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| import json | |
| import os | |
| import random | |
| from collections import defaultdict | |
| from PIL import Image | |
| from .vqa_dataset import VQADataset | |
| QUESTIONS = [ | |
| "What do you think of the above sentence?", | |
| "Can you confirm this statement?", | |
| "How do you interpret the given information?", | |
| "What is your opinion on this matter?", | |
| "Could you provide your perspective on this statement?", | |
| "How would you respond to the provided claim?", | |
| "What are your thoughts regarding the mentioned subject?", | |
| "Can you elaborate on this idea in English?", | |
| "Do you have any insights or feedback on this topic?", | |
| "What's your take on the given statement?", | |
| "What is your perspective on the given statement?", | |
| "How would you interpret this remark?", | |
| "Could you please provide your opinion on this?", | |
| "Can you share your understanding of the above point?", | |
| "Would you mind elaborating on this topic?", | |
| "What are your views about the given statement?", | |
| "How do you feel about the presented information?", | |
| "Could you provide your perspective on this?", | |
| "What is your opinion regarding this statement?", | |
| "Can you share your thoughts about the mentioned claim?", | |
| "How would you interpret the above comment?", | |
| "Would you mind sharing your insights on this issue?", | |
| ] | |
| class SNLIVEDataset(VQADataset): | |
| """Visual Reasoning Dataset.""" | |
| def __init__(self, tokenizer, vis_processor, vis_root, ann_paths, **kwargs): | |
| super().__init__(tokenizer, vis_processor, vis_root, ann_paths=[], **kwargs) | |
| self.annotation = self.load_annotations(ann_paths) | |
| if self.sample_image: | |
| print("randomly sample one annotation for each image") | |
| self.annotation = self.parse_annotation(self.annotation) | |
| self._add_instance_ids() | |
| def load_annotations(ann_paths): | |
| annotation = [] | |
| for ann_path in ann_paths: | |
| with open(ann_path, "r") as f: | |
| for line in f.readlines(): | |
| line = line.strip() | |
| if len(line) != 0: | |
| ann = json.loads(line) | |
| annotation.append(ann) | |
| return annotation | |
| def parse_annotation(self, annotation): | |
| image_list = defaultdict(list) | |
| for ann in annotation: | |
| image_list[ann["Flickr30K_ID"]].append(ann) | |
| annotation = [] | |
| for ann_list in image_list.values(): | |
| annotation.append(random.choice(ann_list)) | |
| return annotation | |
| def process_text(self, ann): | |
| question = ann["sentence2"] + " " + random.choice(QUESTIONS) | |
| answer = ann["gold_label"] | |
| if random.random() < self.option_prob: | |
| instruction = self.prompter(question, ["entailment", "neutral", "contradiction"]) | |
| else: | |
| instruction = self.prompter(question) | |
| return dict(instruction=instruction, answer=answer) | |
| def process_image(self, ann): | |
| image_path = os.path.join(self.vis_root, ann["Flickr30K_ID"] + ".jpg") | |
| image = Image.open(image_path).convert("RGB") | |
| image = self.vis_processor(image) | |
| return image | |