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data_preprocess.py
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data_preprocess.py
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import json
from os.path import join
from tqdm import tqdm
import transformers
from model.blip2 import Blip2Processor
from model.blip2 import Blip2Config
from model.instructblip import InstructBlipProcessor
from PIL import Image
import torch
import numpy as np
from concurrent.futures import ThreadPoolExecutor
import h5py
import fsspec
import copy
import pickle as pk
import glob
from os.path import join
import threading
from datasets.arrow_writer import ArrowWriter
import os
from moviepy.editor import VideoFileClip
from PIL import Image
from random import sample
import random
from tqdm import tqdm
import json
from random import sample
import os
def getsample_vcr(path,types,split_num = -1,store_path =''):
with open(path,'r') as f:
js = json.load(f)
result =[]
for each in tqdm(js['instances']):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text']
if len(each['input']) ==3:
input_image = [each['input'][1]['image']] +each['input'][2]['bbox_list']
else:
input_image = each['input'][1]['image']
if len(input_image) > 10:
continue
output_text = each['output'][0]['answer']
output_image = ""
js_dict ={'input_text':input_text,'input_image':input_image,'output_text':output_text,'output_image':output_image}
result.append(js_dict)
if split_num == -1:
with open(os.path.join(store_path,f'vcr-promot-0-{types}_nature.json'),'w') as f:
for dic in result:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
else:
with open(os.path.join(store_path,f'vcr-promot-0-{types}_sample_{split_num}_nature.json'),'w') as f:
for dic in sample(result,split_num):
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def getsample_llava(path,types,split_num = -1,store_path =''):
with open(path,'r') as f:
js = json.load(f)
result =[]
for each in tqdm(js['instances']):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text']
input_image = each['input'][1]['image']
output_text = each['output'][0]['answer']
output_image = ""
js_dict ={'input_text':input_text,'input_image':input_image,'output_text':output_text,'output_image':output_image}
result.append(js_dict)
if split_num == -1:
with open(os.path.join(store_path,f'llava-promot-0-{types}_nature.json'),'w') as f:
for dic in result:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
else:
with open(os.path.join(store_path,f'llava-promot-0-{types}_sample_{split_num}_nature.json'),'w') as f:
for dic in sample(result,split_num):
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def qa_form_fewshot(instances,text,image,sample_num,nature,output_answer='answer'):
poss_instances= sample(instances,random.randint(0,sample_num))
images=[]
texts = []
i=0
for i,each in enumerate(poss_instances):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text'].replace('<image0>',f'<image{i}>').replace('image 0',f'image {i}')
input_image = inputs[1]['image']
output_text = outputs[0][output_answer]
texts.append(input_text+" "+output_text+"\n")
images.append(input_image)
text = text.replace('<image0>',f'<image{i+1}>').replace('image 0',f'image {i+1}') if i>0 else text
texts.append(text)
images.append(image)
return "\n".join(texts) , images
def getsample_qa(path,types,split_num = -1,sample_icl=5,nature = False,dataset_name ='vqa',store_path=""):
with open(path,'r') as f:
js = json.load(f)
result =[]
if split_num == -1:
instances = js['instances']
else:
instances = sample(js['instances'],split_num)
for each in tqdm(instances):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text']
if len(each['input']) ==3:
input_image = [each['input'][1]['image']] +each['input'][2]['bbox_list']
else:
input_image = each['input'][1]['image']
if len(input_image) > 10 and isinstance(input_image,list):
continue
output_text = each['output'][0]['answer']
if output_text =="":
continue
output_image = ""
input_text,input_image = qa_form_fewshot(instances,input_text,input_image,sample_icl,nature)
js_dict ={'input_text':input_text,'input_image':input_image,'output_text':output_text,'output_image':output_image}
result.append(js_dict)
if nature:
if split_num == -1:
filename = f'{dataset_name}-promot-0-{types}_nature.json'
else:
filename = f'{dataset_name}-promot-0-{types}_sample_{split_num}_nature.json'
else:
if split_num == -1:
filename = f'{dataset_name}-promot-0-{types}.json'
else:
filename = f'{dataset_name}-promot-0-{types}_sample_{split_num}.json'
with open(os.path.join(store_path,filename),'w') as f:
for dic in result:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def getsample_video(path,types,split_num = -1,dataset_name="ivqa",store_path =''):
with open(path,'r') as f:
js = json.load(f)
result =[]
for each in tqdm(js['instances']):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text']
input_image = each['input'][1]['image']
output_text = each['output'][0]['answer']
output_image = ""
js_dict ={'input_text':input_text,'input_image':input_image,'output_text':output_text,'output_image':output_image}
result.append(js_dict)
if split_num == -1:
with open(os.path.join(store_path,f'{dataset_name}-promot-0-{types}_nature.json'),'w') as f:
for dic in result:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
else:
with open(os.path.join(store_path,f'{dataset_name}-promot-0-{types}_sample_{split_num}_nature.json'),'w') as f:
for dic in sample(result,split_num):
