mirror of https://github.com/hpcaitech/ColossalAI
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import torch
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from datasets import load_dataset
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from torch.utils.data import Dataset
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class BeansDataset(Dataset):
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def __init__(self, image_processor, tp_size=1, split='train'):
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super().__init__()
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self.image_processor = image_processor
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self.ds = load_dataset('beans')[split]
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self.label_names = self.ds.features['labels'].names
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while len(self.label_names) % tp_size != 0:
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# ensure that the number of labels is multiple of tp_size
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self.label_names.append(f"pad_label_{len(self.label_names)}")
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self.num_labels = len(self.label_names)
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self.inputs = []
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for example in self.ds:
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self.inputs.append(self.process_example(example))
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def __len__(self):
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return len(self.inputs)
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def __getitem__(self, idx):
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return self.inputs[idx]
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def process_example(self, example):
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input = self.image_processor(example['image'], return_tensors='pt')
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input['labels'] = example['labels']
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return input
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def beans_collator(batch):
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return {
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'pixel_values': torch.cat([data['pixel_values'] for data in batch], dim=0),
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'labels': torch.tensor([data['labels'] for data in batch], dtype=torch.int64)
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}
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