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instruction_data.py
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instruction_data.py
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import os as __os # add "__" if not want to be exported
from copy import deepcopy as __deepcopy
import itertools as __itertools
data_root = "DATAS/TRAIN_TEST"
anno_root_it = f"{data_root}/magic_jsons"
# ============== pretraining datasets=================
available_corpus = dict(
# image
# caption_coco=[
# f"{anno_root_it}/image/caption/coco/train.json",
# f"{data_root}/images/coco",
# ],
# caption_llava=[
# f"{anno_root_it}/image/caption/llava/train.json",
# f"{data_root}/images/coco",
# ],
# caption_minigpt4=[
# f"{anno_root_it}/image/caption/minigpt4/train.json",
# f"{data_root}/images/minigpt4_align/image",
# ],
# caption_paragraph_captioning=[
# f"{anno_root_it}/image/caption/paragraph_captioning/train.json",
# f"{data_root}/images/m3it/image-paragraph-captioning",
# ],
# caption_textcaps=[
# f"{anno_root_it}/image/caption/textcaps/train.json",
# f"{data_root}/images/textcaps",
# ],
# classification_imagenet=[
# f"{anno_root_it}/image/classification/imagenet/train.json",
# f"{data_root}/images/m3it/imagenet",
# ],
# classification_coco_itm=[
# f"{anno_root_it}/image/classification/coco_itm/train.json",
# f"{data_root}/images/coco",
# ],
# conversation_llava=[
# f"{anno_root_it}/image/conversation/llava/train.json",
# f"{data_root}/images/coco",
# ],
# reasoning_clevr=[
# f"{anno_root_it}/image/reasoning/clevr/train.json",
# f"{data_root}/images/m3it/clevr",
# ],
# reasoning_visual_mrc=[
# f"{anno_root_it}/image/reasoning/visual_mrc/train.json",
# f"{data_root}/images/m3it/visual_mrc",
# ],
# reasoning_llava=[
# f"{anno_root_it}/image/reasoning/llava/train.json",
# f"{data_root}/images/coco",
# ],
# vqa_vqav2=[
# f"{anno_root_it}/image/vqa/vqav2/train.json",
# f"{data_root}/images/m3it/vqav2",
# ],
# vqa_gqa=[
# f"{anno_root_it}/image/vqa/gqa/train.json",
# f"{data_root}/images/gqa/images",
# ],
# vqa_okvqa=[
# f"{anno_root_it}/image/vqa/okvqa/train.json",
# f"{data_root}/images/m3it/okvqa",
# ],
# vqa_a_okvqa=[
# f"{anno_root_it}/image/vqa/a_okvqa/train.json",
# f"{data_root}/images/m3it/a_okvqa",
# ],
# vqa_viquae=[
# f"{anno_root_it}/image/vqa/viquae/train.json",
# f"{data_root}/images/viquae_images",
# ],
# vqa_ocr_vqa=[
# f"{anno_root_it}/image/vqa/ocr_vqa/train.json",
# f"{data_root}/images/ocr_vqa/images",
# ],
# vqa_text_vqa=[
# f"{anno_root_it}/image/vqa/text_vqa/train.json",
# f"{data_root}/images/textvqa",
# ],
# vqa_st_vqa=[
# f"{anno_root_it}/image/vqa/st_vqa/train.json",
# f"{data_root}/images/m3it/st-vqa",
# ],
# vqa_docvqa=[
# f"{anno_root_it}/image/vqa/docvqa/train.json",
# f"{data_root}/images/docvqa",
# ],
# origin_llava=[
# f"{anno_root_it}/image/origin_llava/train.json",
# f"{data_root}/images",
# ],
# video
caption_textvr=[
f"{anno_root_it}/video/caption/textvr/train.json",
f"{data_root}/videos/TextVR",
"video"
],
caption_videochat=[
f"{anno_root_it}/video/caption/videochat/train.json",
f"{data_root}/videos/webvid_10m",
"video"
], # not ready, need to read from hdfs
caption_webvid=[
f"{anno_root_it}/video/caption/webvid/train.json",
f"{data_root}/videos/webvid_10m",
"video"
], # not ready, need to read from hdfs
caption_youcook2=[
f"{anno_root_it}/video/caption/youcook2/train.json",
f"{data_root}/videos/YouCook2/split_videos",
"video"
],
classification_k710=[
f"{anno_root_it}/video/classification/k710/train.json",
f"{data_root}/videos/kinetics",
"video"
],
classification_ssv2=[
f"{anno_root_it}/video/classification/ssv2/train.json",
f"{data_root}/videos/20bn-something-something-v2",
"video"
],
conversation_videochat1=[
f"{anno_root_it}/video/conversation/videochat1/train.json",
f"{data_root}/videos/webvid_10m",
"video"
],# not ready, need to read from hdfs
conversation_videochat2=[
f"{anno_root_it}/video/conversation/videochat2/train.json",
f"{data_root}/videos/InternVid-10M-FLT/videos",
"video"
],
conversation_videochatgpt=[