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def caption_form_fewshot(instances,text,image,sample_num,nature,output_answer='answer'):
poss_instances= sample(instances,random.randint(0,sample_num))
images=[]
texts = []
i=0
for i,each in enumerate(poss_instances):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text'].replace('<image0>',f'<image{i}>').replace('image 0',f'image {i}')
input_image = inputs[1]['image']
output_text = outputs[0][output_answer].split("##")[0]
texts.append(input_text+" "+output_text+"\n")
images.append(input_image)
text = text.replace('<image0>',f'<image{i+1}>').replace('image 0',f'image {i+1}') if i>0 else text
texts.append(text)
images.append(image)
return "\n".join(texts) , images
def getsample_caption(path,types,split_num = -1,sample_icl=5,nature = False,dataset_name ='vqa',store_path=""):
with open(path,'r') as f:
js = json.load(f)
result =[]
if split_num == -1:
instances = js['instances']
else:
instances = sample(js['instances'],split_num)
for each in tqdm(instances):
inputs= each['input']
outputs = each['output']
input_text = each['input'][0]['text']
if len(each['input']) ==3:
input_image = [each['input'][1]['image']] +each['input'][2]['bbox_list']
else:
input_image = each['input'][1]['image']
if len(input_image) > 10 and isinstance(input_image,list):
continue
output_text = each['output'][0]['caption']
if output_text =="":
continue
output_image = ""
input_text,input_image = caption_form_fewshot(instances,input_text,input_image,sample_icl,nature,'caption')
js_dict ={'input_text':input_text,'input_image':input_image,'output_text':output_text,'output_image':output_image}
result.append(js_dict)
if nature:
if split_num == -1:
filename = f'{dataset_name}-promot-0-{types}_nature.json'
else:
filename = f'{dataset_name}-promot-0-{types}_sample_{split_num}_nature.json'
else:
if split_num == -1:
filename = f'{dataset_name}-promot-0-{types}.json'
else:
filename = f'{dataset_name}-promot-0-{types}_sample_{split_num}.json'
with open(os.path.join(store_path,filename),'w') as f:
for dic in result:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def generate_jsonl_data_from_instances(store_path):
all_dataset = ['llava', 'textvqa', 'diffusiondb','msrvttqa','msrvtt', 'wikiart','nocaps', 'miniimage','vqa', 'vcr', 'stvqa', 'okvqa', 'nlvr2' ,'gqa', 'refcoco', 'coco' ,'flickr']
qa_dataset = ['vqa', 'stvqa', 'okvqa', 'nlvr2' ,'gqa','textvqa','wikiart','iconqa']
caption_dataset = [ 'refcoco', 'coco' ,'flickr','diffusiondb','miniimage','nocaps']
video_dataset=['msrvttqa','msrvtt']
if not os.path.exists(store_path):
os.makedirs(store_path)
data = {
'llava':{'train':'Vision-PromptSource/tasks/task00001-visual_dialog-llava-prompt-0-subset-train.json'} ,
'textvqa':{'train':'Vision-PromptSource/tasks/task00002-visual_question_answering-textvqa-prompt-0-subset-train.json'} ,
'diffusiondb':{'train':'Vision-PromptSource/tasks/task00006-image_captioning-diffusiondb-prompt-0-subset-train.json'} ,
'wikiart':{'train':'Vision-PromptSource/tasks/task00007-image_generation-wikiart-prompt-0-subset-train.json',
'val':'Vision-PromptSource/tasks/task00008-image_generation-wikiart-prompt-0-subset-val.json'} ,
'nocaps':{'val':'Vision-PromptSource/tasks/task00026-image_captioning-nocaps-prompt-0-subset-val.json'} ,
'miniimage':{'train':'Vision-PromptSource/tasks/task00027-image_classification-miniimage-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00028-image_classification-miniimage-prompt-0-subset-val.json',
'test': 'Vision-PromptSource/tasks/task00029-image_classification-miniimage-prompt-0-subset-test.json'} ,
'vqa': {'train': 'Vision-PromptSource/tasks/task00009-visual_question_answering-vqa-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00010-visual_question_answering-vqa-prompt-0-subset-val.json',
'test': 'Vision-PromptSource/tasks/task00011-visual_question_answering-vqa-prompt-0-subset-test.json'},
'okvqa': {'train': 'Vision-PromptSource/tasks/task00018-visual_question_answering-okvqa-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00019-visual_question_answering-okvqa-prompt-0-subset-val.json'},
'nlvr2': {'train': 'Vision-PromptSource/tasks/task00012-visual_question_answering-nlvr2-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00013-visual_question_answering-nlvr2-prompt-0-subset-val.json',
'test': 'Vision-PromptSource/tasks/task00014-visual_question_answering-nlvr2-prompt-0-subset-test.json'},
'vcr': {'train': 'Vision-PromptSource/tasks/task00004-visual_question_answering-vcr-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00005-visual_question_answering-vcr-prompt-0-subset-val.json'},
'refcoco': {'train': 'Vision-PromptSource/tasks/task00022-phrase_grounding-refcoco-prompt-0-subset-train.json'},
'flickr': {'train': 'Vision-PromptSource/tasks/task00023-image_captioning-flickr-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00024-image_captioning-flickr-prompt-0-subset-val.json',
'test': 'Vision-PromptSource/tasks/task00025-image_captioning-flickr-prompt-0-subset-test.json'},
'coco': {'train': 'Vision-PromptSource/tasks/task00020-image_captioning-coco-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00021-image_captioning-coco-prompt-0-subset-val.json'},
'gqa': {'train': 'Vision-PromptSource/tasks/task00015-visual_question_answering-gqa-prompt-0-subset-train.json',
'val': 'Vision-PromptSource/tasks/task00016-visual_question_answering-gqa-prompt-0-subset-val.json',
