binary_classification.py

# !pip install paddlehub==1.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
# Note: PaddleAiStudio 没有复现成功，最终结果没有区分这两个人，本地使用的是2.x的版本，BaseCVDataset已经被删，不想卸载重装环境，以后有机会再看。

# import paddlehub as hub
# from paddlehub.datasets.base_cv_dataset import BaseCVDataset
#
# module = hub.Module(name="resnet_v2_50_imagenet")
#
#
# class DemoDataset(BaseCVDataset):
#     def __init__(self):
#         # 数据集存放位置
#
#         self.dataset_dir = "data/data"
#         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()
#
#
# 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=5,                           #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)
#
#
# run_states = task.finetune_and_eval()
#
# import numpy as np
# import matplotlib.pyplot as plt
# import matplotlib.image as mpimg
#
# data = ["data/xu/16.jpg","data/yu/16.jpg"]
#
# label_map = dataset.label_dict()
# index = 0
# run_states = task.predict(data=data)
# results = [run_state.run_results for run_state in run_states]
#
# for batch_result in results:
#     print(batch_result)
#     batch_result = np.argmax(batch_result, axis=2)[0]
#     print(batch_result)
#     for result in batch_result:
#         index += 1
#         result = label_map[result]
#         print("input %i is %s, and the predict result is %s" %
#               (index, data[index - 1], result))
#
# img = mpimg.imread(data[0])
# img1 = mpimg.imread(data[1])
# 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("             input1 许佳琪                             input2 虞书欣")