@@ -1,13 +1,101 @@ | |||||
import csv | |||||
from typing import Iterable | from typing import Iterable | ||||
from fastNLP import DataSet, Instance, Vocabulary | |||||
from fastNLP.core.vocabulary import VocabularyOption | |||||
from fastNLP.io.base_loader import DataInfo,DataSetLoader | |||||
from fastNLP.io.embed_loader import EmbeddingOption | |||||
from fastNLP.io.file_reader import _read_json | |||||
from typing import Union, Dict | |||||
from reproduction.Star_transformer.datasets import EmbedLoader | |||||
from reproduction.utils import check_dataloader_paths | |||||
from nltk import Tree | |||||
from fastNLP.io.base_loader import DataInfo, DataSetLoader | |||||
from fastNLP.core.vocabulary import VocabularyOption, Vocabulary | |||||
from fastNLP import DataSet | |||||
from fastNLP import Instance | |||||
from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader | |||||
class SSTLoader(DataSetLoader): | |||||
URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip' | |||||
DATA_DIR = 'sst/' | |||||
""" | |||||
别名::class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader` | |||||
读取SST数据集, DataSet包含fields:: | |||||
words: list(str) 需要分类的文本 | |||||
target: str 文本的标签 | |||||
数据来源: https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip | |||||
:param subtree: 是否将数据展开为子树,扩充数据量. Default: ``False`` | |||||
:param fine_grained: 是否使用SST-5标准,若 ``False`` , 使用SST-2。Default: ``False`` | |||||
""" | |||||
def __init__(self, subtree=False, fine_grained=False): | |||||
self.subtree = subtree | |||||
tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral', | |||||
'3': 'positive', '4': 'very positive'} | |||||
if not fine_grained: | |||||
tag_v['0'] = tag_v['1'] | |||||
tag_v['4'] = tag_v['3'] | |||||
self.tag_v = tag_v | |||||
def _load(self, path): | |||||
""" | |||||
:param str path: 存储数据的路径 | |||||
:return: 一个 :class:`~fastNLP.DataSet` 类型的对象 | |||||
""" | |||||
datalist = [] | |||||
with open(path, 'r', encoding='utf-8') as f: | |||||
datas = [] | |||||
for l in f: | |||||
datas.extend([(s, self.tag_v[t]) | |||||
for s, t in self._get_one(l, self.subtree)]) | |||||
ds = DataSet() | |||||
for words, tag in datas: | |||||
ds.append(Instance(words=words, target=tag)) | |||||
return ds | |||||
@staticmethod | |||||
def _get_one(data, subtree): | |||||
tree = Tree.fromstring(data) | |||||
if subtree: | |||||
return [(t.leaves(), t.label()) for t in tree.subtrees()] | |||||
return [(tree.leaves(), tree.label())] | |||||
def process(self, | |||||
paths, | |||||
train_ds: Iterable[str] = None, | |||||
src_vocab_op: VocabularyOption = None, | |||||
tgt_vocab_op: VocabularyOption = None, | |||||
src_embed_op: EmbeddingOption = None): | |||||
input_name, target_name = 'words', 'target' | |||||
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) | |||||
info = DataInfo(datasets=self.load(paths)) | |||||
_train_ds = [info.datasets[name] | |||||
for name in train_ds] if train_ds else info.datasets.values() | |||||
src_vocab.from_dataset(*_train_ds, field_name=input_name) | |||||
tgt_vocab.from_dataset(*_train_ds, field_name=target_name) | |||||
src_vocab.index_dataset( | |||||
*info.datasets.values(), | |||||
field_name=input_name, new_field_name=input_name) | |||||
tgt_vocab.index_dataset( | |||||
*info.datasets.values(), | |||||
field_name=target_name, new_field_name=target_name) | |||||
info.vocabs = { | |||||
input_name: src_vocab, | |||||
target_name: tgt_vocab | |||||
} | |||||
if src_embed_op is not None: | |||||
src_embed_op.vocab = src_vocab | |||||
init_emb = EmbedLoader.load_with_vocab(**src_embed_op) | |||||
info.embeddings[input_name] = init_emb | |||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input(input_name) | |||||
dataset.set_target(target_name) | |||||
return info | |||||
class sst2Loader(DataSetLoader): | class sst2Loader(DataSetLoader): | ||||
''' | ''' | ||||
@@ -184,6 +184,12 @@ class yelpLoader(DataSetLoader): | |||||
info.vocabs[target_name]=tgt_vocab | info.vocabs[target_name]=tgt_vocab | ||||
