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17 changed files with 639 additions and 203 deletions
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+5 -2docs/requirements.txt
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+36 -0docs/source/fastNLP.api.rst
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+12 -12docs/source/fastNLP.core.rst
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+42 -0docs/source/fastNLP.io.rst
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+0 -36docs/source/fastNLP.loader.rst
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+12 -0docs/source/fastNLP.models.rst
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+6 -0docs/source/fastNLP.modules.encoder.rst
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+0 -5docs/source/fastNLP.modules.interactor.rst
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+6 -1docs/source/fastNLP.modules.rst
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+2 -9docs/source/fastNLP.rst
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+0 -24docs/source/fastNLP.saver.rst
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+20 -18docs/source/index.rst
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+375 -0docs/source/tutorials/fastnlp_10tmin_tutorial.rst
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+111 -0docs/source/tutorials/fastnlp_1_minute_tutorial.rst
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+2 -17docs/source/user/installation.rst
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+4 -79docs/source/user/quickstart.rst
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docs/requirements.txt
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| @@ -1,5 +1,8 @@ | |||||
| numpy>=1.14.2 | numpy>=1.14.2 | ||||
| http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl | |||||
| http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl | |||||
| torchvision>=0.1.8 | torchvision>=0.1.8 | ||||
| sphinx-rtd-theme==0.4.1 | sphinx-rtd-theme==0.4.1 | ||||
| tensorboardX>=1.4 | |||||
| tensorboardX>=1.4 | |||||
| tqdm>=4.28.1 | |||||
| ipython>=6.4.0 | |||||
| ipython-genutils>=0.2.0 | |||||
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docs/source/fastNLP.api.rst
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| fastNLP.api | |||||
| ============ | |||||
| fastNLP.api.api | |||||
| ---------------- | |||||
| .. automodule:: fastNLP.api.api | |||||
| :members: | |||||
| fastNLP.api.converter | |||||
| ---------------------- | |||||
| .. automodule:: fastNLP.api.converter | |||||
| :members: | |||||
| fastNLP.api.model\_zoo | |||||
| ----------------------- | |||||
| .. automodule:: fastNLP.api.model_zoo | |||||
| :members: | |||||
| fastNLP.api.pipeline | |||||
| --------------------- | |||||
| .. automodule:: fastNLP.api.pipeline | |||||
| :members: | |||||
| fastNLP.api.processor | |||||
| ---------------------- | |||||
| .. automodule:: fastNLP.api.processor | |||||
| :members: | |||||
| .. automodule:: fastNLP.api | |||||
| :members: | |||||
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docs/source/fastNLP.core.rst
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| @@ -13,10 +13,10 @@ fastNLP.core.dataset | |||||
| .. automodule:: fastNLP.core.dataset | .. automodule:: fastNLP.core.dataset | ||||
| :members: | :members: | ||||
| fastNLP.core.field | |||||
| ------------------- | |||||
| fastNLP.core.fieldarray | |||||
| ------------------------ | |||||
| .. automodule:: fastNLP.core.field | |||||
| .. automodule:: fastNLP.core.fieldarray | |||||
| :members: | :members: | ||||
| fastNLP.core.instance | fastNLP.core.instance | ||||
| @@ -25,10 +25,10 @@ fastNLP.core.instance | |||||
| .. automodule:: fastNLP.core.instance | .. automodule:: fastNLP.core.instance | ||||
| :members: | :members: | ||||
