Merge branch 'dev0.5.0' into master

reproduction/text_classification/data/IMDBLoader.py

@@ -34,23 +34,36 @@ class IMDBLoader(DataSetLoader):
                 target = parts[0]
                 words = parts[1].split()
                 dataset.append(Instance(words=words, target=target))
-        if len(dataset) == 0:
+
+        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):
-
-        # 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)
 
@@ -80,3 +93,16 @@ 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

@@ -4,13 +4,14 @@ from typing import Iterable
 from fastNLP import DataSet, Instance, Vocabulary
 from fastNLP.core.vocabulary import VocabularyOption
 from fastNLP.io import JsonLoader
-from fastNLP.io.base_loader import DataInfo
+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
 
 
+
 def get_tokenizer():
     try:
         import spacy
@@ -45,26 +46,19 @@ 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
 
-    数据来源: https://www.yelp.com/dataset/download
-
+    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):
+    
+    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'}
@@ -77,6 +71,21 @@ class yelpLoader(JsonLoader):
         self.tokenizer = get_tokenizer()
 
     '''
+    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
+        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):
@@ -85,7 +94,7 @@ class yelpLoader(JsonLoader):
             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:
@@ -109,24 +118,26 @@ class yelpLoader(JsonLoader):
             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
+        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))
+            all_count+=1
+            if len(row)==2:
+                target=self.tag_v[row[0]+".0"]
+                words=clean_str(row[1],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
 
+
+
     def process(self, paths: Union[str, Dict[str, str]],
                 train_ds: Iterable[str] = None,
                 src_vocab_op: VocabularyOption = None,
@@ -142,57 +153,51 @@ class yelpLoader(JsonLoader):
         _train_ds = [info.datasets[name]
                      for name in train_ds] if train_ds else info.datasets.values()
 
-        # vocab = Vocabulary(min_freq=2) if vocab_opt is None else Vocabulary(**vocab_opt)
-        # for name, path in paths.items():
-        #     dataset = self.load(path)
-        #     datasets[name] = dataset
-        #     vocab.from_dataset(dataset, field_name="words")
-        # info.vocabs = vocab
-        # info.datasets = datasets
 
         def wordtochar(words):
-            chars = []
+
+            chars=[]
             for word in words:
-                word = word.lower()
+                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
+        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')
+                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
+            src_vocab.index_dataset(*info.datasets.values(),field_name=input_name, new_field_name=input_name)
+            info.vocabs[input_name]=src_vocab
 
         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)
-        info.vocabs[target_name] = tgt_vocab
 
-        return info
+        info.vocabs[target_name]=tgt_vocab
 
+        return info
 
-if __name__ == "__main__":
-    testloader = yelpLoader()
+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_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)
+    datainfo=testloader.process(datapath,char_level_op=True)
 
-    len_count = 0
+    len_count=0
     for instance in datainfo.datasets["train"]:
-        len_count += len(instance["chars"])
+        len_count+=len(instance["chars"])
 
-    ave_len = len_count / len(datainfo.datasets["train"])
+    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

@@ -1,3 +1,4 @@
+
 import torch
 import torch.nn as nn
 from fastNLP.modules.utils import get_embeddings
@@ -10,11 +11,13 @@ class DPCNN(nn.Module):
         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,
@@ -24,10 +27,12 @@ class DPCNN(nn.Module):
         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)):
@@ -35,6 +40,7 @@ class DPCNN(nn.Module):
                 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
@@ -52,18 +58,20 @@ class DPCNN(nn.Module):
         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
@@ -95,3 +103,4 @@ if __name__ == '__main__':
     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

@@ -20,6 +20,7 @@ os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 
 
+
 # hyper
 
 class Config():
@@ -70,6 +71,7 @@ datainfo = load_data()
 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
@@ -79,9 +81,11 @@ 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)
@@ -118,3 +122,4 @@ trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=l
 
 if __name__ == "__main__":
     print(trainer.train())
+