Merge branch 'dev0.5.0' into lyhuang-reproduction

fastNLP/modules/aggregator/attention.py

@@ -19,7 +19,7 @@ class DotAttention(nn.Module):
         补上文档
     """
     
-    def __init__(self, key_size, value_size, dropout=0):
+    def __init__(self, key_size, value_size, dropout=0.0):
         super(DotAttention, self).__init__()
         self.key_size = key_size
         self.value_size = value_size
@@ -37,7 +37,7 @@ class DotAttention(nn.Module):
         """
         output = torch.matmul(Q, K.transpose(1, 2)) / self.scale
         if mask_out is not None:
-            output.masked_fill_(mask_out, -1e8)
+            output.masked_fill_(mask_out, -1e18)
         output = self.softmax(output)
         output = self.drop(output)
         return torch.matmul(output, V)
@@ -67,9 +67,8 @@ class MultiHeadAttention(nn.Module):
         self.k_in = nn.Linear(input_size, in_size)
         self.v_in = nn.Linear(input_size, in_size)
         # follow the paper, do not apply dropout within dot-product
-        self.attention = DotAttention(key_size=key_size, value_size=value_size, dropout=0)
+        self.attention = DotAttention(key_size=key_size, value_size=value_size, dropout=dropout)
         self.out = nn.Linear(value_size * num_head, input_size)
-        self.drop = TimestepDropout(dropout)
         self.reset_parameters()
     
     def reset_parameters(self):
@@ -105,7 +104,7 @@ class MultiHeadAttention(nn.Module):
         
         # concat all heads, do output linear
         atte = atte.permute(1, 2, 0, 3).contiguous().view(batch, sq, -1)
-        output = self.drop(self.out(atte))
+        output = self.out(atte)
         return output
 
 

reproduction/seqence_labelling/ner/model/dilated_cnn.py

@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from fastNLP.modules.decoder import ConditionalRandomField
+from fastNLP.modules.encoder import Embedding
+from fastNLP.core.utils import seq_len_to_mask
+from fastNLP.core.const import Const as C
+
+
+class IDCNN(nn.Module):
+    def __init__(self, init_embed, char_embed,
+                 num_cls,
+                 repeats, num_layers, num_filters, kernel_size,
+                 use_crf=False, use_projection=False, block_loss=False,
+                 input_dropout=0.3, hidden_dropout=0.2, inner_dropout=0.0):
+        super(IDCNN, self).__init__()
+        self.word_embeddings = Embedding(init_embed)
+        self.char_embeddings = Embedding(char_embed)
+        embedding_size = self.word_embeddings.embedding_dim + \
+            self.char_embeddings.embedding_dim
+
+        self.conv0 = nn.Sequential(
+            nn.Conv1d(in_channels=embedding_size,
+                      out_channels=num_filters,
+                      kernel_size=kernel_size,
+                      stride=1, dilation=1,
+                      padding=kernel_size//2,
+                      bias=True),
+            nn.ReLU(),
+        )
+
+        block = []
+        for layer_i in range(num_layers):
+            dilated = 2 ** layer_i
+            block.append(nn.Conv1d(
+                in_channels=num_filters,
+                out_channels=num_filters,
+                kernel_size=kernel_size,
+                stride=1, dilation=dilated,
+                padding=(kernel_size//2) * dilated,
+                bias=True))
+            block.append(nn.ReLU())
+        self.block = nn.Sequential(*block)
+
+        if use_projection:
+            self.projection = nn.Sequential(
+                nn.Conv1d(
+                    in_channels=num_filters,
+                    out_channels=num_filters//2,
+                    kernel_size=1,
+                    bias=True),
+                nn.ReLU(),)
+            encode_dim = num_filters // 2
+        else:
+            self.projection = None
+            encode_dim = num_filters
+
+        self.input_drop = nn.Dropout(input_dropout)
+        self.hidden_drop = nn.Dropout(hidden_dropout)
+        self.inner_drop = nn.Dropout(inner_dropout)
+        self.repeats = repeats
+        self.out_fc = nn.Conv1d(
+            in_channels=encode_dim,
+            out_channels=num_cls,
+            kernel_size=1,
+            bias=True)
+        self.crf = ConditionalRandomField(
+            num_tags=num_cls) if use_crf else None
+        self.block_loss = block_loss
+
+    def forward(self, words, chars, seq_len, target=None):
+        e1 = self.word_embeddings(words)
+        e2 = self.char_embeddings(chars)
+        x = torch.cat((e1, e2), dim=-1) # b,l,h
+        mask = seq_len_to_mask(seq_len)
+
+        x = x.transpose(1, 2) # b,h,l
+        last_output = self.conv0(x)
+        output = []
+        for repeat in range(self.repeats):
+            last_output = self.block(last_output)
+            hidden = self.projection(last_output) if self.projection is not None else last_output
+            output.append(self.out_fc(hidden))
+
+        def compute_loss(y, t, mask):
+            if self.crf is not None and target is not None:
+                loss = self.crf(y, t, mask)
+            else:
+                t.masked_fill_(mask == 0, -100)
+                loss = F.cross_entropy(y, t, ignore_index=-100)
+            return loss
+
+        if self.block_loss:
+            losses = [compute_loss(o, target, mask) for o in output]
+            loss = sum(losses)
+        else:
+            loss = compute_loss(output[-1], target, mask)
+
+        scores = output[-1]
+        if self.crf is not None:
+            pred = self.crf.viterbi_decode(scores, target, mask)
+        else:
+            pred = scores.max(1)[1] * mask.long()
+
+        return {
+            C.LOSS: loss,
+            C.OUTPUT: pred,
+        }
+
+    def predict(self, words, chars, seq_len):
+        return self.forward(words, chars, seq_len)[C.OUTPUT]

