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107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
import os
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import json
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from PIL import Image
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import torch
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from torch.utils.data import Dataset
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from data.utils import pre_question
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from torchvision.datasets.utils import download_url
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class vqa_dataset(Dataset):
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def __init__(
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self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"
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):
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self.split = split
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self.transform = transform
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self.vqa_root = vqa_root
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self.vg_root = vg_root
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if split == "train":
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urls = {
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"vqa_train": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json",
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"vqa_val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json",
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"vg_qa": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json",
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}
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self.annotation = []
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for f in train_files:
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download_url(urls[f], ann_root)
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self.annotation += json.load(
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open(os.path.join(ann_root, "%s.json" % f), "r")
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)
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else:
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download_url(
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"https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json",
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ann_root,
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)
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self.annotation = json.load(
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open(os.path.join(ann_root, "vqa_test.json"), "r")
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)
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download_url(
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"https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json",
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ann_root,
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)
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self.answer_list = json.load(
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open(os.path.join(ann_root, "answer_list.json"), "r")
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)
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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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if ann["dataset"] == "vqa":
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image_path = os.path.join(self.vqa_root, ann["image"])
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elif ann["dataset"] == "vg":
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image_path = os.path.join(self.vg_root, ann["image"])
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image = Image.open(image_path).convert("RGB")
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image = self.transform(image)
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if self.split == "test":
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question = pre_question(ann["question"])
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question_id = ann["question_id"]
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return image, question, question_id
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elif self.split == "train":
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question = pre_question(ann["question"])
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if ann["dataset"] == "vqa":
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answer_weight = {}
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for answer in ann["answer"]:
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if answer in answer_weight.keys():
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answer_weight[answer] += 1 / len(ann["answer"])
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else:
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answer_weight[answer] = 1 / len(ann["answer"])
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answers = list(answer_weight.keys())
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weights = list(answer_weight.values())
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elif ann["dataset"] == "vg":
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answers = [ann["answer"]]
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weights = [0.2]
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return image, question, answers, weights
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def vqa_collate_fn(batch):
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image_list, question_list, answer_list, weight_list, n = [], [], [], [], []
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for image, question, answer, weights in batch:
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image_list.append(image)
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question_list.append(question)
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weight_list += weights
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answer_list += answer
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n.append(len(answer))
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return (
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torch.stack(image_list, dim=0),
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question_list,
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answer_list,
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torch.Tensor(weight_list),
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n,
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)
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