Merge pull request #543 from lihytotoro/main

Modify eval_mm for MiniCPM-V 2.6

eval_mm/README.md

@@ -1,60 +1,59 @@
 # Evaluation
 
-## opencompass
+## MiniCPM-V 2.6
+
+### opencompass
 First, enter the `vlmevalkit` directory and install all dependencies:
 ```bash
 cd vlmevalkit
-pip install -r requirements.txt
+pip install --upgrade pip
+pip install -e .
+wget https://download.pytorch.org/whl/cu118/torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=4377e0a7fe8ff8ffc4f7c9c6130c1dcd3874050ae4fc28b7ff1d35234fbca423
+wget https://download.pytorch.org/whl/cu118/torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=2e63d62e09d9b48b407d3e1b30eb8ae4e3abad6968e8d33093b60d0657542428
+wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+pip install torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl
+pip install torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl
+pip install flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
 ```
 <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:
+Then, run `scripts/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
+chmod +x ./scripts/run_inference.sh
-./script/run_inference.sh $MODELNAME $DATALIST $MODE
+./scripts/run_inference.sh $MODELNAME $DATALIST $MODE
 ```
 <br />
 
-The three available choices for `MODELNAME` are listed in `vlmeval/config.py`:
+The four available choices for `MODELNAME` are listed in `vlmeval/config.py`:
 ```bash
-ungrouped = {
+minicpm_series = {
     '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'),
+    'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'),
 }
 ```
 <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:
+All available choices for `DATALIST` are listed in `vlmeval/utils/dataset_config.py`. Separate the names of different datasets with spaces and add quotation marks at both ends:
 ```bash
-$DATALIST="POPE ScienceQA_TEST ChartQA_TEST"
+$DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_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:
+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 HallusionBench), you need to run the script according to the following settings:
 ```bash
-# run on all 7 datasets
+# without CoT
-./script/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all
+./scripts/run_inference.sh MiniCPM-V-2_6 "MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" all
-
+./scripts/run_inference.sh MiniCPM-V-2_6 MME all
-# The following are instructions for running on a single dataset
+# with CoT
-# MME
+# While running the CoT version of MME, you need to modify the 'use_cot' function in vlmeval/vlm/minicpm_v.py and add MME to the branch that returns True.
-./script/run_inference.sh MiniCPM-Llama3-V-2_5 MME all
+./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all
-# MMBench_TEST_EN
+./scripts/run_inference.sh MiniCPM-V-2_6 MME all
-./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
+### 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
@@ -112,7 +111,8 @@ chmod +x ./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:
+All optional parameters are listed in `eval_utils/getargs.py`. The meanings of some major parameters are listed as follows.
+For `MiniCPM-V-2_6`, set `model_name` to `minicpmv26`:
 ```bash
 # path to images and their corresponding questions
 # TextVQA
@@ -175,3 +175,188 @@ For the DocVQATest task, in order to upload the inference results to the [offici
 chmod +x ./shell/run_transform.sh
 ./shell/run_transform.sh
 ```
+<br />
+
+## MiniCPM-Llama3-V-2_5
+
+<details>
+<summary>Expand</summary>
+
+### opencompass
+First, enter the `vlmevalkit` directory and install all dependencies:
+```bash
+cd vlmevalkit
+pip install -r requirements.txt
+```
+<br />
+
+Then, run `scripts/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 ./scripts/run_inference.sh
+./scripts/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
+./scripts/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
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MME all
+# MMBench_TEST_EN
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_EN all
+# MMBench_TEST_CN
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_CN all
+# MMMU_DEV_VAL
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMMU_DEV_VAL all
+# MathVista_MINI
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MathVista_MINI all
+# LLaVABench
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 LLaVABench all
+# RealWorldQA
+./scripts/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.
+For `MiniCPM-Llama3-V-2_5`, set `model_name` to `minicpmv`:
+```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
+```
+
+</details>

eval_mm/README_zh.md

@@ -1,61 +1,60 @@
 # Evaluation
 
-## opencompass
+## MiniCPM-V 2.6
+
+### opencompass
 首先，进入 `vlmevalkit` 目录下，安装必要的依赖：
 ```bash
 cd vlmevalkit
-pip install -r requirements.txt
+pip install --upgrade pip
+pip install -e .
+wget https://download.pytorch.org/whl/cu118/torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=4377e0a7fe8ff8ffc4f7c9c6130c1dcd3874050ae4fc28b7ff1d35234fbca423
+wget https://download.pytorch.org/whl/cu118/torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=2e63d62e09d9b48b407d3e1b30eb8ae4e3abad6968e8d33093b60d0657542428
+wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+pip install torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl
+pip install torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl
+pip install flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+rm *.whl
 ```
 <br />
 
-然后，运行 `script/run_inference.sh`，该脚本依次接收三个输入参数：`MODELNAME`, `DATALIST`, `MODE`。`MODELNAME` 为模型名称，`DATALIST` 为目标数据集，`MODE` 为评测模式。
+然后，运行 `scripts/run_inference.sh`，该脚本依次接收三个输入参数：`MODELNAME`, `DATALIST`, `MODE`。`MODELNAME` 为模型名称，`DATALIST` 为目标数据集，`MODE` 为评测模式。
 ```bash
-chmod +x ./script/run_inference.sh
+chmod +x ./scripts/run_inference.sh
-./script/run_inference.sh $MODELNAME $DATALIST $MODE
+./scripts/run_inference.sh $MODELNAME $DATALIST $MODE
 ```
 <br />
 
-`MODELNAME` 有三种选择，位于 `vlmeval/config.py` 中：
+`MODELNAME` 有四种选择，位于 `vlmeval/config.py` 中：
 ```bash
-ungrouped = {
+minicpm_series = {
     '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'),
+    'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'),
 }
 ```
 <br />
 
-可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中，评测单个数据集时，直接调用数据集名称，不加引号；评测多个数据集时，将不同数据集名称以空格隔开，两端加引号：
+可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中。将不同数据集名称以空格隔开，两端加引号：
 ```bash
-$DATALIST="POPE ScienceQA_TEST ChartQA_TEST"
+$DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST"
 ```
 <br />
 
-直接对各 benchmark 进行评分时，设置 `MODE=all`。如果仅需要推理结果，则设置 `MODE=infer`
+直接对各 benchmark 进行评分时，设置 `MODE=all`。如果仅需要推理结果，则设置 `MODE=infer`。
-为了复现出首页展示的表格中的各项结果（MME 到 RealWorldQA 之间的列），需要按照如下设置运行：
+为了复现出首页展示的表格中的各项结果（MME 到 HallusionBench 之间的列），需要按照如下设置运行：
 ```bash
-# 一次性运行 7 个数据集
+# without CoT
-./script/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all
+./scripts/run_inference.sh MiniCPM-V-2_6 "MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" all
-
+./scripts/run_inference.sh MiniCPM-V-2_6 MME all
-# 以下是单独运行 1 个数据集的指令
+# with CoT，运行 CoT 版本的 MME 时，需要改写 vlmeval/vlm/minicpm_v.py 中的 'use_cot' 函数，将 MME 添加到 return True 的分支中
-# MME
+./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all
-./script/run_inference.sh MiniCPM-Llama3-V-2_5 MME all
+./scripts/run_inference.sh MiniCPM-V-2_6 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
+### vqadataset
 首先，进入 `vqaeval` 目录下，安装必要的依赖，并创建 `downloads` 子目录，用于存储下载的数据集：
 ```bash
 cd vqaeval
@@ -112,7 +111,8 @@ chmod +x ./shell/run_inference.sh
 ```
 <br />
 
-可以传入的参数位于 `eval_utils/getargs.py` 中，各主要参数的含义如下：
+可以传入的参数位于 `eval_utils/getargs.py` 中，各主要参数的含义如下。
+对于 `MiniCPM-V-2_6`，需要将 `model_name`设置为 `minicpmv26`：
 ```bash
 # 指定 TextVQA 评测所有图片和问题的路径
 --textVQA_image_dir
@@ -173,3 +173,186 @@ chmod +x ./shell/run_inference.sh
 chmod +x ./shell/run_transform.sh
 ./shell/run_transform.sh
 ```
+<br />
+
+## MiniCPM-Llama3-V-2_5
+
+<details>
+<summary>展开</summary>
+
+### opencompass
+首先，进入 `vlmevalkit` 目录下，安装必要的依赖：
+```bash
+cd vlmevalkit
+pip install -r requirements.txt
+```
+<br />
+
+然后，运行 `scripts/run_inference.sh`，该脚本依次接收三个输入参数：`MODELNAME`, `DATALIST`, `MODE`。`MODELNAME` 为模型名称，`DATALIST` 为目标数据集，`MODE` 为评测模式。
+```bash
+chmod +x ./scripts/run_inference.sh
+./scripts/run_inference.sh $MODELNAME $DATALIST $MODE
+```
+<br />
+
+`MODELNAME` 有三种选择，位于 `vlmeval/config.py` 中：
+```bash
+ungrouped = {
+    'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
+    'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
+    'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
+}
+```
+<br />
+
+可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中，评测单个数据集时，直接调用数据集名称，不加引号；评测多个数据集时，将不同数据集名称以空格隔开，两端加引号：
+```bash
+$DATALIST="POPE ScienceQA_TEST ChartQA_TEST"
+```
+<br />
+
+直接对各 benchmark 进行评分时，设置 `MODE=all`。如果仅需要推理结果，则设置 `MODE=infer`
+为了复现出首页展示的表格中的各项结果（MME 到 RealWorldQA 之间的列），需要按照如下设置运行：
+```bash
+# 一次性运行 7 个数据集
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all
+
+# 以下是单独运行 1 个数据集的指令
+# MME
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MME all
+# MMBench_TEST_EN
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_EN all
+# MMBench_TEST_CN
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_CN all
+# MMMU_DEV_VAL
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMMU_DEV_VAL all
+# MathVista_MINI
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MathVista_MINI all
+# LLaVABench
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 LLaVABench all
+# RealWorldQA
+./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 RealWorldQA all
+```
+<br />
+
+### vqadataset
+首先，进入 `vqaeval` 目录下，安装必要的依赖，并创建 `downloads` 子目录，用于存储下载的数据集：
+```bash
+cd vqaeval
+pip install -r requirements.txt
+mkdir downloads
+```
+<br />
+
+然后，从下列各地址下载数据集并置于指定目录下：
+###### 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
+# 在 https://rrc.cvc.uab.es/?ch=17&com=downloads 下载 Task 1 - Single Page Document Visual Question Answering 下的 Images 和 Annotations
+# 将下载得到的 spdocvqa_images.tar.gz 以及 spdocvqa_qas.zip 置于 DocVQA 目录下
+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 />
+
+`downloads` 目录应当按照下列结构组织：
+```bash
+downloads
+├── TextVQA
+│   ├── train_images
+│   │   ├── ...
+│   ├── TextVQA_0.5.1_val.json
+├── DocVQA
+│   ├── spdocvqa_images
+│   │   ├── ...
+│   ├── val_v1.0_withQT.json
+│   ├── test_v1.0.json
+```
+<br />
+
+准备好相应的数据集之后，修改 `shell/run_inference.sh` 的参数，运行推理：
+
+```bash
+chmod +x ./shell/run_inference.sh
+./shell/run_inference.sh
+```
+<br />
+
+可以传入的参数位于 `eval_utils/getargs.py` 中，各主要参数的含义如下。
+对于 `MiniCPM-Llama3-V-2_5`，需要将 `model_name` 设置为 `minicpmv`：
+```bash
+# 指定 TextVQA 评测所有图片和问题的路径
+--textVQA_image_dir
+--textVQA_ann_path
+# 指定 DocVQA 评测所有图片和问题的路径
+--docVQA_image_dir
+--docVQA_ann_path
+# 指定 DocVQATest 评测所有图片和问题的路径
+--docVQATest_image_dir
+--docVQATest_ann_path
+
+# 决定是否评测某个任务，eval_all 设置为 True 表示所有任务都评测
+--eval_textVQA
+--eval_docVQA
+--eval_docVQATest
+--eval_all
+
+# 模型名称、模型路径（从指定路径加载模型）
+--model_name
+--model_path
+# 从 checkpoint 加载模型
+--ckpt
+# 模型处理输入数据的方式，interleave 表示图文交错式，old 表示非交错式
+--generate_method
+# 推理时的批处理规模，建议推理时设置为 1
+--batchsize
+
+# 输出内容保存的路径
+--answer_path
+```
+<br />
+
+评测三个任务需要设置的参数如下：
+###### 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 />
+
+对于 DocVQATest 任务，为了将推理结果上传到[官方网站](https://rrc.cvc.uab.es/?ch=17)进行评测，还需要运行 `shell/run_transform.sh` 进行格式转换。其中，`input_file_path` 对应原始输出的 json 的路径，`output_file_path` 为自定义的转换后的 json 的路径：
+```bash
+chmod +x ./shell/run_transform.sh
+./shell/run_transform.sh
+```
+
+</details>

eval_mm/vlmevalkit/requirements.txt

@@ -1,33 +1,30 @@
-einops
+decord
-gradio==4.15.0
+gradio
 huggingface_hub
+imageio
 matplotlib
+moviepy
 numpy>=1.23.4
 omegaconf
 openai==1.3.5
 opencv-python>=4.4.0.46
 openpyxl
-pandas>=1.5.3
+pandas
+peft
 pillow
 portalocker
-protobuf
-pycocoevalcap
 python-dotenv
 requests
 rich
-seaborn
 sentencepiece
+setuptools
 sty
 tabulate
 tiktoken
 timeout-decorator
+torch>=2.0.1
 tqdm
+transformers
 typing_extensions==4.7.1
 validators
-visual_genome
 xlsxwriter
-Pillow==10.1.0
-sentencepiece==0.1.99
-transformers==4.40.0
-torch==1.13.1
-torchvision

eval_mm/vlmevalkit/requirements/docs.txt

@@ -0,0 +1,11 @@
+docutils==0.18.1
+modelindex
+myst-parser
+-e git+https://github.com/open-compass/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx==6.1.3
+sphinx-copybutton
+sphinx-design
+sphinx-notfound-page
+sphinx-tabs
+sphinxcontrib-jquery
+tabulate

eval_mm/vlmevalkit/run.py

@@ -1,23 +1,39 @@
 import torch
 import torch.distributed as dist
-from vlmeval.smp import *
+
-from vlmeval.evaluate import *
-from vlmeval.inference import infer_data_job
 from vlmeval.config import supported_VLM
-from vlmeval.utils import dataset_URLs, DATASET_TYPE, abbr2full, MMMU_result_transfer
+from vlmeval.dataset import build_dataset
+from vlmeval.inference import infer_data_job
+from vlmeval.inference_video import infer_data_job_video
+from vlmeval.inference_mt import infer_data_job_mt
+from vlmeval.smp import *
+from vlmeval.utils.result_transfer import MMMU_result_transfer, MMTBench_result_transfer
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
+    # Essential Args
     parser.add_argument('--data', type=str, nargs='+', required=True)
     parser.add_argument('--model', type=str, nargs='+', required=True)
-    parser.add_argument('--work-dir', type=str, default='.', help='select the output directory')
+    # Args that only apply to Video Dataset
+    parser.add_argument('--nframe', type=int, default=8)
+    parser.add_argument('--pack', action='store_true')
+    parser.add_argument('--use-subtitle', action='store_true')
+    # Work Dir
+    parser.add_argument('--work-dir', type=str, default='./outputs', help='select the output directory')
+    # Infer + Eval or Infer Only
     parser.add_argument('--mode', type=str, default='all', choices=['all', 'infer'])
+    # API Kwargs, Apply to API VLMs and Judge API LLMs
     parser.add_argument('--nproc', type=int, default=4, help='Parallel API calling')
     parser.add_argument('--retry', type=int, default=None, help='retry numbers for API VLMs')
+    # Explicitly Set the Judge Model
     parser.add_argument('--judge', type=str, default=None)
-    parser.add_argument('--ignore', action='store_true', help='Ignore failed indices. ')
+    # Logging Utils
     parser.add_argument('--verbose', action='store_true')
+    # Configuration for Resume
+    # Ignore: will not rerun failed VLM inference
+    parser.add_argument('--ignore', action='store_true', help='Ignore failed indices. ')
+    # Rerun: will remove all evaluation temp files
     parser.add_argument('--rerun', action='store_true')
     args = parser.parse_args()
     return args
@@ -51,53 +67,83 @@ def main():
         os.makedirs(pred_root, exist_ok=True)
 
         for _, dataset_name in enumerate(args.data):
-            custom_flag = False
+            dataset_kwargs = {}
+            if dataset_name in ['MMLongBench_DOC', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI']:
+                dataset_kwargs['model'] = model_name
+            if dataset_name == 'MMBench-Video':
+                dataset_kwargs['pack'] = args.pack
+            if dataset_name == 'Video-MME':
+                dataset_kwargs['use_subtitle'] = args.use_subtitle
 
-            if dataset_name not in dataset_URLs:
+            # If distributed, first build the dataset on the main process for doing preparation works
-                dataset_name = abbr2full(dataset_name)
+            if world_size > 1:
-
+                dataset = build_dataset(dataset_name, **dataset_kwargs) if rank == 0 else None
-            if dataset_name not in dataset_URLs:
+                dist.barrier()
-                logger.warning(f'Dataset {dataset_name} is not officially supported. ')
+                dataset_list = [dataset]
-                file_path = osp.join(LMUDataRoot(), f'{dataset_name}.tsv')
+                dist.broadcast_object_list(dataset_list, src=0)
-                if not osp.exists(file_path):
+                dataset = dataset_list[0]
-                    logger.error(f'Cannot find the local dataset {dataset_name}. ')
-                    continue
             else:
-                    custom_flag = True
+                dataset = build_dataset(dataset_name, **dataset_kwargs)
+            if dataset is None:
+                logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ')
+                continue
 
             result_file = f'{pred_root}/{model_name}_{dataset_name}.xlsx'
+            if dataset_name in ['MMBench-Video']:
+                packstr = 'pack' if args.pack else 'nopack'
+                result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
+            elif dataset.MODALITY == 'VIDEO':
+                if args.pack:
+                    logger.info(f'{dataset_name} not support Pack Mode, directly change to unpack')
+                    args.pack = False
+                packstr = 'pack' if args.pack else 'nopack'
+                result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
+                if dataset_name in ['Video-MME']:
+                    subtitlestr = 'subs' if args.use_subtitle else 'nosubs'
+                    result_file = result_file.replace('.xlsx', f'_{subtitlestr}.xlsx')
+
+            if dataset.TYPE == 'MT':
+                result_file = result_file.replace('.xlsx', '.tsv')
+
             if osp.exists(result_file) and args.rerun:
-                os.system(f'rm {pred_root}/{model_name}_{dataset_name}_*')
+                for keyword in ['openai', 'gpt', 'auxmatch']:
+                    os.system(f'rm {pred_root}/{model_name}_{dataset_name}_{keyword}*')
 
             if model is None:
                 model = model_name  # which is only a name
 
+            # Perform the Inference
+            if dataset.MODALITY == 'VIDEO':
+                model = infer_data_job_video(
+                    model,
+                    work_dir=pred_root,
+                    model_name=model_name,
+                    dataset=dataset,
+                    nframe=args.nframe,
+                    pack=args.pack,
+                    verbose=args.verbose,
+                    subtitle=args.use_subtitle,
+                    api_nproc=args.nproc)
+            elif dataset.TYPE == 'MT':
+                model = infer_data_job_mt(
+                    model,
+                    work_dir=pred_root,
+                    model_name=model_name,
+                    dataset=dataset,
+                    verbose=args.verbose,
+                    api_nproc=args.nproc,
+                    ignore_failed=args.ignore)
+            else:
                 model = infer_data_job(
                     model,
                     work_dir=pred_root,
                     model_name=model_name,
-                dataset_name=dataset_name,
+                    dataset=dataset,
                     verbose=args.verbose,
                     api_nproc=args.nproc,
                     ignore_failed=args.ignore)
 
-            if rank == 0:
+            # Set the judge kwargs first before evaluation or dumping
-                if dataset_name in ['MMMU_TEST']:
-                    result_json = MMMU_result_transfer(result_file)
-                    logger.info(f'Transfer MMMU_TEST result to json for official evaluation, json file saved in {result_json}')  # noqa: E501
-                    continue
-
-            if dataset_name in [
-                'MMBench_TEST_CN', 'MMBench_TEST_EN', 'MMBench', 'MMBench_CN'
-                'MMBench_TEST_CN_V11', 'MMBench_TEST_EN_V11', 'MMBench_V11', 'MMBench_CN_V11'
-            ]:
-                if not MMBenchOfficialServer(dataset_name):
-                    logger.error(
-                        f'Can not evaluate {dataset_name} on non-official servers, '
-                        'will skip the evaluation. '
-                    )
-                    continue
-
             judge_kwargs = {
                 'nproc': args.nproc,
                 'verbose': args.verbose,
@@ -107,41 +153,69 @@ def main():
             if args.judge is not None:
                 judge_kwargs['model'] = args.judge
             else:
-                if DATASET_TYPE(dataset_name) in ['multi-choice', 'Y/N']:
+                if dataset.TYPE in ['MCQ', 'Y/N']:
-                    judge_kwargs['model'] = 'chatgpt-0613'
+                    judge_kwargs['model'] = 'chatgpt-0125'
-                elif listinstr(['MMVet', 'MathVista', 'LLaVABench'], dataset_name):
+                elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'], dataset_name):
                     judge_kwargs['model'] = 'gpt-4-turbo'
+                elif listinstr(['MMLongBench', 'MMDU', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI'], dataset_name):
+                    judge_kwargs['model'] = 'gpt-4o'
             if 'OPENAI_API_KEY_JUDGE' in os.environ and len(os.environ['OPENAI_API_KEY_JUDGE']):
                 judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
             if 'OPENAI_API_BASE_JUDGE' in os.environ and len(os.environ['OPENAI_API_BASE_JUDGE']):
                 judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
 
+            if rank == 0:
+                if dataset_name in ['MMMU_TEST']:
+                    result_json = MMMU_result_transfer(result_file)
+                    logger.info(f'Transfer MMMU_TEST result to json for official evaluation, '
+                                f'json file saved in {result_json}')  # noqa: E501
+                    continue
+                elif 'MMT-Bench_ALL' in dataset_name:
+                    submission_file = MMTBench_result_transfer(result_file, **judge_kwargs)
+                    logger.info(f'Extract options from prediction of MMT-Bench FULL split for official evaluation '
+                                f'(https://eval.ai/web/challenges/challenge-page/2328/overview), '
+                                f'submission file saved in {submission_file}')  # noqa: E501
+                    continue
+                elif 'MLLMGuard_DS' in dataset_name:
+                    logger.info('The evaluation of MLLMGuard_DS is not supported yet. ')  # noqa: E501
+                    continue
+                elif 'AesBench_TEST' == dataset_name:
+                    logger.info(f'The results are saved in {result_file}. '
+                                f'Please send it to the AesBench Team via huangyipo@hotmail.com.')  # noqa: E501
+                    continue
+
+            if dataset_name in [
+                'MMBench_TEST_CN', 'MMBench_TEST_EN', 'MMBench', 'MMBench_CN',
+                'MMBench_TEST_CN_V11', 'MMBench_TEST_EN_V11', 'MMBench_V11', 'MMBench_CN_V11'
+            ]:
+                if not MMBenchOfficialServer(dataset_name):
+                    logger.error(
+                        f'Can not evaluate {dataset_name} on non-official servers, '
+                        'will skip the evaluation. '
+                    )
+                    continue
+
+            eval_proxy = os.environ.get('EVAL_PROXY', None)
+            old_proxy = os.environ.get('HTTP_PROXY', '')
+
             if rank == 0 and args.mode == 'all':
-                if DATASET_TYPE(dataset_name) == 'multi-choice':
+                if eval_proxy is not None:
-                    dataset_name = 'default' if custom_flag else dataset_name
+                    proxy_set(eval_proxy)
-                    multiple_choice_eval(
+
-                        result_file,
+                eval_results = dataset.evaluate(result_file, **judge_kwargs)
-                        dataset=dataset_name,
+                if eval_results is not None:
-                        **judge_kwargs)
+                    assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame)
-                elif DATASET_TYPE(dataset_name) == 'Y/N':
+                    logger.info(f'The evaluation of model {model_name} x dataset {dataset_name} has finished! ')
-                    YOrN_eval(
+                    logger.info('Evaluation Results:')
-                        result_file,
+                if isinstance(eval_results, dict):
-                        dataset=dataset_name,
+                    logger.info('\n' + json.dumps(eval_results, indent=4))
-                        **judge_kwargs)
+                elif isinstance(eval_results, pd.DataFrame):
-                elif DATASET_TYPE(dataset_name) == 'Caption':
+                    if len(eval_results) < len(eval_results.columns):
-                    COCO_eval(result_file)
+                        eval_results = eval_results.T
-                elif dataset_name == 'MMVet':
+                    logger.info('\n' + tabulate(eval_results))
-                    MMVet_eval(result_file, **judge_kwargs)
+
-                elif dataset_name == 'OCRBench':
+                if eval_proxy is not None:
-                    OCRBench_eval(result_file)
+                    proxy_set(old_proxy)
-                elif listinstr(['OCRVQA', 'TextVQA', 'ChartQA', 'DocVQA', 'InfoVQA'], dataset_name):
-                    VQAEval(result_file, dataset_name)
-                elif listinstr(['MathVista'], dataset_name):
-                    MathVista_eval(result_file, **judge_kwargs)
-                elif listinstr(['LLaVABench'], dataset_name):
-                    LLaVABench_eval(result_file, **judge_kwargs)
-                else:
-                    logger.error(f'Dataset {dataset_name} is not handled by evaluator, will be skipped. ')
 
 
 if __name__ == '__main__':

eval_mm/vlmevalkit/scripts/run_inference.sh

@@ -11,10 +11,10 @@ export PYTHONPATH=$(dirname $SELF_DIR):$PYTHONPATH
 # int4 7-8G
 
 # model to be used
-# Example: MODELNAME=MiniCPM-Llama3-V-2_5
+# Example: MODELNAME=MiniCPM_V_2_6
 MODELNAME=$1
 # datasets to be tested
-# Example: DATALIST="POPE ScienceQA_TEST ChartQA_TEST"
+# Example: DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST"
 DATALIST=$2
 # test mode, all or infer
 MODE=$3

eval_mm/vlmevalkit/setup.py

@@ -0,0 +1,122 @@
+import re
+import sys
+from os.path import exists
+from setuptools import find_packages, setup
+
+
+def parse_requirements(fname='requirements.txt', with_version=True):
+    """Parse the package dependencies listed in a requirements file but strips
+    specific versioning information.
+
+    Args:
+        fname (str): path to requirements file
+        with_version (bool, default=False): if True include version specs
+
+    Returns:
+        List[str]: list of requirements items
+
+    CommandLine:
+        python -c "import setup; print(setup.parse_requirements())"
+    """
+
+    require_fpath = fname
+
+    def parse_line(line):
+        """Parse information from a line in a requirements text file."""
+        if line.startswith('-r '):
+            # Allow specifying requirements in other files
+            target = line.split(' ')[1]
+            for info in parse_require_file(target):
+                yield info
+        else:
+            info = {'line': line}
+            if line.startswith('-e '):
+                info['package'] = line.split('#egg=')[1]
+            elif '@git+' in line:
+                info['package'] = line
+            else:
+                # Remove versioning from the package
+                pat = '(' + '|'.join(['>=', '==', '>']) + ')'
+                parts = re.split(pat, line, maxsplit=1)
+                parts = [p.strip() for p in parts]
+
+                info['package'] = parts[0]
+                if len(parts) > 1:
+                    op, rest = parts[1:]
+                    if ';' in rest:
+                        # Handle platform specific dependencies
+                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
+                        version, platform_deps = map(str.strip,
+                                                     rest.split(';'))
+                        info['platform_deps'] = platform_deps
+                    else:
+                        version = rest  # NOQA
+                    info['version'] = (op, version)
+            yield info
+
+    def parse_require_file(fpath):
+        with open(fpath, 'r') as f:
+            for line in f.readlines():
+                line = line.strip()
+                if line and not line.startswith('#'):
+                    for info in parse_line(line):
+                        yield info
+
+    def gen_packages_items():
+        if exists(require_fpath):
+            for info in parse_require_file(require_fpath):
+                parts = [info['package']]
+                if with_version and 'version' in info:
+                    parts.extend(info['version'])
+                if not sys.version.startswith('3.4'):
+                    # apparently package_deps are broken in 3.4
+                    platform_deps = info.get('platform_deps')
+                    if platform_deps is not None:
+                        parts.append(';' + platform_deps)
+                item = ''.join(parts)
+                yield item
+
+    packages = list(gen_packages_items())
+    return packages
+
+
+with open('README.md') as f:
+    readme = f.read()
+
+
+def do_setup():
+    setup(
+        name='vlmeval',
+        version='0.1.0',
+        description='OpenCompass VLM Evaluation Kit',
+        author='Haodong Duan',
+        author_email='dhd.efz@gmail.com',
+        maintainer='Haodong Duan',
+        maintainer_email='dhd.efz@gmail.com',
+        long_description=readme,
+        long_description_content_type='text/markdown',
+        cmdclass={},
+        install_requires=parse_requirements('requirements.txt'),
+        setup_requires=[],
+        python_requires='>=3.7.0',
+        packages=find_packages(exclude=[
+            'test*',
+            'paper_test*',
+        ]),
+        keywords=['AI', 'NLP', 'in-context learning'],
+        entry_points={
+            'console_scripts': ['vlmutil = vlmeval:cli']
+        },
+        classifiers=[
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9',
+            'Programming Language :: Python :: 3.10',
+            'Intended Audience :: Developers',
+            'Intended Audience :: Education',
+            'Intended Audience :: Science/Research',
+        ])
+
+
+if __name__ == '__main__':
+    do_setup()

eval_mm/vlmevalkit/vlmeval/__init__.py

@@ -5,9 +5,12 @@ except ImportError:
 
 from .smp import *
 from .api import *
-from .evaluate import *
+from .dataset import *
 from .utils import *
 from .vlm import *
 from .config import *
+from .tools import cli
 
 load_env()
+
+__version__ = '0.2rc1'

eval_mm/vlmevalkit/vlmeval/api/__init__.py

@@ -1,6 +1,5 @@
 from .gpt import OpenAIWrapper, GPT4V
-from .gpt_int import OpenAIWrapperInternal, GPT4V_Internal
 
 __all__ = [
-    'OpenAIWrapper', 'OpenAIWrapperInternal', 'GPT4V', 'GPT4V_Internal'
+    'OpenAIWrapper', 'GPT4V'
 ]

eval_mm/vlmevalkit/vlmeval/api/base.py

@@ -3,7 +3,7 @@ import random as rd
 from abc import abstractmethod
 import os.path as osp
 import copy as cp
-from ..smp import get_logger, parse_file
+from ..smp import get_logger, parse_file, concat_images_vlmeval
 
 
 class BaseAPI:
@@ -62,12 +62,22 @@ class BaseAPI:
         Returns:
             bool: If the API model is working, return True, else return False.
         """
-        retry = 3
+        self.old_timeout = None
+        if hasattr(self, 'timeout'):
+            self.old_timeout = self.timeout
+            self.timeout = 120
+
+        retry = 5
         while retry > 0:
             ret = self.generate('hello')
             if ret is not None and ret != '' and self.fail_msg not in ret:
+                if self.old_timeout is not None:
+                    self.timeout = self.old_timeout
                 return True
             retry -= 1
+
+        if self.old_timeout is not None:
+            self.timeout = self.old_timeout
         return False
 
     def check_content(self, msgs):
@@ -127,6 +137,61 @@ class BaseAPI:
         else:
             return None
 
+    # May exceed the context windows size, so try with different turn numbers.
+    def chat_inner(self, inputs, **kwargs):
+        _ = kwargs.pop('dataset', None)
+        while len(inputs):
+            try:
+                return self.generate_inner(inputs, **kwargs)
+            except:
+                inputs = inputs[1:]
+                while len(inputs) and inputs[0]['role'] != 'user':
+                    inputs = inputs[1:]
+                continue
+        return -1, self.fail_msg + ': ' + 'Failed with all possible conversation turns.', None
+
+    def chat(self, messages, **kwargs1):
+        """The main function for multi-turn chatting. Will call `chat_inner` with the preprocessed input messages."""
+        assert hasattr(self, 'chat_inner'), 'The API model should has the `chat_inner` method. '
+        for msg in messages:
+            assert isinstance(msg, dict) and 'role' in msg and 'content' in msg, msg
+            assert self.check_content(msg['content']) in ['str', 'dict', 'liststr', 'listdict'], msg
+            msg['content'] = self.preproc_content(msg['content'])
+        # merge kwargs
+        kwargs = cp.deepcopy(self.default_kwargs)
+        kwargs.update(kwargs1)
+
+        answer = None
+        # a very small random delay [0s - 0.5s]
+        T = rd.random() * 0.5
+        time.sleep(T)
+
+        assert messages[-1]['role'] == 'user'
+
+        for i in range(self.retry):
+            try:
+                ret_code, answer, log = self.chat_inner(messages, **kwargs)
+                if ret_code == 0 and self.fail_msg not in answer and answer != '':
+                    if self.verbose:
+                        print(answer)
+                    return answer
+                elif self.verbose:
+                    if not isinstance(log, str):
+                        try:
+                            log = log.text
+                        except:
+                            self.logger.warning(f'Failed to parse {log} as an http response. ')
+                    self.logger.info(f'RetCode: {ret_code}\nAnswer: {answer}\nLog: {log}')
+            except Exception as err:
+                if self.verbose:
+                    self.logger.error(f'An error occured during try {i}:')
+                    self.logger.error(err)
+            # delay before each retry
+            T = rd.random() * self.wait * 2
+            time.sleep(T)
+
+        return self.fail_msg if answer in ['', None] else answer
+
     def generate(self, message, **kwargs1):
         """The main function to generate the answer. Will call `generate_inner` with the preprocessed input messages.
 