reproduction/text_classification/data/IMDBLoader.py

@@ -9,6 +9,7 @@ from fastNLP import Const
 # from reproduction.utils import check_dataloader_paths
 from functools import partial
 
+
 class IMDBLoader(DataSetLoader):
     """
     读取IMDB数据集，DataSet包含以下fields:
@@ -33,6 +34,7 @@ class IMDBLoader(DataSetLoader):
                 target = parts[0]
                 words = parts[1].lower().split()
                 dataset.append(Instance(words=words, target=target))
+
         if len(dataset)==0:
             raise RuntimeError(f"{path} has no valid data.")
 
@@ -42,19 +44,32 @@ class IMDBLoader(DataSetLoader):
                 paths: Union[str, Dict[str, str]],
                 src_vocab_opt: VocabularyOption = None,
                 tgt_vocab_opt: VocabularyOption = None,
-                src_embed_opt: EmbeddingOption = None):
-        
-        # paths = check_dataloader_paths(paths)
+                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:
+                word = word.lower()
+                for char in word:
+                    chars.append(char)
+            return chars
+
+        if char_level_op:
+            for dataset in datasets.values():
+                dataset.apply_field(wordtochar, field_name="words", new_field_name='chars')
 
         datasets["train"], datasets["dev"] = datasets["train"].split(0.1, shuffle=False)
 
         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) \
@@ -78,3 +93,18 @@ class IMDBLoader(DataSetLoader):
             dataset.set_target("target")
 
         return info
+
+
+
+if __name__=="__main__":
+    datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv",
+                "test": "/remote-home/ygwang/IMDB_data/test.csv"}
+    datainfo=IMDBLoader().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

@@ -0,0 +1,98 @@
+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 typing import Union, Dict
+from reproduction.Star_transformer.datasets import EmbedLoader
+from reproduction.utils import check_dataloader_paths
+
+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/yelpLoader.py

@@ -1,77 +1,203 @@
-from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader
+import ast
+import csv
+from typing import Iterable
+from fastNLP import DataSet, Instance, Vocabulary
 from fastNLP.core.vocabulary import VocabularyOption
-from fastNLP.io.base_loader import DataSetLoader, DataInfo
-from typing import Union, Dict, List, Iterator
-from fastNLP import DataSet
-from fastNLP import Instance
-from fastNLP import Vocabulary
-from fastNLP import Const
-# from reproduction.utils import check_dataloader_paths
-from functools import partial
-import pandas as pd
+from fastNLP.io import JsonLoader
+from fastNLP.io.base_loader import DataInfo,DataSetLoader
+from fastNLP.io.embed_loader import EmbeddingOption
+from fastNLP.io.file_reader import _read_json
+from typing import Union, Dict
+from reproduction.utils import check_dataloader_paths
 
