@@ -0,0 +1,82 @@ | |||
from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader | |||
from fastNLP.core.vocabulary import VocabularyOption | |||
from fastNLP.io.base_loader import DataSetLoader, DataInfo | |||
from typing import Union, Dict, List, Iterator | |||
from fastNLP import DataSet | |||
from fastNLP import Instance | |||
from fastNLP import Vocabulary | |||
from fastNLP import Const | |||
# from reproduction.utils import check_dataloader_paths | |||
from functools import partial | |||
class IMDBLoader(DataSetLoader): | |||
""" | |||
读取IMDB数据集,DataSet包含以下fields: | |||
words: list(str), 需要分类的文本 | |||
target: str, 文本的标签 | |||
""" | |||
def __init__(self): | |||
super(IMDBLoader, self).__init__() | |||
def _load(self, path): | |||
dataset = DataSet() | |||
with open(path, 'r', encoding="utf-8") as f: | |||
for line in f: | |||
line = line.strip() | |||
if not line: | |||
continue | |||
parts = line.split('\t') | |||
target = parts[0] | |||
words = parts[1].split() | |||
dataset.append(Instance(words=words, target=target)) | |||
if len(dataset) == 0: | |||
raise RuntimeError(f"{path} has no valid data.") | |||
return dataset | |||
def process(self, | |||
paths: Union[str, Dict[str, str]], | |||
src_vocab_opt: VocabularyOption = None, | |||
tgt_vocab_opt: VocabularyOption = None, | |||
src_embed_opt: EmbeddingOption = None): | |||
# paths = check_dataloader_paths(paths) | |||
datasets = {} | |||
info = DataInfo() | |||
for name, path in paths.items(): | |||
dataset = self.load(path) | |||
datasets[name] = dataset | |||
datasets["train"], datasets["dev"] = datasets["train"].split(0.1, shuffle=False) | |||
src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt) | |||
src_vocab.from_dataset(datasets['train'], field_name='words') | |||
# src_vocab.from_dataset(datasets['train'], datasets["dev"], datasets["test"], field_name='words') | |||
src_vocab.index_dataset(*datasets.values(), field_name='words') | |||
tgt_vocab = Vocabulary(unknown=None, padding=None) \ | |||
if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt) | |||
tgt_vocab.from_dataset(datasets['train'], field_name='target') | |||
tgt_vocab.index_dataset(*datasets.values(), field_name='target') | |||
info.vocabs = { | |||
"words": src_vocab, | |||
"target": tgt_vocab | |||
} | |||
info.datasets = datasets | |||
if src_embed_opt is not None: | |||
embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) | |||
info.embeddings['words'] = embed | |||
for name, dataset in info.datasets.items(): | |||
dataset.set_input("words") | |||
dataset.set_target("target") | |||
return info |
@@ -1,4 +1,6 @@ | |||
import ast | |||
import csv | |||
from typing import Iterable | |||
from fastNLP import DataSet, Instance, Vocabulary | |||
from fastNLP.core.vocabulary import VocabularyOption | |||
from fastNLP.io import JsonLoader | |||
@@ -10,11 +12,34 @@ from reproduction.Star_transformer.datasets import EmbedLoader | |||
from reproduction.utils import check_dataloader_paths | |||
def clean_str(sentence, char_lower=False): | |||
""" | |||
heavily borrowed from github | |||
https://github.com/LukeZhuang/Hierarchical-Attention-Network/blob/master/yelp-preprocess.ipynb | |||
:param sentence: is a str | |||
:return: | |||
""" | |||
if char_lower: | |||
sentence = sentence.lower() | |||
import re | |||
nonalpnum = re.compile('[^0-9a-zA-Z?!\']+') | |||
words = sentence.split() | |||
words_collection = [] | |||
for word in words: | |||
if word in ['-lrb-', '-rrb-', '<sssss>', '-r', '-l', 'b-']: | |||
continue | |||
tt = nonalpnum.split(word) | |||
t = ''.join(tt) | |||
if t != '': | |||