@@ -175,7 +240,7 @@ class BaseAPI:
 
         return self.fail_msg if answer in ['', None] else answer
 
-    def message_to_promptimg(self, message):
+    def message_to_promptimg(self, message, dataset=None):
         assert not self.INTERLEAVE
         model_name = self.__class__.__name__
         import warnings
@@ -191,5 +256,10 @@ class BaseAPI:
             image = [x['value'] for x in message if x['type'] == 'image'][0]
         else:
             prompt = '\n'.join([x['value'] if x['type'] == 'text' else '<image>' for x in message])
+            if dataset == 'BLINK':
+                image = concat_images_vlmeval(
+                    [x['value'] for x in message if x['type'] == 'image'],
+                    target_size=512)
+            else:
                 image = [x['value'] for x in message if x['type'] == 'image'][0]
         return prompt, image

eval_mm/vlmevalkit/vlmeval/api/gpt.py

@@ -10,18 +10,18 @@ APIBASES = {
 
 def GPT_context_window(model):
     length_map = {
-        'gpt-4-1106-preview': 128000,
-        'gpt-4-vision-preview': 128000,
         'gpt-4': 8192,
-        'gpt-4-32k': 32768,
         'gpt-4-0613': 8192,
-        'gpt-4-32k-0613': 32768,
+        'gpt-4-turbo-preview': 128000,
+        'gpt-4-1106-preview': 128000,
+        'gpt-4-0125-preview': 128000,
+        'gpt-4-vision-preview': 128000,
+        'gpt-4-turbo': 128000,
+        'gpt-4-turbo-2024-04-09': 128000,
+        'gpt-3.5-turbo': 16385,
+        'gpt-3.5-turbo-0125': 16385,
         'gpt-3.5-turbo-1106': 16385,
-        'gpt-3.5-turbo': 4096,
-        'gpt-3.5-turbo-16k': 16385,
         'gpt-3.5-turbo-instruct': 4096,
-        'gpt-3.5-turbo-0613': 4096,
-        'gpt-3.5-turbo-16k-0613': 16385,
     }
     if model in length_map:
         return length_map[model]
@@ -46,6 +46,7 @@ class OpenAIWrapper(BaseAPI):
                  max_tokens: int = 1024,
                  img_size: int = 512,
                  img_detail: str = 'low',
+                 use_azure: bool = False,
                  **kwargs):
 
         self.model = model
@@ -53,11 +54,26 @@ class OpenAIWrapper(BaseAPI):
         self.fail_msg = 'Failed to obtain answer via API. '
         self.max_tokens = max_tokens
         self.temperature = temperature
+        self.use_azure = use_azure
 
         if 'step-1v' in model:
             env_key = os.environ.get('STEPAI_API_KEY', '')
             if key is None:
                 key = env_key
+        elif 'yi-vision' in model:
+            env_key = os.environ.get('YI_API_KEY', '')
+            if key is None:
+                key = env_key
+        else:
+            if use_azure:
+                env_key = os.environ.get('AZURE_OPENAI_API_KEY', None)
+                assert env_key is not None, 'Please set the environment variable AZURE_OPENAI_API_KEY. '
+
+                if key is None:
+                    key = env_key
+                assert isinstance(key, str), (
+                    'Please set the environment variable AZURE_OPENAI_API_KEY to your openai key. '
+                )
             else:
                 env_key = os.environ.get('OPENAI_API_KEY', '')
                 if key is None:
@@ -66,6 +82,7 @@ class OpenAIWrapper(BaseAPI):
                     f'Illegal openai_key {key}. '
                     'Please set the environment variable OPENAI_API_KEY to your openai key. '
                 )
+
         self.key = key
         assert img_size > 0 or img_size == -1
         self.img_size = img_size
@@ -75,9 +92,26 @@ class OpenAIWrapper(BaseAPI):
 
         super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
 
+        if use_azure:
+            api_base_template = (
+                '{endpoint}openai/deployments/{deployment_name}/chat/completions?api-version={api_version}'
+            )
+            endpoint = os.getenv('AZURE_OPENAI_ENDPOINT', None)
+            assert endpoint is not None, 'Please set the environment variable AZURE_OPENAI_ENDPOINT. '
+            deployment_name = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME', None)
+            assert deployment_name is not None, 'Please set the environment variable AZURE_OPENAI_DEPLOYMENT_NAME. '
+            api_version = os.getenv('OPENAI_API_VERSION', None)
+            assert api_version is not None, 'Please set the environment variable OPENAI_API_VERSION. '
+
+            self.api_base = api_base_template.format(
+                endpoint=os.getenv('AZURE_OPENAI_ENDPOINT'),
+                deployment_name=os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME'),
+                api_version=os.getenv('OPENAI_API_VERSION')
+            )
+        else:
             if api_base is None:
                 if 'OPENAI_API_BASE' in os.environ and os.environ['OPENAI_API_BASE'] != '':
-                self.logger.error('Environment variable OPENAI_API_BASE is set. Will use it as api_base. ')
+                    self.logger.info('Environment variable OPENAI_API_BASE is set. Will use it as api_base. ')
                     api_base = os.environ['OPENAI_API_BASE']
                 else:
                     api_base = 'OFFICIAL'
@@ -91,14 +125,13 @@ class OpenAIWrapper(BaseAPI):
             else:
                 self.logger.error('Unknown API Base. ')
                 sys.exit(-1)
+
         self.logger.info(f'Using API Base: {self.api_base}; API Key: {self.key}')
 
     # inputs can be a lvl-2 nested list: [content1, content2, content3, ...]
     # content can be a string or a list of image & text
-    def prepare_inputs(self, inputs):
+    def prepare_itlist(self, inputs):
-        input_msgs = []
+        assert np.all([isinstance(x, dict) for x in inputs])
-        if self.system_prompt is not None:
-            input_msgs.append(dict(role='system', content=self.system_prompt))
         has_images = np.sum([x['type'] == 'image' for x in inputs])
         if has_images:
             content_list = []
@@ -111,11 +144,24 @@ class OpenAIWrapper(BaseAPI):
                     b64 = encode_image_to_base64(img, target_size=self.img_size)
                     img_struct = dict(url=f'data:image/jpeg;base64,{b64}', detail=self.img_detail)
                     content_list.append(dict(type='image_url', image_url=img_struct))
-            input_msgs.append(dict(role='user', content=content_list))
         else:
             assert all([x['type'] == 'text' for x in inputs])
             text = '\n'.join([x['value'] for x in inputs])
-            input_msgs.append(dict(role='user', content=text))
+            content_list = [dict(type='text', text=text)]
+        return content_list
+
+    def prepare_inputs(self, inputs):
+        input_msgs = []
+        if self.system_prompt is not None:
+            input_msgs.append(dict(role='system', content=self.system_prompt))
+        assert isinstance(inputs, list) and isinstance(inputs[0], dict)
+        assert np.all(['type' in x for x in inputs]) or np.all(['role' in x for x in inputs]), inputs
+        if 'role' in inputs[0]:
+            assert inputs[-1]['role'] == 'user', inputs[-1]
+            for item in inputs:
+                input_msgs.append(dict(role=item['role'], content=self.prepare_itlist(item['content'])))
+        else:
+            input_msgs.append(dict(role='user', content=self.prepare_itlist(inputs)))
         return input_msgs
 
     def generate_inner(self, inputs, **kwargs) -> str:
@@ -133,6 +179,10 @@ class OpenAIWrapper(BaseAPI):
         if max_tokens <= 0:
             return 0, self.fail_msg + 'Input string longer than context window. ', 'Length Exceeded. '
 
+        # Will send request if use Azure, dk how to use openai client for it
+        if self.use_azure:
+            headers = {'Content-Type': 'application/json', 'api-key': self.key}
+        else:
             headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.key}'}
         payload = dict(
             model=self.model,
@@ -141,7 +191,9 @@ class OpenAIWrapper(BaseAPI):
             n=1,
             temperature=temperature,
             **kwargs)
-        response = requests.post(self.api_base, headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1)
+        response = requests.post(
+            self.api_base,
+            headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1)
         ret_code = response.status_code
         ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code
         answer = self.fail_msg
@@ -152,6 +204,26 @@ class OpenAIWrapper(BaseAPI):
             pass
         return ret_code, answer, response
 
+    def get_image_token_len(self, img_path, detail='low'):
+        import math
+        if detail == 'low':
+            return 85
+
+        im = Image.open(img_path)
+        height, width = im.size
+        if width > 1024 or height > 1024:
+            if width > height:
+                height = int(height * 1024 / width)
+                width = 1024
+            else:
+                width = int(width * 1024 / height)
+                height = 1024
+
+        h = math.ceil(height / 512)
+        w = math.ceil(width / 512)
+        total = 85 + 170 * h * w
+        return total
+
     def get_token_len(self, inputs) -> int:
         import tiktoken
         try:
@@ -161,14 +233,12 @@ class OpenAIWrapper(BaseAPI):
         assert isinstance(inputs, list)
         tot = 0
         for item in inputs:
-            if item['type'] == 'text':
+            if 'role' in item:
+                tot += self.get_token_len(item['content'])
+            elif item['type'] == 'text':
                 tot += len(enc.encode(item['value']))
             elif item['type'] == 'image':
-                tot += 85
+                tot += self.get_image_token_len(item['value'], detail=self.img_detail)
-                if self.img_detail == 'high':
-                    img = Image.open(item['value'])
-                    npatch = np.ceil(img.size[0] / 512) * np.ceil(img.size[1] / 512)
-                    tot += npatch * 170
         return tot
 
 

eval_mm/vlmevalkit/vlmeval/api/gpt_int.py

@@ -1,90 +0,0 @@
-import json
-import warnings
-import requests
-from ..smp import *
-from .gpt import GPT_context_window, OpenAIWrapper
-
-url = 'http://ecs.sv.us.alles-apin.openxlab.org.cn/v1/openai/v2/text/chat'
-headers = {
-    'Content-Type': 'application/json'
-}
-
-
-class OpenAIWrapperInternal(OpenAIWrapper):
-
-    is_api: bool = True
-
-    def __init__(self,
-                 model: str = 'gpt-3.5-turbo-0613',
-                 retry: int = 5,
-                 wait: int = 3,
-                 verbose: bool = True,
-                 system_prompt: str = None,
-                 temperature: float = 0,
-                 timeout: int = 60,
-                 max_tokens: int = 1024,
-                 img_size: int = 512,
-                 img_detail: str = 'low',
-                 **kwargs):
-
-        self.model = model
-        if 'KEYS' in os.environ and osp.exists(os.environ['KEYS']):
-            keys = load(os.environ['KEYS'])
-            headers['alles-apin-token'] = keys.get('alles-apin-token', '')
-        elif 'ALLES' in os.environ:
-            headers['alles-apin-token'] = os.environ['ALLES']
-        self.headers = headers
-        self.temperature = temperature
-        self.timeout = timeout
-        self.max_tokens = max_tokens
-
-        assert img_size > 0 or img_size == -1
-        self.img_size = img_size
-        assert img_detail in ['high', 'low']
-        self.img_detail = img_detail
-
-        super(OpenAIWrapper, self).__init__(
-            wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
-
-    def generate_inner(self, inputs, **kwargs) -> str:
-        input_msgs = self.prepare_inputs(inputs)
-
-        temperature = kwargs.pop('temperature', self.temperature)
-        max_tokens = kwargs.pop('max_tokens', self.max_tokens)
-
-        # Held out 100 tokens as buffer
-        context_window = GPT_context_window(self.model)
-        max_tokens = min(max_tokens, context_window - self.get_token_len(inputs))
-        if 0 < max_tokens <= 100:
-            print('Less than 100 tokens left, may exceed the context window with some additional meta symbols. ')
-        if max_tokens <= 0:
-            return 0, self.fail_msg + 'Input string longer than context window. ', 'Length Exceeded. '
-
-        payload = dict(
-            model=self.model,
-            messages=input_msgs,
-            max_tokens=max_tokens,
-            n=1,
-            stop=None,
-            timeout=self.timeout,
-            temperature=temperature,
-            **kwargs)
-
-        response = requests.post(url, headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1)
-        ret_code = response.status_code
-        ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code
-
-        answer = self.fail_msg
-        try:
-            resp_struct = json.loads(response.text)
-            assert resp_struct['msg'] == 'ok' and resp_struct['msgCode'] == '10000', resp_struct
-            answer = resp_struct['data']['choices'][0]['message']['content'].strip()
-        except:
-            pass
-        return ret_code, answer, response
-
-
-class GPT4V_Internal(OpenAIWrapperInternal):
-
-    def generate(self, message, dataset=None):
-        return super(GPT4V_Internal, self).generate(message)

eval_mm/vlmevalkit/vlmeval/config.py

@@ -2,18 +2,18 @@ from vlmeval.vlm import *
 from vlmeval.api import *
 from functools import partial
 
-ungrouped = {
+minicpm_series = {
     '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'),
+    'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'),
 }
 
 supported_VLM = {}
 
 model_groups = [
-    ungrouped
+    minicpm_series
 ]
 
 for grp in model_groups:
     supported_VLM.update(grp)
-

eval_mm/vlmevalkit/vlmeval/dataset/__init__.py

@@ -0,0 +1,186 @@
+import warnings
+
+from .image_base import img_root_map, ImageBaseDataset
+from .image_caption import ImageCaptionDataset
+from .image_yorn import ImageYORNDataset
+from .image_mcq import ImageMCQDataset, MMMUDataset, CustomMCQDataset, MUIRDataset, GMAIMMBenchDataset
+from .image_mt import MMDUDataset
+from .image_vqa import (
+    ImageVQADataset, MathVision, OCRBench, MathVista, LLaVABench, MMVet, MTVQADataset, CustomVQADataset
+)
+
+from .vcr import VCRDataset
+from .mmlongbench import MMLongBench
+from .dude import DUDE
+from .slidevqa import SlideVQA
+
+from .mmbench_video import MMBenchVideo
+from .text_mcq import CustomTextMCQDataset, TextMCQDataset
+from .videomme import VideoMME
+from .mvbench import MVBench, MVBench_MP4
+from .utils import *
+from ..smp import *
+
+
+class ConcatDataset(ImageBaseDataset):
+    # This dataset takes multiple dataset names as input and aggregate them into a single dataset.
+    # Each single dataset should not have a field named `SUB_DATASET`
+
+    DATASET_SETS = {
+        'MMMB': ['MMMB_ar', 'MMMB_cn', 'MMMB_en', 'MMMB_pt', 'MMMB_ru', 'MMMB_tr'],
+        'MTL_MMBench_DEV': [
+            'MMBench_dev_ar', 'MMBench_dev_cn', 'MMBench_dev_en',
+            'MMBench_dev_pt', 'MMBench_dev_ru', 'MMBench_dev_tr'
+        ]
+    }
+
+    def __init__(self, dataset):
+        datasets = self.DATASET_SETS[dataset]
+        self.dataset_map = {}
+        # The name of the compliation
+        self.dataset_name = dataset
+        self.datasets = datasets
+        for dname in datasets:
+            dataset = build_dataset(dname)
+            assert dataset is not None, dataset
+            self.dataset_map[dname] = dataset
+        TYPES = [x.TYPE for x in self.dataset_map.values()]
+        MODALITIES = [x.MODALITY for x in self.dataset_map.values()]
+        assert np.all([x == TYPES[0] for x in TYPES]), (datasets, TYPES)
+        assert np.all([x == MODALITIES[0] for x in MODALITIES]), (datasets, MODALITIES)
+        self.TYPE = TYPES[0]
+        self.MODALITY = MODALITIES[0]
+        data_all = []
+        for dname in datasets:
+            data = self.dataset_map[dname].data
+            data['SUB_DATASET'] = [dname] * len(data)
+            data_new = localize_df(data, dname, nproc=16)
+            data_all.append(data_new)
+
+        data = pd.concat(data_all)
+        data['original_index'] = data.pop('index')
+        data['index'] = np.arange(len(data))
+        self.data = data
+
+    def build_prompt(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+        idx = line['original_index']
+        dname = line['SUB_DATASET']
+        org_data = self.dataset_map[dname].data
+        org_line = cp.deepcopy(org_data[org_data['index'] == idx]).iloc[0]
+        return self.dataset_map[dname].build_prompt(org_line)
+
+    def dump_image(self, line):
+        # Assert all images are pre-dumped
+        assert 'image' not in line
+        assert 'image_path' in line
+        tgt_path = toliststr(line['image_path'])
+        return tgt_path
+
+    @classmethod
+    def supported_datasets(cls):
+        return list(cls.DATASET_SETS)
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        suffix = eval_file.split('.')[-1]
+        # First, split the eval_file by dataset
+        data_all = load(eval_file)
+        for dname in self.datasets:
+            tgt = eval_file.replace(self.dataset_name, dname)
+            data_sub = data_all[data_all['SUB_DATASET'] == dname]
+            data_sub.pop('index')
+            data_sub['index'] = data_sub.pop('original_index')
+            data_sub.pop('SUB_DATASET')
+            dump(data_sub, tgt)
+        # Then, evaluate each dataset separately
+        results_all = []
+        for dname in self.datasets:
+            tgt = eval_file.replace(self.dataset_name, dname)
+            res = self.dataset_map[dname].evaluate(tgt, **judge_kwargs)
+            assert isinstance(res, pd.DataFrame)
+            res['DATASET'] = [dname] * len(res)
+            results_all.append(res)
+        result = pd.concat(results_all)
+        score_file = eval_file.replace(f'.{suffix}', '_acc.csv')
+        dump(result, score_file)
+        return result
+
+
+# Add new supported dataset class here
+IMAGE_DATASET = [
+    ImageCaptionDataset, ImageYORNDataset, ImageMCQDataset, ImageVQADataset, MathVision,
+    MMMUDataset, OCRBench, MathVista, LLaVABench, MMVet, MTVQADataset,
+    MMLongBench, VCRDataset, MMDUDataset, DUDE, SlideVQA, MUIRDataset, GMAIMMBenchDataset
+]
+
+VIDEO_DATASET = [
+    MMBenchVideo, VideoMME, MVBench, MVBench_MP4
+]
+
+TEXT_DATASET = [
+    TextMCQDataset
+]
+
+CUSTOM_DATASET = [
+    CustomMCQDataset, CustomVQADataset, CustomTextMCQDataset
+]
+
+DATASET_COLLECTION = [ConcatDataset]
+
+DATASET_CLASSES = IMAGE_DATASET + VIDEO_DATASET + TEXT_DATASET + CUSTOM_DATASET + DATASET_COLLECTION
+SUPPORTED_DATASETS = []
+for DATASET_CLS in DATASET_CLASSES:
+    SUPPORTED_DATASETS.extend(DATASET_CLS.supported_datasets())
+
+
+def DATASET_TYPE(dataset):
+    for cls in DATASET_CLASSES:
+        if dataset in cls.supported_datasets():
+            if hasattr(cls, 'TYPE'):
+                return cls.TYPE
+    # Have to add specific routine to handle ConcatDataset
+    if dataset in ConcatDataset.DATASET_SETS:
+        dataset_list = ConcatDataset.DATASET_SETS[dataset]
+        TYPES = [DATASET_TYPE(dname) for dname in dataset_list]
+        assert np.all([x == TYPES[0] for x in TYPES]), (dataset_list, TYPES)
+        return TYPES[0]
+
+    if 'openended' in dataset.lower():
+        return 'VQA'
+    warnings.warn(f'Dataset {dataset} is a custom one and not annotated as `openended`, will treat as MCQ. ')
+    return 'MCQ'
+
+
+def build_dataset(dataset_name, **kwargs):
+    for cls in DATASET_CLASSES:
+        if dataset_name in cls.supported_datasets():
+            return cls(dataset=dataset_name, **kwargs)
+
+    warnings.warn(f'Dataset {dataset_name} is not officially supported. ')
+
+    data_file = osp.join(LMUDataRoot(), f'{dataset_name}.tsv')
+    if not osp.exists(data_file):
+        warnings.warn(f'Data file {data_file} does not exist. Dataset building failed. ')
+        return None
+
+    data = load(data_file)
+    if 'question' not in [x.lower() for x in data.columns]:
+        warnings.warn(f'Data file {data_file} does not have a `question` column. Dataset building failed. ')
+        return None
+
+    if 'A' in data and 'B' in data:
+        if 'image' in data or 'image_path' in data:
+            warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom MCQ dataset. ')
+            return CustomMCQDataset(dataset=dataset_name, **kwargs)
+        else:
+            warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom Text MCQ dataset. ')
+            return CustomTextMCQDataset(dataset=dataset_name, **kwargs)
+    else:
+        warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom VQA dataset. ')
+        return CustomVQADataset(dataset=dataset_name, **kwargs)
+
+
+__all__ = [
+    'build_dataset', 'img_root_map', 'build_judge', 'extract_answer_from_item', 'prefetch_answer', 'DEBUG_MESSAGE'
+] + [cls.__name__ for cls in DATASET_CLASSES]

eval_mm/vlmevalkit/vlmeval/dataset/dude.py

@@ -0,0 +1,210 @@
+import math
+from typing import List
+
+from .utils.judge_util import build_judge
+from .image_base import ImageBaseDataset
+from .mmlongbench import concat_images, MMLongBench_auxeval, anls_compute
+from ..smp import *
+
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def DUDE_acc(result_file):
+    data = load(result_file)
+    overall_score = 0.0
+    score_list = list()
+    for i in range(len(data)):
+        item = data.iloc[i]
+        if isinstance(item['answer'], float) and math.isnan(item['answer']):
+            item['answer'] = 'Not answerable'
+
+        item['answer'] = item['answer'].lower()
+        item['pred'] = item['pred'].lower()
+        score = anls_compute(item['answer'], item['pred'])
+        score_list.append(score)
+        overall_score += score
+
+    data['score'] = score_list
+    dump(data, result_file)
+
+    res = dict()
+    res['category'], res['num'], res['avg_score'] = ['anls'], [len(data)], [overall_score / len(data)]
+    res = pd.DataFrame(res)
+    return res
+
+
+class DUDE(ImageBaseDataset):
+
+    TYPE = 'VQA'
+
+    DATASET_URL = {
+        'DUDE': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE.tsv',
+        'DUDE_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE_MINI.tsv',
+    }
+    DATASET_MD5 = {
+        'DUDE': '130d860d08206e1e407cd77150c10d88',
+        'DUDE_MINI': 'e0c0d998114f0cca7516d12039d2b538',
+    }
+
+    SUPPORTED_MODELS = {
+        'GPT4': (1, 1),
+        'GPT4V': (1, 1),
+        'GPT4V_HIGH': (1, 1),
+        'GPT4o': (1, 1),
+        'GPT4o_HIGH': (1, 1),
+        'GPT4o_MINI': (1, 1),
+        'XComposer2d5': (1, -1),
+        'XComposer2_4KHD': (1, -1),
+        'MiniCPM-Llama3-V-2_5': (1, 5),
+        'InternVL-Chat-V1-5': (5, 2),
+    }
+
+    def __init__(self, dataset, **kwargs):
+        self.model_list = list(self.SUPPORTED_MODELS.keys())
+        model_name = kwargs['model']
+        if not listinstr(self.model_list, model_name):
+            raise AssertionError("{} doesn't support the evaluation on DUDE.".format(model_name))
+        super(DUDE, self).__init__(dataset)
+
+        self.is_api = True if listinstr(['GPT4'], model_name) else False
+        self.max_pages = 120
+        concat_num, column_num = self.SUPPORTED_MODELS.get(model_name)
+        self.concat_num = concat_num
+        self.column_num = column_num
+
+    def prepare_tsv(self, url, file_md5=None):
+        data_root = LMUDataRoot()
+        os.makedirs(data_root, exist_ok=True)
+        file_name = url.split('/')[-1]
+        data_path = osp.join(data_root, file_name)
+        if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5):
+            pass
+        else:
+            warnings.warn('The dataset tsv is not downloaded')
+            download_file(url, data_path)
+        return load(data_path)
+
+    def dump_image(self, origin_line):
+        os.makedirs(self.img_root, exist_ok=True)
+        try:
+            import fitz
+        except:
+            warnings.warn('Please use `pip install pymupdf` to parse PDF files.')
+
+        line = origin_line.copy()
+        if not isinstance(line['image_path'], List):
+            line['image_path'] = [line['image_path']]
+        line['image_path'] = line['image_path'][:self.max_pages]
+        skip_pdf_parse = True
+        for im_name in line['image_path']:
+            path = osp.join(self.img_root, im_name)
+            if not read_ok(path):
+                skip_pdf_parse = False
+                break
+
+        # Just for being compatible with the zooped loop: zip(line['image'], line['image_path'])
+        if skip_pdf_parse:
+            line['image'] = line['image_path']
+        else:
+            pdf_data = base64.b64decode(line['image'])
+            pdf_file = io.BytesIO(pdf_data)
+            encoded_images = []
+            with fitz.open(stream=pdf_file, filetype='pdf') as doc:
+                doc = doc[:self.max_pages]
+                for page in doc:
+                    image = page.get_pixmap(dpi=144)
+                    image_file = io.BytesIO(image.tobytes(output='png'))
+                    image = Image.open(image_file)
+                    encoded_image = encode_image_to_base64(image)
+                    encoded_images.append(encoded_image)
+            line['image'] = encoded_images
+            print('process {}'.format(line['doc_id']))
+
+        if 'image' in line:
+            if isinstance(line['image'], list):
+                tgt_path = []
+                assert 'image_path' in line
+                for img, im_name in zip(line['image'], line['image_path']):
+                    path = osp.join(self.img_root, im_name)
+                    if not read_ok(path):
+                        decode_base64_to_image_file(img, path)
+                    tgt_path.append(path)
+            else:
+                tgt_path = osp.join(self.img_root, f"{line['index']}.jpg")
+                if not read_ok(tgt_path):
+                    decode_base64_to_image_file(line['image'], tgt_path)
+                tgt_path = [tgt_path]
+        else:
+            assert 'image_path' in line
+            tgt_path = toliststr(line['image_path'])
+
+        if self.concat_num > 0 and not self.is_api:
+            concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num)
+
+            old_tgt_path = tgt_path
+            assert isinstance(old_tgt_path, list)
+            if self.column_num != -1:
+                tgt_path = [
+                    '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i)
+                    for i in range(len(concatenated_images))
+                ]
+            else:
+                tgt_path = ['_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all.jpg']
+
+            for path, concatenated_image in zip(tgt_path, concatenated_images):
+                if not read_ok(path):
+                    decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path)
+                    num_images, image_size = len(old_tgt_path), concatenated_image.size
+                    print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path))
+        return tgt_path
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        logger = get_logger('Evaluation')
+        model = judge_kwargs['model']
+
+        suffix = eval_file.split('.')[-1]
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+
+        if osp.exists(storage):
+            logger.warning(f'GPT scoring file {storage} already exists, will reuse it in DUDE_eval. ')
+        else:
+            data = load(eval_file)
+            model = build_judge(max_tokens=128, **judge_kwargs)
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = {}
+            if osp.exists(tmp_file):
+                ans = load(tmp_file)
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = list()
+                for model, line in tqdm(tups):
+                    res = MMLongBench_auxeval(model, line)
+                    new_results.append(res)
+
+            log_map, res_map, pred_map = {}, {}, {}
+            all_inds = [line['index'] for line in lines]
+            for k, v in zip(all_inds, new_results):
+                log_map[k] = v['log']
+                res_map[k] = v['res']
+                pred_map[k] = v['pred']
+            data['res'] = [res_map[idx] for idx in data['index']]
+            data['log'] = [log_map[idx] for idx in data['index']]
+            data['pred'] = [pred_map[idx] for idx in data['index']]
+            dump(data, storage)
+
+        score = DUDE_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+
+        dump(score, score_pth)
+        logger.info(f'DUDE successfully finished evaluating {eval_file}, results saved in {score_pth}')
+        logger.info('Score: ')
+        logger.info(score)

eval_mm/vlmevalkit/vlmeval/dataset/image_base.py

@@ -0,0 +1,165 @@
+import pandas as pd
+from abc import abstractmethod
+from ..smp import *
+
+
+def img_root_map(dataset):
+    if 'OCRVQA' in dataset:
+        return 'OCRVQA'
+    if 'COCO_VAL' == dataset:
+        return 'COCO'
+    if 'MMMU' in dataset:
+        return 'MMMU'
+    mmbench_root_map = {
+        'MMBench_DEV_EN': 'MMBench', 'MMBench_TEST_EN': 'MMBench',
+        'MMBench_DEV_CN': 'MMBench', 'MMBench_TEST_CN': 'MMBench',
+        'MMBench': 'MMBench', 'MMBench_CN': 'MMBench',
+        'MMBench_DEV_EN_V11': 'MMBench_V11', 'MMBench_TEST_EN_V11': 'MMBench_V11',
+        'MMBench_DEV_CN_V11': 'MMBench_V11', 'MMBench_TEST_CN_V11': 'MMBench_V11',
+        'MMBench_V11': 'MMBench', 'MMBench_CN_V11': 'MMBench',
+    }
+    if dataset in mmbench_root_map:
+        return mmbench_root_map[dataset]
+    return dataset
+
+
+class ImageBaseDataset:
+
+    MODALITY = 'IMAGE'
+    DATASET_URL = {}
+    DATASET_MD5 = {}
+
+    def __init__(self, dataset='MMBench', skip_noimg=True):
+        ROOT = LMUDataRoot()
+        # You can override this variable to save image files to a different directory
+        self.dataset_name = dataset
+        self.img_root = osp.join(ROOT, 'images', img_root_map(dataset))
+
+        data = self.load_data(dataset)
+        self.skip_noimg = skip_noimg
+        if skip_noimg and 'image' in data:
+            data = data[~pd.isna(data['image'])]
+
+        data['index'] = [str(x) for x in data['index']]
+
+        self.meta_only = True
+
+        # The image field can store the base64 encoded image or another question index (for saving space)
+        if 'image' in data:
+            data['image'] = [str(x) for x in data['image']]
+            image_map = {x: y for x, y in zip(data['index'], data['image'])}
+            for k in image_map:
+                if len(image_map[k]) <= 64:
+                    idx = image_map[k]
+                    assert idx in image_map and len(image_map[idx]) > 64
+                    image_map[k] = image_map[idx]
+
+            images = [toliststr(image_map[k]) for k in data['index']]
+            data['image'] = [x[0] if len(x) == 1 else x for x in images]
+            self.meta_only = False
+
+        if 'image_path' in data:
+            paths = [toliststr(x) for x in data['image_path']]
+            data['image_path'] = [x[0] if len(x) == 1 else x for x in paths]
+
+        if np.all([istype(x, int) for x in data['index']]):
+            data['index'] = [int(x) for x in data['index']]
+
+        self.data = data
+        self.post_build(dataset)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return dict(self.data.iloc[idx])
+
+    def prepare_tsv(self, url, file_md5=None):
+        data_root = LMUDataRoot()
+        os.makedirs(data_root, exist_ok=True)
+        update_flag = False
+        file_name = url.split('/')[-1]
+        data_path = osp.join(data_root, file_name)
+        if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5):
+            pass
+        else:
+            warnings.warn('The dataset tsv is not downloaded')
+            download_file(url, data_path)
+            update_flag = True
+
+        if file_size(data_path, 'GB') > 1:
+            local_path = data_path.replace('.tsv', '_local.tsv')
+            if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None) or update_flag:
+                from ..tools import LOCALIZE
+                LOCALIZE(data_path, local_path)
+            data_path = local_path
+        return load(data_path)
+
+    def dump_image(self, line):
+        os.makedirs(self.img_root, exist_ok=True)
+
+        if 'image' in line:
+            if isinstance(line['image'], list):
+                tgt_path = []
+                assert 'image_path' in line
+                for img, im_name in zip(line['image'], line['image_path']):
+                    path = osp.join(self.img_root, im_name)
+                    if not read_ok(path):
+                        decode_base64_to_image_file(img, path)
+                    tgt_path.append(path)
+            else:
+                tgt_path = osp.join(self.img_root, f"{line['index']}.jpg")
+                if not read_ok(tgt_path):
+                    decode_base64_to_image_file(line['image'], tgt_path)
+                tgt_path = [tgt_path]
+        else:
+            assert 'image_path' in line
+            tgt_path = toliststr(line['image_path'])
+
+        return tgt_path
+
+    def display(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+        assert isinstance(line, pd.Series) or isinstance(line, dict)
+        mmqa_display(line)
+
+    # Return a list of dataset names that are supported by this class, can override
+    @classmethod
+    def supported_datasets(cls):
+        return list(cls.DATASET_URL)
+
+    # Given the dataset name, return the dataset as a pandas dataframe, can override
+    def load_data(self, dataset):
+        url = self.DATASET_URL[dataset]
+        file_md5 = self.DATASET_MD5[dataset] if dataset in self.DATASET_MD5 else None
+        return self.prepare_tsv(url, file_md5)
+
+    # Post built hook, will be called after the dataset is built, can override
+    def post_build(self, dataset):
+        pass
+
+    # Given one data record, return the built prompt (a multi-modal message), can override
+    def build_prompt(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        if self.meta_only:
+            tgt_path = toliststr(line['image_path'])
+        else:
+            tgt_path = self.dump_image(line)
+
+        question = line['question']
+
+        msgs = []
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=question))
+        return msgs
+
+    # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe
+    @abstractmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        pass

eval_mm/vlmevalkit/vlmeval/dataset/image_caption.py

@@ -0,0 +1,75 @@
+from .image_base import ImageBaseDataset
+from ..smp import *
+
+
+class COCO_Caption_Scorer():
+    def __init__(self, ref, gt):
+        from pycocoevalcap.bleu.bleu import Bleu
+        from pycocoevalcap.rouge.rouge import Rouge
+        from pycocoevalcap.cider.cider import Cider
+
+        self.ref = ref
+        self.gt = gt
+        print('setting up scorers...')
+        self.scorers = [
+            (Bleu(4), ['Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4']),
+            (Rouge(), 'ROUGE_L'),
+            (Cider(), 'CIDEr'),
+        ]
+
+    def compute_scores(self):
+        total_scores = {}
+        for scorer, method in self.scorers:
+            print('computing %s score...' % (scorer.method()))
+            score, scores = scorer.compute_score(self.gt, self.ref)
+            if isinstance(method, list):
+                for sc, scs, m in zip(score, scores, method):
+                    print('%s: %0.3f' % (m, sc * 100))
+                total_scores['Bleu'] = [x * 100 for x in score]
+            else:
+                print('%s: %0.3f' % (method, score * 100))
+                total_scores[method] = score * 100
+
+        print('*****DONE*****')
+        for key, value in total_scores.items():
+            print('{}:{}'.format(key, value))
+        return total_scores
+
+
+class ImageCaptionDataset(ImageBaseDataset):
+
+    TYPE = 'Caption'
+
+    DATASET_URL = {
+        'COCO_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/COCO_VAL.tsv',
+    }
+
+    DATASET_MD5 = {
+        'COCO_VAL': '72a5079dead060269ac222c5aa5128af',
+    }
+
+    def load_data(self, dataset):
+        data = super().load_data(dataset)
+        if 'question' not in data:
+            data['question'] = [(
+                'Please describe this image in general. Directly provide the description, '
+                'do not include prefix like "This image depicts". '
+            )] * len(data)
+        return data
+
+    # It returns a dictionary of scores
+    @classmethod
+    def evaluate(self, eval_file, **kwargs):
+        data = load(eval_file)
+        lt = len(data)
+        lines = [data.iloc[i] for i in range(lt)]
+        ref, gt = {}, {}
+        for i, line in enumerate(lines):
+            ref[str(i)] = [str(line['prediction'])]
+            gt[str(i)] = eval(line['answer'])
+
+        scorer = COCO_Caption_Scorer(ref, gt)
+        coco_caption_score_dict = scorer.compute_scores()
+        score_pth = eval_file.replace('.xlsx', '_score.json')
+        dump(coco_caption_score_dict, score_pth)
+        return coco_caption_score_dict

