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Fine-tuning BEiT-3 on VQAv2 (Visual Question Answering)

Setup

  1. Setup environment.
  2. Download COCO 2014 train images, 2014 val images, 2015 test images, annotations (train, val), and questions (train, val, test), then organize the dataset as following structure:
/path/to/your_data/
  train2014/            
    COCO_train2014_000000000009.jpg                
    ...
  val2014/              
    COCO_val2014_000000000042.jpg
    ...  
  test2015/              
    COCO_test2015_000000000001.jpg
    ...         
  vqa/
    v2_OpenEnded_mscoco_train2014_questions.json
    v2_OpenEnded_mscoco_val2014_questions.json
    v2_OpenEnded_mscoco_test2015_questions.json
    v2_OpenEnded_mscoco_test-dev2015_questions.json
    v2_mscoco_train2014_annotations.json
    v2_mscoco_val2014_annotations.json

We then generate the index json files using the following command. beit3.spm is the sentencepiece model used for tokenizing texts.

from datasets import VQAv2Dataset
from transformers import XLMRobertaTokenizer

tokenizer = XLMRobertaTokenizer("/your_beit3_model_path/beit3.spm")

VQAv2Dataset.make_dataset_index(
    data_path="/path/to/your_data",
    tokenizer=tokenizer,
    annotation_data_path="/path/to/your_data/vqa",
)

Example: Fine-tuning BEiT-3 on VQAv2 (Visual Question Answering)

The BEiT-3 base model can be finetuned on VQAv2 using 8 V100-32GB:

python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
        --model beit3_base_patch16_480 \
        --input_size 480 \
        --task vqav2 \
        --batch_size 16 \
        --layer_decay 1.0 \
        --lr 3e-5 \
        --update_freq 1 \
        --randaug \
        --epochs 10 \
        --warmup_epochs 1 \
        --drop_path 0.1 \
        --sentencepiece_model /your_beit3_model_path/beit3.spm \
        --finetune /your_beit3_model_path/beit3_base_patch16_224.pth \
        --data_path /path/to/your_data \
        --output_dir /path/to/save/your_model \
        --log_dir /path/to/save/your_model/log \
        --weight_decay 0.01 \
        --seed 42 \
        --save_ckpt_freq 5 \
        --task_head_lr_weight 20 \
        --opt_betas 0.9 0.98 \
        --enable_deepspeed
  • --batch_size: batch size per GPU. Effective batch size = number of GPUs * --batch_size * --update_freq. So in the above example, the effective batch size is 8*16 = 128.
  • --finetune: weight path of your pretrained models; please download the pretrained model weights in README.md
  • --enable_deepspeed: optional. If you use apex, please enable deepspeed.

The BEiT-3 large model can be finetuned on VQAv2 using 8 V100-32GB:

python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
        --model beit3_large_patch16_480 \
        --input_size 480 \
        --task vqav2 \
        --batch_size 16 \
        --layer_decay 1.0 \
        --lr 2e-5 \
        --update_freq 1 \
        --randaug \
        --epochs 10 \
        --warmup_epochs 1 \
        --drop_path 0.15 \
        --sentencepiece_model /your_beit3_model_path/beit3.spm \
        --finetune /your_beit3_model_path/beit3_large_patch16_224.pth \
        --data_path /path/to/your_data \
        --output_dir /path/to/save/your_model \
        --log_dir /path/to/save/your_model/log \
        --weight_decay 0.01 \
        --seed 42 \
        --save_ckpt_freq 5 \
        --task_head_lr_weight 20 \
        --opt_betas 0.9 0.98 \
        --enable_deepspeed \
        --checkpoint_activations
  • --batch_size: batch size per GPU. Effective batch size = number of GPUs * --batch_size * --update_freq. So in the above example, the effective batch size is 8*16 = 128.
  • --finetune: weight path of your pretrained models; please download the pretrained model weights in README.md
  • --enable_deepspeed: optional. If you use apex, please enable deepspeed.
  • --checkpoint_activations: using gradient checkpointing for saving GPU memory

Example: Evaluate BEiT-3 Finetuned model on VQAv2 (Visual Question Answering)

  • Get the prediction file of the fine-tuned BEiT3-base model on VQAv2 test with 8 V100-32GB:
python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
        --model beit3_base_patch16_480 \
        --input_size 480 \
        --task vqav2 \
        --batch_size 16 \
        --sentencepiece_model /your_beit3_model_path/beit3.spm \
        --finetune /your_beit3_model_path/beit3_base_patch16_480_vqa.pth \
        --data_path /path/to/your_data \
        --output_dir /path/to/save/your_prediction \
        --eval \
        --dist_eval
  • Get the prediction file of the fine-tuned BEiT3-large model on VQAv2 test with 8 V100-32GB:
python -m torch.distributed.launch --nproc_per_node=8 run_beit3_finetuning.py \
        --model beit3_large_patch16_480 \
        --input_size 480 \
        --task vqav2 \
        --batch_size 16 \
        --sentencepiece_model /your_beit3_model_path/beit3.spm \
        --finetune /your_beit3_model_path/beit3_large_patch16_480_vqa.pth \
        --data_path /path/to/your_data \
        --output_dir /path/to/save/your_prediction \
        --eval \
        --dist_eval

Please then submit the prediction file in the output_dir to the evaluation server to obtain the VQAv2 test-dev and test-std results.