diff --git a/README.md b/README.md index c99e5f15..017eae52 100644 --- a/README.md +++ b/README.md @@ -39,30 +39,30 @@ python -m spacy download en ## fastNLP教程 -中文[文档](https://fastnlp.readthedocs.io/)、[教程](https://fastnlp.readthedocs.io/zh/latest/user/tutorials.html) +中文[文档](http://www.fastnlp.top/docs/fastNLP/)、 [教程](http://www.fastnlp.top/docs/fastNLP/user/quickstart.html) ### 快速入门 -- [0. 快速入门](https://fastnlp.readthedocs.io/zh/latest/user/quickstart.html) +- [Quick-1. 文本分类](http://www.fastnlp.top/docs/fastNLP/tutorials/%E6%96%87%E6%9C%AC%E5%88%86%E7%B1%BB.html) +- [Quick-2. 序列标注](http://www.fastnlp.top/docs/fastNLP/tutorials/%E5%BA%8F%E5%88%97%E6%A0%87%E6%B3%A8.html) ### 详细使用教程 -- [1. 使用DataSet预处理文本](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_1_data_preprocess.html) -- [2. 使用Vocabulary转换文本与index](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html) -- [3. 使用Embedding模块将文本转成向量](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_3_embedding.html) -- [4. 使用Loader和Pipe加载并处理数据集](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_4_load_dataset.html) -- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_5_loss_optimizer.html) -- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_6_datasetiter.html) -- [7. 使用Metric快速评测你的模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_7_metrics.html) -- [8. 使用Modules和Models快速搭建自定义模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_8_modules_models.html) -- [9. 快速实现序列标注模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_9_seq_labeling.html) -- [10. 使用Callback自定义你的训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_10_callback.html) +- [1. 使用DataSet预处理文本](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_1_data_preprocess.html) +- [2. 使用Vocabulary转换文本与index](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_2_vocabulary.html) +- [3. 使用Embedding模块将文本转成向量](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_3_embedding.html) +- [4. 使用Loader和Pipe加载并处理数据集](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_4_load_dataset.html) +- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_5_loss_optimizer.html) +- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_6_datasetiter.html) +- [7. 使用Metric快速评测你的模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_7_metrics.html) +- [8. 使用Modules和Models快速搭建自定义模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_8_modules_models.html) +- [9. 使用Callback自定义你的训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_9_callback.html) ### 扩展教程 -- [Extend-1. BertEmbedding的各种用法](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_1_bert_embedding.html) -- [Extend-2. 分布式训练简介](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_2_dist.html) -- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_3_fitlog.html) +- [Extend-1. BertEmbedding的各种用法](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_1_bert_embedding.html) +- [Extend-2. 分布式训练简介](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_2_dist.html) +- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_3_fitlog.html) ## 内置组件 diff --git a/docs/requirements.txt b/docs/requirements.txt index c7d94486..cfa9c93a 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,8 +1,4 @@ -numpy>=1.14.2 -http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl -torchvision>=0.1.8 -sphinx-rtd-theme==0.4.1 -tensorboardX>=1.4 -tqdm>=4.28.1 -ipython>=6.4.0 -ipython-genutils>=0.2.0 \ No newline at end of file +sphinx==3.2.1 +docutils==0.16 +sphinx-rtd-theme==0.5.0 +readthedocs-sphinx-search==0.1.0rc3 \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 4db6dea6..ff77a6fc 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -4,6 +4,10 @@ fastNLP 中文文档 `fastNLP `_ 是一款轻量级的自然语言处理（NLP）工具包。你既可以用它来快速地完成一个NLP任务， 也可以用它在研究中快速构建更复杂的模型。 +.. hint:: + + 如果你是从 readthedocs 访问的该文档，请跳转到我们的 `最新网站 `_ + fastNLP具有如下的特性： - 统一的Tabular式数据容器，简化数据预处理过程; @@ -41,7 +45,7 @@ API 文档 fitlog文档 ---------- -您可以 `点此 `_ 查看fitlog的文档。 +您可以 `点此 `_ 查看fitlog的文档。 fitlog 是由我们团队开发的日志记录+代码管理的工具。 