refactor the PaddleNLP (#4351)

* Update README.md (#4267)

* test=develop (#4269)

* 3d use new api (#4275)

* PointNet++ and PointRCNN use new API

* Update Readme of Dygraph BERT (#4277)

Fix some typos.

* Update run_classifier_multi_gpu.sh (#4279)

remove the CUDA_VISIBLE_DEVICES

* Update README.md (#4280)

* 17 update api (#4294)

* update1.7 save/load & fluid.data

* update datafeed to dataloader

* Update resnet_acnet.py (#4297)

Bias attr of square conv should be "False" rather than None during training mode.

* test=develop

* test=develop

* test=develop

* test=develop

* test
Co-authored-by: NKaipeng Deng <dengkaipeng@baidu.com>
Co-authored-by: Nzhang wenhui <frankwhzhang@126.com>
Co-authored-by: Nparap1uie-s <parap1uie-s@users.noreply.github.com>

.gitmodules

@@ -13,6 +13,3 @@
 [submodule "PaddleSpeech/DeepSpeech"]
 	path = PaddleSpeech/DeepSpeech
 	url = https://github.com/PaddlePaddle/DeepSpeech.git
-[submodule "PaddleNLP/PALM"]
-	path = PaddleNLP/PALM
-	url = https://github.com/PaddlePaddle/PALM

PALM @ 5426f750

-Subproject commit 5426f75073cf5bd416622dbe71b146d3dc8fffb6

ERNIE @ 30b892e3

-Subproject commit 30b892e3c029bff706337f269e6c158b0a223f60

PaddleNLP/README.md

@@ -10,7 +10,7 @@
 
 - **丰富而全面的NLP任务支持：**
 
-  - PaddleNLP为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)等NLP基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK)，[文本生成](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN)等NLP核心技术。同时，PaddleNLP还提供了针对常见NLP大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC)，[对话系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT)等）的特定核心技术和工具组件，模型和预训练参数等，让您在NLP领域畅通无阻。
+  - PaddleNLP为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)等NLP基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_langauge_models)，[文本生成](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/seq2seq)等NLP核心技术。同时，PaddleNLP还提供了针对常见NLP大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension)，[对话系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation)等）的特定核心技术和工具组件，模型和预训练参数等，让您在NLP领域畅通无阻。
 
 - **稳定可靠的NLP模型和强大的预训练参数：**
 
@@ -50,17 +50,17 @@ cd models/PaddleNLP/sentiment_classification
 
 |                      任务场景                      |                        对应项目/目录                         |                             简介                             |
 | :------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
-| **中文分词**，**词性标注**，**命名实体识别**:fire: | [LAC](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis) | LAC，全称为Lexical Analysis of Chinese，是百度内部广泛使用的中文处理工具，功能涵盖从中文分词，词性标注，命名实体识别等常见中文处理任务。 |
-|               **词向量（word2vec）**               |                         [word2vec](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/word2vec)                         | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
-|                    **语言模型**                    | [Language_model](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/language_model) | 基于循环神经网络（RNN）的经典神经语言模型（neural language model）。 |
-|                 **情感分类**:fire:                 | [Senta](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification)，[EmotionDetection](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/emotion_detection) | Senta（Sentiment Classification，简称Senta）和EmotionDetection两个项目分别提供了面向*通用场景*和*人机对话场景专用*的情感倾向性分析模型。 |
-|              **文本相似度计算**:fire:              | [SimNet](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net) | SimNet，又称为Similarity Net，为您提供高效可靠的文本相似度计算工具和预训练模型。 |
-|                 **语义表示**:fire:                 | [PaddleLARK](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK) | PaddleLARK，全称为Paddle LAngauge Representation Toolkit，集成了ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet等热门中英文预训练模型。 |
-|                    **文本生成**                    | [PaddleTextGEN](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN) | Paddle Text Generation为您提供了一些列经典文本生成模型案例，如vanilla seq2seq，seq2seq with attention，variational seq2seq模型等。 |
-|                    **阅读理解**                    | [PaddleMRC](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC) | PaddleMRC，全称为Paddle Machine Reading Comprehension，集合了百度在阅读理解领域相关的模型，工具，开源数据等一系列工作。包括DuReader (百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集)，KT-Net (结合知识的阅读理解模型，SQuAD以及ReCoRD曾排名第一), D-Net (预训练-微调框架，在EMNLP2019 MRQA国际阅读理解评测获得第一)，等。 |
-|                    **对话系统**                    | [PaddleDialogue](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue) | 包括：1）DGU（Dialogue General Understanding，通用对话理解模型）覆盖了包括**检索式聊天系统**中context-response matching任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在6项国际公开数据集中都获得了最佳效果。<br/> 2) knowledge-driven dialogue：百度开源的知识驱动的开放领域对话数据集，发表于ACL2019。<br/>3）ADEM（Auto Dialogue Evaluation Model）：对话自动评估模型，可用于自动评估不同对话生成模型的回复质量。 |
-|                    **机器翻译**                    | [PaddleMT](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT) | 全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型。 |
-|                  **其他前沿工作**                  | [Research](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research) |                    百度最新前沿工作开源。                    |
+| **中文分词**，**词性标注**，**命名实体识别**:fire: | [LAC](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis) | LAC，全称为Lexical Analysis of Chinese，是百度内部广泛使用的中文处理工具，功能涵盖从中文分词，词性标注，命名实体识别等常见中文处理任务。 |
+|               **词向量（word2vec）**               |                         [word2vec](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/word2vec)                         | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
+|                    **语言模型**                    | [Language_model](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/language_model) | 基于循环神经网络（RNN）的经典神经语言模型（neural language model）。 |
+|                 **情感分类**:fire:                 | [Senta](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification)，[EmotionDetection](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/emotion_detection) | Senta（Sentiment Classification，简称Senta）和EmotionDetection两个项目分别提供了面向*通用场景*和*人机对话场景专用*的情感倾向性分析模型。 |
+|              **文本相似度计算**:fire:              | [SimNet](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net) | SimNet，又称为Similarity Net，为您提供高效可靠的文本相似度计算工具和预训练模型。 |
+|                 **语义表示**:fire:                 | [pretrain_langauge_models](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_langauge_models) | 集成了ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet等热门中英文预训练模型。 |
+|                    **文本生成**                    | [seq2seq](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/PaddleTextGEN) | seq2seq为您提供了一些列经典文本生成模型案例，如vanilla seq2seq，seq2seq with attention，variational seq2seq模型等。 |
+|                    **阅读理解**                    | [machine_reading_comprehension](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension) | Paddle Machine Reading Comprehension，集合了百度在阅读理解领域相关的模型，工具，开源数据等一系列工作。包括DuReader (百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集)，KT-Net (结合知识的阅读理解模型，SQuAD以及ReCoRD曾排名第一), D-Net (预训练-微调框架，在EMNLP2019 MRQA国际阅读理解评测获得第一)，等。 |
+|                    **对话系统**                    | [dialogue_system](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system) | 包括：1）DGU（Dialogue General Understanding，通用对话理解模型）覆盖了包括**检索式聊天系统**中context-response matching任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在6项国际公开数据集中都获得了最佳效果。<br/> 2) knowledge-driven dialogue：百度开源的知识驱动的开放领域对话数据集，发表于ACL2019。<br/>3）ADEM（Auto Dialogue Evaluation Model）：对话自动评估模型，可用于自动评估不同对话生成模型的回复质量。 |
+|                    **机器翻译**                    | [machine_translation](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation) | 全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型。 |
+|                  **其他前沿工作**                  | [Research](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research) |                    百度最新前沿工作开源。                    |
 
 
 
@@ -70,13 +70,13 @@ cd models/PaddleNLP/sentiment_classification
 ```text
 .
 ├── Research                          # 百度NLP在research方面的工作集合
-├── PaddleMT                          # 机器翻译相关代码，数据，预训练模型
-├── PaddleDialogue                    # 对话系统相关代码，数据，预训练模型
-├── PaddleMRC                         # 阅读理解相关代码，数据，预训练模型
-├── PaddleLARK                        # 语言表示工具箱
+├── machine_translation               # 机器翻译相关代码，数据，预训练模型
+├── dialogue_system                   # 对话系统相关代码，数据，预训练模型
+├── machcine_reading_comprehension    # 阅读理解相关代码，数据，预训练模型
+├── pretrain_langauge_models          # 语言表示工具箱
 ├── language_model                    # 语言模型
 ├── lexical_analysis                  # LAC词法分析
-├── models                            # 共享网络
+├── shared_modules/models             # 共享网络
 │   ├── __init__.py
 │   ├── classification
 │   ├── dialogue_model_toolkit
@@ -87,7 +87,7 @@ cd models/PaddleNLP/sentiment_classification
 │   ├── representation
 │   ├── sequence_labeling
 │   └── transformer_encoder.py
-├── preprocess                        # 共享文本预处理工具
+├── shared_modules/preprocess        # 共享文本预处理工具
 │   ├── __init__.py
 │   ├── ernie
 │   ├── padding.py

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/README.md → PaddleNLP/dialogue_system/auto_dialogue_evaluation/README.md

@@ -468,7 +468,7 @@ python -u main.py \
       --loss_type="CLS"
 ```
 #### windows环境下：
-评估： 
+评估：
 ```
 python -u main.py --do_eval=true --use_cuda=false --evaluation_file=data\input\data\unlabel_data\test.ids --output_prediction_file=data\output\pretrain_matching_predict --loss_type=CLS
 ```

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/_ce.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/_ce.py

@@ -21,14 +21,16 @@ from kpi import DurationKpi
 
 train_loss_card1 = CostKpi('train_loss_card1', 0.03, 0, actived=True)
 train_loss_card4 = CostKpi('train_loss_card4', 0.03, 0, actived=True)
-train_duration_card1 = DurationKpi('train_duration_card1', 0.01, 0, actived=True)
-train_duration_card4 = DurationKpi('train_duration_card4', 0.01, 0, actived=True)
+train_duration_card1 = DurationKpi(
+    'train_duration_card1', 0.01, 0, actived=True)
+train_duration_card4 = DurationKpi(
+    'train_duration_card4', 0.01, 0, actived=True)
 
 tracking_kpis = [
-        train_loss_card1,
-        train_loss_card4,
-        train_duration_card1,
-        train_duration_card4,
+    train_loss_card1,
+    train_loss_card4,
+    train_duration_card1,
+    train_duration_card4,
 ]
 
 

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/prepare_data_and_model.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/prepare_data_and_model.py

@@ -20,48 +20,52 @@ import sys
 import io
 import os
 
-URLLIB=urllib
-if sys.version_info >= (3, 0): 
+URLLIB = urllib
+if sys.version_info >= (3, 0):
     import urllib.request
-    URLLIB=urllib.request
+    URLLIB = urllib.request
 
-DATA_MODEL_PATH = {"DATA_PATH": "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz", 
-                   "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"} 
+DATA_MODEL_PATH = {
+    "DATA_PATH":
+    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz",
+    "TRAINED_MODEL":
+    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"
+}
 
-PATH_MAP = {'DATA_PATH': "./data/input", 
-            'TRAINED_MODEL': './data/saved_models'}
+PATH_MAP = {'DATA_PATH': "./data/input", 'TRAINED_MODEL': './data/saved_models'}
 
 
-def un_tar(tar_name, dir_name): 
-    try: 
+def un_tar(tar_name, dir_name):
+    try:
         t = tarfile.open(tar_name)
-        t.extractall(path = dir_name)
+        t.extractall(path=dir_name)
         return True
     except Exception as e:
         print(e)
         return False
 
 
-def download_model_and_data(): 
+def download_model_and_data():
     print("Downloading ade data, pretrain model and trained models......")
     print("This process is quite long, please wait patiently............")
-    for path in ['./data/input/data', './data/saved_models/trained_models']: 
-        if not os.path.exists(path): 
+    for path in ['./data/input/data', './data/saved_models/trained_models']:
+        if not os.path.exists(path):
             continue
         shutil.rmtree(path)
-    for path_key in DATA_MODEL_PATH: 
+    for path_key in DATA_MODEL_PATH:
         filename = os.path.basename(DATA_MODEL_PATH[path_key])
-        URLLIB.urlretrieve(DATA_MODEL_PATH[path_key], os.path.join("./", filename))
+        URLLIB.urlretrieve(DATA_MODEL_PATH[path_key],
+                           os.path.join("./", filename))
         state = un_tar(filename, PATH_MAP[path_key])
-        if not state: 
+        if not state:
             print("Tar %s error....." % path_key)
             return False
         os.remove(filename)
     return True
 
 
-if __name__ == "__main__": 
+if __name__ == "__main__":
     state = download_model_and_data()
-    if not state: 
+    if not state:
         exit(1)
     print("Downloading data and models sucess......")