eval_mm/vlmevalkit/vlmeval/dataset/image_mcq.py

@@ -0,0 +1,484 @@
+import warnings
+
+from .image_base import ImageBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..smp import *
+
+
+MMMB_URLS = {
+    'MMMB_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ar.tsv',
+    'MMMB_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_cn.tsv',
+    'MMMB_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_en.tsv',
+    'MMMB_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_pt.tsv',
+    'MMMB_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ru.tsv',
+    'MMMB_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_tr.tsv',
+}
+
+MTL_MMBench_URLS = {
+    'MMBench_dev_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ar.tsv',
+    'MMBench_dev_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_cn.tsv',
+    'MMBench_dev_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_en.tsv',
+    'MMBench_dev_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_pt.tsv',
+    'MMBench_dev_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_tr.tsv',
+    'MMBench_dev_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ru.tsv',
+}
+
+MMMB_MD5 = {
+    'MMMB_ar': 'f3a18b6385f1d9701840aa42de27aead', 'MMMB_cn': '13ed82fa89730037292fcaa27f08f430',
+    'MMMB_en': '1cd781a71ec5a2983c090b84105d6a01', 'MMMB_pt': '548ea2b3bb2da991790386f0015d30d1',
+    'MMMB_ru': 'ce1cc8a0533425ab0d86b326ebfc2984', 'MMMB_tr': '0733739d43090327975294292bc5cd67'
+}
+
+MTL_MMBench_MD5 = {
+    'MMBench_dev_ar': '4271b4a0d0200e1a86380a878e0d64a4', 'MMBench_dev_cn': '2ed5135326fed02c8e51ea50dda8222f',
+    'MMBench_dev_en': 'd9ab776fc018b3d45785e9a5c23431c2', 'MMBench_dev_pt': '4ddfbcd27ef12444b908c03831cd0295',
+    'MMBench_dev_tr': '4fab39d501389d3d6cc90264bb708f11', 'MMBench_dev_ru': '5ba1171ff2e68f80637bf78349e402a5'
+}
+
+
+class ImageMCQDataset(ImageBaseDataset):
+
+    TYPE = 'MCQ'
+
+    DATASET_URL = {
+        # MMBench v1.0
+        'MMBench_DEV_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv',
+        'MMBench_TEST_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv',
+        'MMBench_DEV_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv',
+        'MMBench_TEST_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv',
+        'MMBench': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench.tsv',  # Internal Only
+        'MMBench_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN.tsv',    # Internal Only
+        # MMBench v1.1
+        'MMBench_DEV_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN_V11.tsv',
+        'MMBench_TEST_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN_V11.tsv',
+        'MMBench_DEV_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN_V11.tsv',
+        'MMBench_TEST_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN_V11.tsv',
+        'MMBench_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_V11.tsv',  # Internal Only
+        'MMBench_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN_V11.tsv',    # Internal Only
+        # SEEDBench Series
+        'SEEDBench_IMG': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench_IMG.tsv',
+        'SEEDBench2': 'https://huggingface.co/datasets/VLMEval/SEEDBench2/resolve/main/SEEDBench2.tsv',
+        'SEEDBench2_Plus': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench2_Plus.tsv',
+        # ScienceQA Series
+        'ScienceQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_VAL.tsv',
+        'ScienceQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_TEST.tsv',
+        # MMT-Bench
+        'MMT-Bench_ALL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL_MI.tsv',
+        'MMT-Bench_ALL': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL.tsv',
+        'MMT-Bench_VAL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_VAL_MI.tsv',
+        'MMT-Bench_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_VAL.tsv',
+        # AesBench
+        'AesBench_VAL': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_VAL.tsv',
+        'AesBench_TEST': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_TEST.tsv',
+        # Q-Bench1
+        'Q-Bench1_VAL': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_VAL.tsv',
+        'Q-Bench1_TEST': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_TEST.tsv',
+        # A-Bench
+        'A-Bench_VAL': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_VAL.tsv',
+        'A-Bench_TEST': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_TEST.tsv',
+        # Other Benchmarks
+        'CCBench': 'https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv',
+        'AI2D_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST.tsv',
+        'AI2D_TEST_NO_MASK': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST_NO_MASK.tsv',
+        'MMStar': 'https://opencompass.openxlab.space/utils/VLMEval/MMStar.tsv',
+        'RealWorldQA': 'https://opencompass.openxlab.space/utils/VLMEval/RealWorldQA.tsv',
+        'MLLMGuard_DS': 'https://opencompass.openxlab.space/utils/VLMEval/MLLMGuard_DS.tsv',
+        'BLINK': 'https://opencompass.openxlab.space/utils/VLMEval/BLINK.tsv',
+        'TaskMeAnything_v1_imageqa_random': (
+            'https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-random/'
+            'resolve/main/TaskMeAnything-v1-imageqa-random.tsv'
+        ),
+        'A-OKVQA': 'https://huggingface.co/datasets/Allen8/A-OKVQA/resolve/main/a-okvqa.tsv'
+    }
+
+    DATASET_MD5 = {
+        # MMBench v1.0
+        'MMBench_DEV_EN': 'b6caf1133a01c6bb705cf753bb527ed8',
+        'MMBench_TEST_EN': '6939fadb0ce626fefc0bdc9c64efc528',
+        'MMBench_DEV_CN': '08b8fc3324a5ed74155350f57be69fbd',
+        'MMBench_TEST_CN': '7e1239baf0ee4c8b513e19705a0f317e',
+        'MMBench': '4115aea3383f3dd0083be6a633e0f820',  # Internal Only
+        'MMBench_CN': '2e053ffc90ea598b1feae13c36dc13ee',    # Internal Only
+        # MMBench v1.1
+        'MMBench_DEV_EN_V11': '30c05be8f2f347a50be25aa067248184',
+        'MMBench_TEST_EN_V11': '26f0f15381a21720255091d3e0316ce6',
+        'MMBench_DEV_CN_V11': '593f9b5f6bea453d870a798b34ae4f37',
+        'MMBench_TEST_CN_V11': '74bbe4556dac745613c7cbe5ad787050',
+        'MMBench_V11': 'b9276414f57af1308dcc4d0cd9b42e7c',  # Internal Only
+        'MMBench_CN_V11': '95f6980dd1b4de38e3cbffe0305a3f25',    # Internal Only
+        # SEEDBench
+        'SEEDBench_IMG': '68017231464752261a2526d6ca3a10c0',
+        'SEEDBench2': '4ec15cf864c4f16274112284f531813e',
+        'SEEDBench2_Plus': 'e32d3216dc4f452b0fe497a52015d1fd',
+        # ScienceQA
+        'ScienceQA_VAL': '96320d05e142e585e7204e72affd29f3',
+        'ScienceQA_TEST': 'e42e9e00f9c59a80d8a5db35bc32b71f',
+        # MMT-Bench
+        'MMT-Bench_ALL_MI': '5272157097e19cdd7cb41e412ab3b7c7',
+        'MMT-Bench_ALL': 'b273a2f4c596fe4f2605de0494cd632f',
+        'MMT-Bench_VAL_MI': 'c7d7b998eb5cd9aa36c7d4f721472462',
+        'MMT-Bench_VAL': '8dd4b730f53dbf9c3aed90ca31c928e0',
+        # AesBench
+        'AesBench_VAL': '3edb0c319e9187aa0b97fe7a11700a8c',
+        'AesBench_TEST': '58b1f7ba2cc32e1d68896d6ee716bbf8',
+        # Q-Bench1
+        'Q-Bench1_VAL': '837bdb6cd2da571713543462815187b7',
+        'Q-Bench1_TEST': '15e759bfd58c9d5f30b23a317d347153',
+        # A-Bench
+        'A-Bench_VAL': '218563ec50d34bb336c814143a5bb9c1',
+        'A-Bench_TEST': '567013fb033a20cf23f51d8e865bd16c',
+        # Other Benchmarks
+        'CCBench': 'f5dde47f24dc5a6fb6e595b409b466ac',
+        'AI2D_TEST': '0f593e0d1c7df9a3d69bf1f947e71975',
+        'AI2D_TEST_NO_MASK': 'fd8f463634d4fe9fbd23b876e8eea5be',
+        'MMStar': 'e1ecd2140806c1b1bbf54b43372efb9e',
+        'RealWorldQA': '92321028d2bc29040284b6674721e48f',
+        'MLLMGuard_DS': '975fc0dd7119386e198c37d71e274b3f',
+        'BLINK': '3b6649b6a662184ea046908e5506260e',
+        'TaskMeAnything_v1_imageqa_random': '023fef69e2ca21827afb77c5ec3bc889'
+    }
+
+    DATASET_URL.update(MMMB_URLS)
+    DATASET_URL.update(MTL_MMBench_URLS)
+    DATASET_MD5.update(MMMB_MD5)
+    DATASET_MD5.update(MTL_MMBench_MD5)
+
+    def build_prompt(self, line):
+
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        if self.meta_only:
+            tgt_path = toliststr(line['image_path'])
+        else:
+            tgt_path = self.dump_image(line)
+
+        question = line['question']
+        options = {
+            cand: line[cand]
+            for cand in string.ascii_uppercase
+            if cand in line and not pd.isna(line[cand])
+        }
+        options_prompt = 'Options:\n'
+        for key, item in options.items():
+            options_prompt += f'{key}. {item}\n'
+        hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+        prompt = ''
+        if hint is not None:
+            prompt += f'Hint: {hint}\n'
+        prompt += f'Question: {question}\n'
+        if len(options):
+            prompt += options_prompt
+            prompt += 'Please select the correct answer from the options above. \n'
+
+        msgs = []
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=prompt))
+
+        return msgs
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval
+        # assert dataset is not None
+        dataset_map = {
+            'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11',
+            'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11'
+        }
+        dataset = self.dataset_name
+        if dataset in dataset_map:
+            dataset = dataset_map[dataset]
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        circular = False
+        if listinstr(['mmbench', 'ccbench'], dataset.lower()):
+            data = load(eval_file)
+            data['index'] = [int(x) for x in data['index']]
+            dump(data, eval_file)
+            circular = True
+
+        suffix = eval_file.split('.')[-1]
+        model = judge_kwargs.get('model', 'exact_matching')
+        assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125']
+        name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'}
+        name_str = name_str_map[model] if model in name_str_map else model
+
+        if model == 'exact_matching':
+            model = None
+        elif gpt_key_set():
+            model = build_judge(**judge_kwargs)
+            if not model.working():
+                warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation')
+                warnings.warn(DEBUG_MESSAGE)
+                model = None
+        else:
+            warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
+            model = None
+
+        result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl')
+
+        data = load(eval_file)
+        data = data.sort_values(by='index')
+        data['prediction'] = [str(x) for x in data['prediction']]
+        # If not choice label, then use lower case
+        for k in data.keys():
+            data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
+
+        meta = self.data
+        meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])}
+        data_map = {x: y for x, y in zip(data['index'], data['question'])}
+        for k in data_map:
+            assert k in meta_q_map, (
+                f'eval_file should be the same as or a subset of dataset {self.dataset_name}'
+            )
+
+        if circular:
+            data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name)
+        else:
+            data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name)
+
+        # load split
+        dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+        data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+
+        # May have different report acc functions for different datasets
+        if 'MMT' in dataset:
+            acc = report_acc_MMT(data)
+        else:
+            acc = report_acc(data)
+
+        score_file = eval_file.replace(f'.{suffix}', '_acc.csv')
+        dump(acc, score_file)
+
+        if dataset == 'AesBench_VAL':
+            warnings.warn('Note that AesBench VAL is just a toy version of AesBench TEST. For full results, \
+                           please evaluate on AesBench TEST. The AesBench TEST dataset is more than 20 times \
+                           larger than the VAL dataset and the leaderboard results are based on AesBench TEST.')
+        return acc
+
+
+class MMMUDataset(ImageMCQDataset):
+
+    DATASET_URL = {
+        'MMMU_DEV_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_DEV_VAL.tsv',
+        'MMMU_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_TEST.tsv',
+    }
+
+    DATASET_MD5 = {
+        'MMMU_DEV_VAL': '521afc0f3bf341e6654327792781644d',
+        'MMMU_TEST': 'c19875d11a2d348d07e5eb4bdf33166d',
+    }
+
+    @staticmethod
+    def split_MMMU(msgs):
+        text, images = None, []
+        for s in msgs:
+            if s['type'] == 'image':
+                images.append(s['value'])
+            elif s['type'] == 'text':
+                assert text is None
+                text = s['value']
+        text_segs = text.split('<image ')
+        if len(text_segs) == 1:
+            return msgs
+
+        segs = [dict(type='text', value=text_segs[0])]
+        for i, seg in enumerate(text_segs):
+            if i == 0:
+                continue
+            assert istype(seg[0], int) and seg[1] == '>'
+            image_idx = int(seg[0]) - 1
+            segs.append(dict(type='image', value=images[image_idx]))
+            segs.append(dict(type='text', value=seg[2:]))
+        return segs
+
+    def build_prompt(self, line):
+        msgs = super().build_prompt(line)
+        msgs = self.split_MMMU(msgs)
+        return msgs
+
+
+class MUIRDataset(ImageMCQDataset):
+
+    DATASET_URL = {
+        'MUIRBench': 'http://opencompass.openxxlab.com/utils/VLMEval/MUIRBench.tsv'
+    }
+
+    DATASET_MD5 = {
+        'MUIRBench': '2e5e6fd7699761b08a7cb3ab8c0c2ec8'
+    }
+
+    @staticmethod
+    def split_MUIR(msgs):
+        text, images = None, []
+
+        # Separate images and text from msgs
+        for s in msgs:
+            if s['type'] == 'image':
+                images.append(s['value'])
+            elif s['type'] == 'text':
+                assert text is None  # Ensure only one text entry is expected
+                text = s['value']
+
+        # Split text by <image> tags
+        text_segs = text.split('<image>')
+
+        # Initialize the segments list
+        segs = []
+
+        # Iterate through the text segments and images
+        for i, seg in enumerate(text_segs):
+            # Append the image if this is not the first segment and there are still images left
+            if i > 0 and i - 1 < len(images):
+                segs.append(dict(type='image', value=images[i - 1]))
+            # Append the text segment (if it's non-empty)
+            if len(seg) > 0:
+                segs.append(dict(type='text', value=seg))
+
+        return segs
+
+    def build_prompt(self, line):
+
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        if self.meta_only:
+            tgt_path = toliststr(line['image_path'])
+        else:
+            tgt_path = self.dump_image(line)
+
+        question = line['question']
+        options = {
+            cand: line[cand]
+            for cand in string.ascii_uppercase
+            if cand in line and not pd.isna(line[cand])
+        }
+        # options_prompt = ''
+        options_prompt = '\n'.join([f'{key}. {item}' for key, item in options.items()])
+        # for key, item in options.items():
+        #     options_prompt += f'{key}. {item}\n'
+
+        prompt = ''
+
+        prompt += f'{question}\n'
+        if len(options):
+            prompt += options_prompt
+            prompt += "\nAnswer with the option's letter from the given choices directly."
+
+        msgs = []
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=prompt))
+
+        msgs = self.split_MUIR(msgs)
+        return msgs
+
+
+class GMAIMMBenchDataset(ImageMCQDataset):
+
+    DATASET_URL = {
+        'GMAI-MMBench_VAL': 'https://huggingface.co/datasets/VLMEval/GMAI-MMBench/resolve/main/GMAI-MMBench_VAL.tsv'
+    }
+
+    DATASET_MD5 = {
+        'GMAI-MMBench_VAL': '254bd581627866f1c499d3d6b4422324'
+    }
+
+    def report_acc_by_groups(self, df, group_column):
+        res = defaultdict(list)
+
+        # Check for the 'split' column
+        if 'split' in df:
+            splits = list(set(df['split']))
+            res['split'] = splits
+        else:
+            df['split'] = ['none'] * len(df)
+            res['split'] = ['none']
+
+        res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']]
+
+        if group_column not in df:
+            raise ValueError(f"Column '{group_column}' not found in dataframe.")
+
+        abilities = list(set(df[group_column]))
+        abilities = ['None' if isinstance(ab, float) and pd.isna(ab) else ab for ab in abilities]
+        abilities.sort()
+
+        for ab in abilities:
+            ab_name = ab
+            sub_df = df[df[group_column] == ab]
+            res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']]
+
+        return pd.DataFrame(res)
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.multiple_choice import report_acc, mcq_vanilla_eval
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        suffix = eval_file.split('.')[-1]
+        model = judge_kwargs.get('model', 'exact_matching')
+        assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125']
+        name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'}
+        name_str = name_str_map[model] if model in name_str_map else model
+
+        if model == 'exact_matching':
+            model = None
+        elif gpt_key_set():
+            model = build_judge(**judge_kwargs)
+            if not model.working():
+                warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation')
+                warnings.warn(DEBUG_MESSAGE)
+                model = None
+        else:
+            warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
+            model = None
+
+        result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl')
+
+        data = load(eval_file)
+        data = data.sort_values(by='index')
+        data['prediction'] = [str(x) for x in data['prediction']]
+        # If not choice label, then use lower case
+        for k in data.keys():
+            data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
+
+        meta = self.data
+        meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])}
+        data_map = {x: y for x, y in zip(data['index'], data['question'])}
+        for k in data_map:
+            assert k in meta_q_map, (
+                f'eval_file should be the same as or a subset of dataset {self.dataset_name}'
+            )
+
+        data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name)
+
+        # load split
+        dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+        data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+
+        acc = report_acc(data)
+
+        for group_col in ['clinical vqa task', 'department', 'perceptual granularity']:
+            acc_grouped = self.report_acc_by_groups(data, group_col)
+            score_file_grouped = eval_file.replace(f'.{suffix}', f'_{group_col}_acc.csv')
+            dump(acc_grouped, score_file_grouped)
+
+        return acc
+
+
+class CustomMCQDataset(ImageMCQDataset):
+
+    def load_data(self, dataset):
+        data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv')
+
+        if file_size(data_path, 'GB') > 1:
+            local_path = data_path.replace('.tsv', '_local.tsv')
+            if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None):
+                from ..tools import LOCALIZE
+                LOCALIZE(data_path, local_path)
+            data_path = local_path
+        return load(data_path)

eval_mm/vlmevalkit/vlmeval/dataset/image_mt.py

@@ -0,0 +1,128 @@
+from .image_base import ImageBaseDataset
+from .utils.judge_util import build_judge
+from ..smp import *
+from ..utils import track_progress_rich
+
+
+class ImageMTDataset(ImageBaseDataset):
+
+    TYPE = 'MT'
+
+    def build_prompt(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        if self.meta_only:
+            tgt_path = toliststr(line['image_path'])
+        else:
+            tgt_path = self.dump_image(line)
+
+        questions = toliststr(line['question'])
+        if 'answer' in line:
+            answers = toliststr(line['answer'])
+        else:
+            answers = [''] * len(questions)
+        assert len(questions) == len(answers)
+
+        dlgs, pics_number = [], 0
+        for i in range(len(questions)):
+            q, a = questions[i], answers[i]
+            if '<ImageHere>' in q:
+                content = []
+                tag_number = q.count('<ImageHere>')
+                images = tgt_path[pics_number: pics_number + tag_number]
+                pics_number += tag_number
+                q_split = q.split('<ImageHere>')
+                for i in range(tag_number):
+                    qsp, im = q_split[i], images[i]
+                    if qsp != '':
+                        content.append(dict(type='text', value=qsp))
+                    content.append(dict(type='image', value=im))
+                if q_split[-1] != '':
+                    content.append(dict(type='text', value=q_split[-1]))
+            else:
+                content = [dict(type='text', value=q)]
+            dlgs.append(dict(role='user', content=content))
+            assert '<ImageHere>' not in a, 'We currently do not support images in the answer. '
+            content = [dict(type='text', value=a)]
+            dlgs.append(dict(role='assistant', content=content))
+        return dlgs
+
+
+class MMDUDataset(ImageMTDataset):
+
+    DATASET_URL = {'MMDU': 'https://opencompass.openxlab.space/utils/VLMEval/MMDU.tsv'}
+    DATASET_MD5 = {'MMDU': '848b635a88a078f49aebcc6e39792061'}
+    DIMS = [
+        'Creativity', 'Richness', 'Visual Perception', 'Logical Coherence',
+        'Answer Accuracy', 'Image Relationship Understanding', 'Overall Score'
+    ]
+
+    def calculat_metric(self, ans):
+        all = defaultdict(lambda: 0)
+        tot = defaultdict(lambda: 0)
+        valid = defaultdict(lambda: 0)
+        for k in ans:
+            res = ans[k]['res']
+            assert isinstance(res, pd.DataFrame)
+            lt = len(res)
+            for i in range(lt):
+                line = res.iloc[i]
+                for k in self.DIMS:
+                    tot[k] += 1
+                    if k in line and line[k] is not None:
+                        try:
+                            score = int(line[k])
+                            score = np.clip(score, 0, 10)
+                            all[k] += score
+                            valid[k] += 1
+                        except Exception as e:
+                            print(f'Failed to parse the score: {str(e)}')
+        sp1 = {'set': 'all'}
+        sp1.update({k: all[k] / tot[k] * 10 for k in self.DIMS})
+        sp2 = {'set': 'valid'}
+        sp2.update({k: all[k] / valid[k] * 10 for k in self.DIMS})
+
+        return pd.DataFrame([sp1, sp2])
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        suffix = eval_file.split('.')[-1]
+        model = judge_kwargs['model']
+
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+        score_file = eval_file.replace(f'.{suffix}', f'_{model}_score.csv')
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        data = load(eval_file)
+        model = judge_kwargs.pop('model', 'gpt-4o')
+        judge_model = build_judge(model=model, **judge_kwargs)
+
+        lt = len(data)
+        lines = [data.iloc[i] for i in range(lt)]
+        tups = [(judge_model, line) for line in lines]
+        indices = [line['index'] for line in lines]
+
+        ans = {}
+        if osp.exists(tmp_file):
+            ans = load(tmp_file)
+
+        tups = [x for x, i in zip(tups, indices) if i not in ans]
+        indices = [i for i in indices if i not in ans]
+
+        from .utils.mmdu import mmdu_score
+
+        if len(indices):
+            new_results = track_progress_rich(
+                mmdu_score,
+                tups,
+                nproc=nproc,
+                chunksize=nproc,
+                keys=indices,
+                save=tmp_file,)
+            ans = load(tmp_file)
+            for k, v in zip(indices, new_results):
+                assert k in ans
+
+        metric = self.calculat_metric(ans)
+        dump(metric, score_file)
+        return metric

eval_mm/vlmevalkit/vlmeval/dataset/image_vqa.py

@@ -0,0 +1,433 @@
+from functools import partial
+
+from .image_base import ImageBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..smp import *
+from ..utils import track_progress_rich
+
+
+class ImageVQADataset(ImageBaseDataset):
+    TYPE = 'VQA'
+
+    DATASET_URL = {
+        'OCRVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TEST.tsv',
+        'OCRVQA_TESTCORE': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TESTCORE.tsv',
+        'TextVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/TextVQA_VAL.tsv',
+        'DocVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_VAL.tsv',
+        'DocVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_TEST.tsv',
+        'InfoVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_VAL.tsv',
+        'InfoVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_TEST.tsv',
+        'ChartQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ChartQA_TEST.tsv',
+    }
+
+    DATASET_MD5 = {
+        'OCRVQA_TEST': 'ca46a6d74b403e9d6c0b670f6fc00db9',
+        'OCRVQA_TESTCORE': 'c5239fe77db8bdc1f2ad8e55e0d1fe97',
+        'TextVQA_VAL': 'b233b31f551bbf4056f2f955da3a92cd',
+        'DocVQA_VAL': 'd5ee77e1926ff10690d469c56b73eabf',
+        'DocVQA_TEST': '6a2f28cac26ef2d3447374e8c6f6c8e9',
+        'InfoVQA_VAL': '2342e9c225222f0ef4dec545ebb126fe',
+        'InfoVQA_TEST': 'df535bf51b88dc9718252c34131a6227',
+        'ChartQA_TEST': 'c902e0aa9be5582a7aad6dcf52734b42',
+    }
+
+    def build_prompt(self, line):
+        msgs = super().build_prompt(line)
+        assert msgs[-1]['type'] == 'text'
+        msgs[-1]['value'] += '\nAnswer the question using a single word or phrase.'
+        return msgs
+
+    # It returns a DataFrame
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.vqa_eval import hit_calculate, process_line
+
+        data = load(eval_file)
+        dataset = self.dataset_name
+        assert 'answer' in data and 'prediction' in data
+        data['prediction'] = [str(x) for x in data['prediction']]
+        data['answer'] = [str(x) for x in data['answer']]
+        lt = len(data)
+        pool = mp.Pool(16)
+        lines = [data.iloc[i] for i in range(lt)]
+        if listinstr(['TextVQA'], dataset):
+            res = pool.map(partial(process_line, method='vqa_score'), lines)
+        elif listinstr(['ChartQA'], dataset):
+            res = pool.map(partial(process_line, method='relaxed_accuracy'), lines)
+        elif listinstr(['OCRVQA'], dataset):
+            res = pool.map(partial(process_line, method='accuracy'), lines)
+        elif listinstr(['DocVQA', 'InfoVQA'], dataset):
+            res = pool.map(partial(process_line, method='anls'), lines)
+        else:  # default using vqa_score to calculate score
+            res = pool.map(process_line, lines)
+        hit = hit_calculate(res, dataset)
+        ret = dict()
+        if 'split' in data:
+            splits = set(data['split'])
+            for sp in splits:
+                sub = [r for l, r in zip(lines, res) if l['split'] == sp]
+                # [np.mean(x['match']) >= full_score_weight for x in sub]
+                hit = hit_calculate(sub, dataset)
+                ret[sp] = np.mean(hit) * 100
+            sub = [r for l, r in zip(lines, res)]
+            hit = hit_calculate(sub, dataset)
+            ret['Overall'] = np.mean(hit) * 100
+        else:
+            ret['Overall'] = np.mean(hit) * 100
+            if 'category' in data:
+                cates = list(set(data['category']))
+                cates.sort()
+                for c in cates:
+                    sub = [r for l, r in zip(lines, res) if l['category'] == c]
+                    # [np.mean(x['match']) >= full_score_weight for x in sub]
+                    hit = hit_calculate(sub, dataset)
+                    ret[c] = np.mean(hit) * 100
+        ret = d2df(ret)
+        ret.round(2)
+
+        suffix = eval_file.split('.')[-1]
+        result_file = eval_file.replace(f'.{suffix}', '_acc.csv')
+        dump(ret, result_file)
+        return ret
+
+
+class OCRBench(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {
+        'OCRBench': 'https://opencompass.openxlab.space/utils/VLMEval/OCRBench.tsv'
+    }
+    DATASET_MD5 = {'OCRBench': 'e953d98a987cc6e26ef717b61260b778'}
+
+    # It returns a dictionary
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        OCRBench_score = {
+            'Regular Text Recognition': 0,
+            'Irregular Text Recognition': 0,
+            'Artistic Text Recognition': 0,
+            'Handwriting Recognition': 0,
+            'Digit String Recognition': 0,
+            'Non-Semantic Text Recognition': 0,
+            'Scene Text-centric VQA': 0,
+            'Doc-oriented VQA': 0,
+            'Key Information Extraction': 0,
+            'Handwritten Mathematical Expression Recognition': 0,
+        }
+
+        data = load(eval_file)
+        lt = len(data)
+        lines = [data.iloc[i] for i in range(lt)]
+        for i in tqdm(range(len(lines))):
+            line = lines[i]
+            predict = str(line['prediction'])
+            answers = eval(line['answer'])
+            category = line['category']
+            if category == 'Handwritten Mathematical Expression Recognition':
+                for j in range(len(answers)):
+                    answer = answers[j].strip().replace('\n', ' ').replace(' ', '')
+                    predict = predict.strip().replace('\n', ' ').replace(' ', '')
+                    if answer in predict:
+                        OCRBench_score[category] += 1
+                        break
+            else:
+                for j in range(len(answers)):
+                    answer = answers[j].lower().strip().replace('\n', ' ')
+                    predict = predict.lower().strip().replace('\n', ' ')
+                    if answer in predict:
+                        OCRBench_score[category] += 1
+                        break
+
+        final_score_dict = {}
+        final_score_dict['Text Recognition'] = \
+            (OCRBench_score['Regular Text Recognition'] + OCRBench_score['Irregular Text Recognition']
+             + OCRBench_score['Artistic Text Recognition'] + OCRBench_score['Handwriting Recognition']
+             + OCRBench_score['Digit String Recognition'] + OCRBench_score['Non-Semantic Text Recognition'])
+        final_score_dict['Scene Text-centric VQA'] = OCRBench_score['Scene Text-centric VQA']
+        final_score_dict['Doc-oriented VQA'] = OCRBench_score['Doc-oriented VQA']
+        final_score_dict['Key Information Extraction'] = OCRBench_score['Key Information Extraction']
+        final_score_dict['Handwritten Mathematical Expression Recognition'] = \
+            (OCRBench_score['Handwritten Mathematical Expression Recognition'])
+        final_score_dict['Final Score'] = \
+            (final_score_dict['Text Recognition'] + final_score_dict['Scene Text-centric VQA']
+             + final_score_dict['Doc-oriented VQA'] + final_score_dict['Key Information Extraction']
+             + final_score_dict['Handwritten Mathematical Expression Recognition'])
+        final_score_dict['Final Score Norm'] = (float(final_score_dict['Final Score']) / 10)
+        score_pth = eval_file.replace('.xlsx', '_score.json')
+        dump(final_score_dict, score_pth)
+        return final_score_dict
+
+
+class MathVista(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {
+        'MathVista_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVista_MINI.tsv'
+    }
+    DATASET_MD5 = {'MathVista_MINI': 'f199b98e178e5a2a20e7048f5dcb0464'}
+
+    # It returns a DataFrame
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.mathvista import MathVista_auxeval, MathVista_acc
+
+        model = judge_kwargs['model']
+        suffix = eval_file.split('.')[-1]
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        if not osp.exists(storage):
+            data = load(eval_file)
+            model = build_judge(max_tokens=128, **judge_kwargs)
+            assert model.working(), ('MathVista evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = {}
+            if osp.exists(tmp_file):
+                ans = load(tmp_file)
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = track_progress_rich(
+                    MathVista_auxeval,
+                    tups,
+                    nproc=nproc,
+                    chunksize=nproc,
+                    keys=indices,
+                    save=tmp_file,
+                )
+                ans = load(tmp_file)
+                for k, v in zip(indices, new_results):
+                    assert k in ans
+                    assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res']
+
+            data['res'] = [ans[idx]['res'] for idx in data['index']]
+            data['log'] = [ans[idx]['log'] for idx in data['index']]
+            dump(data, storage)
+
+        score = MathVista_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+        dump(score, score_pth)
+        return score
+
+
+class MathVision(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {
+        'MathVision': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision.tsv',
+        'MathVision_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision_MINI.tsv'
+    }
+    DATASET_MD5 = {
+        'MathVision': '93f6de14f7916e598aa1b7165589831e',
+        'MathVision_MINI': '060fe4fa5d868987ce179307bd5f8a33'
+    }
+
+    # It returns a DataFrame
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.mathv import MATH_V_auxeval, MATH_V_acc
+
+        if 'model' in judge_kwargs:
+            model = judge_kwargs['model']
+        else:
+            model = os.path.basename(os.environ.get('LOCAL_LLM'))
+        suffix = eval_file.split('.')[-1]
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        if not osp.exists(storage):
+            data = load(eval_file)
+            model = build_judge(max_tokens=128, **judge_kwargs)
+            assert model.working(), ('MATH-Vision evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = {}
+            if osp.exists(tmp_file):
+                ans = load(tmp_file)
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = track_progress_rich(
+                    MATH_V_auxeval,
+                    tups,
+                    nproc=nproc,
+                    chunksize=nproc,
+                    keys=indices,
+                    save=tmp_file,
+                )
+                ans = load(tmp_file)
+                for k, v in zip(indices, new_results):
+                    assert k in ans
+                    assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res']
+
+            data['res'] = [ans[idx]['res'] for idx in data['index']]
+            data['log'] = [ans[idx]['log'] for idx in data['index']]
+            dump(data, storage)
+
+        score = MATH_V_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+        dump(score, score_pth)
+        return score
+
+
+class LLaVABench(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {'LLaVABench': 'https://opencompass.openxlab.space/utils/VLMEval/LLaVABench.tsv'}
+    DATASET_MD5 = {'LLaVABench': 'd382a093f749a697820d3dadd61c8428'}
+
+    # It returns a DataFrame
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.llavabench import (
+            build_prompt,
+            LLaVABench_atomeval,
+            LLaVABench_score,
+        )
+
+        suffix = '.' + eval_file.split('.')[-1]
+        record_file = eval_file.replace(suffix, '_openai_result' + suffix)
+        score_file = eval_file.replace(suffix, '_score.csv')
+        nproc = judge_kwargs.pop('nproc', 4)
+        system_prompt = 'You are a helpful and precise assistant for checking the quality of the answer.'
+
+        if not osp.exists(record_file):
+            data = load(eval_file)
+            lines = [data.iloc[i] for i in range(len(data))]
+            model = build_judge(temperature=0.2, system_prompt=system_prompt, **judge_kwargs)
+            assert model.working(), ('LLaVABench evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
+
+            prompts = [build_prompt(line) for line in lines]
+            tups = [(model, prompt) for prompt in prompts]
+            scores = track_progress_rich(LLaVABench_atomeval, tups, nproc=nproc, chunksize=nproc)
+            data['gpt4_score'] = [x[0] for x in scores]
+            data['score'] = [x[1] for x in scores]
+            dump(data, record_file)
+
+        data = load(record_file)
+        ret = LLaVABench_score(data).round(1)
+        dump(ret, score_file)
+        return ret
+
+
+class MMVet(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {
+        'MMVet': 'https://opencompass.openxlab.space/utils/VLMEval/MMVet.tsv'
+    }
+    DATASET_MD5 = {'MMVet': '748aa6d4aa9d4de798306a63718455e3'}
+
+    # It returns a DataFrame
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.mmvet import MMVet_auxeval, MMVet_acc
+
+        suffix = eval_file.split('.')[-1]
+        model = judge_kwargs['model']
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+        nproc = judge_kwargs.pop('nproc', 4)
+        if not osp.exists(storage):
+            data = load(eval_file)
+            model = build_judge(max_tokens=3, **judge_kwargs)
+            assert model.working(), ('MMVet evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
+
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = load(tmp_file) if osp.exists(tmp_file) else {}
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = track_progress_rich(
+                    MMVet_auxeval,
+                    tups,
+                    nproc=nproc,
+                    chunksize=nproc,
+                    keys=indices,
+                    save=tmp_file,
+                )
+                ans = load(tmp_file)
+                for k, v in zip(indices, new_results):
+                    assert k in ans
+                    assert ans[k]['log'] == v['log'] and ans[k]['score'] == v['score']
+            data['score'] = [ans[idx]['score'] for idx in data['index']]
+            data['log'] = [ans[idx]['log'] for idx in data['index']]
+            dump(data, storage)
+
+        score, score_fine = MMVet_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+        score_fine_pth = storage.replace('.xlsx', '_score_fine.csv')
+        dump(score, score_pth)
+        dump(score_fine, score_fine_pth)
+        return score
+
+
+class MTVQADataset(ImageBaseDataset):
+    TYPE = 'VQA'
+    DATASET_URL = {'MTVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MTVQA_TEST.tsv'}
+    DATASET_MD5 = {'MTVQA_TEST': 'd87c17dbab934b7cd89c0a3c1c5657f4'}
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        data = load(eval_file)
+        assert 'answer' in data and 'prediction' in data and 'category' in data
+        data['prediction'] = [str(x) for x in data['prediction']]
+        data['answer'] = [str(x) for x in data['answer']]
+        if 'split' in data:
+            assert np.all([x.lower() == 'test' for x in data['split']]), 'We only support MTVQA_TEST for now. '
+        lt = len(data)
+        category_scores = defaultdict(list)
+        for i in range(lt):
+            line = data.iloc[i]
+            ans = line['answer'].strip().lower().replace('.', '')
+            pred = line['prediction'].strip().lower().replace('.', '')
+            cate = line['category']
+            score = 1.0 if ans in pred else 0.0
+            category_scores[cate].append(score)
+            category_scores['Average'].append(score)
+        # Calculate the average score for each category, the score is normalized to [0, 100]
+        category_averages = {category: np.mean(scores) * 100 for category, scores in category_scores.items()}
+
+        suffix = eval_file.split('.')[-1]
+        result_file = eval_file.replace(f'.{suffix}', '_acc.json')
+        dump(category_averages, result_file)
+
+        return category_averages
+
+    # MT-VQA adopts a custom prompt
+    def build_prompt(self, line):
+        msgs = super().build_prompt(line)
+        assert sum([x['type'] == 'text' for x in msgs]) == 1
+        for item in msgs:
+            if item['type'] == 'text':
+                item['value'] += '\nAnswer the question using a word or phrase in the language of the question.'
+        return msgs
+
+
+class CustomVQADataset(ImageBaseDataset):
+    TYPE = 'VQA'
+
+    def load_data(self, dataset):
+        data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv')
+
+        if file_size(data_path, 'GB') > 1:
+            local_path = data_path.replace('.tsv', '_local.tsv')
+            if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None):
+                from ..tools import LOCALIZE
+
+                LOCALIZE(data_path, local_path)
+            data_path = local_path
+        return load(data_path)
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        raise NotImplementedError