索引与搜索 diff --git a/docs/source/tutorials/extend_3_fitlog.rst b/docs/source/tutorials/extend_3_fitlog.rst index 0fa24143..152e18fe 100644 --- a/docs/source/tutorials/extend_3_fitlog.rst +++ b/docs/source/tutorials/extend_3_fitlog.rst @@ -4,7 +4,7 @@ 本文介绍结合使用 fastNLP 和 fitlog 进行科研的方法。 -首先，我们需要安装 `fitlog `_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。 +首先，我们需要安装 `fitlog `_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。 我们从命令行中进入到一个文件夹，现在我们要在文件夹中创建我们的 fastNLP 项目。你可以在命令行输入 `fitlog init test1` ， 然后你会看到如下提示:: @@ -15,7 +15,7 @@ Fitlog project test1 is initialized. 这表明你已经创建成功了项目文件夹，并且在项目文件夹中已经初始化了 Git。如果你不想初始化 Git， -可以参考文档 `命令行工具 `_ +可以参考文档 `命令行工具 `_ 现在我们进入你创建的项目文件夹 test1 中，可以看到有一个名为 logs 的文件夹，后面我们将会在里面存放你的实验记录。 同时也有一个名为 main.py 的文件，这是我们推荐你使用的训练入口文件。文件的内容如下:: @@ -37,7 +37,7 @@ fitlog.finish() # finish the logging 我们推荐你保留除注释外的四行代码，它们有助于你的实验， -他们的具体用处参见文档 `用户 API `_ +他们的具体用处参见文档 `用户 API `_ 我们假定你要进行前两个教程中的实验，并已经把数据复制到了项目根目录下的 tutorial_sample_dataset.csv 文件中。 现在我们编写如下的训练代码，使用 :class:`~fastNLP.core.callback.FitlogCallback` 进行实验记录保存:: diff --git "a/docs/source/tutorials/\346\226\207\346\234\254\345\210\206\347\261\273.rst" "b/docs/source/tutorials/\346\226\207\346\234\254\345\210\206\347\261\273.rst" index 73686916..30f6cf4f 100644 --- "a/docs/source/tutorials/\346\226\207\346\234\254\345\210\206\347\261\273.rst" +++ "b/docs/source/tutorials/\346\226\207\346\234\254\345\210\206\347\261\273.rst" @@ -291,7 +291,7 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 PS: 使用Bert进行文本分类 -~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python @@ -368,7 +368,7 @@ PS: 使用Bert进行文本分类 PS: 基于词进行文本分类 -~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 由于汉字中没有显示的字与字的边界，一般需要通过分词器先将句子进行分词操作。 下面的例子演示了如何不基于fastNLP已有的数据读取、预处理代码进行文本分类。 diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py index 9c9e505b..45a488d9 100644 --- a/fastNLP/core/dataset.py +++ b/fastNLP/core/dataset.py @@ -53,7 +53,7 @@ from fastNLP import DataSet from fastNLP import Instance instances = [] - winstances.append(Instance(sentence="This is the first instance", + instances.append(Instance(sentence="This is the first instance", ords=['this', 'is', 'the', 'first', 'instance', '.'], seq_len=6)) instances.append(Instance(sentence="Second instance .", diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py index 55ffd9cf..cb05f82d 100644 --- a/fastNLP/core/tester.py +++ b/fastNLP/core/tester.py @@ -148,7 +148,7 @@ def __init__(self, data, model, metrics, batch_size=16, num_workers=0, device=No self._predict_func = self._model.predict self._predict_func_wrapper = self._model.predict else: - if _model_contains_inner_module(model): + if _model_contains_inner_module(self._model): self._predict_func_wrapper = self._model.forward self._predict_func = self._model.module.forward else: diff --git a/fastNLP/io/file_utils.py b/fastNLP/io/file_utils.py index c7893f38..bbf3de1e 100644 --- a/fastNLP/io/file_utils.py +++ b/fastNLP/io/file_utils.py @@ -103,6 +103,11 @@ "yelp-review-polarity": "yelp_review_polarity.tar.gz", "sst-2": "SST-2.zip", "sst": "SST.zip", + 'mr': 'mr.zip', + "R8": "R8.zip", + "R52": "R52.zip", + "20ng": "20ng.zip", + "ohsumed": "ohsumed.zip", # Classification, Chinese "chn-senti-corp": "chn_senti_corp.zip", diff --git a/fastNLP/io/pipe/__init__.py b/fastNLP/io/pipe/__init__.py index 94784515..35965ca3 100644 --- a/fastNLP/io/pipe/__init__.py +++ b/fastNLP/io/pipe/__init__.py @@ -23,15 +23,15 @@ "ChnSentiCorpPipe", "THUCNewsPipe", "WeiboSenti100kPipe", - "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Loader", - + "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Pipe", + "Conll2003NERPipe", "OntoNotesNERPipe", "MsraNERPipe", "WeiboNERPipe", "PeopleDailyPipe", "Conll2003Pipe", - + "MatchingBertPipe", "RTEBertPipe", "SNLIBertPipe", @@ -53,14 +53,20 @@ "RenamePipe", "GranularizePipe", "MachingTruncatePipe", - + "CoReferencePipe", - "CMRC2018BertPipe" + "CMRC2018BertPipe", + + "R52PmiGraphPipe", + "R8PmiGraphPipe", + "OhsumedPmiGraphPipe", + "NG20PmiGraphPipe", + "MRPmiGraphPipe" ] from .classification import CLSBasePipe, YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe, ChnSentiCorpPipe, THUCNewsPipe, \ - WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Loader + WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Pipe from .conll import Conll2003NERPipe, OntoNotesNERPipe, MsraNERPipe, WeiboNERPipe, PeopleDailyPipe from .conll import Conll2003Pipe from .coreference import CoReferencePipe @@ -70,3 +76,5 @@ LCQMCPipe, BQCorpusPipe, LCQMCBertPipe, RenamePipe, GranularizePipe, MachingTruncatePipe from .pipe import Pipe from .qa import CMRC2018BertPipe + +from .construct_graph import MRPmiGraphPipe, R8PmiGraphPipe, R52PmiGraphPipe, NG20PmiGraphPipe, OhsumedPmiGraphPipe diff --git a/fastNLP/io/pipe/construct_graph.py b/fastNLP/io/pipe/construct_graph.py new file mode 100644 index 00000000..d597da9d --- /dev/null +++ b/fastNLP/io/pipe/construct_graph.py @@ -0,0 +1,268 @@ + +__all__ =[ + 'MRPmiGraphPipe', + 'R8PmiGraphPipe', + 'R52PmiGraphPipe', + 'OhsumedPmiGraphPipe', + 'NG20PmiGraphPipe' +] +try: + import networkx as nx + from sklearn.feature_extraction.text import CountVectorizer + from sklearn.feature_extraction.text import TfidfTransformer + from sklearn.pipeline import Pipeline +except: + pass +from collections import defaultdict +import itertools +import math +from tqdm import tqdm +import numpy as np + +from ..data_bundle import DataBundle +from ...core.const import Const +from ..loader.classification import MRLoader, OhsumedLoader, R52Loader, R8Loader, NG20Loader + + +def _get_windows(content_lst: list, window_size:int): + r""" + 滑动窗口处理文本，获取词频和共现词语的词频 + :param content_lst: + :param window_size: + :return: 词频，共现词频，窗口化后文本段的数量 + """ + word_window_freq = defaultdict(int) # w(i) 单词在窗口单位内出现的次数 + word_pair_count = defaultdict(int) # w(i, j) + windows_len = 0 + for words in tqdm(content_lst, desc="Split by window"): + windows = list() + + if isinstance(words, str): + words = words.split() + length = len(words) + + if length <= window_size: + windows.append(words) + else: + for j in range(length - window_size + 1): + window = words[j: j + window_size] + windows.append(list(set(window))) + + for window in windows: + for word in window: + word_window_freq[word] += 1 + + for word_pair in itertools.combinations(window, 2): + word_pair_count[word_pair] += 1 + + windows_len += len(windows) + return word_window_freq, word_pair_count, windows_len + +def _cal_pmi(W_ij, W, word_freq_i, word_freq_j): + r""" + params: w_ij:为词语i,j的共现词频 + w:文本数量 + word_freq_i: 词语i的词频 + word_freq_j: 词语j的词频 + return: 词语i，j的tfidf值 + """ + p_i = word_freq_i / W + p_j = word_freq_j / W + p_i_j = W_ij / W + pmi = math.log(p_i_j / (p_i * p_j)) + + return pmi + +def _count_pmi(windows_len, word_pair_count, word_window_freq, threshold): + r""" + params: windows_len: 文本段数量 + word_pair_count: 词共现频率字典 + word_window_freq: 词频率字典 + threshold: 阈值 + return 词语pmi的list列表,其中元素为[word1, word2, pmi] + """ + word_pmi_lst = list() + for word_pair, W_i_j in tqdm(word_pair_count.items(), desc="Calculate pmi between words"): + word_freq_1 = word_window_freq[word_pair[0]] + word_freq_2 = word_window_freq[word_pair[1]] + + pmi = _cal_pmi(W_i_j, windows_len, word_freq_1, word_freq_2) + if pmi <= threshold: + continue + word_pmi_lst.append([word_pair[0], word_pair[1], pmi]) + return word_pmi_lst + +class GraphBuilderBase: + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + self.graph = nx.Graph() + self.word2id = dict() + self.graph_type = graph_type + self.window_size = widow_size + self.doc_node_num = 0 + self.tr_doc_index = None + self.te_doc_index = None + self.dev_doc_index = None + self.doc = None + self.threshold = threshold + + def _get_doc_edge(self, data_bundle: DataBundle): + r''' + 对输入的DataBundle进行处理，然后生成文档-单词的tfidf值 + ：param: data_bundle中的文本若为英文，形式为[ 'This is the first document.'],若为中文则为['他 喜欢 吃 苹果'] + : return 返回带有具有tfidf边文档-单词稀疏矩阵 + ''' + tr_doc = list(data_bundle.get_dataset("train").get_field(Const.RAW_WORD)) + val_doc = list(data_bundle.get_dataset("dev").get_field(Const.RAW_WORD)) + te_doc = list(data_bundle.get_dataset("test").get_field(Const.RAW_WORD)) + doc = tr_doc + val_doc + te_doc + self.doc = doc + self.tr_doc_index = [ind for ind in range(len(tr_doc))] + self.dev_doc_index = [ind+len(tr_doc) for ind in range(len(val_doc))] + self.te_doc_index = [ind+len(tr_doc)+len(val_doc) for ind in range(len(te_doc))] + text_tfidf = Pipeline([('count', CountVectorizer(token_pattern=r'\S+', min_df=1, max_df=1.0)), + ('tfidf', TfidfTransformer(norm=None, use_idf=True, smooth_idf=False, sublinear_tf=False))]) + + tfidf_vec = text_tfidf.fit_transform(doc) + self.doc_node_num = tfidf_vec.shape[0] + vocab_lst = text_tfidf['count'].get_feature_names() + for ind, word in enumerate(vocab_lst): + self.word2id[word] = ind + for ind, row in enumerate(tfidf_vec): + for col_index, value in zip(row.indices, row.data): + self.graph.add_edge(ind, self.doc_node_num+col_index, weight=value) + return nx.to_scipy_sparse_matrix(self.graph) + + def _get_word_edge(self): + word_window_freq, word_pair_count, windows_len = _get_windows(self.doc, self.window_size) + pmi_edge_lst = _count_pmi(windows_len, word_pair_count, word_window_freq, self.threshold) + for edge_item in pmi_edge_lst: + word_indx1 = self.doc_node_num + self.word2id[edge_item[0]] + word_indx2 = self.doc_node_num + self.word2id[edge_item[1]] + if word_indx1 == word_indx2: + continue + self.graph.add_edge(word_indx1, word_indx2, weight=edge_item[2]) + + def build_graph(self, data_bundle: DataBundle): + r""" + 对输入的DataBundle进行处理，然后返回该scipy_sparse_matrix类型的邻接矩阵。 + + :param ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象 + :return: + """ + raise NotImplementedError + + def build_graph_from_file(self, path: str): + r""" + 传入文件路径，生成处理好的scipy_sparse_matrix对象。paths支持的路径形式可以参考 ：:meth:`fastNLP.io.Loader.load()` + + :param paths: + :return: scipy_sparse_matrix + """ + raise NotImplementedError + + +class MRPmiGraphPipe(GraphBuilderBase): + + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) + + def build_graph(self, data_bundle: DataBundle): + r''' + params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. + return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index. + ''' + self._get_doc_edge(data_bundle) + self._get_word_edge() + return nx.to_scipy_sparse_matrix(self.graph, + nodelist=list(range(self.graph.number_of_nodes())), + weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) + + def build_graph_from_file(self, path: str): + data_bundle = MRLoader().load(path) + return self.build_graph(data_bundle) + +class R8PmiGraphPipe(GraphBuilderBase): + + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) + + def build_graph(self, data_bundle: DataBundle): + r''' + params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. + return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index. + ''' + self._get_doc_edge(data_bundle) + self._get_word_edge() + return nx.to_scipy_sparse_matrix(self.graph, + nodelist=list(range(self.graph.number_of_nodes())), + weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) + + def build_graph_from_file(self, path: str): + data_bundle = R8Loader().load(path) + return self.build_graph(data_bundle) + +class R52PmiGraphPipe(GraphBuilderBase): + + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) + + def build_graph(self, data_bundle: DataBundle): + r''' + params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. + return 返回csr类型的稀疏矩阵;训练集，验证集，测试集，在图中的index. + ''' + self._get_doc_edge(data_bundle) + self._get_word_edge() + return nx.to_scipy_sparse_matrix(self.graph, + nodelist=list(range(self.graph.number_of_nodes())), + weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) + + def build_graph_from_file(self, path: str): + data_bundle = R52Loader().load(path) + return self.build_graph(data_bundle) + +class