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https://gitee.com/fastnlp/fastNLP.git
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216 lines
7.6 KiB
Python
216 lines
7.6 KiB
Python
# 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
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import os
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os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
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os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
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import sys
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sys.path.append('../..')
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from fastNLP.core.const import Const as C
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import torch.nn as nn
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from fastNLP.io.data_loader import YelpLoader
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#from data.sstLoader import sst2Loader
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from model.char_cnn import CharacterLevelCNN
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from fastNLP import CrossEntropyLoss, AccuracyMetric
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from fastNLP.core.trainer import Trainer
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from torch.optim import SGD
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from torch.autograd import Variable
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import torch
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from torch.optim.lr_scheduler import LambdaLR
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from fastNLP.core import LRScheduler
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##hyper
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#todo 这里加入fastnlp的记录
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class Config():
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#seed=7777
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model_dir_or_name="en-base-uncased"
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embedding_grad= False,
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bert_embedding_larers= '4,-2,-1'
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train_epoch= 50
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num_classes=2
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task= "yelp_p"
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#yelp_p
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datapath = {"train": "/remote-home/ygwang/yelp_polarity/train.csv",
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"test": "/remote-home/ygwang/yelp_polarity/test.csv"}
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#IMDB
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#datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv",
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# "test": "/remote-home/ygwang/IMDB_data/test.csv"}
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# sst
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# datapath = {"train": "/remote-home/ygwang/workspace/GLUE/SST-2/train.tsv",
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# "dev": "/remote-home/ygwang/workspace/GLUE/SST-2/dev.tsv"}
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lr=0.01
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batch_size=128
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model_size="large"
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number_of_characters=69
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extra_characters=''
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max_length=1014
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weight_decay = 1e-5
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char_cnn_config={
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"alphabet": {
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"en": {
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"lower": {
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"alphabet": "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}",
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"number_of_characters": 69
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},
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"both": {
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"alphabet": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}",
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"number_of_characters": 95
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}
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}
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},
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"model_parameters": {
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"small": {
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"conv": [
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#依次是channel，kennnel_size，maxpooling_size
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[256,7,3],
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[256,7,3],
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[256,3,-1],
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[256,3,-1],
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[256,3,-1],
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[256,3,3]
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],
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"fc": [1024,1024]
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},
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"large":{
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"conv":[
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[1024, 7, 3],
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[1024, 7, 3],
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[1024, 3, -1],
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[1024, 3, -1],
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[1024, 3, -1],
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[1024, 3, 3]
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],
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"fc": [2048,2048]
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}
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},
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"data": {
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"text_column": "SentimentText",
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"label_column": "Sentiment",
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"max_length": 1014,
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"num_of_classes": 2,
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"encoding": None,
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"chunksize": 50000,
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"max_rows": 100000,
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"preprocessing_steps": ["lower", "remove_hashtags", "remove_urls", "remove_user_mentions"]
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},
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"training": {
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"batch_size": 128,
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"learning_rate": 0.01,
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"epochs": 10,
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"optimizer": "sgd"
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}
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}
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ops=Config
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# set_rng_seeds(ops.seed)
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# print('RNG SEED: {}'.format(ops.seed))
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##1.task相关信息：利用dataloader载入dataInfo
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#dataloader=SST2Loader()
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#dataloader=IMDBLoader()
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dataloader=YelpLoader(fine_grained=True)
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datainfo=dataloader.process(ops.datapath,char_level_op=True,split_dev_op=False)
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char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"]
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ops.number_of_characters=len(char_vocab)
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ops.embedding_dim=ops.number_of_characters
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#chartoindex
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def chartoindex(chars):
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max_seq_len=ops.max_length
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zero_index=len(char_vocab)
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char_index_list=[]
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for char in chars:
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if char in char_vocab:
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char_index_list.append(char_vocab.index(char))
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else:
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#<unk>和<pad>均使用最后一个作为embbeding
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char_index_list.append(zero_index)
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if len(char_index_list) > max_seq_len:
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char_index_list = char_index_list[:max_seq_len]
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elif 0 < len(char_index_list) < max_seq_len:
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char_index_list = char_index_list+[zero_index]*(max_seq_len-len(char_index_list))
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elif len(char_index_list) == 0:
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char_index_list=[zero_index]*max_seq_len
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return char_index_list
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for dataset in datainfo.datasets.values():
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dataset.apply_field(chartoindex,field_name='chars',new_field_name='chars')
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datainfo.datasets['train'].set_input('chars')
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datainfo.datasets['test'].set_input('chars')
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datainfo.datasets['train'].set_target('target')
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datainfo.datasets['test'].set_target('target')
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##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
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class ModelFactory(nn.Module):
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"""
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用于拼装embedding，encoder，decoder 以及设计forward过程
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:param embedding: embbeding model
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:param encoder: encoder model
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:param decoder: decoder model
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"""
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def __int__(self,embedding,encoder,decoder,**kwargs):
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super(ModelFactory,self).__init__()
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self.embedding=embedding
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self.encoder=encoder
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self.decoder=decoder
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def forward(self,x):
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return {C.OUTPUT:None}
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## 2.或直接复用fastNLP的模型
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#vocab=datainfo.vocabs['words']
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vocab_label=datainfo.vocabs['target']
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'''
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# emded_char=CNNCharEmbedding(vocab)
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# embed_word = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50', requires_grad=True)
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# embedding=StackEmbedding([emded_char, embed_word])
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# cnn_char_embed = CNNCharEmbedding(vocab)
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# lstm_char_embed = LSTMCharEmbedding(vocab)
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# embedding = StackEmbedding([cnn_char_embed, lstm_char_embed])
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'''
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#one-hot embedding
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embedding_weight= Variable(torch.zeros(len(char_vocab)+1, len(char_vocab)))
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for i in range(len(char_vocab)):
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embedding_weight[i][i]=1
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embedding=nn.Embedding(num_embeddings=len(char_vocab)+1,embedding_dim=len(char_vocab),padding_idx=len(char_vocab),_weight=embedding_weight)
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for para in embedding.parameters():
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para.requires_grad=False
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#CNNText太过于简单
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#model=CNNText(init_embed=embedding, num_classes=ops.num_classes)
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model=CharacterLevelCNN(ops,embedding)
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## 3. 声明loss,metric,optimizer
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loss=CrossEntropyLoss
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metric=AccuracyMetric
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#optimizer= SGD([param for param in model.parameters() if param.requires_grad==True], lr=ops.lr)
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optimizer = SGD([param for param in model.parameters() if param.requires_grad == True],
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lr=ops.lr, momentum=0.9, weight_decay=ops.weight_decay)
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callbacks = []
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# callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
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callbacks.append(
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LRScheduler(LambdaLR(optimizer, lambda epoch: ops.lr if epoch <
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ops.train_epoch * 0.8 else ops.lr * 0.1))
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)
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## 4.定义train方法
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def train(model,datainfo,loss,metrics,optimizer,num_epochs=100):
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trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss(target='target'),batch_size=ops.batch_size,
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metrics=[metrics(target='target')], dev_data=datainfo.datasets['test'], device=[0,1,2], check_code_level=-1,
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n_epochs=num_epochs)
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print(trainer.train())
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if __name__=="__main__":
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#print(vocab_label)
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#print(datainfo.datasets["train"])
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train(model,datainfo,loss,metric,optimizer,num_epochs=ops.train_epoch)
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