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https://github.com/Vision-CAIR/MiniGPT-4.git
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update evaluation
This commit is contained in:
junchen14
parent
9d8335c1f5
commit
68f42c0570
@ -1,11 +1,12 @@
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model:
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arch: minigpt_v2
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model_type: pretrain
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max_txt_len: 160
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max_txt_len: 500
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end_sym: "</s>"
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low_resource: True
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prompt_template: '[INST] {} [/INST]'
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ckpt: 'please set this value to the path of pretrained checkpoint'
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llama_model: "/ibex/project/c2133/llama_v2/llama-2-7b-chat-pytorch_update"
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ckpt: "/ibex/ai/project/c2090/minigpt4_ckpt/448_conversation_correct_best_v7_ablation1_v5_v6/20231007035/checkpoint_35.pth"
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lora_r: 64
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lora_alpha: 16
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@ -8,15 +8,18 @@ NAME=EXP_NAME
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IMG_PATH=YOUR_IMG_PATH
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EVAL_FILE_PATH=YOUR_EVAL_FILE_PATH
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torchrun --nproc_per_node 1 eval_scripts/eval_ref.py --name ${NAME} \
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--cfg-path ${cfg_path} \
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--ckpt ${CKPT} --dataset refcoco,refcoco+,refcocog --lora_r 64 --lora_alpha 16 \
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--batch_size 64 --max_new_tokens 20 --resample --img_path ${IMG_PATH} --eval_file_path ${EVAL_FILE_PATH}
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# torchrun --nproc_per_node 1 eval_scripts/eval_ref.py --name ${NAME} \
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# --cfg-path ${cfg_path} \
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# --ckpt ${CKPT} --dataset refcoco,refcoco+,refcocog --lora_r 64 --lora_alpha 16 \
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# --batch_size 64 --max_new_tokens 20 --resample --img_path ${IMG_PATH} --eval_file_path ${EVAL_FILE_PATH}
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# torchrun --nproc_per_node 1 eval_scripts/eval_vqa.py --name ${NAME} \
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# --cfg-path ${cfg_path} \
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# --ckpt ${CKPT} --split val,test --dataset okvqa,vizwiz,iconqa,gqa,vsr,hm --lora_r 64 --lora_alpha 16 \
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# --batch_size 32 --max_new_tokens 20 --resample
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torchrun --nproc_per_node 1 eval_scripts/eval_vqa.py --name ${NAME} \
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--cfg-path ${cfg_path} \
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--ckpt ${CKPT} --split val,test --dataset okvqa,vizwiz,aokvqa,iconqa,gqa,vsr,hm --lora_r 64 --lora_alpha 16 \
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--batch_size 32 --max_new_tokens 20 --resample
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torchrun --master-port ${PORT} --nproc_per_node 1 run_textvqa_eval.py --name ${NAME} --ckpt_path ${CKPT} --lora_r 64 --lora_alpha 16 --eval_file_path ${EVAL_FILE_PATH}
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torchrun --master-port ${PORT} --nproc_per_node 1 run_sciencevqa_eval.py --name ${NAME} --ckpt_path ${CKPT} --lora_r 64 --lora_alpha 16
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--ckpt ${CKPT} --split val,test --dataset okvqa --lora_r 64 --lora_alpha 16 \
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--batch_size 32 --max_new_tokens 20
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@ -1,177 +0,0 @@
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import argparse
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import os
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import random
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import requests
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from io import BytesIO
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import numpy as np
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from PIL import Image
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import torch
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import torch.backends.cudnn as cudnn
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import gradio as gr
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import minigpt4.tasks as tasks
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from minigpt4.common.config import Config
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from minigpt4.common.dist_utils import get_rank, init_distributed_mode
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from minigpt4.common.logger import setup_logger
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from minigpt4.common.optims import (
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LinearWarmupCosineLRScheduler,
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LinearWarmupStepLRScheduler,
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)
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from minigpt4.common.registry import registry
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from minigpt4.common.utils import now
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# imports modules for registration
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from minigpt4.datasets.builders import *
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from minigpt4.models import *
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from minigpt4.processors import *
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from minigpt4.runners import *
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from minigpt4.tasks import *
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parser = argparse.ArgumentParser(description="Demo")
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parser.add_argument("--cfg-path", required=False, default='scienceqa_eval.yaml', help="path to configuration file.")
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parser.add_argument("--ckpt_path", required=False, help="path to configuration file.")
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parser.add_argument("--lora_r", type=int, default=64, help="path to configuration file.")
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parser.add_argument("--lora_alpha", type=int, default=16, help="path to configuration file.")
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parser.add_argument("--name", type=str)
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parser.add_argument(
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"--options",
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nargs="+",
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help="override some settings in the used config, the key-value pair "
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"in xxx=yyy format will be merged into config file (deprecate), "
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"change to --cfg-options instead.",
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)
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print('Initializing Chat')
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args = parser.parse_args()
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cfg = Config(args)
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ckpt_list = [args.ckpt_path]
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print('evaluating config:', args.cfg_path)
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print('evaluating checkpoint:', args.ckpt_path)
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for ckpt in ckpt_list:
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cfg.model_cfg.ckpt = ckpt
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cfg.model_cfg.lora_r=args.lora_r
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cfg.model_cfg.lora_alpha=args.lora_alpha
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model_config = cfg.model_cfg
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model_cls = registry.get_model_class(model_config.arch)
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model = model_cls.from_config(model_config).to('cuda:0')
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model.eval()
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print('Initialization Finished')
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vis_processor_cfg = cfg.datasets_cfg.coco_vqa.vis_processor.eval
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vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
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text_processor_cfg = cfg.datasets_cfg.coco_vqa.text_processor.eval
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text_processor = registry.get_processor_class(text_processor_cfg.name).from_config(text_processor_cfg)
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task = tasks.setup_task(cfg)
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from minigpt4.datasets.datasets.vqa_datasets import VQADataset, VQAEvalDataset
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class textVQAEvalDataset(VQADataset):
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def __init__(self, vis_processor, text_processor, vis_root=None, ann_paths=None):
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# super().__init__(vis_processor, text_processor, vis_root, ann_paths)
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from datasets import load_dataset
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from datasets import load_dataset
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self.annotation = load_dataset("derek-thomas/ScienceQA", split='test')
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## select anns with image
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self.annotation = [ann for ann in self.annotation if ann['image']]
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self.vis_processor = vis_processor
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self.text_processor = text_processor
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self.instruction_pool = [
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'[vqa] Question: {} Answer:',
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]
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self.alphabet_options = ["A", "B", "C", "D", "E"]
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self.max_num_choices = len(self.alphabet_options)
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def __getitem__(self, index):
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ann = self.annotation[index]
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image = ann['image'].convert("RGB")
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image = self.vis_processor(image)
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question = self.text_processor(ann["question"])
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lecture = self.text_processor(ann['lecture'])
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hint = self.text_processor(ann['hint'])
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options = ann['choices']
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num_choices = len(ann['choices'])
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if len(ann['choices'])<self.max_num_choices:
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options = options + (self.max_num_choices - len(ann['choices'])) * [""]
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else:
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options = options
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options = [self.text_processor(opt) for opt in options]
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instruction = random.choice(self.instruction_pool).format(question, lecture)
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instruction = "<s>[INST]<Img><ImageHere></Img> {} [/INST]".format(instruction)
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answer = options[ann['answer']]
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return {
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"image": image,
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"question": question,
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"choices": options,
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# "choices": self.alphabet_options,
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"num_choices": num_choices,
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"answer": answer,
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"instruction_input": instruction,
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"question_id": index,
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}
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dataset = textVQAEvalDataset(vis_processor, text_processor)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.run_cfg.batch_size_eval, \
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num_workers=cfg.run_cfg.num_workers)
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print(len(dataset), len(dataloader))
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result_dir = cfg.run_cfg.output_dir
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if not os.path.exists(result_dir):
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os.makedirs(result_dir)
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epoch = cfg.model_cfg.ckpt.split('/')[-1].split('.')[0].split('_')[1]
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exp_id = cfg.model_cfg.ckpt.split('/')[-2] + "_" + epoch
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val_result = task.evaluation(model, dataloader, cuda_enabled=True)
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task.after_evaluation(val_result, split_name=f'{args.name}_scienceqa_val', result_dir=result_dir)
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from minigpt4.common.vqa_tools.vqa import VQA
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from minigpt4.common.vqa_tools.vqa_eval import VQAEval
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import json, logging, os
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result_file = '{}/{}_scienceqa_val.json'.format(result_dir, args.name)
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results = json.load(open(result_file, "r"))
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acc = []
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vqa_tool = VQAEval()
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for res in results:
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gt_ans = res["gt_ans"]
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pred = res["pred_ans"]
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pred = vqa_tool.processPunctuation(pred)
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pred = vqa_tool.processDigitArticle(pred)
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vqa_acc = 1 if pred == gt_ans else 0
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acc.append(vqa_acc)
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accuracy = sum(acc) / len(acc) * 100
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print('scienceqa val acc: ', accuracy, flush=True)
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@ -1,184 +0,0 @@
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import argparse
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import os
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import random
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import requests
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from io import BytesIO
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import numpy as np
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from PIL import Image
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import torch
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import torch.backends.cudnn as cudnn
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import gradio as gr
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import minigpt4.tasks as tasks
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from minigpt4.common.config import Config
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from minigpt4.common.dist_utils import get_rank, init_distributed_mode
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from minigpt4.common.logger import setup_logger
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from minigpt4.common.optims import (
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LinearWarmupCosineLRScheduler,
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LinearWarmupStepLRScheduler,
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)
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from minigpt4.common.registry import registry
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from minigpt4.common.utils import now
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# imports modules for registration
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from minigpt4.datasets.builders import *
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from minigpt4.models import *
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from minigpt4.processors import *
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from minigpt4.runners import *
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from minigpt4.tasks import *
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parser = argparse.ArgumentParser(description="Demo")
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parser.add_argument("--cfg-path", required=False, default='textvqa_eval.yaml', help="path to configuration file.")
