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622 lines
26 KiB
Plaintext
622 lines
26 KiB
Plaintext
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"cell_type": "markdown",
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"id": "213d538c",
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"source": [
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"# T3. dataloader 的内部结构和基本使用\n",
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"\n",
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"  1   fastNLP 中的 dataloader\n",
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" \n",
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"    1.1   dataloader 的基本介绍\n",
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"\n",
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"    1.2   dataloader 的函数创建\n",
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"\n",
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"  2   fastNLP 中 dataloader 的延伸\n",
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"\n",
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"    2.1   collator 的概念与使用\n",
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"\n",
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"    2.2   结合 datasets 框架"
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]
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},
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"cell_type": "markdown",
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"source": [
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"## 1. fastNLP 中的 dataloader\n",
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"\n",
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"### 1.1 dataloader 的基本介绍\n",
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"\n",
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"在`fastNLP 0.8`的开发中，最关键的开发目标就是**实现`fastNLP`对当前主流机器学习框架**，例如\n",
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"\n",
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"  **较为火热的`pytorch`**，以及**国产的`paddle`和`jittor`的兼容**，扩大受众的同时，也是助力国产\n",
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"\n",
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"本着分而治之的思想，我们可以将`fastNLP 0.8`对`pytorch`、`paddle`、`jittor`框架的兼容，划分为\n",
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"\n",
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"    **对数据预处理**、**批量`batch`的划分与补齐**、**模型训练**、**模型评测**，**四个部分的兼容**\n",
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"\n",
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"  针对数据预处理，我们已经在`tutorial-1`中介绍了`dataset`和`vocabulary`的使用\n",
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"\n",
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"    而结合`tutorial-0`，我们可以发现**数据预处理环节本质上是框架无关的**\n",
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"\n",
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"    因为在不同框架下，读取的原始数据格式都差异不大，彼此也很容易转换\n",
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"\n",
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"只有涉及到张量、模型，不同框架才展现出其各自的特色：**`pytorch`中的`tensor`和`nn.Module`**\n",
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"\n",
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"    **在`paddle`中称为`tensor`和`nn.Layer`**，**在`jittor`中则称为`Var`和`Module`**\n",
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"\n",
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"    因此，**模型训练、模型评测**，**是兼容的重难点**，我们将会在`tutorial-5`中详细介绍\n",
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"\n",
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"  针对批量`batch`的处理，作为`fastNLP 0.8`中框架无关部分想框架相关部分的过渡\n",
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"\n",
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"    就是`dataloader`模块的职责，这也是本篇教程`tutorial-3`讲解的重点\n",
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"\n",
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"**`dataloader`模块的职责**，详细划分可以包含以下三部分，**采样划分、补零对齐、框架匹配**\n",
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"\n",
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"    第一，确定`batch`大小，确定采样方式，划分后通过迭代器即可得到`batch`序列\n",
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"\n",
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"    第二，对于序列处理，这也是`fastNLP`主要针对的，将同个`batch`内的数据对齐\n",
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"\n",
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"    第三，**`batch`内数据格式要匹配框架**，**但`batch`结构需保持一致**，**参数匹配机制**\n",
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"\n",
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"  对此，`fastNLP 0.8`给出了 **`TorchDataLoader`、`PaddleDataLoader`和`JittorDataLoader`**\n",
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"\n",
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"    分别针对并匹配不同框架，但彼此之间参数名、属性、方法仍然类似，前两者大致如下表所示\n",
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"\n",
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"| <div align=\"center\">名称</div> | <div align=\"center\">参数</div> | <div align=\"center\">属性</div> | <div align=\"center\">功能</div> | <div align=\"center\">内容</div> |\n",
