diff --git a/fastNLP/modules/aggregator/attention.py b/fastNLP/modules/aggregator/attention.py index 4101b033..2bee7f2e 100644 --- a/fastNLP/modules/aggregator/attention.py +++ b/fastNLP/modules/aggregator/attention.py @@ -19,7 +19,7 @@ class DotAttention(nn.Module): 补上文档 """ - def __init__(self, key_size, value_size, dropout=0): + def __init__(self, key_size, value_size, dropout=0.0): super(DotAttention, self).__init__() self.key_size = key_size self.value_size = value_size @@ -37,7 +37,7 @@ class DotAttention(nn.Module): """ output = torch.matmul(Q, K.transpose(1, 2)) / self.scale if mask_out is not None: - output.masked_fill_(mask_out, -1e8) + output.masked_fill_(mask_out, -1e18) output = self.softmax(output) output = self.drop(output) return torch.matmul(output, V) @@ -67,9 +67,8 @@ class MultiHeadAttention(nn.Module): self.k_in = nn.Linear(input_size, in_size) self.v_in = nn.Linear(input_size, in_size) # follow the paper, do not apply dropout within dot-product - self.attention = DotAttention(key_size=key_size, value_size=value_size, dropout=0) + self.attention = DotAttention(key_size=key_size, value_size=value_size, dropout=dropout) self.out = nn.Linear(value_size * num_head, input_size) - self.drop = TimestepDropout(dropout) self.reset_parameters() def reset_parameters(self): @@ -105,7 +104,7 @@ class MultiHeadAttention(nn.Module): # concat all heads, do output linear atte = atte.permute(1, 2, 0, 3).contiguous().view(batch, sq, -1) - output = self.drop(self.out(atte)) + output = self.out(atte) return output diff --git a/reproduction/seqence_labelling/ner/model/dilated_cnn.py b/reproduction/seqence_labelling/ner/model/dilated_cnn.py new file mode 100644 index 00000000..cd2fa64b --- /dev/null +++ b/reproduction/seqence_labelling/ner/model/dilated_cnn.py @@ -0,0 +1,111 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from fastNLP.modules.decoder import ConditionalRandomField +from fastNLP.modules.encoder import Embedding +from fastNLP.core.utils import seq_len_to_mask +from fastNLP.core.const import Const as C + + +class IDCNN(nn.Module): + def __init__(self, init_embed, char_embed, + num_cls, + repeats, num_layers, num_filters, kernel_size, + use_crf=False, use_projection=False, block_loss=False, + input_dropout=0.3, hidden_dropout=0.2, inner_dropout=0.0): + super(IDCNN, self).__init__() + self.word_embeddings = Embedding(init_embed) + self.char_embeddings = Embedding(char_embed) + embedding_size = self.word_embeddings.embedding_dim + \ + self.char_embeddings.embedding_dim + + self.conv0 = nn.Sequential( + nn.Conv1d(in_channels=embedding_size, + out_channels=num_filters, + kernel_size=kernel_size, + stride=1, dilation=1, + padding=kernel_size//2, + bias=True), + nn.ReLU(), + ) + + block = [] + for layer_i in range(num_layers): + dilated = 2 ** layer_i + block.append(nn.Conv1d( + in_channels=num_filters, + out_channels=num_filters, + kernel_size=kernel_size, + stride=1, dilation=dilated, + padding=(kernel_size//2) * dilated, + bias=True)) + block.append(nn.ReLU()) + self.block = nn.Sequential(*block) + + if use_projection: + self.projection = nn.Sequential( + nn.Conv1d( + in_channels=num_filters, + out_channels=num_filters//2, + kernel_size=1, + bias=True), + nn.ReLU(),) + encode_dim = num_filters // 2 + else: + self.projection = None + encode_dim = num_filters + + self.input_drop = nn.Dropout(input_dropout) + self.hidden_drop = nn.Dropout(hidden_dropout) + self.inner_drop = nn.Dropout(inner_dropout) + self.repeats = repeats + self.out_fc = nn.Conv1d( + in_channels=encode_dim, + out_channels=num_cls, + kernel_size=1, + bias=True) + self.crf = ConditionalRandomField( + num_tags=num_cls) if use_crf else None + self.block_loss = block_loss + + def forward(self, words, chars, seq_len, target=None): + e1 = self.word_embeddings(words) + e2 = self.char_embeddings(chars) + x = torch.cat((e1, e2), dim=-1) # b,l,h + mask = seq_len_to_mask(seq_len) + + x = x.transpose(1, 2) # b,h,l + last_output = self.conv0(x) + output = [] + for repeat in range(self.repeats): + last_output = self.block(last_output) + hidden = self.projection(last_output) if self.projection is not None else last_output + output.append(self.out_fc(hidden)) + + def compute_loss(y, t, mask): + if self.crf is not None and target is not None: + loss = self.crf(y, t, mask) + else: + t.masked_fill_(mask == 0, -100) + loss = F.cross_entropy(y, t, ignore_index=-100) + return loss + + if self.block_loss: + losses = [compute_loss(o, target, mask) for o in output] + loss = sum(losses) + else: + loss = compute_loss(output[-1], target, mask) + + scores = output[-1] + if self.crf is not None: + pred = self.crf.viterbi_decode(scores, target, mask) + else: + pred = scores.max(1)[1] * mask.long() + + return { + C.LOSS: loss, + C.OUTPUT: pred, + } + + def predict(self, words, chars, seq_len): + return self.forward(words, chars, seq_len)[C.OUTPUT] diff --git a/reproduction/text_classification/data/IMDBLoader.py b/reproduction/text_classification/data/IMDBLoader.py index d591cdf8..30daf233 100644 --- a/reproduction/text_classification/data/IMDBLoader.py +++ b/reproduction/text_classification/data/IMDBLoader.py @@ -9,6 +9,7 @@ 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: @@ -33,6 +34,7 @@ class IMDBLoader(DataSetLoader): target = parts[0] words = parts[1].lower().split() dataset.append(Instance(words=words, target=target)) + if len(dataset)==0: raise RuntimeError(f"{path} has no valid data.") @@ -42,19 +44,32 @@ class IMDBLoader(DataSetLoader): 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) + src_embed_opt: EmbeddingOption = None, + char_level_op=False): + datasets = {} info = DataInfo() for name, path in paths.items(): dataset = self.load(path) datasets[name] = dataset + + def wordtochar(words): + chars = [] + for word in words: + word = word.lower() + for char in word: + chars.append(char) + return chars + + if char_level_op: + for dataset in datasets.values(): + dataset.apply_field(wordtochar, field_name="words", new_field_name='chars') 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.index_dataset(*datasets.values(), field_name='words') tgt_vocab = Vocabulary(unknown=None, padding=None) \ @@ -78,3 +93,18 @@ class IMDBLoader(DataSetLoader): dataset.set_target("target") return info + + + +if __name__=="__main__": + datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv", + "test": "/remote-home/ygwang/IMDB_data/test.csv"} + datainfo=IMDBLoader().process(datapath,char_level_op=True) + #print(datainfo.datasets["train"]) + 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) + diff --git a/reproduction/text_classification/data/sstLoader.py b/reproduction/text_classification/data/sstLoader.py new file mode 100644 index 00000000..bffb67fd --- /dev/null +++ b/reproduction/text_classification/data/sstLoader.py @@ -0,0 +1,98 @@ +import csv +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 + +class sst2Loader(DataSetLoader): + ''' + 数据来源"SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', + ''' + def __init__(self): + super(sst2Loader, self).__init__() + + def _load(self, path: str) -> DataSet: + ds = DataSet() + all_count=0 + csv_reader = csv.reader(open(path, encoding='utf-8'),delimiter='\t') + skip_row = 0 + for idx,row in enumerate(csv_reader): + if idx<=skip_row: + continue + target = row[1] + words = row[0].split() + ds.append(Instance(words=words,target=target)) + all_count+=1 + print("all count:", all_count) + return ds + + def process(self, + paths: Union[str, Dict[str, str]], + src_vocab_opt: VocabularyOption = None, + tgt_vocab_opt: VocabularyOption = None, + src_embed_opt: EmbeddingOption = None, + char_level_op=False): + + paths = check_dataloader_paths(paths) + datasets = {} + info = DataInfo() + for name, path in paths.items(): + dataset = self.load(path) + datasets[name] = dataset + + 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 datasets.values(): + dataset.apply_field(wordtochar, field_name="words", new_field_name='chars') + + 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.