'test': 'Vision-PromptSource/tasks/task00017-visual_question_answering-gqa-prompt-0-subset-test.json'},
'stvqa':{'train':'Vision-PromptSource/tasks/task00003-visual_question_answering-stvqa-prompt-0-subset-train.json'},
'msrvttqa':{'train':'Vision-PromptSource/tasks/task00030-video_question_answering-msrvttqa-prompt-0-subset-train.json',
'val':'Vision-PromptSource/tasks/task00031-video_question_answering-msrvttqa-prompt-0-subset-val.json',
'test':'Vision-PromptSource/tasks/task00032-video_question_answering-msrvttqa-prompt-0-subset-test.json'},
'msrvtt':{'train':'Vision-PromptSource/tasks/task00033-video_question_answering-msrvtt-prompt-0-subset-train.json',
'val':'Vision-PromptSource/tasks/task00034-video_question_answering-msrvtt-prompt-0-subset-val.json',
'test':'Vision-PromptSource/tasks/task00035-video_question_answering-msrvtt-prompt-0-subset-test.json'},
'iconqa': {'train':"Vision-PromptSource/tasks/task00044-visual_question_answering-iconqa-prompt-0-subset-train.json",
'val':"Vision-PromptSource/tasks/task00045-visual_question_answering-iconqa-prompt-0-subset-val.json",
'test':"Vision-PromptSource/tasks/task00046-visual_question_answering-iconqa-prompt-0-subset-test.json",}
}
for datasetname in qa_dataset:
print(datasetname)
files = data[datasetname]
for each in files:
getsample_qa(files[each],each,nature=True,sample_icl=sample_number,dataset_name=datasetname,store_path = store_path)
for datasetname in caption_dataset:
print(datasetname)
files = data[datasetname]
for each in files:
getsample_caption(files[each],each,nature=True,sample_icl=sample_number,dataset_name=datasetname,store_path = store_path)
print('vcr')
files = data['vcr']
for each in files:
getsample_vcr(files[each],each,store_path = store_path)
files = data['llava']
for each in files:
print('llava')
getsample_llava(files[each],each,store_path = store_path)
for datasetname in video_dataset:
print(datasetname)
files = data[datasetname]
for each in files:
getsample_video(files[each],each,dataset_name=datasetname,store_path = store_path)
# datasetname ='flickr'
# files = data[datasetname]
# for each in files:
# getsample_caption(files[each],each,sample_icl=0,nature=True,dataset_name=datasetname,store_path = store_path)
# data_json={
# 'coco_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/coco-promot-0-train_nature.json",
# 'coco_val' :"Vision-PromptSource/prompt_data_new_6_12_zero-shot/coco-promot-0-val_nature.json",
# 'coco_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/coco-promot-0-val_nature.json",
# 'flickr_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/flickr-promot-0-train_nature.json",
# 'flickr_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/flickr-promot-0-val_nature.json",
# 'flickr_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/flickr-promot-0-test_nature.json",
# 'gqa_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/gqa-promot-0-train_nature.json",
# 'gqa_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/gqa-promot-0-val_nature.json",
# 'gqa_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/gqa-promot-0-test_nature.json",
# 'okvqa_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/okvqa-promot-0-train_nature.json",
# 'okvqa_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/okvqa-promot-0-val_nature.json",
# 'okvqa_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/okvqa-promot-0-val_nature.json",
# 'nlvr2_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/nlvr2-promot-0-train_nature.json",
# 'nlvr2_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/nlvr2-promot-0-val_nature.json",
# 'nlvr2_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/nlvr2-promot-0-test_nature.json",
# 'vqa_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/vqa-promot-0-train_nature.json",
# 'vqa_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/vqa-promot-0-val_nature.json",
# 'vqa_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/vqa-promot-0-test_nature.json",
# 'miniimage_train':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/miniimage-promot-0-train_nature.json' ,
# 'miniimage_val':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/miniimage-promot-0-val_nature.json' ,
# 'miniimage_test':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/miniimage-promot-0-test_nature.json' ,
# 'wikiart_train':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/wikiart-promot-0-train_nature.json' ,
# 'wikiart_val':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/wikiart-promot-0-val_nature.json' ,
# 'wikiart_test':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/wikiart-promot-0-val_nature.json' ,
# 'stvqa_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/stvqa-promot-0-train_nature.json",
# 'stvqa_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/stvqa-promot-0-train_nature.json",
# 'stvqa_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/stvqa-promot-0-train_nature.json",
# 'refcoco_train' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/refcoco-promot-0-train_nature.json",
# 'refcoco_val' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/refcoco-promot-0-train_nature.json",
# 'refcoco_test' : "Vision-PromptSource/prompt_data_new_6_12_zero-shot/refcoco-promot-0-train_nature.json",
# 'textvqa_train':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/textvqa-promot-0-train_nature.json' ,
# 'textvqa_val':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/textvqa-promot-0-train_nature.json' ,
# 