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