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/utils/input_field.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/utils/input_field.py

@@ -25,8 +25,8 @@ import numpy as np
 import paddle.fluid as fluid
 
 
-class InputField(object): 
-    def __init__(self, input_field): 
+class InputField(object):
+    def __init__(self, input_field):
         """init inpit field"""
         self.context_wordseq = input_field[0]
         self.response_wordseq = input_field[1]

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/utils/model_check.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/utils/model_check.py

@@ -30,7 +30,7 @@ def check_cuda(use_cuda, err = \
 
 
 if __name__ == "__main__":
-    
+
     check_cuda(True)
 
     check_cuda(False)

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/utils/save_load_io.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/utils/save_load_io.py

@@ -69,8 +69,8 @@ def init_from_checkpoint(args, exe, program):
 def init_from_params(args, exe, program):
 
     assert isinstance(args.init_from_params, str)
-    
-    if not os.path.exists(args.init_from_params): 
+
+    if not os.path.exists(args.init_from_params):
         raise Warning("the params path does not exist.")
         return False
 
@@ -122,5 +122,3 @@ def save_param(args, exe, program, dirname):
     print("save parameters at %s" % (os.path.join(param_dir, dirname)))
 
     return True
-
-

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade_net.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade_net.py

@@ -21,14 +21,13 @@ import paddle
 import paddle.fluid as fluid
 
 
-def create_net(
-    is_training,
-    model_input,
-    args, 
-    clip_value=10.0,
-    word_emb_name="shared_word_emb",
-    lstm_W_name="shared_lstm_W",
-    lstm_bias_name="shared_lstm_bias"): 
+def create_net(is_training,
+               model_input,
+               args,
+               clip_value=10.0,
+               word_emb_name="shared_word_emb",
+               lstm_W_name="shared_lstm_W",
+               lstm_bias_name="shared_lstm_bias"):
 
     context_wordseq = model_input.context_wordseq
     response_wordseq = model_input.response_wordseq
@@ -52,17 +51,15 @@ def create_net(
             initializer=fluid.initializer.Normal(scale=0.1)))
 
     #fc to fit dynamic LSTM
-    context_fc = fluid.layers.fc(
-        input=context_emb,
-        size=args.hidden_size * 4,
-        param_attr=fluid.ParamAttr(name='fc_weight'),
-        bias_attr=fluid.ParamAttr(name='fc_bias'))
+    context_fc = fluid.layers.fc(input=context_emb,
+                                 size=args.hidden_size * 4,
+                                 param_attr=fluid.ParamAttr(name='fc_weight'),
+                                 bias_attr=fluid.ParamAttr(name='fc_bias'))
 
-    response_fc = fluid.layers.fc(
-        input=response_emb,
-        size=args.hidden_size * 4,
-        param_attr=fluid.ParamAttr(name='fc_weight'),
-        bias_attr=fluid.ParamAttr(name='fc_bias'))
+    response_fc = fluid.layers.fc(input=response_emb,
+                                  size=args.hidden_size * 4,
+                                  param_attr=fluid.ParamAttr(name='fc_weight'),
+                                  bias_attr=fluid.ParamAttr(name='fc_bias'))
 
     #LSTM
     context_rep, _ = fluid.layers.dynamic_lstm(
@@ -82,7 +79,7 @@ def create_net(
     logits = fluid.layers.bilinear_tensor_product(
         context_rep, response_rep, size=1)
 
-    if args.loss_type == 'CLS': 
+    if args.loss_type == 'CLS':
         label = fluid.layers.cast(x=label, dtype='float32')
         loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label)
         loss = fluid.layers.reduce_mean(
@@ -95,10 +92,10 @@ def create_net(
         loss = fluid.layers.reduce_mean(loss)
     else:
         raise ValueError
-    
-    if is_training: 
+
+    if is_training:
         return loss
-    else: 
+    else:
         return logits
 
 
@@ -106,7 +103,5 @@ def set_word_embedding(word_emb, place, word_emb_name="shared_word_emb"):
     """
     Set word embedding
     """
-    word_emb_param = fluid.global_scope().find_var(
-        word_emb_name).get_tensor()
+    word_emb_param = fluid.global_scope().find_var(word_emb_name).get_tensor()
     word_emb_param.set(word_emb, place)
-

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/eval.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/eval.py

@@ -23,13 +23,13 @@ import ade.evaluate as evaluate
 from ade.utils.configure import PDConfig
 
 
-def do_eval(args): 
+def do_eval(args):
     """evaluate metrics"""
     labels = []
     fr = io.open(args.evaluation_file, 'r', encoding="utf8")
-    for line in fr: 
+    for line in fr:
         tokens = line.strip().split('\t')
-        assert len(tokens) == 3 
+        assert len(tokens) == 3
         label = int(tokens[2])
         labels.append(label)
 
@@ -43,25 +43,25 @@ def do_eval(args):
         score = score.astype(np.float64)
         scores.append(score)
 
-    if args.loss_type == 'CLS': 
+    if args.loss_type == 'CLS':
         recall_dict = evaluate.evaluate_Recall(list(zip(scores, labels)))
         mean_score = sum(scores) / len(scores)
         print('mean score: %.6f' % mean_score)
         print('evaluation recall result:')
         print('1_in_2: %.6f\t1_in_10: %.6f\t2_in_10: %.6f\t5_in_10: %.6f' %
-             (recall_dict['1_in_2'], recall_dict['1_in_10'],
-             recall_dict['2_in_10'], recall_dict['5_in_10']))
-    elif args.loss_type == 'L2': 
+              (recall_dict['1_in_2'], recall_dict['1_in_10'],
+               recall_dict['2_in_10'], recall_dict['5_in_10']))
+    elif args.loss_type == 'L2':
         scores = [x[0] for x in scores]
         mean_score = sum(scores) / len(scores)
         cor = evaluate.evaluate_cor(scores, labels)
         print('mean score: %.6f\nevaluation cor results:%.6f' %
-            (mean_score, cor))
+              (mean_score, cor))
     else:
         raise ValueError
-    
 
-if __name__ == "__main__": 
+
+if __name__ == "__main__":
     args = PDConfig(yaml_file="./data/config/ade.yaml")
     args.build()
 

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/inference_model.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/inference_model.py

@@ -42,22 +42,24 @@ def do_save_inference_model(args):
         with fluid.unique_name.guard():
 
             context_wordseq = fluid.data(
-                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+                name='context_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
             response_wordseq = fluid.data(
-                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
-            labels = fluid.data(
-                    name='labels', shape=[-1, 1], dtype='int64')
+                name='response_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
+            labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(feed_list=input_inst, 
-                        capacity=4, iterable=False)
+            data_reader = fluid.io.PyReader(
+                feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
-                    is_training=False,
-                    model_input=input_field, 
-                    args=args
-                )
+                is_training=False, model_input=input_field, args=args)
 
     if args.use_cuda:
         place = fluid.CUDAPlace(0)
@@ -68,7 +70,7 @@ def do_save_inference_model(args):
     exe.run(startup_prog)
 
     assert (args.init_from_params) or (args.init_from_pretrain_model)
-    
+
     if args.init_from_params:
         save_load_io.init_from_params(args, exe, test_prog)
     elif args.init_from_pretrain_model:
@@ -76,24 +78,22 @@ def do_save_inference_model(args):
 
     # saving inference model
     fluid.io.save_inference_model(
-            args.inference_model_dir,
-            feeded_var_names=[
-                input_field.context_wordseq.name, 
-                input_field.response_wordseq.name,
-            ],
-            target_vars=[
-                logits,
-            ],
-            executor=exe,
-            main_program=test_prog,
-            model_filename="model.pdmodel",
-            params_filename="params.pdparams")
+        args.inference_model_dir,
+        feeded_var_names=[
+            input_field.context_wordseq.name,
+            input_field.response_wordseq.name,
+        ],
+        target_vars=[logits, ],
+        executor=exe,
+        main_program=test_prog,
+        model_filename="model.pdmodel",
+        params_filename="params.pdparams")
 
     print("save inference model at %s" % (args.inference_model_dir))
 
 
 if __name__ == "__main__":
-    args = PDConfig(yaml_file="./data/config/ade.yaml")   
+    args = PDConfig(yaml_file="./data/config/ade.yaml")
     args.build()
 
     check_cuda(args.use_cuda)

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/main.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/main.py

@@ -26,7 +26,6 @@ from inference_model import do_save_inference_model
 
 from ade.utils.configure import PDConfig
 
-
 if __name__ == "__main__":
 
     args = PDConfig(yaml_file="./data/config/ade.yaml")

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/predict.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/predict.py

@@ -32,7 +32,7 @@ from ade.utils.model_check import check_cuda
 import ade.utils.save_load_io as save_load_io
 
 
-def do_predict(args): 
+def do_predict(args):
     """
     predict function
     """
@@ -46,30 +46,32 @@ def do_predict(args):
         with fluid.unique_name.guard():
 
             context_wordseq = fluid.data(
-                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+                name='context_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
             response_wordseq = fluid.data(
-                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
-            labels = fluid.data(
-                    name='labels', shape=[-1, 1], dtype='int64')
+                name='response_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
+            labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(feed_list=input_inst, 
-                        capacity=4, iterable=False)
+            data_reader = fluid.io.PyReader(
+                feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
-                    is_training=False,
-                    model_input=input_field, 
-                    args=args
-                )
+                is_training=False, model_input=input_field, args=args)
             logits.persistable = True
 
             fetch_list = [logits.name]
     #for_test is True if change the is_test attribute of operators to True
     test_prog = test_prog.clone(for_test=True)
-    if args.use_cuda: 
+    if args.use_cuda:
         place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
-    else: 
+    else:
         place = fluid.CPUPlace()
 
     exe = fluid.Executor(place)
@@ -85,42 +87,39 @@ def do_predict(args):
 
     processor = reader.DataProcessor(
         data_path=args.predict_file,
-        max_seq_length=args.max_seq_len, 
+        max_seq_length=args.max_seq_len,
         batch_size=args.batch_size)
 
     batch_generator = processor.data_generator(
-        place=place,
-        phase="test",
-        shuffle=False, 
-        sample_pro=1)
+        place=place, phase="test", shuffle=False, sample_pro=1)
     num_test_examples = processor.get_num_examples(phase='test')
 
     data_reader.decorate_batch_generator(batch_generator)
     data_reader.start()
 
     scores = []
-    while True: 
-        try: 
+    while True:
+        try:
             results = exe.run(compiled_test_prog, fetch_list=fetch_list)
             scores.extend(results[0])
         except fluid.core.EOFException:
             data_reader.reset()
             break
 
-    scores = scores[: num_test_examples]
+    scores = scores[:num_test_examples]
     print("Write the predicted results into the output_prediction_file")
     fw = io.open(args.output_prediction_file, 'w', encoding="utf8")
-    for index, score in enumerate(scores): 
+    for index, score in enumerate(scores):
         fw.write("%s\t%s\n" % (index, score))
     print("finish........................................")
 
 
-if __name__ == "__main__": 
-    
+if __name__ == "__main__":
+
     args = PDConfig(yaml_file="./data/config/ade.yaml")
     args.build()
     args.Print()
 
     check_cuda(args.use_cuda)
 
-    do_predict(args) 
+    do_predict(args)

PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/train.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/train.py

@@ -31,7 +31,7 @@ from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
 import ade.utils.save_load_io as save_load_io
 
-try: 
+try:
     import cPickle as pickle  #python 2
 except ImportError as e:
     import pickle  #python 3
@@ -47,24 +47,26 @@ def do_train(args):
         train_prog.random_seed = args.random_seed
         startup_prog.random_seed = args.random_seed
 
-        with fluid.unique_name.guard(): 
+        with fluid.unique_name.guard():
             context_wordseq = fluid.data(
-                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+                name='context_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
             response_wordseq = fluid.data(
-                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
-            labels = fluid.data(
-                    name='labels', shape=[-1, 1], dtype='int64')
+                name='response_wordseq',
+                shape=[-1, 1],
+                dtype='int64',
+                lod_level=1)
+            labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(feed_list=input_inst, 
-                        capacity=4, iterable=False)
+            data_reader = fluid.io.PyReader(
+                feed_list=input_inst, capacity=4, iterable=False)
 
             loss = create_net(
-                    is_training=True,
-                    model_input=input_field, 
-                    args=args
-                )
+                is_training=True, model_input=input_field, args=args)
             loss.persistable = True
             # gradient clipping
             fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
@@ -74,20 +76,21 @@ def do_train(args):
 
             if args.use_cuda:
                 dev_count = fluid.core.get_cuda_device_count()
-                place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
-            else: 
+                place = fluid.CUDAPlace(
+                    int(os.getenv('FLAGS_selected_gpus', '0')))
+            else:
                 dev_count = int(os.environ.get('CPU_NUM', 1))
                 place = fluid.CPUPlace()
 
             processor = reader.DataProcessor(
                 data_path=args.training_file,
-                max_seq_length=args.max_seq_len, 
+                max_seq_length=args.max_seq_len,
                 batch_size=args.batch_size)
 
             batch_generator = processor.data_generator(
                 place=place,
                 phase="train",
-                shuffle=True, 
+                shuffle=True,
                 sample_pro=args.sample_pro)
 
             num_train_examples = processor.get_num_examples(phase='train')
@@ -105,18 +108,23 @@ def do_train(args):
         args.init_from_pretrain_model == "")
 
     #init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint: 
+    if args.init_from_checkpoint:
         save_load_io.init_from_checkpoint(args, exe, train_prog)
     #init from some pretrain models, to better solve the current task
-    if args.init_from_pretrain_model: 
+    if args.init_from_pretrain_model:
         save_load_io.init_from_pretrain_model(args, exe, train_prog)
 
     if args.word_emb_init:
         print("start loading word embedding init ...")
         if six.PY2:
-            word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'))).astype('float32')
+            word_emb = np.array(
+                pickle.load(io.open(args.word_emb_init, 'rb'))).astype(
+                    'float32')
         else:
-            word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'), encoding="bytes")).astype('float32')
+            word_emb = np.array(
+                pickle.load(
+                    io.open(args.word_emb_init, 'rb'),
+                    encoding="bytes")).astype('float32')
         set_word_embedding(word_emb, place)
         print("finish init word embedding  ...")
 