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parser.add_argument("--ckpt_path", required=False, help="path to configuration file.")
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parser.add_argument("--lora_r", type=int, default=64, help="path to configuration file.")
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parser.add_argument("--lora_alpha", type=int, default=16, help="path to configuration file.")
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parser.add_argument("--name", type=str)
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parser.add_argument("--img_path", type=str)
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parser.add_argument("--eval_file_path", type=str)
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parser.add_argument(
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"--options",
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nargs="+",
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help="override some settings in the used config, the key-value pair "
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"in xxx=yyy format will be merged into config file (deprecate), "
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"change to --cfg-options instead.",
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)
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print('Initializing Chat')
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args = parser.parse_args()
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cfg = Config(args)
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ckpt_list = [args.ckpt_path]
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print('evaluating config:', args.cfg_path)
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print('evaluating checkpoint:', args.ckpt_path)
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import json
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import numpy as np
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f = open(f'{args.eval_file_path}/textocr/TextOCR_0.1_val.json', 'r')
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textcap_dataset_val = json.load(f)
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all_img_ids = list(textcap_dataset_val['imgs'].keys())
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ann_ids = list(textcap_dataset_val['anns'].keys())
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for ckpt in ckpt_list:
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cfg.model_cfg.ckpt = ckpt
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cfg.model_cfg.lora_r=args.lora_r
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cfg.model_cfg.lora_alpha=args.lora_alpha
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model_config = cfg.model_cfg
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model_cls = registry.get_model_class(model_config.arch)
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model = model_cls.from_config(model_config).to('cuda:0')
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model.eval()
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print('Initialization Finished')
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vis_processor_cfg = cfg.datasets_cfg.coco_vqa.vis_processor.eval
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vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
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text_processor_cfg = cfg.datasets_cfg.coco_vqa.text_processor.eval
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text_processor = registry.get_processor_class(text_processor_cfg.name).from_config(text_processor_cfg)
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task = tasks.setup_task(cfg)
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from collections import Counter
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def find_most_frequent_item(items):
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counter = Counter(items)
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most_common = counter.most_common(1)
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if most_common:
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return most_common[0][0]
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else:
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return None
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from minigpt4.datasets.datasets.vqa_datasets import VQADataset, VQAEvalDataset
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class textVQAEvalDataset(VQADataset):
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def __init__(self, vis_processor, text_processor, vis_root=None, ann_paths=None, use_ocr=False):
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# super().__init__(vis_processor, text_processor, vis_root, ann_paths)
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from datasets import load_dataset
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self.annotation = load_dataset("textvqa", split="validation",cache_dir=f"{args.eval_file_path}/textvqa_cache")
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self.vis_processor = vis_processor
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self.text_processor = text_processor
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self.instruction_pool = [
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'[vqa] Question: {} Short answer:',
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]
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self.use_ocr = use_ocr
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def __getitem__(self, index):
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ann = self.annotation[index]
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image_path = ann['flickr_original_url']
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image = ann["image"].convert("RGB")
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img_id = ann['image_id']
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sel_anns = [textcap_dataset_val['anns'][ann] for ann in ann_ids if ann.startswith(img_id)]
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sel_caps = [ann['utf8_string'] for ann in sel_anns]
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sel_caps = [cap for cap in sel_caps if cap not in ['\n', '.']]
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captions = " ".join(sel_caps)
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image = self.vis_processor(image)
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question = self.text_processor(ann["question"])
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captions = self.text_processor(captions)
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instruction = random.choice(self.instruction_pool).format(question)
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if self.use_ocr and img_id in all_img_ids:
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instruction = "<s>[INST] <Img><ImageHere></Img> OCR tokens: {}. {} [/INST]".format(captions, instruction)
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else:
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instruction = "<s>[INST] <Img><ImageHere></Img> {} [/INST]".format(instruction)
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answers = find_most_frequent_item(ann["answers"])
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return {
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"image": image,
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"text_input": question,
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"answer": answers,
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'image_path': image_path,
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"instruction_input": instruction,
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"question_id": ann["question_id"],
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# "instance_id": ann["instance_id"],
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}
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dataset = textVQAEvalDataset(vis_processor, text_processor, use_ocr=cfg.run_cfg.use_ocr)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.run_cfg.batch_size_eval, num_workers=cfg.run_cfg.num_workers)
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result_dir = cfg.run_cfg.output_dir
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if not os.path.exists(result_dir):
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os.makedirs(result_dir)
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epoch = cfg.model_cfg.ckpt.split('/')[-1].split('.')[0].split('_')[1]
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exp_id = cfg.model_cfg.ckpt.split('/')[-2] + "_" + epoch
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val_result = task.evaluation(model, dataloader, cuda_enabled=True)
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task.after_evaluation(val_result, split_name=f'{args.name}_textvqa_val', result_dir=result_dir)
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anno_files = f'{args.eval_file_path}/TextVQA/gt_ann.json'
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ques_files = f'{args.eval_file_path}/TextVQA/gt_ques.json'
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from minigpt4.common.vqa_tools.vqa import VQA
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from minigpt4.common.vqa_tools.vqa_eval import VQAEval
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import logging, json, os
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result_file = '{}/{}_textvqa_val.json'.format(result_dir, args.name)
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vqa = VQA(anno_files, ques_files)
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vqa_result = vqa.loadRes(
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resFile=result_file, quesFile=ques_files
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)
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# create vqaEval object by taking vqa and vqaRes
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# n is precision of accuracy (number of places after decimal), default is 2
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vqa_scorer = VQAEval(vqa, vqa_result, n=2)
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logging.info("Start VQA evaluation.")
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vqa_scorer.evaluate()
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# print accuracies
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overall_acc = vqa_scorer.accuracy["overall"]
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print('textvqa val acc: ', overall_acc, flush=True)
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@ -1,48 +0,0 @@
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model:
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arch: minigpt4
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model_type: pretrain_llama2
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max_txt_len: 160
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end_sym: "</s>"
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low_resource: True
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prompt_template: '[INST] {} [/INST] '
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ckpt: 'please set this value to the path of pretrained checkpoint'
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datasets:
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coco_vqa: # not used
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type: eval
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vis_processor:
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eval:
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name: "blip2_image_eval"
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image_size: 336
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||||
text_processor:
|
||||
eval:
|
||||
name: "blip_caption"
|
||||
|
||||
run:
|
||||
task: scienceqa
|
||||
# optimization-specific
|
||||
batch_size_train: 16
|
||||
batch_size_eval: 32
|
||||
num_workers: 8
|
||||
|
||||
# inference-specific
|
||||
num_ans_candidates: 5
|
||||
max_len: 10
|
||||
min_len: 1
|
||||
num_beams: 5
|
||||
inference_method: "generate"
|
||||
prompt: "Question: {} Short answer:"
|
||||
|
||||
seed: 42
|
||||
output_dir: "results"
|
||||
|
||||
evaluate: True
|
||||
test_splits: ["val"]
|
||||
|
||||
# distribution-specific
|
||||
device: "cuda"
|
||||
world_size: 1
|
||||
dist_url: "env://"
|
||||
distributed: True
|
||||
|
||||
@ -1,48 +0,0 @@
|
||||
model:
|
||||
arch: minigpt4
|
||||
model_type: pretrain_llama2
|
||||
max_txt_len: 160
|
||||
end_sym: "</s>"
|
||||
low_resource: True
|
||||
prompt_template: '[INST] {} [/INST] '
|
||||
ckpt: 'please set this value to the path of pretrained checkpoint'
|
||||
|
||||
datasets:
|
||||
coco_vqa: # not used
|
||||
type: eval
|
||||
vis_processor:
|
||||
eval:
|
||||
name: "blip2_image_eval"
|
||||
image_size: 336
|
||||
text_processor:
|
||||
eval:
|
||||
name: "blip_caption"
|
||||
|
||||
run:
|
||||
task: vqa
|
||||
# optimization-specific
|
||||
batch_size_train: 32
|
||||
batch_size_eval: 128
|
||||
# if do not use OCR token: batch=32, Evaluation Total time: 0:23:47 (9.0954 s / it)
|
||||
use_ocr: True
|
||||
# use OCR token: batch_size=16; Evaluation Total time: 0:36:34 (7.0116 s / it)
|
||||
num_workers: 8
|
||||
|
||||
# inference-specific
|
||||
max_len: 10
|
||||
min_len: 1
|
||||
num_beams: 1
|
||||
inference_method: "generate"
|
||||
prompt: "Question: {} Short answer:"
|
||||
|
||||
seed: 42
|
||||
output_dir: "results"
|
||||
|
||||
evaluate: True
|
||||
test_splits: ["val"]
|
||||
|
||||
# distribution-specific
|
||||
device: "cuda"
|
||||
world_size: 1
|
||||
dist_url: "env://"
|
||||
distributed: True
|
||||
@ -11,13 +11,14 @@ import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from datasets import load_dataset
|
||||
|
||||
from minigpt4.datasets.datasets.vqa_datasets import OKVQAEvalData,VizWizEvalData,AOKVQADAEvalData,AOKVQAMCEvalData,IconQAEvalData,GQAEvalData,VSREvalData,HMEvalData
|
||||
|
||||
from minigpt4.datasets.datasets.vqa_datasets import OKVQAEvalData,VizWizEvalData,IconQAEvalData,GQAEvalData,VSREvalData,HMEvalData
|
||||
|
||||
from minigpt4.common.vqa_tools.VQA.PythonHelperTools.vqaTools.vqa import VQA
|
||||
from minigpt4.common.vqa_tools.VQA.PythonEvaluationTools.vqaEvaluation.vqaEval import VQAEval
|
||||
|
||||
from minigpt4.common.eval_utils import prepare_texts, init_model, eval_parser
|
||||
from minigpt4.conversation.conversation import CONV_VISION_LLama2
|
||||
from minigpt4.conversation.conversation import CONV_VISION_minigptv2
|
||||
import random
|
||||
|
||||
|
||||
@ -37,7 +38,7 @@ print(args.ckpt)
|
||||
print(args.name)
|
||||
|
||||
model, vis_processor = init_model(args)
|
||||
conv_temp = CONV_VISION.copy()
|
||||
conv_temp = CONV_VISION_LLama2.copy()
|
||||
conv_temp.system = ""
|
||||
|
||||
model.eval()
|
||||
@ -45,8 +46,8 @@ model.eval()
|
||||
os.makedirs('results', exist_ok=True)
|
||||
|
||||
if 'okvqa' in args.dataset:
|
||||
img_path=f'{args.img_path}/COCO/cocoapi/data/2017/images/jpeg/train'
|
||||
with open(f'{args.eval_file_path}/okvqa/test_split.json', 'r') as f:
|
||||
img_path=os.path.join(args.img_path,"train")
|
||||
with open(os.path.join(args.eval_file_path,"ok_vqa/test_split.json")) as f:
|
||||
ok_vqa_test_split = json.load(f)
|
||||
|
||||
data = OKVQAEvalData(ok_vqa_test_split, vis_processor, img_path)
|
||||
@ -61,6 +62,8 @@ if 'okvqa' in args.dataset:
|
||||
|
||||
for answer, question_id, question, img_id in zip(answers, question_ids, questions, img_ids):
|
||||
result = dict()
|
||||
if "<unk>" in answer.lower():
|
||||
print("answer: ", answer)
|
||||
answer = answer.lower().replace('<unk>','').strip()
|
||||
result['answer'] = answer
|
||||
result['question_id'] = int(question_id)
|
||||
@ -90,7 +93,7 @@ if 'okvqa' in args.dataset:
|
||||
if len(resamples) == 0:
|
||||
break
|
||||
|
||||
save_path=f'results_correct/{args.name}_okvqa.json'
|
||||
save_path=f'results/{args.name}_okvqa.json'
|
||||
with open(save_path,'w') as f:
|
||||
json.dump(minigpt4_predict, f)
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -1,201 +0,0 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
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|
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|
||||
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|
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APPENDIX: How to apply the Apache License to your work.