-class yelpLoader(DataSetLoader):
+
+
+def get_tokenizer():
+    try:
+        import spacy
+        en = spacy.load('en')
+        print('use spacy tokenizer')
+        return lambda x: [w.text for w in en.tokenizer(x)]
+    except Exception as e:
+        print('use raw tokenizer')
+        return lambda x: x.split()
+
+def clean_str(sentence, tokenizer, char_lower=False):
     """
-    读取IMDB数据集，DataSet包含以下fields:
+    heavily borrowed from github
+    https://github.com/LukeZhuang/Hierarchical-Attention-Network/blob/master/yelp-preprocess.ipynb
+    :param sentence:  is a str
+    :return:
+    """
+    if char_lower:
+        sentence = sentence.lower()
+    import re
+    nonalpnum = re.compile('[^0-9a-zA-Z?!\']+')
+    words = tokenizer(sentence)
+    words_collection = []
+    for word in words:
+        if word in ['-lrb-', '-rrb-', '<sssss>', '-r', '-l', 'b-']:
+            continue
+        tt = nonalpnum.split(word)
+        t = ''.join(tt)
+        if t != '':
+            words_collection.append(t)
 
-        words: list(str), 需要分类的文本
-        target: str, 文本的标签
+    return words_collection
 
 
+class yelpLoader(DataSetLoader):
+    
     """
+    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
 
-    def __init__(self):
+    数据集：yelp_full/yelp_polarity
+    :param fine_grained: 是否使用SST-5标准，若 ``False`` , 使用SST-2。Default: ``False``
+    """
+    
+    def __init__(self, fine_grained=False,lower=False):
         super(yelpLoader, self).__init__()
+        tag_v = {'1.0': 'very negative', '2.0': 'negative', '3.0': 'neutral',
+                 '4.0': 'positive', '5.0': 'very positive'}
+        if not fine_grained:
+            tag_v['1.0'] = tag_v['2.0']
+            tag_v['5.0'] = tag_v['4.0']
+        self.fine_grained = fine_grained
+        self.tag_v = tag_v
+        self.lower = lower
+        self.tokenizer = get_tokenizer()
 
-    def _load(self, path):
-        dataset = DataSet()
-        data = pd.read_csv(path, header=None, sep=",").values
-        for line in data:
-            target = str(line[0])
-            words = str(line[1]).lower().split()
-            dataset.append(Instance(words=words, target=target))
-        if len(dataset)==0:
-            raise RuntimeError(f"{path} has no valid data.")
-
-        return dataset
+    '''
+    读取Yelp数据集, DataSet包含fields:
     
-    def process(self,
-                paths: Union[str, Dict[str, str]],
-                src_vocab_opt: VocabularyOption = None,
-                tgt_vocab_opt: VocabularyOption = None,
-                src_embed_opt: EmbeddingOption = None):
-        
-        # paths = check_dataloader_paths(paths)
-        datasets = {}
-        info = DataInfo()
-        for name, path in paths.items():
-            dataset = self.load(path)
-            datasets[name] = dataset
+        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
+        words: list(str), 需要分类的文本
+        target: str, 文本的标签
+    
+    数据来源: https://www.yelp.com/dataset/download
+    
+
+    def _load_json(self, path):
+        ds = DataSet()
+        for idx, d in _read_json(path, fields=self.fields_list, dropna=self.dropna):
+            d = ast.literal_eval(d)
+            d["words"] = d.pop("text").split()
+            d["target"] = self.tag_v[str(d.pop("stars"))]
+            ds.append(Instance(**d))
+        return ds
+    
+    def _load_yelp2015_broken(self,path):
+        ds = DataSet()
+        with open (path,encoding='ISO 8859-1') as f:
+            row=f.readline()
+            all_count=0
+            exp_count=0
+            while row:
+                row=row.split("\t\t")
+                all_count+=1
+                if len(row)>=3:
+                    words=row[-1].split()
+                    try:
+                        target=self.tag_v[str(row[-2])+".0"]
+                        ds.append(Instance(words=words, target=target))
+                    except KeyError:
+                        exp_count+=1
+                else:
+                    exp_count+=1
+                row = f.readline()
+            print("error sample count:",exp_count)
+            print("all count:",all_count)
+        return ds
+    '''
+    
+    def _load(self, path):
+        ds = DataSet()
+        csv_reader=csv.reader(open(path,encoding='utf-8'))
+        all_count=0
+        real_count=0
+        for row in csv_reader:
+            all_count+=1
+            if len(row)==2:
+                target=self.tag_v[row[0]+".0"]
+                words=clean_str(row[1],self.tokenizer,self.lower)
+                if len(words)!=0:
+                    ds.append(Instance(words=words,target=target))
+                    real_count += 1
+        print("all count:", all_count)
+        print("real count:", real_count)
+        return ds
 