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 + + return info + +if __name__=="__main__": + datapath = {"train": "/remote-home/ygwang/workspace/GLUE/SST-2/train.tsv", + "dev": "/remote-home/ygwang/workspace/GLUE/SST-2/dev.tsv"} + datainfo=sst2Loader().process(datapath,char_level_op=True) + #print(datainfo.datasets["train"]) + 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) \ No newline at end of file diff --git a/reproduction/text_classification/data/yelpLoader.py b/reproduction/text_classification/data/yelpLoader.py index 680b3488..0e65fb20 100644 --- a/reproduction/text_classification/data/yelpLoader.py +++ b/reproduction/text_classification/data/yelpLoader.py @@ -1,77 +1,203 @@ -from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader +import ast +import csv +from typing import Iterable +from fastNLP import DataSet, Instance, Vocabulary 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 -import pandas as pd +from fastNLP.io import JsonLoader +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.utils import check_dataloader_paths -class yelpLoader(DataSetLoader): + + +def get_tokenizer(): + try: + import spacy + en = spacy.load('en') + print('use spacy tokenizer') + return lambda x: [w.text for w in en.tokenizer(x)] + except Exception as e: + print('use raw tokenizer') + return lambda x: x.split() + +def clean_str(sentence, tokenizer, char_lower=False): """ - 读取IMDB数据集，DataSet包含以下fields: + 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 = tokenizer(sentence) + words_collection = [] + for word in words: + if word in ['-lrb-', '-rrb-', '', '-r', '-l', 'b-']: + continue + tt = nonalpnum.split(word) + t = ''.join(tt) + if t != '': + words_collection.append(t) - words: list(str), 需要分类的文本 - target: str, 文本的标签 + return words_collection +class yelpLoader(DataSetLoader): + """ + 读取Yelp_full/Yelp_polarity数据集, DataSet包含fields: + words: list(str), 需要分类的文本 + target: str, 文本的标签 + chars:list(str),未index的字符列表 - def __init__(self): + 数据集：yelp_full/yelp_polarity + :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,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'} + 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 + self.lower = lower + self.tokenizer = get_tokenizer() - def _load(self, path): - dataset = DataSet() - data = pd.read_csv(path, header=None, sep=",").values - for line in data: - target = str(line[0]) - words = str(line[1]).lower().split() - dataset.append(Instance(words=words, target=target)) - if len(dataset)==0: - raise RuntimeError(f"{path} has no valid data.") - - return dataset + ''' + 读取Yelp数据集, DataSet包含fields: - 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 + review_id: str, 22 character unique review id + user_id: str, 22 character unique user id + business_id: str, 22 character business id + useful: int, number of useful votes received + funny: int, number of funny votes received + cool: int, number of cool votes received + 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 + + + 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) + d["words"] = d.pop("text").split() + d["target"] = self.tag_v[str(d.pop("stars"))] + ds.append(Instance(**d)) + return ds + + 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.tokenizer,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 - 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.index_dataset(*datasets.values(), field_name='words') + 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(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_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') + 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() + + + def wordtochar(words): - info.vocabs = { - "words": src_vocab, - "target": tgt_vocab - } + chars=[] + for word in words: + word=word.lower() + for char in word: + chars.append(char) + return chars - info.datasets = datasets + 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 - if src_embed_opt is not None: - embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) - info.embeddings['words'] = embed + 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) - for name, dataset in