@@ -124,69 +132,74 @@ def do_train(args):
     build_strategy.enable_inplace = True
 
     compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
-                loss_name=loss.name, build_strategy=build_strategy)
+        loss_name=loss.name, build_strategy=build_strategy)
 
     steps = 0
     begin_time = time.time()
-    time_begin =  time.time()
+    time_begin = time.time()
 
-    for epoch_step in range(args.epoch): 
+    for epoch_step in range(args.epoch):
         data_reader.start()
         sum_loss = 0.0
         ce_loss = 0.0
         while True:
-            try: 
+            try:
                 fetch_list = [loss.name]
                 outputs = exe.run(compiled_train_prog, fetch_list=fetch_list)
                 np_loss = outputs
                 sum_loss += np.array(np_loss).mean()
                 ce_loss = np.array(np_loss).mean()
 
-                if steps % args.print_steps == 0: 
+                if steps % args.print_steps == 0:
                     time_end = time.time()
                     used_time = time_end - time_begin
                     current_time = time.strftime('%Y-%m-%d %H:%M:%S',
-                                                time.localtime(time.time()))
-                    print('%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s' % (current_time, epoch_step, steps, sum_loss / args.print_steps, args.print_steps / used_time))
+                                                 time.localtime(time.time()))
+                    print(
+                        '%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s'
+                        % (current_time, epoch_step, steps, sum_loss /
+                           args.print_steps, args.print_steps / used_time))
                     sum_loss = 0.0
                     time_begin = time.time()
 
-                if steps % args.save_steps == 0: 
+                if steps % args.save_steps == 0:
                     if args.save_checkpoint:
-                        save_load_io.save_checkpoint(args, exe, train_prog, "step_" + str(steps))
-                    if args.save_param: 
-                        save_load_io.save_param(args, exe, train_prog, "step_" + str(steps))
+                        save_load_io.save_checkpoint(args, exe, train_prog,
+                                                     "step_" + str(steps))
+                    if args.save_param:
+                        save_load_io.save_param(args, exe, train_prog,
+                                                "step_" + str(steps))
                 steps += 1
-            except fluid.core.EOFException:  
+            except fluid.core.EOFException:
                 data_reader.reset()
                 break
-    
-    if args.save_checkpoint: 
+
+    if args.save_checkpoint:
         save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
-    if args.save_param: 
+    if args.save_param:
         save_load_io.save_param(args, exe, train_prog, "step_final")
 
-    def get_cards(): 
+    def get_cards():
         num = 0
         cards = os.environ.get('CUDA_VISIBLE_DEVICES', '')
-        if cards != '': 
+        if cards != '':
             num = len(cards.split(","))
         return num
 
-    if args.enable_ce: 
+    if args.enable_ce:
         card_num = get_cards()
         pass_time_cost = time.time() - begin_time
         print("test_card_num", card_num)
         print("kpis\ttrain_duration_card%s\t%s" % (card_num, pass_time_cost))
         print("kpis\ttrain_loss_card%s\t%f" % (card_num, ce_loss))
-        
+
 
 if __name__ == '__main__':
-    
+
     args = PDConfig(yaml_file="./data/config/ade.yaml")
     args.build()
     args.Print()
 
     check_cuda(args.use_cuda)
-    
+
     do_train(args)

PaddleNLP/PaddleDialogue/dialogue_general_understanding/README.md → PaddleNLP/dialogue_system/dialogue_general_understanding/README.md

@@ -62,7 +62,7 @@ SWDA：Switchboard Dialogue Act Corpus;
     数据集、相关模型下载：
     linux环境下：
 ```
-python dgu/prepare_data_and_model.py 
+python dgu/prepare_data_and_model.py
 ```
     数据路径：data/input/data
 
@@ -72,7 +72,7 @@ python dgu/prepare_data_and_model.py
 
     windows环境下：
 ```
-python dgu\prepare_data_and_model.py 
+python dgu\prepare_data_and_model.py
 ```
 
     下载的数据集中已提供了训练集，测试集和验证集，用户如果需要重新生成某任务数据集的训练数据，可执行：
@@ -164,19 +164,19 @@ task_type: train，predict, evaluate, inference, all, 选择5个参数选项中
 训练示例： bash run.sh atis_intent train
 ```
 
-    如果为CPU训练: 
+    如果为CPU训练:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 1、export CUDA_VISIBLE_DEVICES=
 ```
 
-    如果为GPU训练: 
+    如果为GPU训练:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 1、如果为单卡训练（用户指定空闲的单卡）：
-export CUDA_VISIBLE_DEVICES=0 
+export CUDA_VISIBLE_DEVICES=0
 2、如果为多卡训练（用户指定空闲的多张卡）：
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 ```
@@ -252,19 +252,19 @@ task_type: train，predict, evaluate, inference, all, 选择5个参数选项中
 预测示例： bash run.sh atis_intent predict
 ```
 
-    如果为CPU预测: 
+    如果为CPU预测:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 1、export CUDA_VISIBLE_DEVICES=
 ```
 
-    如果为GPU预测: 
+    如果为GPU预测:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 支持单卡预测（用户指定空闲的单卡）：
-export CUDA_VISIBLE_DEVICES=0 
+export CUDA_VISIBLE_DEVICES=0
 ```
 
 注：预测时，如采用方式一，用户可通过修改run.sh中init_from_params参数来指定自己训练好的需要预测的模型，目前代码中默认为加载官方已经训练好的模型;
@@ -348,7 +348,7 @@ task_type: train，predict, evaluate, inference, all, 选择5个参数选项中
 
 注：评估计算ground_truth和predict_label之间的打分，默认CPU计算即可；
 
-####     方式二: 执行评估相关的代码: 
+####     方式二: 执行评估相关的代码:
 
 ```
 TASK_NAME="atis_intent"  #指定预测的任务名称
@@ -363,7 +363,7 @@ python -u main.py \
 
 #### windows环境下
 ```
-python -u main.py --task_name=atis_intent --use_cuda=false --do_eval=true --evaluation_file=data\input\data\atis\atis_intent\test.txt --output_prediction_file=data\output\pred_atis_intent 
+python -u main.py --task_name=atis_intent --use_cuda=false --do_eval=true --evaluation_file=data\input\data\atis\atis_intent\test.txt --output_prediction_file=data\output\pred_atis_intent
 ```
 
 ### 模型推断
@@ -378,22 +378,22 @@ task_type: train，predict, evaluate, inference, all, 选择5个参数选项中
 保存模型示例： bash run.sh atis_intent inference
 ```
 
-    如果为CPU执行inference model过程: 
+    如果为CPU执行inference model过程:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 1、export CUDA_VISIBLE_DEVICES=
 ```
 
     如果为GPU执行inference model过程:
 
 ```
-请将run.sh内参数设置为: 
+请将run.sh内参数设置为:
 1、单卡模型推断（用户指定空闲的单卡）：
 export CUDA_VISIBLE_DEVICES=0
 ```
 