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"|:--|:--:|:--:|:--|:--|\n",
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"| **`dataset`** | √ | √ | 指定`dataloader`的数据内容 | |\n",
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"| `batch_size` | √ | √ | 指定`dataloader`的`batch`大小 | 默认`16` |\n",
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"| `shuffle` | √ | √ | 指定`dataloader`的数据是否打乱 | 默认`False` |\n",
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"| `collate_fn` | √ | √ | 指定`dataloader`的`batch`打包方法 | 视框架而定 |\n",
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"| `sampler` | √ | √ | 指定`dataloader`的`__len__`和`__iter__`函数的实现 | 默认`None` |\n",
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"| `batch_sampler` | √ | √ | 指定`dataloader`的`__len__`和`__iter__`函数的实现 | 默认`None` |\n",
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"| `drop_last` | √ | √ | 指定`dataloader`划分`batch`时是否丢弃剩余的 | 默认`False` |\n",
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"| `cur_batch_indices` | | √ | 记录`dataloader`当前遍历批量序号 | |\n",
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"| `num_workers` | √ | √ | 指定`dataloader`开启子进程数量 | 默认`0` |\n",
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"| `worker_init_fn` | √ | √ | 指定`dataloader`子进程初始方法 | 默认`None` |\n",
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"| `generator` | √ | √ | 指定`dataloader`子进程随机种子 | 默认`None` |\n",
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"| `prefetch_factor` | | √ | 指定为每个`worker`装载的`sampler`数量 | 默认`2` |"
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]
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},
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"source": [
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"  论及`dataloader`的函数，其中，`get_batch_indices`用来获取当前遍历到的`batch`序号，其他函数\n",
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"\n",
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"    包括`set_ignore`、`set_pad`和`databundle`类似，请参考`tutorial-2`，此处不做更多介绍\n",
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"\n",
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"    以下是`tutorial-2`中已经介绍过的数据预处理流程，接下来是对相关数据进行`dataloader`处理"
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"\u001b[38;5;2m[i 0604 15:44:29.773860 92 log.cc:351] Load log_sync: 1\u001b[m\n"
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"+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n",
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"| SentenceId | Sentence | Sentiment | input_ids | token_type_ids | attention_mask | target |\n",
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"+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n",
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"| 1 | A series of... | negative | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 1 |\n",
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"| 4 | A positivel... | neutral | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 2 |\n",
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"| 3 | Even fans o... | negative | [101, 2130,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 1 |\n",
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"| 5 | A comedy-dr... | positive | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 0 |\n",
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"+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"sys.path.append('..')\n",
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"\n",
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"import pandas as pd\n",
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"from functools import partial\n",
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"from fastNLP.transformers.torch import BertTokenizer\n",
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"\n",
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"from fastNLP import DataSet\n",
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"from fastNLP import Vocabulary\n",
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"from fastNLP.io import DataBundle\n",
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"\n",
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"\n",
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"class PipeDemo:\n",
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" def __init__(self, tokenizer='bert-base-uncased'):\n",
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" self.tokenizer = BertTokenizer.from_pretrained(tokenizer)\n",
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"\n",
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" def process_from_file(self, path='./data/test4dataset.tsv'):\n",
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" datasets = DataSet.from_pandas(pd.read_csv(path, sep='\\t'))\n",
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" train_ds, test_ds = datasets.split(ratio=0.7)\n",
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" train_ds, dev_ds = datasets.split(ratio=0.8)\n",