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Copyright 2022 Allen Institute for Artificial Intelligence
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@ -1,207 +0,0 @@
|
||||
# A-OKVQA
|
||||
|
||||
Official repository for **A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**.
|
||||
|
||||
Links: [[Paper]](https://arxiv.org/abs/2206.01718) [[Website]](http://a-okvqa.allenai.org) [[Leaderboard]](https://leaderboard.allenai.org/a-okvqa/submissions/public)
|
||||
|
||||
### Abstract
|
||||
|
||||
The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. Despite a proliferation of VQA datasets, this goal is hindered by a set of common limitations. These include a reliance on relatively simplistic questions that are repetitive in both concepts and linguistic structure, little world knowledge needed outside of the paired image, and limited reasoning required to arrive at the correct answer. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense reasoning about the scene depicted in the image. We demonstrate the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state-of-the-art vision–language models.
|
||||
|
||||
|
||||
|
||||
<hr>
|
||||
|
||||
#### Table of Contents
|
||||
|
||||
- [Getting started](#getting-started)
|
||||
* [Downloading the dataset](#downloading-the-dataset)
|
||||
- [Evaluation & Leaderboard](#evaluation)
|
||||
- [Codebase](#codebase)
|
||||
* [Preparing data](#preparing-data)
|
||||
* [Models and Predictions](#models-and-predictions)
|
||||
|
||||
<hr>
|
||||
|
||||
## Getting started
|
||||
|
||||
```bash
|
||||
git clone --single-branch --recurse-submodules https://github.com/allenai/aokvqa.git
|
||||
|
||||
cd aokvqa
|
||||
export PYTHONPATH=.
|
||||
|
||||
conda env create --name aokvqa
|
||||
conda activate aokvqa
|
||||
```
|
||||
|
||||
### Downloading the dataset
|
||||
|
||||
```bash
|
||||
export AOKVQA_DIR=./datasets/aokvqa/
|
||||
mkdir -p ${AOKVQA_DIR}
|
||||
|
||||
curl -fsSL https://prior-datasets.s3.us-east-2.amazonaws.com/aokvqa/aokvqa_v1p0.tar.gz | tar xvz -C ${AOKVQA_DIR}
|
||||
```
|
||||
|
||||
<details> <summary><b>Downloading COCO 2017</b></summary>
|
||||
|
||||
```bash
|
||||
export COCO_DIR=./datasets/coco/
|
||||
mkdir -p ${COCO_DIR}
|
||||
|
||||
for split in train val test; do
|
||||
wget "http://images.cocodataset.org/zips/${split}2017.zip"
|
||||
unzip "${split}2017.zip" -d ${COCO_DIR}; rm "${split}2017.zip"
|
||||
done
|
||||
|
||||
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
|
||||
unzip annotations_trainval2017.zip -d ${COCO_DIR}; rm annotations_trainval2017.zip
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
Loading our dataset is easy! Just grab our [load_aokvqa.py](https://github.com/allenai/aokvqa/blob/main/load_aokvqa.py) file and refer to the following code.
|
||||
|
||||
```python
|
||||
import os
|
||||
aokvqa_dir = os.getenv('AOKVQA_DIR')
|
||||
|
||||
from load_aokvqa import load_aokvqa, get_coco_path
|
||||
train_dataset = load_aokvqa(aokvqa_dir, 'train') # also 'val' or 'test'
|
||||
```
|
||||
|
||||
<details> <summary><b>Example dataset entry</b></summary>
|
||||
|
||||
```python
|
||||
dataset_example = train_dataset[0]
|
||||
|
||||
print(dataset_example['question_id'])
|
||||
# 22MexNkBPpdZGX6sxbxVBH
|
||||
|
||||
coco_dir = os.getenv('COCO_DIR')
|
||||
image_path = get_coco_path('train', dataset_example['image_id'], coco_dir)
|
||||
print(image_path)
|
||||
# ./datasets/coco/train2017/000000299207.jpg
|
||||
|
||||
print(dataset_example['question'])
|
||||
print(dataset_example['choices'])
|
||||
# What is the man by the bags awaiting?
|
||||
# ['skateboarder', 'train', 'delivery', 'cab']
|
||||
|
||||
correct_choice = dataset_example['choices'][ dataset_example['correct_choice_idx'] ]
|
||||
# Corrrect: cab
|
||||
|
||||
print(dataset_example['rationales'][0])
|
||||
# A train would not be on the street, he would not have luggage waiting for a delivery, and the skateboarder is there and not paying attention to him so a cab is the only possible answer.
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Evaluation
|
||||
|
||||
Please prepare `predictions_{split}.json` files (for `split: {val,test}`) in the format below. You may omit either `multiple_choice` or `direct_answer` field if you only want to evaluate one setting.
|
||||
|
||||
```python
|
||||
{
|
||||
'<question_id>' : {
|
||||
'multiple_choice' : '<prediction>',
|
||||
'direct_answer' : '<prediction>'
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
You can run evaluation on the validation set as follows.
|
||||
|
||||
```bash
|
||||
python evaluation/eval_predictions.py --aokvqa-dir ${AOKVQA_DIR} --split val --preds ./predictions_val.json
|
||||
```
|
||||
|
||||
### Leaderboard
|
||||
|
||||
You may submit `predictions_test.json` to the [leaderboard](https://leaderboard.allenai.org/a-okvqa/submissions/get-started).
|
||||
|
||||
## Codebase
|
||||
|
||||
We provide all code and pretrained models necessary to replicate our experiments for Large-Scale Pretrained Models (sec. 5.2) and Rationale Generation (sec. 5.3).
|
||||
|
||||
### Preparing data
|
||||
|
||||
```bash
|
||||
export FEATURES_DIR=./features/
|
||||
mkdir -p ${FEATURES_DIR}
|
||||
```
|
||||
|
||||
You can compute CLIP features for our vocabulary and dataset. These are most commonly used by our other experiments.
|
||||
|
||||
```bash
|
||||
python data_scripts/encode_vocab_clip.py --vocab ${AOKVQA_DIR}/large_vocab_train.csv --model-type ViT-B/32 --out ${FEATURES_DIR}/clip-ViT-B-32_large_vocab.pt
|
||||
|
||||
for split in train val test; do
|
||||
python data_scripts/extract_clip_features.py --aokvqa-dir ${AOKVQA_DIR} --coco-dir ${COCO_DIR} --split ${split} --model-type ViT-B/32 --out ${FEATURES_DIR}/clip-ViT-B-32_${split}.pt
|
||||
done
|
||||
```
|
||||
|
||||
<details> <summary><b>For training ClipCap with a transformer mapping network</b></summary>
|
||||
|
||||
If you want to train our ClipCap models with the transformer mapping network (instead of an MLP, like we do), you'll also need to run `extract_clip_features.py` with `--model-type RN50x4`.
|
||||
|
||||
</details>
|
||||
|
||||
<details> <summary><b>For ResNet and BERT input features</b></summary>
|
||||
|
||||
Our ResNet and BERT classification experiments require these respective features instead of CLIP. To generate these, please run the following commands:
|
||||
|
||||
```bash
|
||||
# ResNet
|
||||
for split in train val test; do
|
||||
python data_scripts/extract_resnet_features.py --aokvqa-dir ${AOKVQA_DIR} --coco-dir ${COCO_DIR} --split ${split} --out ${FEATURES_DIR}/resnet_${split}.pt
|
||||
done
|
||||
|
||||
# BERT
|
||||
for split in train val test; do
|
||||
python data_scripts/extract_bert_features.py --aokvqa-dir ${AOKVQA_DIR} --split ${split} --out ${FEATURES_DIR}/bert_${split}.pt
|
||||
done
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Models and Predictions
|
||||
|
||||
```bash
|
||||
export LOG_DIR=./logs/
|
||||
export PREDS_DIR=./predictions/
|
||||
export PT_MODEL_DIR=./pretrained_models/
|
||||
mkdir -p ${LOG_DIR} ${PREDS_DIR} ${PT_MODEL_DIR}
|
||||
```
|
||||
|
||||
<details> <summary><b>Download our pretrained model weights</b></summary>
|
||||
|
||||
```bash
|
||||
# Checkpoints for transfer learning experiments
|
||||
curl -fsSL https://prior-model-weights.s3.us-east-2.amazonaws.com/aokvqa/transfer_exp_checkpoints.tar.gz | tar xvz -C ${PT_MODEL_DIR}/aokvqa_models
|
||||
|
||||
# Checkpoints for ClipCap models (generating answers and rationales)
|
||||
curl -fsSL https://prior-model-weights.s3.us-east-2.amazonaws.com/aokvqa/clipcap_checkpoints.tar.gz | tar xvz -C ${PT_MODEL_DIR}/aokvqa_models
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
We have included instructions for replicating each of our experiments (see README.md files below).
