Merge branch 'dev0.5.0' of https://github.com/SrWYG/fastNLP into pr

reproduction/text_classification/README.md

@@ -0,0 +1,22 @@
+# text_classification任务模型复现
+这里使用fastNLP复现以下模型：
+char_cnn :论文链接[Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626v3.pdf)
+dpcnn:论文链接[Deep Pyramid Convolutional Neural Networks for TextCategorization](https://ai.tencent.com/ailab/media/publications/ACL3-Brady.pdf)
+HAN:论文链接[Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf)
+#待补充
+awd_lstm:
+lstm_self_attention(BCN?):
+awd-sltm:
+
+# 数据集及复现结果汇总
+
+使用fastNLP复现的结果vs论文汇报结果(/前为fastNLP实现，后面为论文报道,-表示论文没有在该数据集上列出结果)
+
+model name | yelp_p | sst-2|IMDB| 
+:---: | :---: | :---: | :---: 
+char_cnn | 93.80/95.12 | - |- |
+dpcnn | 95.50/97.36 | - |- |
+HAN |- | - |-|
+BCN| - |- |-|
+awd-lstm| - |- |-|
+

reproduction/text_classification/data/IMDBLoader.py

@@ -32,27 +32,27 @@ class IMDBLoader(DataSetLoader):
                     continue
                 parts = line.split('\t')
                 target = parts[0]
-                words = parts[1].split()
+                words = parts[1].lower().split()
                 dataset.append(Instance(words=words, target=target))
 
         if len(dataset)==0:
             raise RuntimeError(f"{path} has no valid data.")
 
         return dataset
-
+    
     def process(self,
                 paths: Union[str, Dict[str, str]],
                 src_vocab_opt: VocabularyOption = None,
                 tgt_vocab_opt: VocabularyOption = None,
                 src_embed_opt: EmbeddingOption = None,
                 char_level_op=False):
-
+      
         datasets = {}
         info = DataInfo()
         for name, path in paths.items():
             dataset = self.load(path)
             datasets[name] = dataset
-
+            
         def wordtochar(words):
             chars = []
             for word in words:
@@ -69,7 +69,7 @@ class IMDBLoader(DataSetLoader):
 
         src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt)
         src_vocab.from_dataset(datasets['train'], field_name='words')
-        # src_vocab.from_dataset(datasets['train'], datasets["dev"], datasets["test"], field_name='words')
+
         src_vocab.index_dataset(*datasets.values(), field_name='words')
 
         tgt_vocab = Vocabulary(unknown=None, padding=None) \
@@ -95,6 +95,7 @@ class IMDBLoader(DataSetLoader):
         return info
 
 
+
 if __name__=="__main__":
     datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv",
                 "test": "/remote-home/ygwang/IMDB_data/test.csv"}
@@ -106,3 +107,4 @@ if __name__=="__main__":
 
     ave_len = len_count / len(datainfo.datasets["train"])
     print(ave_len)
+

reproduction/text_classification/data/MTL16Loader.py

@@ -32,7 +32,7 @@ class MTL16Loader(DataSetLoader):
                     continue
                 parts = line.split('\t')
                 target = parts[0]
-                words = parts[1].split()
+                words = parts[1].lower().split()
                 dataset.append(Instance(words=words, target=target))
         if len(dataset)==0:
             raise RuntimeError(f"{path} has no valid data.")
@@ -72,4 +72,8 @@ class MTL16Loader(DataSetLoader):
             embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
             info.embeddings['words'] = embed
 
+        for name, dataset in info.datasets.items():
+            dataset.set_input("words")
+            dataset.set_target("target")
+
         return info