eval_mm/vlmevalkit/vlmeval/dataset/image_yorn.py

@@ -0,0 +1,88 @@
+from ..smp import *
+from ..utils import *
+from .image_base import ImageBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+
+
+class ImageYORNDataset(ImageBaseDataset):
+
+    TYPE = 'Y/N'
+
+    DATASET_URL = {
+        'MME': 'https://opencompass.openxlab.space/utils/VLMEval/MME.tsv',
+        'HallusionBench': 'https://opencompass.openxlab.space/utils/VLMEval/HallusionBench.tsv',
+        'POPE': 'https://opencompass.openxlab.space/utils/VLMEval/POPE.tsv',
+    }
+
+    DATASET_MD5 = {
+        'MME': 'b36b43c3f09801f5d368627fb92187c3',
+        'HallusionBench': '0c23ac0dc9ef46832d7a24504f2a0c7c',
+        'POPE': 'c12f5acb142f2ef1f85a26ba2fbe41d5',
+    }
+
+    # It returns a dataframe
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.yorn import YOrN_Extraction, YOrN_auxeval
+        from .utils.yorn import default_rating, MME_rating, Hallusion_rating, POPE_rating
+
+        dataset = self.dataset_name
+        data = load(eval_file)
+        data['prediction'] = [str(x) for x in data['prediction']]
+        storage = eval_file.replace('.xlsx', '_auxmatch.xlsx')
+        tmp_file = eval_file.replace('.xlsx', '_tmp.pkl')
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        if not osp.exists(storage):
+            ans_map = {k: YOrN_Extraction(v) for k, v in zip(data['index'], data['prediction'])}
+            if osp.exists(tmp_file):
+                tmp = load(tmp_file)
+                for k in tmp:
+                    if ans_map[k] == 'Unknown' and tmp[k] != 'Unknown':
+                        ans_map[k] = tmp[k]
+
+            data['extracted'] = [ans_map[x] for x in data['index']]
+            unknown = data[data['extracted'] == 'Unknown']
+
+            model = judge_kwargs.get('model', 'exact_matching')
+            if model == 'exact_matching':
+                model = None
+            elif gpt_key_set():
+                model = build_judge(**judge_kwargs)
+                if not model.working():
+                    warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation')
+                    warnings.warn(DEBUG_MESSAGE)
+                    model = None
+            else:
+                model = None
+                warnings.warn('OPENAI_API_KEY is not working properly, will use exact matching for evaluation')
+
+            if model is not None:
+                lt = len(unknown)
+                lines = [unknown.iloc[i] for i in range(lt)]
+                tups = [(model, line) for line in lines]
+                indices = list(unknown['index'])
+                if len(tups):
+                    res = track_progress_rich(
+                        YOrN_auxeval, tups, nproc=nproc, chunksize=nproc, keys=indices, save=tmp_file)
+                    for k, v in zip(indices, res):
+                        ans_map[k] = v
+
+            data['extracted'] = [ans_map[x] for x in data['index']]
+            dump(data, storage)
+
+        data = load(storage)
+        data['score'] = (data['answer'] == data['extracted'])
+        dump(data, storage)
+
+        if dataset is not None and listinstr(['MME'], dataset):
+            score = MME_rating(storage)
+        elif dataset is not None and listinstr(['Hallusion'], dataset):
+            score = Hallusion_rating(storage)
+        elif dataset is not None and listinstr(['POPE'], dataset):
+            score = POPE_rating(storage)
+        else:
+            score = default_rating(storage)
+
+        score_tgt = eval_file.replace('.xlsx', '_score.csv')
+        dump(score, score_tgt)
+        return score

eval_mm/vlmevalkit/vlmeval/dataset/mmbench_video.py

@@ -0,0 +1,252 @@
+from huggingface_hub import snapshot_download
+from ..smp import *
+from .video_base import VideoBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..utils import track_progress_rich
+
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def unwrap_hf_pkl(pth, suffix='.mp4'):
+    base_dir = os.path.join(pth, 'video_pkl/')
+    target_dir = os.path.join(pth, 'video/')
+    pickle_files = [os.path.join(base_dir, file) for file in os.listdir(base_dir)]
+    pickle_files.sort()
+
+    if not os.path.exists(target_dir):
+        os.makedirs(target_dir, exist_ok=True)
+        for pickle_file in pickle_files:
+            with open(pickle_file, 'rb') as file:
+                video_data = pickle.load(file)
+            # For each video file in the pickle file, write its contents to a new mp4 file
+            for video_name, video_content in video_data.items():
+                output_path = os.path.join(target_dir, f'{video_name}{suffix}')
+                with open(output_path, 'wb') as output_file:
+                    output_file.write(video_content)
+        print('The video file has been restored and stored from the pickle file.')
+    else:
+        print('The video file already exists.')
+
+
+class MMBenchVideo(VideoBaseDataset):
+
+    MD5 = '98f7df3eb1007fc375ea6fe88a98e2ff'
+    SYS = 'You are an AI assistant responsible for answering questions about videos.'
+    FRAMES_TMPL_PACK = """
+You will be provided with {} separate frames uniformly sampled from a video, \
+the frames are provided in chronological order of the video.
+Please analyze these images and provide the answer / answers to the \
+following question / questions about the video content.
+If multiple questions are provided (with indices I1, I2, I3, ...), \
+you should organize your answers in the following json format:
+{{
+    'I1': 'Answer to Question I1',
+    'I2': 'Answer to Question I2',
+    ...
+}}
+Otherwise, please directly reply with your response to the only question.
+Even if the information in these separate frames is not enough to give an answer,
+PLEASE GIVE A RESPONSE TO EACH OF THE QUESTIONS IN THE FORMAT DESCRIBED ABOVE.
+"""
+
+    FRAMES_TMPL_NOPACK = """
+You will be provided with {} separate frames uniformly sampled from a video, \
+the frames are provided in chronological order of the video.
+Please analyze these images and provide the answer to the question about the video content.
+Please directly reply with your response to the only question.
+"""
+
+    TYPE = 'VQA'
+
+    def __init__(self, dataset='MMBench-Video', pack=False):
+        super().__init__(dataset=dataset, pack=pack)
+
+    @classmethod
+    def supported_datasets(cls):
+        return ['MMBench-Video']
+
+    def prepare_dataset(self, dataset_name='MMBench-Video', repo_id='nebulae09/MMBench-Video'):
+        def check_integrity(pth):
+            data_file = osp.join(pth, f'{dataset_name}.tsv')
+            if md5(data_file) != self.MD5:
+                return False
+            data = load(data_file)
+            for video_pth in data['video_path']:
+                if not osp.exists(osp.join(pth, video_pth)):
+                    return False
+            return True
+
+        cache_path = get_cache_path(repo_id)
+        if cache_path is not None and check_integrity(cache_path):
+            dataset_path = cache_path
+        else:
+            dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset')
+            unwrap_hf_pkl(dataset_path)
+        self.video_path = osp.join(dataset_path, 'video/')
+        data_file = osp.join(dataset_path, f'{dataset_name}.tsv')
+
+        return dict(data_file=data_file, root=osp.join(dataset_path, 'video'))
+
+    def build_prompt_pack(self, line, num_frames):
+        if isinstance(line, int):
+            assert line < len(self)
+            video = self.videos[line]
+        elif isinstance(line, pd.Series):
+            video = line['video']
+        elif isinstance(line, str):
+            video = line
+
+        frames = self.save_video_frames(video, num_frames)
+        sub = self.data[self.data['video'] == video]
+        sys_prompt = self.SYS + self.FRAMES_TMPL_PACK.format(num_frames)
+        message = [dict(type='text', value=sys_prompt)]
+        for im in frames:
+            message.append(dict(type='image', value=im))
+        nq = len(sub)
+        prompt = 'Questions: \n{}\nAnswers: \n'
+        qs = {int(sub.iloc[i]['index']): sub.iloc[i]['question'] for i in range(nq)}
+        prompt = prompt.format(json.dumps(qs))
+        message.append(dict(type='text', value=prompt))
+        return message
+
+    def build_prompt_nopack(self, line, num_frames, video_llm):
+        if isinstance(line, int):
+            assert line < len(self)
+            line = self.data.iloc[line]
+        if video_llm:
+            question = line['question']
+            prefix, video_idx_path = os.path.split(line['video_path'])
+            message = [dict(type='text', value=question)]
+            message.append(dict(type='video', value=os.path.join(self.video_path, video_idx_path)))
+            return message
+        else:
+            frames = self.save_video_frames(line['video'], num_frames)
+            sys_prompt = self.FRAMES_TMPL_NOPACK.format(num_frames)
+            message = [dict(type='text', value=sys_prompt)]
+            for im in frames:
+                message.append(dict(type='image', value=im))
+            prompt = 'Question: {}\nAnswer: '.format(line['question'])
+            message.append(dict(type='text', value=prompt))
+        return message
+
+    def build_prompt(self, line, num_frames, video_llm):
+        if self.pack and not video_llm:
+            return self.build_prompt_pack(line, num_frames)
+        else:
+            return self.build_prompt_nopack(line, num_frames, video_llm)
+
+    @staticmethod
+    def remove_side_quote(s, syms=[',', '"', "'"]):
+        if np.all([x in syms for x in s]):
+            return ''
+        while s[0] in syms:
+            s = s[1:]
+        while s[-1] in syms:
+            s = s[:-1]
+        return s
+
+    @staticmethod
+    def robust_json_load(s):
+        try:
+            jsons = list(extract_json_objects(s))
+            assert len(jsons) == 1
+            return jsons[0]
+        except:
+            if '{' in s and s.find('{') == s.rfind('{'):
+                sub_str = s[s.find('{') + 1:].strip()
+                lines = sub_str.split('\n')
+                res = {}
+                for l in lines:
+                    l = l.strip()
+                    if ': ' in l:
+                        key = l.split(': ')[0].strip()
+                        val = l.split(': ')[1].strip()
+                        key = MMBenchVideo.remove_side_quote(key)
+                        val = MMBenchVideo.remove_side_quote(val)
+                        if len(key) and len(val):
+                            res[key] = val
+                return res
+            return None
+
+    def load_pack_answers(self, data_raw):
+        vstats = defaultdict(lambda: 0)
+        data = defaultdict(lambda: {})
+
+        for k in data_raw:
+            ans = data_raw[k].strip()
+            if FAIL_MSG in ans:
+                vstats['GEN_FAIL'] += 1
+                continue
+            res = self.robust_json_load(ans)
+            if res is not None:
+                data[k] = res
+                vstats['PARSE_OK'] += 1
+            else:
+                vstats['PARSE_FAIL'] += 1
+
+        # return data
+        meta = cp.deepcopy(self.data)
+        lt = len(meta)
+        prediction = []
+        for i in range(lt):
+            line = meta.iloc[i]
+            vid = line['video']
+            idx = str(line['index'])
+            prediction.append(data[vid][idx] if idx in data[vid] else None)
+        meta['prediction'] = prediction
+        vstats['VALIDQ'] = len([x for x in prediction if x is not None])
+        vstats['INVALIDQ'] = len([x for x in prediction if x is None])
+        return meta, vstats
+
+    # It returns a dictionary
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.mmbench_video import get_dimension_rating, system_prompt, build_prompt
+
+        assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file'
+        judge = judge_kwargs['model']
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        tmp_file = eval_file.replace('.xlsx', f'_{judge}_tmp.pkl')
+        tgt_file = eval_file.replace('.xlsx', f'_{judge}_rating.json')
+        score_file = eval_file.replace('.xlsx', f'_{judge}_score.xlsx')
+
+        model = build_judge(system_prompt=system_prompt, **judge_kwargs)
+        assert model.working(), 'MMBench-Video evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE
+
+        if not osp.exists(score_file):
+            res = {} if not osp.exists(tmp_file) else load(tmp_file)
+            res = {k: v for k, v in res.items() if model.fail_msg not in v}
+
+            data = load(eval_file)
+            data_un = data[~data['index'].isin(res)]
+            data_un = data_un[~pd.isna(data_un['prediction'])]
+            lt = len(data_un)
+            prompts = [build_prompt(data_un.iloc[i]) for i in range(lt)]
+            indices = [data_un.iloc[i]['index'] for i in range(lt)]
+
+            if len(prompts):
+                _ = track_progress_rich(
+                    model.generate,
+                    prompts,
+                    keys=indices,
+                    save=tmp_file,
+                    nproc=nproc,
+                    chunksize=nproc
+                )
+            score_map = load(tmp_file)
+            data['score'] = [score_map[idx] if idx in score_map else -1 for idx in data['index']]
+            rejected = [x for x in score_map.values() if FAIL_MSG in x]
+            data['score'] = [int(x) if istype(x, int) else -1 for x in data['score']]
+            print(
+                f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(score_map)} questions, '
+                f'failed to obtain the score for another {len(rejected)} questions. '
+                f'Those questions will be counted as 0 score in ALL rating, and will not be counted in VALID rating.'
+            )
+
+            dump(data, score_file)
+
+        rating = get_dimension_rating(score_file)
+        dump(rating, tgt_file)
+        return rating

eval_mm/vlmevalkit/vlmeval/dataset/mmlongbench.py

@@ -0,0 +1,582 @@
+import re
+import math
+from urllib.request import urlopen
+from PIL import Image, ImageDraw, ImageFont
+import torchvision.transforms as transforms
+
+from vlmeval.dataset.utils import build_judge, levenshtein_distance
+from vlmeval.smp import *
+from .image_base import ImageBaseDataset
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def get_gpt4_ICE():
+    example_1 = """
+---
+Question: List the primary questions asked about the services in this report.
+Analysis:  The primary questions asked about the services in the report for The Limes Residential Home are:\n\n
+1. Is the service safe?\n
+2. Is the service effective?\n
+3. Is the service caring?\n
+4. Is the service responsive?\n
+5. Is the service well-led?
+Extracted answer: [
+    'Is the servife safe?',
+    'Is the service effective',
+    'Is the serve caring?',
+    'Is the service responsive?',
+    'Is the service well-led?'
+]
+Answer format: List\n
+"""
+
+    example_2 = """
+---
+Question: How many regulations of the HSCA 2008 are breached in all according to this report?
+Analysis: According to the report, the provider breached 10 Health and Social Care Act 2008 (Regulated Activities)
+Regulations in total. Here are the specifics:\n\n1. Regulation 13: Safeguarding service users from abuse and
+improper treatment\n2. Regulation 12: Safe care and treatment\n3. Regulation 18: Staffing\n4. Regulation 11:
+Need for consent\n5. Regulation 10: Dignity and respect\n6. Regulation 9: Person-centred care\n7. Regulation 17:
+Good governance\n8. Regulation 18 (CQC Registration Regulations 2009): Notification of other incidents\n9.
+Regulation 18: Failure to maintain an accurate and up-to-date care plan\n10. Regulation 11: Failure to implement
+the Mental Capacity Act 2005 code of practice effectively\n\nThese breaches involve issues concerning staffing,
+safeguarding, medicines management, dignity and respect, consent, care planning, governance, and failure to
+notify the CQC of incidents.
+Extracted answer: 10
+Answer format: Integer\n
+"""
+
+    example_3 = """
+---
+Question: According to the survey that is the percentage of Chinese who are paying more or
+about the same attention to politics after Trump's election?
+Analysis: The survey provided does not specify the percentage of Chinese individuals specifically who are paying
+more or about the same attention to politics after Trump's election. The report focuses primarily on American
+demographics and does not include specific details about the Chinese population in relation to this question. If
+you need information about a different demographic or a summary of the findings from the American demographic,
+I can certainly help with that!
+Extracted answer: Not answerable
+Answer format: String\n
+"""
+
+    example_4 = """
+---
+Question: How many quotations from male respondent over 50 years old are included in this report?
+Analysis: The image you've provided appears to be a screenshot of a document with multiple charts. However, the
+text is too small and blurry to read accurately. If you can provide a clearer image or more context, I might be
+able to help you with your question.
+Extracted answer: Fail to answer
+Answer format: String\n
+"""
+
+    return [example_1, example_2, example_3, example_4]
+
+
+def build_mmlongbench_gpt4_prompt(line):
+    task_description = """
+Given the question and analysis, you are tasked to extract answers with required formats from the free-form analysis.
+- Your extracted answers should be one of the following formats: (1) Integer, (2) Float, (3) String and (4) List.
+If you find the analysis the question can not be answered from the given documents, type "Not answerable".
+Exception: If the analysis only tells you that it can not read/understand the images or documents,
+type "Fail to answer".
+- Please make your response as concise as possible. Also note that your response should be formatted as below:
+```
+Extracted answer: [answer]
+Answer format: [answer format]
+```
+Please read the following example, then extract the answer from the model response
+and type it at the end of the prompt.\n
+"""
+    question = line['question']
+    prediction = str(line['prediction'])
+    prompt = task_description
+    examples = get_gpt4_ICE()
+    for example in examples:
+        prompt += example
+    prompt += '---\nQuestion:' + question + '\n'
+    prompt += 'Analysis: ' + prediction
+    return prompt
+
+
+def anls_compute(groundtruth, prediction, threshold=0.5):
+    dist = levenshtein_distance(groundtruth, prediction)
+    length = max(len(groundtruth.upper()), len(prediction.upper()))
+    value = 0.0 if length == 0 else float(dist) / float(length)
+    anls = 1.0 - value
+    if anls <= threshold:
+        anls = 0.0
+    return anls
+
+
+def is_float_equal(reference, prediction, include_percentage: bool = False, is_close: float = False) -> bool:
+    def get_precision(gt_ans: float) -> int:
+        precision = 3
+        if '.' in str(gt_ans):
+            precision = len(str(gt_ans).split('.')[-1])
+        return precision
+
+    reference = float(str(reference).strip().rstrip('%').strip())
+    try:
+        prediction = float(str(prediction).strip().rstrip('%').strip())
+    except:
+        return False
+
+    if include_percentage:
+        gt_result = [reference / 100, reference, reference * 100]
+    else:
+        gt_result = [reference]
+    for item in gt_result:
+        try:
+            if is_close:
+                if math.isclose(item, prediction, rel_tol=0.01):
+                    return True
+            precision = max(min(get_precision(prediction), get_precision(item)), 2)
+            if round(prediction, precision) == round(item, precision):
+                return True
+        except Exception:
+            continue
+    return False
+
+
+def get_clean_string(s):
+    s = str(s).lower().strip()
+    if s.endswith('mile'):
+        s.rstrip('mile').strip()
+    if s.endswith('miles'):
+        s.rstrip('miles').strip()
+    if s.endswith('million'):
+        s.rstrip('million').strip()
+    # remove parenthesis
+    s = re.sub(r'\s*\([^)]*\)', '', s).strip()
+    # remove quotes
+    s = re.sub(r"^['\"]|['\"]$", '', s).strip()
+    s = s.strip().lstrip('$').strip()
+    s = s.strip().rstrip('%').strip()
+    return s
+
+
+def is_exact_match(s):
+    flag = False
+    # Website
+    if 'https://' in s:
+        flag = True
+    # code file
+    if s.endswith('.py') or s.endswith('ipynb'):
+        flag = True
+    if s.startswith('page'):
+        flag = True
+    # telephone number
+    if re.fullmatch(r'\b\d+(-\d+|\s\d+)?\b', s):
+        flag = True
+    # time
+    if 'a.m.' in s or 'p.m.' in s:
+        flag = True
+    # YYYY-MM-DD
+    if re.fullmatch(r'\b\d{4}[-\s]\d{2}[-\s]\d{2}\b', s):
+        flag = True
+    # YYYY-MM
+    if re.fullmatch(r'\b\d{4}[-\s]\d{2}\b', s):
+        flag = True
+    # Email address
+    if re.fullmatch(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', s):
+        flag = True
+    return flag
+
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def get_font():
+    try:
+        truetype_url = 'http://opencompass.openxlab.space/utils/Fonts/SimHei.ttf'
+        ff = urlopen(truetype_url)
+        font = ImageFont.truetype(ff, size=40)
+    except:
+        print('Fail to download the font. Use the default one.')
+        font = ImageFont.load_default(size=40)
+    return font
+
+
+def frame2img(img_path_list, font, save_path=None, idx_start=0):
+    imgs = [Image.open(img_path) for img_path in img_path_list]
+
+    new_imgs = []
+    for img in imgs:
+        w, h = img.size
+        scale = w / h
+        if w > h:
+            new_w = 560 * 2
+            new_h = int(560 * 2 / scale)
+        else:
+            new_w = int(560 * 2 * scale)
+            new_h = 560 * 2
+        img = transforms.functional.resize(img, [new_h, new_w],)
+        new_imgs.append(img)
+    imgs = new_imgs
+    new_w = 0
+    new_h = 0
+    pad = 40
+    if w > h:
+        for im in imgs:
+            w, h = im.size
+            new_w = max(new_w, w)
+            new_h += h + 10 + pad
+        new_img = Image.new('RGB', (new_w, new_h), 'white')
+        draw = ImageDraw.Draw(new_img)
+        curr_h = 0
+        for idx, im in enumerate(imgs):
+            w, h = im.size
+            new_img.paste(im, (0, pad + curr_h))
+            draw.text((0, curr_h), f'<IMAGE {idx+idx_start}>', font=font, fill='black')
+            if idx + 1 < len(imgs):
+                draw.line([(0, pad + curr_h + h + 5), (new_w, pad + curr_h + h + 5)], fill='black', width=2)
+            curr_h += h + 10 + pad
+    else:
+        for im in imgs:
+            w, h = im.size
+            new_w += w + 10
+            new_h = max(new_h, h)
+        new_h += pad
+        new_img = Image.new('RGB', (new_w, new_h), 'white')
+        draw = ImageDraw.Draw(new_img)
+        curr_w = 0
+        for idx, im in enumerate(imgs):
+            w, h = im.size
+            new_img.paste(im, (curr_w, pad))
+            draw.text((curr_w, 0), f'<IMAGE {idx+idx_start}>', font=font, fill='black')
+            if idx + 1 < len(imgs):
+                draw.line([(curr_w + w + 5, 0), (curr_w + w + 5, new_h)], fill='black', width=2)
+            curr_w += w + 10
+
+    if save_path is not None:
+        new_img.save(save_path)
+
+    return new_img
+
+
+def concat_images(image_list, max_concat=1, column_num=1):
+    concatenated_images = []
+    if column_num == -1:
+        MAX_COLUMN_NUM = 20
+        max_concat = 1
+        while len(image_list) / max_concat > MAX_COLUMN_NUM:
+            max_concat += 1
+        interval = max(math.ceil(len(image_list) / max_concat), 1)
+        for i in range(0, len(image_list), interval):
+            batch_images = image_list[i:i + interval]
+            concatenated_image = frame2img(batch_images, font=get_font(), idx_start=i)
+            concatenated_images.append(concatenated_image)
+    else:
+        interval = max(math.ceil(len(image_list) / max_concat), 1)
+        for i in range(0, len(image_list), interval):
+            batch_images = [Image.open(filename) for filename in image_list[i:i + interval]]
+            if column_num == 1:
+                total_height = batch_images[0].height * len(batch_images)
+            else:
+                total_height = batch_images[0].height * ((len(batch_images) - 1) // column_num + 1)
+            concatenated_image = Image.new('RGB', (batch_images[0].width * column_num, total_height), 'white')
+
+            x_offset, y_offset = 0, 0
+            for count, image in enumerate(batch_images):
+                concatenated_image.paste(image, (x_offset, y_offset))
+                x_offset += image.width
+                if (count + 1) % column_num == 0:
+                    y_offset += image.height
+                    x_offset = 0
+            concatenated_images.append(concatenated_image)
+    return concatenated_images
+
+
+def eval_score(gt, pred, answer_type):
+    if answer_type == 'Int':
+        try:
+            gt, pred = int(gt), int(float(pred))
+        except:
+            pred = ''
+        score = (gt == pred)
+    elif answer_type == 'Float':
+        try:
+            gt = float(get_clean_string(str(gt)))
+            pred = float(get_clean_string(str(pred)))
+        except:
+            pred = ''
+        score = is_float_equal(gt, pred, include_percentage=True, is_close=True)
+    elif answer_type == 'Str':
+        gt = get_clean_string(gt)
+        pred = get_clean_string(pred)
+        if is_exact_match(gt):
+            score = (gt == pred)
+        else:
+            score = anls_compute(gt, pred)
+    else:
+        if isinstance(gt, str) and gt.startswith('['):
+            gt = eval(gt)
+        if not isinstance(gt, list):
+            gt = [gt]
+        if isinstance(pred, str) and pred.startswith('['):
+            pred = eval(pred)
+        if not isinstance(pred, list):
+            pred = [pred]
+        print(len(gt), len(pred))
+        if len(gt) != len(pred):
+            score = 0.0
+        else:
+            gt = sorted([get_clean_string(a) for a in gt])
+            pred = sorted([get_clean_string(a) for a in pred])
+            print(gt, pred)
+            if isfloat(gt[0]) or is_exact_match(gt[0]):
+                score = ('-'.join(gt) == '-'.join(pred))
+            else:
+                score = min([anls_compute(gt_v, pred_v) for gt_v, pred_v in zip(gt, pred)])
+
+    return float(score)
+
+
+def MMLongBench_auxeval(model, line):
+    prompt = build_mmlongbench_gpt4_prompt(line)
+    log = ''
+    retry = 5
+
+    for i in range(retry):
+        prediction = line['prediction']
+        res = model.generate(prompt, temperature=i * 0.5)
+
+        if FAIL_MSG in res:
+            log += f'Try {i}: output is {prediction}, failed to parse.\n'
+        else:
+            log += 'Succeed'
+            try:
+                pred = res.split('Answer format:')[0].split('Extracted answer:')[1].strip()
+            except:
+                pred = ''
+            return dict(log=log, res=res, pred=pred)
+    log += 'All 5 retries failed.\n'
+    return dict(log=log, res='', pred='')
+
+
+def get_f1(data):
+    gt_pos_data = data[data.apply(lambda k: k['answer'] != 'Not answerable', axis=1)]
+    pred_pos_data = data[data.apply(lambda k: k['pred'] != 'Not answerable', axis=1)]
+    recall = sum(gt_pos_data['score'].tolist()) / len(gt_pos_data)
+    precision = sum(pred_pos_data['score'].tolist()) / len(pred_pos_data)
+    return 2 * recall * precision / (recall + precision)
+
+
+def MMLongBench_acc(result_file):
+    data = load(result_file)
+    overall_score = 0.0
+    score_list = list()
+    for i in range(len(data)):
+        item = data.iloc[i]
+        try:
+            score = eval_score(item['answer'], item['pred'], item['answer_format'])
+        except:
+            score = 0.0
+        score_list.append(score)
+        overall_score += score
+
+    data['score'] = score_list
+    dump(data, result_file)
+
+    data_chart = data[data.apply(lambda k: 'Chart' in eval(k['evidence_sources']), axis=1)]
+    data_table = data[data.apply(lambda k: 'Table' in eval(k['evidence_sources']), axis=1)]
+    data_image = data[data.apply(lambda k: 'Figure' in eval(k['evidence_sources']), axis=1)]
+    data_text = data[data.apply(lambda k: 'Pure-text (Plain-text)' in eval(k['evidence_sources']), axis=1)]
+    data_layout = data[data.apply(lambda k: 'Generalized-text (Layout)' in eval(k['evidence_sources']), axis=1)]
+
+    data_single = data[data.apply(lambda k: len(eval(k['evidence_pages'])) == 1, axis=1)]
+    data_multi = data[data.apply(lambda k: len(eval(k['evidence_pages'])) > 1, axis=1)]
+    data_unans = data[data.apply(lambda k: len(eval(k['evidence_pages'])) == 0, axis=1)]
+
+    res = dict()
+    res['category'] = [
+        'overall_f1', 'overall_acc', 'text', 'layout', 'table', 'chart',
+        'image', 'single-page', 'multi-page', 'unanswerable'
+    ]
+    res['num'] = [
+        len(data), len(data), len(data_text), len(data_layout), len(data_table),
+        len(data_chart), len(data_image), len(data_single), len(data_multi), len(data_unans)
+    ]
+    res['avg_score'] = [
+        get_f1(data),
+        overall_score / len(data),
+        sum(data_text['score'].tolist()) / len(data_text) if len(data_text) > 0 else 0.0,
+        sum(data_layout['score'].tolist()) / len(data_layout) if len(data_layout) > 0 else 0.0,
+        sum(data_table['score'].tolist()) / len(data_table) if len(data_table) > 0 else 0.0,
+        sum(data_chart['score'].tolist()) / len(data_chart) if len(data_chart) > 0 else 0.0,
+        sum(data_image['score'].tolist()) / len(data_image) if len(data_image) > 0 else 0.0,
+        sum(data_single['score'].tolist()) / len(data_single) if len(data_single) > 0 else 0.0,
+        sum(data_multi['score'].tolist()) / len(data_multi) if len(data_multi) > 0 else 0.0,
+        sum(data_unans['score'].tolist()) / len(data_unans) if len(data_unans) > 0 else 0.0,
+    ]
+    res = pd.DataFrame(res)
+    return res
+
+
+class MMLongBench(ImageBaseDataset):
+
+    TYPE = 'VQA'
+
+    DATASET_URL = {
+        'MMLongBench_DOC': 'https://opencompass.openxlab.space/utils/VLMEval/MMLongBench_DOC.tsv',
+    }
+    DATASET_MD5 = {
+        'MMLongBench_DOC': '9b393e1f4c52718380d50586197eac9b',
+    }
+
+    SUPPORTED_MODELS = {
+        'GPT4': (1, 1),
+        'GPT4V': (1, 1),
+        'GPT4V_HIGH': (1, 1),
+        'GPT4o': (1, 1),
+        'GPT4o_HIGH': (1, 1),
+        'GPT4o_MINI': (1, 1),
+        'MiniCPM-Llama3-V-2_5': (1, 5),
+        'InternVL-Chat-V1-5': (5, 2),
+        'XComposer2_4KHD': (1, 5),
+        'XComposer2d5': (1, -1),
+    }
+
+    def __init__(self, dataset, **kwargs):
+        self.model_list = list(self.SUPPORTED_MODELS.keys())
+        model_name = kwargs['model']
+        if not listinstr(self.model_list, model_name):
+            raise AssertionError("{} doesn't support the evaluation on MMLongBench_DOC.".format(model_name))
+        super(MMLongBench, self).__init__(dataset)
+
+        self.is_api = True if listinstr(['GPT4'], model_name) else False
+        self.max_pages = 120
+        concat_num, column_num = self.SUPPORTED_MODELS.get(model_name)
+        self.concat_num = concat_num
+        self.column_num = column_num
+
+    def dump_image(self, origin_line):
+        os.makedirs(self.img_root, exist_ok=True)
+        try:
+            import fitz
+        except:
+            warnings.warn('Please use `pip install pymupdf` to parse PDF files.')
+
+        line = origin_line.copy()
+        line['image_path'] = line['image_path'][:self.max_pages]
+        skip_pdf_parse = True
+        for im_name in line['image_path']:
+            path = osp.join(self.img_root, im_name)
+            if not read_ok(path):
+                skip_pdf_parse = False
+                break
+
+        # Just for being compatible with the zooped loop: zip(line['image'], line['image_path'])
+        if skip_pdf_parse:
+            line['image'] = line['image_path']
+        else:
+            pdf_data = base64.b64decode(line['image'])
+            pdf_file = io.BytesIO(pdf_data)
+            encoded_images = []
+            with fitz.open(stream=pdf_file, filetype='pdf') as doc:
+                doc = doc[:self.max_pages]
+                for page in doc:
+                    image = page.get_pixmap(dpi=144)
+                    image_file = io.BytesIO(image.tobytes(output='png'))
+                    image = Image.open(image_file)
+                    encoded_image = encode_image_to_base64(image)
+                    encoded_images.append(encoded_image)
+            line['image'] = encoded_images
+            print('process {}'.format(line['doc_id']))
+
+        if 'image' in line:
+            if isinstance(line['image'], list):
+                tgt_path = []
+                assert 'image_path' in line
+                for img, im_name in zip(line['image'], line['image_path']):
+                    path = osp.join(self.img_root, im_name)
+                    if not read_ok(path):
+                        decode_base64_to_image_file(img, path)
+                    tgt_path.append(path)
+            else:
+                tgt_path = osp.join(self.img_root, f"{line['index']}.jpg")
+                if not read_ok(tgt_path):
+                    decode_base64_to_image_file(line['image'], tgt_path)
+                tgt_path = [tgt_path]
+        else:
+            assert 'image_path' in line
+            tgt_path = toliststr(line['image_path'])
+
+        if self.concat_num > 0 and not self.is_api:
+            concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num)
+
+            old_tgt_path = tgt_path
+            assert isinstance(old_tgt_path, list)
+            if self.column_num != -1:
+                tgt_path = [
+                    '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i)
+                    for i in range(len(concatenated_images))
+                ]
+            else:
+                tgt_path = [
+                    '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all_{}.jpg'.format(i)
+                    for i in range(len(concatenated_images))
+                ]
+
+            for path, concatenated_image in zip(tgt_path, concatenated_images):
+                if not read_ok(path):
+                    decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path)
+                    num_images, image_size = len(old_tgt_path), concatenated_image.size
+                    print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path))
+        return tgt_path
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        logger = get_logger('Evaluation')
+        model = judge_kwargs['model']
+
+        suffix = eval_file.split('.')[-1]
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+
+        if osp.exists(storage):
+            logger.warning(f'GPT scoring file {storage} already exists, will reuse it in MMLongBench_eval. ')
+        else:
+            data = load(eval_file)
+            model = build_judge(max_tokens=128, **judge_kwargs)
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = {}
+            if osp.exists(tmp_file):
+                ans = load(tmp_file)
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = list()
+                for model, line in tqdm(tups):
+                    res = MMLongBench_auxeval(model, line)
+                    new_results.append(res)
+
+            log_map, res_map, pred_map = {}, {}, {}
+            all_inds = [line['index'] for line in lines]
+            for k, v in zip(all_inds, new_results):
+                log_map[k] = v['log']
+                res_map[k] = v['res']
+                pred_map[k] = v['pred']
+            data['res'] = [res_map[idx] for idx in data['index']]
+            data['log'] = [log_map[idx] for idx in data['index']]
+            data['pred'] = [pred_map[idx] for idx in data['index']]
+            dump(data, storage)
+
+        score = MMLongBench_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+
+        dump(score, score_pth)
+        logger.info(f'MMLongBench_eval successfully finished evaluating {eval_file}, results saved in {score_pth}')
+        logger.info('Score: ')
+        logger.info(score)