|
||||
|
||||
All Python scripts should be run from the root of this repository. Please be sure to first run the installation and data preparation as directed above.
|
||||
|
||||
- [Heuristics](./heuristics/README.md)
|
||||
- [Transfer Learning Experiments](./transfer_experiments/README.md)
|
||||
- [Querying GPT-3](./gpt3/README.md)
|
||||
- [ClipCap](https://github.com/allenai/aokvqa/blob/ClipCap/README.md)
|
||||
- [Generating Captions & Rationales](https://github.com/allenai/aokvqa/blob/ClipCap/README.md)
|
||||
|
||||
For each experiment, we follow this prediction file naming scheme: `{model-name}_{split}-{setting}.json` (e.g. `random-weighted_val-mc.json` or `random-weighted_test-da.json`). As examples in these Readme files, we produce predictions on the validation set.
|
||||
|
||||
We unify predictions for each split before evaluation. (You can omit one of `--mc` or `--da` prediction file if you only want to evaluate one setting.)
|
||||
|
||||
```bash
|
||||
python evaluation/prepare_predictions.py --aokvqa-dir ${AOKVQA_DIR} --split val --mc ./predictions_val-mc.json --da ./predictions_val-da.json --out ./predictions_val.json
|
||||
# repeat for test split ...
|
||||
```
|
||||
@ -1,45 +0,0 @@
|
||||
import os
|
||||
import argparse
|
||||
from collections import Counter
|
||||
import pathlib
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
# Build vocab from train set: correct choices + (direct answers appearing in >= 3 )
|
||||
|
||||
train_set = load_aokvqa(args.aokvqa_dir, 'train')
|
||||
|
||||
vocab = []
|
||||
all_choices = Counter()
|
||||
direct_answers = Counter()
|
||||
|
||||
for i in train_set:
|
||||
vocab.append( i['choices'][i['correct_choice_idx']] )
|
||||
all_choices.update(i['choices'])
|
||||
direct_answers.update(set(i['direct_answers']))
|
||||
vocab += [k for k,v in all_choices.items() if v >= 3]
|
||||
vocab += [k for k,v in direct_answers.items() if v >= 3]
|
||||
|
||||
vocab = sorted(set(vocab))
|
||||
print(f"Vocab size: {len(vocab)}")
|
||||
|
||||
# Save vocabulary Output
|
||||
|
||||
with open(args.output_file, 'w') as f:
|
||||
for v in vocab:
|
||||
print(v, file=f)
|
||||
|
||||
## Check validation set coverage
|
||||
|
||||
val_set = load_aokvqa(args.aokvqa_dir, 'val')
|
||||
|
||||
val_acc = [v['choices'][v['correct_choice_idx']] in vocab for v in val_set]
|
||||
val_acc = sum(val_acc) / len(val_acc) * 100
|
||||
print(f"Val set coverage: {val_acc:.2f}" )
|
||||
@ -1,26 +0,0 @@
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
import torch
|
||||
import clip
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--vocab', type=pathlib.Path, required=True, dest='vocab_file')
|
||||
parser.add_argument('--model-type', type=str, choices=['RN50', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'], required=True, dest='model_type')
|
||||
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.output_file.suffix == '.pt'
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
model, preprocess = clip.load(args.model_type, device=device)
|
||||
|
||||
with torch.no_grad():
|
||||
a = open(args.vocab_file).read().splitlines()
|
||||
mc_text = clip.tokenize(a).to(device)
|
||||
mc_text_features = torch.stack([model.encode_text(mct.unsqueeze(0)).cpu() for mct in tqdm(mc_text)], dim=1)[0]
|
||||
mc_text_features = mc_text_features.float()
|
||||
model_name = args.model_type.replace('/', '-').replace('@', '-')
|
||||
torch.save(mc_text_features, args.output_file)
|
||||
@ -1,50 +0,0 @@
|
||||
import os
|
||||
import argparse
|
||||
import pathlib
|
||||
from tqdm import tqdm
|
||||
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.output_file.suffix == '.pt'
|
||||
|
||||
## Load dataset
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
## Load model
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
|
||||
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
model = model.to(device)
|
||||
model.eval()
|
||||
|
||||
def mean_pooling(model_output, attention_mask):
|
||||
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
|
||||
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
||||
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
||||
|
||||
## Encoding loop
|
||||
|
||||
with torch.no_grad():
|
||||
embeddings = {}
|
||||
|
||||
for d in tqdm(dataset):
|
||||
encoded_input = tokenizer([d['question']], padding=True, return_tensors='pt')
|
||||
encoded_input = {k:v.to(device) for k,v in encoded_input.items()}
|
||||
e = mean_pooling(model(**encoded_input), encoded_input['attention_mask'])
|
||||
embeddings[d['question_id']] = {
|
||||
'question' : e[0].cpu()
|
||||
}
|
||||
|
||||
torch.save(embeddings, args.output_file)
|
||||
@ -1,51 +0,0 @@
|
||||
import os
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
import torch
|
||||
import clip
|
||||
|
||||
from load_aokvqa import load_aokvqa, get_coco_path
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--coco-dir', type=pathlib.Path, required=True, dest='coco_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--model-type', type=str, choices=['RN50', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'], required=True, dest='model_type')
|
||||
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.output_file.suffix == '.pt'
|
||||
|
||||
## Load dataset
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
## Load model
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
model, preprocess = clip.load(args.model_type, device=device)
|
||||
|
||||
## Encoding loop
|
||||
|
||||
with torch.no_grad():
|
||||
embeddings = {}
|
||||
|
||||
for d in tqdm(dataset):
|
||||
q = d["question"]
|
||||
q_text = clip.tokenize(q).to(device)
|
||||
q_text_features = model.encode_text(q_text)
|
||||
|
||||
img = Image.open(get_coco_path(args.split, d['image_id'], args.coco_dir))
|
||||
img = preprocess(img).unsqueeze(0).to(device)
|
||||
image_features = model.encode_image(img)
|
||||
|
||||
embeddings[d['question_id']] = {
|
||||
'question' : q_text_features[0].float().cpu(),
|
||||
'image' : image_features[0].float().cpu(),
|
||||
}
|
||||
|
||||
torch.save(embeddings, args.output_file)
|
||||
@ -1,62 +0,0 @@
|
||||
import os
|
||||
import argparse
|
||||
import pathlib
|
||||
from tqdm import tqdm
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torchvision import models
|
||||
from torchvision import transforms as T
|
||||
|
||||
from load_aokvqa import load_aokvqa, get_coco_path
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--coco-dir', type=pathlib.Path, required=True, dest='coco_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.output_file.suffix == '.pt'
|
||||
|
||||
## Load dataset
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
## Load model
|
||||
|
||||
resnet_preprocess = T.Compose([
|
||||
T.Resize(size=224, interpolation=T.InterpolationMode.BICUBIC),
|
||||
T.CenterCrop(size=(224, 224)),
|
||||
T.ToTensor(),
|
||||
T.Normalize(
|
||||
mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225]
|
||||
)
|
||||
])
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
resnet_model = models.resnet50(pretrained=True)
|
||||
resnet_model = torch.nn.Sequential(
|
||||
*list(resnet_model.children())[:-1],
|
||||
nn.Flatten()
|
||||
) # strip classification layer
|
||||
resnet_model = resnet_model.to(device)
|
||||
|
||||
## Encoding loop
|
||||
|
||||
with torch.no_grad():
|
||||
embeddings = {}
|
||||
|
||||
for d in tqdm(dataset):
|
||||
img = Image.open(get_coco_path(args.split, d['image_id'], args.coco_dir)).convert('RGB')
|
||||
resnet_input = resnet_preprocess(img).unsqueeze(0).to(device)
|
||||
resnet_features = resnet_model(resnet_input)
|
||||
embeddings[d['question_id']] = {
|
||||
'image' : resnet_features[0].cpu()
|
||||
}
|
||||
|
||||
torch.save(embeddings, args.output_file)
|
||||
@ -1,36 +0,0 @@
|
||||
name: aokvqa
|
||||
channels:
|
||||
- pytorch
|
||||
- nvidia
|
||||
- huggingface
|
||||
- conda-forge
|
||||
- defaults
|
||||
dependencies:
|
||||
- python=3.7
|
||||
- cudatoolkit=11.3
|
||||
- numpy=1.21.6
|
||||
- pytorch=1.11.0
|
||||
- torchvision=0.12.0
|
||||
- pytorch-lightning=1.6.3
|
||||
- torchmetrics=0.8.1
|
||||
- gdown=4.4.0
|
||||
- pip=22.0.4
|
||||
- pip:
|
||||
- argparse==1.4.0
|
||||
- Pillow==9.0.1
|
||||
- tensorboard==2.9.0
|
||||
- ftfy==6.1.1
|
||||
- regex==2022.3.15
|
||||
- tqdm==4.64.0
|
||||
- clip @ git+https://github.com/openai/CLIP.git@b46f5ac7587d2e1862f8b7b1573179d80dcdd620
|
||||
- openai==0.18.1
|
||||
- nltk==3.7
|
||||
- sacrebleu==2.0.0
|
||||
- sacremoses==0.0.53
|
||||
- sentence-transformers==2.2.0
|
||||
- datasets==2.1.0
|
||||
- tokenizers==0.10.3
|
||||
- transformers==4.10.3
|
||||
|
||||
# Next: resolve conflict between sentence-transfomers and pytorch-lightning
|
||||
# pip uninstall sentencepiece
|
||||
Binary file not shown.