words_collection.append(t) | |||
return words_collection | |||
class yelpLoader(JsonLoader): | |||
""" | |||
读取Yelp数据集, DataSet包含fields: | |||
review_id: str, 22 character unique review id | |||
user_id: str, 22 character unique user id | |||
business_id: str, 22 character business id | |||
@@ -24,23 +49,25 @@ class yelpLoader(JsonLoader): | |||
date: str, date formatted YYYY-MM-DD | |||
words: list(str), 需要分类的文本 | |||
target: str, 文本的标签 | |||
数据来源: https://www.yelp.com/dataset/download | |||
:param fine_grained: 是否使用SST-5标准,若 ``False`` , 使用SST-2。Default: ``False`` | |||
""" | |||
def __init__(self, fine_grained=False): | |||
def __init__(self, fine_grained=False, lower=False): | |||
super(yelpLoader, self).__init__() | |||
tag_v = {'1.0': 'very negative', '2.0': 'negative', '3.0': 'neutral', | |||
'4.0': 'positive', '5.0': 'very positive'} | |||
'4.0': 'positive', '5.0': 'very positive'} | |||
if not fine_grained: | |||
tag_v['1.0'] = tag_v['2.0'] | |||
tag_v['5.0'] = tag_v['4.0'] | |||
self.fine_grained = fine_grained | |||
self.tag_v = tag_v | |||
def _load(self, path): | |||
self.lower = lower | |||
''' | |||
def _load_json(self, path): | |||
ds = DataSet() | |||
for idx, d in _read_json(path, fields=self.fields_list, dropna=self.dropna): | |||
d = ast.literal_eval(d) | |||
@@ -49,20 +76,113 @@ class yelpLoader(JsonLoader): | |||
ds.append(Instance(**d)) | |||
return ds | |||
def process(self, paths: Union[str, Dict[str, str]], vocab_opt: VocabularyOption = None, | |||
embed_opt: EmbeddingOption = None): | |||
def _load_yelp2015_broken(self,path): | |||
ds = DataSet() | |||
with open (path,encoding='ISO 8859-1') as f: | |||
row=f.readline() | |||
all_count=0 | |||
exp_count=0 | |||
while row: | |||
row=row.split("\t\t") | |||
all_count+=1 | |||
if len(row)>=3: | |||
words=row[-1].split() | |||
try: | |||
target=self.tag_v[str(row[-2])+".0"] | |||
ds.append(Instance(words=words, target=target)) | |||
except KeyError: | |||
exp_count+=1 | |||
else: | |||
exp_count+=1 | |||
row = f.readline() | |||
print("error sample count:",exp_count) | |||
print("all count:",all_count) | |||
return ds | |||
''' | |||
def _load(self, path): | |||
ds = DataSet() | |||
csv_reader = csv.reader(open(path, encoding='utf-8')) | |||
all_count = 0 | |||
real_count = 0 | |||
for row in csv_reader: | |||
all_count += 1 | |||
if len(row) == 2: | |||
target = self.tag_v[row[0] + ".0"] | |||
words = clean_str(row[1], self.lower) | |||
if len(words) != 0: | |||
ds.append(Instance(words=words, target=target)) | |||
real_count += 1 | |||
print("all count:", all_count) | |||
print("real count:", real_count) | |||
return ds | |||
def process(self, paths: Union[str, Dict[str, str]], | |||
train_ds: Iterable[str] = None, | |||
src_vocab_op: VocabularyOption = None, | |||
tgt_vocab_op: VocabularyOption = None, | |||
embed_opt: EmbeddingOption = None, | |||
char_level_op=False): | |||
paths = check_dataloader_paths(paths) | |||
datasets = {} | |||
info = DataInfo() | |||
vocab = Vocabulary(min_freq=2) if vocab_opt is None else Vocabulary(**vocab_opt) | |||
for name, path in paths.items(): | |||
dataset = self.load(path) | |||
datasets[name] = dataset | |||
vocab.from_dataset(dataset, field_name="words") | |||
info.vocabs = vocab | |||
info.datasets = datasets | |||
if embed_opt is not None: | |||
embed = EmbedLoader.load_with_vocab(**embed_opt, vocab=vocab) | |||
info.embeddings['words'] = embed | |||
info = DataInfo(datasets=self.load(paths)) | |||
src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op) | |||
tgt_vocab = Vocabulary(unknown=None, padding=None) \ | |||