info.datasets.items(): - dataset.set_input("words") - dataset.set_target("target") + 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) diff --git a/reproduction/text_classification/model/char_cnn.py b/reproduction/text_classification/model/char_cnn.py index f87f5c14..ac370082 100644 --- a/reproduction/text_classification/model/char_cnn.py +++ b/reproduction/text_classification/model/char_cnn.py @@ -1 +1,90 @@ -# TODO \ No newline at end of file +''' +@author: https://github.com/ahmedbesbes/character-based-cnn +这里借鉴了上述链接中char-cnn model的代码，改动主要为将其改动为符合fastnlp的pipline +''' +import torch +import torch.nn as nn +from fastNLP.core.const import Const as C + +class CharacterLevelCNN(nn.Module): + def __init__(self, args,embedding): + super(CharacterLevelCNN, self).__init__() + + self.config=args.char_cnn_config + self.embedding=embedding + + conv_layers = [] + for i, conv_layer_parameter in enumerate(self.config['model_parameters'][args.model_size]['conv']): + if i == 0: + #in_channels = args.number_of_characters + len(args.extra_characters) + in_channels = args.embedding_dim + out_channels = conv_layer_parameter[0] + else: + in_channels, out_channels = conv_layer_parameter[0], conv_layer_parameter[0] + + if conv_layer_parameter[2] != -1: + conv_layer = nn.Sequential(nn.Conv1d(in_channels, + out_channels, + kernel_size=conv_layer_parameter[1], padding=0), + nn.ReLU(), + nn.MaxPool1d(conv_layer_parameter[2])) + else: + conv_layer = nn.Sequential(nn.Conv1d(in_channels, + out_channels, + kernel_size=conv_layer_parameter[1], padding=0), + nn.ReLU()) + conv_layers.append(conv_layer) + self.conv_layers = nn.ModuleList(conv_layers) + + input_shape = (args.batch_size, args.max_length, + args.number_of_characters + len(args.extra_characters)) + dimension = self._get_conv_output(input_shape) + + print('dimension :', dimension) + + fc_layer_parameter = self.config['model_parameters'][args.model_size]['fc'][0] + fc_layers = nn.ModuleList([ + nn.Sequential( + nn.Linear(dimension, fc_layer_parameter), nn.Dropout(0.5)), + nn.Sequential(nn.Linear(fc_layer_parameter, + fc_layer_parameter), nn.Dropout(0.5)), + nn.Linear(fc_layer_parameter, args.num_classes), + ]) + + self.fc_layers = fc_layers + + if args.model_size == 'small': + self._create_weights(mean=0.0, std=0.05) + elif args.model_size == 'large': + self._create_weights(mean=0.0, std=0.02) + + def _create_weights(self, mean=0.0, std=0.05): + for module in self.modules(): + if isinstance(module, nn.Conv1d) or isinstance(module, nn.Linear): + module.weight.data.normal_(mean, std) + + def _get_conv_output(self, shape): + input = torch.rand(shape) + output = input.transpose(1, 2) + # forward pass through conv layers + for i in range(len(self.conv_layers)): + output = self.conv_layers[i](output) + + output = output.view(output.size(0), -1) + n_size = output.size(1) + return n_size + + def forward(self, chars): + input=self.embedding(chars) + output = input.transpose(1, 2) + # forward pass through conv layers + for i in range(len(self.conv_layers)): + output = self.conv_layers[i](output) + + output = output.view(output.size(0), -1) + + # forward pass through fc layers + for i in range(len(self.fc_layers)): + output = self.fc_layers[i](output) + + return {C.OUTPUT: output} \ No newline at end of file diff --git a/reproduction/text_classification/model/dpcnn.py b/reproduction/text_classification/model/dpcnn.py index f87f5c14..c31307bc 100644 --- a/reproduction/text_classification/model/dpcnn.py +++ b/reproduction/text_classification/model/dpcnn.py @@ -1 +1,106 @@ -# TODO \ No newline at end of file + +import torch +import torch.nn as nn +from fastNLP.modules.utils import get_embeddings +from fastNLP.core import Const as C + + +class DPCNN(nn.Module): + def __init__(self, init_embed, num_cls, n_filters=256, + kernel_size=3, n_layers=7, embed_dropout=0.1, cls_dropout=0.1): + super().