value printed in the log is the current batch, and the metric is the average value of previous step. step 7/7 [==============================] - loss: 0.0000e+00 - acc: 0.7143 - 30ms/step Eval samples: 7 {'loss': [0.0], 'acc': 0.7142857142857143}
预测
对test_dataset数据进行预测
print('测试数据集样本量：{}'.format(len(test_dataset)))
测试数据集样本量：7
# 执行预测 result = model.predict(test_dataset)
Predict begin... step 7/7 [==============================] - 32ms/step Predict samples: 7
# 打印前10条看看结果 for idx in range(7): predict_label = str(np.argmax(result[0][idx])) real_label = str(test_dataset.__getitem__(idx)[1]) print('样本ID：{}, 真实标签：{}, 预测值：{}'.format(idx, real_label, predict_label))
样本ID：0, 真实标签：0, 预测值：0 样本ID：1, 真实标签：0, 预测值：0 样本ID：2, 真实标签：2, 预测值：2 样本ID：3, 真实标签：3, 预测值：3 样本ID：4, 真实标签：3, 预测值：3 样本ID：5, 真实标签：4, 预测值：0 样本ID：6, 真实标签：4, 预测值：1
# 定义画图方法 from PIL import Image import matplotlib.font_manager as font_manager import matplotlib.pyplot as plt %matplotlib inline fontpath = 'MINGHEI_R.TTF' font = font_manager.FontProperties(fname=fontpath, size=10) def show_img(img, predict): plt.figure() plt.title(predict, FontProperties=font) plt.imshow(img, cmap=plt.cm.binary) plt.show() # 抽样展示 origin_data=test_dataset.get_origin_data() for i in range(7): img_path=origin_data[i][0] real_label=str(origin_data[i][1]) predict_label= str(np.argmax(result[0][i])) img=Image.open(img_path) title='样本ID：{}, 真实标签：{}, 预测值：{}'.format(idx, real_label, predict_label) show_img(img, title)