if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op) | |||
_train_ds = [info.datasets[name] | |||
for name in train_ds] if train_ds else info.datasets.values() | |||
# vocab = Vocabulary(min_freq=2) if vocab_opt is None else Vocabulary(**vocab_opt) | |||
# for name, path in paths.items(): | |||
# dataset = self.load(path) | |||
# datasets[name] = dataset | |||
# vocab.from_dataset(dataset, field_name="words") | |||
# info.vocabs = vocab | |||
# info.datasets = datasets | |||
def wordtochar(words): | |||
chars = [] | |||
for word in words: | |||
word = word.lower() | |||
for char in word: | |||
chars.append(char) | |||
return chars | |||
input_name, target_name = 'words', 'target' | |||
info.vocabs = {} | |||
# 就分隔为char形式 | |||
if char_level_op: | |||
for dataset in info.datasets.values(): | |||
dataset.apply_field(wordtochar, field_name="words", new_field_name='chars') | |||
# if embed_opt is not None: | |||
# embed = EmbedLoader.load_with_vocab(**embed_opt, vocab=vocab) | |||
# info.embeddings['words'] = embed | |||
else: | |||
src_vocab.from_dataset(*_train_ds, field_name=input_name) | |||
src_vocab.index_dataset(*info.datasets.values(), field_name=input_name, new_field_name=input_name) | |||
info.vocabs[input_name] = src_vocab | |||
tgt_vocab.from_dataset(*_train_ds, field_name=target_name) | |||
tgt_vocab.index_dataset( | |||
*info.datasets.values(), | |||
field_name=target_name, new_field_name=target_name) | |||
info.vocabs[target_name] = tgt_vocab | |||
return info | |||
if __name__ == "__main__": | |||
testloader = yelpLoader() | |||
# datapath = {"train": "/remote-home/ygwang/yelp_full/train.csv", | |||
# "test": "/remote-home/ygwang/yelp_full/test.csv"} | |||
# datapath={"train": "/remote-home/ygwang/yelp_full/test.csv"} | |||
datapath = {"train": "/remote-home/ygwang/yelp_polarity/train.csv", | |||
"test": "/remote-home/ygwang/yelp_polarity/test.csv"} | |||
datainfo = testloader.process(datapath, char_level_op=True) | |||
len_count = 0 | |||
for instance in datainfo.datasets["train"]: | |||
len_count += len(instance["chars"]) | |||
ave_len = len_count / len(datainfo.datasets["train"]) | |||
print(ave_len) |
@@ -1,65 +1,83 @@ | |||
# 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 | |||
from torch.optim.lr_scheduler import CosineAnnealingLR | |||
import torch.cuda | |||
from torch.optim import SGD | |||
from fastNLP.core.trainer import Trainer | |||
from fastNLP import CrossEntropyLoss, AccuracyMetric | |||
from fastNLP.modules.encoder.embedding import StaticEmbedding, CNNCharEmbedding, StackEmbedding | |||
from reproduction.text_classification.model.dpcnn import DPCNN | |||
from .data.yelpLoader import yelpLoader | |||
from fastNLP.io.dataset_loader import SSTLoader | |||
import torch.nn as nn | |||
from fastNLP.core import LRScheduler | |||
from fastNLP.core.const import Const as C | |||
import sys | |||
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' | |||
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" | |||
from fastNLP.core.const import Const as C | |||
from fastNLP.core import LRScheduler | |||
import torch.nn as nn | |||
from fastNLP.io.dataset_loader import SSTLoader | |||
from reproduction.text_classification.model.dpcnn import DPCNN | |||
from fastNLP.modules.encoder.embedding import StaticEmbedding, CNNCharEmbedding, StackEmbedding | |||
from fastNLP import CrossEntropyLoss, AccuracyMetric | |||
from fastNLP.core.trainer import Trainer | |||
from torch.optim import SGD | |||
import torch.cuda | |||
from torch.optim.lr_scheduler import CosineAnnealingLR | |||
sys.path.append('../..') | |||
