[bugfix]: Fix bug for input v3

docs/source/component/backbone.md

@@ -1,6 +1,6 @@
 # 为何需要组件化
 
-## 1. 灵活搭建模型，多思即所得
+## 1. 灵活搭建模型，所思即所得
 
 依靠动态可插拔的公共组件，以“搭积木”的方式快速构建想要的模型结构。框架提供了"胶水"语法，实现组件间的无缝衔接。
 

docs/source/train.md

@@ -267,6 +267,7 @@ EasyRec支持两种损失函数配置方式：1）使用单个损失函数；2
   - 表示损失函数的权重设定为归一化的随机数
 
 参考论文：
+
 - 《 Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics 》
 - 《 [Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning](https://arxiv.org/abs/2111.10603) 》
 

easy_rec/python/compat/early_stopping.py

@@ -21,9 +21,9 @@
 import os
 import threading
 import time
-from distutils.version import LooseVersion
 
 import tensorflow as tf
+from distutils.version import LooseVersion
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import init_ops

easy_rec/python/input/odps_input_v3.py

@@ -4,7 +4,6 @@
 import logging
 import sys
 
-import numpy as np
 import tensorflow as tf
 
 from easy_rec.python.input.input import Input
@@ -72,7 +71,7 @@ def _odps_read(self):
       batch_num = int(total_records_num / self._data_config.batch_size)
       res_num = total_records_num - batch_num * self._data_config.batch_size
       batch_defaults = [
-          np.array([x] * self._data_config.batch_size) for x in record_defaults
+          [x] * self._data_config.batch_size for x in record_defaults
       ]
       for batch_id in range(batch_num):
         batch_data_np = [x.copy() for x in batch_defaults]

easy_rec/python/model/multi_task_model.py

@@ -9,8 +9,8 @@
 from easy_rec.python.layers.dnn import DNN
 from easy_rec.python.model.rank_model import RankModel
 from easy_rec.python.protos import tower_pb2
-from easy_rec.python.protos.loss_pb2 import LossType
 from easy_rec.python.protos.easy_rec_model_pb2 import EasyRecModel
+from easy_rec.python.protos.loss_pb2 import LossType
 
 if tf.__version__ >= '2.0':
   tf = tf.compat.v1

easy_rec/python/test/train_eval_test.py

@@ -7,11 +7,11 @@
 import threading
 import time
 import unittest
-from distutils.version import LooseVersion
 
 import numpy as np
 import six
 import tensorflow as tf
+from distutils.version import LooseVersion
 from tensorflow.python.platform import gfile
 
 from easy_rec.python.main import predict