平台aistudio 笔记本

python 3.7

paddle 1.6 1.8都测试过

模型resnet_v2_50_imagenet

paddle.enable_static()
module = hub.Module(name="resnet_v2_50_imagenet")

from paddlehub.dataset.base_cv_dataset import BaseCVDataset
   
class DemoDataset(BaseCVDataset):	
   def __init__(self):	
       # 数据集存放位置
       
       self.dataset_dir = "data/data84458"
       super(DemoDataset, self).__init__(
           base_path=self.dataset_dir,
           train_list_file="train_list.txt",
           validate_list_file="validate_list.txt",
           test_list_file="test_list.txt",
           #predict_file="predict_list.txt",
           label_list_file="label_list.txt",
           )
dataset = DemoDataset()

import matplotlib.pyplot as plt 
import matplotlib.image as mpimg
img = mpimg.imread('data/data84458/yangmi/ym85.jpg') 
img1 = mpimg.imread('data/data84458/liuyifei/lyf14.jpg') 
plt.figure(figsize=(10,10))
plt.subplot(1,2,1)
plt.imshow(img)
plt.axis('off') 
plt.subplot(1,2,2)
plt.imshow(img1)
plt.axis('off') 
plt.show()
print("杨幂\t\t\t\t\t\t\t\t刘亦菲")

data_reader = hub.reader.ImageClassificationReader(
    image_width=module.get_expected_image_width(),
    image_height=module.get_expected_image_height(),
    images_mean=module.get_pretrained_images_mean(),
    images_std=module.get_pretrained_images_std(),
    dataset=dataset)

config = hub.RunConfig(
    use_cuda=True,                              #是否使用GPU训练，默认为False；
    num_epoch=3,                                #Fine-tune的轮数；
    checkpoint_dir="cv_finetune_turtorial_demo",#模型checkpoint保存路径, 若用户没有指定，程序会自动生成；
    batch_size=3,                              #训练的批大小，如果使用GPU，请根据实际情况调整batch_size；
    eval_interval=10,                           #模型评估的间隔，默认每100个step评估一次验证集；
    strategy=hub.finetune.strategy.DefaultFinetuneStrategy())  #Fine-tune优化策略；

input_dict, output_dict, program = module.context(trainable=True)
img = input_dict["image"]
feature_map = output_dict["feature_map"]
feed_list = [img.name]

# task = hub.ImageClassifierTask(
#     data_reader=data_reader,
#     feed_list=feed_list,
#     feature=feature_map,
#     num_classes=dataset.num_labels,
#     config=config)

task = hub.ImageClassifierTask(
    data_reader=data_reader,
    feed_list=feed_list,
    feature=feature_map,
    num_classes=dataset.num_labels,
    config=config)