Chinese Information Extraction

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Project description

CNN4IE

中文信息抽取工具。使用CNN的不同变体进行信息抽取，以后会持续加入不同模型。该项目使用pytorch，python开发。

CNN4IE将各种改进版本的conv进行改动用于中文信息抽取。

Guide

Intro
Model
Evaluate
Install
Dataset
Todo
Cite
Reference

Intro

目前主要实现中文实体抽取：

训练样本以B、I、O形式进行标注。

Model

模型

模型里面的conv块部分主要来自后面的paper中的部分模块。

1.MultiLayerResCNN(cnn4ie/mlrescnn)：多层残差CNN(+CRF)， Convolutional Sequence to Sequence Learning 。
2.MultiLayerResDSCNN(cnn4ie/dscnn)：多层深度可分离depthwise_separable_convolutionCNN(+CRF)， Xception: Deep Learning with Depthwise Separable Convolutions 。
3.MultiLayerAugmentedCNN(cnn4ie/attention_augmented_cnn)：多层注意力增强CNN(+CRF)， Attention Augmented Convolutional Networks 。
4.MultiLayerLambdaCNN(cnn4ie/lambda_cnn)：多层LambdaCNN(+CRF)， LambdaNetworks: Modeling long-range Interactions without Attention 。
5.MultiLayerResLWCNN(cnn4ie/lcnn)：多层轻量LightweightCNN(+CRF)， Pay Less Attention with Lightweight and Dynamic Convolutions 。
6.MultiLayerResDYCNN(cnn4ie/dcnn)：多层动态DynamicCNN(+CRF)， Pay Less Attention with Lightweight and Dynamic Convolutions 。
7.MultiLayerStdAttnCNN(cnn4ie/stand_alone_self_attention_cnn)：多层独立自注意力stand_alone_self_attention_CNN(+CRF)，Stand-Alone Self-Attention in Vision Models 。
8.MultiLayerCSAttCNN(cnn4ie/channel_spatial_attention_cnn)，多层联合通道和空间注意力channel_spatial_attention_CNN(+CRF)，CBAM: Convolutional Block Attention Module 。
9.MultiLayerSACNN(cnn4ie/self_attention_cnn)，多层self-attention_CNN(+CRF)，Self-Attention Generative Adversarial Networks 。
10.MultiLayerGroupMixedCNN(cnn4ie/mixed_depthwise_cnn)，多层mixed_depthwise_CNN(+CRF)，MixConv: Mixed Depthwise Convolutional Kernels 。
11.MultiLayerMultiCNN(cnn4ie/multi_cnn)，多层multiconv_CNN(+CRF)，Character-Level Translation with Self-attention 。
12.MultiLayerMixedAttCNN(cnn4ie/mixed_attention_cnn)，多层混合(全局和局部attention)mixed_attention_cnn，ConvBERT: Improving BERT with Span-based Dynamic Convolution 。

Usage

相关参数的配置config见每个模型文件夹中的config.cfg文件，训练和预测时会加载此文件。

训练及预测(支持加载预训练的embedding向量)

1.MultiLayerResCNN(cnn4ie/mlrescnn)

(1).训练

from cnn4ie.mlrescnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 199 | Time: 0m 4s
  Train Loss: 228.545 | Train PPL: 1.802960293422957e+99
   Val. Loss: 433.577 |  Val. PPL: 1.9966207577208172e+188
   Val. report:               precision    recall  f1-score   support

         1       1.00      1.00      1.00      4539
         2       0.98      0.99      0.99      4926
         3       0.90      0.83      0.86       166
         4       0.74      0.98      0.84        52
         5       0.94      0.77      0.84       120
         6       0.76      0.97      0.85        39
         7       0.82      0.87      0.85        54
         8       0.93      0.74      0.82        68
         9       0.95      0.77      0.85        26
        10       1.00      0.80      0.89        10

  accuracy                           0.98     10000
  macro avg       0.90      0.87      0.88     10000
  weighted avg       0.99      0.98      0.98     10000

