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123 lines
4.2 KiB
Python
123 lines
4.2 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 torch.cuda
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from fastNLP.core.utils import cache_results
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from torch.optim import SGD
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from fastNLP.core.trainer import Trainer
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from fastNLP import CrossEntropyLoss, AccuracyMetric
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from fastNLP.embeddings import StaticEmbedding
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from reproduction.text_classification.model.dpcnn import DPCNN
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from fastNLP.io.data_loader import YelpLoader
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from fastNLP.core.sampler import BucketSampler
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from fastNLP.core import LRScheduler
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from fastNLP.core.const import Const as C
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from fastNLP.core.vocabulary import VocabularyOption
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from utils.util_init import set_rng_seeds
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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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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# hyper
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class Config():
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seed = 12345
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model_dir_or_name = "dpcnn-yelp-f"
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embedding_grad = True
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train_epoch = 30
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batch_size = 100
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task = "yelp_f"
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#datadir = 'workdir/datasets/SST'
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# datadir = 'workdir/datasets/yelp_polarity'
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datadir = 'workdir/datasets/yelp_full'
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#datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"}
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datafile = {"train": "train.csv", "test": "test.csv"}
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lr = 1e-3
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src_vocab_op = VocabularyOption(max_size=100000)
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embed_dropout = 0.3
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cls_dropout = 0.1
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weight_decay = 1e-5
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def __init__(self):
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self.datadir = os.path.join(os.environ['HOME'], self.datadir)
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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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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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#datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train'])
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@cache_results(ops.model_dir_or_name+'-data-cache')
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def load_data():
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datainfo = YelpLoader(fine_grained=True, lower=True).process(
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paths=ops.datapath, train_ds=['train'], src_vocab_op=ops.src_vocab_op)
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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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embedding = StaticEmbedding(
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datainfo.vocabs['words'], model_dir_or_name='en-glove-840b-300', requires_grad=ops.embedding_grad,
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normalize=False
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)
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return datainfo, embedding
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datainfo, embedding = load_data()
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embedding.embedding.weight.data /= embedding.embedding.weight.data.std()
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print(embedding.embedding.weight.mean(), embedding.embedding.weight.std())
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# 2.或直接复用fastNLP的模型
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# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)])
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print(datainfo)
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print(datainfo.datasets['train'][0])
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model = DPCNN(init_embed=embedding, num_cls=len(datainfo.vocabs[C.TARGET]),
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embed_dropout=ops.embed_dropout, cls_dropout=ops.cls_dropout)
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print(model)
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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=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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# callbacks.append(
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# FitlogCallback(data=datainfo.datasets, verbose=1)
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# )
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device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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print(device)
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# 4.定义train方法
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trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
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sampler=BucketSampler(num_buckets=50, batch_size=ops.batch_size),
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metrics=[metric],
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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=ops.train_epoch, num_workers=4)
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if __name__ == "__main__":
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print(trainer.train())
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