| fastNLP.core.loss | |||||
| ------------------ | |||||
| fastNLP.core.losses | |||||
| -------------------- | |||||
| .. automodule:: fastNLP.core.loss | |||||
| .. automodule:: fastNLP.core.losses | |||||
| :members: | :members: | ||||
| fastNLP.core.metrics | fastNLP.core.metrics | ||||
| @@ -49,12 +49,6 @@ fastNLP.core.predictor | |||||
| .. automodule:: fastNLP.core.predictor | .. automodule:: fastNLP.core.predictor | ||||
| :members: | :members: | ||||
| fastNLP.core.preprocess | |||||
| ------------------------ | |||||
| .. automodule:: fastNLP.core.preprocess | |||||
| :members: | |||||
| fastNLP.core.sampler | fastNLP.core.sampler | ||||
| --------------------- | --------------------- | ||||
| @@ -73,6 +67,12 @@ fastNLP.core.trainer | |||||
| .. automodule:: fastNLP.core.trainer | .. automodule:: fastNLP.core.trainer | ||||
| :members: | :members: | ||||
| fastNLP.core.utils | |||||
| ------------------- | |||||
| .. automodule:: fastNLP.core.utils | |||||
| :members: | |||||
| fastNLP.core.vocabulary | fastNLP.core.vocabulary | ||||
| ------------------------ | ------------------------ | ||||
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docs/source/fastNLP.io.rst
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| fastNLP.io | |||||
| =========== | |||||
| fastNLP.io.base\_loader | |||||
| ------------------------ | |||||
| .. automodule:: fastNLP.io.base_loader | |||||
| :members: | |||||
| fastNLP.io.config\_io | |||||
| ---------------------- | |||||
| .. automodule:: fastNLP.io.config_io | |||||
| :members: | |||||
| fastNLP.io.dataset\_loader | |||||
| --------------------------- | |||||
| .. automodule:: fastNLP.io.dataset_loader | |||||
| :members: | |||||
| fastNLP.io.embed\_loader | |||||
| ------------------------- | |||||
| .. automodule:: fastNLP.io.embed_loader | |||||
| :members: | |||||
| fastNLP.io.logger | |||||
| ------------------ | |||||
| .. automodule:: fastNLP.io.logger | |||||
| :members: | |||||
| fastNLP.io.model\_io | |||||
| --------------------- | |||||
| .. automodule:: fastNLP.io.model_io | |||||
| :members: | |||||
| .. automodule:: fastNLP.io | |||||
| :members: | |||||
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docs/source/fastNLP.loader.rst
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| fastNLP.loader | |||||
| =============== | |||||
| fastNLP.loader.base\_loader | |||||
| ---------------------------- | |||||
| .. automodule:: fastNLP.loader.base_loader | |||||
| :members: | |||||
| fastNLP.loader.config\_loader | |||||
| ------------------------------ | |||||
| .. automodule:: fastNLP.loader.config_loader | |||||
| :members: | |||||
| fastNLP.loader.dataset\_loader | |||||
| ------------------------------- | |||||
| .. automodule:: fastNLP.loader.dataset_loader | |||||
| :members: | |||||
| fastNLP.loader.embed\_loader | |||||
| ----------------------------- | |||||
| .. automodule:: fastNLP.loader.embed_loader | |||||
| :members: | |||||
| fastNLP.loader.model\_loader | |||||
| ----------------------------- | |||||
| .. automodule:: fastNLP.loader.model_loader | |||||
| :members: | |||||
| .. automodule:: fastNLP.loader | |||||
| :members: | |||||
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docs/source/fastNLP.models.rst
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| @@ -7,6 +7,12 @@ fastNLP.models.base\_model | |||||
| .. automodule:: fastNLP.models.base_model | .. automodule:: fastNLP.models.base_model | ||||
| :members: | :members: | ||||
| fastNLP.models.biaffine\_parser | |||||
| -------------------------------- | |||||
| .. automodule:: fastNLP.models.biaffine_parser | |||||
| :members: | |||||
| fastNLP.models.char\_language\_model | fastNLP.models.char\_language\_model | ||||
| ------------------------------------- | ------------------------------------- | ||||