info.datasets['train'],info.datasets['dev']=info.datasets['train'].split(0.1, shuffle=False) | |||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input("words") | |||||
dataset.set_target("target") | |||||
return info | return info | ||||
if __name__=="__main__": | if __name__=="__main__": | ||||
@@ -0,0 +1,109 @@ | |||||
import torch | |||||
import torch.nn as nn | |||||
from torch.autograd import Variable | |||||
from fastNLP.modules.utils import get_embeddings | |||||
from fastNLP.core import Const as C | |||||
def pack_sequence(tensor_seq, padding_value=0.0): | |||||
if len(tensor_seq) <= 0: | |||||
return | |||||
length = [v.size(0) for v in tensor_seq] | |||||
max_len = max(length) | |||||
size = [len(tensor_seq), max_len] | |||||
size.extend(list(tensor_seq[0].size()[1:])) | |||||
ans = torch.Tensor(*size).fill_(padding_value) | |||||
if tensor_seq[0].data.is_cuda: | |||||
ans = ans.cuda() | |||||
ans = Variable(ans) | |||||
for i, v in enumerate(tensor_seq): | |||||
ans[i, :length[i], :] = v | |||||
return ans | |||||
class HANCLS(nn.Module): | |||||
def __init__(self, init_embed, num_cls): | |||||
super(HANCLS, self).__init__() | |||||
self.embed = get_embeddings(init_embed) | |||||
self.han = HAN(input_size=300, | |||||
output_size=num_cls, | |||||
word_hidden_size=50, word_num_layers=1, word_context_size=100, | |||||
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100 | |||||
) | |||||
def forward(self, input_sents): | |||||
# input_sents [B, num_sents, seq-len] dtype long | |||||
# target | |||||
B, num_sents, seq_len = input_sents.size() | |||||
input_sents = input_sents.view(-1, seq_len) # flat | |||||
words_embed = self.embed(input_sents) # should be [B*num-sent, seqlen , word-dim] | |||||
words_embed = words_embed.view(B, num_sents, seq_len, -1) # recover # [B, num-sent, seqlen , word-dim] | |||||
out = self.han(words_embed) | |||||
return {C.OUTPUT: out} | |||||
def predict(self, input_sents): | |||||
x = self.forward(input_sents)[C.OUTPUT] | |||||
return {C.OUTPUT: torch.argmax(x, 1)} | |||||
class HAN(nn.Module): | |||||
def __init__(self, input_size, output_size, | |||||
word_hidden_size, word_num_layers, word_context_size, | |||||
sent_hidden_size, sent_num_layers, sent_context_size): | |||||
super(HAN, self).__init__() | |||||
self.word_layer = AttentionNet(input_size, | |||||
word_hidden_size, | |||||
word_num_layers, | |||||
word_context_size) | |||||
self.sent_layer = AttentionNet(2 * word_hidden_size, | |||||
sent_hidden_size, | |||||
sent_num_layers, | |||||
sent_context_size) | |||||
self.output_layer = nn.Linear(2 * sent_hidden_size, output_size) | |||||
self.softmax = nn.LogSoftmax(dim=1) | |||||
def forward(self, batch_doc): | |||||
# input is a sequence of matrix | |||||
doc_vec_list = [] | |||||
for doc in batch_doc: | |||||
sent_mat = self.word_layer(doc) # doc's dim (num_sent, seq_len, word_dim) | |||||
doc_vec_list.append(sent_mat) # sent_mat's dim (num_sent, vec_dim) | |||||
doc_vec = self.sent_layer(pack_sequence(doc_vec_list)) | |||||
output = self.softmax(self.output_layer(doc_vec)) | |||||
return output | |||||
class AttentionNet(nn.Module): | |||||
def __init__(self, input_size, gru_hidden_size, gru_num_layers, context_vec_size): | |||||
super(AttentionNet, self).__init__() | |||||
self.input_size = input_size | |||||
self.gru_hidden_size = gru_hidden_size | |||||
self.gru_num_layers = gru_num_layers | |||||
self.context_vec_size = context_vec_size | |||||
# Encoder | |||||
self.gru = nn.GRU(input_size=input_size, | |||||
hidden_size=gru_hidden_size, | |||||
num_layers=gru_num_layers, | |||||
batch_first=True, | |||||
bidirectional=True) | |||||
# Attention | |||||
self.fc = nn.Linear(2 * gru_hidden_size, context_vec_size) | |||||
self.tanh = nn.Tanh() | |||||
self.softmax = nn.Softmax(dim=1) | |||||
# context vector | |||||
self.context_vec = nn.Parameter(torch.Tensor(context_vec_size, 1)) | |||||
self.context_vec.data.uniform_(-0.1, 0.1) | |||||
def forward(self, inputs): | |||||