@ -1,97 +0,0 @@
|
||||
import argparse
|
||||
import pathlib
|
||||
import json
|
||||
import glob
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
def eval_aokvqa(dataset, preds, multiple_choice=False, strict=True):
|
||||
|
||||
if isinstance(dataset, list):
|
||||
dataset = { dataset[i]['question_id'] : dataset[i] for i in range(len(dataset)) }
|
||||
|
||||
if multiple_choice is False:
|
||||
dataset = {k:v for k,v in dataset.items() if v['difficult_direct_answer'] is False}
|
||||
|
||||
if strict:
|
||||
dataset_qids = set(dataset.keys())
|
||||
preds_qids = set(preds.keys())
|
||||
assert dataset_qids.issubset(preds_qids)
|
||||
|
||||
# dataset = q_id (str) : dataset element (dict)
|
||||
# preds = q_id (str) : prediction (str)
|
||||
|
||||
acc = []
|
||||
|
||||
for q in dataset.keys():
|
||||
if q not in preds.keys():
|
||||
acc.append(0.0)
|
||||
continue
|
||||
|
||||
pred = preds[q]
|
||||
choices = dataset[q]['choices']
|
||||
direct_answers = dataset[q]['direct_answers']
|
||||
|
||||
## Multiple Choice setting
|
||||
if multiple_choice:
|
||||
if strict:
|
||||
assert pred in choices, 'Prediction must be a valid choice'
|
||||
correct_choice_idx = dataset[q]['correct_choice_idx']
|
||||
acc.append( float(pred == choices[correct_choice_idx]) )
|
||||
## Direct Answer setting
|
||||
else:
|
||||
num_match = sum([pred.lower() == da.lower() for da in direct_answers])
|
||||
vqa_acc = min(1.0, num_match / 3.0)
|
||||
acc.append(vqa_acc)
|
||||
|
||||
acc = sum(acc) / len(acc) * 100
|
||||
|
||||
return acc
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--preds', type=str, required=True, dest='prediction_files')
|
||||
args = parser.parse_args()
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
for prediction_file in glob.glob(args.prediction_files):
|
||||
predictions = json.load(open(prediction_file, 'r'))
|
||||
|
||||
# Multiple choice
|
||||
|
||||
mc_predictions = {}
|
||||
|
||||
for q in predictions.keys():
|
||||
if 'multiple_choice' in predictions[q].keys():
|
||||
mc_predictions[q] = predictions[q]['multiple_choice']
|
||||
|
||||
if mc_predictions != {}:
|
||||
mc_acc = eval_aokvqa(
|
||||
dataset,
|
||||
mc_predictions,
|
||||
multiple_choice=True,
|
||||
strict=False
|
||||
)
|
||||
print(prediction_file, 'MC', mc_acc)
|
||||
|
||||
# Direct Answer
|
||||
|
||||
da_predictions = {}
|
||||
|
||||
for q in predictions.keys():
|
||||
if 'direct_answer' in predictions[q].keys():
|
||||
da_predictions[q] = predictions[q]['direct_answer']
|
||||
|
||||
if da_predictions != {}:
|
||||
da_acc = eval_aokvqa(
|
||||
dataset,
|
||||
da_predictions,
|
||||
multiple_choice=False,
|
||||
strict=False
|
||||
)
|
||||
print(prediction_file, 'DA', da_acc)
|
||||
@ -1,13 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
|
||||
|
||||
def load_aokvqa(aokvqa_dir, split, version='v1p0'):
|
||||
assert split in ['train', 'val', 'test', 'test_w_ans']
|
||||
dataset = json.load(open(
|
||||
os.path.join(aokvqa_dir, f"aokvqa_{version}_{split}.json")
|
||||
))
|
||||
return dataset
|
||||
|
||||
def get_coco_path(split, image_id, coco_dir):
|
||||
return os.path.join(coco_dir, f"{split}2017", f"{image_id:012}.jpg")
|
||||
@ -1,31 +0,0 @@
|
||||
import argparse
|
||||
import pathlib
|
||||
import json
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--mc', type=argparse.FileType('r'), dest='mc_pred_file')
|
||||
parser.add_argument('--da', type=argparse.FileType('r'), dest='da_pred_file')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), dest='output_file')
|
||||
args = parser.parse_args()
|
||||
assert args.mc_pred_file or args.da_pred_file
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
mc_preds = json.load(args.mc_pred_file) if args.mc_pred_file else None
|
||||
da_preds = json.load(args.da_pred_file) if args.da_pred_file else None
|
||||
predictions = {}
|
||||
|
||||
for d in dataset:
|
||||
q = d['question_id']
|
||||
predictions[q] = {}
|
||||
if mc_preds and q in mc_preds.keys():
|
||||
predictions[q]['multiple_choice'] = mc_preds[q]
|
||||
if da_preds and q in da_preds.keys():
|
||||
predictions[q]['direct_answer'] = da_preds[q]
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,44 +0,0 @@
|
||||
import argparse
|
||||
import pathlib
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
|
||||
from sentence_transformers import SentenceTransformer
|
||||
from sentence_transformers.util import cos_sim
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
def map_to_choices(dataset, predictions, device='cpu'):
|
||||
if isinstance(dataset, list):
|
||||
dataset = { dataset[i]['question_id'] : dataset[i] for i in range(len(dataset)) }
|
||||
|
||||
if all([p in dataset[q]['choices'] for q, p in predictions.items()]):
|
||||
return predictions
|
||||
|
||||
model = SentenceTransformer('sentence-transformers/average_word_embeddings_glove.6B.300d')
|
||||
model.to(device)
|
||||
for q in tqdm(predictions.keys()):
|
||||
choices = dataset[q]['choices']
|
||||
if predictions[q] not in choices:
|
||||
choice_embeddings = model.encode([predictions[q]] + choices, convert_to_tensor=True)
|
||||
a_idx = cos_sim(choice_embeddings[0], choice_embeddings[1:]).argmax().item()
|
||||
predictions[q] = choices[a_idx]
|
||||
|
||||
return predictions
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--pred', type=argparse.FileType('r'), required=True, dest='prediction_file')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
dataset = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
predictions = json.load(args.prediction_file)
|
||||
predictions = map_to_choices(dataset, predictions)
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,14 +0,0 @@
|
||||
## Querying GPT-3
|
||||
|
||||
To follow our experiments which use GPT-3, you must have access to the [OpenAI API](https://openai.com/api/) (at cost). Please retrieve your [organization](https://beta.openai.com/account/org-settings) and [API](https://beta.openai.com/account/api-keys) keys and set them in your environment variables.
|
||||
|
||||
```bash
|
||||
export OPENAI_ORG=....
|
||||
export OPENAI_API_KEY=...
|
||||
```
|
||||
|
||||
For producing predictions for both DA and MC settings, run:
|
||||
```bash
|
||||
python gpt3/query_gpt3.py --aokvqa-dir ${AOKVQA_DIR} --split val --out ${PREDS_DIR}/gpt3_val-da.json
|
||||
python remap_predictions.py --aokvqa-dir ${AOKVQA_DIR} --split val --pred ${PREDS_DIR}/gpt3_val-da.json --out ${PREDS_DIR}/gpt3_val-mc.json
|
||||
```
|
||||
@ -1,23 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--coco-dir', type=pathlib.Path, required=True, dest='coco_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val'], required=True)
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
aokvqa_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
coco_captions = json.load(open(os.path.join(args.coco_dir, 'annotations', f'captions_{args.split}2017.json')))['annotations']
|
||||
coco_captions = {c['image_id'] : c['caption'] for c in coco_captions}
|
||||
|
||||
captions = { d['question_id'] : coco_captions[d['image_id']] for d in aokvqa_set }
|
||||
|
||||
json.dump(captions, args.output_file)
|
||||
@ -1,79 +0,0 @@
|
||||
import os
|
||||
import random
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
import openai
|
||||
openai.organization = os.getenv('OPENAI_ORG')
|
||||
openai.api_key = os.getenv('OPENAI_API_KEY')
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
random.seed(0)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--n', type=int, default=10, dest='num_examples')
|
||||
parser.add_argument('--train-context', type=argparse.FileType('r'), dest='train_context_file')
|
||||
parser.add_argument('--prefix', type=str, default='', dest='prompt_prefix')
|
||||
parser.add_argument('--include-choices', action='store_true', dest='include_choices')
|
||||
parser.add_argument('--context', type=argparse.FileType('r'), dest='context_file')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
train_set = load_aokvqa(args.aokvqa_dir, 'train')
|
||||
eval_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
train_context = {}
|
||||
context = {}
|
||||
if args.context_file is not None:
|
||||
train_context = json.load(args.train_context_file)
|
||||
context = json.load(args.context_file)
|
||||
|
||||
predictions = {}
|
||||
|
||||
for d in tqdm(eval_set):
|
||||
q = d['question_id']
|
||||
|
||||
prompt = args.prompt_prefix
|
||||
for e in random.sample(train_set, args.num_examples):
|
||||
prompt += prompt_element(e,
|
||||
context=train_context.get(q, None),
|
||||
include_choices=args.include_choices,
|
||||
answer=True
|
||||
)
|
||||
prompt += '\n\n'
|
||||
|
||||
prompt += prompt_element(d,
|
||||
context=context.get(q, None),
|
||||
include_choices=args.include_choices,
|
||||
answer=False
|
||||
)
|
||||
|
||||
response = openai.Completion.create(
|
||||
engine="text-curie-001",
|
||||
prompt=prompt,
|
||||
temperature=0.0,
|
||||
max_tokens=10,
|
||||
)
|
||||
|
||||
predictions[q] = response.choices[0].text.strip()
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
|
||||
|
||||
def prompt_element(d, context=None, include_choices=False, answer=False):
|
||||
return (f"Context: {context}\n" if context is not None else '') + \
|
||||
f"Q: {d['question']}\n" + \
|
||||
(f"Choices: {', '.join(d['choices'])}.\n" if include_choices else '') + \
|
||||
f"A:" + (f" {d['choices'][d['correct_choice_idx']]}" if answer else '')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,16 +0,0 @@
|
||||
import json
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test_w_ans'], required=True)
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
aokvqa_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
rationales = {d['question_id'] : d['rationales'][0] for d in aokvqa_set}
|
||||
json.dump(rationales, args.output_file)