eval_mm/vlmevalkit/vlmeval/dataset/mvbench.py

@@ -0,0 +1,577 @@
+import huggingface_hub
+from huggingface_hub import snapshot_download
+from ..smp import *
+from .video_base import VideoBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..utils import track_progress_rich
+import torchvision.transforms as T
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+from decord import VideoReader, cpu
+import imageio
+import cv2
+import zipfile
+import os
+import glob
+from moviepy.editor import VideoFileClip, ImageSequenceClip
+import moviepy.config_defaults
+from .utils.mvbench import *
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+moviepy.config_defaults.LOGGER_LEVEL = logging.CRITICAL + 1
+
+
+class MVBench(VideoBaseDataset):
+
+    MD5 = 'ae2a2607e2f8618155709220c6e927a6'
+    SYS = """Carefully watch the video and pay attention to the cause and sequence of events, \
+the detail and movement of objects, and the action and pose of persons. \
+Based on your observations, select the best option that accurately addresses the question.
+"""
+
+    TYPE = 'MCQ'
+
+    def __init__(self, dataset='MVBench', pack=False):
+        self.type_data_list = {
+            'Action Sequence': ('action_sequence.json',
+                                'your_data_path/star/Charades_v1_480/', 'video', True),  # has start & end
+            'Action Prediction': ('action_prediction.json',
+                                  'your_data_path/star/Charades_v1_480/', 'video', True),  # has start & end
+            'Action Antonym': ('action_antonym.json',
+                               'your_data_path/ssv2_video/', 'video', False),
+            'Fine-grained Action': ('fine_grained_action.json',
+                                    'your_data_path/Moments_in_Time_Raw/videos/', 'video', False),
+            'Unexpected Action': ('unexpected_action.json',
+                                  'your_data_path/FunQA_test/test/', 'video', False),
+            'Object Existence': ('object_existence.json',
+                                 'your_data_path/clevrer/video_validation/', 'video', False),
+            'Object Interaction': ('object_interaction.json',
+                                   'your_data_path/star/Charades_v1_480/', 'video', True),  # has start & end
+            'Object Shuffle': ('object_shuffle.json',
+                               'your_data_path/perception/videos/', 'video', False),
+            'Moving Direction': ('moving_direction.json',
+                                 'your_data_path/clevrer/video_validation/', 'video', False),
+            'Action Localization': ('action_localization.json',
+                                    'your_data_path/sta/sta_video/', 'video', True),   # has start & end
+            'Scene Transition': ('scene_transition.json',
+                                 'your_data_path/scene_qa/video/', 'video', False),
+            'Action Count': ('action_count.json',
+                             'your_data_path/perception/videos/', 'video', False),
+            'Moving Count': ('moving_count.json',
+                             'your_data_path/clevrer/video_validation/', 'video', False),
+            'Moving Attribute': ('moving_attribute.json',
+                                 'your_data_path/clevrer/video_validation/', 'video', False),
+            'State Change': ('state_change.json',
+                             'your_data_path/perception/videos/', 'video', False),
+            'Fine-grained Pose': ('fine_grained_pose.json',
+                                  'your_data_path/nturgbd/', 'video', False),
+            'Character Order': ('character_order.json',
+                                'your_data_path/perception/videos/', 'video', False),
+            'Egocentric Navigation': ('egocentric_navigation.json',
+                                      'your_data_path/vlnqa/', 'video', False),
+            'Episodic Reasoning': ('episodic_reasoning.json',
+                                   'your_data_path/tvqa/frames_fps3_hq/', 'frame', True),  # has start & end, read frame
+            'Counterfactual Inference': ('counterfactual_inference.json',
+                                         'your_data_path/clevrer/video_validation/', 'video', False),
+        }
+        super().__init__(dataset=dataset, pack=pack)
+
+    @classmethod
+    def supported_datasets(cls):
+        return ['MVBench']
+
+    def prepare_dataset(self, dataset_name='MVBench', repo_id='OpenGVLab/MVBench'):
+        def check_integrity(pth):
+            data_file = osp.join(pth, f'{dataset_name}.tsv')
+
+            if not os.path.exists(data_file):
+                return False
+
+            if md5(data_file) != self.MD5:
+                return False
+
+            data = load(data_file)
+            for idx, item in data.iterrows():
+                if not osp.exists(osp.join(pth, item['prefix'], item['video'])):
+                    return False
+            return True
+
+        cache_path = get_cache_path(repo_id, branch='main')
+        if cache_path is not None and check_integrity(cache_path):
+            dataset_path = cache_path
+        else:
+            def unzip_hf_zip(pth):
+                pth = os.path.join(pth, 'video/')
+                for filename in os.listdir(pth):
+                    if filename.endswith('.zip'):
+                        # 构建完整的文件路径
+                        zip_path = os.path.join(pth, filename)
+
+                        # 解压 ZIP 文件
+                        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                            zip_ref.extractall(pth)
+
+            def generate_tsv(pth):
+                data_file = osp.join(pth, f'{dataset_name}.tsv')
+                if os.path.exists(data_file) and md5(data_file) == self.MD5:
+                    return
+                json_data_dir = os.path.join(dataset_path, 'json')
+                self.data_list = []
+                for k, v in self.type_data_list.items():
+                    with open(os.path.join(json_data_dir, v[0]), 'r') as f:
+                        json_data = json.load(f)
+                    for data in json_data:
+                        self.data_list.append({
+                            'task_type': k,
+                            'prefix': v[1].replace('your_data_path', os.path.join(dataset_path, 'video')),
+                            'data_type': v[2],
+                            'bound': v[3],
+                            'start': data['start'] if 'start' in data.keys() else None,
+                            'end': data['end'] if 'end' in data.keys() else None,
+                            'video': data['video'],
+                            'question': data['question'],
+                            'answer': data['answer'],
+                            'candidates': data['candidates']
+                        })
+
+                data_df = pd.DataFrame(self.data_list)
+                data_df = data_df.assign(index=range(len(data_df)))
+                data_df.to_csv(data_file, sep='\t', index=False)
+
+            def move_files(pth):
+                # special for mvbench
+                src_folder = os.path.join(pth, 'video/data0613')
+                for subdir in os.listdir(src_folder):
+                    subdir_path = os.path.join(src_folder, subdir)
+                    if os.path.isdir(subdir_path):
+                        for subsubdir in os.listdir(subdir_path):
+                            subsubdir_path = os.path.join(subdir_path, subsubdir)
+                            if os.path.isdir(subsubdir_path):
+                                for item in os.listdir(subsubdir_path):
+                                    item_path = os.path.join(subsubdir_path, item)
+                                    target_folder = os.path.join(pth, 'video', subdir, subsubdir, item)
+                                    if not os.path.exists(target_folder):
+                                        shutil.move(item_path, os.path.join(target_folder, item))
+
+            hf_token = os.environ.get('HUGGINGFACE_TOKEN')
+            huggingface_hub.login(hf_token)
+            dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset')
+            move_files(dataset_path)
+            unzip_hf_zip(dataset_path)
+            generate_tsv(dataset_path)
+
+        data_file = osp.join(dataset_path, f'{dataset_name}.tsv')
+
+        self.decord_method = {
+            'video': self.read_video,
+            'gif': self.read_gif,
+            'frame': self.read_frame,
+        }
+
+        self.nframe = 8
+        self.resolution = 224
+        self.frame_fps = 3
+
+        # transform
+        crop_size = self.resolution
+        scale_size = self.resolution
+        input_mean = [0.48145466, 0.4578275, 0.40821073]
+        input_std = [0.26862954, 0.26130258, 0.27577711]
+        self.transform = T.Compose([
+            GroupScale(int(scale_size), interpolation=InterpolationMode.BICUBIC),
+            GroupCenterCrop(crop_size),
+            Stack(),
+            ToTorchFormatTensor(),
+            GroupNormalize(input_mean, input_std)
+        ])
+
+        return dict(root=dataset_path, data_file=data_file)
+
+    def get_index(self, bound, fps, max_frame, first_idx=0):
+        if bound:
+            start, end = bound[0], bound[1]
+        else:
+            start, end = -100000, 100000
+        start_idx = max(first_idx, round(start * fps))
+        end_idx = min(round(end * fps), max_frame)
+        seg_size = float(end_idx - start_idx) / self.num_segments
+        frame_indices = np.array([
+            int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+            for idx in range(self.num_segments)
+        ])
+        return frame_indices
+
+    def read_video(self, video_path, bound=None):
+        vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+        max_frame = len(vr) - 1
+        fps = float(vr.get_avg_fps())
+
+        images_group = list()
+        frame_indices = self.get_index(bound, fps, max_frame, first_idx=0)
+        for frame_index in frame_indices:
+            img = Image.fromarray(vr[frame_index].asnumpy())
+            images_group.append(img)
+        torch_imgs = self.transform(images_group)
+        return torch_imgs
+
+    def read_gif(self, video_path, bound=None, fps=25):
+        gif = imageio.get_reader(video_path)
+        max_frame = len(gif) - 1
+
+        images_group = list()
+        frame_indices = self.get_index(bound, fps, max_frame, first_idx=0)
+        for index, frame in enumerate(gif):
+            if index in frame_indices:
+                img = cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB)
+                img = Image.fromarray(img)
+                images_group.append(img)
+        torch_imgs = self.transform(images_group)
+        return torch_imgs
+
+    def read_frame(self, video_path, bound=None, fps=3):
+        max_frame = len(os.listdir(video_path))
+        images_group = list()
+        frame_indices = self.get_index(bound, fps, max_frame, first_idx=1)  # frame_idx starts from 1
+        for frame_index in frame_indices:
+            img = Image.open(os.path.join(video_path, f'{frame_index:05d}.jpg'))
+            images_group.append(img)
+        torch_imgs = self.transform(images_group)
+        return torch_imgs
+
+    def save_video_frames(self, imgs, video_name, frames):
+
+        frame_paths = self.frame_paths(video_name, frames)
+        flag = np.all([osp.exists(p) for p in frame_paths])
+
+        if not flag:
+            block_size = imgs.size(0) // frames
+            split_tensors = torch.split(imgs, block_size)
+            to_pil = transforms.ToPILImage()
+            images = [to_pil(arr) for arr in split_tensors]
+            for im, pth in zip(images, frame_paths):
+                if not osp.exists(pth):
+                    im.save(pth)
+
+        return frame_paths
+
+    def qa_template(self, data):
+        question = f"Question: {data['question']}\n"
+        question += 'Options:\n'
+        answer = data['answer']
+        answer_idx = -1
+        for idx, c in enumerate(eval(data['candidates'])):
+            question += f"({chr(ord('A') + idx)}) {c}\n"
+            if c == answer:
+                answer_idx = idx
+        question = question.rstrip()
+        answer = f"({chr(ord('A') + answer_idx)}) {answer}"
+        return question, answer
+
+    def load_into_video_and_process(self, line):
+        video_path = os.path.join(line['prefix'], line['video'])
+
+        if line['data_type'] in ['gif'] or os.path.splitext(video_path)[1] in ['.webm']:
+            processed_video_path = video_path.replace(os.path.splitext(video_path)[1], '.mp4')
+            if not os.path.exists(processed_video_path):
+                # using MoviePy to transform GIF, webm into mp4 format
+                gif_clip = VideoFileClip(video_path)
+                gif_clip.write_videofile(processed_video_path, codec='libx264')
+                gif_clip.close()
+        elif line['data_type'] in ['frame']:
+            input_images = os.path.join(video_path, '*.jpg')
+            processed_video_path = f'{video_path}.mp4'
+            if not os.path.exists(processed_video_path):
+                # using MoviePy to transform images into mp4
+                image_files = sorted(glob.glob(input_images))
+                image_clip = ImageSequenceClip(image_files, fps=self.frame_fps)
+                image_clip.write_videofile(processed_video_path, codec='libx264')
+                image_clip.close()
+        else:
+            processed_video_path = video_path
+
+        if line['bound']:
+            base_name, suffix = os.path.splitext(processed_video_path)
+            output_video_path = f'{base_name}_processed{suffix}'
+            if not os.path.exists(output_video_path):
+                video_clip = VideoFileClip(processed_video_path)
+                clip = video_clip.subclip(line['start'], min(line['end'], video_clip.duration))
+                clip.write_videofile(output_video_path)
+                clip.close()
+        else:
+            output_video_path = processed_video_path
+
+        return output_video_path
+
+    def build_prompt(self, line, num_frames, video_llm):
+        if isinstance(line, int):
+            assert line < len(self)
+            line = self.data.iloc[line]
+
+        question, answer = self.qa_template(line)
+        message = [dict(type='text', value=self.SYS)]
+        message.append(dict(type='text', value=question))
+        if video_llm:
+            new_video_path = self.load_into_video_and_process(line)
+            message.append(dict(type='video', value=new_video_path))
+        else:
+            bound = None
+            if line['bound']:
+                bound = (
+                    line['start'],
+                    line['end'],
+                )
+            video_path = os.path.join(line['prefix'], line['video'])
+            decord_method = self.decord_method[line['data_type']]
+            self.num_segments = num_frames if num_frames > 0 else self.nframe
+            torch_imgs = decord_method(video_path, bound)
+            img_frame_paths = self.save_video_frames(torch_imgs, line['video'], self.num_segments)
+            for im in img_frame_paths:
+                message.append(dict(type='image', value=im))
+        message.append(dict(type='text', value='\nOnly give the best option.'))
+        message.append(dict(type='text', value='Best option:('))
+        return message
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+
+        assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file'
+
+        tmp_file = eval_file.replace('.xlsx', '_tmp.pkl')
+        tgt_file = eval_file.replace('.xlsx', '_rating.json')
+        score_file = eval_file.replace('.xlsx', '_score.xlsx')
+
+        if not osp.exists(score_file):
+            res = {} if not osp.exists(tmp_file) else load(tmp_file)
+            res = {k: v for k, v in res.items() if FAIL_MSG not in v}
+
+            data = load(eval_file)
+            data_un = data[~pd.isna(data['prediction'])]
+
+            for idx in data['index']:
+                ans = data.loc[data['index'] == idx, 'answer'].values[0]
+                pred = data.loc[data['index'] == idx, 'prediction'].values[0]
+                options = eval(data.loc[data['index'] == idx, 'candidates'].values[0])
+                answer_idx = -1
+                for id, c in enumerate(options):
+                    if c == ans:
+                        answer_idx = id
+                ans = f"({chr(ord('A') + answer_idx)}) {ans}"
+
+                if FAIL_MSG in pred:
+                    data.loc[idx, 'score'] = -1
+                else:
+                    data.loc[idx, 'score'] = int(check_ans(pred, ans))
+
+            rejected = [x for x in data['score'] if x == -1]
+
+            print(
+                f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, '
+                f'failed to obtain the score for another {len(rejected)} questions. '
+                f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.'
+            )
+
+            dump(data, score_file)
+
+        rating = get_dimension_rating(score_file)
+        dump(rating, tgt_file)
+        return rating
+
+
+class MVBench_MP4(VideoBaseDataset):
+
+    MP4_MD5 = '7b4608045347904c28c153015a7a2b6b'
+    SYS = """Carefully watch the video and pay attention to the cause and sequence of events, \
+the detail and movement of objects, and the action and pose of persons. \
+Based on your observations, select the best option that accurately addresses the question.
+"""
+    TYPE = 'MCQ'
+
+    def __init__(self, dataset='MVBench_MP4', pack=False):
+        super().__init__(dataset=dataset, pack=pack)
+
+    @classmethod
+    def supported_datasets(cls):
+        return ['MVBench_MP4']
+
+    def prepare_dataset(self, dataset_name='MVBench_MP4', repo_id='OpenGVLab/MVBench'):
+        def check_integrity(pth):
+            data_file = osp.join(pth, f'{dataset_name}.tsv')
+
+            if not os.path.exists(data_file):
+                return False
+
+            if md5(data_file) != self.MP4_MD5:
+                return False
+
+            data = load(data_file)
+            for idx, item in data.iterrows():
+                if not osp.exists(osp.join(pth, item['prefix'], item['video'])):
+                    return False
+            return True
+
+        cache_path = get_cache_path(repo_id, branch='video')
+        if cache_path is not None and check_integrity(cache_path):
+            dataset_path = cache_path
+        else:
+            def generate_tsv(pth):
+                data_file = osp.join(pth, f'{dataset_name}.tsv')
+                if os.path.exists(data_file) and md5(data_file) == self.MD5:
+                    return
+                json_data_path = os.path.join(dataset_path, 'test.json')
+                json_data = load(json_data_path)
+                root_data_dict = json_data['root']
+                self.data_list = []
+                for k, v in json_data['meta'].items():
+                    for item in v:
+                        self.data_list.append({
+                            'task_type': k,
+                            'prefix': root_data_dict[k],
+                            'video': item['video'],
+                            'question': item['question'],
+                            'answer': item['answer'],
+                            'candidates': item['candidates']
+                        })
+                data_df = pd.DataFrame(self.data_list)
+                data_df = data_df.assign(index=range(len(data_df)))
+                data_df.to_csv(data_file, sep='\t', index=False)
+
+            hf_token = os.environ.get('HUGGINGFACE_TOKEN')
+            huggingface_hub.login(hf_token)
+            dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset', revision='video')
+            generate_tsv(dataset_path)
+
+        data_file = osp.join(dataset_path, f'{dataset_name}.tsv')
+
+        self.nframe = 8
+        self.resolution = 224
+
+        # transform
+        crop_size = self.resolution
+        scale_size = self.resolution
+        input_mean = [0.48145466, 0.4578275, 0.40821073]
+        input_std = [0.26862954, 0.26130258, 0.27577711]
+        self.transform = T.Compose([
+            GroupScale(int(scale_size), interpolation=InterpolationMode.BICUBIC),
+            GroupCenterCrop(crop_size),
+            Stack(),
+            ToTorchFormatTensor(),
+            GroupNormalize(input_mean, input_std)
+        ])
+
+        return dict(root=dataset_path, data_file=data_file)
+
+    def qa_template(self, data):
+        question = f"Question: {data['question']}\n"
+        question += 'Options:\n'
+        answer = data['answer']
+        answer_idx = -1
+        for idx, c in enumerate(eval(data['candidates'])):
+            question += f"({chr(ord('A') + idx)}) {c}\n"
+            if c == answer:
+                answer_idx = idx
+        question = question.rstrip()
+        answer = f"({chr(ord('A') + answer_idx)}) {answer}"
+        return question, answer
+
+    def get_index(self, max_frame):
+        seg_size = float(max_frame) / self.num_segments
+        frame_indices = np.array([
+            int((seg_size / 2) + np.round(seg_size * idx))
+            for idx in range(self.num_segments)
+        ])
+        return frame_indices
+
+    def read_video(self, video_path, bound=None):
+        vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+        max_frame = len(vr) - 1
+
+        images_group = list()
+        frame_indices = self.get_index(max_frame)
+        for frame_index in frame_indices:
+            img = Image.fromarray(vr[frame_index].asnumpy())
+            images_group.append(img)
+        torch_imgs = self.transform(images_group)
+        return torch_imgs
+
+    def save_video_frames(self, imgs, video_name, frames):
+
+        frame_paths = self.frame_paths(video_name, frames)
+        flag = np.all([osp.exists(p) for p in frame_paths])
+
+        if not flag:
+            block_size = imgs.size(0) // frames
+            split_tensors = torch.split(imgs, block_size)
+            to_pil = transforms.ToPILImage()
+            images = [to_pil(arr) for arr in split_tensors]
+            for im, pth in zip(images, frame_paths):
+                if not osp.exists(pth):
+                    im.save(pth)
+
+        return frame_paths
+
+    def build_prompt(self, line, num_frames, video_llm):
+        if isinstance(line, int):
+            assert line < len(self)
+            line = self.data.iloc[line]
+
+        question, answer = self.qa_template(line)
+        message = [dict(type='text', value=self.SYS)]
+        message.append(dict(type='text', value=question))
+        video_path = os.path.join(self.data_root, line['prefix'], line['video'])
+        if video_llm:
+            message.append(dict(type='video', value=video_path))
+        else:
+            video_path = os.path.join(self.data_root, line['prefix'], line['video'])
+            self.num_segments = num_frames if num_frames > 0 else self.nframe
+            torch_imgs = self.read_video(video_path)
+            img_frame_paths = self.save_video_frames(torch_imgs, line['video'], self.num_segments)
+            for im in img_frame_paths:
+                message.append(dict(type='image', value=im))
+        message.append(dict(type='text', value='\nOnly give the best option.'))
+        message.append(dict(type='text', value='Best option:('))
+        return message
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+
+        assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file'
+
+        tmp_file = eval_file.replace('.xlsx', '_tmp.pkl')
+        tgt_file = eval_file.replace('.xlsx', '_rating.json')
+        score_file = eval_file.replace('.xlsx', '_score.xlsx')
+
+        if not osp.exists(score_file):
+            res = {} if not osp.exists(tmp_file) else load(tmp_file)
+            res = {k: v for k, v in res.items() if FAIL_MSG not in v}
+
+            data = load(eval_file)
+            data_un = data[~pd.isna(data['prediction'])]
+
+            for idx in data['index']:
+                ans = data.loc[data['index'] == idx, 'answer'].values[0]
+                pred = data.loc[data['index'] == idx, 'prediction'].values[0]
+                options = eval(data.loc[data['index'] == idx, 'candidates'].values[0])
+                answer_idx = -1
+                for id, c in enumerate(options):
+                    if c == ans:
+                        answer_idx = id
+                ans = f"({chr(ord('A') + answer_idx)}) {ans}"
+
+                if FAIL_MSG in pred:
+                    data.loc[idx, 'score'] = -1
+                else:
+                    data.loc[idx, 'score'] = int(check_ans(pred, ans))
+
+            rejected = [x for x in data['score'] if x == -1]
+
+            print(
+                f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, '
+                f'failed to obtain the score for another {len(rejected)} questions. '
+                f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.'
+            )
+
+            dump(data, score_file)
+
+        rating = get_dimension_rating(score_file)
+        dump(rating, tgt_file)
+        return rating

eval_mm/vlmevalkit/vlmeval/dataset/slidevqa.py

@@ -0,0 +1,189 @@
+import re
+import math
+from typing import List
+
+from vlmeval.dataset.utils.judge_util import build_judge
+from vlmeval.smp import *
+from .image_base import ImageBaseDataset
+from .mmlongbench import concat_images, MMLongBench_auxeval, anls_compute
+
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def get_f1(gt, pred):
+    gt_bow, pred_bow = gt.strip().split(), pred.strip().split()
+    if not gt_bow or not pred_bow:
+        return 0.0
+
+    recall = len([pred_e for pred_e in pred_bow if pred_e in gt_bow]) / len(gt_bow)
+    precision = len([pred_e for pred_e in pred_bow if pred_e in gt_bow]) / len(pred_bow)
+    f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 1e-4 else 0.0
+    return f1
+
+
+def SlideVQA_acc(result_file):
+    data = load(result_file)
+    anls_list, em_list, f1_list = list(), list(), list()
+    for i in range(len(data)):
+        item = data.iloc[i]
+        if isinstance(item['answer'], float) and math.isnan(item['answer']):
+            item['answer'] = 'Not answerable'
+
+        item['answer'] = re.sub('\n', '', item['answer']).lower()
+        item['pred'] = str(item['pred']).lower()
+        anls_score = anls_compute(item['answer'], item['pred'])
+        em_score = (item['answer'].strip() == item['pred'].strip())
+        f1_score = get_f1(item['answer'], item['pred'])
+        anls_list.append(anls_score)
+        em_list.append(em_score)
+        f1_list.append(f1_score)
+        print('---------------------')
+        print(item['answer'], item['pred'], anls_score, em_score, f1_score)
+
+    data['anls'] = anls_list
+    data['em'] = em_list
+    data['f1'] = f1_list
+    dump(data, result_file)
+
+    res = dict()
+    res['category'], res['num'] = ['anls', 'EM', 'F1'], [len(data), len(data), len(data)]
+    res['avg'] = [sum(anls_list) / len(data), sum(em_list) / len(data), sum(f1_list) / len(data)]
+    res = pd.DataFrame(res)
+    return res
+
+
+class SlideVQA(ImageBaseDataset):
+
+    TYPE = 'VQA'
+
+    DATASET_URL = {
+        'SLIDEVQA_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/SLIDEVQA_MINI.tsv',
+        'SLIDEVQA': 'https://opencompass.openxlab.space/utils/VLMEval/SLIDEVQA.tsv',
+    }
+    DATASET_MD5 = {
+        'SLIDEVQA_MINI': '6d9a8d8814fa5b7669deb2af3a3208eb',
+        'SLIDEVQA': '5e822c2f800e94c1e23badfd478326b6',
+    }
+
+    SUPPORTED_MODELS = {
+        'GPT4': (1, 1),
+        'GPT4V': (1, 1),
+        'GPT4V_HIGH': (1, 1),
+        'GPT4o': (1, 1),
+        'GPT4o_HIGH': (1, 1),
+        'GPT4o_MINI': (1, 1),
+        'XComposer2d5': (1, -1),
+        'XComposer2_4KHD': (1, -1),
+        'MiniCPM-Llama3-V-2_5': (1, 5),
+        'InternVL-Chat-V1-5': (5, 2),
+    }
+
+    def __init__(self, dataset, **kwargs):
+        self.model_list = list(self.SUPPORTED_MODELS.keys())
+        model_name = kwargs['model']
+        if not listinstr(self.model_list, model_name):
+            raise AssertionError("{} doesn't support the evaluation on SlideVQA.".format(model_name))
+        super(SlideVQA, self).__init__(dataset)
+
+        self.is_api = True if listinstr(['GPT4'], model_name) else False
+        self.max_pages = 120
+        concat_num, column_num = self.SUPPORTED_MODELS.get(model_name)
+        self.concat_num = concat_num
+        self.column_num = column_num
+
+    def dump_image(self, origin_line):
+        os.makedirs(self.img_root, exist_ok=True)
+
+        line = origin_line.copy()
+        if not isinstance(line['image_path'], List):
+            line['image_path'] = [line['image_path']]
+        line['image_path'] = line['image_path'][:self.max_pages]
+
+        if 'image' in line:
+            if isinstance(line['image'], list):
+                tgt_path = []
+                assert 'image_path' in line
+                for img, im_name in zip(line['image'], line['image_path']):
+                    path = osp.join(self.img_root, im_name)
+                    if not read_ok(path):
+                        decode_base64_to_image_file(img, path)
+                    tgt_path.append(path)
+            else:
+                tgt_path = osp.join(self.img_root, f"{line['index']}.jpg")
+                if not read_ok(tgt_path):
+                    decode_base64_to_image_file(line['image'], tgt_path)
+                tgt_path = [tgt_path]
+        else:
+            assert 'image_path' in line
+            tgt_path = toliststr(line['image_path'])
+
+        if self.concat_num > 0 and not self.is_api:
+            concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num)
+
+            old_tgt_path = tgt_path
+            assert isinstance(old_tgt_path, list)
+            if self.column_num != -1:
+                tgt_path = [
+                    '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i)
+                    for i in range(len(concatenated_images))
+                ]
+            else:
+                tgt_path = ['_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all.jpg']
+
+            for path, concatenated_image in zip(tgt_path, concatenated_images):
+                if not read_ok(path):
+                    decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path)
+                    num_images, image_size = len(old_tgt_path), concatenated_image.size
+                    print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path))
+        return tgt_path
+
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        logger = get_logger('Evaluation')
+        model = judge_kwargs['model']
+
+        suffix = eval_file.split('.')[-1]
+        storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
+        tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
+
+        if osp.exists(storage):
+            logger.warning(f'GPT scoring file {storage} already exists, will reuse it in SlideVQA_eval. ')
+        else:
+            data = load(eval_file)
+            model = build_judge(max_tokens=128, **judge_kwargs)
+            lt = len(data)
+            lines = [data.iloc[i] for i in range(lt)]
+            tups = [(model, line) for line in lines]
+            indices = [line['index'] for line in lines]
+
+            ans = {}
+            if osp.exists(tmp_file):
+                ans = load(tmp_file)
+            tups = [x for x, i in zip(tups, indices) if i not in ans]
+            indices = [i for i in indices if i not in ans]
+
+            if len(indices):
+                new_results = list()
+                for model, line in tqdm(tups):
+                    res = MMLongBench_auxeval(model, line)
+                    new_results.append(res)
+
+            log_map, res_map, pred_map = {}, {}, {}
+            all_inds = [line['index'] for line in lines]
+            for k, v in zip(all_inds, new_results):
+                log_map[k] = v['log']
+                res_map[k] = v['res']
+                pred_map[k] = v['pred']
+            data['res'] = [res_map[idx] for idx in data['index']]
+            data['log'] = [log_map[idx] for idx in data['index']]
+            data['pred'] = [pred_map[idx] for idx in data['index']]
+            dump(data, storage)
+
+        score = SlideVQA_acc(storage)
+        score_pth = storage.replace('.xlsx', '_score.csv')
+
+        dump(score, score_pth)
+        logger.info(f'SlideVQA successfully finished evaluating {eval_file}, results saved in {score_pth}')
+        logger.info('Score: ')
+        logger.info(score)

eval_mm/vlmevalkit/vlmeval/dataset/text_base.py

@@ -0,0 +1,88 @@
+from abc import abstractmethod
+from ..smp import *
+
+
+class TextBaseDataset:
+    MODALITY = 'TEXT'
+    DATASET_URL = {}
+    DATASET_MD5 = {}
+
+    def __init__(self, dataset='MMBench', **kwargs):
+        self.dataset_name = dataset
+
+        data = self.load_data(dataset)
+
+        data['index'] = [str(x) for x in data['index']]
+
+        if np.all([istype(x, int) for x in data['index']]):
+            data['index'] = [int(x) for x in data['index']]
+
+        self.data = data
+        self.post_build(dataset)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return dict(self.data.iloc[idx])
+
+    def prepare_tsv(self, url, file_md5=None):
+        data_root = LMUDataRoot()
+        os.makedirs(data_root, exist_ok=True)
+        update_flag = False
+        file_name = url.split('/')[-1]
+        data_path = osp.join(data_root, file_name)
+        if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5):
+            pass
+        else:
+            warnings.warn('The dataset tsv is not downloaded')
+            download_file(url, data_path)
+            update_flag = True
+
+        if file_size(data_path, 'GB') > 1:
+            local_path = data_path.replace('.tsv', '_local.tsv')
+            if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None) or update_flag:
+                from ..tools import LOCALIZE
+                LOCALIZE(data_path, local_path)
+            data_path = local_path
+        return load(data_path)
+
+    def dump_image(self, line):
+        return []
+
+    def display(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+        assert isinstance(line, pd.Series) or isinstance(line, dict)
+        mmqa_display(line)
+
+    # Return a list of dataset names that are supported by this class, can override
+    @classmethod
+    def supported_datasets(cls):
+        return list(cls.DATASET_URL)
+
+    # Given the dataset name, return the dataset as a pandas dataframe, can override
+    def load_data(self, dataset):
+        url = self.DATASET_URL[dataset]
+        file_md5 = self.DATASET_MD5[dataset]
+        return self.prepare_tsv(url, file_md5)
+
+    # Post built hook, will be called after the dataset is built, can override
+    def post_build(self, dataset):
+        pass
+
+    # Given one data record, return the built prompt (a multi-modal message), can override
+    def build_prompt(self, line):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        question = line['question']
+
+        msgs = []
+        msgs.append(dict(type='text', value=question))
+        return msgs
+
+    # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe
+    @abstractmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        pass

eval_mm/vlmevalkit/vlmeval/dataset/text_mcq.py

@@ -0,0 +1,123 @@
+from .text_base import TextBaseDataset
+from .utils import build_judge, DEBUG_MESSAGE
+from ..smp import *
+
+
+class TextMCQDataset(TextBaseDataset):
+    TYPE = 'MCQ'
+
+    DATASET_URL = {}
+
+    DATASET_MD5 = {}
+
+    def build_prompt(self, line):
+
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        question = line['question']
+        options = {
+            cand: line[cand]
+            for cand in string.ascii_uppercase
+            if cand in line and not pd.isna(line[cand])
+        }
+        options_prompt = 'Options:\n'
+        for key, item in options.items():
+            options_prompt += f'{key}. {item}\n'
+        hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+        prompt = ''
+        if hint is not None:
+            prompt += f'Hint: {hint}\n'
+        prompt += f'Question: {question}\n'
+        if len(options):
+            prompt += options_prompt
+            prompt += 'Please select the correct answer from the options above. \n'
+
+        msgs = []
+
+        msgs.append(dict(type='text', value=prompt))
+
+        return msgs
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval
+        # assert dataset is not None
+        dataset_map = {
+            'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11',
+            'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11'
+        }
+        dataset = self.dataset_name
+        if dataset in dataset_map:
+            dataset = dataset_map[dataset]
+        nproc = judge_kwargs.pop('nproc', 4)
+
+        circular = False
+
+        suffix = eval_file.split('.')[-1]
+        model = judge_kwargs.get('model', 'exact_matching')
+        assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125']
+        name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'}
+        name_str = name_str_map[model] if model in name_str_map else model
+
+        if model == 'exact_matching':
+            model = None
+        elif gpt_key_set():
+            model = build_judge(**judge_kwargs)
+            if not model.working():
+                warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation')
+                warnings.warn(DEBUG_MESSAGE)
+                model = None
+        else:
+            warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
+            model = None
+
+        result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl')
+
+        data = load(eval_file)
+        data = data.sort_values(by='index')
+        data['prediction'] = [str(x) for x in data['prediction']]
+        # If not choice label, then use lower case
+        for k in data.keys():
+            data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
+
+        meta = self.data
+        meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])}
+        data_map = {x: y for x, y in zip(data['index'], data['question'])}
+        for k in data_map:
+            assert k in meta_q_map, (
+                f'eval_file should be the same as or a subset of dataset {self.dataset_name}'
+            )
+
+        if circular:
+            data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name)
+        else:
+            data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name)
+
+        # load split
+        dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+        data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
+
+        # May have different report acc functions for different datasets
+        if 'MMT' in dataset:
+            acc = report_acc_MMT(data)
+        else:
+            acc = report_acc(data)
+
+        score_file = eval_file.replace(f'.{suffix}', '_acc.csv')
+        dump(acc, score_file)
+
+        return acc
+
+
+class CustomTextMCQDataset(TextMCQDataset):
+
+    def load_data(self, dataset):
+        data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv')
+
+        if file_size(data_path, 'GB') > 1:
+            local_path = data_path.replace('.tsv', '_local.tsv')
+            if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None):
+                from ..tools import LOCALIZE
+                LOCALIZE(data_path, local_path)
+            data_path = local_path
+        return load(data_path)

eval_mm/vlmevalkit/vlmeval/dataset/utils/__init__.py

@@ -0,0 +1,9 @@
+from .judge_util import build_judge, DEBUG_MESSAGE
+from .multiple_choice import extract_answer_from_item, prefetch_answer
+from .vqa_eval import levenshtein_distance
+
+
+__all__ = [
+    'build_judge', 'extract_answer_from_item', 'prefetch_answer',
+    'levenshtein_distance', 'DEBUG_MESSAGE'
+]

eval_mm/vlmevalkit/vlmeval/dataset/utils/judge_util.py

@@ -0,0 +1,41 @@
+import os
+from ...api import OpenAIWrapper
+from ...smp import load_env
+
+INTERNAL = os.environ.get('INTERNAL', 0)
+
+
+def build_judge(**kwargs):
+    model = kwargs.pop('model', None)
+    kwargs.pop('nproc', None)
+    load_env()
+    LOCAL_LLM = os.environ.get('LOCAL_LLM', None)
+    if LOCAL_LLM is None:
+        model_map = {
+            'gpt-4-turbo': 'gpt-4-1106-preview',
+            'gpt-4-0613': 'gpt-4-0613',
+            'gpt-4-0125': 'gpt-4-0125-preview',
+            'gpt-4-0409': 'gpt-4-turbo-2024-04-09',
+            'chatgpt-1106': 'gpt-3.5-turbo-1106',
+            'chatgpt-0125': 'gpt-3.5-turbo-0125',
+            'gpt-4o': 'gpt-4o-2024-05-13',
+            'gpt-4o-mini': 'gpt-4o-mini-2024-07-18',
+        }
+        model_version = model_map[model]
+    else:
+        model_version = LOCAL_LLM
+    model = OpenAIWrapper(model_version, **kwargs)
+    return model
+
+
+DEBUG_MESSAGE = """
+To debug the OpenAI API, you can try the following scripts in python:
+```python
+from vlmeval.api import OpenAIWrapper
+model = OpenAIWrapper('gpt-4-1106-preview', verbose=True)
+msgs = [dict(type='text', value='Hello!')]
+code, answer, resp = model.generate_inner(msgs)
+print(code, answer, resp)
+```
+You cam see the specific error if the API call fails.
+"""

eval_mm/vlmevalkit/vlmeval/dataset/utils/llavabench.py

@@ -1,10 +1,6 @@
-import argparse
 import numpy as np
 import pandas as pd
-import os.path as osp
+from ...smp import *
-from vlmeval.evaluate.misc import build_judge
-from vlmeval.smp import *
-from vlmeval.utils import track_progress_rich
 
 rule_dict = {
     'llava_bench_conv': {'role': 'Assistant', 'prompt': 'We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.'},  # noqa: E501
@@ -67,54 +63,3 @@ def LLaVABench_score(data):
         ret['VLM Score'].append(np.mean(sub['score']) * 10)
         ret['GPT4 Score'].append(np.mean(sub['gpt4_score']) * 10)
     return pd.DataFrame(ret)
-
-
-def LLaVABench_eval(eval_file, **judge_kwargs):
-    suffix = '.' + eval_file.split('.')[-1]
-    record_file = eval_file.replace(suffix, '_openai_result' + suffix)
-    score_file = eval_file.replace(suffix, '_score.csv')
-    nproc = judge_kwargs.pop('nproc', 4)
-
-    if not osp.exists(record_file):
-        data = load(eval_file)
-        lines = [data.iloc[i] for i in range(len(data))]
-        model = build_judge(
-            temperature=0.2,
-            system_prompt='You are a helpful and precise assistant for checking the quality of the answer.',
-            **judge_kwargs)
-        prompts = [build_prompt(line) for line in lines]
-        tups = [(model, prompt) for prompt in prompts]
-        scores = track_progress_rich(LLaVABench_atomeval, tups, nproc=nproc, chunksize=nproc)
-        data['gpt4_score'] = [x[0] for x in scores]
-        data['score'] = [x[1] for x in scores]
-        dump(data, record_file)
-
-    data = load(record_file)
-    ret = LLaVABench_score(data).round(1)
-    print(ret)
-    dump(ret, score_file)
-    return ret
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='LLaVABench Evaluation. ')
-    parser.add_argument('data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument(
-        '--model', type=str, help='The LLM (GPT) used for inference. ', default='gpt-4-turbo',
-        choices=['gpt-4-0613', 'gpt-4-turbo', 'chatgpt-1106', 'chatgpt-0613', 'gpt-4-0314'])
-    parser.add_argument('--nproc', type=int, default=4)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    load_env()
-    args = parse_args()
-    judge_kwargs = dict(model=args.model, nproc=args.nproc, verbose=args.verbose)
-    if 'OPENAI_API_KEY_JUDGE' in os.environ and os.environ['OPENAI_API_KEY_JUDGE']:
-        judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
-    if 'OPENAI_API_BASE_JUDGE' in os.environ and os.environ['OPENAI_API_BASE_JUDGE']:
-        judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
-
-    LLaVABench_eval(eval_file=args.data, **judge_kwargs)