-        datasets["train"], datasets["dev"] = datasets["train"].split(0.1, shuffle=False)
 
-        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')
 
+    def process(self, paths: Union[str, Dict[str, str]],
+                train_ds: Iterable[str] = None,
+                src_vocab_op: VocabularyOption = None,
+                tgt_vocab_op: VocabularyOption = None,
+                embed_opt: EmbeddingOption = None,
+                char_level_op=False):
+        paths = check_dataloader_paths(paths)
+        datasets = {}
+        info = DataInfo(datasets=self.load(paths))
+        src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op)
         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')
+            if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
+        _train_ds = [info.datasets[name]
+                     for name in train_ds] if train_ds else info.datasets.values()
+
+
+        def wordtochar(words):
 
-        info.vocabs = {
-            "words": src_vocab,
-            "target": tgt_vocab
-        }
+            chars=[]
+            for word in words:
+                word=word.lower()
+                for char in word:
+                    chars.append(char)
+            return chars
 
-        info.datasets = datasets
+        input_name, target_name = 'words', 'target'
+        info.vocabs={}
+        #就分隔为char形式
+        if char_level_op:
+            for dataset in info.datasets.values():
+                dataset.apply_field(wordtochar, field_name="words",new_field_name='chars')
+        # if embed_opt is not None:
+        #     embed = EmbedLoader.load_with_vocab(**embed_opt, vocab=vocab)
+        #     info.embeddings['words'] = embed
+        else:
+            src_vocab.from_dataset(*_train_ds, field_name=input_name)
+            src_vocab.index_dataset(*info.datasets.values(),field_name=input_name, new_field_name=input_name)
+            info.vocabs[input_name]=src_vocab
 
-        if src_embed_opt is not None:
-            embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
-            info.embeddings['words'] = embed
+        tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
+        tgt_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=target_name, new_field_name=target_name)
 
-        for name, dataset in info.datasets.items():
-            dataset.set_input("words")
-            dataset.set_target("target")
+        info.vocabs[target_name]=tgt_vocab
 
         return info
+
+if __name__=="__main__":
+    testloader=yelpLoader()
+    # datapath = {"train": "/remote-home/ygwang/yelp_full/train.csv",
+    #             "test": "/remote-home/ygwang/yelp_full/test.csv"}
+    #datapath={"train": "/remote-home/ygwang/yelp_full/test.csv"}
+    datapath = {"train": "/remote-home/ygwang/yelp_polarity/train.csv",
+                "test": "/remote-home/ygwang/yelp_polarity/test.csv"}
+    datainfo=testloader.process(datapath,char_level_op=True)
+
+    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/model/char_cnn.py