|
||||
@ -1,11 +0,0 @@
|
||||
## Heuristics
|
||||
|
||||
```bash
|
||||
# These scripts accept the same arguments.
|
||||
# heuristics/random_unweighted.py
|
||||
# heuristics/random_weighted.py
|
||||
# heuristics/most_common_answer.py
|
||||
|
||||
python heuristics/random_unweighted.py --aokvqa-dir ${AOKVQA_DIR} --split val --mc --out ${PREDS_DIR}/random-unweighted_val-mc.json
|
||||
# Exclude --mc for the direct answer setting
|
||||
```
|
||||
@ -1,39 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
import argparse
|
||||
import pathlib
|
||||
from collections import Counter
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--mc', action='store_true', dest='multiple_choice')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
train_set = load_aokvqa(args.aokvqa_dir, 'train')
|
||||
train_freq = dict(Counter(
|
||||
[d['choices'][d['correct_choice_idx']] for d in train_set]
|
||||
))
|
||||
most_common_answer = max(train_freq.keys(), key=train_freq.get)
|
||||
|
||||
##
|
||||
|
||||
eval_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
predictions = {}
|
||||
|
||||
for d in eval_set:
|
||||
q = d['question_id']
|
||||
predictions[q] = most_common_answer
|
||||
|
||||
if args.multiple_choice:
|
||||
choices = [c for c in d['choices'] if c in train_freq.keys()]
|
||||
if len(choices) > 0:
|
||||
predictions[q] = max(choices, key=train_freq.get)
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,38 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
from random import seed, sample
|
||||
import argparse
|
||||
import pathlib
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--mc', action='store_true', dest='multiple_choice')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
seed(0)
|
||||
|
||||
train_set = load_aokvqa(args.aokvqa_dir, 'train')
|
||||
|
||||
if args.multiple_choice is False:
|
||||
choices = list(set(
|
||||
[d['choices'][d['correct_choice_idx']] for d in train_set]
|
||||
))
|
||||
|
||||
##
|
||||
|
||||
predictions = {}
|
||||
|
||||
eval_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
for d in eval_set:
|
||||
q = d['question_id']
|
||||
if args.multiple_choice:
|
||||
choices = d['choices']
|
||||
predictions[q] = sample(choices, 1)[0]
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,46 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
import numpy as np
|
||||
import argparse
|
||||
import pathlib
|
||||
from collections import Counter
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--mc', action='store_true', dest='multiple_choice')
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
|
||||
args = parser.parse_args()
|
||||
|
||||
np.random.seed(0)
|
||||
|
||||
train_set = load_aokvqa(args.aokvqa_dir, 'train')
|
||||
train_freq = dict(Counter(
|
||||
[d['choices'][d['correct_choice_idx']] for d in train_set]
|
||||
))
|
||||
|
||||
if args.multiple_choice is False:
|
||||
choices = list(train_freq.keys())
|
||||
probs = [f / len(train_set) for f in train_freq.values()]
|
||||
|
||||
##
|
||||
|
||||
predictions = {}
|
||||
|
||||
eval_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
for d in eval_set:
|
||||
if args.multiple_choice:
|
||||
choices = d['choices']
|
||||
probs = [train_freq.get(c, 0) for c in choices]
|
||||
if probs == [0, 0, 0, 0]:
|
||||
probs = [1, 1, 1, 1]
|
||||
probs = [p / sum(probs) for p in probs]
|
||||
|
||||
q = d['question_id']
|
||||
predictions[q] = np.random.choice(choices, size=1, p=probs)[0]
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,13 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
|
||||
|
||||
def load_aokvqa(aokvqa_dir, split, version='v1p0'):
|
||||
assert split in ['train', 'val', 'test', 'test_w_ans']
|
||||
dataset = json.load(open(
|
||||
os.path.join(aokvqa_dir, f"aokvqa_{version}_{split}.json")
|
||||
))
|
||||
return dataset
|
||||
|
||||
def get_coco_path(split, image_id, coco_dir):
|
||||
return os.path.join(coco_dir, f"{split}2017", f"{image_id:012}.jpg")
|
||||
@ -1,41 +0,0 @@
|
||||
## Transfer Learning Experiments
|
||||
|
||||
We use the following training/prediction scripts for the classifier, zero-shot, and contrastive experiments in Table 3.
|
||||
|
||||
```bash
|
||||
## Training
|
||||
python transfer_experiments/train.py --aokvqa-dir ${AOKVQA_DIR} --vocab ${AOKVQA_DIR}/large_vocab_train.csv --log-dir ${LOG_DIR}
|
||||
|
||||
--backbone clip --clip-model-type ViT-B/32 --train-features ${FEATURES_DIR}/clip-ViT-B-32_train.pt --val-features ${FEATURES_DIR}/clip-ViT-B-32_val.pt
|
||||
--inputs question # OR --inputs image # OR --inputs question image
|
||||
# OR
|
||||
--backbone resnet --train-features ${FEATURES_DIR}/resnet_train.pt --val-features ${FEATURES_DIR}/resnet_val.pt --inputs image
|
||||
# OR
|
||||
--backbone bert --train-features ${FEATURES_DIR}/bert_train.pt --val-features ${FEATURES_DIR}/bert_val.pt --inputs question
|
||||
|
||||
--objective classifier
|
||||
# OR
|
||||
--objective contrastive --vocab-features ${FEATURE_DIR}/clip-ViT-B-32_large_vocab.pt
|
||||
```
|
||||
|
||||
You can make predictions for CLIP zero-shot or from a classifier/contrastive checkpoint trained above.
|
||||
|
||||
```bash
|
||||
## Predicting
|
||||
python transfer_experiments/predict.py --aokvqa-dir ${AOKVQA_DIR} --out ${PREDS_DIR}/clip-classifier_val-mc.json
|
||||
|
||||
--split val # or test
|
||||
--features ${FEATURE_DIR}/clip-ViT-B-32_val.pt # adjust for backbone and eval split
|
||||
|
||||
--ckpt path/to/model.ckpt
|
||||
# OR
|
||||
--zero-shot --clip-model-type ViT-B/32
|
||||
--inputs question # OR --inputs image # OR --inputs question image
|
||||
|
||||
--mc # Multiple-choice. Exclude for direct-answer.
|
||||
|
||||
# IF classifier OR direct-answer
|
||||
--vocab ${AOKVQA_DIR}/large_vocab_train.csv
|
||||
# IF contrastive/zero-shot AND direct-answer
|
||||
--vocab-features ${FEATURES_DIR}/clip-ViT-B-32_large_vocab.pt
|
||||
```
|
||||
@ -1,126 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
import argparse
|
||||
import pathlib
|
||||
from tqdm import tqdm
|
||||
import json
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
# https://github.com/PyTorchLightning/pytorch-lightning/issues/11663
|
||||
import sentencepiece; import pytorch_lightning as pl; import clip
|
||||
|
||||
from transfer_experiments.train import LinearClassifier
|
||||
from load_aokvqa import load_aokvqa
|
||||
from evaluation.remap_predictions import map_to_choices
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--features', type=pathlib.Path, required=True)
|
||||
parser.add_argument('--out', type=argparse.FileType('w'), dest='output_file')
|
||||
#
|
||||
parser_weights = parser.add_mutually_exclusive_group(required=True)
|
||||
|
||||
parser_weights.add_argument('--ckpt', type=pathlib.Path, dest='checkpoint_path')
|
||||
|
||||
parser_weights.add_argument('--zero-shot', action='store_true', dest='clip_zero_shot')
|
||||
parser.add_argument('--inputs', nargs='+', type=str, choices=['question', 'image'], required=('--zero-shot' in sys.argv))
|
||||
#
|
||||
parser.add_argument('--vocab', type=argparse.FileType('r'))
|
||||
parser.add_argument('--vocab-features', type=pathlib.Path, dest='vocab_features')
|
||||
parser.add_argument('--mc', action='store_true', dest='multiple_choice')
|
||||
|
||||
parser.add_argument('--clip-model-type', type=str,
|
||||
choices=['RN50', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'],
|
||||
dest='clip_model_type', required=('--zero-shot' in sys.argv and '--mc' in sys.argv))
|
||||
#
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
## Load dataset
|
||||
|
||||
aokvqa_set = load_aokvqa(args.aokvqa_dir, args.split)
|
||||
|
||||
## Load models
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
if args.checkpoint_path is not None:
|
||||
classifier = LinearClassifier.load_from_checkpoint(args.checkpoint_path)
|
||||
classifier.to(device)
|
||||
hp = classifier.hparams
|
||||
elif args.clip_zero_shot:
|
||||
classifier = nn.Identity().to(device)
|
||||
hp = pl.utilities.AttributeDict(backbone='clip', clip_model_type=args.clip_model_type, objective='zero-shot', inputs=args.inputs)
|
||||
|
||||
# Load input features
|
||||
|
||||
embeddings = torch.load(args.features)
|
||||
if hp.backbone == 'clip':
|
||||
for q in embeddings.keys():
|
||||
embeddings[q]['question'] = embeddings[q]['question'] / embeddings[q]['question'].norm(dim=-1, keepdim=True)
|
||||
embeddings[q]['image'] = embeddings[q]['image'] / embeddings[q]['image'].norm(dim=-1, keepdim=True)
|
||||
|
||||
# Load vocab, vocab features, clip
|
||||
|
||||
if (hp.objective == 'classifier') or \
|
||||
(hp.objective in ['contrastive', 'zero-shot'] and args.multiple_choice is False):
|
||||
vocab = args.vocab.read().splitlines()
|
||||
|
||||
if hp.objective in ['contrastive', 'zero-shot']:
|
||||
if args.multiple_choice is False:
|
||||
vocab_features = torch.load(args.vocab_features).cpu()
|
||||
vocab_features /= vocab_features.norm(dim=-1, keepdim=True)