reproduction/text_classification/data/SSTLoader.py

@@ -0,0 +1,187 @@
+from typing import Iterable
+from nltk import Tree
+from fastNLP.io.base_loader import DataInfo, DataSetLoader
+from fastNLP.core.vocabulary import VocabularyOption, Vocabulary
+from fastNLP import DataSet
+from fastNLP import Instance
+from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader
+import csv
+from typing import Union, Dict
+
+class SSTLoader(DataSetLoader):
+    URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
+    DATA_DIR = 'sst/'
+
+    """
+    别名：:class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader`
+
+    读取SST数据集, DataSet包含fields::
+
+        words: list(str) 需要分类的文本
+        target: str 文本的标签
+
+    数据来源: https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip
+
+    :param subtree: 是否将数据展开为子树，扩充数据量. Default: ``False``
+    :param fine_grained: 是否使用SST-5标准，若 ``False`` , 使用SST-2。Default: ``False``
+    """
+
+    def __init__(self, subtree=False, fine_grained=False):
+        self.subtree = subtree
+
+        tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral',
+                 '3': 'positive', '4': 'very positive'}
+        if not fine_grained:
+            tag_v['0'] = tag_v['1']
+            tag_v['4'] = tag_v['3']
+        self.tag_v = tag_v
+
+    def _load(self, path):
+        """
+
+        :param str path: 存储数据的路径
+        :return: 一个 :class:`~fastNLP.DataSet` 类型的对象
+        """
+        datalist = []
+        with open(path, 'r', encoding='utf-8') as f:
+            datas = []
+            for l in f:
+                datas.extend([(s, self.tag_v[t])
+                              for s, t in self._get_one(l, self.subtree)])
+        ds = DataSet()
+        for words, tag in datas:
+            ds.append(Instance(words=words, target=tag))
+        return ds
+
+    @staticmethod
+    def _get_one(data, subtree):
+        tree = Tree.fromstring(data)
+        if subtree:
+            return [(t.leaves(), t.label()) for t in tree.subtrees()]
+        return [(tree.leaves(), tree.label())]
+
+    def process(self,
+                paths,
+                train_ds: Iterable[str] = None,
+                src_vocab_op: VocabularyOption = None,
+                tgt_vocab_op: VocabularyOption = None,
+                src_embed_op: EmbeddingOption = None):
+        input_name, target_name = 'words', 'target'
+        src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op)
+        tgt_vocab = Vocabulary(unknown=None, padding=None) \
+            if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
+
+        info = DataInfo(datasets=self.load(paths))
+        _train_ds = [info.datasets[name]
+                     for name in train_ds] if train_ds else info.datasets.values()
+        src_vocab.from_dataset(*_train_ds, field_name=input_name)
+        tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
+        src_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=input_name, new_field_name=input_name)
+        tgt_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=target_name, new_field_name=target_name)
+        info.vocabs = {
+            input_name: src_vocab,
+            target_name: tgt_vocab
+        }
+
+        if src_embed_op is not None:
+            src_embed_op.vocab = src_vocab
+            init_emb = EmbedLoader.load_with_vocab(**src_embed_op)
+            info.embeddings[input_name] = init_emb
+
+        for name, dataset in info.datasets.items():
+            dataset.set_input(input_name)
+            dataset.set_target(target_name)
+
+        return info
+
+class sst2Loader(DataSetLoader):
+    '''
+    数据来源"SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8',
+    '''
+    def __init__(self):
+        super(sst2Loader, self).__init__()
+
+    def _load(self, path: str) -> DataSet:
+        ds = DataSet()
+        all_count=0
+        csv_reader = csv.reader(open(path, encoding='utf-8'),delimiter='\t')
+        skip_row = 0
+        for idx,row in enumerate(csv_reader):
+            if idx<=skip_row:
+                continue
+            target = row[1]
+            words = row[0].split()
+            ds.append(Instance(words=words,target=target))
+            all_count+=1
+        print("all count:", all_count)
+        return ds
+
+    def process(self,
+                paths: Union[str, Dict[str, str]],
+                src_vocab_opt: VocabularyOption = None,
+                tgt_vocab_opt: VocabularyOption = None,
+                src_embed_opt: EmbeddingOption = None,
+                char_level_op=False):
+
+        paths = check_dataloader_paths(paths)
+        datasets = {}
+        info = DataInfo()
+        for name, path in paths.items():
+            dataset = self.load(path)
+            datasets[name] = dataset
+
+        def wordtochar(words):
+            chars=[]
+            for word in words:
+                word=word.lower()
+                for char in word:
+                    chars.append(char)
+            return chars
+
+        input_name, target_name = 'words', 'target'
+        info.vocabs={}
+
+        # 就分隔为char形式
+        if char_level_op:
+            for dataset in datasets.values():
+                dataset.apply_field(wordtochar, field_name="words", new_field_name='chars')
+
+        src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt)
+        src_vocab.from_dataset(datasets['train'], field_name='words')
+        src_vocab.index_dataset(*datasets.values(), field_name='words')
+
+        tgt_vocab = Vocabulary(unknown=None, padding=None) \
+            if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt)
+        tgt_vocab.from_dataset(datasets['train'], field_name='target')
+        tgt_vocab.index_dataset(*datasets.values(), field_name='target')
+
+
+        info.vocabs = {
+            "words": src_vocab,
+            "target": tgt_vocab
+        }
+
+        info.datasets = datasets
+
+
+        if src_embed_opt is not None:
+            embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
+            info.embeddings['words'] = embed
+
+        return info
+
+if __name__=="__main__":
+    datapath = {"train": "/remote-home/ygwang/workspace/GLUE/SST-2/train.tsv",
+                "dev": "/remote-home/ygwang/workspace/GLUE/SST-2/dev.tsv"}
+    datainfo=sst2Loader().process(datapath,char_level_op=True)
+    #print(datainfo.datasets["train"])
+    len_count = 0
+    for instance in datainfo.datasets["train"]:
+        len_count += len(instance["chars"])
+
+    ave_len = len_count / len(datainfo.datasets["train"])
+    print(ave_len)