'textvqa_test':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/textvqa-promot-0-train_nature.json' ,
# 'diffusiondb_train':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/diffusiondb-promot-0-train_nature.json' ,
# 'diffusiondb_val':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/diffusiondb-promot-0-train_nature.json' ,
# 'diffusiondb_test':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/diffusiondb-promot-0-train_nature.json' ,
# 'nocaps_train':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/nocaps-promot-0-val_nature.json' ,
# 'nocaps_val':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/nocaps-promot-0-val_nature.json' ,
# 'nocaps_test':'Vision-PromptSource/prompt_data_new_6_12_zero-shot/nocaps-promot-0-val_nature.json' ,
# }
# data_json={
# 'coco_train' : "Vision-PromptSource/prompt_data_new_6_5/coco-promot-0-train_nature.json",
# 'coco_val' :"Vision-PromptSource/prompt_data_new_6_5/coco-promot-0-val_nature.json",
# 'coco_test' : "Vision-PromptSource/prompt_data_new_6_5/coco-promot-0-val_nature.json",
# 'flickr_train' : "Vision-PromptSource/prompt_data_new_6_5/flickr-promot-0-train_nature.json",
# 'flickr_val' : "Vision-PromptSource/prompt_data_new_6_5/flickr-promot-0-val_nature.json",
# 'flickr_test' : "Vision-PromptSource/prompt_data_new_6_5/flickr-promot-0-test_nature.json",
# 'gqa_train' : "Vision-PromptSource/prompt_data_new_6_5/gqa-promot-0-train_nature.json",
# 'gqa_val' : "Vision-PromptSource/prompt_data_new_6_5/gqa-promot-0-val_nature.json",
# 'gqa_test' : "Vision-PromptSource/prompt_data_new_6_5/gqa-promot-0-test_nature.json",
# 'okvqa_train' : "Vision-PromptSource/prompt_data_new_6_5/okvqa-promot-0-train_nature.json",
# 'okvqa_val' : "Vision-PromptSource/prompt_data_new_6_5/okvqa-promot-0-val_nature.json",
# 'okvqa_test' : "Vision-PromptSource/prompt_data_new_6_5/okvqa-promot-0-val_nature.json",
# 'nlvr2_train' : "Vision-PromptSource/prompt_data_new_6_5/nlvr2-promot-0-train_nature.json",
# 'nlvr2_val' : "Vision-PromptSource/prompt_data_new_6_5/nlvr2-promot-0-val_nature.json",
# 'nlvr2_test' : "Vision-PromptSource/prompt_data_new_6_5/nlvr2-promot-0-test_nature.json",
# 'vqa_train' : "Vision-PromptSource/prompt_data_new_6_5/vqa-promot-0-train_nature.json",
# 'vqa_val' : "Vision-PromptSource/prompt_data_new_6_5/vqa-promot-0-val_nature.json",
# 'vqa_test' : "Vision-PromptSource/prompt_data_new_6_5/vqa-promot-0-test_nature.json",
# 'vcr_train' : "Vision-PromptSource/prompt_data_new_6_5/vcr-promot-0-train_nature.json",
# 'vcr_val' : "Vision-PromptSource/prompt_data_new_6_5/vcr-promot-0-val_nature.json",
# 'vcr_test' : "Vision-PromptSource/prompt_data_new_6_5/vcr-promot-0-val_nature.json",
# 'miniimage_train':'Vision-PromptSource/prompt_data_new_6_5/miniimage-promot-0-train_nature.json' ,
# 'miniimage_val':'Vision-PromptSource/prompt_data_new_6_5/miniimage-promot-0-val_nature.json' ,
# 'miniimage_test':'Vision-PromptSource/prompt_data_new_6_5/miniimage-promot-0-test_nature.json' ,
# 'wikiart_train':'Vision-PromptSource/prompt_data_new_6_5/wikiart-promot-0-train_nature.json' ,
# 'wikiart_val':'Vision-PromptSource/prompt_data_new_6_5/wikiart-promot-0-val_nature.json' ,
# 'wikiart_test':'Vision-PromptSource/prompt_data_new_6_5/wikiart-promot-0-val_nature.json' ,
# 'stvqa_train' : "Vision-PromptSource/prompt_data_new_6_5/stvqa-promot-0-train_nature.json",
# 'stvqa_val' : "Vision-PromptSource/prompt_data_new_6_5/stvqa-promot-0-train_nature.json",
# 'stvqa_test' : "Vision-PromptSource/prompt_data_new_6_5/stvqa-promot-0-train_nature.json",
# 'refcoco_train' : "Vision-PromptSource/prompt_data_new_6_5/refcoco-promot-0-train_nature.json",
# 'refcoco_val' : "Vision-PromptSource/prompt_data_new_6_5/refcoco-promot-0-train_nature.json",
# 'refcoco_test' : "Vision-PromptSource/prompt_data_new_6_5/refcoco-promot-0-train_nature.json",
# 'llava_train':'Vision-PromptSource/prompt_data_new_6_5/llava-promot-0-train_nature.json' ,
# 'llava_val':'Vision-PromptSource/prompt_data_new_6_5/llava-promot-0-train_nature.json' ,
# 'llava_test':'Vision-PromptSource/prompt_data_new_6_5/llava-promot-0-train_nature.json' ,
# 'textvqa_train':'Vision-PromptSource/prompt_data_new_6_5/textvqa-promot-0-train_nature.json' ,
# 'textvqa_val':'Vision-PromptSource/prompt_data_new_6_5/textvqa-promot-0-train_nature.json' ,
# 'textvqa_test':'Vision-PromptSource/prompt_data_new_6_5/textvqa-promot-0-train_nature.json' ,
# 'diffusiondb_train':'Vision-PromptSource/prompt_data_new_6_5/diffusiondb-promot-0-train_nature.json' ,
# 'diffusiondb_val':'Vision-PromptSource/prompt_data_new_6_5/diffusiondb-promot-0-train_nature.json' ,
# 'diffusiondb_test':'Vision-PromptSource/prompt_data_new_6_5/diffusiondb-promot-0-train_nature.json' ,
# 'nocaps_train':'Vision-PromptSource/prompt_data_new_6_5/nocaps-promot-0-val_nature.json' ,
# 'nocaps_val':'Vision-PromptSource/prompt_data_new_6_5/nocaps-promot-0-val_nature.json' ,
# 'nocaps_test':'Vision-PromptSource/prompt_data_new_6_5/nocaps-promot-0-val_nature.json' ,
# 'msrvtt_train':'Vision-PromptSource/prompt_data_new_6_5/msrvtt-promot-0-train_nature.json',
# 'msrvtt_val':'Vision-PromptSource/prompt_data_new_6_5/msrvtt-promot-0-val_nature.json',
# 'msrvtt_test':'Vision-PromptSource/prompt_data_new_6_5/msrvtt-promot-0-test_nature.json',
# 'msrvttqa_train':'Vision-PromptSource/prompt_data_new_6_5/msrvttqa-promot-0-train_nature.json',
# 'msrvttqa_val':'Vision-PromptSource/prompt_data_new_6_5/msrvttqa-promot-0-val_nature.json',
# 'msrvttqa_test':'Vision-PromptSource/prompt_data_new_6_5/msrvttqa-promot-0-test_nature.json',
# }
data_json={