|
||||
else:
|
||||
clip_model = clip.load(hp.clip_model_type, device=device)[0]
|
||||
logit_scale = clip_model.logit_scale.exp().cpu()
|
||||
|
||||
## Prediction loop
|
||||
|
||||
predictions = {}
|
||||
|
||||
with torch.no_grad():
|
||||
for o in tqdm(aokvqa_set):
|
||||
q = o['question_id']
|
||||
|
||||
# Load input embedding (from question / image)
|
||||
if hp.objective == 'zero-shot' and ('question' in hp.inputs and 'image' in hp.inputs):
|
||||
e = embeddings[q]['question'] + embeddings[q]['image']
|
||||
elif 'question' in hp.inputs and 'image' in hp.inputs:
|
||||
e = torch.cat((embeddings[q]['question'], embeddings[q]['image']))
|
||||
elif 'question' in hp.inputs:
|
||||
e = embeddings[q]['question']
|
||||
elif 'image' in hp.inputs:
|
||||
e = embeddings[q]['image']
|
||||
|
||||
# Pass inputs through model
|
||||
e = e.unsqueeze(0).to(device)
|
||||
x = classifier(e)[0].cpu()
|
||||
|
||||
# Predict
|
||||
if hp.objective in ['contrastive', 'zero-shot']:
|
||||
if args.multiple_choice:
|
||||
vocab = o['choices']
|
||||
# Encode choices
|
||||
vocab_features = clip.tokenize(vocab).to(device)
|
||||
vocab_features = torch.stack([
|
||||
clip_model.encode_text(v.unsqueeze(0)) for v in vocab_features
|
||||
], dim=1)[0]
|
||||
vocab_features /= vocab_features.norm(dim=-1, keepdim=True)
|
||||
vocab_features = vocab_features.float().cpu()
|
||||
|
||||
x = logit_scale * x @ vocab_features.t()
|
||||
x = x.softmax(dim=-1)
|
||||
|
||||
predictions[q] = vocab[x.argmax().item()]
|
||||
|
||||
## Save and evaluate predictions
|
||||
|
||||
# Map prediction to nearest neighbor choice (by word embeddings)
|
||||
if args.multiple_choice and hp.objective == 'classifier':
|
||||
predictions = map_to_choices(aokvqa_set, predictions)
|
||||
|
||||
json.dump(predictions, args.output_file)
|
||||
@ -1,263 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
import pathlib
|
||||
import random
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
|
||||
# https://github.com/PyTorchLightning/pytorch-lightning/issues/11663
|
||||
import sentencepiece; import pytorch_lightning as pl
|
||||
|
||||
import torchmetrics.functional as MF
|
||||
|
||||
from load_aokvqa import load_aokvqa
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
|
||||
parser.add_argument('--vocab', type=argparse.FileType('r'), required=True)
|
||||
parser.add_argument('--log-dir', type=pathlib.Path, dest='log_dir', required=True)
|
||||
#
|
||||
parser.add_argument('--backbone', type=str, choices=['clip', 'resnet', 'bert'], required=True)
|
||||
parser.add_argument('--clip-model-type', type=str,
|
||||
choices=['RN50', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'],
|
||||
dest='clip_model_type', required=('clip' in sys.argv))
|
||||
parser.add_argument('--train-features', type=pathlib.Path, required=True, dest='train_features')
|
||||
parser.add_argument('--val-features', type=pathlib.Path, required=True, dest='val_features')
|
||||
parser.add_argument('--vocab-features', type=pathlib.Path, required=('contrastive' in sys.argv), dest='vocab_features')
|
||||
#
|
||||
parser.add_argument('--objective', type=str, choices=['classifier', 'contrastive'], required=True)
|
||||
parser.add_argument('--inputs', nargs='+', type=str, choices=['question', 'image'], required=True)
|
||||
# Defaults
|
||||
parser.add_argument('--bs', type=int, default=128, dest='batch_size')
|
||||
parser.add_argument('--lr', type=float, default=0.01)
|
||||
parser.add_argument('--epochs', type=int, default=500)
|
||||
parser.add_argument('--gpus', type=int, default=1)
|
||||
args = parser.parse_args()
|
||||
|
||||
pl.seed_everything(1)
|
||||
vocab = args.vocab.read().splitlines()
|
||||
|
||||
## Data loading
|
||||
|
||||
dm = AokvqaEmbeddingsDataModule(
|
||||
args.aokvqa_dir,
|
||||
args.train_features,
|
||||
args.val_features,
|
||||
args.objective,
|
||||
args.backbone,
|
||||
args.inputs,
|
||||
vocab,
|
||||
args.vocab_features,
|
||||
batch_size=args.batch_size,
|
||||
num_workers=16
|
||||
)
|
||||
|
||||
## Model definition
|
||||
|
||||
model = LinearClassifier(
|
||||
args.objective,
|
||||
args.backbone,
|
||||
args.clip_model_type,
|
||||
args.inputs,
|
||||
len(vocab),
|
||||
args.lr
|
||||
)
|
||||
|
||||
## Training and testing loops
|
||||
|
||||
logger = pl.loggers.TensorBoardLogger(
|
||||
args.log_dir,
|
||||
name=f'{args.backbone}-{args.objective}',
|
||||
version=f"inputs:{'+'.join(args.inputs)}"
|
||||
)
|
||||
|
||||
trainer = pl.Trainer(
|
||||
logger=logger,
|
||||
gpus=args.gpus,
|
||||
max_epochs=args.epochs,
|
||||
callbacks=[
|
||||
pl.callbacks.ModelCheckpoint(
|
||||
monitor="val_acc",
|
||||
filename="{epoch:02d}-{val_acc:.2f}",
|
||||
mode="max"
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
trainer.fit(model, dm)
|
||||
|
||||
|
||||
class AokvqaEmbeddingsDataset(Dataset):
|
||||
def __init__(self, aokvqa_dir, split, input_features, objective, backbone, inputs, vocab, vocab_features):
|
||||
|
||||
aokvqa_set = load_aokvqa(aokvqa_dir, split)
|
||||
|
||||
assert ( backbone == 'resnet' and inputs == ['image'] and objective == 'classifier' ) \
|
||||
or ( backbone == 'bert' and inputs == ['question'] and objective == 'classifier' ) \
|
||||
or ( backbone == 'clip' )
|
||||
|
||||
embeddings = torch.load(input_features)
|
||||
if backbone == 'clip':
|
||||
for q in embeddings.keys():
|
||||
embeddings[q]['question'] /= embeddings[q]['question'].norm(dim=-1, keepdim=True)
|
||||
embeddings[q]['image'] /= embeddings[q]['image'].norm(dim=-1, keepdim=True)
|
||||
if objective == 'contrastive':
|
||||
vocab_embeddings = torch.load(vocab_features)
|
||||
vocab_embeddings /= vocab_embeddings.norm(dim=-1, keepdim=True)
|
||||
|
||||
self.objective = objective
|
||||
self.vocab_len = len(vocab)
|
||||
|
||||
self.embeddings = []
|
||||
self.answers = []
|
||||
|
||||
for o in aokvqa_set:
|
||||
correct_answers = set([o['choices'][o['correct_choice_idx']]] + o['direct_answers'])
|
||||
correct_answers = [vocab.index(a) for a in correct_answers if a in vocab]
|
||||
if self.objective == 'contrastive':
|
||||
correct_answers = [vocab_embeddings[a] for a in correct_answers]
|
||||
if len(correct_answers) == 0: continue
|
||||
self.answers.append(correct_answers)
|
||||
|
||||
q = o['question_id']
|
||||
if 'question' in inputs and 'image' in inputs:
|
||||
e = torch.cat((embeddings[q]['question'], embeddings[q]['image']))
|
||||
elif 'question' in inputs and 'image' not in inputs:
|
||||
e = embeddings[q]['question']
|
||||
elif 'question' not in inputs and 'image' in inputs:
|
||||
e = embeddings[q]['image']
|
||||
self.embeddings.append(e)
|
||||
|
||||
def __getitem__(self, index):
|
||||
e = self.embeddings[index]
|
||||
a = self.answers[index]
|
||||
if self.objective == 'classifier':
|
||||
a = torch.sum(F.one_hot(torch.tensor(a), num_classes=self.vocab_len), dim=0)
|
||||
elif self.objective == 'contrastive':
|
||||
a = random.sample(a, 1)[0]
|
||||
return e, a
|
||||
|
||||
def __len__(self):
|
||||
return len(self.embeddings)
|
||||
|
||||
|
||||
class AokvqaEmbeddingsDataModule(pl.LightningDataModule):
|
||||
|
||||
def __init__(self, aokvqa_dir, train_features, val_features, objective, backbone, inputs, vocab, vocab_features, batch_size=1, num_workers=0):
|
||||
super().__init__()
|
||||
self.aokvqa_dir = aokvqa_dir
|
||||
self.train_features = train_features
|
||||
self.val_features = val_features
|
||||
self.objective = objective
|
||||
self.backbone = backbone
|
||||
self.inputs = inputs
|
||||
self.vocab = vocab
|
||||
self.vocab_features = vocab_features
|
||||
self.batch_size = batch_size
|
||||
self.num_workers = num_workers
|
||||
|
||||
def setup(self, stage=None):
|
||||
self.train_dataset = AokvqaEmbeddingsDataset(
|
||||
self.aokvqa_dir, 'train', self.train_features, self.objective,
|
||||
self.backbone, self.inputs, self.vocab, self.vocab_features
|
||||
)
|
||||
self.val_dataset = AokvqaEmbeddingsDataset(
|
||||
self.aokvqa_dir, 'val', self.val_features, self.objective,
|
||||
self.backbone, self.inputs, self.vocab, self.vocab_features
|
||||
)
|
||||
|
||||
def train_dataloader(self):
|
||||
return DataLoader(
|
||||
self.train_dataset, batch_size=self.batch_size, shuffle=True,
|
||||
num_workers=int(0.8 * self.num_workers)
|
||||
)
|
||||
|
||||
def val_dataloader(self):
|
||||
return DataLoader(
|
||||
self.val_dataset, batch_size=self.batch_size, shuffle=False,
|
||||
num_workers=int(0.2 * self.num_workers)
|
||||
)
|
||||
|
||||
|
||||
class LinearClassifier(pl.LightningModule):
|
||||
def __init__(self, objective, backbone, clip_model_type, inputs, vocab_len, lr=0.001):
|
||||
super().__init__()
|
||||
self.save_hyperparameters(ignore=['lr'])
|
||||
self.lr = lr
|
||||
|
||||
if self.hparams.backbone == 'clip':
|
||||
clip_dim = {
|
||||
'RN50' : 1024,
|
||||
'RN50x4' : 640,
|
||||
'RN50x16' : 768,
|
||||
'RN50x64' : 1024,
|
||||
'RN101' : 512,