@@ -1 +1,90 @@
-# TODO
+'''
+@author: https://github.com/ahmedbesbes/character-based-cnn
+这里借鉴了上述链接中char-cnn model的代码，改动主要为将其改动为符合fastnlp的pipline
+'''
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+
+class CharacterLevelCNN(nn.Module):
+    def __init__(self, args,embedding):
+        super(CharacterLevelCNN, self).__init__()
+
+        self.config=args.char_cnn_config
+        self.embedding=embedding
+
+        conv_layers = []
+        for i, conv_layer_parameter in enumerate(self.config['model_parameters'][args.model_size]['conv']):
+            if i == 0:
+                #in_channels = args.number_of_characters + len(args.extra_characters)
+                in_channels = args.embedding_dim
+                out_channels = conv_layer_parameter[0]
+            else:
+                in_channels, out_channels = conv_layer_parameter[0], conv_layer_parameter[0]
+
+            if conv_layer_parameter[2] != -1:
+                conv_layer = nn.Sequential(nn.Conv1d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=conv_layer_parameter[1], padding=0),
+                                           nn.ReLU(),
+                                           nn.MaxPool1d(conv_layer_parameter[2]))
+            else:
+                conv_layer = nn.Sequential(nn.Conv1d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=conv_layer_parameter[1], padding=0),
+                                           nn.ReLU())
+            conv_layers.append(conv_layer)
+        self.conv_layers = nn.ModuleList(conv_layers)
+
+        input_shape = (args.batch_size, args.max_length,
+                       args.number_of_characters + len(args.extra_characters))
+        dimension = self._get_conv_output(input_shape)
+
+        print('dimension :', dimension)
+
+        fc_layer_parameter = self.config['model_parameters'][args.model_size]['fc'][0]
+        fc_layers = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(dimension, fc_layer_parameter), nn.Dropout(0.5)),
+            nn.Sequential(nn.Linear(fc_layer_parameter,
+                                    fc_layer_parameter), nn.Dropout(0.5)),
+            nn.Linear(fc_layer_parameter, args.num_classes),
+        ])
+
+        self.fc_layers = fc_layers
+
+        if args.model_size == 'small':
+            self._create_weights(mean=0.0, std=0.05)
+        elif args.model_size == 'large':
+            self._create_weights(mean=0.0, std=0.02)
+
+    def _create_weights(self, mean=0.0, std=0.05):
+        for module in self.modules():
+            if isinstance(module, nn.Conv1d) or isinstance(module, nn.Linear):
+                module.weight.data.normal_(mean, std)
+
+    def _get_conv_output(self, shape):
+        input = torch.rand(shape)
+        output = input.transpose(1, 2)
+        # forward pass through conv layers
+        for i in range(len(self.conv_layers)):
+            output = self.conv_layers[i](output)
+
+        output = output.view(output.size(0), -1)
+        n_size = output.size(1)
+        return n_size
+
+    def forward(self, chars):
+        input=self.embedding(chars)
+        output = input.transpose(1, 2)
+        # forward pass through conv layers
+        for i in range(len(self.conv_layers)):
+            output = self.conv_layers[i](output)
+
+        output = output.view(output.size(0), -1)
+
+        # forward pass through fc layers
+        for i in range(len(self.fc_layers)):
+            output = self.fc_layers[i](output)
+
+        return {C.OUTPUT: output}