reproduction/text_classification/data/sstLoader.py

@@ -1,13 +1,102 @@
-import csv
 from typing import Iterable
-from fastNLP import DataSet, Instance, Vocabulary
-from fastNLP.core.vocabulary import VocabularyOption
-from fastNLP.io.base_loader import DataInfo,DataSetLoader
-from fastNLP.io.embed_loader import EmbeddingOption
-from fastNLP.io.file_reader import _read_json
+from nltk import Tree
+from fastNLP.io.base_loader import DataInfo, DataSetLoader
+from fastNLP.core.vocabulary import VocabularyOption, Vocabulary
+from fastNLP import DataSet
+from fastNLP import Instance
+from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader
+import csv
 from typing import Union, Dict
-from reproduction.Star_transformer.datasets import EmbedLoader
-from reproduction.utils import check_dataloader_paths
+
+class SSTLoader(DataSetLoader):
+    URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
+    DATA_DIR = 'sst/'
+
+    """
+    别名：:class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader`
+
+    读取SST数据集, DataSet包含fields::
+
+        words: list(str) 需要分类的文本
+        target: str 文本的标签
+
+    数据来源: https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip
+
+    :param subtree: 是否将数据展开为子树，扩充数据量. Default: ``False``
+    :param fine_grained: 是否使用SST-5标准，若 ``False`` , 使用SST-2。Default: ``False``
+    """
+
+    def __init__(self, subtree=False, fine_grained=False):
+        self.subtree = subtree
+
+        tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral',
+                 '3': 'positive', '4': 'very positive'}
+        if not fine_grained:
+            tag_v['0'] = tag_v['1']
+            tag_v['4'] = tag_v['3']
+        self.tag_v = tag_v
+
+    def _load(self, path):
+        """
+
+        :param str path: 存储数据的路径
+        :return: 一个 :class:`~fastNLP.DataSet` 类型的对象
+        """
+        datalist = []
+        with open(path, 'r', encoding='utf-8') as f:
+            datas = []
+            for l in f:
+                datas.extend([(s, self.tag_v[t])
+                              for s, t in self._get_one(l, self.subtree)])
+        ds = DataSet()
+        for words, tag in datas:
+            ds.append(Instance(words=words, target=tag))
+        return ds
+
+    @staticmethod
+    def _get_one(data, subtree):
+        tree = Tree.fromstring(data)
+        if subtree:
+            return [(t.leaves(), t.label()) for t in tree.subtrees()]
+        return [(tree.leaves(), tree.label())]
+
+    def process(self,
+                paths,
+                train_ds: Iterable[str] = None,
+                src_vocab_op: VocabularyOption = None,
+                tgt_vocab_op: VocabularyOption = None,
+                src_embed_op: EmbeddingOption = None):
+        input_name, target_name = 'words', 'target'
+        src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op)
+        tgt_vocab = Vocabulary(unknown=None, padding=None) \
+            if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
+
+        info = DataInfo(datasets=self.load(paths))
+        _train_ds = [info.datasets[name]
+                     for name in train_ds] if train_ds else info.datasets.values()
+        src_vocab.from_dataset(*_train_ds, field_name=input_name)
+        tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
+        src_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=input_name, new_field_name=input_name)
+        tgt_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=target_name, new_field_name=target_name)
+        info.vocabs = {
+            input_name: src_vocab,
+            target_name: tgt_vocab
+        }
+
+        if src_embed_op is not None:
+            src_embed_op.vocab = src_vocab
+            init_emb = EmbedLoader.load_with_vocab(**src_embed_op)
+            info.embeddings[input_name] = init_emb
+
+        for name, dataset in info.datasets.items():
+            dataset.set_input(input_name)
+            dataset.set_target(target_name)
+
+        return info
 