'coco_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/coco-promot-0-train_nature.json",
'coco_val' :"Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/coco-promot-0-val_nature.json",
'coco_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/coco-promot-0-val_nature.json",
'flickr_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/flickr-promot-0-train_nature.json",
'flickr_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/flickr-promot-0-val_nature.json",
'flickr_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/flickr-promot-0-test_nature.json",
'gqa_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/gqa-promot-0-train_nature.json",
'gqa_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/gqa-promot-0-val_nature.json",
'gqa_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/gqa-promot-0-test_nature.json",
'okvqa_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/okvqa-promot-0-train_nature.json",
'okvqa_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/okvqa-promot-0-val_nature.json",
'okvqa_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/okvqa-promot-0-val_nature.json",
'nlvr2_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nlvr2-promot-0-train_nature.json",
'nlvr2_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nlvr2-promot-0-val_nature.json",
'nlvr2_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nlvr2-promot-0-test_nature.json",
'vqa_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vqa-promot-0-train_nature.json",
'vqa_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vqa-promot-0-val_nature.json",
'vqa_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vqa-promot-0-test_nature.json",
'vcr_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vcr-promot-0-train_nature.json",
'vcr_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vcr-promot-0-val_nature.json",
'vcr_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/vcr-promot-0-val_nature.json",
'miniimage_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/miniimage-promot-0-train_nature.json' ,
'miniimage_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/miniimage-promot-0-val_nature.json' ,
'miniimage_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/miniimage-promot-0-test_nature.json' ,
'wikiart_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/wikiart-promot-0-train_nature.json' ,
'wikiart_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/wikiart-promot-0-val_nature.json' ,
'wikiart_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/wikiart-promot-0-val_nature.json' ,
'stvqa_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/stvqa-promot-0-train_nature.json",
'stvqa_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/stvqa-promot-0-train_nature.json",
'stvqa_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/stvqa-promot-0-train_nature.json",
'refcoco_train' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/refcoco-promot-0-train_nature.json",
'refcoco_val' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/refcoco-promot-0-train_nature.json",
'refcoco_test' : "Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/refcoco-promot-0-train_nature.json",
'llava_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/llava-promot-0-train_nature.json' ,
'llava_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/llava-promot-0-train_nature.json' ,
'llava_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/llava-promot-0-train_nature.json' ,
'textvqa_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/textvqa-promot-0-train_nature.json' ,
'textvqa_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/textvqa-promot-0-train_nature.json' ,
'textvqa_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/textvqa-promot-0-train_nature.json' ,
'diffusiondb_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/diffusiondb-promot-0-train_nature.json' ,
'diffusiondb_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/diffusiondb-promot-0-train_nature.json' ,
'diffusiondb_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/diffusiondb-promot-0-train_nature.json' ,
'nocaps_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nocaps-promot-0-val_nature.json' ,
'nocaps_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nocaps-promot-0-val_nature.json' ,
'nocaps_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/nocaps-promot-0-val_nature.json' ,
'msrvtt_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvtt-promot-0-train_nature.json',
'msrvtt_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvtt-promot-0-val_nature.json',
'msrvtt_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvtt-promot-0-test_nature.json',
'msrvttqa_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvttqa-promot-0-train_nature.json',
'msrvttqa_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvttqa-promot-0-val_nature.json',
'msrvttqa_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/msrvttqa-promot-0-test_nature.json',
'iconqa_train':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/iconqa-promot-0-train_nature.json',
'iconqa_val':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/iconqa-promot-0-val_nature.json',
'iconqa_test':'Vision-PromptSource/prompt_data_8_11_vicuna_fewshot/iconqa-promot-0-test_nature.json',
}
data_size= {
"vqa": {
"train": 70000,
"test": 1000,
"val": 1000