__init__() + self.region_embed = RegionEmbedding( + init_embed, out_dim=n_filters, kernel_sizes=[1, 3, 5]) + + embed_dim = self.region_embed.embedding_dim + self.conv_list = nn.ModuleList() + for i in range(n_layers): + self.conv_list.append(nn.Sequential( + nn.ReLU(), + + nn.Conv1d(n_filters, n_filters, kernel_size, + padding=kernel_size//2), + nn.Conv1d(n_filters, n_filters, kernel_size, + padding=kernel_size//2), + )) + self.pool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1) + self.embed_drop = nn.Dropout(embed_dropout) + self.classfier = nn.Sequential( + nn.Dropout(cls_dropout), + + nn.Linear(n_filters, num_cls), + ) + self.reset_parameters() + + + def reset_parameters(self): + for m in self.modules(): + if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Linear)): + nn.init.normal_(m.weight, mean=0, std=0.01) + if m.bias is not None: + nn.init.normal_(m.bias, mean=0, std=0.01) + + + def forward(self, words, seq_len=None): + words = words.long() + # get region embeddings + x = self.region_embed(words) + x = self.embed_drop(x) + + # not pooling on first conv + x = self.conv_list[0](x) + x + for conv in self.conv_list[1:]: + x = self.pool(x) + x = conv(x) + x + + # B, C, L => B, C + x, _ = torch.max(x, dim=2) + x = self.classfier(x) + return {C.OUTPUT: x} + + + def predict(self, words, seq_len=None): + x = self.forward(words, seq_len)[C.OUTPUT] + return {C.OUTPUT: torch.argmax(x, 1)} + +class RegionEmbedding(nn.Module): + def __init__(self, init_embed, out_dim=300, kernel_sizes=None): + super().__init__() + if kernel_sizes is None: + kernel_sizes = [5, 9] + + assert isinstance( + kernel_sizes, list), 'kernel_sizes should be List(int)' + + self.embed = get_embeddings(init_embed) + try: + embed_dim = self.embed.embedding_dim + except Exception: + embed_dim = self.embed.embed_size + self.region_embeds = nn.ModuleList() + for ksz in kernel_sizes: + self.region_embeds.append(nn.Sequential( + nn.Conv1d(embed_dim, embed_dim, ksz, padding=ksz // 2), + )) + self.linears = nn.ModuleList([nn.Conv1d(embed_dim, out_dim, 1) + for _ in range(len(kernel_sizes))]) + self.embedding_dim = embed_dim + + def forward(self, x): + x = self.embed(x) + x = x.transpose(1, 2) + # B, C, L + out = 0 + for conv, fc in zip(self.region_embeds, self.linears[1:]): + conv_i = conv(x) + out = out + fc(conv_i) + # B, C, L + return out + + +if __name__ == '__main__': + x = torch.randint(0, 10000, size=(5, 15), dtype=torch.long) + model = DPCNN((10000, 300), 20) + y = model(x) + print(y.size(), y.mean(1), y.std(1)) + diff --git a/reproduction/text_classification/train_char_cnn.py b/reproduction/text_classification/train_char_cnn.py index e69de29b..c2c983a4 100644 --- a/reproduction/text_classification/train_char_cnn.py +++ b/reproduction/text_classification/train_char_cnn.py @@ -0,0 +1,206 @@ +# 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.core.const import Const as C +import torch.nn as nn +from fastNLP.io.dataset_loader import SSTLoader +from data.yelpLoader import yelpLoader +from data.sstLoader import sst2Loader +from data.IMDBLoader import IMDBLoader +from model.char_cnn import CharacterLevelCNN +from fastNLP.core.vocabulary import Vocabulary +from fastNLP.models.cnn_text_classification import CNNText +from fastNLP.modules.encoder.embedding import CNNCharEmbedding,StaticEmbedding,StackEmbedding,LSTMCharEmbedding +from fastNLP import CrossEntropyLoss, AccuracyMetric +from fastNLP.core.trainer import Trainer +from torch.optim import SGD +from torch.autograd import Variable +import torch +from fastNLP import BucketSampler + +##hyper +#todo 这里加入fastnlp的记录 +class Config(): + model_dir_or_name="en-base-uncased" + embedding_grad= False, + bert_embedding_larers= '4,-2,-1' + train_epoch= 50 + num_classes=2 + task= "IMDB" + #yelp_p + datapath = {"train": "/remote-home/ygwang/yelp_polarity/train.csv", + "test": "/remote-home/ygwang/yelp_polarity/test.csv"} + #IMDB + #datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv", + # "test": "/remote-home/ygwang/IMDB_data/test.csv"} + # sst + # datapath = {"train": "/remote-home/ygwang/workspace/GLUE/SST-2/train.tsv", + # "dev": "/remote-home/ygwang/workspace/GLUE/SST-2/dev.tsv"} + + lr=0.01 + batch_size=128 + model_size="large" + number_of_characters=69 + extra_characters='' + max_length=1014 + + char_cnn_config={ + "alphabet": { + "en": { + "lower": { + "alphabet": "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}", + "number_of_characters": 69 + }, + "both": { + "alphabet": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}", + "number_of_characters": 95 + } + } + }, + "model_parameters": { + "small": { + "conv": [ + #依次是channel，kennnel_size，maxpooling_size + [256,7,3], + [256,7,3], + [256,3,-1], + [256,3,-1], + [256,3,-1], + [256,3,3] + ], + "fc": [1024,1024] + }, + "large":{ + "conv":[ + [1024, 7, 3], + [1024, 7, 3], + [1024, 3, -1], + [1024, 3, -1], + [1024, 3, -1], + [1024, 3, 3] + ], + "fc": [2048,2048] + } + }, + "data": { + "text_column": "SentimentText", + "label_column": "Sentiment", + "max_length": 1014, + "num_of_classes": 2, + "encoding": None, + "chunksize": 50000, + "max_rows": 100000, + "preprocessing_steps": ["lower", "remove_hashtags", "remove_urls", "remove_user_mentions"] + }, + "training": { + "batch_size": 128, + "learning_rate": 0.01, + "epochs": 10, + "optimizer": "sgd" + } + } +ops=Config + + +##1.task相关信息：利用dataloader载入dataInfo +dataloader=sst2Loader() +dataloader=IMDBLoader() +#dataloader=yelpLoader(fine_grained=True) +datainfo=dataloader.process(ops.datapath,char_level_op=True) +char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"] +ops.number_of_characters=len(char_vocab) +ops.embedding_dim=ops.number_of_characters + +#chartoindex +def chartoindex(chars): + max_seq_len=ops.max_length + zero_index=len(char_vocab) + char_index_list=[] + for char in chars: + if char in char_vocab: + char_index_list.append(char_vocab.index(char)) + else: + #和均使用最后一个作为embbeding + char_index_list.append(zero_index) + if len(char_index_list) > max_seq_len: + char_index_list = char_index_list[:max_seq_len] + elif 0 < len(char_index_list) < max_seq_len: + char_index_list = char_index_list+[zero_index]*(max_seq_len-len(char_index_list)) + elif len(char_index_list) == 0: + char_index_list=[zero_index]*max_seq_len + return char_index_list + +for dataset in datainfo.datasets.values(): + dataset.apply_field(chartoindex,field_name='chars',new_field_name='chars') + +datainfo.datasets['train'].set_input('chars') +datainfo.datasets['test'].set_input('chars') +datainfo.datasets['train'].set_target('target') +datainfo.datasets['test'].set_target('target') + +##2. 定义/组装模型，这里可以随意，就如果是fastNLP封装好的，类似CNNText就直接用初始化调用就好了，这里只是给出一个伪框架表示占位，在这里建立符合fastNLP输入输出规范的model +class ModelFactory(nn.Module): + """ + 用于拼装embedding，encoder，decoder 以及设计forward过程 + + :param embedding: embbeding model + :param encoder: encoder model + :param decoder: decoder model + + """ + def __int__(self,embedding,encoder,decoder,**kwargs): + super(ModelFactory,self).__init__() + self.embedding=embedding + self.encoder=encoder + self.decoder=decoder + + def forward(self,x): + return {C.OUTPUT:None} + +## 2.或直接复用fastNLP的模型 +#vocab=datainfo.vocabs['words'] +vocab_label=datainfo.vocabs['target'] +''' +# emded_char=CNNCharEmbedding(vocab) +# embed_word = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50', requires_grad=True) +# embedding=StackEmbedding([emded_char, embed_word]) +# cnn_char_embed = CNNCharEmbedding(vocab) +# lstm_char_embed = LSTMCharEmbedding(vocab) +# embedding = StackEmbedding([cnn_char_embed, lstm_char_embed]) +''' +#one-hot embedding +embedding_weight= Variable(torch.zeros(len(char_vocab)+1, len(char_vocab))) + +for i in range(len(char_vocab)): + embedding_weight[i][i]=1 +embedding=nn.Embedding(num_embeddings=len(char_vocab)+1,embedding_dim=len(char_vocab),padding_idx=len(char_vocab),_weight=embedding_weight) +for para in embedding.parameters(): + para.requires_grad=False +#CNNText太过于简单 +#model=CNNText(init_embed=embedding, num_classes=ops.num_classes) +model=CharacterLevelCNN(ops,embedding) + +## 3. 声明loss,metric,optimizer +loss=CrossEntropyLoss +metric=AccuracyMetric +optimizer= SGD([param for param in model.parameters() if param.requires_grad==True], lr=ops.lr) + +## 4.