eval_mm/vlmevalkit/vlmeval/dataset/utils/mathv.py

@@ -0,0 +1,170 @@
+from ...smp import *
+from ...utils import can_infer
+try:
+    from latex2sympy2 import latex2sympy
+except ImportError:
+    print('Please install latex2sympy2 by running "pip install latex2sympy2"')
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def is_equal(asw: str, gt_asw: str) -> bool:
+    if not isinstance(asw, str) != str or not isinstance(gt_asw, str):
+        print('Warning: input is not string')
+        print(asw, gt_asw)
+    asw = str(asw).lower().strip()
+    gt_asw = str(gt_asw).lower().strip()
+    if gt_asw == asw:
+        return True
+    try:
+        a = eval(gt_asw)
+        b = eval(asw)
+        if abs(a - b) < 1e-6:
+            return True
+    except:
+        pass
+    try:
+        a = latex2sympy(gt_asw)
+        b = latex2sympy(asw)
+        if abs(eval(str(a)) - eval(str(b))) < 1e-6:
+            return True
+        if abs(a - b) < 1e-6:
+            return True
+    except:
+        pass
+    return False
+
+
+def get_gpt4_ICE():
+    example_1 = """
+Hint: Please answer the question and provide the final answer at the end.\n
+Question: Which number is missing?\n
+Model response: The number missing in the sequence is 14.\n
+Extracted answer: 14
+"""
+
+    example_2 = """
+Hint: Please answer the question and provide the final answer at the end.\n
+Question: What is the fraction of females facing the camera?\n
+Model response: The fraction of females facing the camera is 0.6,
+which means that six out of ten females in the group are facing the camera.\n
+Extracted answer: 0.6
+"""
+
+    example_3 = """
+Hint: Please answer the question and provide the final answer at the end.\n
+Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n
+Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n
+Extracted answer: 1.45
+"""
+
+    example_4 = """
+Hint: Please answer the question and provide the final answer at the end.\n
+Question: Between which two years does the line graph saw its maximum peak?\n
+Model response: The line graph saw its maximum peak between 2007 and 2008.\n
+Extracted answer: [2007, 2008]
+"""
+
+    example_5 = """
+Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n
+Question: What fraction of the shape is blue?\n
+Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n
+Model response: The correct answer is (B) 8/11.\n
+Extracted answer: B
+"""
+
+    return [example_1, example_2, example_3, example_4, example_5]
+
+
+def build_mathv_gpt4_prompt(line):
+    task_description = """
+Please read the following example.
+Then extract the answer from the model response and type it at the end of the prompt.\n
+"""
+    question = line['question']
+    prediction = str(line['prediction'])
+    prompt = task_description
+    examples = get_gpt4_ICE()
+    for example in examples:
+        prompt += example + '\n'
+    prompt += question + '\n'
+    prompt += 'Model respone: ' + prediction
+    prompt += 'Extracted answer:'
+    return prompt
+
+
+def list_to_dict(lst):
+    return {chr(65 + i): val for i, val in enumerate(lst)}
+
+
+def post_check(line, prefetch=False):
+    res = None
+    ans = line['answer']
+    response = line['prediction'] if prefetch else line['res']
+    try:
+        if len(eval(line['choices'])) > 0:
+            ans = line['answer']
+            choices = list_to_dict(eval(line['choices']))
+            res = can_infer(response, choices)
+            if prefetch:
+                return res
+        else:
+            res = str(response)
+            ans = str(ans)
+    except ValueError:
+        pass
+
+    if is_equal(res, ans):
+        return res if prefetch else True
+    else:
+        return False
+
+
+def MATH_V_auxeval(model, line):
+    prompt = build_mathv_gpt4_prompt(line)
+    log = ''
+    retry = 5
+    if post_check(line, prefetch=True):
+        res = post_check(line, prefetch=True)
+        return dict(log='Prefetch succeed', res=res)
+    for i in range(retry):
+        prediction = line['prediction']
+        res = model.generate(prompt, temperature=i * 0.5)
+
+        if FAIL_MSG in res:
+            log += f'Try {i}: output is {prediction}, failed to parse.\n'
+        else:
+            log += 'Succeed'
+            return dict(log=log, res=res)
+    log += 'All 5 retries failed.\n'
+    return dict(log=log, res='')
+
+
+def MATH_V_acc(result_file):
+    data = load(result_file)
+    tot = defaultdict(lambda: 0)
+    fetch = defaultdict(lambda: 0)
+    hit = defaultdict(lambda: 0)
+    lt = len(data)
+    for i in range(lt):
+        item = data.iloc[i]
+        cate = item['category']
+        tot['Overall'] += 1
+        tot[cate] += 1
+        if item['log'] == 'Prefetch succeed':
+            fetch['Overall'] += 1
+            fetch[cate] += 1
+        if post_check(item, prefetch=False):
+            hit['Overall'] += 1
+            hit[cate] += 1
+
+    res = defaultdict(list)
+    for k in tot.keys():
+        res['Subject'].append(k)
+        res['tot'].append(tot[k])
+        res['prefetch'].append(fetch[k])
+        res['hit'].append(hit[k])
+        res['prefetch_rate'].append(fetch[k] / tot[k] * 100)
+        res['acc'].append(hit[k] / tot[k] * 100)
+    res = pd.DataFrame(res).sort_values('Subject', ignore_index=True)
+    return res

eval_mm/vlmevalkit/vlmeval/dataset/utils/mathvista.py

@@ -1,7 +1,8 @@
-from vlmeval.evaluate.misc import build_judge
+from ...smp import *
-from vlmeval.smp import *
+from ...utils import can_infer
-from vlmeval.utils import track_progress_rich
+
-from vlmeval.utils.matching_util import can_infer
+
+FAIL_MSG = 'Failed to obtain answer via API.'
 
 
 def get_gpt4_ICE():
@@ -110,7 +111,8 @@ def MathVista_auxeval(model, line):
     for i in range(retry):
         prediction = line['prediction']
         res = model.generate(prompt, temperature=i * 0.5)
-        if res is None:
+
+        if FAIL_MSG in res:
             log += f'Try {i}: output is {prediction}, failed to parse.\n'
         else:
             log += 'Succeed'
@@ -160,81 +162,3 @@ def MathVista_acc(result_file):
         res['acc'].append(hit[k] / tot[k] * 100)
     res = pd.DataFrame(res)
     return res
-
-
-def MathVista_eval(eval_file, **judge_kwargs):
-    logger = get_logger('Evaluation')
-    model = judge_kwargs['model']
-
-    suffix = eval_file.split('.')[-1]
-    storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
-    tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
-    nproc = judge_kwargs.pop('nproc', 4)
-
-    if osp.exists(storage):
-        logger.warning(f'GPT scoring file {storage} already exists, will reuse it in MathVista_eval. ')
-    else:
-        data = load(eval_file)
-        model = build_judge(max_tokens=128, **judge_kwargs)
-        lt = len(data)
-        lines = [data.iloc[i] for i in range(lt)]
-        tups = [(model, line) for line in lines]
-        indices = [line['index'] for line in lines]
-
-        ans = {}
-        if osp.exists(tmp_file):
-            ans = load(tmp_file)
-        tups = [x for x, i in zip(tups, indices) if i not in ans]
-        indices = [i for i in indices if i not in ans]
-
-        if len(indices):
-            new_results = track_progress_rich(
-                MathVista_auxeval, tups, nproc=nproc, chunksize=nproc,
-                keys=indices, save=tmp_file)
-            ans = load(tmp_file)
-            for k, v in zip(indices, new_results):
-                assert k in ans
-                assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res']
-
-        log_map, res_map = {}, {}
-        all_inds = [line['index'] for line in lines]
-        for k in all_inds:
-            log_map[k] = ans[k]['log']
-            res_map[k] = ans[k]['res']
-        data['res'] = [res_map[idx] for idx in data['index']]
-        data['log'] = [log_map[idx] for idx in data['index']]
-        dump(data, storage)
-
-    score = MathVista_acc(storage)
-    score_pth = storage.replace('.xlsx', '_score.csv')
-
-    dump(score, score_pth)
-    logger.info(f'MathVista_eval successfully finished evaluating {eval_file}, results saved in {score_pth}')
-    logger.info('Score: ')
-    logger.info(score)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Inference LLM Answers. ')
-    parser.add_argument('data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument(
-        '--model',
-        type=str,
-        help='The LLM (GPT) used for inference. ',
-        default='gpt-4-turbo',
-        choices=['gpt-4-0613', 'gpt-4-turbo', 'chatgpt-1106', 'chatgpt-0613'])
-    parser.add_argument('--nproc', type=int, default=4)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    load_env()
-    args = parse_args()
-    judge_kwargs = dict(model=args.model, nproc=args.nproc, verbose=args.verbose)
-    if 'OPENAI_API_KEY_JUDGE' in os.environ and os.environ['OPENAI_API_KEY_JUDGE']:
-        judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
-    if 'OPENAI_API_BASE_JUDGE' in os.environ and os.environ['OPENAI_API_BASE_JUDGE']:
-        judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
-    MathVista_eval(eval_file=args.data, **judge_kwargs)

eval_mm/vlmevalkit/vlmeval/dataset/utils/mmbench_video.py

@@ -0,0 +1,70 @@
+from ...smp import *
+import numpy as np
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+system_prompt = """
+As an AI assistant, your task is to evaluate a candidate answer in comparison to a given correct answer.
+The question itself, the correct 'groundtruth' answer, and the candidate answer will be provided to you.
+Your assessment should range from 0 to 3, \
+based solely on the semantic similarity between the groundtruth and the candidate answer, \
+disregarding any grammatical differences.
+A rating of 0 suggests no similarity, implying the candidate answer is entirely incorrect.
+A rating of 1 suggests low similarity, meaning the candidate answer is largely incorrect.
+A rating of 2 suggests high similarity, meaning the candidate answer is largely correct.
+Lastly, a rating of 3 indicates complete similarity, which means the candidate answer is entirely correct.
+Your response should be a single integer from 0, 1, 2, or 3.
+"""
+
+MMV_DIMENSIONS = {
+    'CP': ['Video Topic', 'Video Emotion', 'Video Scene', 'Video Style'],
+    'FP-S': ['OCR', 'Object Recognition', 'Attribute Recognition', 'Event Recognition', 'Human Motion', 'Counting'],
+    'FP-C': ['Spatial Relationship', 'Human-object Interaction', 'Human Interaction'],
+    'HL': ['Hallucination'],
+    'LR': ['Structuralized Image-Text Understanding', 'Mathematical Calculation'],
+    'AR': ['Physical Property', 'Function Reasoning', 'Identity Reasoning'],
+    'RR': ['Natural Relation', 'Physical Relation', 'Social Relation'],
+    'CSR': ['Common Sense Reasoning'],
+    'TR': ['Counterfactual Reasoning', 'Causal Reasoning', 'Future Prediction'],
+}
+L3_DIMS = []
+for k, v in MMV_DIMENSIONS.items():
+    L3_DIMS.extend(v)
+
+MMV_DIMENSIONS['Perception'] = []
+MMV_DIMENSIONS['Reasoning'] = []
+MMV_DIMENSIONS['Overall'] = []
+for k in ['CP', 'FP-C', 'FP-S', 'HL']:
+    MMV_DIMENSIONS['Perception'].extend(MMV_DIMENSIONS[k])
+    MMV_DIMENSIONS['Overall'].extend(MMV_DIMENSIONS[k])
+for k in ['LR', 'AR', 'RR', 'CSR', 'TR']:
+    MMV_DIMENSIONS['Reasoning'].extend(MMV_DIMENSIONS[k])
+    MMV_DIMENSIONS['Overall'].extend(MMV_DIMENSIONS[k])
+
+
+def get_dimension_rating(data_path):
+    data = load(data_path)
+    coarse_rating = {k: [] for k in MMV_DIMENSIONS}
+    fine_rating = {k: [] for k in L3_DIMS}
+
+    for i in range(len(data)):
+        cate = data.iloc[i]['dimensions']
+        cates = eval(cate)
+
+        for c in cates:
+            fine_rating[c].append(data.iloc[i]['score'])
+
+        for d in MMV_DIMENSIONS:
+            if np.any([x in MMV_DIMENSIONS[d] for x in cates]):
+                coarse_rating[d].append(data.iloc[i]['score'])
+
+    coarse_all = {k: f'{np.mean([max(x, 0) for x in v]):.2f}' for k, v in coarse_rating.items()}
+    coarse_valid = {k: f'{np.mean([x for x in v if x >= 0]):.2f}' for k, v in coarse_rating.items()}
+    fine_all = {k: f'{np.mean([max(x, 0) for x in v]):.2f}' for k, v in fine_rating.items()}
+    fine_valid = {k: f'{np.mean([x for x in v if x >= 0]):.2f}' for k, v in fine_rating.items()}
+    return dict(coarse_all=coarse_all, coarse_valid=coarse_valid, fine_all=fine_all, fine_valid=fine_valid)
+
+
+def build_prompt(item):
+    tmpl = 'Question: {}\nGroundtruth answer: {}\nCandidate answer: {}\nYour response: '
+    return tmpl.format(item['question'], item['answer'], item['prediction'])

eval_mm/vlmevalkit/vlmeval/dataset/utils/mmdu.py

@@ -0,0 +1,126 @@
+from ...smp import *
+
+meta_prompt = """
+You are an assistant skilled at evaluating the quality of creative text.
+Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to \
+the user question displayed below. You'll need to assess the response on the following dimensions: \
+Creativity, Richness, Visual Perception, Logical Coherence, Answer Accuracy and Image Relationship Understanding. \
+We will provide you with a creative question and the AI model's response and a reference answer for your evaluation. \
+As you begin your assessment, follow this process:
+1. Evaluate the AI model's answers on different dimensions, pointing out its strengths or weaknesses \
+in each dimension and assigning a score of 1 to 10 for each.
+2. Finally, based on the assessments across dimensions, \
+provide an overall score of 1 to 10 for the AI model's response.
+3. Your scoring should be as stringent as possible and follow the scoring rules below:
+In general, the higher the quality of the model's response and its strict adherence to user needs, \
+the higher the score. Responses that do not meet user needs will receive lower scores.
+Scoring rules:
+Creativity:
+Scores 1-2 when there is no innovation or uniqueness in the content.
+Scores 3-4 when providing partially original content but with low creative quality.
+Scores 5-6 when mostly creative but lacks significant novelty, with moderate quality.
+Scores 7-8 when having novelty and high-quality content.
+Scores 9-10 when highly novel and of exceptional quality compared to the reference answer.
+Richness:
+Scores 1-2 when lacking depth and breadth, with very limited information.
+Scores 3-4 when limited in depth and breadth, with fewer explanations and examples, showing low diversity.
+Scores 5-6 when limited in depth and breadth but provides basic necessary information.
+Scores 7-8 when providing depth and useful additional information.
+Scores 9-10 when providing exceptional depth, breadth, and high diversity compared to the reference answer.
+Visual Perception:
+Scores 1-2 when the description of the visual information in the image contains errors or \
+is significantly inconsistent with the content of the image.
+Scores 3-4 When the description of the visual information in the image reflects only a small amount \
+of the image's information and contains some errors.
+Scores 5-6 when the description of the visual information in the image includes the basic information \
+of the image but contains minimal information.
+Scores 7-8 when the description of the visual information in the image matches the image well and is rich in content, \
+providing a substantial amount of information about the image.
+Scores 9-10 when the description of the visual information in the image not only matches the image \
+but also is more detailed and informative compared to the reference answer, providing more information about the image.
+Logical Coherence:
+Scores 1-2 when entirely incoherent, lacking any logic, and not matching the question or known information.
+Scores 3-4 when somewhat coherent but with many logical errors or inconsistencies.
+Scores 5-6 when mostly coherent, with few errors, but may struggle to maintain complete coherence in complex situations.
+Scores 7-8 when excellent logical handling, very few errors.
+Scores 9-10 when flawless logic, impeccable in handling complexity, \
+and significantly higher logical coherence compared to the reference answer.
+Answer Accuracy:
+Scores 1-2 when the answer is significantly inconsistent with the question or contains obvious errors.
+Scores 3-4 when the answer is partially correct but contains some errors or is incomplete.
+Scores 5-6 when the answer is basically correct but lacks details or is not sufficiently detailed.
+Scores 7-8 when the answer is accurate and detailed, fully corresponding to the question.
+Scores 9-10 when the answer is not only accurate and detailed but also provides additional useful information, \
+exceeding expectations.
+Image Relationship Understanding:
+Scores 1-2 when there are significant errors or confusion in distinguishing and describing different images, \
+unable to correctly identify and relate the content of the images.
+Scores 3-4 when the description of different images reflects only minimal distinguishing information, \
+contains some errors and confusion, and fails to clearly differentiate and relate the images.
+Scores 5-6 when the description of different images includes basic distinguishing information, \
+is able to correctly identify and relate the images in a basic manner, \
+but the information provided is minimal and lacks detail.
+Scores 7-8 when the description of different images is accurate and detailed, \
+clearly distinguishing and relating the images, \
+with rich content that points out the main commonalities and differences between the images.
+Scores 9-10 when the description of different images is not only accurate and detailed but also \
+provides richer information and analysis, clearly distinguishing and relating the images, \
+more comprehensively pointing out the commonalities and differences \
+between the images compared to the reference answer.
+Overall Score:
+Scores 1-2 when irrelevant to the question, factually incorrect, or generates harmful content.
+Scores 3-4 when no serious errors, mostly harmless, but of low quality and does not meet requirements.
+Scores 5-6 when basically meeting requirements but performing poorly in some dimensions, with moderate quality.
+Scores 7-8 when performing well in all dimensions.
+Scores 9-10 when fully addressing user questions and all requirements, significantly surpassing the reference answer.
+Please remember, you must evaluate and explain before scoring. After your explanation for each dimension, \
+add the score for that dimension. Finally, at the end of your response, \
+in the format of the dictionary (including brackets), return all your scoring results, \
+ensuring your scores are integers:
+{'Dimension One': Score, 'Dimension Two': Score, ..., 'Overall Score': Score}, \
+for example: {'Creativity': 9, 'Richness': 6, ..., 'Overall Score': 7}.\n
+"""
+question_begin_prompt = '[Question]'
+reference_begin_prompt = '[The Start of Reference Answer]'
+reference_end_prompt = '[The End of Reference Answer]'
+answers_begin_prompt = '[The Start of Assistant’s Answer]'
+answers_end_prompt = '[The End of Assistant’s Answer]'
+
+
+def mmdu_score(model, line):
+    question = eval(line['question'])
+    gt = eval(line['answer'])
+    prediction = eval(line['prediction'])
+
+    DIMS = [
+        'Creativity', 'Richness', 'Visual Perception', 'Logical Coherence',
+        'Answer Accuracy', 'Image Relationship Understanding', 'Overall Score'
+    ]
+
+    all_result_dict = []
+    logs = []
+    for j in range(len(question)):
+        try:
+            prompt = meta_prompt + question_begin_prompt + '\n' + question[j] + '\n\n' + \
+                reference_begin_prompt + '\n' + gt[j] + '\n' + reference_end_prompt + '\n\n' + \
+                answers_begin_prompt + '\n' + prediction[j] + '\n' + answers_end_prompt
+            response = model.generate(prompt)
+            start_index = response.find('{')
+            end_index = response.rfind('}') + 1
+            dictionary_str = response[start_index: end_index]
+            result_dict = eval(dictionary_str)
+            all_result_dict.append(result_dict)
+            if all([x in result_dict for x in DIMS]):
+                logs.append('Succeed')
+            else:
+                logs.append(
+                    f'Following Dims are not in results of turn {j}: '
+                    f'{",".join([x for x in DIMS if x not in result_dict])}'
+                )
+        except Exception as e:
+            print({e})
+            all_result_dict.append({d: None for d in DIMS})
+            logs.append(str(e))
+
+    df = pd.DataFrame(all_result_dict)
+    return dict(res=df, log='\n'.join(logs))

eval_mm/vlmevalkit/vlmeval/dataset/utils/mmvet.py

@@ -1,6 +1,4 @@
-from vlmeval.evaluate.misc import build_judge
+from ...smp import *
-from vlmeval.smp import *
-from vlmeval.utils import track_progress_rich
 
 
 def build_mmvet_gpt4_prompt(line):
@@ -106,86 +104,3 @@ def MMVet_acc(result_file):
     res = pd.DataFrame(res)
     res2 = pd.DataFrame(res2)
     return res, res2
-
-
-def MMVet_eval(eval_file, **judge_kwargs):
-    logger = get_logger('Evaluation')
-
-    suffix = eval_file.split('.')[-1]
-    model = judge_kwargs['model']
-    storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
-    tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
-    nproc = judge_kwargs.pop('nproc', 4)
-    if osp.exists(storage):
-        logger.warning(f'GPT scoring file {storage} already exists, will reuse it in MMVet_eval. ')
-    else:
-        data = load(eval_file)
-        model = build_judge(max_tokens=3, **judge_kwargs)
-
-        lt = len(data)
-        lines = [data.iloc[i] for i in range(lt)]
-        tups = [(model, line) for line in lines]
-        indices = [line['index'] for line in lines]
-
-        ans = {}
-        if osp.exists(tmp_file):
-            ans = load(tmp_file)
-        tups = [x for x, i in zip(tups, indices) if i not in ans]
-        indices = [i for i in indices if i not in ans]
-
-        if len(indices):
-            new_results = track_progress_rich(
-                MMVet_auxeval, tups, nproc=nproc, chunksize=nproc,
-                keys=indices, save=tmp_file)
-            ans = load(tmp_file)
-            for k, v in zip(indices, new_results):
-                assert k in ans
-                assert ans[k]['log'] == v['log'] and ans[k]['score'] == v['score']
-
-        log_map, score_map = {}, {}
-        all_inds = [line['index'] for line in lines]
-        for k in all_inds:
-            log_map[k] = ans[k]['log']
-            score_map[k] = ans[k]['score']
-        data['score'] = [score_map[idx] for idx in data['index']]
-        data['log'] = [log_map[idx] for idx in data['index']]
-        dump(data, storage)
-
-    score, score_fine = MMVet_acc(storage)
-    score_pth = storage.replace('.xlsx', '_score.csv')
-    score_fine_pth = storage.replace('.xlsx', '_score_fine.csv')
-
-    dump(score, score_pth)
-    dump(score_fine, score_fine_pth)
-    logger.info(
-        f'MMVet_eval successfully finished evaluating {eval_file}, '
-        f'results saved in {score_pth} and {score_fine_pth}'
-    )
-    logger.info('Score: ')
-    logger.info(score)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Inference LLM Answers. ')
-    parser.add_argument('data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument(
-        '--model',
-        type=str,
-        help='The LLM (GPT) used for inference. ',
-        default='gpt-4-turbo',
-        choices=['gpt-4-0613', 'gpt-4-turbo', 'chatgpt-1106', 'chatgpt-0613'])
-    parser.add_argument('--nproc', type=int, default=4)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    load_env()
-    args = parse_args()
-    judge_kwargs = dict(model=args.model, nproc=args.nproc, verbose=args.verbose)
-    if 'OPENAI_API_KEY_JUDGE' in os.environ and os.environ['OPENAI_API_KEY_JUDGE']:
-        judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
-    if 'OPENAI_API_BASE_JUDGE' in os.environ and os.environ['OPENAI_API_BASE_JUDGE']:
-        judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
-    MMVet_eval(eval_file=args.data, **judge_kwargs)

eval_mm/vlmevalkit/vlmeval/dataset/utils/multiple_choice.py

@@ -0,0 +1,442 @@
+import pandas as pd
+from ...utils import can_infer, track_progress_rich
+from ...smp import *
+import numpy as np
+
+MMB_abbrs = {
+    'coarse_perception': 'CP',
+    'finegrained_perception (instance-level)': 'FP-S',
+    'finegrained_perception (cross-instance)': 'FP-C',
+    'logic_reasoning': 'LR',
+    'relation_reasoning': 'RR',
+    'attribute_reasoning': 'AR'
+}
+
+MMT_abbrs = {
+    'visual_recognition': 'VR',
+    'localization': 'Loc',
+    'ocr': 'OCR',
+    'counting': 'Count',
+    'hallucination': 'HLN',
+    'image_retrieval': 'IR',
+    'threed': '3D',
+    'visual_captioning': 'VC',
+    'visual_grounding': 'VG',
+    'doc_understanding': 'DU',
+    'action_recognition': 'AR',
+    'pixel_level_perception': 'PLP',
+    'image-to-image_translation': 'I2IT',
+    'relation_reasoning': 'RR',
+    'intelligence_quotient_test': 'IQT',
+    'emotion': 'Emo',
+    'visual_illusion': 'VI',
+    'meme_understanding': 'MemU',
+    'visual_prompt_understanding': 'VPU',
+    'anomaly_detection': 'AND',
+    'keypoint_detection': 'KD',
+    'visual_commonsense_reasoning': 'VCR',
+    'image_evaluation_judgement': 'IEJ',
+    'multiple_image_analysis': 'MIA',
+    'cross_image_matching': 'CIM',
+    'temporal_understanding': 'TU',
+    'visual_code': 'VP',
+    'medical_understanding': 'MedU',
+    'autonomous_driving': 'AUD',
+    'discipline_knowledge_reasoning': 'DKR',
+    'embodied_ai': 'EA',
+    'gui_navigation': 'GN'
+}
+
+
+def MMMU_preproc(data):
+    logger = get_logger('Evaluation')
+    cnt = 0
+    As, Bs, Ans = list(data['A']), list(data['B']), list(data['answer'])
+    lt = len(data)
+    for i in range(lt):
+        if pd.isna(As[i]):
+            As[i] = Ans[i]
+            Bs[i] = 'Other Answers'
+            cnt += 1
+    logger.info(f'During MMMU_preproc in Evaluation, {cnt} open questions are re-formulated to multi-choice ones. ')
+    data['A'] = As
+    data['B'] = Bs
+    return data
+
+
+def report_acc(df):
+    # assert group in [None, 'category', 'l2-category']
+    res = defaultdict(list)
+
+    if 'split' in df:
+        splits = list(set(df['split']))
+        res['split'] = splits
+    else:
+        df['split'] = ['none'] * len(df)
+        res['split'] = ['none']
+
+    for group in [None, 'l2-category', 'category']:
+        if group is None:
+            res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']]
+        elif group not in df:
+            continue
+        else:
+            abilities = list(set(df[group]))
+            abilities.sort()
+            for ab in abilities:
+                ab_name = MMB_abbrs[ab] if ab in MMB_abbrs else ab
+                sub_df = df[df[group] == ab]
+                res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']]
+    return pd.DataFrame(res)
+
+
+def report_acc_MMT(df):
+    # assert group in [None, 'category', 'l2-category']
+    res = defaultdict(list)
+    res['split'] = list()
+    res['Overall'] = list()
+    for _, name in MMT_abbrs.items():
+        res[name] = list()
+
+    if 'split' in df:
+        splits = list(set(df['split']))
+        res['split'] = splits
+
+    else:
+        df['split'] = ['none'] * len(df)
+        res['split'] = ['none']
+
+    for group in [None, 'category', 'l2-category']:
+        if group is None:
+            res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']]
+            res['Overall'].extend([np.mean(df['hit'])])
+        elif group not in df:
+            continue
+        elif group == 'category':
+            abilities = list(set(df[group]))
+            abilities.sort()
+            for ab in abilities:
+                ab_name = ab
+                sub_df = df[df[group] == ab]
+                res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']]
+                res[ab_name].extend([np.mean(sub_df['hit'])])
+        else:
+            abilities = list(set(df[group]))
+            abilities.sort()
+            for ab in abilities:
+                sub_task_name_list = df[df['l2-category'] == ab]['category'].unique()
+                sub_task_acc = []
+                for sub_task_name in sub_task_name_list:
+                    sub_df = df[df['category'] == sub_task_name]
+                    sub_task_acc.append([np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']])
+
+                new_acc = []
+                for i in range(len(sub_task_acc[0])):
+                    new_acc.append(sum([_[i] for _ in sub_task_acc]) / len([_ for _ in sub_task_acc]))
+                ab_name = MMT_abbrs[ab] if ab in MMT_abbrs else ab
+                res[ab_name] = new_acc
+
+                sub_task_acc = []
+                for sub_task_name in sub_task_name_list:
+                    sub_df = df[df['category'] == sub_task_name]
+                    sub_task_acc.append([np.mean(sub_df['hit'])])
+                new_acc = []
+                for i in range(len(sub_task_acc[0])):
+                    new_acc.append(sum([_[i] for _ in sub_task_acc]) / len([_ for _ in sub_task_acc]))
+
+                res[ab_name].extend(new_acc)
+
+    res['split'].append('ALL')
+    return pd.DataFrame(res)
+
+
+def build_prompt(question, options, prediction):
+    tmpl = (
+        'You are an AI assistant who will help me to match '
+        'an answer with several options of a single-choice question. '
+        'You are provided with a question, several options, and an answer, '
+        'and you need to find which option is most similar to the answer. '
+        'If the meaning of all options are significantly different from the answer, output Z. '
+        'Your should output a single uppercase character in A, B, C, D (if they are valid options), and Z. \n'
+        'Example 1: \n'
+        'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n'
+        'Answer: a cute teddy bear\nYour output: A\n'
+        'Example 2: \n'
+        'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n'
+        'Answer: Spider\nYour output: Z\n'
+        'Example 3: \n'
+        'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: '
+    )
+    return tmpl.format(question, options, prediction)
+
+
+def build_prompt_blink(question, options, prediction):
+    tmpl = (
+        'You are an AI assistant who will help me to match an answer with several options of a single-choice question. '
+        'You are provided with a question, several options, and an answer, '
+        'and you need to find which option is most similar to the answer. '
+        "If the answer says things like refuse to answer, I'm sorry cannot help, etc., output Z."
+        'If the meaning of all options are significantly different from the answer, '
+        'or the answer does not select any option, output Z. '
+        'Your should output one of the choices, A, B, C, D (if they are valid options), or Z.\n'
+        'Example 1: \n'
+        'Question: Which point is closer to the camera?\nSelect from the following choices.\n'
+        'Options: A. Point A\nB. Point B\n(Z) Failed\n'
+        'Answer: Point B, where the child is sitting, is closer to the camera.\nYour output: (B)\n'
+        'Example 2: \n'
+        'Question: Which point is closer to the camera?\nSelect from the following choices.\n'
+        'Options: (A) Point A\n(B) Point B\n(Z) Failed\n'
+        "Answer: I'm sorry, but I can't assist with that request.\nYour output: (Z)\n"
+        'Example 3: \n'
+        'Question: Which point is corresponding to the reference point?\nSelect from the following choices.\n'
+        'Options: (A) Point A\n(B) Point B\n(Z) Failed\n'
+        'Answer:The reference point (REF) on the first image is at the tip of the pot, '
+        'which is the part used to Poke if the pots were used for that action. Looking at the second image, '
+        'we need to find the part of the object that would correspond to poking.\n'
+        "(A) Point A is at the tip of the spoon's handle, which is not used for poking.\n"
+        '(B) Point B is at the bottom of the spoon, which is not used for poking.\n'
+        '(C) Point C is on the side of the pspoonot, which is not used for poking.\n'
+        '(D) Point D is at the tip of the spoon, which is not used for poking.\n'
+        '\nTherefore, there is no correct answer in the choices\nYour output: (Z)\n'
+        'Example 4: \n'
+        'Question: {}?\nOptions: {}\n(Z) Failed\nAnswer: {}\nYour output: '
+    )
+    return tmpl.format(question, options, prediction)
+
+
+def build_prompt_cn(question, options, prediction):
+    tmpl = (
+        '你是一个帮助我匹配答案与单选题中多个选项的 AI 助手。'
+        '你会被提供：一个问题，多个选项，一个答案。你的任务是找到与答案意义最相近的选项。'
+        '如果所有选项的意义都与答案显著不同，则输出 Z。'
+        '你应该输出一个单个的大写字母，例如 A, B, C, D（如果它们是有效选项），或 Z。'
+        '例 1:'
+        '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 一只可爱的泰迪熊\n输出: A\n'
+        '例 2: \n'
+        '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 蜘蛛\n输出: Z\n'
+        '例 3: \n'
+        '问题: {}?\n选项: {}\n答案: {}\n输出: '
+    )
+    return tmpl.format(question, options, prediction)
+
+
+def build_choices(item):
+    ret = {}
+    for ch in string.ascii_uppercase:
+        if ch in item and (not pd.isna(item[ch])):
+            ret[ch] = item[ch]
+    return ret
+
+
+def prefetch_answer(item):
+    choices = build_choices(item)
+    return can_infer(item['prediction'], choices)
+
+
+def extract_answer_from_item(model, item, dataset_name=None):
+    logger = get_logger('Evaluation')
+    # It will return: (pred, raw, llm_time)
+    choices = build_choices(item)
+    option_str = build_option_str(choices)
+
+    if dataset_name == 'BLINK':
+        prompt = build_prompt_blink(item['question'], option_str, item['prediction'])
+    elif cn_string(item['question']):
+        prompt = build_prompt_cn(item['question'], option_str, item['prediction'])
+    else:
+        prompt = build_prompt(item['question'], option_str, item['prediction'])
+    retry = 3
+
+    ret = can_infer(item['prediction'], choices)
+    if ret:
+        return dict(opt=ret, log=item['prediction'])
+    if model is None:
+        return dict(opt='Z', log='Failed in Prefetch, no GPT-based answer matching under `exact_matching` policy.')
+
+    while retry:
+        ans = model.generate(prompt)
+        if 'Failed to obtain answer via API' in ans:
+            logger.warning('GPT API failed to answer. ')
+        else:
+            ret = can_infer(ans, choices)
+            if ret:
+                return dict(opt=ret, log=ans)
+            else:
+                logger.warning(f'Output includes 0 / > 1 letter among candidates {set(choices)} and Z: {ans}')
+        retry -= 1
+
+        if retry == 0:
+            options = list(choices) + ['Z'] if 'Z' not in choices else []
+            return dict(opt=rd.choice(options), log='Failed to predict, thus randomly generate one. ')
+
+
+# For Circular Evaluation
+def prefetch_circular_group(sub_data, verbose=False):
+    lt = len(sub_data)
+    GT, PRED = [], []
+    for i in range(lt):
+        item = sub_data.iloc[i]
+        GT.append(item['GT'])
+        PRED.append(prefetch_answer(item))
+        if PRED[-1] and (GT[-1] != PRED[-1]):
+            log = (
+                f'Failed in Prefetching Rolling {i}: Answer is {GT[-1]}, '
+                f"Prediction is {item['prediction']}, Pre-fetched is {PRED[-1]}. "
+            )
+            return dict(hit=0, log=log)
+    flag = True
+    for g, p in zip(GT, PRED):
+        if g != p:
+            flag = False
+    ret = (dict(hit=1, log='Succeed During Pre-fetching'), ) if flag else (None, )
+    ret = ret + (GT, PRED) if verbose else ret
+    return ret if len(ret) > 1 else ret[0]
+
+
+def eval_vanilla(model, item, dataset_name=None):
+    res = extract_answer_from_item(model, item, dataset_name=dataset_name)
+    opt, match_log = res['opt'], res['log']
+    if opt == item['GT']:
+        return dict(hit=1, log=f'Match Log: {match_log}. ')
+    else:
+        return dict(hit=0, log=f'Match Log: {match_log}. ')
+
+
+# For Circular Evaluation
+def eval_circular_group(model, sub_data, dataset_name=None):
+    res, GT, PRED = prefetch_circular_group(sub_data, verbose=True)
+    if res is not None:
+        return res
+
+    lt = len(sub_data)
+    log = ''
+    for i in range(lt):
+        if PRED[i]:
+            log += f'Rolling {i} Matched.\n'
+        else:
+            res = extract_answer_from_item(model, sub_data.iloc[i], dataset_name=dataset_name)
+            opt, match_log = res['opt'], res['log']
+            PRED[i] = opt
+            if PRED[i] != GT[i]:
+                log += (
+                    f"Failed in Rolling {i}: Answer is {GT[i]}; Prediction is {sub_data.iloc[i]['prediction']}; "
+                    f'Pre-fetched is {PRED[i]}; Match Log is {match_log}.\n'
+                )
+                return dict(hit=0, log=log)
+            else:
+                log += (
+                    f"Rolling {i}: Answer is {GT[i]}, Prediction is {sub_data.iloc[i]['prediction']}, "
+                    f'Pre-fetched is {PRED[i]}.\n'
+                )
+
+    return dict(hit=1, log=log)
+
+
+# data, meta are pd.DataFrame, result_file is a path
+def mcq_vanilla_eval(model, data, meta, nproc, result_file, dataset_name=None):
+    result = {}
+    if osp.exists(result_file):
+        result = load(result_file)
+    answer_map = {i: c for i, c in zip(meta['index'], meta['answer'])}
+
+    if 'MMMU' in dataset_name:
+        data = MMMU_preproc(data)
+        answer_map = {k: (v if v in list(string.ascii_uppercase) else 'A') for k, v in answer_map.items()}
+
+    data = data[data['index'].isin(answer_map)]
+    data['GT'] = [answer_map[idx] for idx in data['index']]
+    items = []
+
+    for i in range(len(data)):
+        # Dealing with the normal part
+        item = data.iloc[i]
+        if item['index'] not in result:
+            items.append(item)
+
+    tups = [dict(model=model, item=x, dataset_name=dataset_name) for x in items]
+    keys = [x['index'] for x in items]
+    if len(tups):
+        res = track_progress_rich(eval_vanilla, tups, nproc=nproc, chunksize=nproc, save=result_file, keys=keys)
+        result = load(result_file)
+        for k, v in zip(keys, res):
+            if k in result:
+                assert result[k]['hit'] == v['hit'] and result[k]['log'] == v['log']
+            else:
+                result[k] = v
+    data['hit'] = [result[i]['hit'] for i in data['index']]
+    data['log'] = [result[i]['log'] for i in data['index']]
+    if 'GT' in data:
+        data.pop('GT')
+    return data
+
+
+# data, meta are pd.DataFrame, result_file is a path
+def mcq_circular_eval(model, data, meta, nproc, result_file, dataset_name=None):
+    result = {}
+    if osp.exists(result_file):
+        result = load(result_file)
+    # Build Answer Map
+    answer_map = {i: c for i, c in zip(meta['index'], meta['answer'])}
+
+    for idx in list(meta['index']) + list(data['index']):
+        assert istype(idx, int)
+
+    # Only keep those lines in the meta data
+    data = data[data['index'].isin(answer_map)]
+    data['GT'] = [answer_map[idx] for idx in data['index']]
+    data_main = data[data['index'] < int(1e6)]
+
+    data_groups = []
+    for i in range(len(data_main)):
+        # Dealing with the normal part
+        idx = data_main.iloc[i]['index']
+        if idx not in result:
+            sub_data = data[data['index'] % int(1e6) == idx]
+            data_groups.append(sub_data)
+
+    if len(data_groups):
+        prefetched = [prefetch_circular_group(g, verbose=False) for g in data_groups]
+        remain = []
+        for dg, pf in zip(data_groups, prefetched):
+            if pf is not None:
+                result[dg.iloc[0]['index'] % 1e6] = pf
+            else:
+                remain.append(dg)
+        dump(result, result_file)
+
+        tups = [dict(model=model, sub_data=x, dataset_name=dataset_name) for x in remain]
+        keys = [x.iloc[0]['index'] % 1e6 for x in remain]
+
+        if len(tups) == 0:
+            pass
+        elif model is None:
+            logger = get_logger('Evaluation')
+            logger.warning('Exact Matching mode, will not do GPT-based answer matching. ')
+            for k in keys:
+                result[k] = dict(
+                    hit=0, log='Failed in Prefetch, no GPT-based answer matching under `exact_matching` policy.')
+        else:
+            res = track_progress_rich(
+                eval_circular_group,
+                tups,
+                nproc=nproc,
+                chunksize=nproc,
+                save=result_file,
+                keys=keys)
+            result = load(result_file)
+            for k, v in zip(keys, res):
+                if k in result:
+                    assert result[k]['hit'] == v['hit'] and result[k]['log'] == v['log']
+                else:
+                    result[k] = v
+
+    tmp_pth = f'/tmp/{timestr()}.xlsx'
+    dump(data_main, tmp_pth)
+    data_main = load(tmp_pth)
+    indices = data_main['index']
+    data_main['hit'] = [result[i]['hit'] for i in indices]
+    data_main['log'] = [result[i]['log'] for i in indices]
+    if 'GT' in data_main:
+        data_main.pop('GT')
+
+    return data_main