| @@ -25,6 +31,12 @@ fastNLP.models.sequence\_modeling | |||||
| .. automodule:: fastNLP.models.sequence_modeling | .. automodule:: fastNLP.models.sequence_modeling | ||||
| :members: | :members: | ||||
| fastNLP.models.snli | |||||
| -------------------- | |||||
| .. automodule:: fastNLP.models.snli | |||||
| :members: | |||||
| .. automodule:: fastNLP.models | .. automodule:: fastNLP.models | ||||
| :members: | :members: | ||||
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docs/source/fastNLP.modules.encoder.rst
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| @@ -43,6 +43,12 @@ fastNLP.modules.encoder.masked\_rnn | |||||
| .. automodule:: fastNLP.modules.encoder.masked_rnn | .. automodule:: fastNLP.modules.encoder.masked_rnn | ||||
| :members: | :members: | ||||
| fastNLP.modules.encoder.transformer | |||||
| ------------------------------------ | |||||
| .. automodule:: fastNLP.modules.encoder.transformer | |||||
| :members: | |||||
| fastNLP.modules.encoder.variational\_rnn | fastNLP.modules.encoder.variational\_rnn | ||||
| ----------------------------------------- | ----------------------------------------- | ||||
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docs/source/fastNLP.modules.interactor.rst
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| fastNLP.modules.interactor | |||||
| =========================== | |||||
| .. automodule:: fastNLP.modules.interactor | |||||
| :members: | |||||
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docs/source/fastNLP.modules.rst
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| @@ -6,7 +6,12 @@ fastNLP.modules | |||||
| fastNLP.modules.aggregator | fastNLP.modules.aggregator | ||||
| fastNLP.modules.decoder | fastNLP.modules.decoder | ||||
| fastNLP.modules.encoder | fastNLP.modules.encoder | ||||
| fastNLP.modules.interactor | |||||
| fastNLP.modules.dropout | |||||
| ------------------------ | |||||
| .. automodule:: fastNLP.modules.dropout | |||||
| :members: | |||||
| fastNLP.modules.other\_modules | fastNLP.modules.other\_modules | ||||
| ------------------------------- | ------------------------------- | ||||
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docs/source/fastNLP.rst
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| @@ -3,18 +3,11 @@ fastNLP | |||||
| .. toctree:: | .. toctree:: | ||||
| fastNLP.api | |||||
| fastNLP.core | fastNLP.core | ||||
| fastNLP.loader | |||||
| fastNLP.io | |||||
| fastNLP.models | fastNLP.models | ||||
| fastNLP.modules | fastNLP.modules | ||||
| fastNLP.saver | |||||
| fastNLP.fastnlp | |||||
| ---------------- | |||||
| .. automodule:: fastNLP.fastnlp | |||||
| :members: | |||||
| .. automodule:: fastNLP | .. automodule:: fastNLP | ||||
| :members: | :members: | ||||
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docs/source/fastNLP.saver.rst
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| fastNLP.saver | |||||
| ============== | |||||
| fastNLP.saver.config\_saver | |||||
| ---------------------------- | |||||
| .. automodule:: fastNLP.saver.config_saver | |||||
| :members: | |||||
| fastNLP.saver.logger | |||||
| --------------------- | |||||
| .. automodule:: fastNLP.saver.logger | |||||
| :members: | |||||
| fastNLP.saver.model\_saver | |||||
| --------------------------- | |||||
| .. automodule:: fastNLP.saver.model_saver | |||||
| :members: | |||||
| .. automodule:: fastNLP.saver | |||||
| :members: | |||||
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docs/source/index.rst
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| @@ -1,33 +1,35 @@ | |||||
| fastNLP documentation | fastNLP documentation | ||||
| ===================== | ===================== | ||||
| fastNLP，目前仍在孵化中。 | |||||
| A Modularized and Extensible Toolkit for Natural Language Processing. Currently still in incubation. | |||||