 class sst2Loader(DataSetLoader):
     '''

reproduction/text_classification/data/yelpLoader.py

@@ -34,18 +34,10 @@ def clean_str(sentence, tokenizer, char_lower=False):
     return words_collection
 
 
-class yelpLoader(JsonLoader):
+class yelpLoader(DataSetLoader):
     
     """
-    读取Yelp数据集, DataSet包含fields:
-    
-        review_id: str, 22 character unique review id
-        user_id: str, 22 character unique user id
-        business_id: str, 22 character business id
-        useful: int, number of useful votes received
-        funny: int, number of funny votes received
-        cool: int, number of cool votes received
-        date: str, date formatted YYYY-MM-DD
+    读取Yelp_full/Yelp_polarity数据集, DataSet包含fields:
         words: list(str), 需要分类的文本
         target: str, 文本的标签
         chars:list(str),未index的字符列表
@@ -180,6 +172,12 @@ class yelpLoader(JsonLoader):
 
         info.vocabs[target_name]=tgt_vocab
 
+        info.datasets['train'],info.datasets['dev']=info.datasets['train'].split(0.1, shuffle=False)
+
+        for name, dataset in info.datasets.items():
+            dataset.set_input("words")
+            dataset.set_target("target")
+
         return info
 
 if __name__=="__main__":
@@ -196,4 +194,4 @@ if __name__=="__main__":
         len_count+=len(instance["chars"])
 
     ave_len=len_count/len(datainfo.datasets["train"])
-    print(ave_len)
+    print(ave_len)