reproduction/text_classification/model/dpcnn.py

@@ -1 +1,106 @@
-# TODO
+
+import torch
+import torch.nn as nn
+from fastNLP.modules.utils import get_embeddings
+from fastNLP.core import Const as C
+
+
+class DPCNN(nn.Module):
+    def __init__(self, init_embed, num_cls, n_filters=256,
+                 kernel_size=3, n_layers=7, embed_dropout=0.1, cls_dropout=0.1):
+        super().__init__()
+        self.region_embed = RegionEmbedding(
+            init_embed, out_dim=n_filters, kernel_sizes=[1, 3, 5])
+
+        embed_dim = self.region_embed.embedding_dim
+        self.conv_list = nn.ModuleList()
+        for i in range(n_layers):
+            self.conv_list.append(nn.Sequential(
+                nn.ReLU(),
+
+                nn.Conv1d(n_filters, n_filters, kernel_size,
+                          padding=kernel_size//2),
+                nn.Conv1d(n_filters, n_filters, kernel_size,
+                          padding=kernel_size//2),
+            ))
+        self.pool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
+        self.embed_drop = nn.Dropout(embed_dropout)
+        self.classfier = nn.Sequential(
+            nn.Dropout(cls_dropout),
+
+            nn.Linear(n_filters, num_cls),
+        )
+        self.reset_parameters()
+
+
+    def reset_parameters(self):
+        for m in self.modules():
+            if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+                nn.init.normal_(m.weight, mean=0, std=0.01)
+                if m.bias is not None:
+                    nn.init.normal_(m.bias, mean=0, std=0.01)
+
+
+    def forward(self, words, seq_len=None):
+        words = words.long()
+        # get region embeddings
+        x = self.region_embed(words)
+        x = self.embed_drop(x)
+
+        # not pooling on first conv
+        x = self.conv_list[0](x) + x
+        for conv in self.conv_list[1:]:
+            x = self.pool(x)
+            x = conv(x) + x
+
+        # B, C, L => B, C
+        x, _ = torch.max(x, dim=2)
+        x = self.classfier(x)
+        return {C.OUTPUT: x}
+
+
+    def predict(self, words, seq_len=None):
+        x = self.forward(words, seq_len)[C.OUTPUT]
+        return {C.OUTPUT: torch.argmax(x, 1)}
+
+class RegionEmbedding(nn.Module):
+    def __init__(self, init_embed, out_dim=300, kernel_sizes=None):
+        super().__init__()
+        if kernel_sizes is None:
+            kernel_sizes = [5, 9]
+
+        assert isinstance(
+            kernel_sizes, list), 'kernel_sizes should be List(int)'
+
+        self.embed = get_embeddings(init_embed)
+        try:
+            embed_dim = self.embed.embedding_dim
+        except Exception:
+            embed_dim = self.embed.embed_size
+        self.region_embeds = nn.ModuleList()
+        for ksz in kernel_sizes:
+            self.region_embeds.append(nn.Sequential(
+                nn.Conv1d(embed_dim, embed_dim, ksz, padding=ksz // 2),
+            ))
+        self.linears = nn.ModuleList([nn.Conv1d(embed_dim, out_dim, 1)
+                                      for _ in range(len(kernel_sizes))])
+        self.embedding_dim = embed_dim
+
+    def forward(self, x):
+        x = self.embed(x)
+        x = x.transpose(1, 2)
+        # B, C, L
+        out = 0
+        for conv, fc in zip(self.region_embeds, self.linears[1:]):
+            conv_i = conv(x)
+            out = out + fc(conv_i)
+        # B, C, L
+        return out
+
+
+if __name__ == '__main__':
+    x = torch.randint(0, 10000, size=(5, 15), dtype=torch.long)
+    model = DPCNN((10000, 300), 20)
+    y = model(x)
+    print(y.size(), y.mean(1), y.std(1))
+