},
"gqa": {
"train": 70000,
"test": 1000,
"val": 1000
},
"okvqa": {
"train": 9000,
"test": 2000,
"val": 2000
},
"nlvr2": {
"train": 20000,
"test": 500,
"val": 500
},
"coco": {
"train": 60000,
"test": 1000,
"val": 1000
},
"flickr": {
"train": 40000,
"test": 1000,
"val": 1000
},
"vcr": {
"train": 118000,
"test": 2000,
"val": 2000
},
"stvqa": {
"train": 30000,
"test": 0,
"val": 0
},
"refcoco": {
"train": 60000,
"test": 0,
"val": 0
},
"miniimage": {
"train": 15000,
"test": 500,
"val": 500
},
"wikiart": {
"train": 8000,
"test": 500,
"val": 500
},
"llava": {
"train": 150000,
"test": 0,
"val": 0
},
"textvqa": {
"train": 25000,
"test": 0,
"val": 0
},
"diffusiondb": {
"train": 15000,
"test": 0,
"val": 0
},
"nocaps": {
"train": 0,
"test": 2500,
"val": 2500
},
"msrvtt": {
"train": 25000,
"test": 1000,
"val": 1000
},
"msrvttqa": {
"train": 30000,
"test": 1500,
"val": 1500
},
"msrvttqa": {
"train": 50000,
"test": 1500,
"val": 1500
},
"iconqa": {
"train": 15000,
"test": 1500,
"val": 1500
},
}
def get_json_file(file_path):
js =[]
with open(file_path,'r')as f:
for line in f.readlines():
js.append(json.loads(line))
return js
def generate_new_json(data_json,data_size,file_name):
json_train=[]
json_test=[]
json_val=[]
if not os.path.exists(f'Vision-PromptSource/{save_dir_name}'):
os.makedirs(f'Vision-PromptSource/{save_dir_name}')
if not os.path.exists(f'Vision-PromptSource/{save_dir_name}/train'):
os.makedirs(f'Vision-PromptSource/{save_dir_name}/train')
if not os.path.exists(f'Vision-PromptSource/{save_dir_name}/val'):
os.makedirs(f'Vision-PromptSource/{save_dir_name}/val')
if not os.path.exists(f'Vision-PromptSource/{save_dir_name}/test'):
os.makedirs(f'Vision-PromptSource/{save_dir_name}/test')
for each in tqdm(data_size):
js_train = sample(get_json_file(data_json[f'{each}_train']),data_size[each]['train']) if data_size[each]['train'] != -1 else get_json_file(data_json[f'{each}_train'])
js_test = sample(get_json_file(data_json[f'{each}_test']),data_size[each]['test']) if data_size[each]['test'] != -1 else get_json_file(data_json[f'{each}_test'])
js_val = sample(get_json_file(data_json[f'{each}_val']),data_size[each]['val']) if data_size[each]['val'] != -1 else get_json_file(data_json[f'{each}_val'])
json_train.extend(js_train)
json_test.extend(js_test)
json_val.extend(js_val)
# random.shuffle(json_train)
# random.shuffle(json_test)
# random.shuffle(json_val)
if data_size[each]['train'] !=0:
sample_num = data_size[each]['train']
with open(f'Vision-PromptSource/{save_dir_name}/train/{file_name}-{each}-sample_{sample_num}-train.json','w') as f:
for dic in js_train:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
if data_size[each]['test'] !=0:
sample_num = data_size[each]['test']
with open(f'Vision-PromptSource/{save_dir_name}/test/{file_name}-{each}-sample_{sample_num}-test.json','w') as f:
for dic in js_test:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
if data_size[each]['val'] !=0:
sample_num = data_size[each]['val']
with open(f'Vision-PromptSource/{save_dir_name}/val/{file_name}-{each}-sample_{sample_num}-val.json','w') as f:
for dic in js_val:
f.write(json.dumps(dic, ensure_ascii=False)+"\n")
# with open(f'Vision-PromptSource/{save_dir_name}/{file_name}-train.json','w') as f:
# for dic in json_train:
# f.write(json.dumps(dic, ensure_ascii=False)+"\n")
# with open(f'Vision-PromptSource/{save_dir_name}/{file_name}-test.json','w') as f:
# for dic in json_test:
# f.write(json.dumps(dic, ensure_ascii=False)+"\n")
# with open(f'Vision-PromptSource/{save_dir_name}/{file_name}-val.json','w') as f:
# for dic in json_val:
# f.write(json.dumps(dic, ensure_ascii=False)+"\n")
def save_pickle_img(path,file):
with open(join(path_dir,path),'ab') as f:
pk.dump(file,f)
def extract_frames(video_path, num_frames):
clip = VideoFileClip(video_path)
duration = clip.duration
frame_times = [duration * i / (num_frames + 1) for i in range(1, num_frames + 1)]
frames = []
for t in frame_times:
frame = clip.get_frame(t)
image = Image.fromarray(frame)
frames.append(image)
clip.close()
return frames
def read_image(postfix,img_path):
if postfix == 'png':
image = Image.open(join("Vision-PromptSource",img_path))
elif postfix == 'h5':
image = h5py.File(join("Vision-PromptSource",img_path), 'r')
else:
image = Image.open(join("Vision-PromptSource", img_path))
return image
def preprocess_function(input_text,input_image,output_text):
result = {}
flag = isinstance(input_image,list)
result["pixel_values"] = []
if flag:
postfix = input_image[0][1:].split('.')[-1]
for img_path in input_image:
img_path = img_path[1:] if img_path[0] == '.' and img_path[1] !='/' else img_path
img = read_image(postfix,img_path)
result["pixel_values"].append(processor(images = img)["pixel_values"][0])
else:
postfix = input_image[1:].split('.')[-1]
img_path = input_image[1:] if input_image[0] == '.' and input_image[1] !='/' else input_image
img_path = img_path.replace('_/',"_")
if postfix =='mp4':
images = extract_frames(img_path,NUM_FRAMES)
for img in images:
result["pixel_values"].append(processor(images = img)["pixel_values"][0])
else:
img = read_image(postfix,img_path)
result["pixel_values"].append(processor(images = img)["pixel_values"][0])
return result
def concat_text_input_output( input_ids, input_atts, output_ids, output_atts):
input_part_targets_len = []
llm_tokens = {"input_ids": [], "attention_mask": []}
this_input_ones = sum(input_atts)
input_part_targets_len.append(this_input_ones)