reproduction/text_classification/model/HAN.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from fastNLP.modules.utils import get_embeddings
+from fastNLP.core import Const as C
+
+
+def pack_sequence(tensor_seq, padding_value=0.0):
+    if len(tensor_seq) <= 0:
+        return
+    length = [v.size(0) for v in tensor_seq]
+    max_len = max(length)
+    size = [len(tensor_seq), max_len]
+    size.extend(list(tensor_seq[0].size()[1:]))
+    ans = torch.Tensor(*size).fill_(padding_value)
+    if tensor_seq[0].data.is_cuda:
+        ans = ans.cuda()
+    ans = Variable(ans)
+    for i, v in enumerate(tensor_seq):
+        ans[i, :length[i], :] = v
+    return ans
+
+
+class HANCLS(nn.Module):
+    def __init__(self, init_embed, num_cls):
+        super(HANCLS, self).__init__()
+
+        self.embed = get_embeddings(init_embed)
+        self.han = HAN(input_size=300,
+                       output_size=num_cls,
+                       word_hidden_size=50, word_num_layers=1, word_context_size=100,
+                       sent_hidden_size=50, sent_num_layers=1, sent_context_size=100
+                       )
+
+    def forward(self, input_sents):
+        # input_sents [B, num_sents, seq-len] dtype long
+        # target
+        B, num_sents, seq_len = input_sents.size()
+        input_sents = input_sents.view(-1, seq_len)  # flat
+        words_embed = self.embed(input_sents)  # should be [B*num-sent, seqlen , word-dim]
+        words_embed = words_embed.view(B, num_sents, seq_len, -1)  # recover # [B, num-sent, seqlen , word-dim]
+        out = self.han(words_embed)
+
+        return {C.OUTPUT: out}
+
+    def predict(self, input_sents):
+        x = self.forward(input_sents)[C.OUTPUT]
+        return {C.OUTPUT: torch.argmax(x, 1)}
+
+
+class HAN(nn.Module):
+    def __init__(self, input_size, output_size,
+                 word_hidden_size, word_num_layers, word_context_size,
+                 sent_hidden_size, sent_num_layers, sent_context_size):
+        super(HAN, self).__init__()
+
+        self.word_layer = AttentionNet(input_size,
+                                       word_hidden_size,
+                                       word_num_layers,
+                                       word_context_size)
+        self.sent_layer = AttentionNet(2 * word_hidden_size,
+                                       sent_hidden_size,
+                                       sent_num_layers,
+                                       sent_context_size)
+        self.output_layer = nn.Linear(2 * sent_hidden_size, output_size)
+        self.softmax = nn.LogSoftmax(dim=1)
+
+    def forward(self, batch_doc):
+        # input is a sequence of matrix
+        doc_vec_list = []
+        for doc in batch_doc:
+            sent_mat = self.word_layer(doc)  # doc's dim (num_sent, seq_len, word_dim)
+            doc_vec_list.append(sent_mat)  # sent_mat's dim (num_sent, vec_dim)
+        doc_vec = self.sent_layer(pack_sequence(doc_vec_list))
+        output = self.softmax(self.output_layer(doc_vec))
+        return output
+
+
+class AttentionNet(nn.Module):
+    def __init__(self, input_size, gru_hidden_size, gru_num_layers, context_vec_size):
+        super(AttentionNet, self).__init__()
+
+        self.input_size = input_size
+        self.gru_hidden_size = gru_hidden_size
+        self.gru_num_layers = gru_num_layers
+        self.context_vec_size = context_vec_size
+
+        # Encoder
+        self.gru = nn.GRU(input_size=input_size,
+                          hidden_size=gru_hidden_size,
+                          num_layers=gru_num_layers,
+                          batch_first=True,
+                          bidirectional=True)
+        # Attention
+        self.fc = nn.Linear(2 * gru_hidden_size, context_vec_size)
+        self.tanh = nn.Tanh()
+        self.softmax = nn.Softmax(dim=1)
+        # context vector
+        self.context_vec = nn.Parameter(torch.Tensor(context_vec_size, 1))
+        self.context_vec.data.uniform_(-0.1, 0.1)
+
+    def forward(self, inputs):
+        # GRU part
+        h_t, hidden = self.gru(inputs)  # inputs's dim (batch_size, seq_len,  word_dim)
+        u = self.tanh(self.fc(h_t))
+        # Attention part
+        alpha = self.softmax(torch.matmul(u, self.context_vec))  # u's dim (batch_size, seq_len, context_vec_size)
+        output = torch.bmm(torch.transpose(h_t, 1, 2), alpha)  # alpha's dim (batch_size, seq_len, 1)
+        return torch.squeeze(output, dim=2)  # output's dim (batch_size, 2*hidden_size, 1)

reproduction/text_classification/model/awd_lstm.py

@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from .awdlstm_module import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class AWDLSTMSentiment(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 nfc=128,
+                 wdrop=0.5):
+        super(AWDLSTMSentiment,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True, wdrop=wdrop)
+        self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        output = self.mlp(output[:,-1,:])
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}
+