OhsumedPmiGraphPipe(GraphBuilderBase): + + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) + + def build_graph(self, data_bundle: DataBundle): + r''' + params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. + return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index. + ''' + self._get_doc_edge(data_bundle) + self._get_word_edge() + return nx.to_scipy_sparse_matrix(self.graph, + nodelist=list(range(self.graph.number_of_nodes())), + weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) + + def build_graph_from_file(self, path: str): + data_bundle = OhsumedLoader().load(path) + return self.build_graph(data_bundle) + + +class NG20PmiGraphPipe(GraphBuilderBase): + + def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): + super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) + + def build_graph(self, data_bundle: DataBundle): + r''' + params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. + return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index. + ''' + self._get_doc_edge(data_bundle) + self._get_word_edge() + return nx.to_scipy_sparse_matrix(self.graph, + nodelist=list(range(self.graph.number_of_nodes())), + weight='weight', dtype=np.float32, format='csr'), ( + self.tr_doc_index, self.dev_doc_index, self.te_doc_index) + + def build_graph_from_file(self, path: str): + r''' + param: path->数据集的路径. + return: 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index. + ''' + data_bundle = NG20Loader().load(path) + return self.build_graph(data_bundle) diff --git a/fastNLP/models/biaffine_parser.py b/fastNLP/models/biaffine_parser.py index dff4809c..cd874e7c 100644 --- a/fastNLP/models/biaffine_parser.py +++ b/fastNLP/models/biaffine_parser.py @@ -376,7 +376,7 @@ def forward(self, words1, words2, seq_len, target1=None): if self.encoder_name.endswith('lstm'): sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) x = x[sort_idx] - x = nn.utils.rnn.pack_padded_sequence(x, sort_lens, batch_first=True) + x = nn.utils.rnn.pack_padded_sequence(x, sort_lens.cpu(), batch_first=True) feat, _ = self.encoder(x) # -> [N,L,C] feat, _ = nn.utils.rnn.pad_packed_sequence(feat, batch_first=True) _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) diff --git a/fastNLP/modules/encoder/_elmo.py b/fastNLP/modules/encoder/_elmo.py index 13843f83..7a2cf4bc 100644 --- a/fastNLP/modules/encoder/_elmo.py +++ b/fastNLP/modules/encoder/_elmo.py @@ -251,7 +251,7 @@ def __init__(self, config): def forward(self, inputs, seq_len): sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) inputs = inputs[sort_idx] - inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=self.batch_first) + inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=self.batch_first) output, hx = self.encoder(inputs, None) # -> [N,L,C] output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=self.batch_first) _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) @@ -316,7 +316,7 @@ def forward(self, inputs, seq_len): max_len = inputs.size(1) sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) inputs = inputs[sort_idx] - inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=True) + inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=True) output, _ = self._lstm_forward(inputs, None) _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) output = output[:, unsort_idx] diff --git a/readthedocs.yml b/readthedocs.yml index e6d5bafd..5ff803a0 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -7,10 +7,11 @@ build: image: latest python: - version: 3.6 + version: 3.8 install: + - requirements: docs/requirements.txt - method: setuptools path: . formats: - - htmlzip \ No newline at end of file + - htmlzip diff --git a/setup.py b/setup.py index d4a71c33..a3f47009 100644 --- a/setup.py +++ b/setup.py @@ -23,7 +23,7 @@ long_description_content_type='text/markdown', license='Apache License', author='Fudan FastNLP Team', - python_requires='>=3.6', + python_requires='>=3.7', packages=pkgs, install_requires=reqs.strip().split('\n'), )