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" data_bundle = DataBundle(datasets={'train': train_ds, 'dev': dev_ds, 'test': test_ds})\n",
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"\n",
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" encode = partial(self.tokenizer.encode_plus, max_length=100, truncation=True,\n",
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" return_attention_mask=True)\n",
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" data_bundle.apply_field_more(encode, field_name='Sentence', progress_bar='tqdm')\n",
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" \n",
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" target_vocab = Vocabulary(padding=None, unknown=None)\n",
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"\n",
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" target_vocab.from_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='Sentiment')\n",
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" target_vocab.index_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='Sentiment',\n",
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" new_field_name='target')\n",
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"\n",
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" data_bundle.set_pad('input_ids', pad_val=self.tokenizer.pad_token_id)\n",
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" data_bundle.set_ignore('SentenceId', 'Sentence', 'Sentiment') \n",
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" return data_bundle\n",
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"\n",
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" \n",
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"pipe = PipeDemo(tokenizer='bert-base-uncased')\n",
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"\n",
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"data_bundle = pipe.process_from_file('./data/test4dataset.tsv')\n",
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"\n",
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"print(data_bundle.get_dataset('train'))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76e6b8ab",
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"metadata": {},
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"source": [
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"### 1.2 dataloader 的函数创建\n",
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"\n",
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"在`fastNLP 0.8`中，**更方便、可能更常用的`dataloader`创建方法是通过`prepare_xx_dataloader`函数**\n",
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"\n",
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"  例如下方的`prepare_torch_dataloader`函数，指定必要参数，读取数据集，生成对应`dataloader`\n",
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"\n",
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"  类型为`TorchDataLoader`，只能适用于`pytorch`框架，因此对应`trainer`初始化时`driver='torch'`\n",
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"\n",
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"同时我们看还可以发现，在`fastNLP 0.8`中，**`batch`表示为字典`dict`类型**，**`key`值就是原先数据集中各个字段**\n",
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"\n",
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"  **除去经过`DataBundle.set_ignore`函数隐去的部分**，而`value`值为`pytorch`框架对应的`torch.Tensor`类型"
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]
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},
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"text": [
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"<class 'fastNLP.core.dataloaders.torch_dataloader.fdl.TorchDataLoader'>\n",
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"<class 'dict'> <class 'torch.Tensor'> ['input_ids', 'token_type_ids', 'attention_mask', 'target']\n",
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"{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
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" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n",
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" 'input_ids': tensor([[ 101, 1037, 4038, 1011, 3689, 1997, 3053, 8680, 19173, 15685,\n",
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" 1999, 1037, 18006, 2836, 2011, 1996, 2516, 2839, 14996, 3054,\n",
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" 15509, 5325, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0],\n",
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" [ 101, 1037, 2186, 1997, 9686, 17695, 18673, 14313, 1996, 15262,\n",
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" 3351, 2008, 2054, 2003, 2204, 2005, 1996, 13020, 2003, 2036,\n",
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" 2204, 2005, 1996, 25957, 4063, 1010, 2070, 1997, 2029, 5681,\n",
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" 2572, 25581, 2021, 3904, 1997, 2029, 8310, 2000, 2172, 1997,\n",
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" 1037, 2466, 1012, 102],\n",
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" [ 101, 2130, 4599, 1997, 19214, 6432, 1005, 1055, 2147, 1010,\n",