reproduction/text_classification/model/awdlstm_module.py

@@ -0,0 +1,86 @@
+"""
+轻量封装的 Pytorch LSTM 模块.
+可在 forward 时传入序列的长度, 自动对padding做合适的处理.
+"""
+__all__ = [
+    "LSTM"
+]
+
+import torch
+import torch.nn as nn
+import torch.nn.utils.rnn as rnn
+
+from fastNLP.modules.utils import initial_parameter
+from torch import autograd
+from .weight_drop import WeightDrop
+
+
+class LSTM(nn.Module):
+    """
+    别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
+
+    LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
+        为1; 且可以应对DataParallel中LSTM的使用问题。
+
+    :param input_size:  输入 `x` 的特征维度
+    :param hidden_size: 隐状态 `h` 的特征维度.
+    :param num_layers: rnn的层数. Default: 1
+    :param dropout: 层间dropout概率. Default: 0
+    :param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False``
+    :param batch_first: 若为 ``True``, 输入和输出 ``Tensor`` 形状为
+        :(batch, seq, feature). Default: ``False``
+    :param bias: 如果为 ``False``, 模型将不会使用bias. Default: ``True``
+    """
+    
+    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True,
+                 bidirectional=False, bias=True, wdrop=0.5):
+        super(LSTM, self).__init__()
+        self.batch_first = batch_first
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first,
+                            dropout=dropout, bidirectional=bidirectional)
+        self.lstm = WeightDrop(self.lstm, ['weight_hh_l0'], dropout=wdrop)
+        self.init_param()
+
+    def init_param(self):
+        for name, param in self.named_parameters():
+            if 'bias' in name:
+                # based on https://github.com/pytorch/pytorch/issues/750#issuecomment-280671871
+                param.data.fill_(0)
+                n = param.size(0)
+                start, end = n // 4, n // 2
+                param.data[start:end].fill_(1)
+            else:
+                nn.init.xavier_uniform_(param)
+
+    def forward(self, x, seq_len=None, h0=None, c0=None):
+        """
+
+        :param x: [batch, seq_len, input_size] 输入序列
+        :param seq_len: [batch, ] 序列长度, 若为 ``None``, 所有输入看做一样长. Default: ``None``
+        :param h0: [batch, hidden_size] 初始隐状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :param c0: [batch, hidden_size] 初始Cell状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :return (output, ht) 或 output: 若 ``get_hidden=True`` [batch, seq_len, hidden_size*num_direction] 输出序列
+            和 [batch, hidden_size*num_direction] 最后时刻隐状态.
+        """
+        batch_size, max_len, _ = x.size()
+        if h0 is not None and c0 is not None:
+            hx = (h0, c0)
+        else:
+            hx = None
+        if seq_len is not None and not isinstance(x, rnn.PackedSequence):
+            sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
+            if self.batch_first:
+                x = x[sort_idx]
+            else:
+                x = x[:, sort_idx]
+            x = rnn.pack_padded_sequence(x, sort_lens, batch_first=self.batch_first)
+            output, hx = self.lstm(x, hx)  # -> [N,L,C]
+            output, _ = rnn.pad_packed_sequence(output, batch_first=self.batch_first, total_length=max_len)
+            _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)
+            if self.batch_first:
+                output = output[unsort_idx]
+            else:
+                output = output[:, unsort_idx]
+        else:
+            output, hx = self.lstm(x, hx)
+        return output, hx

reproduction/text_classification/model/lstm.py

@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from fastNLP.modules.encoder.lstm import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class BiLSTMSentiment(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 nfc=128):
+        super(BiLSTMSentiment,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
+        self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        output = self.mlp(output[:,-1,:])
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}
+

reproduction/text_classification/model/lstm_self_attention.py

@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from fastNLP.modules.encoder.lstm import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.aggregator.attention import SelfAttention
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class BiLSTM_SELF_ATTENTION(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 attention_unit=256,
+                 attention_hops=1,
+                 nfc=128):
+        super(BiLSTM_SELF_ATTENTION,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
+        self.attention = SelfAttention(input_size=hidden_dim * 2 , attention_unit=attention_unit, attention_hops=attention_hops)
+        self.mlp = MLP(size_layer=[hidden_dim* 2*attention_hops, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        after_attention, penalty = self.attention(output,words)
+        after_attention =after_attention.view(after_attention.size(0),-1)
+        output = self.mlp(after_attention)
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}