(2).预测

from cnn4ie.mlrescnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config_cfg')
result = predict.predict('据新华社报道，安徽省六安市被评上十大易居城市！')

print(result)

[{'start': 7, 'stop': 13, 'word': '安徽省六安市', 'type': 'LOC'}, {'start': 1, 'stop': 4, 'word': '新华社', 'type': 'ORG'}]

2.MultiLayerResDSCNN(cnn4ie/dscnn)

(1).训练

from cnn4ie.dscnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 192 | Time: 0m 3s
  Train Loss: 191.273 | Train PPL: 1.172960293422957e+99
   Val. Loss: 533.260 |  Val. PPL: 5.2866207577208172e+188
   Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.98      0.98      0.98      4926
         3       0.92      0.82      0.87       166
         4       0.82      0.88      0.85        52
         5       0.84      0.76      0.80       120
         6       0.90      0.95      0.92        39
         7       0.90      0.85      0.88        54
         8       0.84      0.71      0.77        68
         9       0.85      0.65      0.74        26
        10       1.00      0.70      0.82        10

  accuracy                           0.98     10000
  macro avg       0.91      0.83      0.86     10000
  weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.dscnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

3.MultiLayerAugmentedCNN(cnn4ie/attention_augmented_cnn)

(1).训练

from cnn4ie.attention_augmented_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 192 | Time: 0m 3s
      Train Loss: 185.204 | Train PPL: 2.711303579086953e+80
       Val. Loss: 561.592 |  Val. PPL: 7.877783034926193e+243
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.98      0.99      0.98      4926
         3       0.96      0.77      0.85       166
         4       0.81      0.85      0.83        52
         5       0.88      0.71      0.78       120
         6       0.90      0.90      0.90        39
         7       0.90      0.85      0.88        54
         8       0.85      0.69      0.76        68
         9       1.00      0.42      0.59        26
        10       1.00      0.50      0.67        10

  accuracy                           0.98     10000
  macro avg       0.93      0.77      0.82     10000
  weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.attention_augmented_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

4.MultiLayerLambdaCNN(cnn4ie/lambda_cnn)

(1).训练

from cnn4ie.lambda_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 197 | Time: 0m 2s
      Train Loss: 198.344 | Train PPL: 1.3800537707438322e+86
       Val. Loss: 668.780 |  Val. PPL: 2.8022239331403918e+290
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.98      0.98      0.98      4926
         3       0.80      0.78      0.79       166
         4       0.89      0.90      0.90        52
         5       0.86      0.77      0.81       120
         6       0.90      0.92      0.91        39
         7       0.81      0.87      0.84        54
         8       0.88      0.75      0.81        68
         9       0.93      0.54      0.68        26
        10       1.00      0.70      0.82        10

  accuracy                           0.98     10000
  macro avg       0.90      0.82      0.85     10000
  weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.lambda_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

5.MultiLayerResLWCNN(cnn4ie/lcnn)

(1).训练

from cnn4ie.lcnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 190 | Time: 0m 4s
      Train Loss: 195.472 | Train PPL: 7.807223255192846e+84
       Val. Loss: 453.642 |  Val. PPL: 1.0328983269312897e+197
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      5925
         2       0.99      0.98      0.98      5501
         3       0.90      0.85      0.87       174
         4       0.72      0.93      0.81        57
         5       0.92      0.81      0.86       122
         6       0.82      0.91      0.86        44
         7       0.84      0.85      0.85        62
         8       0.92      0.77      0.84        71
         9       0.66      0.81      0.72        31
        10       0.91      0.77      0.83        13

 accuracy                           0.98     12000
 macro avg       0.86      0.87      0.86     12000
 weighted avg       0.98      0.98      0.98     12000

(2).预测

from cnn4ie.lcnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

6.MultiLayerResDYCNN(cnn4ie/dcnn)