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" 1045, 8343, 1010, 2052, 2031, 1037, 2524, 2051, 3564, 2083,\n",
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" 2023, 2028, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0],\n",
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" [ 101, 1037, 13567, 26162, 5257, 1997, 3802, 7295, 9888, 1998,\n",
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" 2035, 1996, 20014, 27611, 1010, 14583, 1010, 11703, 20175, 1998,\n",
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" 4028, 1997, 1037, 8101, 2319, 10576, 2030, 1037, 28900, 7815,\n",
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" 3850, 1012, 102, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0]]),\n",
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" 'target': tensor([0, 1, 1, 2]),\n",
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" 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
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]
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}
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],
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"source": [
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"from fastNLP import prepare_torch_dataloader\n",
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"\n",
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"train_dataset = data_bundle.get_dataset('train')\n",
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"evaluate_dataset = data_bundle.get_dataset('dev')\n",
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"\n",
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"train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
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"evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)\n",
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"\n",
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"print(type(train_dataloader))\n",
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"\n",
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"import pprint\n",
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"\n",
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"for batch in train_dataloader:\n",
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" print(type(batch), type(batch['input_ids']), list(batch))\n",
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" pprint.pprint(batch, width=1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9f457a6e",
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||
"metadata": {},
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"source": [
|
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"之所以说`prepare_xx_dataloader`函数更方便，是因为其**导入对象不仅可也是`DataSet`类型**，**还可以**\n",
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"\n",
|
||
"  **是`DataBundle`类型**，不过数据集名称需要是`'train'`、`'dev'`、`'test'`供`fastNLP`识别\n",
|
||
"\n",
|
||
"例如下方就是**直接通过`prepare_paddle_dataloader`函数生成基于`PaddleDataLoader`的字典**\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "7827557d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'fastNLP.core.dataloaders.paddle_dataloader.fdl.PaddleDataLoader'>\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import prepare_paddle_dataloader\n",
|
||
"\n",
|
||
"dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)\n",
|
||
"\n",
|
||
"print(type(dl_bundle['train']))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d898cf40",
|
||
"metadata": {},
|
||
"source": [
|
||
"  而在接下来`trainer`的初始化过程中，按如下方式使用即可，除了初始化时`driver='paddle'`外\n",
|
||
"\n",
|
||
"  这里也可以看出`trainer`模块中，**`evaluate_dataloaders`的设计允许评测可以针对多个数据集**\n",
|
||
"\n",
|
||
"```python\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" train_dataloader=dl_bundle['train'],\n",
|
||
" optimizers=optimizer,\n",
|
||
"\t...\n",
|
||
"\tdriver='paddle',\n",
|
||
"\tdevice='gpu',\n",
|
||
"\t...\n",
|
||
" evaluate_dataloaders={'dev': dl_bundle['dev'], 'test': dl_bundle['test']}, \n",
|
||
" metrics={'acc': Accuracy()},\n",
|
||
"\t...\n",
|
||
")\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d74d0523",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2. fastNLP 中 dataloader 的延伸\n",
|
||
"\n",
|
||
"### 2.1 collator 的概念与使用\n",
|
||
"\n",
|
||
"在`fastNLP 0.8`中，在数据加载模块`dataloader`内部，如之前表格所列举的，还存在其他的一些模块\n",
|
||
"\n",
|
||
"  例如，**实现序列的补零对齐的核对器`collator`模块**；注：`collate vt. 整理（文件或书等）；核对，校勘`\n",
|
||
"\n",
|
||
"在`fastNLP 0.8`中，虽然`dataloader`随框架不同，但`collator`模块却是统一的，主要属性、方法如下表所示\n",
|
||
"\n",
|
||
"| <div align=\"center\">名称</div> | <div align=\"center\">属性</div> | <div align=\"center\">方法</div> | <div align=\"center\">功能</div> | <div align=\"center\">内容</div> |\n",
|
||
"|:--|:--:|:--:|:--|:--|\n",
|
||
"| `backend` | √ | | 记录`collator`对应框架 | 字符串型，如`'torch'` |\n",
|
||
"| `padders` | √ | | 记录各字段对应的`padder`，每个负责具体补零对齐  | 字典类型 |\n",
|
||
"| `ignore_fields` | √ | | 记录`dataloader`采样`batch`时不予考虑的字段 | 集合类型 |\n",
|
||
"| `input_fields` | √ | | 记录`collator`每个字段的补零值、数据类型等 | 字典类型 |\n",
|
||
"| `set_backend` | | √ | 设置`collator`对应框架 | 字符串型，如`'torch'` |\n",
|
||
"| `set_ignore` | | √ | 设置`dataloader`采样`batch`时不予考虑的字段 | 字符串型，表示`field_name`  |\n",