# GRU part | |||||
h_t, hidden = self.gru(inputs) # inputs's dim (batch_size, seq_len, word_dim) | |||||
u = self.tanh(self.fc(h_t)) | |||||
# Attention part | |||||
alpha = self.softmax(torch.matmul(u, self.context_vec)) # u's dim (batch_size, seq_len, context_vec_size) | |||||
output = torch.bmm(torch.transpose(h_t, 1, 2), alpha) # alpha's dim (batch_size, seq_len, 1) | |||||
return torch.squeeze(output, dim=2) # output's dim (batch_size, 2*hidden_size, 1) |
@@ -0,0 +1,109 @@ | |||||
# 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 | |||||
import sys | |||||
sys.path.append('../../') | |||||
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.data.yelpLoader import yelpLoader | |||||
from reproduction.text_classification.model.HAN import HANCLS | |||||
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 | |||||
##hyper | |||||
class Config(): | |||||
model_dir_or_name = "en-base-uncased" | |||||
embedding_grad = False, | |||||
train_epoch = 30 | |||||
batch_size = 100 | |||||
num_classes = 5 | |||||
task = "yelp" | |||||
#datadir = '/remote-home/lyli/fastNLP/yelp_polarity/' | |||||
datadir = '/remote-home/ygwang/yelp_polarity/' | |||||
datafile = {"train": "train.csv", "test": "test.csv"} | |||||
lr = 1e-3 | |||||
def __init__(self): | |||||
self.datapath = {k: os.path.join(self.datadir, v) | |||||
for k, v in self.datafile.items()} | |||||
ops = Config() | |||||
##1.task相关信息:利用dataloader载入dataInfo | |||||
datainfo = yelpLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train']) | |||||
print(len(datainfo.datasets['train'])) | |||||
print(len(datainfo.datasets['test'])) | |||||
# post process | |||||
def make_sents(words): | |||||
sents = [words] | |||||
return sents | |||||
for dataset in datainfo.datasets.values(): | |||||
dataset.apply_field(make_sents, field_name='words', new_field_name='input_sents') | |||||
datainfo = datainfo | |||||
datainfo.datasets['train'].set_input('input_sents') | |||||
datainfo.datasets['test'].set_input('input_sents') | |||||
datainfo.datasets['train'].set_target('target') | |||||
datainfo.datasets['test'].set_target('target') | |||||
## 2.或直接复用fastNLP的模型 | |||||
vocab = datainfo.vocabs['words'] | |||||
# embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)]) | |||||
embedding = StaticEmbedding(vocab) | |||||
print(len(vocab)) | |||||
print(len(datainfo.vocabs['target'])) | |||||
# model = DPCNN(init_embed=embedding, num_cls=ops.num_classes) | |||||
model = HANCLS(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) | |||||
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(): | |||||
ds.apply_field(len, C.INPUT, C.INPUT_LEN) | |||||
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): | |||||
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, | |||||
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) |
@@ -7,7 +7,6 @@ import sys | |||||
sys.path.append('../..') | sys.path.append('../..') | ||||
from fastNLP.core.const import Const as C | from fastNLP.core.const import Const as C | ||||
import torch.nn as nn | import torch.nn as nn | ||||
from fastNLP.io.dataset_loader import SSTLoader | |||||
from data.yelpLoader import yelpLoader | from data.yelpLoader import yelpLoader | ||||
from data.sstLoader import sst2Loader | from data.sstLoader import sst2Loader | ||||
from data.IMDBLoader import IMDBLoader | from data.IMDBLoader import IMDBLoader | ||||
@@ -107,9 +106,9 @@ ops=Config | |||||
##1.task相关信息:利用dataloader载入dataInfo | ##1.task相关信息:利用dataloader载入dataInfo | ||||
dataloader=sst2Loader() | |||||
dataloader=IMDBLoader() | |||||
#dataloader=yelpLoader(fine_grained=True) | |||||
#dataloader=sst2Loader() | |||||
#dataloader=IMDBLoader() | |||||
dataloader=yelpLoader(fine_grained=True) | |||||
datainfo=dataloader.process(ops.datapath,char_level_op=True) | datainfo=dataloader.process(ops.datapath,char_level_op=True) | ||||
char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"] | char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"] | ||||
ops.number_of_characters=len(char_vocab) | ops.number_of_characters=len(char_vocab) | ||||