f"{anno_root_it}/video/conversation/videochatgpt/train.json",
f"{data_root}/videos/AVideo_ChatGPT",
"video"
],
reasoning_next_qa=[
f"{anno_root_it}/video/reasoning/next_qa/train.json",
f"{data_root}/videos/NExTVideo",
"video"
],
reasoning_clevrer_qa=[
f"{anno_root_it}/video/reasoning/clevrer_qa/train.json",
f"{data_root}/videos/CLEVRER",
"video"
],
reasoning_clevrer_mc=[
f"{anno_root_it}/video/reasoning/clevrer_mc/train.json",
f"{data_root}/videos/CLEVRER",
"video"
],
vqa_ego_qa=[
f"{anno_root_it}/video/vqa/ego_qa/train.json",
f"{data_root}/videos/ego4d_data/split_videos",
"video"
],
vqa_tgif_frame_qa=[
f"{anno_root_it}/video/vqa/tgif_frame_qa/train.json",
f"{data_root}/videos/tgif",
"video"
],
vqa_tgif_transition_qa=[
f"{anno_root_it}/video/vqa/tgif_transition_qa/train.json",
f"{data_root}/videos/tgif",
"video"
],
vqa_webvid_qa=[
f"{anno_root_it}/video/vqa/webvid_qa/train.json",
f"{data_root}/videos/webvid_10m",
"video"
],# not ready, need to read from hdfs
origin_videochatgpt=[
f"{anno_root_it}/video/origin_videochatgpt/train.json",
f"{data_root}/videos/Video_ChatGPT",
"video"
],
)
available_corpus["videochat2_instruction_full"] = [
available_corpus["caption_coco"],
available_corpus["caption_llava"],
available_corpus["caption_minigpt4"],
available_corpus["caption_paragraph_captioning"],
available_corpus["caption_textcaps"],
available_corpus["classification_imagenet"],
available_corpus["classification_coco_itm"],
available_corpus["conversation_llava"],
available_corpus["reasoning_clevr"],
available_corpus["reasoning_visual_mrc"],
available_corpus["reasoning_llava"],
available_corpus["vqa_vqav2"],
available_corpus["vqa_gqa"],
available_corpus["vqa_okvqa"],
available_corpus["vqa_a_okvqa"],
available_corpus["vqa_viquae"],
available_corpus["vqa_ocr_vqa"],
available_corpus["vqa_text_vqa"],
available_corpus["vqa_st_vqa"],
available_corpus["vqa_docvqa"],
available_corpus["caption_textvr"],
available_corpus["caption_youcook2"],
available_corpus["classification_k710"],
available_corpus["classification_ssv2"],
available_corpus["conversation_videochat2"],
available_corpus["conversation_videochatgpt"],
available_corpus["reasoning_next_qa"],
available_corpus["reasoning_clevrer_qa"],
available_corpus["reasoning_clevrer_mc"],
available_corpus["vqa_ego_qa"],
available_corpus["vqa_tgif_frame_qa"],
available_corpus["vqa_tgif_transition_qa"],
available_corpus["conversation_videochat1"],
available_corpus["vqa_webvid_qa"],
available_corpus["caption_videochat"],
available_corpus["caption_webvid"],
]
available_corpus["videochat2_video"] = [
available_corpus["caption_textvr"],
available_corpus["caption_youcook2"],
available_corpus["classification_k710"],
available_corpus["classification_ssv2"],
available_corpus["conversation_videochat2"],
available_corpus["conversation_videochatgpt"],
available_corpus["reasoning_next_qa"],
available_corpus["reasoning_clevrer_qa"],
available_corpus["reasoning_clevrer_mc"],
available_corpus["vqa_ego_qa"],
available_corpus["vqa_tgif_frame_qa"],
available_corpus["vqa_tgif_transition_qa"],
available_corpus["conversation_videochat1"],
available_corpus["vqa_webvid_qa"],
available_corpus["caption_videochat"],
available_corpus["caption_webvid"],
]
# ============== for debug=================
available_corpus["videochat2_instruction_debug"] = [
# available_corpus["caption_minigpt4"],
available_corpus["caption_textvr"],
# available_corpus["vqa_ego_qa"],
# available_corpus["classification_k710"],
# available_corpus["reasoning_next_qa"],
# available_corpus["caption_textvr"],
# available_corpus["caption_youcook2"],
# available_corpus["caption_textcaps"], # realistic caption foucsing in real life text
# available_corpus["caption_textvr"], # good realistic captioning, also focusing on text
]
if __name__ == '__main__':
print(len(list(
__itertools.chain(
available_corpus['conversation_data'],
available_corpus['reasoning_data'],
available_corpus['conversation_videochat2'],
available_corpus['caption_data'],
available_corpus['classification_data'],
)
)))
print(len(available_corpus['videochat2_instruction_full']))