|
||
"| `set_pad` | | √ | 设置`collator`每个字段的补零值、数据类型等 | |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "d0795b3e",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'function'>\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"train_dataloader.collate_fn\n",
|
||
"\n",
|
||
"print(type(train_dataloader.collate_fn))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5f816ef5",
|
||
"metadata": {},
|
||
"source": [
|
||
"此外，还可以**手动定义`dataloader`中的`collate_fn`**，而不是使用`fastNLP 0.8`中自带的`collator`模块\n",
|
||
"\n",
|
||
"  该函数的定义可以大致如下，需要注意的是，**定义`collate_fn`之前需要了解`batch`作为字典的格式**\n",
|
||
"\n",
|
||
"  该函数通过`collate_fn`参数传入`dataloader`，**在`batch`分发**（**而不是`batch`划分**）**时调用**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "ff8e405e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"\n",
|
||
"def collate_fn(batch):\n",
|
||
" input_ids, atten_mask, labels = [], [], []\n",
|
||
" max_length = [0] * 3\n",
|
||
" for each_item in batch:\n",
|
||
" input_ids.append(each_item['input_ids'])\n",
|
||
" max_length[0] = max(len(each_item['input_ids']), max_length[0])\n",
|
||
" atten_mask.append(each_item['token_type_ids'])\n",
|
||
" max_length[1] = max(len(each_item['token_type_ids']), max_length[1])\n",
|
||
" labels.append(each_item['attention_mask'])\n",
|
||
" max_length[2] = max(len(each_item['attention_mask']), max_length[2])\n",
|
||
"\n",
|
||
" for i in range(3):\n",
|
||
" each = (input_ids, atten_mask, labels)[i]\n",
|
||
" for item in each:\n",
|
||
" item.extend([0] * (max_length[i] - len(item)))\n",
|
||
" return {'input_ids': torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
|
||
" 'token_type_ids': torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
|
||
" 'attention_mask': torch.cat([torch.tensor(item) for item in labels], dim=0)}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "487b75fb",
|
||
"metadata": {},
|
||
"source": [
|
||
"注意：使用自定义的`collate_fn`函数，`trainer`的`collate_fn`变量也会自动调整为`function`类型"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "e916d1ac",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'fastNLP.core.dataloaders.torch_dataloader.fdl.TorchDataLoader'>\n",
|
||
"<class 'function'>\n",
|
||
"{'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0]),\n",
|
||
" 'input_ids': tensor([[ 101, 1037, 4038, 1011, 3689, 1997, 3053, 8680, 19173, 15685,\n",
|
||
" 1999, 1037, 18006, 2836, 2011, 1996, 2516, 2839, 14996, 3054,\n",
|
||
" 15509, 5325, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0],\n",
|
||
" [ 101, 1037, 2186, 1997, 9686, 17695, 18673, 14313, 1996, 15262,\n",
|
||
" 3351, 2008, 2054, 2003, 2204, 2005, 1996, 13020, 2003, 2036,\n",
|
||
" 2204, 2005, 1996, 25957, 4063, 1010, 2070, 1997, 2029, 5681,\n",
|
||
" 2572, 25581, 2021, 3904, 1997, 2029, 8310, 2000, 2172, 1997,\n",
|
||
" 1037, 2466, 1012, 102],\n",
|
||
" [ 101, 2130, 4599, 1997, 19214, 6432, 1005, 1055, 2147, 1010,\n",
|
||
" 1045, 8343, 1010, 2052, 2031, 1037, 2524, 2051, 3564, 2083,\n",
|
||
" 2023, 2028, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0],\n",
|
||
" [ 101, 1037, 13567, 26162, 5257, 1997, 3802, 7295, 9888, 1998,\n",
|
||
" 2035, 1996, 20014, 27611, 1010, 14583, 1010, 11703, 20175, 1998,\n",
|
||
" 4028, 1997, 1037, 8101, 2319, 10576, 2030, 1037, 28900, 7815,\n",
|
||
" 3850, 1012, 102, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0]]),\n",
|
||
" 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"train_dataloader = prepare_torch_dataloader(train_dataset, collate_fn=collate_fn, shuffle=True)\n",
|
||
"evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, collate_fn=collate_fn, shuffle=True)\n",
|
||
"\n",
|
||
"print(type(train_dataloader))\n",
|
||
"print(type(train_dataloader.collate_fn))\n",
|
||
"\n",
|
||
"for batch in train_dataloader:\n",
|
||
" pprint.pprint(batch, width=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0bd98365",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 2.2 fastNLP 与 datasets 的结合\n",
|
||
"\n",
|
||
"从`tutorial-1`至`tutorial-3`，我们已经完成了对`fastNLP v0.8`数据读取、预处理、加载，整个流程的介绍\n",
|
||
"\n",
|
||
"  不过在实际使用中，我们往往也会采取更为简便的方法读取数据，例如使用`huggingface`的`datasets`模块\n",
|
||
"\n",
|
||
"**使用`datasets`模块中的`load_dataset`函数**，通过指定数据集两级的名称，示例中即是**`GLUE`标准中的`SST-2`数据集**\n",
|
||
"\n",
|
||
"  即可以快速从网上下载好`SST-2`数据集读入，之后以`pandas.DataFrame`作为中介，再转化成`fastNLP.DataSet`\n",
|
||
"\n",
|
||
"  之后的步骤就和其他关于`dataset`、`databundle`、`vocabulary`、`dataloader`中介绍的相关使用相同了"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "91879c30",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "639a0ad3c63944c6abef4e8ee1f7bf7c",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
" 0%| | 0/3 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from datasets import load_dataset\n",
|
||
"\n",
|
||
"sst2data = load_dataset('glue', 'sst2')\n",
|
||
"\n",
|
||
"dataset = DataSet.from_pandas(sst2data['train'].to_pandas())"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.13"
|
||
},
|
||
"pycharm": {
|
||
"stem_cell": {
|
||
"cell_type": "raw",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": []
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|