-####     方式二: 执行inference model相关的代码: 
+####     方式二: 执行inference model相关的代码:
 
 ```
 TASK_NAME="atis_intent"  #指定预测的任务名称
@@ -459,7 +459,7 @@ python -u main.py \
 
     用户也可以根据自己的需求，组建自定义的模型，具体方法如下所示：
 
-    a、自定义数据 
+    a、自定义数据
 
       如用户目前有数据集为**task_name**, 则在**data/input/data**下定义**task_name**文件夹，将数据集存放进去；在**dgu/reader.py**中，新增自定义的数据处理的类，如**udc**数据集对应**UDCProcessor**;  在**train.py**内设置**task_name**和**processor**的对应关系(如**processors = {'udc': reader.UDCProcessor}**).
 
@@ -481,7 +481,7 @@ python -u main.py \
 - Elizabeth Shriberg, Raj Dhillon, Sonali Bhagat, JeremyAng, and Hannah Carvey. 2004. The icsi meetingrecorder dialog act (mrda) corpus. Technical report,INTERNATIONAL COMPUTER SCIENCE INSTBERKELEY CA.
 - Andreas Stolcke, Klaus Ries, Noah Coccaro, Eliza-beth Shriberg, Rebecca Bates, Daniel Jurafsky, PaulTaylor, Rachel Martin, Carol Van Ess-Dykema, andMarie Meteer. 2000. Dialogue act modeling for au-tomatic tagging and recognition of conversationalspeech.Computational linguistics, 26(3):339–373.
 - Ye-Yi Wang, Li Deng, and Alex Acero. 2005.  Spo-ken language understanding.IEEE Signal Process-ing Magazine, 22(5):16–31.Jason Williams, Antoine Raux, Deepak Ramachan-dran, and Alan Black. 2013. The dialog state tracking challenge.  InProceedings of the SIGDIAL 2013Conference, pages 404–413.
-- Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc VLe,  Mohammad Norouzi,  Wolfgang Macherey,Maxim  Krikun,  Yuan  Cao,  Qin  Gao,  KlausMacherey,  et al. 2016.   Google’s neural ma-chine translation system: Bridging the gap betweenhuman and machine translation.arXiv preprintarXiv:1609.08144.Kaisheng 
+- Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc VLe,  Mohammad Norouzi,  Wolfgang Macherey,Maxim  Krikun,  Yuan  Cao,  Qin  Gao,  KlausMacherey,  et al. 2016.   Google’s neural ma-chine translation system: Bridging the gap betweenhuman and machine translation.arXiv preprintarXiv:1609.08144.Kaisheng
 - Yao, Geoffrey Zweig, Mei-Yuh Hwang,Yangyang Shi, and Dong Yu. 2013. Recurrent neu-ral networks for language understanding. InInter-speech, pages 2524–2528.
 - Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, YingChen, Wayne Xin Zhao, Dianhai Yu, and Hua Wu.2018.  Multi-turn response selection for chatbotswith deep attention matching network. InProceed-ings of the 56th Annual Meeting of the Associationfor Computational Linguistics (Volume 1: Long Pa-pers), volume 1, pages 1118–1127.
 - Su Zhu and Kai Yu. 2017.  Encoder-decoder withfocus-mechanism for sequence labelling based spo-ken language understanding. In2017 IEEE Interna-tional Conference on Acoustics, Speech and SignalProcessing (ICASSP), pages 5675–5679. IEEE.

PaddleNLP/PaddleDialogue/dialogue_general_understanding/_ce.py → PaddleNLP/dialogue_system/dialogue_general_understanding/_ce.py

@@ -20,20 +20,26 @@ from kpi import CostKpi
 from kpi import DurationKpi
 from kpi import AccKpi
 
-each_step_duration_atis_slot_card1 = DurationKpi('each_step_duration_atis_slot_card1', 0.01, 0, actived=True)
-train_loss_atis_slot_card1 = CostKpi('train_loss_atis_slot_card1', 0.08, 0, actived=True)
-train_acc_atis_slot_card1 = CostKpi('train_acc_atis_slot_card1', 0.01, 0, actived=True)
-each_step_duration_atis_slot_card4 = DurationKpi('each_step_duration_atis_slot_card4', 0.06, 0, actived=True)
-train_loss_atis_slot_card4 = CostKpi('train_loss_atis_slot_card4', 0.03, 0, actived=True)
-train_acc_atis_slot_card4 = CostKpi('train_acc_atis_slot_card4', 0.01, 0, actived=True)
+each_step_duration_atis_slot_card1 = DurationKpi(
+    'each_step_duration_atis_slot_card1', 0.01, 0, actived=True)
+train_loss_atis_slot_card1 = CostKpi(
+    'train_loss_atis_slot_card1', 0.08, 0, actived=True)
+train_acc_atis_slot_card1 = CostKpi(
+    'train_acc_atis_slot_card1', 0.01, 0, actived=True)
+each_step_duration_atis_slot_card4 = DurationKpi(
+    'each_step_duration_atis_slot_card4', 0.06, 0, actived=True)
+train_loss_atis_slot_card4 = CostKpi(
+    'train_loss_atis_slot_card4', 0.03, 0, actived=True)
+train_acc_atis_slot_card4 = CostKpi(
+    'train_acc_atis_slot_card4', 0.01, 0, actived=True)
 
 tracking_kpis = [
-        each_step_duration_atis_slot_card1,
-        train_loss_atis_slot_card1,
-        train_acc_atis_slot_card1,
-        each_step_duration_atis_slot_card4,
-        train_loss_atis_slot_card4,
-        train_acc_atis_slot_card4,
+    each_step_duration_atis_slot_card1,
+    train_loss_atis_slot_card1,
+    train_acc_atis_slot_card1,
+    each_step_duration_atis_slot_card4,
+    train_loss_atis_slot_card4,
+    train_acc_atis_slot_card4,
 ]
 
 

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/batching.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/batching.py

@@ -75,8 +75,8 @@ def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
 
 
 def prepare_batch_data(task_name,
-                       insts, 
-                       max_len, 
+                       insts,
+                       max_len,
                        total_token_num,
                        voc_size=0,
                        pad_id=None,
@@ -98,14 +98,18 @@ def prepare_batch_data(task_name,
     # compatible with squad, whose example includes start/end positions, 
     # or unique id
 
-    if isinstance(insts[0][3], list): 
-        if task_name == "atis_slot": 
-            labels_list = [inst[3] + [0] * (max_len - len(inst[3])) for inst in insts]
-            labels_list = [np.array(labels_list).astype("int64").reshape([-1, max_len])]
-        elif task_name == "dstc2": 
+    if isinstance(insts[0][3], list):
+        if task_name == "atis_slot":
+            labels_list = [
+                inst[3] + [0] * (max_len - len(inst[3])) for inst in insts
+            ]
+            labels_list = [
+                np.array(labels_list).astype("int64").reshape([-1, max_len])
+            ]
+        elif task_name == "dstc2":
             labels_list = [inst[3] for inst in insts]
             labels_list = [np.array(labels_list).astype("int64")]
-    else: 
+    else:
         for i in range(3, len(insts[0]), 1):
             labels = [inst[i] for inst in insts]
             labels = np.array(labels).astype("int64").reshape([-1, 1])
@@ -124,28 +128,25 @@ def prepare_batch_data(task_name,
         out = batch_src_ids
     # Second step: padding
     src_id, self_input_mask = pad_batch_data(
-        out, 
-        max_len, 
-        pad_idx=pad_id, 
-        return_input_mask=True)
+        out, max_len, pad_idx=pad_id, return_input_mask=True)
     pos_id = pad_batch_data(
-        batch_pos_ids, 
-        max_len, 
-        pad_idx=pad_id, 
-        return_pos=False, 
+        batch_pos_ids,
+        max_len,
+        pad_idx=pad_id,
+        return_pos=False,
         return_input_mask=False)
     sent_id = pad_batch_data(
-        batch_sent_ids, 
-        max_len, 
-        pad_idx=pad_id, 
-        return_pos=False, 
+        batch_sent_ids,
+        max_len,
+        pad_idx=pad_id,
+        return_pos=False,
         return_input_mask=False)
 
     if mask_id >= 0:
         return_list = [
             src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos
         ] + labels_list
-    else: 
+    else:
         return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list
 
     return return_list if len(return_list) > 1 else return_list[0]
@@ -163,13 +164,13 @@ def pad_batch_data(insts,
     corresponding position data and attention bias.
     """
     return_list = []
-    max_len = max_len_in if max_len_in != -1 else max(len(inst) for inst in insts)
+    max_len = max_len_in if max_len_in != -1 else max(
+        len(inst) for inst in insts)
     # Any token included in dict can be used to pad, since the paddings' loss
     # will be masked out by weights and make no effect on parameter gradients.
 
     inst_data = np.array(
-        [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts
-    ])
+        [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts])
     return_list += [inst_data.astype("int64").reshape([-1, max_len])]
 
     # position data
@@ -183,10 +184,10 @@ def pad_batch_data(insts,
 
     if return_input_mask:
         # This is used to avoid attention on paddings.
-        input_mask_data = np.array([[1] * len(inst) + [0] * 
+        input_mask_data = np.array([[1] * len(inst) + [0] *
                                     (max_len - len(inst)) for inst in insts])
         input_mask_data = np.expand_dims(input_mask_data, axis=-1)
-        return_list += [input_mask_data.astype("float32")] 
+        return_list += [input_mask_data.astype("float32")]
 
     if return_max_len:
         return_list += [max_len]

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/define_predict_pack.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_predict_pack.py

@@ -21,31 +21,34 @@ import paddle
 import paddle.fluid as fluid
 
 
-class DefinePredict(object): 
+class DefinePredict(object):
     """
     Packaging Prediction Results
     """
-    def __init__(self): 
+
+    def __init__(self):
         """
         init
         """
-        self.task_map = {'udc': 'get_matching_res', 
-                         'swda': 'get_cls_res', 
-                         'mrda': 'get_cls_res', 
-                         'atis_intent': 'get_cls_res',
-                         'atis_slot': 'get_sequence_tagging',
-                         'dstc2': 'get_multi_cls_res', 
-                         'dstc2_asr': 'get_multi_cls_res', 
-                         'multi-woz': 'get_multi_cls_res'}
+        self.task_map = {
+            'udc': 'get_matching_res',
+            'swda': 'get_cls_res',
+            'mrda': 'get_cls_res',
+            'atis_intent': 'get_cls_res',
+            'atis_slot': 'get_sequence_tagging',
+            'dstc2': 'get_multi_cls_res',
+            'dstc2_asr': 'get_multi_cls_res',
+            'multi-woz': 'get_multi_cls_res'
+        }
 
-    def get_matching_res(self, probs, params=None): 
+    def get_matching_res(self, probs, params=None):
         """
         get matching score
         """
         probs = list(probs)
         return probs[1]
 
-    def get_cls_res(self, probs, params=None): 
+    def get_cls_res(self, probs, params=None):
         """
         get da classify tag
         """
@@ -54,7 +57,7 @@ class DefinePredict(object):
         tag = probs.index(max_prob)
         return tag
 
-    def get_sequence_tagging(self, probs, params=None): 
+    def get_sequence_tagging(self, probs, params=None):
         """
         get sequence tagging tag
         """
@@ -63,23 +66,19 @@ class DefinePredict(object):
         labels = [" ".join([str(l) for l in list(l_l)]) for l_l in batch_labels]
         return labels
 
-    def get_multi_cls_res(self, probs, params=None): 
+    def get_multi_cls_res(self, probs, params=None):
         """
         get dst classify tag
         """
         labels = []
         probs = list(probs)
-        for i in range(len(probs)): 
-            if probs[i] >= 0.5: 
+        for i in range(len(probs)):
+            if probs[i] >= 0.5:
                 labels.append(i)
-        if not labels: 
+        if not labels:
             max_prob = max(probs)
             label_str = str(probs.index(max_prob))
-        else: 
+        else:
             label_str = " ".join([str(l) for l in sorted(labels)])
 
         return label_str
-
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/prepare_data_and_model.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/prepare_data_and_model.py

@@ -20,51 +20,60 @@ import sys
 import io
 import os
 
-
-URLLIB=urllib
-if sys.version_info >= (3, 0): 
+URLLIB = urllib
+if sys.version_info >= (3, 0):
     import urllib.request
-    URLLIB=urllib.request
+    URLLIB = urllib.request
 
-DATA_MODEL_PATH = {"DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz", 
-                   "PRETRAIN_MODEL": "https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz", 
-                   "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"} 
+DATA_MODEL_PATH = {
+    "DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz",
+    "PRETRAIN_MODEL":
+    "https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz",
+    "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"
+}
 
-PATH_MAP = {'DATA_PATH': "./data/input", 
-            'PRETRAIN_MODEL': './data/pretrain_model', 
-            'TRAINED_MODEL': './data/saved_models'}
+PATH_MAP = {
+    'DATA_PATH': "./data/input",
+    'PRETRAIN_MODEL': './data/pretrain_model',
+    'TRAINED_MODEL': './data/saved_models'
+}
 
 
-def un_tar(tar_name, dir_name): 
-    try: 
+def un_tar(tar_name, dir_name):
+    try:
         t = tarfile.open(tar_name)
-        t.extractall(path = dir_name)
+        t.extractall(path=dir_name)
         return True
     except Exception as e:
         print(e)
         return False
 
 
-def download_model_and_data(): 
+def download_model_and_data():
     print("Downloading dgu data, pretrain model and trained models......")
     print("This process is quite long, please wait patiently............")
-    for path in ['./data/input/data', './data/pretrain_model/uncased_L-12_H-768_A-12', './data/saved_models/trained_models']: 
-        if not os.path.exists(path): 
+    for path in [
+            './data/input/data',
+            './data/pretrain_model/uncased_L-12_H-768_A-12',
+            './data/saved_models/trained_models'
+    ]:
+        if not os.path.exists(path):
             continue
         shutil.rmtree(path)
-    for path_key in DATA_MODEL_PATH: 
+    for path_key in DATA_MODEL_PATH:
         filename = os.path.basename(DATA_MODEL_PATH[path_key])
-        URLLIB.urlretrieve(DATA_MODEL_PATH[path_key], os.path.join("./", filename))
+        URLLIB.urlretrieve(DATA_MODEL_PATH[path_key],
+                           os.path.join("./", filename))
         state = un_tar(filename, PATH_MAP[path_key])
-        if not state: 
+        if not state:
             print("Tar %s error....." % path_key)
             return False
         os.remove(filename)
     return True
 
 
-if __name__ == "__main__": 
+if __name__ == "__main__":
     state = download_model_and_data()
-    if not state: 
+    if not state:
         exit(1)
     print("Downloading data and models sucess......")

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/README.md → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/README.md

@@ -6,7 +6,7 @@ scripts：运行数据处理脚本目录, 将官方公开数据集转换成模
 python run_build_data.py udc
 生成数据在dialogue_general_understanding/data/input/data/udc
 
-2)、生成DA任务所需要的训练集、开发集、测试集时: 
+2)、生成DA任务所需要的训练集、开发集、测试集时:
   python run_build_data.py swda
   python run_build_data.py mrda
   生成数据分别在dialogue_general_understanding/data/input/data/swda和dialogue_general_understanding/data/input/data/mrda
@@ -19,6 +19,3 @@ python run_build_data.py udc
   python run_build_data.py atis