|
||||
'ViT-B/32' : 512,
|
||||
'ViT-B/16' : 512,
|
||||
'ViT-L/14' : 768,
|
||||
'ViT-L/14@336px' : 768,
|
||||
}[clip_model_type]
|
||||
emb_dim = clip_dim * len(inputs)
|
||||
elif self.hparams.backbone == 'resnet':
|
||||
emb_dim = 2048
|
||||
elif self.hparams.backbone == 'bert':
|
||||
emb_dim = 768
|
||||
|
||||
if self.hparams.objective == 'classifier':
|
||||
out_dim = vocab_len
|
||||
elif self.hparams.objective == 'contrastive':
|
||||
out_dim = clip_dim
|
||||
|
||||
self.linear = nn.Linear(emb_dim, out_dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear(x)
|
||||
if self.hparams.objective == 'classifier':
|
||||
x = torch.sigmoid(x)
|
||||
return x
|
||||
|
||||
def compute_loss(self, batch):
|
||||
x, y = batch
|
||||
|
||||
y_pred = self.forward(x)
|
||||
|
||||
if self.hparams.objective == 'classifier':
|
||||
loss = F.binary_cross_entropy(y_pred, y.float())
|
||||
elif self.hparams.objective == 'contrastive':
|
||||
indices = torch.arange(0, x.shape[0], dtype=torch.int64, device=self.device)
|
||||
sim = (y_pred @ y.T).softmax(dim=-1)
|
||||
loss = F.cross_entropy(sim, indices)
|
||||
|
||||
if self.hparams.objective == 'classifier':
|
||||
acc = MF.f1_score(y_pred, y)
|
||||
elif self.hparams.objective == 'contrastive':
|
||||
acc = torch.mean(sim[indices, indices])
|
||||
|
||||
return loss, acc
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
loss, acc = self.compute_loss(batch)
|
||||
self.log("train_loss", loss)
|
||||
self.log("train_acc", acc)
|
||||
return loss
|
||||
|
||||
def validation_step(self, batch, batch_idx):
|
||||
loss, acc = self.compute_loss(batch)
|
||||
self.log("val_loss", loss)
|
||||
self.log("val_acc", acc)
|
||||
return loss
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
|
||||
return optimizer
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -11,7 +11,7 @@ model:
|
||||
# generation configs
|
||||
prompt: ""
|
||||
|
||||
llama_model: "please set this value to the path of llama2-chat-7b"
|
||||
llama_model: "/ibex/project/c2133/llama_v2/llama-2-7b-chat-pytorch"
|
||||
lora_r: 64
|
||||
lora_alpha: 16
|
||||
|
||||
|
||||
@ -127,7 +127,14 @@ CONV_VISION_LLama2 = Conversation(
|
||||
sep="",
|
||||
)
|
||||
|
||||
|
||||
CONV_VISION_minigptv2 = Conversation(
|
||||
system="",
|
||||
roles=("<s>[INST] ", " [/INST]"),
|
||||
messages=[],
|
||||
offset=2,
|
||||
sep_style=SeparatorStyle.SINGLE,
|
||||
sep="",
|
||||
)
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class Chat:
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def __init__(self, model, vis_processor, device='cuda:0', stopping_criteria=None):
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|
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@ -3,7 +3,6 @@ import json
|
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import pickle
|
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import random
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import time
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# import iterto
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import numpy as np
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from PIL import Image
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import skimage.io as io
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@ -65,7 +65,6 @@ class OKVQAEvalData(torch.utils.data.Dataset):
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image = Image.open(image_path).convert('RGB')
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image = self.vis_processor(image)
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question = f"[vqa] Based on the image, respond to this question with a short answer: {question}"
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# question = f"[vqa] {question} "
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return image, question, question_id, img_id
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class VizWizEvalData(torch.utils.data.Dataset):
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@ -86,54 +85,9 @@ class VizWizEvalData(torch.utils.data.Dataset):
|
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image_path = os.path.join(self.root_path, img_id)
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image = Image.open(image_path).convert('RGB')
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image = self.vis_processor(image)
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# question = f"[vqa] Based on the image, respond to this question with a short answer: {question} "
|
||||
question = f"[vqa] Based on the image, respond to this question with a short answer: {question} and reply 'unanswerable' if you could not answer it"
|
||||
question = f"[vqa] The question is'{question}' Based on the image, answer the question with a single word or phrase. and reply 'unanswerable' when the provided information is insufficient" # 52.0
|
||||
return image, question, answers
|
||||
|
||||
class AOKVQADAEvalData(torch.utils.data.Dataset):
|
||||
def __init__(self, loaded_data, vis_processor, root_path):
|
||||
self.loaded_data = loaded_data
|
||||
self.root_path = root_path
|
||||
self.vis_processor = vis_processor
|
||||
|
||||
def __len__(self):
|
||||
return len(self.loaded_data)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
data = self.loaded_data[idx]
|
||||
img_file = data['image']
|
||||
question = data['question']
|
||||
question_id = data['question_id']
|
||||
image_path = os.path.join(self.root_path, img_file)
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
image = self.vis_processor(image)
|
||||
question = f"[vqa] Based on the image, respond to this question with a short answer: {question}"
|
||||
# question = f"[vqa] {question} "
|
||||
return image, question, question_id
|
||||
|
||||
class AOKVQAMCEvalData(torch.utils.data.Dataset):
|
||||
def __init__(self, loaded_data, vis_processor, root_path):
|
||||
self.loaded_data = loaded_data
|
||||
self.root_path = root_path
|
||||
self.vis_processor = vis_processor
|
||||
|
||||
def __len__(self):
|
||||
return len(self.loaded_data)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
data = self.loaded_data[idx]
|
||||
img_file = data['image']
|
||||
question = data['question']
|
||||
question_id = data['question_id']
|
||||
image_path = os.path.join(self.root_path, img_file)
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
image = self.vis_processor(image).half().cuda()
|
||||
candidates=data['choices']
|
||||
# question = f"Given this image, choose one correct answer from {candidates} for this question: {question} "
|
||||
question = f"[vqa] Based on the image, respond to this question with a short answer: {question}"
|
||||
# question = f"[vqa] {question} "
|
||||
return image, question, question_id, candidates
|
||||
|
||||
class IconQAEvalData(torch.utils.data.Dataset):
|
||||
def __init__(self, loaded_data, vis_processor, root_path):
|
||||
self.loaded_data = loaded_data
|
||||
@ -152,9 +106,7 @@ class IconQAEvalData(torch.utils.data.Dataset):
|
||||
image = self.vis_processor(image).half().cuda()
|
||||
candidates = '_'.join(data['choices'])
|
||||
answer = data['answer']
|
||||
# question = f"Given this image, choose one correct answer from {candidates} for this question: {question} "
|
||||
question = f"[vqa] Based on the image, respond to this question with a short answer: {question}"
|
||||
# question = f"[vqa] {question} "
|
||||
return image, question, candidates, answer
|
||||
|
||||
class GQAEvalData(torch.utils.data.Dataset):
|
||||
@ -173,9 +125,7 @@ class GQAEvalData(torch.utils.data.Dataset):
|
||||
image = Image.open(image_path).convert("RGB")
|
||||
image = self.vis_processor(image)
|
||||
question = ann["question"]
|
||||
# question = f'Question: {question} Short answer: '
|
||||
question = f"[vqa] Based on the image, respond to this question with a short answer: {question}"
|
||||
# question = f"[vqa] {question} "
|
||||
labels = ann["answer"]
|
||||
|
||||
return image, question, labels
|
||||
@ -196,7 +146,7 @@ class HMEvalData(torch.utils.data.Dataset):
|
||||
image = Image.open(image_path).convert("RGB")
|
||||
image = self.vis_processor(image)
|
||||
question = ann["text"]
|
||||
question = f"[vqa] This is an image with: \"{question}\" written on it. Is it hateful? Answer:"
|
||||
question = f"This is an image writting '{question}'. Is this image hateful? Answer yes or no. Answer:"
|
||||
labels = ann["label"]
|
||||
|
||||
return image, question, labels
|
||||
@ -217,7 +167,6 @@ class VSREvalData(torch.utils.data.Dataset):
|
||||
image = self.vis_processor(image)
|
||||
question = ann["caption"]
|
||||
question = f'[vqa] Based on the image, is this statement true or false? {question}'
|
||||
question_id = ann["image"].split('.')[0]
|
||||
labels = 'true' if ann["label"] == 1 else 'false'
|
||||
|
||||
return image, question, labels
|
||||
Loading…
Reference in New Issue
Block a user