llm_tokens['input_ids'].append(
np.concatenate([
input_ids[:this_input_ones],
output_ids[1:],
input_ids[this_input_ones:]
])
)
llm_tokens['attention_mask'].append(
np.concatenate([
input_atts[:this_input_ones],
output_atts[1:],
input_atts[this_input_ones:]
])
)
llm_tokens['input_ids'] = np.stack(llm_tokens['input_ids'])
llm_tokens['attention_mask'] = np.stack(llm_tokens['attention_mask'])
return llm_tokens, input_part_targets_len
def preprocess_function_batched(result,input_text,output_text):
if 'vicuna' in model_type:
processor.tokenizer.padding_side = "right"
processor.tokenizer.truncation_side = 'left'
replace_token = "".join(32*[image_placeholder])
input_text = input_text.replace('图',replace_token)
re = processor.tokenizer(
input_text,
padding="longest",
truncation=True,
max_length=max_seq_length,
)
processor.tokenizer.truncation_side = 'right'
out = processor.tokenizer(
output_text,
padding="longest",
truncation=True,
max_length=256)
re, input_part_targets_len = concat_text_input_output(
re['input_ids'],
re['attention_mask'],
out['input_ids'],
out['attention_mask'],
)
re['input_ids'] = np.array(re['input_ids'],dtype=np.int32)
re['attention_mask'] = np.array(re['attention_mask'],dtype=np.bool_)
# do not apply loss to the padding
targets = copy.deepcopy(re['input_ids'])
targets[targets == processor.tokenizer.pad_token_id] = -100
# do not apply loss to the text input (i.e., instruction)
for i, l in enumerate(input_part_targets_len):
targets[i][:l] = -100
m= {
'input_ids': re['input_ids'],
'attention_mask': re['attention_mask'],
'label': targets,
}
result.update(m)
else:
re= processor.tokenizer(input_text, padding='max_length', max_length=max_seq_length, truncation=True)
re['input_ids'] = np.array(re['input_ids'],dtype=np.int32)
re['attention_mask'] = np.array(re['attention_mask'],dtype=np.bool_)
# result['label'] = np.array(processor.tokenizer(output_text, padding='max_length', max_length=32, truncation=True)["input_ids"],dtype=np.int32)
out = processor.tokenizer(output_text, padding='max_length', max_length=128, truncation=True)
result['label'] = np.array(out["input_ids"],dtype=np.int32)
result['label_attention_mask'] = np.array(out["attention_mask"],dtype=np.bool_)
result.update(re)
return result
def process_raw_datajson_to_pickle(json_data,types):
json_data = get_json_file(json_data)
with ThreadPoolExecutor(max_workers=10) as executor:
for each in tqdm(json_data):
input_text = each['input_text']
input_imgs = each['input_image']
output_text = each['output_text']
temp = preprocess_function(input_text,input_imgs,output_text)
temp = preprocess_function_batched(temp,input_text,output_text)
executor.submit(save_pickle_img, f"bilp2-prompt-{types}.pkl",temp)
def save_to_arrow(path,temp):
with ArrowWriter(path=path) as writer:
writer.write_batch(temp)
writer.finalize()
def process_raw_datajson_to_arrow(json_data,file_name,types,sub_length = -1,big_file_name=None,dataset_name=None):
if big_file_name is None:
big_file_name = file_name
if dataset_name is not None:
big_file_name = join(big_file_name,dataset_name)
if not os.path.exists(f'{big_file_name}/arrow_data_{file_name}_{types}'):
os.makedirs(f'{big_file_name}/arrow_data_{file_name}_{types}')
if sub_length>0:
json_data = json_data[:sub_length]
save_arrow_data={'pixel_values':[], 'label':[], 'input_ids':[], 'attention_mask':[]}
index_arrow=0
threads = []
for idx,each in enumerate(tqdm(json_data)):
input_text = each['input_text']
input_imgs = each['input_image']
output_text = each['output_text']
try:
temp = preprocess_function(input_text,input_imgs,output_text)
except Exception as e:
print(e)
continue
temp = preprocess_function_batched(temp,input_text,output_text)
for each in save_arrow_data:
save_arrow_data[each].append(temp[each])
if idx %1000 == 0 and idx !=0:
if sub_length>0:
path = f"{big_file_name}/arrow_data_{file_name}_{types}/bilp2-temp-{types}-{index_arrow}-length{sub_length}.arrow"
else:
path = f"{big_file_name}/arrow_data_{file_name}_{types}/bilp2-temp-{types}-{index_arrow}.arrow"
t = threading.Thread(target=save_to_arrow, args=(path, save_arrow_data))
threads.append(t)
t.start()
save_arrow_data={'pixel_values':[], 'label':[], 'input_ids':[], 'attention_mask':[]}
index_arrow+=1
for t in threads:
t.join()
if sub_length>0:
path = f"{big_file_name}/arrow_data_{file_name}_{types}/bilp2-temp-{types}-{index_arrow}-length{sub_length}.arrow"
else:
path = f"{big_file_name}/arrow_data_{file_name}_{types}/bilp2-temp-{types}-{index_arrow}.arrow"
save_to_arrow(path,save_arrow_data)
def to_pickle(file_name):
train_js =f'Vision-PromptSource/prompt_data/{file_name}-train.json'
test_js =f'Vision-PromptSource/prompt_data/{file_name}-test.json'
val_js =f'Vision-PromptSource/prompt_data/{file_name}-val.json'
print('start process training data')
process_raw_datajson_to_pickle(train_js,'train')
print('start process testing data')
process_raw_datajson_to_pickle(test_js,'test')
print('start process val data')
process_raw_datajson_to_pickle(val_js,'val')
def to_arrow(file_name,length=-1,do_train = True,convert_file_name=None):
if convert_file_name is None:
convert_file_name = file_name
train_js =f'Vision-PromptSource/prompt_data/{file_name}-train.json'
test_js =f'Vision-PromptSource/prompt_data/{file_name}-test.json'
val_js =f'Vision-PromptSource/prompt_data/{file_name}-val.json'
if do_train:
train_js = get_json_file(train_js)
print('start process training data')
process_raw_datajson_to_arrow(train_js,convert_file_name,'train',length)
