Add eval_mm dir

eval_mm/README.md

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+# Evaluation
+
+## opencompass
+First, enter the `vlmevalkit` directory and install all dependencies:
+```bash
+cd vlmevalkit
+pip install -r requirements.txt
+```
+<br />
+
+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:
+```bash
+chmod +x ./script/run_inference.sh
+./script/run_inference.sh $MODELNAME $DATALIST $MODE
+```
+<br />
+
+The three available choices for `MODELNAME` are listed in `vlmeval/config.py`:
+```bash
+ungrouped = {
+    'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
+    'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
+    'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
+}
+```
+<br />
+
+All available choices for `DATALIST` are listed in `vlmeval/utils/dataset_config.py`. While evaluating on a single dataset, call the dataset name directly without quotation marks; while evaluating on multiple datasets, separate the names of different datasets with spaces and add quotation marks at both ends:
+```bash
+$DATALIST="POPE ScienceQA_TEST ChartQA_TEST"
+```
+<br />
+
+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:
+```bash
+# 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
+```
+<br />
+
+## vqadataset
+First, enter the `vqaeval` directory and install all dependencies. Then, create `downloads` subdirectory to store the downloaded dataset for all tasks:
+```bash
+cd vqaeval
+pip install -r requirements.txt
+mkdir downloads
+```
+<br />
+
+Download the datasets from the following links and place it in the specified directories:
+###### TextVQA
+```bash
+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
+
+```bash
+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 ../..
+```
+<br />
+
+The `downloads` directory should be organized according to the following structure:
+```bash
+downloads
+├── TextVQA
+│   ├── train_images
+│   │   ├── ...
+│   ├── TextVQA_0.5.1_val.json
+├── DocVQA
+│   ├── spdocvqa_images
+│   │   ├── ...
+│   ├── val_v1.0_withQT.json
+│   ├── test_v1.0.json
+```
+<br />
+
+Modify the parameters in `shell/run_inference.sh` and run inference:
+
+```bash
+chmod +x ./shell/run_inference.sh
+./shell/run_inference.sh
+```
+<br />
+
+All optional parameters are listed in `eval_utils/getargs.py`. The meanings of some major parameters are listed as follows:
+```bash
+# 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
+```
+<br />
+
+While evaluating on different tasks, parameters need to be set as follows:
+###### TextVQA
+```bash
+--eval_textVQA
+--textVQA_image_dir ./downloads/TextVQA/train_images
+--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json
+```
+
+###### DocVQA
+```bash
+--eval_docVQA
+--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images
+--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json
+```
+
+###### DocVQATest
+```bash
+--eval_docVQATest
+--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images
+--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json
+```
+
+<br />
+
+For the DocVQATest task, in order to upload the inference results to the [official website](https://rrc.cvc.uab.es/?ch=17) 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:
+```bash
+chmod +x ./shell/run_transform.sh
+./shell/run_transform.sh
+```