reproduction/text_classification/model/weight_drop.py

@@ -0,0 +1,99 @@
+import torch
+from torch.nn import Parameter
+from functools import wraps
+
+class WeightDrop(torch.nn.Module):
+    def __init__(self, module, weights, dropout=0, variational=False):
+        super(WeightDrop, self).__init__()
+        self.module = module
+        self.weights = weights
+        self.dropout = dropout
+        self.variational = variational
+        self._setup()
+
+    def widget_demagnetizer_y2k_edition(*args, **kwargs):
+        # We need to replace flatten_parameters with a nothing function
+        # It must be a function rather than a lambda as otherwise pickling explodes
+        # We can't write boring code though, so ... WIDGET DEMAGNETIZER Y2K EDITION!
+        # (╯°□°）╯︵ ┻━┻
+        return
+
+    def _setup(self):
+        # Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN
+        if issubclass(type(self.module), torch.nn.RNNBase):
+            self.module.flatten_parameters = self.widget_demagnetizer_y2k_edition
+
+        for name_w in self.weights:
+            print('Applying weight drop of {} to {}'.format(self.dropout, name_w))
+            w = getattr(self.module, name_w)
+            del self.module._parameters[name_w]
+            self.module.register_parameter(name_w + '_raw', Parameter(w.data))
+
+    def _setweights(self):
+        for name_w in self.weights:
+            raw_w = getattr(self.module, name_w + '_raw')
+            w = None
+            if self.variational:
+                mask = torch.autograd.Variable(torch.ones(raw_w.size(0), 1))
+                if raw_w.is_cuda: mask = mask.cuda()
+                mask = torch.nn.functional.dropout(mask, p=self.dropout, training=True)
+                w = mask.expand_as(raw_w) * raw_w
+            else:
+                w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training)
+            setattr(self.module, name_w, w)
+
+    def forward(self, *args):
+        self._setweights()
+        return self.module.forward(*args)
+
+if __name__ == '__main__':
+    import torch
+    from weight_drop import WeightDrop
+
+    # Input is (seq, batch, input)
+    x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda()
+    h0 = None
+
+    ###
+
+    print('Testing WeightDrop')
+    print('=-=-=-=-=-=-=-=-=-=')
+
+    ###
+
+    print('Testing WeightDrop with Linear')
+
+    lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9)
+    lin.cuda()
+    run1 = [x.sum() for x in lin(x).data]
+    run2 = [x.sum() for x in lin(x).data]
+
+    print('All items should be different')
+    print('Run 1:', run1)
+    print('Run 2:', run2)
+
+    assert run1[0] != run2[0]
+    assert run1[1] != run2[1]
+
+    print('---')
+
+    ###
+
+    print('Testing WeightDrop with LSTM')
+
+    wdrnn = WeightDrop(torch.nn.LSTM(10, 10), ['weight_hh_l0'], dropout=0.9)
+    wdrnn.cuda()
+
+    run1 = [x.sum() for x in wdrnn(x, h0)[0].data]
+    run2 = [x.sum() for x in wdrnn(x, h0)[0].data]
+
+    print('First timesteps should be equal, all others should differ')
+    print('Run 1:', run1)
+    print('Run 2:', run2)
+
+    # First time step, not influenced by hidden to hidden weights, should be equal
+    assert run1[0] == run2[0]
+    # Second step should not
+    assert run1[1] != run2[1]
+
+    print('---')

