# 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 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: #和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)