   生成槽位识别数据在dialogue_general_understanding/data/input/data/atis/atis_slot
   生成意图识别数据在dialogue_general_understanding/data/input/data/atis/atis_intent
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_atis_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_atis_dataset.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """build swda train dev test dataset"""
 
 import json
@@ -23,11 +22,12 @@ import io
 import re
 
 
-class ATIS(object): 
+class ATIS(object):
     """
     nlu dataset atis data process
     """
-    def __init__(self): 
+
+    def __init__(self):
         """
         init instance
         """
@@ -41,91 +41,94 @@ class ATIS(object):
         self.map_tag_slot = "../../data/input/data/atis/atis_slot/map_tag_slot_id.txt"
         self.map_tag_intent = "../../data/input/data/atis/atis_intent/map_tag_intent_id.txt"
 
-    def _load_file(self, data_type): 
+    def _load_file(self, data_type):
         """
         load dataset filename
         """
         slot_stat = os.path.exists(self.out_slot_dir)
-        if not slot_stat: 
+        if not slot_stat:
             os.makedirs(self.out_slot_dir)
         intent_stat = os.path.exists(self.out_intent_dir)
-        if not intent_stat: 
+        if not intent_stat:
             os.makedirs(self.out_intent_dir)
         src_examples = []
         json_file = os.path.join(self.src_dir, "%s.json" % data_type)
         load_f = io.open(json_file, 'r', encoding="utf8")
         json_dict = json.load(load_f)
         examples = json_dict['rasa_nlu_data']['common_examples']
-        for example in examples: 
+        for example in examples:
             text = example.get('text')
             intent = example.get('intent')
             entities = example.get('entities')
             src_examples.append((text, intent, entities))
         return src_examples
 
-    def _parser_intent_data(self, examples, data_type): 
+    def _parser_intent_data(self, examples, data_type):
         """
         parser intent dataset
         """
         out_filename = "%s/%s.txt" % (self.out_intent_dir, data_type)
         fw = io.open(out_filename, 'w', encoding="utf8")
-        for example in examples: 
-            if example[1] not in self.intent_dict: 
+        for example in examples:
+            if example[1] not in self.intent_dict:
                 self.intent_dict[example[1]] = self.intent_id
                 self.intent_id += 1
-            fw.write(u"%s\t%s\n" % (self.intent_dict[example[1]], example[0].lower()))
+            fw.write(u"%s\t%s\n" %
+                     (self.intent_dict[example[1]], example[0].lower()))
 
         fw = io.open(self.map_tag_intent, 'w', encoding="utf8")
-        for tag in self.intent_dict: 
+        for tag in self.intent_dict:
             fw.write(u"%s\t%s\n" % (tag, self.intent_dict[tag]))
 
-    def _parser_slot_data(self, examples, data_type): 
+    def _parser_slot_data(self, examples, data_type):
         """
         parser slot dataset
         """
         out_filename = "%s/%s.txt" % (self.out_slot_dir, data_type)
         fw = io.open(out_filename, 'w', encoding="utf8")
-        for example in examples: 
+        for example in examples:
             tags = []
             text = example[0]
             entities = example[2]
-            if not entities: 
+            if not entities:
                 tags = [str(self.slot_dict['O'])] * len(text.strip().split())
                 continue
-            for i in range(len(entities)): 
+            for i in range(len(entities)):
                 enty = entities[i]
                 start = enty['start']
                 value_num = len(enty['value'].split())
                 tags_slot = []
-                for j in range(value_num): 
-                    if j == 0: 
+                for j in range(value_num):
+                    if j == 0:
                         bround_tag = "B"
-                    else: 
+                    else:
                         bround_tag = "I"
                     tag = "%s-%s" % (bround_tag, enty['entity'])
-                    if tag not in self.slot_dict: 
+                    if tag not in self.slot_dict:
                         self.slot_dict[tag] = self.slot_id
                         self.slot_id += 1
                     tags_slot.append(str(self.slot_dict[tag]))
-                if i == 0: 
-                    if start not in [0, 1]: 
-                        prefix_num = len(text[: start].strip().split())
+                if i == 0:
+                    if start not in [0, 1]:
+                        prefix_num = len(text[:start].strip().split())
                         tags.extend([str(self.slot_dict['O'])] * prefix_num)
                     tags.extend(tags_slot)
-                else: 
-                    prefix_num = len(text[entities[i - 1]['end']: start].strip().split())
+                else:
+                    prefix_num = len(text[entities[i - 1]['end']:start].strip()
+                                     .split())
                     tags.extend([str(self.slot_dict['O'])] * prefix_num)
                     tags.extend(tags_slot)
-            if entities[-1]['end'] < len(text): 
+            if entities[-1]['end'] < len(text):
                 suffix_num = len(text[entities[-1]['end']:].strip().split())
                 tags.extend([str(self.slot_dict['O'])] * suffix_num)
-            fw.write(u"%s\t%s\n" % (text.encode('utf8'), " ".join(tags).encode('utf8')))
-        
+            fw.write(u"%s\t%s\n" %
+                     (text.encode('utf8'), " ".join(tags).encode('utf8')))
+
         fw = io.open(self.map_tag_slot, 'w', encoding="utf8")
-        for slot in self.slot_dict: 
+        for slot in self.slot_dict:
             fw.write(u"%s\t%s\n" % (slot, self.slot_dict[slot]))
 
-    def get_train_dataset(self): 
+    def get_train_dataset(self):
         """
         parser train dataset and print train.txt
         """
@@ -133,7 +136,7 @@ class ATIS(object):
         self._parser_intent_data(train_examples, "train")
         self._parser_slot_data(train_examples, "train")
 
-    def get_test_dataset(self): 
+    def get_test_dataset(self):
         """
         parser test dataset and print test.txt
         """
@@ -141,7 +144,7 @@ class ATIS(object):
         self._parser_intent_data(test_examples, "test")
         self._parser_slot_data(test_examples, "test")
 
-    def main(self): 
+    def main(self):
         """
         run data process
         """
@@ -149,10 +152,6 @@ class ATIS(object):
         self.get_test_dataset()
 
 
-if __name__ == "__main__": 
+if __name__ == "__main__":
     atis_inst = ATIS()
     atis_inst.main()
-
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_dstc2_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_dstc2_dataset.py

@@ -24,11 +24,12 @@ import re
 import commonlib
 
 
-class DSTC2(object): 
+class DSTC2(object):
     """
     dialogue state tracking dstc2 data process
     """
-    def __init__(self): 
+
+    def __init__(self):
         """
         init instance
         """
@@ -42,16 +43,17 @@ class DSTC2(object):
         self._load_file()
         self._load_ontology()
 
-    def _load_file(self): 
+    def _load_file(self):
         """
         load dataset filename
         """
         self.data_dict = commonlib.load_dict(self.data_list)
-        for data_type in self.data_dict: 
-            for i in range(len(self.data_dict[data_type])): 
-                self.data_dict[data_type][i] = os.path.join(self.src_dir, self.data_dict[data_type][i])
+        for data_type in self.data_dict:
+            for i in range(len(self.data_dict[data_type])):
+                self.data_dict[data_type][i] = os.path.join(
+                    self.src_dir, self.data_dict[data_type][i])
 
-    def _load_ontology(self): 
+    def _load_ontology(self):
         """
         load ontology tag
         """
@@ -60,8 +62,8 @@ class DSTC2(object):
         fr = io.open(self.onto_json, 'r', encoding="utf8")
         ontology = json.load(fr)
         slots_values = ontology['informable']
-        for slot in slots_values: 
-            for value in slots_values[slot]: 
+        for slot in slots_values:
+            for value in slots_values[slot]:
                 key = "%s_%s" % (slot, value)
                 self.map_tag_dict[key] = tag_id
                 tag_id += 1
@@ -69,22 +71,22 @@ class DSTC2(object):
             self.map_tag_dict[key] = tag_id
             tag_id += 1
 
-    def _parser_dataset(self, data_type): 
+    def _parser_dataset(self, data_type):
         """
         parser train dev test dataset
         """
         stat = os.path.exists(self.out_dir)
-        if not stat: 
+        if not stat:
             os.makedirs(self.out_dir)
         asr_stat = os.path.exists(self.out_asr_dir)
-        if not asr_stat: 
+        if not asr_stat:
             os.makedirs(self.out_asr_dir)
         out_file = os.path.join(self.out_dir, "%s.txt" % data_type)
         out_asr_file = os.path.join(self.out_asr_dir, "%s.txt" % data_type)
         fw = io.open(out_file, 'w', encoding="utf8")
         fw_asr = io.open(out_asr_file, 'w', encoding="utf8")
         data_list = self.data_dict.get(data_type)
-        for fn in data_list: 
+        for fn in data_list:
             log_file = os.path.join(fn, "log.json")
             label_file = os.path.join(fn, "label.json")
             f_log = io.open(log_file, 'r', encoding="utf8")
@@ -93,49 +95,59 @@ class DSTC2(object):
             label_json = json.load(f_label)
             session_id = log_json['session-id']
             assert len(label_json["turns"]) == len(log_json["turns"])
-            for i in range(len(label_json["turns"])): 
+            for i in range(len(label_json["turns"])):
                 log_turn = log_json["turns"][i]
                 label_turn = label_json["turns"][i]
                 assert log_turn["turn-index"] == label_turn["turn-index"]
-                labels = ["%s_%s" % (slot, label_turn["goal-labels"][slot]) for slot in label_turn["goal-labels"]]
-                labels_ids = " ".join([str(self.map_tag_dict.get(label, self.map_tag_dict["%s_none" % label.split('_')[0]])) for label in labels])
+                labels = [
+                    "%s_%s" % (slot, label_turn["goal-labels"][slot])
+                    for slot in label_turn["goal-labels"]
+                ]
+                labels_ids = " ".join([
+                    str(
+                        self.map_tag_dict.get(label, self.map_tag_dict[
+                            "%s_none" % label.split('_')[0]]))
+                    for label in labels
+                ])
                 mach = log_turn['output']['transcript']
                 user = label_turn['transcription']
-                if not labels_ids.strip(): 
+                if not labels_ids.strip():
                     labels_ids = self.map_tag_dict['none']
                 out = "%s\t%s\1%s\t%s" % (session_id, mach, user, labels_ids)
-                user_asr = log_turn['input']['live']['asr-hyps'][0]['asr-hyp'].strip()
-                out_asr = "%s\t%s\1%s\t%s" % (session_id, mach, user_asr, labels_ids)
+                user_asr = log_turn['input']['live']['asr-hyps'][0][
+                    'asr-hyp'].strip()
+                out_asr = "%s\t%s\1%s\t%s" % (session_id, mach, user_asr,
+                                              labels_ids)
                 fw.write(u"%s\n" % out.encode('utf8'))
                 fw_asr.write(u"%s\n" % out_asr.encode('utf8'))
 
-    def get_train_dataset(self): 
+    def get_train_dataset(self):
         """
         parser train dataset and print train.txt
         """
         self._parser_dataset("train")
 
-    def get_dev_dataset(self): 
+    def get_dev_dataset(self):
         """
         parser dev dataset and print dev.txt
         """
         self._parser_dataset("dev")
 
-    def get_test_dataset(self): 
+    def get_test_dataset(self):
         """
         parser test dataset and print test.txt
         """
         self._parser_dataset("test")
 
-    def get_labels(self): 
+    def get_labels(self):
         """
         get tag and map ids file
         """
         fw = io.open(self.map_tag, 'w', encoding="utf8")
-        for elem in self.map_tag_dict: 
+        for elem in self.map_tag_dict:
             fw.write(u"%s\t%s\n" % (elem, self.map_tag_dict[elem]))
 
-    def main(self): 
+    def main(self):
         """
         run data process
         """
@@ -144,10 +156,7 @@ class DSTC2(object):
         self.get_test_dataset()
         self.get_labels()
 
-if __name__ == "__main__": 
+
+if __name__ == "__main__":
     dstc_inst = DSTC2()
     dstc_inst.main()
-
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_mrda_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_mrda_dataset.py

@@ -23,11 +23,12 @@ import re
 import commonlib
 
 
-class MRDA(object): 
+class MRDA(object):
     """
     dialogue act dataset mrda data process
     """
-    def __init__(self): 
+
+    def __init__(self):
         """
         init instance
         """
@@ -41,7 +42,7 @@ class MRDA(object):
         self._load_file()
         self.tag_dict = commonlib.load_voc(self.voc_map_tag)
 
-    def _load_file(self): 
+    def _load_file(self):
         """
         load dataset filename
         """
@@ -49,30 +50,30 @@ class MRDA(object):
         self.trans_dict = {}
         self.data_dict = commonlib.load_dict(self.data_list)
         file_list, file_path = commonlib.get_file_list(self.src_dir)
-        for i in range(len(file_list)): 
+        for i in range(len(file_list)):
             name = file_list[i]
             keyword = name.split('.')[0]
-            if 'dadb' in name: 
+            if 'dadb' in name:
                 self.dadb_dict[keyword] = file_path[i]
-            if 'trans' in name: 
+            if 'trans' in name:
                 self.trans_dict[keyword] = file_path[i]
 
-    def load_dadb(self, data_type): 
+    def load_dadb(self, data_type):
         """
         load dadb dataset
         """
         dadb_dict = {}
         conv_id_list = []
         dadb_list = self.data_dict[data_type]
-        for dadb_key in dadb_list: 
+        for dadb_key in dadb_list:
             dadb_file = self.dadb_dict[dadb_key]
             fr = io.open(dadb_file, 'r', encoding="utf8")
-            row = csv.reader(fr, delimiter = ',')
-            for line in row: 
+            row = csv.reader(fr, delimiter=',')
+            for line in row:
                 elems = line
                 conv_id = elems[2]
                 conv_id_list.append(conv_id)
-                if len(elems) != 14: 
+                if len(elems) != 14:
                     continue
                 error_code = elems[3]
                 da_tag = elems[-9]
@@ -80,17 +81,17 @@ class MRDA(object):