| Introduction | Introduction | ||||
| ------------ | ------------ | ||||
| fastNLP是一个基于PyTorch的模块化自然语言处理系统，用于快速开发NLP工具。 | |||||
| 它将基于深度学习的NLP模型划分为不同的模块。 | |||||
| 这些模块分为4类：encoder（编码），interaction（交互）, aggregration（聚合） and decoder（解码）， | |||||
| 而每个类别包含不同的实现模块。 | |||||
| FastNLP is a modular Natural Language Processing system based on | |||||
| PyTorch, built for fast development of NLP models. | |||||
| 大多数当前的NLP模型可以构建在这些模块上，这极大地简化了开发NLP模型的过程。 | |||||
| fastNLP的架构如图所示： | |||||
| A deep learning NLP model is the composition of three types of modules: | |||||
| .. image:: figures/procedures.PNG | |||||
| +-----------------------+-----------------------+-----------------------+ | |||||
| | module type | functionality | example | | |||||
| +=======================+=======================+=======================+ | |||||
| | encoder | encode the input into | embedding, RNN, CNN, | | |||||
| | | some abstract | transformer | | |||||
| | | representation | | | |||||
| +-----------------------+-----------------------+-----------------------+ | |||||
| | aggregator | aggregate and reduce | self-attention, | | |||||
| | | information | max-pooling | | |||||
| +-----------------------+-----------------------+-----------------------+ | |||||
| | decoder | decode the | MLP, CRF | | |||||
| | | representation into | | | |||||
| | | the output | | | |||||
| +-----------------------+-----------------------+-----------------------+ | |||||
| 在constructing model部分，以序列标注和文本分类为例进行说明： | |||||
| .. image:: figures/text_classification.png | |||||
| .. image:: figures/sequence_labeling.PNG | |||||
| :width: 400 | |||||
| * encoder module：将输入编码为一些抽象表示，输入的是单词序列，输出向量序列。 | |||||
| * interaction module：使表示中的信息相互交互，输入的是向量序列，输出的也是向量序列。 | |||||
| * aggregation module：聚合和减少信息，输入向量序列，输出一个向量。 | |||||
| * decoder module：将表示解码为输出，输出一个label（文本分类）或者输出label序列（序列标注） | |||||
| For example: | |||||
| 其中interaction module和aggregation module在模型中不一定存在，例如上面的序列标注模型。 | |||||
| .. image:: figures/text_classification.png | |||||
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docs/source/tutorials/fastnlp_10tmin_tutorial.rst
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| fastNLP上手教程 | |||||
| =============== | |||||
| fastNLP提供方便的数据预处理，训练和测试模型的功能 | |||||
| DataSet & Instance | |||||
| ------------------ | |||||
| fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。 | |||||
| 有一些read\_\*方法，可以轻松从文件读取数据，存成DataSet。 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import DataSet | |||||
| from fastNLP import Instance | |||||
| # 从csv读取数据到DataSet | |||||
| win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" | |||||
| dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') | |||||
| print(dataset[0]) | |||||
| .. parsed-literal:: | |||||
| {'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story ., | |||||
| 'label': 1} | |||||
| .. code:: ipython3 | |||||
| # DataSet.append(Instance)加入新数据 | |||||
| dataset.append(Instance(raw_sentence='fake data', label='0')) | |||||
| dataset[-1] | |||||
| .. parsed-literal:: | |||||
| {'raw_sentence': fake data, | |||||
| 'label': 0} | |||||
| .. code:: ipython3 | |||||
| # DataSet.apply(func, new_field_name)对数据预处理 | |||||
| # 将所有数字转为小写 | |||||
| dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') | |||||
| # label转int | |||||
| dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) | |||||
| # 使用空格分割句子 | |||||
| dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0) | |||||
| def split_sent(ins): | |||||
| return ins['raw_sentence'].split() | |||||
| dataset.apply(split_sent, new_field_name='words', is_input=True) | |||||
| .. code:: ipython3 | |||||
| # DataSet.drop(func)筛除数据 | |||||
| # 删除低于某个长度的词语 | |||||
| dataset.drop(lambda x: len(x['words']) <= 3) | |||||
| .. code:: ipython3 | |||||