(1).训练

from cnn4ie.dcnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 192 | Time: 0m 4s
      Train Loss: 182.916 | Train PPL: 2.7491663642617552e+79
       Val. Loss: 463.782 |  Val. PPL: 2.618555606950152e+201
       Val. report:               precision    recall  f1-score   support

         1       1.00      1.00      1.00      5925
         2       0.99      0.98      0.98      5501
         3       0.86      0.86      0.86       174
         4       0.80      0.93      0.86        57
         5       0.84      0.79      0.81       122
         6       0.83      0.89      0.86        44
         7       0.83      0.87      0.85        62
         8       0.88      0.75      0.81        71
         9       0.92      0.71      0.80        31
        10       1.00      0.85      0.92        13

 accuracy                           0.98     12000
 macro avg       0.89      0.86      0.88     12000
 weighted avg       0.98      0.98      0.98     12000

(2).预测

from cnn4ie.dcnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

7.MultiLayerStdAttnCNN(cnn4ie/stand_alone_self_attention_cnn)

(1).训练

from cnn4ie.stand_alone_self_attention_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 195 | Time: 0m 3s
      Train Loss: 247.570 | Train PPL: 3.29768182789317e+107
       Val. Loss: 681.482 |  Val. PPL: 9.20623044303632e+295
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.99      0.99      0.99      4926
         3       0.95      0.86      0.90       166
         4       0.93      0.96      0.94        52
         5       0.91      0.78      0.84       120
         6       0.93      0.97      0.95        39
         7       0.80      0.89      0.84        54
         8       0.91      0.72      0.80        68
         9       1.00      0.69      0.82        26
        10       1.00      0.90      0.95        10

 accuracy                           0.98     10000
 macro avg       0.94      0.88      0.90     10000
 weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.stand_alone_self_attention_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 19, 'stop': 26, 'word': '全国人大常委会', 'type': 'ORG'}, {'start': 32, 'stop': 36, 'word': ' 今天下午', 'type': 'T'}, {'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}]

8.MultiLayerCSAttCNN(cnn4ie/channel_spatial_attention_cnn)

(1).训练

from cnn4ie.channel_spatial_attention_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 181 | Time: 0m 3s
      Train Loss: 112.922 | Train PPL: 1.1001029953413096e+49
       Val. Loss: 493.448 |  Val. PPL: 2.002428912702234e+214
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.98      0.98      0.98      4926
         3       0.89      0.81      0.85       166
         4       0.77      0.88      0.82        52
         5       0.90      0.73      0.81       120
         6       0.84      0.92      0.88        39
         7       0.81      0.89      0.85        54
         8       0.90      0.69      0.78        68
         9       0.85      0.85      0.85        26
        10       0.82      0.90      0.86        10

 accuracy                           0.98     10000
 macro avg       0.88      0.87      0.87     10000
 weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.channel_spatial_attention_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

9.MultiLayerSACNN(cnn4ie/self_attention_cnn)

(1).训练

from cnn4ie.self_attention_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 198 | Time: 0m 2s
      Train Loss: 241.123 | Train PPL: 5.227354818437855e+104
       Val. Loss: 421.708 |  Val. PPL: 1.3982772880257424e+183
       Val. report:               precision    recall  f1-score   support

         1       0.99      1.00      1.00      4539
         2       0.98      0.98      0.98      4926
         3       0.89      0.87      0.88       166
         4       0.84      0.92      0.88        52
         5       0.76      0.74      0.75       120
         6       0.88      0.95      0.91        39
         7       0.83      0.91      0.87        54
         8       0.80      0.71      0.75        68
         9       1.00      0.54      0.70        26
        10       1.00      0.70      0.82        10

 accuracy                           0.98     10000
 macro avg       0.90      0.83      0.85     10000
 weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.self_attention_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}, {'start': 19, 'stop': 26, 'word': '全国人大常委会', 'type': 'ORG'}, {'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}]