                 dadb_dict[conv_id] = (error_code, da_ori_tag, da_tag)
         return dadb_dict, conv_id_list
 
-    def load_trans(self, data_type): 
+    def load_trans(self, data_type):
         """load trans data"""
         trans_dict = {}
         trans_list = self.data_dict[data_type]
-        for trans_key in trans_list: 
+        for trans_key in trans_list:
             trans_file = self.trans_dict[trans_key]
             fr = io.open(trans_file, 'r', encoding="utf8")
-            row = csv.reader(fr, delimiter = ',')
-            for line in row: 
+            row = csv.reader(fr, delimiter=',')
+            for line in row:
                 elems = line
-                if len(elems) != 3: 
+                if len(elems) != 3:
                     continue
                 conv_id = elems[0]
                 text = elems[1]
@@ -98,7 +99,7 @@ class MRDA(object):
                 trans_dict[conv_id] = (text, text_process)
         return trans_dict
 
-    def _parser_dataset(self, data_type): 
+    def _parser_dataset(self, data_type):
         """
         parser train dev test dataset
         """
@@ -106,50 +107,51 @@ class MRDA(object):
         dadb_dict, conv_id_list = self.load_dadb(data_type)
         trans_dict = self.load_trans(data_type)
         fw = io.open(out_filename, 'w', encoding="utf8")
-        for elem in conv_id_list: 
+        for elem in conv_id_list:
             v_dadb = dadb_dict[elem]
             v_trans = trans_dict[elem]
             da_tag = v_dadb[2]
-            if da_tag not in self.tag_dict: 
+            if da_tag not in self.tag_dict:
                 continue
             tag = self.tag_dict[da_tag]
-            if tag == "Z": 
+            if tag == "Z":
                 continue
-            if tag not in self.map_tag_dict: 
+            if tag not in self.map_tag_dict:
                 self.map_tag_dict[tag] = self.tag_id
                 self.tag_id += 1
             caller = elem.split('_')[0].split('-')[-1]
             conv_no = elem.split('_')[0].split('-')[0]
-            out = "%s\t%s\t%s\t%s" % (conv_no, self.map_tag_dict[tag], caller, v_trans[0])
+            out = "%s\t%s\t%s\t%s" % (conv_no, self.map_tag_dict[tag], caller,
+                                      v_trans[0])
             fw.write(u"%s\n" % out)
 
-    def get_train_dataset(self): 
+    def get_train_dataset(self):
         """
         parser train dataset and print train.txt
         """
         self._parser_dataset("train")
 
-    def get_dev_dataset(self): 
+    def get_dev_dataset(self):
         """
         parser dev dataset and print dev.txt
         """
         self._parser_dataset("dev")
 
-    def get_test_dataset(self): 
+    def get_test_dataset(self):
         """
         parser test dataset and print test.txt
         """
         self._parser_dataset("test")
 
-    def get_labels(self): 
+    def get_labels(self):
         """
         get tag and map ids file
         """
         fw = io.open(self.map_tag, 'w', encoding="utf8")
-        for elem in self.map_tag_dict: 
+        for elem in self.map_tag_dict:
             fw.write(u"%s\t%s\n" % (elem, self.map_tag_dict[elem]))
 
-    def main(self): 
+    def main(self):
         """
         run data process
         """
@@ -158,10 +160,7 @@ class MRDA(object):
         self.get_test_dataset()
         self.get_labels()
 
-if __name__ == "__main__": 
+
+if __name__ == "__main__":
     mrda_inst = MRDA()
     mrda_inst.main()
-
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_swda_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_swda_dataset.py

@@ -23,11 +23,12 @@ import re
 import commonlib
 
 
-class SWDA(object): 
+class SWDA(object):
     """
     dialogue act dataset swda data process
     """
-    def __init__(self): 
+
+    def __init__(self):
         """
         init instance
         """
@@ -39,94 +40,94 @@ class SWDA(object):
         self.src_dir = "../../data/input/data/swda/source_data/swda"
         self._load_file()
 
-    def _load_file(self): 
+    def _load_file(self):
         """
         load dataset filename
         """
         self.data_dict = commonlib.load_dict(self.data_list)
         self.file_dict = {}
         child_dir = commonlib.get_dir_list(self.src_dir)
-        for chd in child_dir: 
+        for chd in child_dir:
             file_list, file_path = commonlib.get_file_list(chd)
-            for i in range(len(file_list)): 
-                name = file_list[i] 
+            for i in range(len(file_list)):
+                name = file_list[i]
                 keyword = "sw%s" % name.split('.')[0].split('_')[-1]
                 self.file_dict[keyword] = file_path[i]
 
-    def _parser_dataset(self, data_type): 
+    def _parser_dataset(self, data_type):
         """
         parser train dev test dataset
         """
         out_filename = "%s/%s.txt" % (self.out_dir, data_type)
         fw = io.open(out_filename, 'w', encoding='utf8')
-        for name in self.data_dict[data_type]: 
+        for name in self.data_dict[data_type]:
             file_path = self.file_dict[name]
             fr = io.open(file_path, 'r', encoding="utf8")
             idx = 0
-            row = csv.reader(fr, delimiter = ',')
-            for r in row: 
-                if idx == 0: 
+            row = csv.reader(fr, delimiter=',')
+            for r in row:
+                if idx == 0:
                     idx += 1
                     continue
                 out = self._parser_utterence(r)
                 fw.write(u"%s\n" % out)
 
-    def _clean_text(self, text): 
+    def _clean_text(self, text):
         """
         text cleaning for dialogue act dataset
         """
-        if text.startswith('<') and text.endswith('>.'): 
+        if text.startswith('<') and text.endswith('>.'):
             return text
         if "[" in text or "]" in text:
             stat = True
-        else: 
+        else:
             stat = False
         group = re.findall("\[.*?\+.*?\]", text)
-        while group and stat: 
-            for elem in group: 
+        while group and stat:
+            for elem in group:
                 elem_src = elem
                 elem = re.sub('\+', '', elem.lstrip('[').rstrip(']'))
                 text = text.replace(elem_src, elem)
-            if "[" in text or "]" in text: 
+            if "[" in text or "]" in text:
                 stat = True
-            else: 
+            else:
                 stat = False
             group = re.findall("\[.*?\+.*?\]", text)
-        if "{" in text or "}" in text: 
+        if "{" in text or "}" in text:
             stat = True
-        else: 
+        else:
             stat = False
         group = re.findall("{[A-Z].*?}", text)
-        while group and stat: 
+        while group and stat:
             child_group = re.findall("{[A-Z]*(.*?)}", text)
-            for i in range(len(group)):  
+            for i in range(len(group)):
                 text = text.replace(group[i], child_group[i])
-            if "{" in text or "}" in text: 
+            if "{" in text or "}" in text:
                 stat = True
-            else: 
+            else:
                 stat = False
             group = re.findall("{[A-Z].*?}", text)
-        if "(" in text or ")" in text: 
+        if "(" in text or ")" in text:
             stat = True
-        else: 
+        else:
             stat = False
         group = re.findall("\(\(.*?\)\)", text)
-        while group and stat: 
-            for elem in group: 
-                if elem: 
+        while group and stat:
+            for elem in group:
+                if elem:
                     elem_clean = re.sub("\(|\)", "", elem)
                     text = text.replace(elem, elem_clean)
-                else: 
+                else:
                     text = text.replace(elem, "mumblex")
             if "(" in text or ")" in text:
                 stat = True
-            else: 
+            else:
                 stat = False
             group = re.findall("\(\((.*?)\)\)", text)
 
         group = re.findall("\<.*?\>", text)
-        if group: 
-            for elem in group: 
+        if group:
+            for elem in group:
                 text = text.replace(elem, "")
 
         text = re.sub(r" \'s", "\'s", text)
@@ -137,24 +138,24 @@ class SWDA(object):
         text = re.sub("\[|\]|\+|\>|\<|\{|\}", "", text)
         return text.strip().lower()
 
-    def _map_tag(self, da_tag): 
+    def _map_tag(self, da_tag):
         """
         map tag to 42 classes
         """
         curr_da_tags = []
         curr_das = re.split(r"\s*[,;]\s*", da_tag)
-        for curr_da in curr_das: 
+        for curr_da in curr_das:
             if curr_da == "qy_d" or curr_da == "qw^d" or curr_da == "b^m":
                 pass
             elif curr_da == "nn^e":
                 curr_da = "ng"
             elif curr_da == "ny^e":
                 curr_da = "na"
-            else: 
+            else:
                 curr_da = re.sub(r'(.)\^.*', r'\1', curr_da)
                 curr_da = re.sub(r'[\(\)@*]', '', curr_da)
                 tag = curr_da
-                if tag in ('qr', 'qy'): 
+                if tag in ('qr', 'qy'):
                     tag = 'qy'
                 elif tag in ('fe', 'ba'):
                     tag = 'ba'
@@ -170,12 +171,12 @@ class SWDA(object):
                     tag = 'fo_o_fw_"_by_bc'
                 curr_da = tag
             curr_da_tags.append(curr_da)
-        if curr_da_tags[0] not in self.map_tag_dict: 
+        if curr_da_tags[0] not in self.map_tag_dict:
             self.map_tag_dict[curr_da_tags[0]] = self.tag_id
             self.tag_id += 1
         return self.map_tag_dict[curr_da_tags[0]]
-    
-    def _parser_utterence(self, line): 
+
+    def _parser_utterence(self, line):
         """
         parser one turn dialogue
         """
@@ -188,34 +189,34 @@ class SWDA(object):
 
         out = "%s\t%s\t%s\t%s" % (conversation_no, act_tag, caller, text)
         return out
-        
-    def get_train_dataset(self): 
+
+    def get_train_dataset(self):
         """
         parser train dataset and print train.txt
         """
         self._parser_dataset("train")
 
-    def get_dev_dataset(self): 
+    def get_dev_dataset(self):
         """
         parser dev dataset and print dev.txt
         """
         self._parser_dataset("dev")
 
-    def get_test_dataset(self): 
+    def get_test_dataset(self):
         """
         parser test dataset and print test.txt
         """
         self._parser_dataset("test")
 
-    def get_labels(self): 
+    def get_labels(self):
         """
         get tag and map ids file
         """
         fw = io.open(self.map_tag, 'w', encoding='utf8')
-        for elem in self.map_tag_dict: 
+        for elem in self.map_tag_dict:
             fw.write(u"%s\t%s\n" % (elem, self.map_tag_dict[elem]))
 
-    def main(self): 
+    def main(self):
         """
         run data process
         """
@@ -224,10 +225,7 @@ class SWDA(object):
         self.get_test_dataset()
         self.get_labels()
 
-if __name__ == "__main__": 
+
+if __name__ == "__main__":
     swda_inst = SWDA()
     swda_inst.main()
-
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/commonlib.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/commonlib.py

@@ -25,52 +25,49 @@ def get_file_list(dir_name):
     file_list = list()
     file_path = list()
     for root, dirs, files in os.walk(dir_name):
-        for file in files: 
+        for file in files:
             file_list.append(file)
             file_path.append(os.path.join(root, file))
     return file_list, file_path
 
 
-def get_dir_list(dir_name): 
+def get_dir_list(dir_name):
     """
     get directory names
     """
     child_dir = []
     dir_list = os.listdir(dir_name)
-    for cur_file in dir_list: 
+    for cur_file in dir_list:
         path = os.path.join(dir_name, cur_file)
-        if not os.path.isdir(path): 
+        if not os.path.isdir(path):
             continue
         child_dir.append(path)
     return child_dir
 
 
-def load_dict(conf): 
+def load_dict(conf):
     """
     load swda dataset config
     """
     conf_dict = dict()
     fr = io.open(conf, 'r', encoding="utf8")
-    for line in fr: 
+    for line in fr:
         line = line.strip()
         elems = line.split('\t')
-        if elems[0] not in conf_dict: 
+        if elems[0] not in conf_dict:
             conf_dict[elems[0]] = []
         conf_dict[elems[0]].append(elems[1])
     return conf_dict
 
 
-def load_voc(conf): 
+def load_voc(conf):
     """
     load map dict
     """
     map_dict = {}
     fr = io.open(conf, 'r', encoding="utf8")
-    for line in fr:   
+    for line in fr:
         line = line.strip()
         elems = line.split('\t')
         map_dict[elems[0]] = elems[1]
     return map_dict
-
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/run_build_data.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/run_build_data.py

@@ -20,29 +20,29 @@ from build_dstc2_dataset import DSTC2
 from build_mrda_dataset import MRDA
 from build_swda_dataset import SWDA
 
-
-if __name__ == "__main__": 
+if __name__ == "__main__":
     task_name = sys.argv[1]
     task_name = task_name.lower()
-    
-    if task_name not in ['swda', 'mrda', 'atis', 'dstc2', 'udc']: 
+
+    if task_name not in ['swda', 'mrda', 'atis', 'dstc2', 'udc']:
         print("task name error: we support [swda|mrda|atis|dstc2|udc]")
         exit(1)
-    
-    if task_name == 'swda': 
+
+    if task_name == 'swda':
         swda_inst = SWDA()
         swda_inst.main()
-    elif task_name == 'mrda': 
+    elif task_name == 'mrda':
         mrda_inst = MRDA()
         mrda_inst.main()
-    elif task_name == 'atis': 
+    elif task_name == 'atis':
         atis_inst = ATIS()
         atis_inst.main()
-        shutil.copyfile("../../data/input/data/atis/atis_slot/test.txt", "../../data/input/data/atis/atis_slot/dev.txt")
-        shutil.copyfile("../../data/input/data/atis/atis_intent/test.txt", "../../data/input/data/atis/atis_intent/dev.txt")
-    elif task_name == 'dstc2': 
+        shutil.copyfile("../../data/input/data/atis/atis_slot/test.txt",
+                        "../../data/input/data/atis/atis_slot/dev.txt")
+        shutil.copyfile("../../data/input/data/atis/atis_intent/test.txt",
+                        "../../data/input/data/atis/atis_intent/dev.txt")
+    elif task_name == 'dstc2':
         dstc_inst = DSTC2()
         dstc_inst.main()
-    else: 
+    else:
         exit(0)
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/tokenization.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/tokenization.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tokenization classes."""
 
 from __future__ import absolute_import