reproduction/text_classification/train_char_cnn.py

@@ -0,0 +1,206 @@
+# 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 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
+from model.char_cnn import CharacterLevelCNN
+from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.models.cnn_text_classification import CNNText
+from fastNLP.modules.encoder.embedding import CNNCharEmbedding,StaticEmbedding,StackEmbedding,LSTMCharEmbedding
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP.core.trainer import Trainer
+from torch.optim import SGD
+from torch.autograd import Variable
+import torch
+from fastNLP import BucketSampler
+
+##hyper
+#todo 这里加入fastnlp的记录
+class Config():
+    model_dir_or_name="en-base-uncased"
+    embedding_grad= False,
+    bert_embedding_larers= '4,-2,-1'
+    train_epoch= 50
+    num_classes=2
+    task= "IMDB"
+    #yelp_p
+    datapath = {"train": "/remote-home/ygwang/yelp_polarity/train.csv",
+               "test": "/remote-home/ygwang/yelp_polarity/test.csv"}
+    #IMDB
+    #datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv",
+    #           "test": "/remote-home/ygwang/IMDB_data/test.csv"}
+    # sst
+    # datapath = {"train": "/remote-home/ygwang/workspace/GLUE/SST-2/train.tsv",
+    #           "dev": "/remote-home/ygwang/workspace/GLUE/SST-2/dev.tsv"}
+
+    lr=0.01
+    batch_size=128
+    model_size="large"
+    number_of_characters=69
+    extra_characters=''
+    max_length=1014
+
+    char_cnn_config={
+        "alphabet": {
+            "en": {
+                "lower": {
+                    "alphabet": "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}",
+                    "number_of_characters": 69
+                },
+                "both": {
+                    "alphabet": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}",
+                    "number_of_characters": 95
+                }
+            }
+        },
+        "model_parameters": {
+            "small": {
+                "conv": [
+                    #依次是channel，kennnel_size，maxpooling_size
+                    [256,7,3],
+                    [256,7,3],
+                    [256,3,-1],
+                    [256,3,-1],
+                    [256,3,-1],
+                    [256,3,3]
+                ],
+                "fc": [1024,1024]
+            },
+            "large":{
+                "conv":[
+                    [1024, 7, 3],
+                    [1024, 7, 3],
+                    [1024, 3, -1],
+                    [1024, 3, -1],
+                    [1024, 3, -1],
+                    [1024, 3, 3]
+                ],
+                "fc": [2048,2048]
+            }
+        },
+        "data": {
+            "text_column": "SentimentText",
+            "label_column": "Sentiment",
+            "max_length": 1014,
+            "num_of_classes": 2,
+            "encoding": None,
+            "chunksize": 50000,
+            "max_rows": 100000,
+            "preprocessing_steps": ["lower", "remove_hashtags", "remove_urls", "remove_user_mentions"]
+        },
+        "training": {
+            "batch_size": 128,
+            "learning_rate": 0.01,
+            "epochs": 10,
+            "optimizer": "sgd"
+        }
+    }
+ops=Config
+
+
+##1.task相关信息：利用dataloader载入dataInfo
+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)
+ops.embedding_dim=ops.number_of_characters
+
+#chartoindex
+def chartoindex(chars):
+    max_seq_len=ops.max_length
+    zero_index=len(char_vocab)
+    char_index_list=[]
+    for char in chars:
+        if char in char_vocab:
+             char_index_list.append(char_vocab.index(char))
+        else:
+            #<unk>和<pad>均使用最后一个作为embbeding
+            char_index_list.append(zero_index)
+    if len(char_index_list) > max_seq_len:
+        char_index_list = char_index_list[:max_seq_len]
+    elif 0 < len(char_index_list) < max_seq_len:
+        char_index_list = char_index_list+[zero_index]*(max_seq_len-len(char_index_list))
+    elif len(char_index_list) == 0:
+        char_index_list=[zero_index]*max_seq_len
+    return char_index_list
+
+for dataset in datainfo.datasets.values():
+    dataset.apply_field(chartoindex,field_name='chars',new_field_name='chars')
+
+datainfo.datasets['train'].set_input('chars')
+datainfo.datasets['test'].set_input('chars')
+datainfo.datasets['train'].set_target('target')
+datainfo.datasets['test'].set_target('target')
+
+##2. 定义/组装模型，这里可以随意，就如果是fastNLP封装好的，类似CNNText就直接用初始化调用就好了，这里只是给出一个伪框架表示占位，在这里建立符合fastNLP输入输出规范的model
+class ModelFactory(nn.Module):
+    """
+        用于拼装embedding，encoder，decoder 以及设计forward过程
+
+        :param embedding:  embbeding model
+        :param encoder: encoder model
+        :param decoder: decoder model
+
+        """
+    def __int__(self,embedding,encoder,decoder,**kwargs):
+        super(ModelFactory,self).__init__()
+        self.embedding=embedding
+        self.encoder=encoder
+        self.decoder=decoder
+
+    def forward(self,x):
+        return {C.OUTPUT:None}
+
+## 2.或直接复用fastNLP的模型
+#vocab=datainfo.vocabs['words']
+vocab_label=datainfo.vocabs['target']
+'''
+# emded_char=CNNCharEmbedding(vocab)
+# embed_word = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50', requires_grad=True)
+# embedding=StackEmbedding([emded_char, embed_word])
+# cnn_char_embed = CNNCharEmbedding(vocab)
+# lstm_char_embed = LSTMCharEmbedding(vocab)
+# embedding = StackEmbedding([cnn_char_embed, lstm_char_embed])
+'''
+#one-hot embedding
+embedding_weight= Variable(torch.zeros(len(char_vocab)+1, len(char_vocab)))
+
+for i in range(len(char_vocab)):
+    embedding_weight[i][i]=1
+embedding=nn.Embedding(num_embeddings=len(char_vocab)+1,embedding_dim=len(char_vocab),padding_idx=len(char_vocab),_weight=embedding_weight)
+for para in embedding.parameters():
+    para.requires_grad=False
+#CNNText太过于简单
+#model=CNNText(init_embed=embedding, num_classes=ops.num_classes)
+model=CharacterLevelCNN(ops,embedding)
+
+## 3. 声明loss,metric,optimizer
+loss=CrossEntropyLoss
+metric=AccuracyMetric
+optimizer= SGD([param for param in model.parameters() if param.requires_grad==True], lr=ops.lr)
+
+## 4.定义train方法
+def train(model,datainfo,loss,metrics,optimizer,num_epochs=100):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss(target='target'),
+                      metrics=[metrics(target='target')], dev_data=datainfo.datasets['test'], device=0, check_code_level=-1,
+                      n_epochs=num_epochs)
+    print(trainer.train())
+
+
+
+if __name__=="__main__":
+    #print(vocab_label)
+
+    #print(datainfo.datasets["train"])
+    train(model,datainfo,loss,metric,optimizer,num_epochs=ops.train_epoch)
+    