| # 分出测试集、训练集 | |||||
| test_data, train_data = dataset.split(0.3) | |||||
| print("Train size: ", len(test_data)) | |||||
| print("Test size: ", len(train_data)) | |||||
| .. parsed-literal:: | |||||
| Train size: 54 | |||||
| Test size: | |||||
| Vocabulary | |||||
| ---------- | |||||
| fastNLP中的Vocabulary轻松构建词表，将词转成数字 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import Vocabulary | |||||
| # 构建词表, Vocabulary.add(word) | |||||
| vocab = Vocabulary(min_freq=2) | |||||
| train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) | |||||
| vocab.build_vocab() | |||||
| # index句子, Vocabulary.to_index(word) | |||||
| train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||||
| test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||||
| print(test_data[0]) | |||||
| .. parsed-literal:: | |||||
| {'raw_sentence': the plot is romantic comedy boilerplate from start to finish ., | |||||
| 'label': 2, | |||||
| 'label_seq': 2, | |||||
| 'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'], | |||||
| 'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]} | |||||
| .. code:: ipython3 | |||||
| # 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset | |||||
| from fastNLP.core.batch import Batch | |||||
| from fastNLP.core.sampler import RandomSampler | |||||
| batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler()) | |||||
| for batch_x, batch_y in batch_iterator: | |||||
| print("batch_x has: ", batch_x) | |||||
| print("batch_y has: ", batch_y) | |||||
| break | |||||
| .. parsed-literal:: | |||||
| batch_x has: {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']), | |||||
| list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])], | |||||
| dtype=object), 'word_seq': tensor([[ 19, 184, 6, 1, 481, 9, 206, 50, 91, 1210, 1609, 1330, | |||||
| 495, 5, 63, 4, 1269, 4, 1, 1184, 7, 50, 1050, 10, | |||||
| 8, 1611, 16, 21, 1039, 1, 2], | |||||
| [ 3, 711, 22, 9, 1282, 16, 2482, 2483, 200, 2, 0, 0, | |||||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | |||||
| 0, 0, 0, 0, 0, 0, 0]])} | |||||
| batch_y has: {'label_seq': tensor([3, 2])} | |||||
| Model | |||||
| ----- | |||||
| .. code:: ipython3 | |||||
| # 定义一个简单的Pytorch模型 | |||||
| from fastNLP.models import CNNText | |||||
| model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) | |||||
| model | |||||
| .. parsed-literal:: | |||||
| CNNText( | |||||
| (embed): Embedding( | |||||
| (embed): Embedding(77, 50, padding_idx=0) | |||||
| (dropout): Dropout(p=0.0) | |||||
| ) | |||||
| (conv_pool): ConvMaxpool( | |||||
| (convs): ModuleList( | |||||
| (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,)) | |||||
| (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,)) | |||||
| (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,)) | |||||
| ) | |||||
| ) | |||||
| (dropout): Dropout(p=0.1) | |||||
| (fc): Linear( | |||||
| (linear): Linear(in_features=12, out_features=5, bias=True) | |||||
| ) | |||||
| ) | |||||
| Trainer & Tester | |||||
| ---------------- | |||||
| 使用fastNLP的Trainer训练模型 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import Trainer | |||||
| from copy import deepcopy | |||||
| from fastNLP import CrossEntropyLoss | |||||
| from fastNLP import AccuracyMetric | |||||
| .. code:: ipython3 | |||||
| # 进行overfitting测试 | |||||
| copy_model = deepcopy(model) | |||||
| overfit_trainer = Trainer(model=copy_model, | |||||
| train_data=test_data, | |||||
| dev_data=test_data, | |||||
| loss=CrossEntropyLoss(pred="output", target="label_seq"), | |||||
| metrics=AccuracyMetric(), | |||||
| n_epochs=10, | |||||
| save_path=None) | |||||
| overfit_trainer.train() | |||||
| .. parsed-literal:: | |||||
| training epochs started 2018-12-07 14:07:20 | |||||
| .. parsed-literal:: | |||||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='… | |||||
