Created by: zhuyingjun-zyj

paddlepaddle -version：1.8.1 paddlehub -version :1.7.1

我按照https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.6/demo/text_classification/predict.py操作，设置了自定义数据集和模型保存路径，进行fine-tune，在预测时候报错： 一下是预测代码：

import paddlehub as hub
module = hub.Module(name="ernie")

path = 'D://project//test//textPaddle//testdata'

from paddlehub.dataset.base_nlp_dataset import BaseNLPDataset

class ThuTastData(BaseNLPDataset):
    def __init__(self):
        # 数据集存放位置
        self.dataset_dir = path
        super(ThuTastData, self).__init__(
            base_path=self.dataset_dir,
            train_file="train.txt",
            dev_file="valid.txt",
            test_file="test.txt",
            train_file_with_header=True,
            dev_file_with_header=True,
            test_file_with_header=True,
            # 数据集类别集合
            label_list=["5", "2", "4", "3", "1"])

dataset = ThuTastData()

# for e in dataset.get_train_examples()[:3]:
#     print("{}\t{}\t{}".format(e.guid, e.text_a, e.label))

reader = hub.reader.ClassifyReader(
    dataset=dataset,
    vocab_path=module.get_vocab_path(),
    sp_model_path=module.get_spm_path(),
    word_dict_path=module.get_word_dict_path(),
    max_seq_len=128)

strategy = hub.AdamWeightDecayStrategy(
    weight_decay=0.01,
    warmup_proportion=0.1,
    learning_rate=5e-5)

config = hub.RunConfig(
    log_interval=3,
    save_ckpt_interval=10,
    checkpoint_dir='./modle.ckpt',
    num_epoch=20,
    use_cuda=False,
    batch_size=32,
    eval_interval=50,
    strategy=strategy)

inputs, outputs, program = module.context(
    trainable=True, max_seq_len=128)

# Use "pooled_output" for classification tasks on an entire sentence.
pooled_output = outputs["pooled_output"]

feed_list = [
    inputs["input_ids"].name,
    inputs["position_ids"].name,
    inputs["segment_ids"].name,
    inputs["input_mask"].name,
]

cls_task = hub.TextClassifierTask(
        data_reader=reader,
        feature=pooled_output,
        feed_list=feed_list,
        num_classes=dataset.num_labels,
        config=config,

        metrics_choices=["acc"])
# run_states = cls_task.finetune_and_eval()
data = [["环境不好??太乱了??东西又贵又不好吃"], ["交通方便；环境很好；服务态度很好 房间较小"],
            ["19天硬盘就罢工了~~~算上运来的一周都没用上15天~~~可就是不能换了~~~唉~~~~你说这算什么事呀~~~"]]
print(cls_task.predict(data=data, return_result=True))

一下是报错日志：

!!! The default number of CPU_NUM=1.
[2020-06-10 15:10:17,401] [    INFO] - PaddleHub predict start
[2020-06-10 15:10:17,401] [    INFO] - Load the best model from ./modle.ckpt\best_model
C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\paddle\fluid\executor.py:1093: UserWarning: There are no operators in the program to be executed. If you pass Program manually, please use fluid.program_guard to ensure the current Program is being used.
  warnings.warn(error_info)
[2020-06-10 15:10:18,218] [    INFO] - Try loading checkpoint from ./modle.ckpt\ckpt.meta
[2020-06-10 15:10:19,297] [    INFO] - PaddleHub model checkpoint loaded. current_epoch=1, global_step=10, best_score=-999.00000
Traceback (most recent call last):
  File "D:/project/test/textPaddle/testPaddleFinetune.py", line 77, in <module>
    print(cls_task.predict(data=data, return_result=True))
  File "C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\paddlehub\finetune\task\base_task.py", line 1011, in predict
    self.init_if_load_best_model()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\paddlehub\finetune\task\base_task.py", line 382, in init_if_load_best_model
    self.init_if_necessary()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\paddlehub\finetune\task\base_task.py", line 367, in init_if_necessary
    if not self.load_checkpoint():
  File "C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\paddlehub\finetune\task\base_task.py", line 837, in load_checkpoint
    self.max_train_steps = self.env.current_step + self.max_train_steps / self.config.num_epoch * (
AttributeError: 'TextClassifierTask' object has no attribute 'max_train_steps'