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108 lines
3.4 KiB
Python
108 lines
3.4 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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import sys
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sys.path.append('../../')
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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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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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from fastNLP.core.const import Const as C
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from fastNLP.core import LRScheduler
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from fastNLP.io.data_loader import YelpLoader
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from reproduction.text_classification.model.HAN import HANCLS
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from fastNLP.embeddings import StaticEmbedding
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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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import torch.cuda
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from torch.optim.lr_scheduler import CosineAnnealingLR
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##hyper
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class Config():
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model_dir_or_name = "en-base-uncased"
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embedding_grad = False,
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train_epoch = 30
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batch_size = 100
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num_classes = 5
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task = "yelp"
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#datadir = '/remote-home/lyli/fastNLP/yelp_polarity/'
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datadir = '/remote-home/ygwang/yelp_polarity/'
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datafile = {"train": "train.csv", "test": "test.csv"}
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lr = 1e-3
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def __init__(self):
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self.datapath = {k: os.path.join(self.datadir, v)
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for k, v in self.datafile.items()}
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ops = Config()
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##1.task相关信息：利用dataloader载入dataInfo
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datainfo = YelpLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train'])
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print(len(datainfo.datasets['train']))
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print(len(datainfo.datasets['test']))
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# post process
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def make_sents(words):
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sents = [words]
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return sents
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for dataset in datainfo.datasets.values():
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dataset.apply_field(make_sents, field_name='words', new_field_name='input_sents')
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datainfo = datainfo
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datainfo.datasets['train'].set_input('input_sents')
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datainfo.datasets['test'].set_input('input_sents')
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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.或直接复用fastNLP的模型
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vocab = datainfo.vocabs['words']
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# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)])
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embedding = StaticEmbedding(vocab)
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print(len(vocab))
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print(len(datainfo.vocabs['target']))
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# model = DPCNN(init_embed=embedding, num_cls=ops.num_classes)
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model = HANCLS(init_embed=embedding, num_cls=ops.num_classes)
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## 3. 声明loss,metric,optimizer
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loss = CrossEntropyLoss(pred=C.OUTPUT, target=C.TARGET)
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metric = AccuracyMetric(pred=C.OUTPUT, target=C.TARGET)
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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=0)
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callbacks = []
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callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
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device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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print(device)
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for ds in datainfo.datasets.values():
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ds.apply_field(len, C.INPUT, C.INPUT_LEN)
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ds.set_input(C.INPUT, C.INPUT_LEN)
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ds.set_target(C.TARGET)
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## 4.定义train方法
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def train(model, datainfo, loss, metrics, optimizer, num_epochs=ops.train_epoch):
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trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
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metrics=[metrics], dev_data=datainfo.datasets['test'], device=device,
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check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks,
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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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train(model, datainfo, loss, metric, optimizer)
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