| .. parsed-literal:: | |||||
| Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037 | |||||
| Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296 | |||||
| Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333 | |||||
| Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556 | |||||
| Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111 | |||||
| Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481 | |||||
| Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963 | |||||
| Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185 | |||||
| Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222 | |||||
| Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778 | |||||
| .. code:: ipython3 | |||||
| # 实例化Trainer，传入模型和数据，进行训练 | |||||
| trainer = Trainer(model=model, | |||||
| train_data=train_data, | |||||
| dev_data=test_data, | |||||
| loss=CrossEntropyLoss(pred="output", target="label_seq"), | |||||
| metrics=AccuracyMetric(), | |||||
| n_epochs=5) | |||||
| trainer.train() | |||||
| print('Train finished!') | |||||
| .. parsed-literal:: | |||||
| training epochs started 2018-12-07 14:08:10 | |||||
| .. parsed-literal:: | |||||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i… | |||||
| .. parsed-literal:: | |||||
| Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037 | |||||
| Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037 | |||||
| Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037 | |||||
| Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185 | |||||
| Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741 | |||||
| Train finished! | |||||
| .. code:: ipython3 | |||||
| from fastNLP import Tester | |||||
| tester = Tester(data=test_data, model=model, metrics=AccuracyMetric()) | |||||
| acc = tester.test() | |||||
| .. parsed-literal:: | |||||
| [tester] | |||||
| AccuracyMetric: acc=0.240741 | |||||
| In summary | |||||
| ---------- | |||||
| fastNLP Trainer的伪代码逻辑 | |||||
| --------------------------- | |||||
| 1. 准备DataSet，假设DataSet中共有如下的fields | |||||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||||
| :: | |||||
| ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label'] | |||||
| 通过 | |||||
| DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input | |||||
| 通过 | |||||
| DataSet.set_target('label', flag=True)将'label'设置为target | |||||
| 2. 初始化模型 | |||||
| ~~~~~~~~~~~~~ | |||||
| :: | |||||
| class Model(nn.Module): | |||||
| def __init__(self): | |||||
| xxx | |||||
| def forward(self, word_seq1, word_seq2): | |||||
| # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的 | |||||
| # (2) input field的数量可以多于这里的形参数量。但是不能少于。 | |||||
| xxxx | |||||
| # 输出必须是一个dict | |||||
| 3. Trainer的训练过程 | |||||
| ~~~~~~~~~~~~~~~~~~~~ | |||||
| :: | |||||
| (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward | |||||
| (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。 | |||||
| 由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; | |||||
| 另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target； | |||||
| 为了解决以上的问题，我们的loss提供映射机制 | |||||
| 比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target | |||||
| 那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可 | |||||
| (3) 对于Metric是同理的 | |||||
| Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值 | |||||
| 一些问题. | |||||
| --------- | |||||
| 1. DataSet中为什么需要设置input和target | |||||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||||
| :: | |||||
| 只有被设置为input或者target的数据才会在train的过程中被取出来 | |||||
| (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。 | |||||
| (1.2) 我们在传递值给losser或者metric的时候会使用来自: | |||||
| (a)Model.forward的output | |||||
| (b)被设置为target的field | |||||
| 2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数 | |||||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||||
| :: | |||||
| (1.1) 构建模型过程中， | |||||