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/transformer_encoder.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/transformer_encoder.py

@@ -113,7 +113,7 @@ def multi_head_attention(queries,
         """
         Scaled Dot-Product Attention
         """
-        scaled_q = layers.scale(x=q, scale=d_key ** -0.5)
+        scaled_q = layers.scale(x=q, scale=d_key**-0.5)
         product = layers.matmul(x=scaled_q, y=k, transpose_y=True)
         if attn_bias:
             product += attn_bias

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/utils/input_field.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/utils/input_field.py

@@ -25,8 +25,8 @@ import numpy as np
 import paddle.fluid as fluid
 
 
-class InputField(object): 
-    def __init__(self, input_field): 
+class InputField(object):
+    def __init__(self, input_field):
         """init inpit field"""
         self.src_ids = input_field[0]
         self.pos_ids = input_field[1]

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/utils/model_check.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/utils/model_check.py

@@ -30,7 +30,7 @@ def check_cuda(use_cuda, err = \
 
 
 if __name__ == "__main__":
-    
+
     check_cuda(True)
 
     check_cuda(False)

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/utils/save_load_io.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/utils/save_load_io.py

@@ -69,8 +69,8 @@ def init_from_checkpoint(args, exe, program):
 def init_from_params(args, exe, program):
 
     assert isinstance(args.init_from_params, str)
-    
-    if not os.path.exists(args.init_from_params): 
+
+    if not os.path.exists(args.init_from_params):
         raise Warning("the params path does not exist.")
         return False
 
@@ -113,7 +113,7 @@ def save_param(args, exe, program, dirname):
 
     if not os.path.exists(param_dir):
         os.makedirs(param_dir)
-    
+
     fluid.io.save_params(
         exe,
         os.path.join(param_dir, dirname),
@@ -122,5 +122,3 @@ def save_param(args, exe, program, dirname):
     print("save parameters at %s" % (os.path.join(param_dir, dirname)))
 
     return True
-
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu_net.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu_net.py

@@ -23,14 +23,9 @@ from dgu.bert import BertModel
 from dgu.utils.configure import JsonConfig
 
 
-def create_net(
-        is_training,
-        model_input,
-        num_labels,
-        paradigm_inst,
-        args): 
+def create_net(is_training, model_input, num_labels, paradigm_inst, args):
     """create dialogue task model"""
-    
+
     src_ids = model_input.src_ids
     pos_ids = model_input.pos_ids
     sent_ids = model_input.sent_ids
@@ -48,14 +43,15 @@ def create_net(
         config=bert_conf,
         use_fp16=False)
 
-    params = {'num_labels': num_labels,
-              'src_ids': src_ids,
-              'pos_ids': pos_ids,
-              'sent_ids': sent_ids,
-              'input_mask': input_mask,
-              'labels': labels,
-              'is_training': is_training}
+    params = {
+        'num_labels': num_labels,
+        'src_ids': src_ids,
+        'pos_ids': pos_ids,
+        'sent_ids': sent_ids,
+        'input_mask': input_mask,
+        'labels': labels,
+        'is_training': is_training
+    }
 
     results = paradigm_inst.paradigm(bert, params)
     return results
-

PaddleNLP/PaddleDialogue/dialogue_general_understanding/eval.py → PaddleNLP/dialogue_system/dialogue_general_understanding/eval.py

@@ -20,17 +20,17 @@ from dgu.evaluation import evaluate
 from dgu.utils.configure import PDConfig
 
 
-def do_eval(args): 
+def do_eval(args):
 
     task_name = args.task_name.lower()
     reference = args.evaluation_file
     predicitions = args.output_prediction_file
-    
+
     evaluate(task_name, predicitions, reference)
 
 
-if __name__ == "__main__": 
-    
+if __name__ == "__main__":
+
     args = PDConfig(yaml_file="./data/config/dgu.yaml")
     args.build()
 

PaddleNLP/PaddleDialogue/dialogue_general_understanding/inference_model.py → PaddleNLP/dialogue_system/dialogue_general_understanding/inference_model.py

@@ -29,10 +29,10 @@ import dgu.utils.save_load_io as save_load_io
 
 import dgu.reader as reader
 from dgu_net import create_net
-import dgu.define_paradigm as define_paradigm 
+import dgu.define_paradigm as define_paradigm
 
 
-def do_save_inference_model(args): 
+def do_save_inference_model(args):
     """save inference model function"""
 
     task_name = args.task_name.lower()
@@ -57,35 +57,36 @@ def do_save_inference_model(args):
         with fluid.unique_name.guard():
 
             # define inputs of the network
-            num_labels = len(processors[task_name].get_labels()) 
+            num_labels = len(processors[task_name].get_labels())
 
             src_ids = fluid.data(
-                        name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
             pos_ids = fluid.data(
-                        name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
             sent_ids = fluid.data(
-                        name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
             input_mask = fluid.data(
-                        name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
-            if args.task_name == 'atis_slot': 
+                name='input_mask',
+                shape=[-1, args.max_seq_len],
+                dtype='float32')
+            if args.task_name == 'atis_slot':
                 labels = fluid.data(
-                        name='labels', shape=[-1, args.max_seq_len], dtype='int64')
+                    name='labels', shape=[-1, args.max_seq_len], dtype='int64')
             elif args.task_name in ['dstc2', 'dstc2_asr', 'multi-woz']:
                 labels = fluid.data(
-                        name='labels', shape=[-1, num_labels], dtype='int64')
-            else: 
-                labels = fluid.data(
-                        name='labels', shape=[-1, 1], dtype='int64')
-            
+                    name='labels', shape=[-1, num_labels], dtype='int64')
+            else:
+                labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
+
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            
+
             results = create_net(
-                    is_training=False, 
-                    model_input=input_field, 
-                    num_labels=num_labels,
-                    paradigm_inst=paradigm_inst,
-                    args=args)
+                is_training=False,
+                model_input=input_field,
+                num_labels=num_labels,
+                paradigm_inst=paradigm_inst,
+                args=args)
             probs = results.get("probs", None)
 
     if args.use_cuda:
@@ -97,7 +98,7 @@ def do_save_inference_model(args):
     exe.run(startup_prog)
 
     assert (args.init_from_params) or (args.init_from_pretrain_model)
-    
+
     if args.init_from_params:
         save_load_io.init_from_params(args, exe, test_prog)
     elif args.init_from_pretrain_model:
@@ -105,20 +106,16 @@ def do_save_inference_model(args):
 
     # saving inference model
     fluid.io.save_inference_model(
-            args.inference_model_dir,
-            feeded_var_names=[
-                input_field.src_ids.name, 
-                input_field.pos_ids.name,
-                input_field.sent_ids.name, 
-                input_field.input_mask.name
-            ],
-            target_vars=[
-                probs
-            ],
-            executor=exe,
-            main_program=test_prog,
-            model_filename="model.pdmodel",
-            params_filename="params.pdparams")
+        args.inference_model_dir,
+        feeded_var_names=[
+            input_field.src_ids.name, input_field.pos_ids.name,
+            input_field.sent_ids.name, input_field.input_mask.name
+        ],
+        target_vars=[probs],
+        executor=exe,
+        main_program=test_prog,
+        model_filename="model.pdmodel",
+        params_filename="params.pdparams")
 
     print("save inference model at %s" % (args.inference_model_dir))
 

PaddleNLP/PaddleDialogue/dialogue_general_understanding/main.py → PaddleNLP/dialogue_system/dialogue_general_understanding/main.py

@@ -26,7 +26,6 @@ from inference_model import do_save_inference_model
 
 from dgu.utils.configure import PDConfig
 
-
 if __name__ == "__main__":
 
     args = PDConfig(yaml_file="./data/config/dgu.yaml")

PaddleNLP/PaddleDialogue/dialogue_general_understanding/train.py → PaddleNLP/dialogue_system/dialogue_general_understanding/train.py

@@ -28,7 +28,7 @@ import paddle.fluid as fluid
 from dgu_net import create_net
 import dgu.reader as reader
 from dgu.optimization import optimization
-import dgu.define_paradigm as define_paradigm 
+import dgu.define_paradigm as define_paradigm
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
@@ -37,7 +37,7 @@ import dgu.utils.save_load_io as save_load_io
 
 def do_train(args):
     """train function"""
-    
+
     task_name = args.task_name.lower()
     paradigm_inst = define_paradigm.Paradigm(task_name)
 
@@ -53,34 +53,35 @@ def do_train(args):
     train_prog = fluid.default_main_program()
     startup_prog = fluid.default_startup_program()
 
-    with fluid.program_guard(train_prog, startup_prog): 
+    with fluid.program_guard(train_prog, startup_prog):
         train_prog.random_seed = args.random_seed
         startup_prog.random_seed = args.random_seed
-        with fluid.unique_name.guard(): 
+        with fluid.unique_name.guard():
             num_labels = len(processors[task_name].get_labels())
 
             src_ids = fluid.data(
-                        name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
             pos_ids = fluid.data(
-                        name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
             sent_ids = fluid.data(
-                        name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
+                name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
             input_mask = fluid.data(
-                        name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
-            if args.task_name == 'atis_slot': 
+                name='input_mask',
+                shape=[-1, args.max_seq_len],
+                dtype='float32')
+            if args.task_name == 'atis_slot':
                 labels = fluid.data(
-                        name='labels', shape=[-1, args.max_seq_len], dtype='int64')
+                    name='labels', shape=[-1, args.max_seq_len], dtype='int64')
             elif args.task_name in ['dstc2']:
                 labels = fluid.data(
-                        name='labels', shape=[-1, num_labels], dtype='int64')
-            else: 
-                labels = fluid.data(
-                        name='labels', shape=[-1, 1], dtype='int64')
-            
+                    name='labels', shape=[-1, num_labels], dtype='int64')
+            else:
+                labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
+
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(feed_list=input_inst, 
-                        capacity=4, iterable=False)
+            data_reader = fluid.io.PyReader(
+                feed_list=input_inst, capacity=4, iterable=False)
             processor = processors[task_name](data_dir=args.data_dir,
                                               vocab_path=args.vocab_path,
                                               max_seq_len=args.max_seq_len,
@@ -90,12 +91,12 @@ def do_train(args):
                                               random_seed=args.random_seed)
 
             results = create_net(
-                    is_training=True, 
-                    model_input=input_field, 
-                    num_labels=num_labels,
-                    paradigm_inst=paradigm_inst,
-                    args=args)
-            
+                is_training=True,
+                model_input=input_field,
+                num_labels=num_labels,
+                paradigm_inst=paradigm_inst,
+                args=args)
+
             loss = results.get("loss", None)
             probs = results.get("probs", None)
             accuracy = results.get("accuracy", None)
@@ -103,21 +104,19 @@ def do_train(args):
 
             loss.persistable = True
             probs.persistable = True
-            if accuracy: 
+            if accuracy:
                 accuracy.persistable = True
             num_seqs.persistable = True
 
-            if args.use_cuda: 
+            if args.use_cuda:
                 dev_count = fluid.core.get_cuda_device_count()
-            else: 
+            else:
                 dev_count = int(os.environ.get('CPU_NUM', 1))
-            
+
             batch_generator = processor.data_generator(
-                batch_size=args.batch_size,
-                phase='train',
-                shuffle=True)
+                batch_size=args.batch_size, phase='train', shuffle=True)
             num_train_examples = processor.get_num_examples(phase='train')
-            
+
             if args.in_tokens:
                 max_train_steps = args.epoch * num_train_examples // (
                     args.batch_size // args.max_seq_len) // dev_count
@@ -147,32 +146,32 @@ def do_train(args):
         place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
     else:
         place = fluid.CPUPlace()
-    
+
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
     assert (args.init_from_checkpoint == "") or (
-            args.init_from_pretrain_model == "")
+        args.init_from_pretrain_model == "")
 
     # init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint: 
+    if args.init_from_checkpoint:
         save_load_io.init_from_checkpoint(args, exe, train_prog)
-    
+
     # init from some pretrain models, to better solve the current task
-    if args.init_from_pretrain_model: 
+    if args.init_from_pretrain_model:
         save_load_io.init_from_pretrain_model(args, exe, train_prog)
 
     build_strategy = fluid.compiler.BuildStrategy()
     build_strategy.enable_inplace = True
 
     compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
-                loss_name=loss.name, build_strategy=build_strategy)
-    
+        loss_name=loss.name, build_strategy=build_strategy)
+
     # start training
     steps = 0
     time_begin = time.time()
     ce_info = []
-    for epoch_step in range(args.epoch): 
+    for epoch_step in range(args.epoch):
         data_reader.start()
         while True:
             try:
@@ -216,43 +215,38 @@ def do_train(args):
                     used_time = time_end - time_begin
                     current_time = time.strftime('%Y-%m-%d %H:%M:%S',
                                                  time.localtime(time.time()))