eval_mm/vlmevalkit/vlmeval/dataset/utils/mvbench.py

@@ -0,0 +1,450 @@
+from ...smp import *
+from PIL import Image, ImageOps
+import torchvision
+import random
+import numbers
+import math
+import torch
+
+
+def get_dimension_rating(data_path):
+    data = load(data_path)
+    result_board = {}
+    for idx, item in data.iterrows():
+        if item['task_type'] not in result_board:
+            result_board[item['task_type']] = [0, 0]
+        result_board[item['task_type']][1] += 1
+        if item['score']:
+            result_board[item['task_type']][0] += 1
+
+    correct = 0
+    total = 0
+    for key, value in result_board.items():
+        correct += value[0]
+        total += value[1]
+        result_board[key].append(f'{value[0] / value[1] * 100 :.2f}%')
+
+    result_board['overall'] = [correct, total, f'{correct / total * 100 :.2f}%']
+
+    return result_board
+
+
+def check_ans(pred, gt):
+    flag = False
+
+    pred_list = pred.lower().split(' ')
+    pred_option, _ = pred_list[0], ' '.join(pred_list[1:])
+    gt_list = gt.lower().split(' ')
+    gt_option, gt_content = gt_list[0], ' '.join(gt_list[1:])
+    if gt_content[-1] == '.':
+        gt_content = gt_content[:-1]
+
+    if pred_option.replace('.', '') in gt_option:
+        flag = True
+    elif gt_option in pred_option:
+        flag = True
+
+    return flag
+
+
+class GroupRandomCrop(object):
+    def __init__(self, size):
+        if isinstance(size, numbers.Number):
+            self.size = (int(size), int(size))
+        else:
+            self.size = size
+
+    def __call__(self, img_group):
+
+        w, h = img_group[0].size
+        th, tw = self.size
+
+        out_images = list()
+
+        x1 = random.randint(0, w - tw)
+        y1 = random.randint(0, h - th)
+
+        for img in img_group:
+            assert (img.size[0] == w and img.size[1] == h)
+            if w == tw and h == th:
+                out_images.append(img)
+            else:
+                out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
+
+        return out_images
+
+
+class MultiGroupRandomCrop(object):
+    def __init__(self, size, groups=1):
+        if isinstance(size, numbers.Number):
+            self.size = (int(size), int(size))
+        else:
+            self.size = size
+        self.groups = groups
+
+    def __call__(self, img_group):
+
+        w, h = img_group[0].size
+        th, tw = self.size
+
+        out_images = list()
+
+        for i in range(self.groups):
+            x1 = random.randint(0, w - tw)
+            y1 = random.randint(0, h - th)
+
+            for img in img_group:
+                assert (img.size[0] == w and img.size[1] == h)
+                if w == tw and h == th:
+                    out_images.append(img)
+                else:
+                    out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
+
+        return out_images
+
+
+class GroupCenterCrop(object):
+    def __init__(self, size):
+        self.worker = torchvision.transforms.CenterCrop(size)
+
+    def __call__(self, img_group):
+        return [self.worker(img) for img in img_group]
+
+
+class GroupRandomHorizontalFlip(object):
+    """Randomly horizontally flips the given PIL.Image with a probability of 0.5
+    """
+
+    def __init__(self, is_flow=False):
+        self.is_flow = is_flow
+
+    def __call__(self, img_group, is_flow=False):
+        v = random.random()
+        if v < 0.5:
+            ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
+            if self.is_flow:
+                for i in range(0, len(ret), 2):
+                    # invert flow pixel values when flipping
+                    ret[i] = ImageOps.invert(ret[i])
+            return ret
+        else:
+            return img_group
+
+
+class GroupNormalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, tensor):
+        rep_mean = self.mean * (tensor.size()[0] // len(self.mean))
+        rep_std = self.std * (tensor.size()[0] // len(self.std))
+
+        # TODO: make efficient
+        for t, m, s in zip(tensor, rep_mean, rep_std):
+            t.sub_(m).div_(s)
+
+        return tensor
+
+
+class GroupScale(object):
+    """ Rescales the input PIL.Image to the given 'size'.
+    'size' will be the size of the smaller edge.
+    For example, if height > width, then image will be
+    rescaled to (size * height / width, size)
+    size: size of the smaller edge
+    interpolation: Default: PIL.Image.BILINEAR
+    """
+
+    def __init__(self, size, interpolation=Image.BILINEAR):
+        self.worker = torchvision.transforms.Resize(size, interpolation)
+
+    def __call__(self, img_group):
+        return [self.worker(img) for img in img_group]
+
+
+class GroupOverSample(object):
+    def __init__(self, crop_size, scale_size=None, flip=True):
+        self.crop_size = crop_size if not isinstance(
+            crop_size, int) else (crop_size, crop_size)
+
+        if scale_size is not None:
+            self.scale_worker = GroupScale(scale_size)
+        else:
+            self.scale_worker = None
+        self.flip = flip
+
+    def __call__(self, img_group):
+
+        if self.scale_worker is not None:
+            img_group = self.scale_worker(img_group)
+
+        image_w, image_h = img_group[0].size
+        crop_w, crop_h = self.crop_size
+
+        offsets = GroupMultiScaleCrop.fill_fix_offset(
+            False, image_w, image_h, crop_w, crop_h)
+        oversample_group = list()
+        for o_w, o_h in offsets:
+            normal_group = list()
+            flip_group = list()
+            for i, img in enumerate(img_group):
+                crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
+                normal_group.append(crop)
+                flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
+
+                if img.mode == 'L' and i % 2 == 0:
+                    flip_group.append(ImageOps.invert(flip_crop))
+                else:
+                    flip_group.append(flip_crop)
+
+            oversample_group.extend(normal_group)
+            if self.flip:
+                oversample_group.extend(flip_group)
+        return oversample_group
+
+
+class GroupFullResSample(object):
+    def __init__(self, crop_size, scale_size=None, flip=True):
+        self.crop_size = crop_size if not isinstance(
+            crop_size, int) else (crop_size, crop_size)
+
+        if scale_size is not None:
+            self.scale_worker = GroupScale(scale_size)
+        else:
+            self.scale_worker = None
+        self.flip = flip
+
+    def __call__(self, img_group):
+
+        if self.scale_worker is not None:
+            img_group = self.scale_worker(img_group)
+
+        image_w, image_h = img_group[0].size
+        crop_w, crop_h = self.crop_size
+
+        w_step = (image_w - crop_w) // 4
+        h_step = (image_h - crop_h) // 4
+
+        offsets = list()
+        offsets.append((0 * w_step, 2 * h_step))  # left
+        offsets.append((4 * w_step, 2 * h_step))  # right
+        offsets.append((2 * w_step, 2 * h_step))  # center
+
+        oversample_group = list()
+        for o_w, o_h in offsets:
+            normal_group = list()
+            flip_group = list()
+            for i, img in enumerate(img_group):
+                crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
+                normal_group.append(crop)
+                if self.flip:
+                    flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
+
+                    if img.mode == 'L' and i % 2 == 0:
+                        flip_group.append(ImageOps.invert(flip_crop))
+                    else:
+                        flip_group.append(flip_crop)
+
+            oversample_group.extend(normal_group)
+            oversample_group.extend(flip_group)
+        return oversample_group
+
+
+class GroupMultiScaleCrop(object):
+
+    def __init__(self, input_size, scales=None, max_distort=1,
+                 fix_crop=True, more_fix_crop=True):
+        self.scales = scales if scales is not None else [1, .875, .75, .66]
+        self.max_distort = max_distort
+        self.fix_crop = fix_crop
+        self.more_fix_crop = more_fix_crop
+        self.input_size = input_size if not isinstance(input_size, int) else [
+            input_size, input_size]
+        self.interpolation = Image.BILINEAR
+
+    def __call__(self, img_group):
+
+        im_size = img_group[0].size
+
+        crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size)
+        crop_img_group = [
+            img.crop(
+                (offset_w,
+                 offset_h,
+                 offset_w + crop_w,
+                 offset_h + crop_h)) for img in img_group]
+        ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation)
+                         for img in crop_img_group]
+        return ret_img_group
+
+    def _sample_crop_size(self, im_size):
+        image_w, image_h = im_size[0], im_size[1]
+
+        # find a crop size
+        base_size = min(image_w, image_h)
+        crop_sizes = [int(base_size * x) for x in self.scales]
+        crop_h = [
+            self.input_size[1] if abs(
+                x - self.input_size[1]) < 3 else x for x in crop_sizes]
+        crop_w = [
+            self.input_size[0] if abs(
+                x - self.input_size[0]) < 3 else x for x in crop_sizes]
+
+        pairs = []
+        for i, h in enumerate(crop_h):
+            for j, w in enumerate(crop_w):
+                if abs(i - j) <= self.max_distort:
+                    pairs.append((w, h))
+
+        crop_pair = random.choice(pairs)
+        if not self.fix_crop:
+            w_offset = random.randint(0, image_w - crop_pair[0])
+            h_offset = random.randint(0, image_h - crop_pair[1])
+        else:
+            w_offset, h_offset = self._sample_fix_offset(
+                image_w, image_h, crop_pair[0], crop_pair[1])
+
+        return crop_pair[0], crop_pair[1], w_offset, h_offset
+
+    def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h):
+        offsets = self.fill_fix_offset(
+            self.more_fix_crop, image_w, image_h, crop_w, crop_h)
+        return random.choice(offsets)
+
+    @staticmethod
+    def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h):
+        w_step = (image_w - crop_w) // 4
+        h_step = (image_h - crop_h) // 4
+
+        ret = list()
+        ret.append((0, 0))  # upper left
+        ret.append((4 * w_step, 0))  # upper right
+        ret.append((0, 4 * h_step))  # lower left
+        ret.append((4 * w_step, 4 * h_step))  # lower right
+        ret.append((2 * w_step, 2 * h_step))  # center
+
+        if more_fix_crop:
+            ret.append((0, 2 * h_step))  # center left
+            ret.append((4 * w_step, 2 * h_step))  # center right
+            ret.append((2 * w_step, 4 * h_step))  # lower center
+            ret.append((2 * w_step, 0 * h_step))  # upper center
+
+            ret.append((1 * w_step, 1 * h_step))  # upper left quarter
+            ret.append((3 * w_step, 1 * h_step))  # upper right quarter
+            ret.append((1 * w_step, 3 * h_step))  # lower left quarter
+            ret.append((3 * w_step, 3 * h_step))  # lower righ quarter
+
+        return ret
+
+
+class GroupRandomSizedCrop(object):
+    """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size
+    and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio
+    This is popularly used to train the Inception networks
+    size: size of the smaller edge
+    interpolation: Default: PIL.Image.BILINEAR
+    """
+
+    def __init__(self, size, interpolation=Image.BILINEAR):
+        self.size = size
+        self.interpolation = interpolation
+
+    def __call__(self, img_group):
+        for attempt in range(10):
+            area = img_group[0].size[0] * img_group[0].size[1]
+            target_area = random.uniform(0.08, 1.0) * area
+            aspect_ratio = random.uniform(3. / 4, 4. / 3)
+
+            w = int(round(math.sqrt(target_area * aspect_ratio)))
+            h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if random.random() < 0.5:
+                w, h = h, w
+
+            if w <= img_group[0].size[0] and h <= img_group[0].size[1]:
+                x1 = random.randint(0, img_group[0].size[0] - w)
+                y1 = random.randint(0, img_group[0].size[1] - h)
+                found = True
+                break
+        else:
+            found = False
+            x1 = 0
+            y1 = 0
+
+        if found:
+            out_group = list()
+            for img in img_group:
+                img = img.crop((x1, y1, x1 + w, y1 + h))
+                assert (img.size == (w, h))
+                out_group.append(
+                    img.resize(
+                        (self.size, self.size), self.interpolation))
+            return out_group
+        else:
+            # Fallback
+            scale = GroupScale(self.size, interpolation=self.interpolation)
+            crop = GroupRandomCrop(self.size)
+            return crop(scale(img_group))
+
+
+class ConvertDataFormat(object):
+    def __init__(self, model_type):
+        self.model_type = model_type
+
+    def __call__(self, images):
+        if self.model_type == '2D':
+            return images
+        tc, h, w = images.size()
+        t = tc // 3
+        images = images.view(t, 3, h, w)
+        images = images.permute(1, 0, 2, 3)
+        return images
+
+
+class Stack(object):
+
+    def __init__(self, roll=False):
+        self.roll = roll
+
+    def __call__(self, img_group):
+        if img_group[0].mode == 'L':
+            return np.concatenate([np.expand_dims(x, 2)
+                                   for x in img_group], axis=2)
+        elif img_group[0].mode == 'RGB':
+            if self.roll:
+                return np.concatenate([np.array(x)[:, :, ::-1]
+                                       for x in img_group], axis=2)
+            else:
+                # print(np.concatenate(img_group, axis=2).shape)
+                # print(img_group[0].shape)
+                return np.concatenate(img_group, axis=2)
+
+
+class ToTorchFormatTensor(object):
+    """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
+    to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
+
+    def __init__(self, div=True):
+        self.div = div
+
+    def __call__(self, pic):
+        if isinstance(pic, np.ndarray):
+            # handle numpy array
+            img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
+        else:
+            # handle PIL Image
+            img = torch.ByteTensor(
+                torch.ByteStorage.from_buffer(
+                    pic.tobytes()))
+            img = img.view(pic.size[1], pic.size[0], len(pic.mode))
+            # put it from HWC to CHW format
+            # yikes, this transpose takes 80% of the loading time/CPU
+            img = img.transpose(0, 1).transpose(0, 2).contiguous()
+        return img.float().div(255) if self.div else img.float()
+
+
+class IdentityTransform(object):
+
+    def __call__(self, data):
+        return data

eval_mm/vlmevalkit/vlmeval/dataset/utils/ocrbench.py

@@ -1,4 +1,4 @@
-from vlmeval.smp import *
+from ...smp import *
 
 
 def OCRBench_eval(eval_file):

eval_mm/vlmevalkit/vlmeval/dataset/utils/videomme.py

@@ -0,0 +1,140 @@
+from ...smp import *
+import numpy as np
+import re
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+DURATIONS = [
+    'short',
+    'medium',
+    'long',
+]
+
+DOMAINS = [
+    'Knowledge',
+    'Film & Television',
+    'Sports Competition',
+    'Artistic Performance',
+    'Life Record',
+    'Multilingual'
+]
+
+SUB_CATEGORIES = [
+    'Humanity & History',
+    'Literature & Art',
+    'Biology & Medicine',
+    'Finance & Commerce',
+    'Astronomy',
+    'Geography',
+    'Law',
+    'Life Tip',
+    'Technology',
+    'Animation',
+    'Movie & TV Show',
+    'Documentary',
+    'News Report',
+    'Esports',
+    'Basketball',
+    'Football',
+    'Athletics',
+    'Other Sports',
+    'Stage Play',
+    'Magic Show',
+    'Variety Show',
+    'Acrobatics',
+    'Handicraft',
+    'Food',
+    'Fashion',
+    'Daily Life',
+    'Travel',
+    'Pet & Animal',
+    'Exercise',
+    'Multilingual'
+]
+
+TASK_CATEGORIES = [
+    'Temporal Perception',
+    'Spatial Perception',
+    'Attribute Perception',
+    'Action Recognition',
+    'Object Recognition',
+    'OCR Problems',
+    'Counting Problem',
+    'Temporal Reasoning',
+    'Spatial Reasoning',
+    'Action Reasoning',
+    'Object Reasoning',
+    'Information Synopsis',
+]
+
+
+def get_dimension_rating(data_path):
+    data = load(data_path)
+
+    duration_rating = {k: {} for k in DURATIONS}
+    for duration in DURATIONS + ['overall']:
+        duration_rating[duration] = {
+            'overall': '',
+            'domain': {k: [] for k in DOMAINS},
+            'sub_category': {k: [] for k in SUB_CATEGORIES},
+            'task_type': {k: [] for k in TASK_CATEGORIES}
+        }
+
+    for i in range(len(data)):
+
+        domain = data.iloc[i]['domain']
+        sub_ctg = data.iloc[i]['sub_category']
+        task_ctg = data.iloc[i]['task_type']
+
+        duration = data.iloc[i]['duration']
+        duration_rating[duration]['domain'][domain].append(data.iloc[i]['score'])
+        duration_rating[duration]['sub_category'][sub_ctg].append(data.iloc[i]['score'])
+        duration_rating[duration]['task_type'][task_ctg].append(data.iloc[i]['score'])
+
+        duration_rating['overall']['domain'][domain].append(data.iloc[i]['score'])
+        duration_rating['overall']['sub_category'][sub_ctg].append(data.iloc[i]['score'])
+        duration_rating['overall']['task_type'][task_ctg].append(data.iloc[i]['score'])
+
+    for duration in DURATIONS + ['overall']:
+
+        overall_res_dur = f'{np.mean([x for x in sum(duration_rating[duration]["domain"].values(), []) if x >= 0]):.2f}'
+        duration_rating[duration]['overall'] = overall_res_dur
+
+        for domain in DOMAINS:
+            domain_res_dur = f'{np.mean([x for x in duration_rating[duration]["domain"][domain] if x >= 0]):.2f}'
+            duration_rating[duration]['domain'][domain] = domain_res_dur
+
+        for sub_ctg in SUB_CATEGORIES:
+            sub_res_dur = f'{np.mean([x for x in duration_rating[duration]["sub_category"][sub_ctg] if x >= 0]):.2f}'
+            duration_rating[duration]['sub_category'][sub_ctg] = sub_res_dur
+
+        for task_ctg in TASK_CATEGORIES:
+            task_res_dur = f'{np.mean([x for x in duration_rating[duration]["task_type"][task_ctg] if x >= 0]):.2f}'
+            duration_rating[duration]['task_type'][task_ctg] = task_res_dur
+
+    return duration_rating
+
+
+def extract_characters_regex(s):
+    s = s.strip()
+    answer_prefixes = [
+        'The best answer is',
+        'The correct answer is',
+        'The answer is',
+        'The answer',
+        'The best option is'
+        'The correct option is',
+        'Best answer:'
+        'Best option:',
+        'Answer:',
+        'Option:',
+    ]
+    for answer_prefix in answer_prefixes:
+        s = s.replace(answer_prefix, '')
+
+    if len(s.split()) > 10 and not re.search('[ABCD]', s):
+        return ''
+    matches = re.search(r'[ABCD]', s)
+    if matches is None:
+        return ''
+    return matches[0]

eval_mm/vlmevalkit/vlmeval/dataset/utils/vqa_eval.py

@@ -2,10 +2,8 @@
 # Partly adopted from https://github.com/GT-Vision-Lab/VQA
 # Copyright (c) 2014, Aishwarya Agrawal
 
-import re
+from ...smp import *
-from vlmeval.smp import *
 from typing import Optional
-from functools import partial
 
 
 def _process_digit_article(inText):
@@ -163,7 +161,6 @@ def hit_calculate(result, dataset_name, anls_threshold=0.5):
     if listinstr(['TextVQA'], dataset_name):
         return [np.mean(x['match']) for x in result]
     elif listinstr(['DocVQA', 'InfoVQA'], dataset_name):
-        # return [1 - np.min(x['match']) >= anls_threshold for x in result]
         return [0.0 if 1 - np.min(x['match']) < anls_threshold else 1 - np.min(x['match']) for x in result]
     elif listinstr(['ChartQA', 'OCRVQA'], dataset_name):
         return [np.max(x['match']) for x in result]
@@ -286,55 +283,3 @@ def process_line(line, method='vqa_score'):
         ret['match'] = [x == ret['pred'] for x in ret['gt']]
 
     return ret
-
-
-def VQAEval(eval_file, dataset_name, **kwargs):
-    logger = get_logger('Evaluation')
-    data = load(eval_file)
-    assert 'answer' in data and 'prediction' in data
-    data['prediction'] = [str(x) for x in data['prediction']]
-    data['answer'] = [str(x) for x in data['answer']]
-    lt = len(data)
-    pool = mp.Pool(16)
-    lines = [data.iloc[i] for i in range(lt)]
-    if listinstr(['TextVQA'], dataset_name):
-        res = pool.map(partial(process_line, method='vqa_score'), lines)
-    elif listinstr(['ChartQA'], dataset_name):
-        res = pool.map(partial(process_line, method='relaxed_accuracy'), lines)
-    elif listinstr(['OCRVQA'], dataset_name):
-        res = pool.map(partial(process_line, method='accuracy'), lines)
-    elif listinstr(['DocVQA', 'InfoVQA'], dataset_name):
-        res = pool.map(partial(process_line, method='anls'), lines)
-    else:  # default using vqa_score to calculate score
-        res = pool.map(process_line, lines)
-    # [np.mean(x['match']) >= full_score_weight for x in res]
-    hit = hit_calculate(res, dataset_name)
-    ret = dict()
-    if 'split' in data:
-        splits = set(data['split'])
-        for sp in splits:
-            sub = [r for l, r in zip(lines, res) if l['split'] == sp]
-            # [np.mean(x['match']) >= full_score_weight for x in sub]
-            hit = hit_calculate(sub, dataset_name)
-            ret[sp] = np.mean(hit) * 100
-        sub = [r for l, r in zip(lines, res)]
-        hit = hit_calculate(sub, dataset_name)
-        ret['Overall'] = np.mean(hit) * 100
-    else:
-        ret['Overall'] = np.mean(hit) * 100
-        if 'category' in data:
-            cates = list(set(data['category']))
-            cates.sort()
-            for c in cates:
-                sub = [r for l, r in zip(lines, res) if l['category'] == c]
-                # [np.mean(x['match']) >= full_score_weight for x in sub]
-                hit = hit_calculate(sub, dataset_name)
-                ret[c] = np.mean(hit) * 100
-    ret = d2df(ret)
-    ret.round(2)
-
-    suffix = eval_file.split('.')[-1]
-    result_file = eval_file.replace(f'.{suffix}', '_acc.csv')
-    logger.info(f'VQA Eval Finished. Saved to {result_file}. ')
-    logger.info(ret)
-    dump(ret, result_file)

eval_mm/vlmevalkit/vlmeval/dataset/utils/yorn.py

@@ -1,8 +1,4 @@
-from vlmeval.evaluate.misc import build_judge
+from ...smp import *
-from vlmeval.smp import *
-from vlmeval.utils import track_progress_rich
-
-INTERNAL = os.environ.get('INTERNAL', 0)
 
 
 def MME_rating(data_file):
@@ -205,93 +201,3 @@ def YOrN_auxeval(model, line):
         if ans != 'Unknown':
             return ans
     return 'Unknown'
-
-
-def YOrN_eval(eval_file, dataset=None, **judge_kwargs):
-    logger = get_logger('Evaluation')
-    data = load(eval_file)
-    data['prediction'] = [str(x) for x in data['prediction']]
-    storage = eval_file.replace('.xlsx', '_auxmatch.xlsx')
-    tmp_file = eval_file.replace('.xlsx', '_tmp.pkl')
-    nproc = judge_kwargs.pop('nproc', 4)
-
-    if not osp.exists(storage):
-        ans_map = {k: YOrN_Extraction(v) for k, v in zip(data['index'], data['prediction'])}
-        if osp.exists(tmp_file):
-            tmp = load(tmp_file)
-            for k in tmp:
-                if ans_map[k] == 'Unknown' and tmp[k] != 'Unknown':
-                    ans_map[k] = tmp[k]
-
-        data['extracted'] = [ans_map[x] for x in data['index']]
-        unknown = data[data['extracted'] == 'Unknown']
-
-        if INTERNAL or gpt_key_set():
-            model = build_judge(**judge_kwargs)
-        else:
-            logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
-            model = None
-
-        if model is not None:
-            lt = len(unknown)
-            lines = [unknown.iloc[i] for i in range(lt)]
-            tups = [(model, line) for line in lines]
-            indices = list(unknown['index'])
-            if len(tups):
-                res = track_progress_rich(
-                    YOrN_auxeval, tups, nproc=nproc, chunksize=nproc, keys=indices, save=tmp_file)
-                for k, v in zip(indices, res):
-                    ans_map[k] = v
-
-        data['extracted'] = [ans_map[x] for x in data['index']]
-        dump(data, storage)
-    else:
-        logger.warning(f'GPT matching file {storage} already exists, will reuse it in YOrN_eval. ')
-
-    data = load(storage)
-    data['score'] = (data['answer'] == data['extracted'])
-    dump(data, storage)
-
-    if dataset is not None and listinstr(['MME'], dataset):
-        score = MME_rating(storage)
-    elif dataset is not None and listinstr(['Hallusion'], dataset):
-        score = Hallusion_rating(storage)
-    elif dataset is not None and listinstr(['POPE'], dataset):
-        score = POPE_rating(storage)
-    else:
-        score = default_rating(storage)
-
-    score_tgt = eval_file.replace('.xlsx', '_score.csv')
-    dump(score, score_tgt)
-
-    logger.info(f'YOrN_eval successfully finished evaluating {eval_file}, results saved in {score_tgt}')
-    logger.info('Score: ')
-    logger.info(score)
-    return score
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Inference LLM Answers. ')
-    parser.add_argument('data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument(
-        '--model',
-        type=str,
-        help='The LLM (GPT) used for inference. ',
-        default='chatgpt-0613',
-        choices=['chatgpt-0613'])
-    parser.add_argument('--nproc', type=int, default=4)
-    parser.add_argument('--dataset', type=str, default=None)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    load_env()
-    args = parse_args()
-    judge_kwargs = dict(model=args.model, nproc=args.nproc, verbose=args.verbose)
-    if 'OPENAI_API_KEY_JUDGE' in os.environ and os.environ['OPENAI_API_KEY_JUDGE']:
-        judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
-    if 'OPENAI_API_BASE_JUDGE' in os.environ and os.environ['OPENAI_API_BASE_JUDGE']:
-        judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
-    acc = YOrN_eval(eval_file=args.data, dataset=args.dataset, **judge_kwargs)

