| see [read_pyarrow.py](https://gist.github.com/csarron/df712e53c9e0dcaad4eb6843e7a3d51c#file-read_pyarrow-py) for how to read one pyarrow file. | |
| example PyTorch dataset: | |
| ```python | |
| from torch.utils.data import Dataset | |
| class ImageCaptionArrowDataset(Dataset): | |
| def __init__( | |
| self, | |
| dataset_file, | |
| tokenizer, | |
| ): | |
| import pyarrow as pa | |
| data = [pa.ipc.open_file(pa.memory_map(f, "rb")).read_all() for f in glob.glob(dataset_file)] | |
| self.data = pa.concat_tables(data) | |
| # do other initialization, like init image preprocessing fn, | |
| def __getitem__(self, index): | |
| # item_id = self.data["id"][index].as_py() | |
| text = self.data["text"][index].as_py() # get text | |
| if isinstance(text, list): | |
| text = random.choice(text) | |
| img_bytes = self.data["image"][index].as_py() # get image bytes | |
| # do some processing with image and text, return the features | |
| # img_feat = self.image_bytes_to_tensor(img_bytes) | |
| # inputs = self.tokenizer( | |
| # text, | |
| # padding="max_length", | |
| # max_length=self.max_text_len, | |
| # truncation=True, | |
| # return_token_type_ids=True, | |
| # return_attention_mask=True, | |
| # add_special_tokens=True, | |
| # return_tensors="pt", | |
| # ) | |
| # input_ids = inputs.input_ids.squeeze(0) | |
| # attention_mask = inputs.attention_mask.squeeze(0) | |
| # return { | |
| # # "item_ids": item_id, | |
| # "text_ids": input_ids, | |
| # "input_ids": input_ids, | |
| # "text_masks": attention_mask, | |
| # "pixel_values": img_feat, | |
| # } | |
| def __len__(self): | |
| return len(self.data) | |
| ``` |