eval_mm/README.md

Evaluation

opencompass

First, enter the vlmevalkit directory and install all dependencies:

cd vlmevalkit
pip install -r requirements.txt

Then, run script/run_inference.sh, which receives three input parameters in sequence: MODELNAME, DATALIST, and MODE. MODELNAME represents the name of the model, DATALIST represents the datasets used for inference, and MODE represents evaluation mode:

chmod +x ./script/run_inference.sh
./script/run_inference.sh $MODELNAME $DATALIST $MODE

The three available choices for MODELNAME are listed in vlmeval/config.py:

ungrouped = {
    'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
    'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
    'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
}

All available choices for DATALIST are listed in vlmeval/utils/dataset_config.py. While evaluating on a single dataset, call the dataset name directly without quotation marks; while evaluating on multiple datasets, separate the names of different datasets with spaces and add quotation marks at both ends:

$DATALIST="POPE ScienceQA_TEST ChartQA_TEST"

While scoring on each benchmark directly, set MODE=all. If only inference results are required, set MODE=infer. In order to reproduce the results in the table displayed on the homepage (columns between MME and RealWorldQA), you need to run the script according to the following settings:

# run on all 7 datasets
./script/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all

# The following are instructions for running on a single dataset
# MME
./script/run_inference.sh MiniCPM-Llama3-V-2_5 MME all
# MMBench_TEST_EN
./script/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_EN all
# MMBench_TEST_CN
./script/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_CN all
# MMMU_DEV_VAL
./script/run_inference.sh MiniCPM-Llama3-V-2_5 MMMU_DEV_VAL all
# MathVista_MINI
./script/run_inference.sh MiniCPM-Llama3-V-2_5 MathVista_MINI all
# LLaVABench
./script/run_inference.sh MiniCPM-Llama3-V-2_5 LLaVABench all
# RealWorldQA
./script/run_inference.sh MiniCPM-Llama3-V-2_5 RealWorldQA all

vqadataset

First, enter the vqaeval directory and install all dependencies. Then, create downloads subdirectory to store the downloaded dataset for all tasks:

cd vqaeval
pip install -r requirements.txt
mkdir downloads

Download the datasets from the following links and place it in the specified directories:

TextVQA

cd downloads
mkdir TextVQA && cd TextVQA
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
unzip train_val_images.zip && rm train_val_images.zip
mv train_val_images/train_images . && rm -rf train_val_images
wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json
cd ../..

DocVQA / DocVQATest

cd downloads
mkdir DocVQA && cd DocVQA && mkdir spdocvqa_images
# Download Images and Annotations from Task 1 - Single Page Document Visual Question Answering at https://rrc.cvc.uab.es/?ch=17&com=downloads
# Move the spdocvqa_images.tar.gz and spdocvqa_qas.zip to DocVQA directory
tar -zxvf spdocvqa_images.tar.gz -C spdocvqa_images && rm spdocvqa_images.tar.gz
unzip spdocvqa_qas.zip && rm spdocvqa_qas.zip
cp spdocvqa_qas/val_v1.0_withQT.json . && cp spdocvqa_qas/test_v1.0.json .  && rm -rf spdocvqa_qas
cd ../..

The downloads directory should be organized according to the following structure:

downloads
├── TextVQA
│   ├── train_images
│   │   ├── ...
│   ├── TextVQA_0.5.1_val.json
├── DocVQA
│   ├── spdocvqa_images
│   │   ├── ...
│   ├── val_v1.0_withQT.json
│   ├── test_v1.0.json

Modify the parameters in shell/run_inference.sh and run inference:

chmod +x ./shell/run_inference.sh
./shell/run_inference.sh

All optional parameters are listed in eval_utils/getargs.py. The meanings of some major parameters are listed as follows:

# path to images and their corresponding questions
# TextVQA
--textVQA_image_dir
--textVQA_ann_path
# DocVQA
--docVQA_image_dir
--docVQA_ann_path
# DocVQATest
--docVQATest_image_dir
--docVQATest_ann_path

# whether to eval on certain task
--eval_textVQA
--eval_docVQA
--eval_docVQATest
--eval_all

# model name and model path
--model_name
--model_path
# load model from ckpt
--ckpt
# the way the model processes input data, "interleave" represents interleaved image-text form, while "old" represents non-interleaved.
--generate_method

--batchsize

# path to save the outputs
--answer_path

While evaluating on different tasks, parameters need to be set as follows:

TextVQA

--eval_textVQA
--textVQA_image_dir ./downloads/TextVQA/train_images
--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json

DocVQA

--eval_docVQA
--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images
--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json

DocVQATest

--eval_docVQATest
--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images
--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json

For the DocVQATest task, in order to upload the inference results to the official website for evaluation, run shell/run_transform.sh for format transformation after inference. input_file_path represents the path to the original output json, output_file_path represents the path to the transformed json:

chmod +x ./shell/run_transform.sh
./shell/run_transform.sh