| 例如: | |||||
| DataSet中x，seq_lens是input，那么forward就应该是 | |||||
| def forward(self, x, seq_lens): | |||||
| pass | |||||
| 我们是通过形参名称进行匹配的field的 | |||||
| 1. 加载数据到DataSet | |||||
| ~~~~~~~~~~~~~~~~~~~~ | |||||
| 2. 使用apply操作对DataSet进行预处理 | |||||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||||
| :: | |||||
| (2.1) 处理过程中将某些field设置为input，某些field设置为target | |||||
| 3. 构建模型 | |||||
| ~~~~~~~~~~~ | |||||
| :: | |||||
| (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。 | |||||
| 例如: | |||||
| DataSet中x，seq_lens是input，那么forward就应该是 | |||||
| def forward(self, x, seq_lens): | |||||
| pass | |||||
| 我们是通过形参名称进行匹配的field的 | |||||
| (3.2) 模型的forward的output需要是dict类型的。 | |||||
| 建议将输出设置为{"pred": xx}. | |||||
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docs/source/tutorials/fastnlp_1_minute_tutorial.rst
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| @@ -0,0 +1,111 @@ | |||||
| FastNLP 1分钟上手教程 | |||||
| ===================== | |||||
| step 1 | |||||
| ------ | |||||
| 读取数据集 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import DataSet | |||||
| # linux_path = "../test/data_for_tests/tutorial_sample_dataset.csv" | |||||
| win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" | |||||
| ds = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') | |||||
| step 2 | |||||
| ------ | |||||
| 数据预处理 1. 类型转换 2. 切分验证集 3. 构建词典 | |||||
| .. code:: ipython3 | |||||
| # 将所有数字转为小写 | |||||
| ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') | |||||
| # label转int | |||||
| ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) | |||||
| def split_sent(ins): | |||||
| return ins['raw_sentence'].split() | |||||
| ds.apply(split_sent, new_field_name='words', is_input=True) | |||||
| .. code:: ipython3 | |||||
| # 分割训练集/验证集 | |||||
| train_data, dev_data = ds.split(0.3) | |||||
| print("Train size: ", len(train_data)) | |||||
| print("Test size: ", len(dev_data)) | |||||
| .. parsed-literal:: | |||||
| Train size: 54 | |||||
| Test size: 23 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import Vocabulary | |||||
| vocab = Vocabulary(min_freq=2) | |||||
| train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) | |||||
| # index句子, Vocabulary.to_index(word) | |||||
| train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||||
| dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||||
| step 3 | |||||
| ------ | |||||
| 定义模型 | |||||
| .. code:: ipython3 | |||||
| from fastNLP.models import CNNText | |||||
| model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) | |||||
| step 4 | |||||
| ------ | |||||
| 开始训练 | |||||
| .. code:: ipython3 | |||||
| from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric | |||||
| trainer = Trainer(model=model, | |||||
| train_data=train_data, | |||||
| dev_data=dev_data, | |||||
| loss=CrossEntropyLoss(), | |||||
| metrics=AccuracyMetric() | |||||
| ) | |||||
| trainer.train() | |||||
| print('Train finished!') | |||||
| .. parsed-literal:: | |||||
| training epochs started 2018-12-07 14:03:41 | |||||
| .. parsed-literal:: | |||||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i… | |||||
| .. parsed-literal:: | |||||
| Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.26087 | |||||
| Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.347826 | |||||
| Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.608696 | |||||
| Train finished! | |||||
| 本教程结束。更多操作请参考进阶教程。 | |||||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||||
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docs/source/user/installation.rst
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| @@ -6,26 +6,11 @@ Installation | |||||
| :local: | :local: | ||||
| Cloning From GitHub | |||||
| ~~~~~~~~~~~~~~~~~~~ | |||||
| If you just want to use fastNLP, use: | |||||
