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80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
import os
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import json
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import random
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from torch.utils.data import Dataset
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from torchvision.datasets.utils import download_url
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from PIL import Image
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from data.utils import pre_caption
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class nlvr_dataset(Dataset):
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def __init__(self, transform, image_root, ann_root, split):
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"""
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image_root (string): Root directory of images
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ann_root (string): directory to store the annotation file
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split (string): train, val or test
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"""
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urls = {
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"train": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json",
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"val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json",
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"test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json",
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}
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filenames = {
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"train": "nlvr_train.json",
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"val": "nlvr_dev.json",
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"test": "nlvr_test.json",
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}
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download_url(urls[split], ann_root)
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self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))
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self.transform = transform
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self.image_root = image_root
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def __len__(self):
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return len(self.annotation)
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def __getitem__(self, index):
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ann = self.annotation[index]
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image0_path = os.path.join(self.image_root, ann["images"][0])
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image0 = Image.open(image0_path).convert("RGB")
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image0 = self.transform(image0)
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image1_path = os.path.join(self.image_root, ann["images"][1])
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image1 = Image.open(image1_path).convert("RGB")
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image1 = self.transform(image1)
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sentence = pre_caption(ann["sentence"], 40)
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if ann["label"] == "True":
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label = 1
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else:
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label = 0
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words = sentence.split(" ")
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if "left" not in words and "right" not in words:
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if random.random() < 0.5:
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return image0, image1, sentence, label
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else:
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return image1, image0, sentence, label
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else:
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if random.random() < 0.5:
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return image0, image1, sentence, label
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else:
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new_words = []
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for word in words:
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if word == "left":
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new_words.append("right")
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elif word == "right":
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new_words.append("left")
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else:
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new_words.append(word)
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sentence = " ".join(new_words)
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return image1, image0, sentence, label
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