10.MultiLayerGroupMixedCNN(cnn4ie/mixed_depthwise_cnn)

(1).训练

from cnn4ie.mixed_depthwise_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 200 | Time: 0m 1s
      Train Loss: 310.169 | Train PPL: 5.0653182367925945e+134
       Val. Loss: 451.143 |  Val. PPL: 8.489160946059989e+195
       Val. report:               precision    recall  f1-score   support

         1       1.00      1.00      1.00      4539
         2       0.98      0.99      0.99      4926
         3       0.93      0.83      0.88       166
         4       0.89      0.90      0.90        52
         5       0.89      0.75      0.81       120
         6       0.92      0.92      0.92        39
         7       0.91      0.93      0.92        54
         8       0.86      0.71      0.77        68
         9       1.00      0.58      0.73        26
        10       1.00      0.70      0.82        10

 accuracy                           0.99     10000
 macro avg       0.94      0.83      0.87     10000
 weighted avg       0.98      0.99      0.98     10000

(2).预测

from cnn4ie.mixed_depthwise_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 19, 'stop': 24, 'word': '全国人大常', 'type': 'ORG'}, {'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}, {'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}]

11.MultiLayerMultiCNN(cnn4ie/multi_cnn)

(1).训练

from cnn4ie.multi_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 200 | Time: 0m 1s
      Train Loss: 234.673 | Train PPL: 8.267382310706752e+101
       Val. Loss: 444.010 |  Val. PPL: 6.779999895568844e+192
       Val. report:               precision    recall  f1-score   support

         1       1.00      1.00      1.00      4539
         2       0.98      0.99      0.98      4926
         3       0.92      0.84      0.88       166
         4       0.81      0.96      0.88        52
         5       0.83      0.78      0.81       120
         6       0.86      0.95      0.90        39
         7       0.92      0.91      0.92        54
         8       0.80      0.71      0.75        68
         9       1.00      0.69      0.82        26
        10       1.00      0.70      0.82        10

 accuracy                           0.98     10000
 macro avg       0.91      0.85      0.88     10000
 weighted avg       0.98      0.98      0.98     10000

(2).预测

from cnn4ie.multi_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}, {'start': 20, 'stop': 25, 'word': '国人大常委', 'type': 'ORG'}, {'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}]

12.MultiLayerMixedAttCNN(cnn4ie/mixed_attention_cnn)

(1).训练

from cnn4ie.mixed_attention_cnn.train import Train
train = Train()
train.train_model('config.cfg')

Epoch: 250 | Time: 0m 3s
  Train Loss: 107.790 | Train PPL: 6.497928061494806e+46
   Val. Loss: 283.142 |  Val. PPL: 9.269305878179231e+122
   Val. report:               precision    recall  f1-score   support

         1       1.00      1.00      1.00      4539
         2       0.98      0.99      0.99      4926
         3       0.91      0.84      0.87       166
         4       0.86      0.98      0.92        52
         5       0.91      0.78      0.84       120
         6       0.93      0.97      0.95        39
         7       0.88      0.91      0.89        54
         8       0.92      0.72      0.81        68
         9       1.00      0.65      0.79        26
        10       1.00      0.80      0.89        10

 accuracy                           0.99     10000
 macro avg       0.94      0.86      0.89     10000
 weighted avg       0.99      0.99      0.99     10000

(2).预测

from cnn4ie.mixed_attention_cnn.predict import Predict

predict = Predict()
predict.load_model_vocab('config.cfg')
result = predict.predict('本报北京２月２８日讯记者苏宁报道：八届全国人大常委会第三十次会议今天下午在京闭幕。')

print(result)

[{'start': 32, 'stop': 36, 'word': '今天下午', 'type': 'T'}, {'start': 2, 'stop': 4, 'word': '北京', 'type': 'LOC'}, {'start': 12, 'stop': 14, 'word': '苏宁', 'type': 'LOC'}]

