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https://github.com/yunjey/pytorch-tutorial.git
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101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
import torch
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import torchvision.transforms as transforms
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import torch.utils.data as data
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import os
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import sys
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import pickle
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import numpy as np
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import nltk
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from PIL import Image
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from vocab import Vocabulary
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sys.path.append('../../../coco/PythonAPI')
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from pycocotools.coco import COCO
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class CocoDataset(data.Dataset):
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"""COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
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def __init__(self, root, json, vocab, transform=None):
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"""Set the path for images, captions and vocabulary wrapper.
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Args:
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root: image directory.
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json: coco annotation file path.
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vocab: vocabulary wrapper.
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transform: image transformer
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"""
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self.root = root
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self.coco = COCO(json)
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self.ids = list(self.coco.anns.keys())
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self.vocab = vocab
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self.transform = transform
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def __getitem__(self, index):
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"""Returns one data pair (image and caption)."""
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coco = self.coco
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vocab = self.vocab
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ann_id = self.ids[index]
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caption = coco.anns[ann_id]['caption']
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img_id = coco.anns[ann_id]['image_id']
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path = coco.loadImgs(img_id)[0]['file_name']
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image = Image.open(os.path.join(self.root, path)).convert('RGB')
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if self.transform is not None:
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image = self.transform(image)
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# Convert caption (string) to word ids.
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tokens = nltk.tokenize.word_tokenize(str(caption).lower())
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caption = []
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caption.append(vocab('<start>'))
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caption.extend([vocab(token) for token in tokens])
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caption.append(vocab('<end>'))
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target = torch.Tensor(caption)
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return image, target
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def __len__(self):
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return len(self.ids)
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def collate_fn(data):
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"""Creates mini-batch tensors from the list of tuples (image, caption).
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Args:
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data: list of tuple (image, caption).
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- image: torch tensor of shape (3, 256, 256).
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- caption: torch tensor of shape (?); variable length.
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Returns:
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images: torch tensor of shape (batch_size, 3, 256, 256).
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targets: torch tensor of shape (batch_size, padded_length).
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lengths: list; valid length for each padded caption.
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"""
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# Sort a data list by caption length
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data.sort(key=lambda x: len(x[1]), reverse=True)
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images, captions = zip(*data)
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# Merge images (from tuple of 3D tensor to 4D tensor)
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images = torch.stack(images, 0)
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# Merge captions (from tuple of 1D tensor to 2D tensor)
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lengths = [len(cap) for cap in captions]
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targets = torch.zeros(len(captions), max(lengths)).long()
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for i, cap in enumerate(captions):
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end = lengths[i]
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targets[i, :end] = cap[:end]
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return images, targets, lengths
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def get_data_loader(root, json, vocab, transform, batch_size, shuffle, num_workers):
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"""Returns torch.utils.data.DataLoader for custom coco dataset."""
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# COCO dataset
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coco = CocoDataset(root=root,
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json=json,
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vocab = vocab,
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transform=transform)
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# Data loader for COCO dataset
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data_loader = torch.utils.data.DataLoader(dataset=coco,
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batch_size=batch_size,
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shuffle=shuffle,
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num_workers=num_workers,
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collate_fn=collate_fn)
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return data_loader |