reproduction/text_classification/train_dpcnn.py

@@ -0,0 +1,125 @@
+# 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 torch.cuda
+from fastNLP.core.utils import cache_results
+from torch.optim import SGD
+from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
+from fastNLP.core.trainer import Trainer
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP.modules.encoder.embedding import StaticEmbedding, CNNCharEmbedding, StackEmbedding
+from reproduction.text_classification.model.dpcnn import DPCNN
+from data.yelpLoader import yelpLoader
+import torch.nn as nn
+from fastNLP.core import LRScheduler
+from fastNLP.core.const import Const as C
+from fastNLP.core.vocabulary import VocabularyOption
+from utils.util_init import set_rng_seeds
+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'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+
+
+
+# hyper
+
+class Config():
+    seed = 12345
+    model_dir_or_name = "dpcnn-yelp-p"
+    embedding_grad = True
+    train_epoch = 30
+    batch_size = 100
+    num_classes = 2
+    task = "yelp_p"
+    #datadir = '/remote-home/yfshao/workdir/datasets/SST'
+    datadir = '/remote-home/yfshao/workdir/datasets/yelp_polarity'
+    #datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"}
+    datafile = {"train": "train.csv",  "test": "test.csv"}
+    lr = 1e-3
+    src_vocab_op = VocabularyOption()
+    embed_dropout = 0.3
+    cls_dropout = 0.1
+    weight_decay = 1e-4
+
+    def __init__(self):
+        self.datapath = {k: os.path.join(self.datadir, v)
+                         for k, v in self.datafile.items()}
+
+
+ops = Config()
+
+set_rng_seeds(ops.seed)
+print('RNG SEED: {}'.format(ops.seed))
+
+# 1.task相关信息：利用dataloader载入dataInfo
+
+#datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train'])
+@cache_results(ops.model_dir_or_name+'-data-cache')
+def load_data():
+    datainfo = yelpLoader(fine_grained=True, lower=True).process(
+        paths=ops.datapath, train_ds=['train'], src_vocab_op=ops.src_vocab_op)
+    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)
+    return datainfo
+
+datainfo = load_data()
+
+# 2.或直接复用fastNLP的模型
+
+vocab = datainfo.vocabs['words']
+# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)])
+#embedding = StaticEmbedding(vocab)
+
+embedding = StaticEmbedding(
+    vocab, model_dir_or_name='en-word2vec-300', requires_grad=ops.embedding_grad,
+    normalize=False
+)
+
+print(len(datainfo.datasets['train']))
+print(len(datainfo.datasets['test']))
+print(datainfo.datasets['train'][0])
+
+
+print(len(vocab))
+print(len(datainfo.vocabs['target']))
+
+
+model = DPCNN(init_embed=embedding, num_cls=ops.num_classes,
+              embed_dropout=ops.embed_dropout, cls_dropout=ops.cls_dropout)
+print(model)
+
+# 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=ops.weight_decay)
+
+callbacks = []
+callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
+# callbacks.append
+#     LRScheduler(LambdaLR(optimizer, lambda epoch: ops.lr if epoch <
+#                          ops.train_epoch * 0.8 else ops.lr * 0.1))
+# )
+
+# callbacks.append(
+#     FitlogCallback(data=datainfo.datasets, verbose=1)
+# )
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+print(device)
+
+# 4.定义train方法
+trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                  metrics=[metric],
+                  dev_data=datainfo.datasets['test'], device=device,
+                  check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks,
+                  n_epochs=ops.train_epoch, num_workers=4)
+
+
+
+if __name__ == "__main__":
+    print(trainer.train())
+

reproduction/text_classification/utils/util_init.py

@@ -0,0 +1,11 @@
+import numpy
+import torch
+import random
+
+
+def set_rng_seeds(seed):
+    random.seed(seed)
+    numpy.random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    # print('RNG_SEED {}'.format(seed))