mmclassification/mmpretrain/datasets/vg_vqa.py

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[Feature] Support multiple multi-modal algorithms and inferencers. (#1561) * [Feat] Migrate blip caption to mmpretrain. (#50) * Migrate blip caption to mmpretrain * minor fix * support train * [Feature] Support OFA caption task. (#51) * [Feature] Support OFA caption task. * Remove duplicated files. * [Feature] Support OFA vqa task. (#58) * [Feature] Support OFA vqa task. * Fix lint. * [Feat] Add BLIP retrieval to mmpretrain. (#55) * init * minor fix for train * fix according to comments * refactor * Update Blip retrieval. (#62) * [Feature] Support OFA visual grounding task. (#59) * [Feature] Support OFA visual grounding task. * minor add TODO --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feat] Add flamingos coco caption and vqa. (#60) * first init * init flamingo coco * add vqa * minor fix * remove unnecessary modules * Update config * Use `ApplyToList`. --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature]: BLIP2 coco retrieval (#53) * [Feature]: Add blip2 retriever * [Feature]: Add blip2 all modules * [Feature]: Refine model * [Feature]: x1 * [Feature]: Runnable coco ret * [Feature]: Runnable version * [Feature]: Fix lint * [Fix]: Fix lint * [Feature]: Use 364 img size * [Feature]: Refactor blip2 * [Fix]: Fix lint * refactor files * minor fix * minor fix --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * Remove * fix blip caption inputs (#68) * [Feat] Add BLIP NLVR support. (#67) * first init * init flamingo coco * add vqa * add nlvr * refactor nlvr * minor fix * minor fix * Update dataset --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature]: BLIP2 Caption (#70) * [Feature]: Add language model * [Feature]: blip2 caption forward * [Feature]: Reproduce the results * [Feature]: Refactor caption * refine config --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feat] Migrate BLIP VQA to mmpretrain (#69) * reformat * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * refactor code --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * Update RefCOCO dataset * [Fix] fix lint * [Feature] Implement inference APIs for multi-modal tasks. (#65) * [Feature] Implement inference APIs for multi-modal tasks. * [Project] Add gradio demo. * [Improve] Update requirements * Update flamingo * Update blip * Add NLVR inferencer * Update flamingo * Update hugging face model register * Update ofa vqa * Update BLIP-vqa (#71) * Update blip-vqa docstring (#72) * Refine flamingo docstring (#73) * [Feature]: BLIP2 VQA (#61) * [Feature]: VQA forward * [Feature]: Reproduce accuracy * [Fix]: Fix lint * [Fix]: Add blank line * minor fix --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feature]: BLIP2 docstring (#74) * [Feature]: Add caption docstring * [Feature]: Add docstring to blip2 vqa * [Feature]: Add docstring to retrieval * Update BLIP-2 metafile and README (#75) * [Feature]: Add readme and docstring * Update blip2 results --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature] BLIP Visual Grounding on MMPretrain Branch (#66) * blip grounding merge with mmpretrain * remove commit * blip grounding test and inference api * refcoco dataset * refcoco dataset refine config * rebasing * gitignore * rebasing * minor edit * minor edit * Update blip-vqa docstring (#72) * rebasing * Revert "minor edit" This reverts commit 639cec757c215e654625ed0979319e60f0be9044. * blip grounding final * precommit * refine config * refine config * Update blip visual grounding --------- Co-authored-by: Yiqin Wang 王逸钦 <wyq1217@outlook.com> Co-authored-by: mzr1996 <mzr1996@163.com> * Update visual grounding metric * Update OFA docstring, README and metafiles. (#76) * [Docs] Update installation docs and gradio demo docs. (#77) * Update OFA name * Update Visual Grounding Visualizer * Integrate accelerate support * Fix imports. * Fix timm backbone * Update imports * Update README * Update circle ci * Update flamingo config * Add gradio demo README * [Feature]: Add scienceqa (#1571) * [Feature]: Add scienceqa * [Feature]: Change param name * Update docs * Update video --------- Co-authored-by: Hubert <42952108+yingfhu@users.noreply.github.com> Co-authored-by: yingfhu <yingfhu@gmail.com> Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> Co-authored-by: Yiqin Wang 王逸钦 <wyq1217@outlook.com> Co-authored-by: Rongjie Li <limo97@163.com>
2023-05-19 16:50:04 +08:00
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List
from mmengine.fileio import load
from mmpretrain.registry import DATASETS
from .base_dataset import BaseDataset
@DATASETS.register_module()
class VGVQA(BaseDataset):
"""Visual Genome VQA dataset."""
def load_data_list(self) -> List[dict]:
"""Load data list.
Compare to BaseDataset, the only difference is that coco_vqa annotation
file is already a list of data. There is no 'metainfo'.
"""
raw_data_list = load(self.ann_file)
if not isinstance(raw_data_list, list):
raise TypeError(
f'The VQA annotations loaded from annotation file '
f'should be a dict, but got {type(raw_data_list)}!')
# load and parse data_infos.
data_list = []
for raw_data_info in raw_data_list:
# parse raw data information to target format
data_info = self.parse_data_info(raw_data_info)
if isinstance(data_info, dict):
# For VQA tasks, each `data_info` looks like:
# {
# "question_id": 986769,
# "question": "How many people are there?",
# "answer": "two",
# "image": "image/1.jpg",
# "dataset": "vg"
# }
# change 'image' key to 'img_path'
# TODO: This process will be removed, after the annotation file
# is preprocess.
data_info['img_path'] = data_info['image']
del data_info['image']
if 'answer' in data_info:
# add answer_weight & answer_count, delete duplicate answer
if data_info['dataset'] == 'vqa':
answer_weight = {}
for answer in data_info['answer']:
if answer in answer_weight.keys():
answer_weight[answer] += 1 / len(
data_info['answer'])
else:
answer_weight[answer] = 1 / len(
data_info['answer'])
data_info['answer'] = list(answer_weight.keys())
data_info['answer_weight'] = list(
answer_weight.values())
data_info['answer_count'] = len(answer_weight)
elif data_info['dataset'] == 'vg':
data_info['answers'] = [data_info['answer']]
data_info['answer_weight'] = [0.2]
data_info['answer_count'] = 1
data_list.append(data_info)
else:
raise TypeError(
f'Each VQA data element loaded from annotation file '
f'should be a dict, but got {type(data_info)}!')
return data_list