定义train方法 +def train(model,datainfo,loss,metrics,optimizer,num_epochs=100): + trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss(target='target'), + metrics=[metrics(target='target')], dev_data=datainfo.datasets['test'], device=0, check_code_level=-1, + n_epochs=num_epochs) + print(trainer.train()) + + + +if __name__=="__main__": + #print(vocab_label) + + #print(datainfo.datasets["train"]) + train(model,datainfo,loss,metric,optimizer,num_epochs=ops.train_epoch) + \ No newline at end of file diff --git a/reproduction/text_classification/train_dpcnn.py b/reproduction/text_classification/train_dpcnn.py index e69de29b..fcfa138b 100644 --- a/reproduction/text_classification/train_dpcnn.py +++ b/reproduction/text_classification/train_dpcnn.py @@ -0,0 +1,125 @@ +# 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 torch.cuda +from fastNLP.core.utils import cache_results +from torch.optim import SGD +from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR +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 +import torch.nn as nn +from fastNLP.core import LRScheduler +from fastNLP.core.const import Const as C +from fastNLP.core.vocabulary import VocabularyOption +from utils.util_init import set_rng_seeds +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" + + + +# hyper + +class Config(): + seed = 12345 + model_dir_or_name = "dpcnn-yelp-p" + embedding_grad = True + train_epoch = 30 + batch_size = 100 + num_classes = 2 + task = "yelp_p" + #datadir = '/remote-home/yfshao/workdir/datasets/SST' + datadir = '/remote-home/yfshao/workdir/datasets/yelp_polarity' + #datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"} + datafile = {"train": "train.csv", "test": "test.csv"} + lr = 1e-3 + src_vocab_op = VocabularyOption() + embed_dropout = 0.3 + cls_dropout = 0.1 + weight_decay = 1e-4 + + def __init__(self): + self.datapath = {k: os.path.join(self.datadir, v) + for k, v in self.datafile.items()} + + +ops = Config() + +set_rng_seeds(ops.seed) +print('RNG SEED: {}'.format(ops.seed)) + +# 1.task相关信息：利用dataloader载入dataInfo + +#datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train']) +@cache_results(ops.model_dir_or_name+'-data-cache') +def load_data(): + datainfo = yelpLoader(fine_grained=True, lower=True).process( + paths=ops.datapath, train_ds=['train'], src_vocab_op=ops.src_vocab_op) + 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) + return datainfo + +datainfo = load_data() + +# 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, model_dir_or_name='en-word2vec-300', requires_grad=ops.embedding_grad, + normalize=False +) + +print(len(datainfo.datasets['train'])) +print(len(datainfo.datasets['test'])) +print(datainfo.datasets['train'][0]) + + +print(len(vocab)) +print(len(datainfo.vocabs['target'])) + + +model = DPCNN(init_embed=embedding, num_cls=ops.num_classes, + embed_dropout=ops.embed_dropout, cls_dropout=ops.cls_dropout) +print(model) + +# 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=ops.weight_decay) + +callbacks = [] +callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5))) +# callbacks.append +# LRScheduler(LambdaLR(optimizer, lambda epoch: ops.lr if epoch < +# ops.train_epoch * 0.8 else ops.lr * 0.1)) +# ) + +# callbacks.append( +# FitlogCallback(data=datainfo.datasets, verbose=1) +# ) + +device = 'cuda:0' if torch.cuda.is_available() else 'cpu' + +print(device) + +# 4.定义train方法 +trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, + metrics=[metric], + dev_data=datainfo.datasets['test'], device=device, + check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks, + n_epochs=ops.train_epoch, num_workers=4) + + + +if __name__ == "__main__": + print(trainer.train()) + diff --git a/reproduction/text_classification/utils/util_init.py b/reproduction/text_classification/utils/util_init.py new file mode 100644 index 00000000..fcb8fffb --- /dev/null +++ b/reproduction/text_classification/utils/util_init.py @@ -0,0 +1,11 @@ +import numpy +import torch +import random + + +def set_rng_seeds(seed): + random.seed(seed) + numpy.random.seed(seed) + torch.random.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # print('RNG_SEED {}'.format(seed))