# hyper | |||
##hyper | |||
class Config(): | |||
model_dir_or_name="en-base-uncased" | |||
embedding_grad= False, | |||
train_epoch= 30 | |||
model_dir_or_name = "en-base-uncased" | |||
embedding_grad = False, | |||
train_epoch = 30 | |||
batch_size = 100 | |||
num_classes=5 | |||
task= "SST" | |||
datadir = '/remote-home/yfshao/workdir/datasets/SST' | |||
datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"} | |||
lr=1e-3 | |||
num_classes = 2 | |||
task = "yelp_p" | |||
#datadir = '/remote-home/yfshao/workdir/datasets/SST' | |||
datadir = '/remote-home/ygwang/yelp_polarity' | |||
#datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"} | |||
datafile = {"train": "train.csv", "test": "test.csv"} | |||
lr = 1e-3 | |||
def __init__(self): | |||
self.datapath = {k:os.path.join(self.datadir, v) | |||
self.datapath = {k: os.path.join(self.datadir, v) | |||
for k, v in self.datafile.items()} | |||
ops=Config() | |||
ops = Config() | |||
##1.task相关信息:利用dataloader载入dataInfo | |||
datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds='train') | |||
# 1.task相关信息:利用dataloader载入dataInfo | |||
#datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train']) | |||
datainfo = yelpLoader(fine_grained=True, lower=True).process( | |||
paths=ops.datapath, train_ds=['train']) | |||
print(len(datainfo.datasets['train'])) | |||
print(len(datainfo.datasets['dev'])) | |||
print(len(datainfo.datasets['test'])) | |||
## 2.或直接复用fastNLP的模型 | |||
vocab = datainfo.vocabs['words'] | |||
# 2.或直接复用fastNLP的模型 | |||
vocab = datainfo.vocabs['words'] | |||
# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)]) | |||
embedding = StaticEmbedding(vocab) | |||
#embedding = StaticEmbedding(vocab) | |||
embedding = StaticEmbedding( | |||
vocab, model_dir_or_name='en-word2vec-300', requires_grad=True) | |||
print(len(vocab)) | |||
print(len(datainfo.vocabs['target'])) | |||
model = DPCNN(init_embed=embedding, num_cls=ops.num_classes) | |||
## 3. 声明loss,metric,optimizer | |||
loss=CrossEntropyLoss(pred=C.OUTPUT, target=C.TARGET) | |||
metric=AccuracyMetric(pred=C.OUTPUT, target=C.TARGET) | |||
optimizer= SGD([param for param in model.parameters() if param.requires_grad==True], | |||
lr=ops.lr, momentum=0.9, weight_decay=0) | |||
# 3. 声明loss,metric,optimizer | |||
loss = CrossEntropyLoss(pred=C.OUTPUT, target=C.TARGET) | |||
metric = AccuracyMetric(pred=C.OUTPUT, target=C.TARGET) | |||
optimizer = SGD([param for param in model.parameters() if param.requires_grad == True], | |||
lr=ops.lr, momentum=0.9, weight_decay=0) | |||
callbacks = [] | |||
callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5))) | |||
device = 'cuda:0' if torch.cuda.is_available() else 'cpu' | |||
print(device) | |||
for ds in datainfo.datasets.values(): | |||
@@ -67,14 +85,17 @@ for ds in datainfo.datasets.values(): | |||
ds.set_input(C.INPUT, C.INPUT_LEN) | |||
ds.set_target(C.TARGET) | |||
## 4.定义train方法 | |||
def train(model,datainfo,loss,metrics,optimizer,num_epochs=ops.train_epoch): | |||
# 4.定义train方法 | |||
def train(model, datainfo, loss, metrics, optimizer, num_epochs=ops.train_epoch): | |||
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, | |||
metrics=[metrics], dev_data=datainfo.datasets['dev'], device=device, | |||
metrics=[metrics], | |||
dev_data=datainfo.datasets['test'], device=device, | |||
check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks, | |||
n_epochs=num_epochs) | |||
print(trainer.train()) | |||
if __name__=="__main__": | |||
train(model,datainfo,loss,metric,optimizer) | |||
if __name__ == "__main__": | |||
train(model, datainfo, loss, metric, optimizer) |