Evaluate

评估采用的是P、R、F1、PPL等。评估方法可利用scikit-learn中的precision_recall_fscore_support或classification_report。

Install

安装：pip install CNN4IE
下载源码：

git clone https://github.com/jiangnanboy/CNN4IE.git
cd CNN4IE
python setup.py install

通过以上两种方法的任何一种完成安装都可以。如果不想安装，可以下载github源码包

Dataset

这里利用data(来自人民日报，识别的是[ORG, PER, LOC, T, O])中的数据进行训练评估，模型1的训练及评估结果（分为带预训练向量和不带预训练向量的训练结果）见examples/mlrescnn（其它模型可自行运行评估）。

预训练embedding向量：sgns.sogou.char.bz2

数据集的格式见data，分为train与dev，其中source与target为中文对应的实体标注。

数据被处理成csv格式。

Todo

持续加入更多模型......

Cite

如果你在研究中使用了CNN4IE，请按如下格式引用：

@software{CNN4IE,
  author = {Shi Yan},
  title = {CNN4IE: Chinese Information Extraction Tool},
  year = {2021},
  url = {https://github.com/jiangnanboy/CNN4IE},
}

License

CNN4IE 的授权协议为 Apache License 2.0，可免费用做商业用途。请在产品说明中附加CNN4IE的链接和授权协议。CNN4IE受版权法保护，侵权必究。

Update

(1).CNN4IE 0.1.0 init commit

(2).CNN4IE 0.1.1 update self.max_len

(3).CNN4IE 0.1.2 update new model -> [MultiLayerResDSCNN]

(4).CNN4IE 0.1.3 update new model -> [MultiLayerAugmentedCNN]、[MultiLayerLambdaCNN]

(5).CNN4IE 0.1.4 update new model -> [MultiLayerResLWCNN]、[MultiLayerResDYCNN]

(6).CNN4IE 0.1.5 update new model -> [MultiLayerStdAttnCNN]

(7).CNN4IE 0.1.6 update new model -> [MultiLayerCSAttCNN]

(8).CNN4IE 0.1.7 update new model -> [MultiLayerSACNN]、[MultiLayerGroupMixedCNN]

(9).CNN4IE 0.1.8 update new model -> [MultiLayerMultiCNN]

(10).CNN4IE 0.1.9 update new model -> [MultiLayerMixedAttCNN]

Reference

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This version

0.1.9

Oct 23, 2021

0.1.8

Oct 14, 2021

0.1.7

Oct 13, 2021

0.1.6

Oct 12, 2021

0.1.5

Oct 12, 2021

0.1.4

Oct 11, 2021

0.1.3

Oct 9, 2021

0.1.2

Oct 1, 2021

0.1.1

Jul 10, 2021

0.1.0

Jun 19, 2021

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CNN4IE 0.1.9

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CNN4IE

Intro

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模型

Usage

1.MultiLayerResCNN(cnn4ie/mlrescnn)

2.MultiLayerResDSCNN(cnn4ie/dscnn)

3.MultiLayerAugmentedCNN(cnn4ie/attention_augmented_cnn)

4.MultiLayerLambdaCNN(cnn4ie/lambda_cnn)

5.MultiLayerResLWCNN(cnn4ie/lcnn)

6.MultiLayerResDYCNN(cnn4ie/dcnn)

7.MultiLayerStdAttnCNN(cnn4ie/stand_alone_self_attention_cnn)

8.MultiLayerCSAttCNN(cnn4ie/channel_spatial_attention_cnn)

9.MultiLayerSACNN(cnn4ie/self_attention_cnn)

10.MultiLayerGroupMixedCNN(cnn4ie/mixed_depthwise_cnn)

11.MultiLayerMultiCNN(cnn4ie/multi_cnn)

12.MultiLayerMixedAttCNN(cnn4ie/mixed_attention_cnn)

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