print('start process testing data')
test_js = get_json_file(test_js)
process_raw_datajson_to_arrow(test_js,convert_file_name,'test',length)
print('start process val data')
val_js = get_json_file(val_js)
process_raw_datajson_to_arrow(val_js,convert_file_name,'val',length)
def zero_preprocess_json(json_file):
re =[]
for j in json_file:
m={'output_image':""}
if 'vcr' in j['input_image'][0]:
m["input_text"] = f"image 0 is <image0>{image_placeholder}.\n"+j['input_text'].split(f'{image_placeholder}.\n')[-1]
m["input_image"] = [j["input_image"][0]]
m['output_text'] = j["output_text"]
else:
image_id = len(j['input_text'].split('\n'))-1
m["input_text"] = (f"image 0 is <image0>{image_placeholder}.\n"+j['input_text'].split('\n')[-1]).replace(f'image {image_id}','image 0').replace(f'<image{image_id}>','<image0>')
m["input_image"] = [j["input_image"][-1]]
m['output_text'] = j["output_text"]
replace_token = "".join(32*[image_placeholder])
m['input_text'] = m['input_text'].replace(image_placeholder,replace_token)
re.append(m)
return re
def zero_shot_to_arrow(file_name,length=-1,do_train = True,convert_file_name=None):
if convert_file_name is None:
convert_file_name = file_name
train_js =f'Vision-PromptSource/prompt_data/{file_name}-train.json'
test_js =f'Vision-PromptSource/prompt_data/{file_name}-test.json'
val_js =f'Vision-PromptSource/prompt_data/{file_name}-val.json'
if do_train:
train_js = get_json_file(train_js)[:length] if length>0 else get_json_file(train_js)
zero_train_js = zero_preprocess_json(train_js)
print('start process training data')
process_raw_datajson_to_arrow(zero_train_js,convert_file_name,'train_zeroshot',length)
print('start process testing data')
test_js = get_json_file(test_js)[:length] if length>0 else get_json_file(test_js)
zero_test_js = zero_preprocess_json(test_js)
process_raw_datajson_to_arrow(zero_test_js,convert_file_name,'test_zeroshot',length)
print('start process val data')
val_js = get_json_file(val_js)[:length] if length>0 else get_json_file(val_js)
zero_val_js = zero_preprocess_json(val_js)
process_raw_datajson_to_arrow(zero_val_js,convert_file_name,'val_zeroshot',length)
# generate_new_json(data_json,data_size,file_name)
# to_arrow(file_name,2000,False,convert_file_name)
# zero_shot_to_arrow(file_name,2000,False,convert_file_name)
# generate_jsonl_data_from_instances()
from multiprocessing import Pool
from functools import partial
def get_json_file(file_path):
js =[]
with open(file_path,'r')as f:
for line in f.readlines():
js.append(json.loads(line))
return js
def process_train_data(train,length,big_file_name):
if '-1' not in train:
dataset_name = train.split('/')[-1].split('-')[-3]
else:
dataset_name = train.split('/')[-1].split('-')[-4]
train_json = get_json_file(train)
print(f'start process testing data {dataset_name}')
process_raw_datajson_to_arrow(train_json, convert_file_name, 'train', length, big_file_name, dataset_name)
def process_val_data(val,length,big_file_name):
if '-1' not in val:
dataset_name = val.split('/')[-1].split('-')[-3]
else:
dataset_name = val.split('/')[-1].split('-')[-4]
val_json = get_json_file(val)
print(f'start process testing data {dataset_name}')
process_raw_datajson_to_arrow(val_json, convert_file_name, 'val', length, big_file_name, dataset_name)
def process_test_data(test,length,big_file_name):
if '-1' not in test:
dataset_name = test.split('/')[-1].split('-')[-3]
else:
dataset_name = test.split('/')[-1].split('-')[-4]
test_json = get_json_file(test)
print(f'start process testing data {dataset_name}')
process_raw_datajson_to_arrow(test_json, convert_file_name, 'test', length, big_file_name, dataset_name)
def to_arrowByDataset(file_name,length=-1,do_train = True,convert_file_name=None):
if convert_file_name is None:
convert_file_name = file_name
big_file_name = f"{save_dir_name}/{file_name}"
train_js =glob.glob(f'Vision-PromptSource/{save_dir_name}/train/*')
test_js =glob.glob(f'Vision-PromptSource/{save_dir_name}/test/*')
val_js =glob.glob(f'Vision-PromptSource/{save_dir_name}/val/*')
# if do_train:
# for train in train_js:
# # if 'llava' in train or 'okvqa' in train or 'textvqa' in train or 'flickr' in train or 'vcr' in train or 'diffusiondb' in train or 'miniimage' in train or 'refcoco' in train or 'wikiart' in train:
# # # ocr-vqa & miniimage wikiart not finished
# # continue
# # if 'msrvtt' in train :
# dataset_name = train.split('/')[-1].split('-')[-3]
# train_json = get_json_file(train)
# print(f'start process training data {dataset_name}')
# process_raw_datajson_to_arrow(train_json,convert_file_name,'train',length,big_file_name,dataset_name)
# for test in test_js:
# # ocr-vqa & miniimage not finished
# # continue
# dataset_name = test.split('/')[-1].split('-')[-4]
# # if 'nocaps' == dataset_name:
# test_json = get_json_file(test)
# print(f'start process testing data {dataset_name}')
# process_raw_datajson_to_arrow(test_json,convert_file_name,'test',length,big_file_name,dataset_name)
# 创建进程池
with Pool() as pool:
# 使用进程池的map方法并行处理任务
partial_process_train_data = partial(process_train_data, length=length, big_file_name=big_file_name)
pool.map(partial_process_train_data, train_js)
partial_process_test_data = partial(process_test_data, length=length, big_file_name=big_file_name)
pool.map(partial_process_test_data, test_js)
partial_process_val_data = partial(process_val_data, length=length, big_file_name=big_file_name)
pool.map(partial_process_val_data, val_js)