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Merge pull request #211 from lyhuang18/lyhuang-reproduction

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datasetloader改成pipe
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lyhuang18 2019-08-30 01:23:41 +08:00 committed by GitHub
commit b134c9f7e7
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3 changed files with 32 additions and 41 deletions
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23
reproduction/text_classification/train_awdlstm.py
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@ -1,11 +1,9 @@
# 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 sys
sys.path.append('../..')
from fastNLP.io.data_loader import IMDBLoader
from fastNLP.io.pipe.classification import IMDBPipe
from fastNLP.embeddings import StaticEmbedding
from model.awd_lstm import AWDLSTMSentiment
@ -32,15 +30,14 @@ opt=Config()
# load data
dataloader=IMDBLoader()
datainfo=dataloader.process(opt.datapath)
data_bundle=IMDBPipe.process_from_file(opt.datapath)
# print(datainfo.datasets["train"])
# print(datainfo)
# print(data_bundle.datasets["train"])
# print(data_bundle)
# define model
vocab=datainfo.vocabs['words']
vocab=data_bundle.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)
@ -52,11 +49,11 @@ optimizer= Adam([param for param in model.parameters() if param.requires_grad==T
def train(datainfo, model, optimizer, loss, metrics, opt):
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
metrics=metrics, dev_data=datainfo.datasets['test'], device=0, check_code_level=-1,
trainer = Trainer(data_bundle.datasets['train'], model, optimizer=optimizer, loss=loss,
metrics=metrics, dev_data=data_bundle.datasets['test'], device=0, check_code_level=-1,
n_epochs=opt.train_epoch, save_path=opt.save_model_path)
trainer.train()
if __name__ == "__main__":
train(datainfo, model, optimizer, loss, metrics, opt)
train(data_bundle, model, optimizer, loss, metrics, opt)

25
reproduction/text_classification/train_lstm.py
View File

@ -1,9 +1,7 @@
# 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.io.data_loader import IMDBLoader
from fastNLP.io.pipe.classification import IMDBPipe
from fastNLP.embeddings import StaticEmbedding
from model.lstm import BiLSTMSentiment
@ -29,15 +27,14 @@ opt=Config()
# load data
dataloader=IMDBLoader()
datainfo=dataloader.process(opt.datapath)
data_bundle=IMDBPipe.process_from_file(opt.datapath)
# print(datainfo.datasets["train"])
# print(datainfo)
# print(data_bundle.datasets["train"])
# print(data_bundle)
# define model
vocab=datainfo.vocabs['words']
vocab=data_bundle.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)
@ -48,12 +45,12 @@ 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['test'], device=0, check_code_level=-1,
def train(data_bundle, model, optimizer, loss, metrics, opt):
trainer = Trainer(data_bundle.datasets['train'], model, optimizer=optimizer, loss=loss,
metrics=metrics, dev_data=data_bundle.datasets['test'], device=0, check_code_level=-1,
n_epochs=opt.train_epoch, save_path=opt.save_model_path)
trainer.train()
if __name__ == "__main__":
train(datainfo, model, optimizer, loss, metrics, opt)
train(data_bundle, model, optimizer, loss, metrics, opt)

25
reproduction/text_classification/train_lstm_att.py
View File

@ -1,9 +1,7 @@
# 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.io.data_loader import IMDBLoader
from fastNLP.io.pipe.classification import IMDBPipe
from fastNLP.embeddings import StaticEmbedding
from model.lstm_self_attention import BiLSTM_SELF_ATTENTION
@ -31,15 +29,14 @@ opt=Config()
# load data
dataloader=IMDBLoader()
datainfo=dataloader.process(opt.datapath)
data_bundle=IMDBPipe.process_from_file(opt.datapath)
# print(datainfo.datasets["train"])
# print(datainfo)
# print(data_bundle.datasets["train"])
# print(data_bundle)
# define model
vocab=datainfo.vocabs['words']
vocab=data_bundle.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)
@ -50,12 +47,12 @@ 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['test'], device=0, check_code_level=-1,
def train(data_bundle, model, optimizer, loss, metrics, opt):
trainer = Trainer(data_bundle.datasets['train'], model, optimizer=optimizer, loss=loss,
metrics=metrics, dev_data=data_bundle.datasets['test'], device=0, check_code_level=-1,
n_epochs=opt.train_epoch, save_path=opt.save_model_path)
trainer.train()
if __name__ == "__main__":
train(datainfo, model, optimizer, loss, metrics, opt)
train(data_bundle, model, optimizer, loss, metrics, opt)