reproduction/text_classification/train_HAN.py

@@ -0,0 +1,109 @@
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+
+import os
+import sys
+sys.path.append('../../')
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+
+from fastNLP.core.const import Const as C
+from fastNLP.core import LRScheduler
+import torch.nn as nn
+from fastNLP.io.dataset_loader import SSTLoader
+from reproduction.text_classification.data.yelpLoader import yelpLoader
+from reproduction.text_classification.model.HAN import HANCLS
+from fastNLP.modules.encoder.embedding import StaticEmbedding, CNNCharEmbedding, StackEmbedding
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP.core.trainer import Trainer
+from torch.optim import SGD
+import torch.cuda
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+
+##hyper
+
+class Config():
+    model_dir_or_name = "en-base-uncased"
+    embedding_grad = False,
+    train_epoch = 30
+    batch_size = 100
+    num_classes = 5
+    task = "yelp"
+    #datadir = '/remote-home/lyli/fastNLP/yelp_polarity/'
+    datadir = '/remote-home/ygwang/yelp_polarity/'
+    datafile = {"train": "train.csv", "test": "test.csv"}
+    lr = 1e-3
+
+    def __init__(self):
+        self.datapath = {k: os.path.join(self.datadir, v)
+                         for k, v in self.datafile.items()}
+
+
+ops = Config()
+
+##1.task相关信息：利用dataloader载入dataInfo
+
+datainfo = yelpLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train'])
+print(len(datainfo.datasets['train']))
+print(len(datainfo.datasets['test']))
+
+
+# post process
+def make_sents(words):
+    sents = [words]
+    return sents
+
+
+for dataset in datainfo.datasets.values():
+    dataset.apply_field(make_sents, field_name='words', new_field_name='input_sents')
+
+datainfo = datainfo
+datainfo.datasets['train'].set_input('input_sents')
+datainfo.datasets['test'].set_input('input_sents')
+datainfo.datasets['train'].set_target('target')
+datainfo.datasets['test'].set_target('target')
+
+## 2.或直接复用fastNLP的模型
+
+vocab = datainfo.vocabs['words']
+# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)])
+embedding = StaticEmbedding(vocab)
+
+print(len(vocab))
+print(len(datainfo.vocabs['target']))
+
+# model = DPCNN(init_embed=embedding, num_cls=ops.num_classes)
+model = HANCLS(init_embed=embedding, num_cls=ops.num_classes)
+
+## 3. 声明loss,metric,optimizer
+loss = CrossEntropyLoss(pred=C.OUTPUT, target=C.TARGET)
+metric = AccuracyMetric(pred=C.OUTPUT, target=C.TARGET)
+optimizer = SGD([param for param in model.parameters() if param.requires_grad == True],
+                lr=ops.lr, momentum=0.9, weight_decay=0)
+
+callbacks = []
+callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+print(device)
+
+for ds in datainfo.datasets.values():
+    ds.apply_field(len, C.INPUT, C.INPUT_LEN)
+    ds.set_input(C.INPUT, C.INPUT_LEN)
+    ds.set_target(C.TARGET)
+
+
+## 4.定义train方法
+def train(model, datainfo, loss, metrics, optimizer, num_epochs=ops.train_epoch):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                      metrics=[metrics], dev_data=datainfo.datasets['test'], device=device,
+                      check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks,
+                      n_epochs=num_epochs)
+
+    print(trainer.train())
+
+
+if __name__ == "__main__":
+    train(model, datainfo, loss, metric, optimizer)

reproduction/text_classification/train_awdlstm.py

@@ -0,0 +1,102 @@
+# 这个模型需要在pytorch=0.4下运行，weight_drop不支持1.0
+
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.awd_lstm import AWDLSTMSentiment
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    nfc=128
+    wdrop=0.5
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=AWDLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc, wdrop=opt.wdrop)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()

reproduction/text_classification/train_char_cnn.py

@@ -7,7 +7,6 @@ import sys
 sys.path.append('../..')
 from fastNLP.core.const import Const as C
 import torch.nn as nn
-from fastNLP.io.dataset_loader import SSTLoader
 from data.yelpLoader import yelpLoader
 from data.sstLoader import sst2Loader
 from data.IMDBLoader import IMDBLoader
@@ -107,9 +106,9 @@ ops=Config
 
 
 ##1.task相关信息：利用dataloader载入dataInfo
-dataloader=sst2Loader()
-dataloader=IMDBLoader()
-#dataloader=yelpLoader(fine_grained=True)
+#dataloader=sst2Loader()
+#dataloader=IMDBLoader()
+dataloader=yelpLoader(fine_grained=True)
 datainfo=dataloader.process(ops.datapath,char_level_op=True)
 char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"]
 ops.number_of_characters=len(char_vocab)

reproduction/text_classification/train_lstm.py

@@ -0,0 +1,99 @@
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.lstm import BiLSTMSentiment
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    nfc=128
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=BiLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()

reproduction/text_classification/train_lstm_att.py

@@ -0,0 +1,101 @@
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.lstm_self_attention import BiLSTM_SELF_ATTENTION
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    attention_unit=256
+    attention_hops=1
+    nfc=128
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=BiLSTM_SELF_ATTENTION(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, attention_unit=opt.attention_unit, attention_hops=opt.attention_hops, nfc=opt.nfc)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()