-                    if accuracy is not None: 
-                        print(
-                            "%s epoch: %d, step: %d, ave loss: %f, "
-                            "ave acc: %f, speed: %f steps/s" %
-                            (current_time, epoch_step, steps,
-                             np.mean(np_loss),
-                             np.mean(np_acc),
-                             args.print_steps / used_time))
+                    if accuracy is not None:
+                        print("%s epoch: %d, step: %d, ave loss: %f, "
+                              "ave acc: %f, speed: %f steps/s" %
+                              (current_time, epoch_step, steps,
+                               np.mean(np_loss), np.mean(np_acc),
+                               args.print_steps / used_time))
                         ce_info.append([
-                            np.mean(np_loss),
-                            np.mean(np_acc),
+                            np.mean(np_loss), np.mean(np_acc),
                             args.print_steps / used_time
                         ])
                     else:
-                        print(
-                            "%s epoch: %d, step: %d, ave loss: %f, "
-                            "speed: %f steps/s" %
-                            (current_time, epoch_step, steps,
-                             np.mean(np_loss),
-                             args.print_steps / used_time))
-                        ce_info.append([
-                            np.mean(np_loss),
-                            args.print_steps / used_time
-                        ])
+                        print("%s epoch: %d, step: %d, ave loss: %f, "
+                              "speed: %f steps/s" %
+                              (current_time, epoch_step, steps,
+                               np.mean(np_loss), args.print_steps / used_time))
+                        ce_info.append(
+                            [np.mean(np_loss), args.print_steps / used_time])
                     time_begin = time.time()
 
-                if steps % args.save_steps == 0: 
+                if steps % args.save_steps == 0:
                     save_path = "step_" + str(steps)
-                    if args.save_checkpoint: 
-                        save_load_io.save_checkpoint(args, exe, train_prog, save_path)
+                    if args.save_checkpoint:
+                        save_load_io.save_checkpoint(args, exe, train_prog,
+                                                     save_path)
                     if args.save_param:
-                        save_load_io.save_param(args, exe, train_prog, save_path)
-                 
-            except fluid.core.EOFException:  
+                        save_load_io.save_param(args, exe, train_prog,
+                                                save_path)
+
+            except fluid.core.EOFException:
                 data_reader.reset()
                 break
-    if args.save_checkpoint: 
+    if args.save_checkpoint:
         save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
     if args.save_param:
         save_load_io.save_param(args, exe, train_prog, "step_final")
@@ -264,7 +258,7 @@ def do_train(args):
         if cards != '':
             num = len(cards.split(","))
         return num
-    
+
     if args.enable_ce:
         card_num = get_cards()
         print("test_card_num", card_num)
@@ -283,8 +277,8 @@ def do_train(args):
         print("kpis\ttrain_acc_%s_card%s\t%f" % (task_name, card_num, ce_acc))
 
 
-if __name__ == '__main__': 
-    
+if __name__ == '__main__':
+
     args = PDConfig(yaml_file="./data/config/dgu.yaml")
     args.build()
     args.Print()

PaddleNLP/emotion_detection/inference_model.py

@@ -19,8 +19,7 @@ from __future__ import print_function
 
 import os
 import sys
-sys.path.append("../")
-
+sys.path.append("../shared_modules/")
 import paddle
 import paddle.fluid as fluid
 import numpy as np

PaddleNLP/emotion_detection/run_classifier.py

@@ -23,7 +23,7 @@ import os
 import time
 import multiprocessing
 import sys
-sys.path.append("../")
+sys.path.append("../shared_modules/")
 
 import paddle
 import paddle.fluid as fluid

PaddleNLP/emotion_detection/run_ernie_classifier.py

@@ -24,7 +24,7 @@ import time
 import argparse
 import multiprocessing
 import sys
-sys.path.append("../")
+sys.path.append("../shared_modules/")
 
 import paddle
 import paddle.fluid as fluid

PaddleNLP/language_model/train.py

@@ -36,7 +36,7 @@ import sys
 if sys.version[0] == '2':
     reload(sys)
     sys.setdefaultencoding("utf-8")
-sys.path.append('../')
+sys.path.append('../shared_modules/')
 import os
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 

PaddleNLP/lexical_analysis/creator.py

@@ -26,7 +26,7 @@ from paddle.fluid.initializer import NormalInitializer
 from reader import Dataset
 from ernie_reader import SequenceLabelReader
 
-sys.path.append("..")
+sys.path.append("../shared_modules/")
 from models.sequence_labeling import nets
 from models.representation.ernie import ernie_encoder, ernie_pyreader
 
@@ -35,7 +35,8 @@ def create_model(args, vocab_size, num_labels, mode='train'):
     """create lac model"""
 
     # model's input data
-    words = fluid.data(name='words', shape=[None, 1], dtype='int64', lod_level=1)
+    words = fluid.data(
+        name='words', shape=[None, 1], dtype='int64', lod_level=1)
     targets = fluid.data(
         name='targets', shape=[None, 1], dtype='int64', lod_level=1)
 
@@ -88,7 +89,8 @@ def create_pyreader(args,
                     return_reader=False,
                     mode='train'):
     # init reader
-    device_count = len(fluid.cuda_places()) if args.use_cuda else len(fluid.cpu_places())
+    device_count = len(fluid.cuda_places()) if args.use_cuda else len(
+        fluid.cpu_places())
 
     if model == 'lac':
         pyreader = fluid.io.DataLoader.from_generator(
@@ -107,14 +109,14 @@ def create_pyreader(args,
                     fluid.io.shuffle(
                         reader.file_reader(file_name),
                         buf_size=args.traindata_shuffle_buffer),
-                    batch_size=args.batch_size/device_count),
+                    batch_size=args.batch_size / device_count),
                 places=place)
         else:
             pyreader.set_sample_list_generator(
                 fluid.io.batch(
                     reader.file_reader(
                         file_name, mode=mode),
-                    batch_size=args.batch_size/device_count),
+                    batch_size=args.batch_size / device_count),
                 places=place)
 
     elif model == 'ernie':

PaddleNLP/lexical_analysis/ernie_reader.py

@@ -20,7 +20,7 @@ import sys
 from collections import namedtuple
 import numpy as np
 
-sys.path.append("..")
+sys.path.append("../shared_modules/")
 from preprocess.ernie.task_reader import BaseReader, tokenization
 
 

PaddleNLP/lexical_analysis/eval.py

@@ -24,7 +24,7 @@ import paddle
 import utils
 import reader
 import creator
-sys.path.append('../models/')
+sys.path.append('../shared_modules/models/')
 from model_check import check_cuda
 from model_check import check_version
 

PaddleNLP/lexical_analysis/inference_model.py

@@ -10,7 +10,7 @@ import paddle.fluid as fluid
 import creator
 import reader
 import utils
-sys.path.append('../models/')
+sys.path.append('../shared_modules/models/')
 from model_check import check_cuda
 from model_check import check_version
 

PaddleNLP/lexical_analysis/predict.py

@@ -24,7 +24,7 @@ import paddle
 import utils
 import reader
 import creator
-sys.path.append('../models/')
+sys.path.append('../shared_modules/models/')
 from model_check import check_cuda
 from model_check import check_version
 

PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py

@@ -34,7 +34,7 @@ import paddle.fluid as fluid
 
 import creator
 import utils
-sys.path.append("..")
+sys.path.append("../shared_modules/")
 from models.representation.ernie import ErnieConfig
 from models.model_check import check_cuda
 from models.model_check import check_version
@@ -187,8 +187,8 @@ def do_train(args):
                      end_time - start_time, train_pyreader.queue.size()))
 
             if steps % args.save_steps == 0:
-                save_path = os.path.join(args.model_save_dir, "step_" + str(steps),
-                                         "checkpoint")
+                save_path = os.path.join(args.model_save_dir,
+                                         "step_" + str(steps), "checkpoint")
                 print("\tsaving model as %s" % (save_path))
                 fluid.save(train_program, save_path)
 
@@ -196,9 +196,10 @@ def do_train(args):
                 evaluate(exe, test_program, test_pyreader, train_ret)
 
     save_path = os.path.join(args.model_save_dir, "step_" + str(steps),
-                                         "checkpoint")
+                             "checkpoint")
     fluid.save(train_program, save_path)
 
+
 def do_eval(args):
     # init executor
     if args.use_cuda:

PaddleNLP/lexical_analysis/train.py

@@ -29,7 +29,7 @@ import reader
 import utils
 import creator
 from eval import test_process
-sys.path.append('../models/')
+sys.path.append('../shared_modules/models/')
 from model_check import check_cuda
 from model_check import check_version
 
@@ -151,8 +151,7 @@ def do_train(args):
             # save checkpoints
             if step % args.save_steps == 0 and step != 0:
                 save_path = os.path.join(args.model_save_dir,
-                                         "step_" + str(step),
-                                         "checkpoint")
+                                         "step_" + str(step), "checkpoint")
                 fluid.save(train_program, save_path)
             step += 1
 

PaddleNLP/PaddleMRC/README.md → PaddleNLP/machine_reading_comprehension/README.md

@@ -14,12 +14,12 @@ DuReader是一个大规模、面向真实应用、由人类生成的中文阅读
  - 答案由人类生成
  - 面向真实应用场景
  - 标注更加丰富细致
- 
+
 更多关于DuReader数据集的详细信息可在[DuReader官网](https://ai.baidu.com//broad/subordinate?dataset=dureader)找到。
 
 ### DuReader基线系统
 
-DuReader基线系统利用[PaddlePaddle](http://paddlepaddle.org)深度学习框架，针对**DuReader阅读理解数据集**实现并升级了一个经典的阅读理解模型 —— BiDAF. 
+DuReader基线系统利用[PaddlePaddle](http://paddlepaddle.org)深度学习框架，针对**DuReader阅读理解数据集**实现并升级了一个经典的阅读理解模型 —— BiDAF.
 
 
 ## [KT-Net](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2019-KTNET)
@@ -30,7 +30,7 @@ KT-NET是百度NLP提出的具有开创性意义的语言表示与知识表示
  - 被ACL 2019录用为长文 ([文章链接](https://www.aclweb.org/anthology/P19-1226/))
 
 此外，KT-NET具备很强的通用性，不仅适用于机器阅读理解任务，对其他形式的语言理解任务，如自然语言推断、复述识别、语义相似度判断等均有帮助。
- 
+
 
 ## [D-NET](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/MRQA2019-D-NET)
 D-NET是一个以提升**阅读理解模型泛化能力**为目标的“预训练-微调”框架。D-NET的特点包括：
@@ -39,4 +39,3 @@ D-NET是一个以提升**阅读理解模型泛化能力**为目标的“预训
 - 在微调阶段引入多任务、多领域的学习策略 (基于[PALM](https://github.com/PaddlePaddle/PALM)多任务学习框架)，有效的提升了模型在不同领域的泛化能力
 
 百度利用D-NET框架在EMNLP 2019 [MRQA](https://mrqa.github.io/shared)国际阅读理解评测中以超过第二名近两个百分点的成绩夺得冠军，同时，在全部12个测试数据集中的10个排名第一。
-

PaddleNLP/PaddleMT/transformer/desc.py → PaddleNLP/machine_translation/transformer/desc.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 def get_input_descs(args):
     """
     Generate a dict mapping data fields to the corresponding data shapes and
@@ -42,11 +43,12 @@ def get_input_descs(args):
         # encoder.
         # The actual data shape of src_slf_attn_bias is:
         # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
-        "src_slf_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"],
+        "src_slf_attn_bias":
+        [(batch_size, n_head, seq_len, seq_len), "float32"],
         # The actual data shape of trg_word is:
         # [batch_size, max_trg_len_in_batch, 1]
         "trg_word": [(batch_size, seq_len), "int64",
-                    2],  # lod_level is only used in fast decoder.
+                     2],  # lod_level is only used in fast decoder.
         # The actual data shape of trg_pos is:
         # [batch_size, max_trg_len_in_batch, 1]
         "trg_pos": [(batch_size, seq_len), "int64"],
@@ -54,12 +56,14 @@ def get_input_descs(args):
         # subsequent words in the decoder.
         # The actual data shape of trg_slf_attn_bias is:
         # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
-        "trg_slf_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"],
+        "trg_slf_attn_bias":
+        [(batch_size, n_head, seq_len, seq_len), "float32"],
         # This input is used to remove attention weights on paddings of the source
         # input in the encoder-decoder attention.
         # The actual data shape of trg_src_attn_bias is:
         # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
-        "trg_src_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"],
+        "trg_src_attn_bias":
+        [(batch_size, n_head, seq_len, seq_len), "float32"],
         # This input is used in independent decoder program for inference.
         # The actual data shape of enc_output is:
         # [batch_size, max_src_len_in_batch, d_model]
@@ -80,6 +84,7 @@ def get_input_descs(args):
 
     return input_descs
 
+
 # Names of word embedding table which might be reused for weight sharing.
 word_emb_param_names = (
     "src_word_emb_table",

PaddleNLP/PaddleMT/transformer/inference_model.py → PaddleNLP/machine_translation/transformer/inference_model.py

@@ -87,13 +87,14 @@ def do_save_inference_model(args):
 
     # saving inference model
 
-    fluid.io.save_inference_model(args.inference_model_dir,
-                                  feeded_var_names=list(input_field_names),
-                                  target_vars=[out_ids, out_scores],
-                                  executor=exe,
-                                  main_program=test_prog,
-                                  model_filename="model.pdmodel",
-                                  params_filename="params.pdparams")
+    fluid.io.save_inference_model(
+        args.inference_model_dir,
+        feeded_var_names=list(input_field_names),
+        target_vars=[out_ids, out_scores],
+        executor=exe,
+        main_program=test_prog,
+        model_filename="model.pdmodel",
+        params_filename="params.pdparams")
 
     print("save inference model at %s" % (args.inference_model_dir))
 

PaddleNLP/PaddleMT/transformer/main.py → PaddleNLP/machine_translation/transformer/main.py

@@ -25,7 +25,6 @@ from train import do_train
 from predict import do_predict
 from inference_model import do_save_inference_model
 
-
 if __name__ == "__main__":
     LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
     logging.basicConfig(
@@ -43,4 +42,4 @@ if __name__ == "__main__":
         do_predict(args)
 
     if args.do_save_inference_model:
-        do_save_inference_model(args)
\ No newline at end of file
+        do_save_inference_model(args)