| Run the following commands to install fastNLP package: | |||||
| .. code:: shell | .. code:: shell | ||||
| git clone https://github.com/fastnlp/fastNLP | |||||
| cd fastNLP | |||||
| pip install fastNLP | |||||
| PyTorch Installation | |||||
| ~~~~~~~~~~~~~~~~~~~~ | |||||
| Visit the [PyTorch official website] for installation instructions based | |||||
| on your system. In general, you could use: | |||||
| .. code:: shell | |||||
| # using conda | |||||
| conda install pytorch torchvision -c pytorch | |||||
| # or using pip | |||||
| pip3 install torch torchvision | |||||
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docs/source/user/quickstart.rst
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| @@ -1,84 +1,9 @@ | |||||
| ========== | |||||
| Quickstart | Quickstart | ||||
| ========== | ========== | ||||
| Example | |||||
| ------- | |||||
| Basic Usage | |||||
| ~~~~~~~~~~~ | |||||
| A typical fastNLP routine is composed of four phases: loading dataset, | |||||
| pre-processing data, constructing model and training model. | |||||
| .. code:: python | |||||
| from fastNLP.models.base_model import BaseModel | |||||
| from fastNLP.modules import encoder | |||||
| from fastNLP.modules import aggregation | |||||
| from fastNLP.modules import decoder | |||||
| from fastNLP.loader.dataset_loader import ClassDataSetLoader | |||||
| from fastNLP.loader.preprocess import ClassPreprocess | |||||
| from fastNLP.core.trainer import ClassificationTrainer | |||||
| from fastNLP.core.inference import ClassificationInfer | |||||
| class ClassificationModel(BaseModel): | |||||
| """ | |||||
| Simple text classification model based on CNN. | |||||
| """ | |||||
| def __init__(self, num_classes, vocab_size): | |||||
| super(ClassificationModel, self).__init__() | |||||
| self.emb = encoder.Embedding(nums=vocab_size, dims=300) | |||||
| self.enc = encoder.Conv( | |||||
| in_channels=300, out_channels=100, kernel_size=3) | |||||
| self.agg = aggregation.MaxPool() | |||||
| self.dec = decoder.MLP([100, num_classes]) | |||||
| def forward(self, x): | |||||
| x = self.emb(x) # [N,L] -> [N,L,C] | |||||
| x = self.enc(x) # [N,L,C_in] -> [N,L,C_out] | |||||
| x = self.agg(x) # [N,L,C] -> [N,C] | |||||
| x = self.dec(x) # [N,C] -> [N, N_class] | |||||
| return x | |||||
| data_dir = 'data' # directory to save data and model | |||||
| train_path = 'test/data_for_tests/text_classify.txt' # training set file | |||||
| # load dataset | |||||
| ds_loader = ClassDataSetLoader("train", train_path) | |||||
| data = ds_loader.load() | |||||
| # pre-process dataset | |||||
| pre = ClassPreprocess(data_dir) | |||||
| vocab_size, n_classes = pre.process(data, "data_train.pkl") | |||||
| # construct model | |||||
| model_args = { | |||||
| 'num_classes': n_classes, | |||||
| 'vocab_size': vocab_size | |||||
| } | |||||
| model = ClassificationModel(num_classes=n_classes, vocab_size=vocab_size) | |||||
| .. toctree:: | |||||
| :maxdepth: 1 | |||||
| # train model | |||||
| train_args = { | |||||
| "epochs": 20, | |||||
| "batch_size": 50, | |||||
| "pickle_path": data_dir, | |||||
| "validate": False, | |||||
| "save_best_dev": False, | |||||
| "model_saved_path": None, | |||||
| "use_cuda": True, | |||||
| "learn_rate": 1e-3, | |||||
| "momentum": 0.9} | |||||
| trainer = ClassificationTrainer(train_args) | |||||
| trainer.train(model) | |||||
| ../tutorials/fastnlp_1_minute_tutorial | |||||
| ../tutorials/fastnlp_10tmin_tutorial | |||||
| # predict using model | |||||
| seqs = [x[0] for x in data] | |||||
| infer = ClassificationInfer(data_dir) | |||||
| labels_pred = infer.predict(model, seqs) | |||||
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| @@ -0,0 +1,6 @@ | |||||
| build: | |||||
| image: latest | |||||
| python: | |||||
| version: 3.6 | |||||
| setup_py_install: true | |||||