eval_mm/vlmevalkit/vlmeval/dataset/vcr.py

@@ -0,0 +1,332 @@
+import uuid
+from functools import partial
+from .image_base import ImageBaseDataset
+from ..smp import *
+
+rouge = None
+nlp_en = None
+nlp_zh = None
+nlp = None
+
+
+def initialize():
+    import evaluate
+    import spacy
+
+    global rouge, nlp_en, nlp_zh, nlp
+
+    try:
+        rouge = evaluate.load('rouge', experiment_id=str(uuid.uuid4()))
+    except:
+        warnings.warn('Please first `pip install rouge_score`.')
+
+    try:
+        nlp_en = spacy.load('en_core_web_sm')
+    except:
+        warnings.warn('Will automatically download en_core_web_sm via spacy.')
+        spacy.cli.download('en_core_web_sm')
+        nlp_en = spacy.load('en_core_web_sm')
+
+    try:
+        nlp_zh = spacy.load('zh_core_web_sm')
+    except:
+        warnings.warn('Will automatically download zh_core_web_sm via spacy.')
+        spacy.cli.download('zh_core_web_sm')
+        nlp_zh = spacy.load('zh_core_web_sm')
+
+    nlp = {'en': nlp_en, 'zh': nlp_zh}
+
+
+def rough_filter(answer_text):
+    if "I can't" in answer_text:
+        return False
+    elif 'I cannot' in answer_text:
+        return False
+    elif 'sorry' in answer_text.lower():
+        return False
+    if '无法' in answer_text:
+        return False
+    elif '抱歉' in answer_text:
+        return False
+    else:
+        return True
+
+
+def zero_template(crossed_text):
+    return {
+        'crossed_text': crossed_text,
+        'max_sim_val': 0,
+        'max_sim_string': '',
+        'precision': 0,
+        'recall': 0,
+        'f1': 0,
+        'jaccard': 0,
+        'rouge1': 0,
+        'exact_match': 0,
+    }
+
+
+def tokenize(text, language):
+    """
+    Tokenize the text and return the tokens.
+
+    Parameters:
+    text (str): The text to tokenize.
+    language (str): The language of the text.
+
+    Returns:
+    list: The list of tokens.
+    """
+    assert language in ['en', 'zh']
+    nlp_language = nlp[language]
+    processed_text = nlp_language(text)
+    return [token.text for token in processed_text]
+
+
+def find_best_match(needle, hay, language, rouge):
+    """
+    Finds the best matching n-gram in the haystack for the given needle.
+
+    Parameters:
+    needle (str): The string to find.
+    hay (str): The text to search within.
+
+    Returns:
+    tuple: The highest similarity value and the best matching string.
+    """
+    assert language in ['en', 'zh']
+    from nltk.util import ngrams
+    from difflib import SequenceMatcher as SM
+
+    tokens_hay = tokenize(hay, language)
+    tokens_needle = tokenize(needle, language)
+
+    splitter = '' if language == 'zh' else ' '
+    ngrams_ = ngrams(tokens_hay, len(tokens_needle))
+    max_sim_val = 0
+    max_sim_string = ''
+    max_sim_ngram = []
+    tokens_needle_set = set(tokens_needle)
+    ngrams_hasjoint = [
+        ngram
+        for ngram in ngrams_
+        if not set(ngram).isdisjoint(tokens_needle_set)
+    ]
+
+    for ngram in ngrams_hasjoint:
+        hay_ngram = splitter.join(ngram)
+        similarity = SM(None, hay_ngram, needle).ratio()
+        if similarity > max_sim_val:
+            max_sim_val = similarity
+            max_sim_string = hay_ngram
+            max_sim_ngram = ngram
+
+    # Evaluate
+    if len(max_sim_ngram) == 0:
+        return {
+            'crossed_text': needle,
+            'max_sim_val': 0,
+            'max_sim_string': '',
+            'precision': 0,
+            'recall': 0,
+            'f1': 0,
+            'jaccard': 0,
+            'rouge1': 0,
+            'exact_match': 0,
+        }
+    pred_set = set(max_sim_ngram)
+    ref_set = set(tokens_needle)
+    correct_tokens = pred_set.intersection(ref_set)
+    len_correct_tokens = len(correct_tokens)
+
+    precision = len_correct_tokens / len(pred_set)
+    recall = len_correct_tokens / len(ref_set)
+    if (precision + recall) == 0:
+        f1 = 0
+    else:
+        f1 = 2 * precision * recall / (precision + recall)
+    union = pred_set.union(ref_set)
+    jaccard = len_correct_tokens / len(union) if len(union) > 0 else 0
+    rouge_1 = rouge.compute(
+        predictions=[max_sim_string],
+        references=[needle],
+        tokenizer=partial(tokenize, language=language),
+        rouge_types=['rouge1'],
+    )['rouge1']
+    exact_match = float(list(max_sim_ngram) == list(tokens_needle))
+    out = {
+        'crossed_text': needle,
+        'max_sim_string': max_sim_string,
+        'max_sim_val': max_sim_val,
+        'precision': precision,
+        'recall': recall,
+        'f1': f1,
+        'jaccard': jaccard,
+        'rouge1': rouge_1,
+        'exact_match': exact_match,
+    }
+    return out
+
+
+def process_match_single_new(
+        image_id, prediction, answer, language, progress
+):
+    """
+    process the inference results for a single image and calculate the metrics
+
+    Parameters:
+    image_id (int): The image id (question id).
+    prediction (str): The prediction text.
+    answer (Union[str, List[str]]): The answer text, or a list of answer texts. The masked n-grams in the image.
+    language (str): The language of the text. Can be "en" or "zh".
+    rouge (rouge): The rouge metric object.
+    progress (multiprocessing.Queue): The progress queue.
+
+    Returns:
+    tuple: The image id (question_id, int) and the result per id (dict of dict of dict).
+    """
+    result_per_id = {image_id: {}}
+    if isinstance(answer, str):
+        answer = eval(answer)
+    assert isinstance(answer, list)
+    result = prediction.split('Assistant: ')[-1]
+    for i, crossed_text in enumerate(answer):
+        if rough_filter(result):
+            find_best_match_result = find_best_match(
+                crossed_text, result, language, rouge
+            )
+            if i == 0:
+                result_per_id[image_id] = {str(i): find_best_match_result}
+            else:
+                result_per_id[image_id][str(i)] = find_best_match_result
+        else:
+            if i == 0:
+                result_per_id[image_id] = {str(i): zero_template(crossed_text)}
+            else:
+                result_per_id[image_id][str(i)] = zero_template(crossed_text)
+    progress.put(1)
+    return image_id, result_per_id
+
+
+class VCRDataset(ImageBaseDataset):
+    TYPE = 'VQA'
+
+    URL_PREFIX = 'https://huggingface.co/datasets/vcr-org'
+
+    DATASET_URL = {
+        'VCR_EN_EASY_500': f'{URL_PREFIX}/VCR-wiki-en-easy-test-500/resolve/main/VCR-wiki-en-easy-test-500.tsv',
+        'VCR_EN_EASY_100': f'{URL_PREFIX}/VCR-wiki-en-easy-test-100/resolve/main/VCR-wiki-en-easy-test-100.tsv',
+        'VCR_EN_EASY_ALL': f'{URL_PREFIX}/VCR-wiki-en-easy-test/resolve/main/VCR-wiki-en-easy-test.tsv',
+        'VCR_EN_HARD_500': f'{URL_PREFIX}/VCR-wiki-en-hard-test-500/resolve/main/VCR-wiki-en-hard-test-500.tsv',
+        'VCR_EN_HARD_100': f'{URL_PREFIX}/VCR-wiki-en-hard-test-100/resolve/main/VCR-wiki-en-hard-test-100.tsv',
+        'VCR_EN_HARD_ALL': f'{URL_PREFIX}/VCR-wiki-en-hard-test/resolve/main/VCR-wiki-en-hard-test.tsv',
+        'VCR_ZH_EASY_500': f'{URL_PREFIX}/VCR-wiki-zh-easy-test-500/resolve/main/VCR-wiki-zh-easy-test-500.tsv',
+        'VCR_ZH_EASY_100': f'{URL_PREFIX}/VCR-wiki-zh-easy-test-100/resolve/main/VCR-wiki-zh-easy-test-100.tsv',
+        'VCR_ZH_EASY_ALL': f'{URL_PREFIX}/VCR-wiki-zh-easy-test/resolve/main/VCR-wiki-zh-easy-test.tsv',
+        'VCR_ZH_HARD_500': f'{URL_PREFIX}/VCR-wiki-zh-hard-test-500/resolve/main/VCR-wiki-zh-hard-test-500.tsv',
+        'VCR_ZH_HARD_100': f'{URL_PREFIX}/VCR-wiki-zh-hard-test-100/resolve/main/VCR-wiki-zh-hard-test-100.tsv',
+        'VCR_ZH_HARD_ALL': f'{URL_PREFIX}/VCR-wiki-zh-hard-test/resolve/main/VCR-wiki-zh-hard-test.tsv',
+    }
+
+    DATASET_MD5 = {
+        'VCR_EN_EASY_500': 'fd9258db52f8685dc710619a0ea0a261',
+        'VCR_EN_EASY_100': '9df5d7266683458621ecbe122beb72f0',
+        'VCR_EN_EASY_ALL': '8a9b96885f251d1c85f42f84073327f1',
+        'VCR_EN_HARD_500': '0a22a85080b6a1f52b1f95e302d43df4',
+        'VCR_EN_HARD_100': '1b20f5cbcbeae0b0bec77f7a36143958',
+        'VCR_EN_HARD_ALL': '2d8b8b1ee0eba0e0b618fd3aa7d9710e',
+        'VCR_ZH_EASY_500': 'beca5fd54176adf44cf94bd9b50cf048',
+        'VCR_ZH_EASY_100': '4a86a5678a79844d6d22ab0629c51cd5',
+        'VCR_ZH_EASY_ALL': '5050fe7f0027ad2068fd4c7f220edaea',
+        'VCR_ZH_HARD_500': '617e3360f75c54455625cb0a8da5c1e7',
+        'VCR_ZH_HARD_100': 'b0e38c85f5d5e63894a3b881c372a62b',
+        'VCR_ZH_HARD_ALL': '54bbfef448206518b03127ef8b61404c',
+    }
+
+    def __init__(self, dataset='VCR_EN_EASY_500', skip_noimg=True):
+        super().__init__(dataset, skip_noimg)
+
+        initialize()
+        self.language = 'en' if 'EN' in dataset else 'zh'
+        self.difficulty = 'easy' if 'EASY' in dataset else 'hard'
+
+    # def build_prompt(self, line):
+    #     msgs = super().build_prompt(line)
+    #     assert msgs[-1]['type'] == 'text'
+    #     if self.language == 'zh':
+    #         msgs[-1]['value'] += '图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。'
+    #     else:
+    #         msgs[-1]['value'] += ('What is the covered texts in the image? '
+    #                               'Please restore the covered texts without outputting the explanations.')
+    #     return msgs
+
+    def evaluate(self, eval_file, **judge_kwargs):
+        import multiprocessing
+
+        vcr_score_list = {'Exact_Match': [], 'Jaccard': []}
+        vcr_score = {'Exact_Match': 0, 'Jaccard': 0}
+        logger = get_logger('Evaluation')
+        data = load(eval_file)
+
+        lt = len(data)
+        lines = [data.iloc[i] for i in range(lt)]
+
+        pool = multiprocessing.Pool()
+        manager = multiprocessing.Manager()
+        progress_queue = manager.Queue()
+        results = []
+
+        overall_results = {str(image_id): {} for image_id in range(len(lines))}
+
+        for instance_id, instance in enumerate(lines):
+            results.append(
+                pool.apply_async(
+                    process_match_single_new,
+                    args=(
+                        str(instance_id),
+                        instance['prediction'],
+                        instance['answer'],
+                        self.language,
+                        progress_queue,
+                    ),
+                )
+            )
+        pool.close()
+
+        # Display progress bar
+        for _ in tqdm(range(len(results))):
+            progress_queue.get()
+
+        pool.join()
+
+        # Merging results into overall_result
+        for result in results:
+            image_id, result_per_id = result.get()
+            overall_results[str(image_id)].update(result_per_id[image_id])
+            for blank_id_str in result_per_id[image_id].keys():
+                vcr_score_list['Exact_Match'].append(
+                    result_per_id[image_id][blank_id_str]['exact_match']
+                )
+                vcr_score_list['Jaccard'].append(
+                    result_per_id[image_id][blank_id_str]['jaccard']
+                )
+            vcr_score['Exact_Match'] = np.mean(vcr_score_list['Exact_Match'])
+            vcr_score['Jaccard'] = np.mean(vcr_score_list['Jaccard'])
+        results_out = {
+            k: v for i in range(len(results)) for k, v in results[i].get()[1].items()
+        }
+        results_with_metrics = {
+            'Exact_Match': vcr_score['Exact_Match'],
+            'Jaccard': vcr_score['Jaccard'],
+            'Predictions': results_out,
+        }
+        score_pth = eval_file.replace(
+            '.xlsx', f'{self.language}_{self.difficulty}_score.json'
+        )
+        dump(results_with_metrics, score_pth)
+        logger.info(
+            f'VCR successfully finished evaluating {eval_file}, results saved in {score_pth}'
+        )
+        logger.info('Score: ')
+        for key, value in vcr_score.items():
+            logger.info('{}:{}'.format(key, value))

eval_mm/vlmevalkit/vlmeval/dataset/video_base.py

@@ -0,0 +1,87 @@
+from abc import abstractmethod
+from ..smp import *
+
+
+class VideoBaseDataset:
+
+    MODALITY = 'VIDEO'
+
+    def __init__(self,
+                 dataset='MMBench-Video',
+                 pack=False):
+        try:
+            import decord
+        except:
+            warnings.warn('Please install decord via `pip install decord`.')
+
+        self.dataset_name = dataset
+        ret = self.prepare_dataset(dataset)
+        assert ret is not None
+        lmu_root = LMUDataRoot()
+        self.frame_root = osp.join(lmu_root, 'images', dataset)
+        os.makedirs(self.frame_root, exist_ok=True)
+        self.frame_tmpl = 'frame-{}-of-{}.jpg'
+
+        self.data_root = ret['root']
+        self.data_file = ret['data_file']
+        self.data = load(self.data_file)
+
+        assert 'question' in self.data and 'video' in self.data
+        videos = list(set(self.data['video']))
+        videos.sort()
+        self.videos = videos
+        self.pack = pack
+
+    def __len__(self):
+        return len(self.videos) if self.pack else len(self.data)
+
+    def __getitem__(self, idx):
+        if self.pack:
+            assert idx < len(self.videos)
+            sub_data = self.data[self.data['video'] == self.videos[idx]]
+            return sub_data
+        else:
+            assert idx < len(self.data)
+            return dict(self.data.iloc[idx])
+
+    def frame_paths(self, video, num_frames=8):
+        frame_root = osp.join(self.frame_root, video)
+        os.makedirs(frame_root, exist_ok=True)
+        return [osp.join(frame_root, self.frame_tmpl.format(i, num_frames)) for i in range(1, num_frames + 1)]
+
+    def save_video_frames(self, video, num_frames=8):
+        frame_paths = self.frame_paths(video, num_frames)
+        flag = np.all([osp.exists(p) for p in frame_paths])
+        if flag:
+            return frame_paths
+        vid_path = osp.join(self.data_root, video + '.mp4')
+        vid = decord.VideoReader(vid_path)
+        step_size = len(vid) / (num_frames + 1)
+        indices = [int(i * step_size) for i in range(1, num_frames + 1)]
+        images = [vid[i].numpy() for i in indices]
+        images = [Image.fromarray(arr) for arr in images]
+        for im, pth in zip(images, frame_paths):
+            if not osp.exists(pth):
+                im.save(pth)
+        return frame_paths
+
+    # Return a list of dataset names that are supported by this class, can override
+    @classmethod
+    def supported_datasets(cls):
+        return ['MMBench-Video', 'Video-MME', 'MVBench']
+
+    # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe
+    @abstractmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        pass
+
+    @abstractmethod
+    def build_prompt(self, idx, num_frames=8):
+        pass
+
+    @abstractmethod
+    def prepare_dataset(self, dataset):
+        # The prepare_dataset function should return a dictionary containing:
+        # `root` (directory that containing video files)
+        # `data_file` (the TSV dataset file)
+        pass

eval_mm/vlmevalkit/vlmeval/dataset/videomme.py

@@ -0,0 +1,250 @@
+from huggingface_hub import snapshot_download
+from ..smp import *
+from .video_base import VideoBaseDataset
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def unwrap_hf_pkl(pth, suffix='.mp4'):
+    base_dir = os.path.join(pth, 'video_pkl/')
+    target_dir = os.path.join(pth, 'video/')
+    pickle_files = [os.path.join(base_dir, file) for file in os.listdir(base_dir)]
+    pickle_files.sort()
+
+    if not os.path.exists(target_dir):
+        os.makedirs(target_dir, exist_ok=True)
+        for pickle_file in pickle_files:
+            with open(pickle_file, 'rb') as file:
+                video_data = pickle.load(file)
+            # For each video file in the pickle file, write its contents to a new mp4 file
+            for video_name, video_content in video_data.items():
+                output_path = os.path.join(target_dir, f'{video_name}{suffix}')
+                with open(output_path, 'wb') as output_file:
+                    output_file.write(video_content)
+        print('The video file has been restored and stored from the pickle file.')
+    else:
+        print('The video file already exists.')
+
+
+class VideoMME(VideoBaseDataset):
+
+    MD5 = '2f16cd40b1c125b67e661e59da2f6cd0'
+    SYS = ''
+
+    FRAMES_TMPL_NOSUB = """
+These are the frames of a video. \
+Select the best answer to the following multiple-choice question based on the video. \
+Respond with only the letter (A, B, C, or D) of the correct option.
+"""
+
+    FRAMES_TMPL_SUB = """
+These are the frames of a video. \
+This video's subtitles are listed below:
+{}
+Select the best answer to the following multiple-choice question based on the video. \
+Respond with only the letter (A, B, C, or D) of the correct option.
+"""
+
+    TYPE = 'MCQ'
+
+    def __init__(self, dataset='Video-MME', use_subtitle=False):
+        super().__init__(dataset=dataset)
+        self.use_subtitle = use_subtitle
+
+    @classmethod
+    def supported_datasets(cls):
+        return ['Video-MME']
+
+    def prepare_dataset(self, dataset_name='Video-MME', repo_id='lmms-lab/Video-MME'):
+
+        def check_integrity(pth):
+            data_file = osp.join(pth, f'{dataset_name}.tsv')
+
+            if not os.path.exists(data_file):
+                return False
+
+            if md5(data_file) != self.MD5:
+                return False
+            data = load(data_file)
+            for video_pth in data['video_path']:
+                if not osp.exists(osp.join(pth, video_pth)):
+                    return False
+            return True
+
+        cache_path = get_cache_path(repo_id)
+        if cache_path is not None and check_integrity(cache_path):
+            dataset_path = cache_path
+        else:
+
+            def unzip_hf_zip(pth):
+                import zipfile
+                base_dir = pth
+                target_dir = os.path.join(pth, 'video/')
+                zip_files = [
+                    os.path.join(base_dir, file) for file in os.listdir(base_dir)
+                    if file.endswith('.zip') and file.startswith('video')
+                ]
+                zip_files.sort()
+
+                if not os.path.exists(target_dir):
+                    os.makedirs(target_dir, exist_ok=True)
+                    for zip_file in zip_files:
+                        with zipfile.ZipFile(zip_file, 'r') as zip_ref:
+                            for member in zip_ref.namelist():
+                                # Check if the member is a file (not a directory)
+                                if not member.endswith('/'):
+                                    # Extract the file to the specified directory
+                                    source = zip_ref.open(member)
+                                    target = open(os.path.join(target_dir, os.path.basename(member)), 'wb')
+                                    with source, target:
+                                        target.write(source.read())
+                    print('The video file has been restored and stored from the zip file.')
+                else:
+                    print('The video file already exists.')
+
+                subtitle_zip_file = os.path.join(base_dir, 'subtitle.zip')
+                subtitle_target_dir = os.path.join(base_dir, 'subtitle')
+
+                if not os.path.exists(subtitle_target_dir):
+                    os.makedirs(subtitle_target_dir, exist_ok=True)
+                    with zipfile.ZipFile(subtitle_zip_file, 'r') as zip_ref:
+                        for member in zip_ref.namelist():
+                            # Check if the member is a file (not a directory)
+                            if not member.endswith('/'):
+                                # Extract the file to the specified directory
+                                source = zip_ref.open(member)
+                                target = open(os.path.join(subtitle_target_dir, os.path.basename(member)), 'wb')
+                                with source, target:
+                                    target.write(source.read())
+                    print('The subtitle file has been restored and stored from the zip file.')
+                else:
+                    print('The subtitle file already exists.')
+
+            def generate_tsv(pth):
+
+                data_file = osp.join(pth, f'{dataset_name}.tsv')
+                if os.path.exists(data_file) and md5(data_file) == self.MD5:
+                    return
+
+                data_file = pd.read_parquet(os.path.join(pth, 'videomme/test-00000-of-00001.parquet'))
+                data_file = data_file.assign(index=range(len(data_file)))
+                data_file['video'] = data_file['videoID']
+                data_file['video_path'] = data_file['videoID'].apply(lambda x: f'./video/{x}.mp4')
+                data_file['subtitle_path'] = data_file['videoID'].apply(lambda x: f'./subtitle/{x}.srt')
+                data_file['question'] += '\n' + data_file['options'].apply(lambda x: '\n'.join(x))
+
+                data_file = data_file[['index', 'video', 'video_path', 'duration', 'domain',
+                                       'sub_category', 'task_type', 'subtitle_path', 'question', 'answer']]
+
+                data_file.to_csv(osp.join(pth, f'{dataset_name}.tsv'), sep='\t', index=False)
+
+            dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset')
+            unzip_hf_zip(dataset_path)
+            generate_tsv(dataset_path)
+
+        data_file = osp.join(dataset_path, f'{dataset_name}.tsv')
+
+        return dict(data_file=data_file, root=dataset_path)
+
+    def save_video_frames(self, video, num_frames=8):
+
+        vid_path = osp.join(self.data_root, 'video', video + '.mp4')
+        vid = decord.VideoReader(vid_path)
+        step_size = len(vid) / (num_frames + 1)
+        indices = [int(i * step_size) for i in range(1, num_frames + 1)]
+
+        video_info = {
+            'fps': vid.get_avg_fps(),
+            'n_frames': len(vid),
+        }
+
+        frame_paths = self.frame_paths(video, num_frames)
+        flag = np.all([osp.exists(p) for p in frame_paths])
+
+        if not flag:
+            images = [vid[i].numpy() for i in indices]
+            images = [Image.fromarray(arr) for arr in images]
+            for im, pth in zip(images, frame_paths):
+                if not osp.exists(pth):
+                    im.save(pth)
+
+        return frame_paths, indices, video_info
+
+    def build_prompt(self, line, num_frames, video_llm):
+        if isinstance(line, int):
+            assert line < len(self)
+            line = self.data.iloc[line]
+
+        frames, indices, video_info = self.save_video_frames(line['video'], num_frames)
+
+        if self.use_subtitle and os.path.exists(osp.join(self.data_root, line['subtitle_path'])):
+            import pysubs2
+            subs = pysubs2.load(osp.join(self.data_root, line['subtitle_path']), encoding='utf-8')
+            subtitles = []
+
+            for seleced_frame_id in indices:
+                sub_text = ''
+                cur_time = pysubs2.make_time(fps=video_info['fps'], frames=seleced_frame_id)
+                for sub in subs:
+                    if sub.start < cur_time and sub.end > cur_time:
+                        sub_text = sub.text.replace('\\N', ' ')
+                        break
+                if sub_text.strip():
+                    subtitles.append(sub_text)
+            subtitles = '\n'.join(subtitles)
+        else:
+            subtitles = ''
+
+        message = [dict(type='text', value=self.SYS)]
+        if video_llm:
+            message.append(dict(type='video', value=osp.join(self.data_root, 'video', line['video'] + '.mp4')))
+        else:
+            for im in frames:
+                message.append(dict(type='image', value=im))
+
+        text_prompt = self.FRAMES_TMPL_NOSUB if not self.use_subtitle else self.FRAMES_TMPL_SUB.format(subtitles)
+        message.append(dict(type='text', value=text_prompt))
+        prompt = 'Question: {}\nAnswer: '.format(line['question'])
+        message.append(dict(type='text', value=prompt))
+        return message
+
+    # It returns a dictionary
+    @classmethod
+    def evaluate(self, eval_file, **judge_kwargs):
+        from .utils.videomme import get_dimension_rating, extract_characters_regex
+
+        assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file'
+
+        tmp_file = eval_file.replace('.xlsx', '_tmp.pkl')
+        tgt_file = eval_file.replace('.xlsx', '_rating.json')
+        score_file = eval_file.replace('.xlsx', '_score.xlsx')
+
+        if not osp.exists(score_file):
+            res = {} if not osp.exists(tmp_file) else load(tmp_file)
+            res = {k: v for k, v in res.items() if FAIL_MSG not in v}
+
+            data = load(eval_file)
+            data_un = data[~pd.isna(data['prediction'])]
+
+            for idx in data['index']:
+                ans = data.loc[data['index'] == idx, 'answer'].values[0]
+                pred = data.loc[data['index'] == idx, 'prediction'].values[0]
+
+                if extract_characters_regex(pred) == '':
+                    data.loc[idx, 'score'] = -1
+                else:
+                    data.loc[idx, 'score'] = int(extract_characters_regex(pred) == ans)
+
+            rejected = [x for x in data['score'] if x == -1]
+
+            print(
+                f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, '
+                f'failed to obtain the score for another {len(rejected)} questions. '
+                f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.'
+            )
+
+            dump(data, score_file)
+
+        rating = get_dimension_rating(score_file)
+        dump(rating, tgt_file)
+        return rating

eval_mm/vlmevalkit/vlmeval/evaluate/__init__.py

@@ -1,9 +0,0 @@
-from .yes_or_no import default_rating, MME_rating, YOrN_eval
-from .mmvet_eval import MMVet_eval
-from .multiple_choice import multiple_choice_eval
-from .coco_eval import COCO_eval
-from .vqa_eval import VQAEval
-from .mathvista_eval import MathVista_eval
-from .llavabench import LLaVABench_eval
-from .misc import build_judge
-from .OCRBench import OCRBench_eval

eval_mm/vlmevalkit/vlmeval/evaluate/coco_eval.py

@@ -1,74 +0,0 @@
-from vlmeval.smp import *
-from pycocoevalcap.bleu.bleu import Bleu
-from pycocoevalcap.rouge.rouge import Rouge
-from pycocoevalcap.cider.cider import Cider
-
-
-class COCO_Caption_Scorer():
-    def __init__(self, ref, gt):
-        self.ref = ref
-        self.gt = gt
-        print('setting up scorers...')
-        self.scorers = [
-            (Bleu(4), ['Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4']),
-            # (Meteor(), "METEOR"), # need java version 11.0.16+
-            (Rouge(), 'ROUGE_L'),
-            (Cider(), 'CIDEr'),
-            # (Spice(), "SPICE"), # need java version 11.0.16+
-        ]
-
-    def compute_scores(self):
-        total_scores = {}
-        for scorer, method in self.scorers:
-            print('computing %s score...' % (scorer.method()))
-            score, scores = scorer.compute_score(self.gt, self.ref)
-            if type(method) == list:
-                for sc, scs, m in zip(score, scores, method):
-                    print('%s: %0.3f' % (m, sc * 100))
-                total_scores['Bleu'] = [x * 100 for x in score]
-            else:
-                print('%s: %0.3f' % (method, score * 100))
-                total_scores[method] = score * 100
-
-        print('*****DONE*****')
-        for key, value in total_scores.items():
-            print('{}:{}'.format(key, value))
-        return total_scores
-
-
-def COCO_eval(eval_file, nproc=4, verbose=False):
-    logger = get_logger('Evaluation')
-
-    data = load(eval_file)
-
-    lt = len(data)
-    lines = [data.iloc[i] for i in range(lt)]
-    ref = {}
-    gt = {}
-    for i, line in enumerate(lines):
-        ref[str(i)] = [str(line['prediction'])]
-        gt[str(i)] = eval(line['answer'])
-
-    scorer = COCO_Caption_Scorer(ref, gt)
-    coco_caption_score_dict = scorer.compute_scores()
-
-    score_pth = eval_file.replace('.xlsx', '_score.json')
-    dump(coco_caption_score_dict, score_pth)
-    logger.info(f'COCO_eval successfully finished evaluating {eval_file}, results saved in {score_pth}')
-    logger.info('Score: ')
-    for key, value in coco_caption_score_dict.items():
-        logger.info('{}:{}'.format(key, value))
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Inference LLM Answers. ')
-    parser.add_argument('--data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument('--nproc', type=int, default=4)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    COCO_eval(eval_file=args.data, nproc=args.nproc, verbose=args.verbose)

eval_mm/vlmevalkit/vlmeval/evaluate/misc.py

@@ -1,29 +0,0 @@
-import os
-from vlmeval.api import OpenAIWrapper, OpenAIWrapperInternal
-from vlmeval.smp import load_env
-
-INTERNAL = os.environ.get('INTERNAL', 0)
-
-
-def build_judge(**kwargs):
-    model = kwargs.pop('model', None)
-    load_env()
-    LOCAL_LLM = os.environ.get('LOCAL_LLM', None)
-    if LOCAL_LLM is None:
-        model_map = {
-            'gpt-4-turbo': 'gpt-4-1106-preview',
-            'gpt-4-0613': 'gpt-4-0613',
-            'gpt-4-0314': 'gpt-4-0314',
-            'gpt-4-0125': 'gpt-4-0125-preview',
-            'chatgpt-1106': 'gpt-3.5-turbo-1106',
-            'chatgpt-0613': 'gpt-3.5-turbo-0613',
-            'chatgpt-0125': 'gpt-3.5-turbo-0125'
-        }
-        model_version = model_map[model]
-    else:
-        model_version = LOCAL_LLM
-    if INTERNAL:
-        model = OpenAIWrapperInternal(model_version, **kwargs)
-    else:
-        model = OpenAIWrapper(model_version, **kwargs)
-    return model

eval_mm/vlmevalkit/vlmeval/evaluate/multiple_choice.py

@@ -1,399 +0,0 @@
-import os.path as osp
-import pandas as pd
-from tqdm import tqdm
-from vlmeval.evaluate.misc import build_judge
-from vlmeval.utils import can_infer, track_progress_rich, TSVDataset
-from vlmeval.smp import *
-import numpy as np
-
-INTERNAL = os.environ.get('INTERNAL', 0)
-
-abbrs = {
-    'coarse_perception': 'CP',
-    'finegrained_perception (instance-level)': 'FP-S',
-    'finegrained_perception (cross-instance)': 'FP-C',
-    'logic_reasoning': 'LR',
-    'relation_reasoning': 'RR',
-    'attribute_reasoning': 'AR'
-}
-
-
-def MMMU_preproc(data):
-    logger = get_logger('Evaluation')
-    cnt = 0
-    As, Bs, Ans = list(data['A']), list(data['B']), list(data['answer'])
-    lt = len(data)
-    for i in range(lt):
-        if pd.isna(As[i]):
-            As[i] = Ans[i]
-            Bs[i] = 'Other Answers'
-            cnt += 1
-    logger.info(f'During MMMU_preproc in Evaluation, {cnt} open questions are re-formulated to multi-choice ones. ')
-    data['A'] = As
-    data['B'] = Bs
-    return data
-
-
-def report_acc(df):
-    # assert group in [None, 'category', 'l2-category']
-    res = defaultdict(list)
-
-    if 'split' in df:
-        splits = list(set(df['split']))
-        res['split'] = splits
-    else:
-        df['split'] = ['none'] * len(df)
-        res['split'] = ['none']
-
-    for group in [None, 'l2-category', 'category']:
-        if group is None:
-            res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']]
-        elif group not in df:
-            continue
-        else:
-            abilities = list(set(df[group]))
-            abilities.sort()
-            for ab in abilities:
-                ab_name = abbrs[ab] if ab in abbrs else ab
-                sub_df = df[df[group] == ab]
-                res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']]
-    return pd.DataFrame(res)
-
-
-def build_prompt(question, options, prediction):
-    tmpl = (
-        'You are an AI assistant who will help me to match '
-        'an answer with several options of a single-choice question. '
-        'You are provided with a question, several options, and an answer, '
-        'and you need to find which option is most similar to the answer. '
-        'If the meaning of all options are significantly different from the answer, output Z. '
-        'Your should output a single uppercase character in A, B, C, D (if they are valid options), and Z. \n'
-        'Example 1: \n'
-        'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n'
-        'Answer: a cute teddy bear\nYour output: A\n'
-        'Example 2: \n'
-        'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n'
-        'Answer: Spider\nYour output: Z\n'
-        'Example 3: \n'
-        'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: '
-    )
-    return tmpl.format(question, options, prediction)
-
-
-def build_prompt_cn(question, options, prediction):
-    tmpl = (
-        '你是一个帮助我匹配答案与单选题中多个选项的 AI 助手。'
-        '你会被提供：一个问题，多个选项，一个答案。你的任务是找到与答案意义最相近的选项。'
-        '如果所有选项的意义都与答案显著不同，则输出 Z。'
-        '你应该输出一个单个的大写字母，例如 A, B, C, D（如果它们是有效选项），或 Z。'
-        '例 1:'
-        '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 一只可爱的泰迪熊\n输出: A\n'
-        '例 2: \n'
-        '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 蜘蛛\n输出: Z\n'
-        '例 3: \n'
-        '问题: {}?\n选项: {}\n答案: {}\n输出: '
-    )
-    return tmpl.format(question, options, prediction)
-
-
-def build_choices(item):
-    ret = {}
-    for ch in string.ascii_uppercase:
-        if ch in item and (not pd.isna(item[ch])):
-            ret[ch] = item[ch]
-    return ret
-
-
-def prefetch_answer(item):
-    choices = build_choices(item)
-    return can_infer(item['prediction'], choices)
-
-
-def extract_answer_from_item(model, item):
-    logger = get_logger('Evaluation')
-    # It will return: (pred, raw, llm_time)
-    choices = build_choices(item)
-    option_str = build_option_str(choices)
-
-    if cn_string(item['question']):
-        prompt = build_prompt_cn(item['question'], option_str, item['prediction'])
-    else:
-        prompt = build_prompt(item['question'], option_str, item['prediction'])
-    retry = 3
-
-    ret = can_infer(item['prediction'], choices)
-    if ret:
-        return dict(opt=ret, log=item['prediction'])
-
-    while retry:
-        ans = model.generate(prompt)
-        if 'Failed to obtain answer via API' in ans:
-            logger.warning('GPT API failed to answer. ')
-        else:
-            ret = can_infer(ans, choices)
-            if ret:
-                return dict(opt=ret, log=ans)
-            else:
-                logger.warning(f'Output includes 0 / > 1 letter among candidates {set(choices)} and Z: {ans}')
-        retry -= 1
-
-        if retry == 0:
-            options = list(choices) + ['Z'] if 'Z' not in choices else []
-            return dict(opt=rd.choice(options), log='Failed to predict, thus randomly generate one. ')
-
-
-def prefetch_sub_data(sub_data, answer_map, verbose=False):
-    lt = len(sub_data)
-    GT, PRED = [], []
-    for i in range(lt):
-        item = sub_data.iloc[i]
-        idx = item['index']
-        GT.append(answer_map[idx])
-        PRED.append(prefetch_answer(item))
-        if PRED[-1] and (GT[-1] != PRED[-1]):
-            log = (
-                f'Failed in Prefetching Rolling {i}: Answer is {GT[-1]}, '
-                f"Prediction is {item['prediction']}, Pre-fetched is {PRED[-1]}. "
-            )
-            return dict(hit=0, log=log)
-    flag = True
-    for g, p in zip(GT, PRED):
-        if g != p:
-            flag = False
-    ret = (dict(hit=1, log='Succeed During Pre-fetching'), ) if flag else (None, )
-    ret = ret + (GT, PRED) if verbose else ret
-    return ret if len(ret) > 1 else ret[0]
-
-
-def eval_sub_data(model, sub_data, answer_map):
-    res, GT, PRED = prefetch_sub_data(sub_data, answer_map, verbose=True)
-    if res is not None:
-        return res
-
-    lt = len(sub_data)
-    log = ''
-    for i in range(lt):
-        if PRED[i]:
-            log += f'Rolling {i} Matched.\n'
-        else:
-            res = extract_answer_from_item(model, sub_data.iloc[i])
-            opt, match_log = res['opt'], res['log']
-            PRED[i] = opt
-            if PRED[i] != GT[i]:
-                log += (
-                    f"Failed in Rolling {i}: Answer is {GT[i]}; Prediction is {sub_data.iloc[i]['prediction']}; "
-                    f'Pre-fetched is {PRED[i]}; Match Log is {match_log}.\n'
-                )
-                return dict(hit=0, log=log)
-            else:
-                log += (
-                    f"Rolling {i}: Answer is {GT[i]}, Prediction is {sub_data.iloc[i]['prediction']}, "
-                    f'Pre-fetched is {PRED[i]}.\n'
-                )
-
-    return dict(hit=1, log=log)
-
-
-def eval_data_groups(model, data_groups, answer_map, result, result_file, nproc=16):
-    prefetched = [prefetch_sub_data(g, answer_map, verbose=False) for g in data_groups]
-    remain = []
-    for dg, pf in zip(data_groups, prefetched):
-        if pf:
-            result[dg.iloc[0]['index'] % 1e6] = pf
-        else:
-            remain.append(dg)
-    dump(result, result_file)
-    tups = [(model, x, answer_map) for x in remain]
-    keys = [x.iloc[0]['index'] % 1e6 for x in remain]
-    if len(tups) == 0:
-        return
-
-    if model is None:
-        logger = get_logger('Evaluation')
-        logger.warning('Exact Matching mode, will not do GPT-based answer matching. ')
-        for k in keys:
-            result[k] = dict(
-                hit=0, log='Failed in Prefetch, no GPT-based answer matching under `exact_matching` policy.')
-        dump(result, result_file)
-        return
-
-    res = track_progress_rich(
-        eval_sub_data,
-        tups,
-        nproc=nproc,
-        chunksize=nproc,
-        save=result_file,
-        keys=keys)
-    result = load(result_file)
-    for k, v in zip(keys, res):
-        if k in result:
-            assert result[k]['hit'] == v['hit'] and result[k]['log'] == v['log']
-        else:
-            result[k] = v
-    dump(result, result_file)
-
-
-def multiple_choice_eval(eval_file, dataset='default', **judge_kwargs):
-    logger = get_logger('Evaluation')
-
-    # assert dataset is not None
-    dataset_map = {
-        'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11',
-        'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11'
-    }
-    if dataset in dataset_map:
-        dataset = dataset_map[dataset]
-    nproc = judge_kwargs.pop('nproc', 4)
-
-    if listinstr(['mmbench', 'ccbench'], dataset.lower()):
-        data = load(eval_file)
-        data['index'] = [int(x) for x in data['index']]
-        dump(data, eval_file)
-
-    rd.seed(2680)
-    suffix = eval_file.split('.')[-1]
-    model = judge_kwargs['model']
-    assert model in ['chatgpt-0613', 'exact_matching', 'gpt-4-0125']
-    name_str_map = {
-        'chatgpt-0613': 'openai',
-        'gpt-4-0125': 'gpt4'
-    }
-    name_str = name_str_map[model] if model in name_str_map else model
-
-    if model == 'exact_matching':
-        model = None
-    else:
-        if INTERNAL or gpt_key_set():
-            model = build_judge(**judge_kwargs)
-        else:
-            logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
-            model = None
-
-    logger.info(f'Evaluating {eval_file}')
-    result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl')
-    result = {}
-    if osp.exists(result_file):
-        result = load(result_file)
-
-    data = load(eval_file)
-    data = data.sort_values(by='index')
-    data['prediction'] = [str(x) for x in data['prediction']]
-    for k in data.keys():
-        data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
-
-    if dataset != 'default':
-        meta = TSVDataset(dataset).data
-    else:
-        logger.warning('Dataset is not provided, try to use the original `eval_file` as meta data. ')
-        meta = load(eval_file)
-        assert 'index' in meta and 'answer' in meta, 'Essentail columns missing in the eval_file.'
-
-    answer_map = {i: c for i, c in zip(meta['index'], meta['answer'])}
-    cate_map = {i: c for i, c in zip(meta['index'], meta['category'])} if 'category' in meta else None
-    l2_cate_map = {i: c for i, c in zip(meta['index'], meta['l2-category'])} if 'l2-category' in meta else None
-    split_map = {i: c for i, c in zip(meta['index'], meta['split'])} if 'split' in meta else None
-
-    if cate_map is not None and np.all([pd.isna(x) for x in cate_map.values()]):
-        cate_map = None
-    if l2_cate_map is not None and np.all([pd.isna(x) for x in l2_cate_map.values()]):
-        l2_cate_map = None
-    if split_map is not None and np.all([pd.isna(x) for x in split_map.values()]):
-        split_map = None
-
-    if listinstr(['MMMU'], dataset):
-        data = MMMU_preproc(data)
-        answer_map = {k: (v if v in list(string.ascii_uppercase) else 'A') for k, v in answer_map.items()}
-
-    data = data[data['index'].isin(answer_map)]
-    data_main = data[data['index'] < int(1e6)]
-    meta_idx_set = set(meta['index'])
-    data_main = data_main[data_main['index'].isin(meta_idx_set)]
-
-    lt = len(data_main)
-    hit, tot = 0, 0
-
-    data_groups = []
-    for i in tqdm(range(lt)):
-        # Dealing with the normal part
-        item_main = data_main.iloc[i]
-        idx = item_main['index']
-
-        if idx in result:
-            correct = result[idx]['hit']
-            assert correct in [0, 1]
-            hit += correct
-            tot += 1
-            continue
-
-        sub_data = data[data['index'] % int(1e6) == idx]
-        data_groups.append(sub_data)
-
-    if len(data_groups):
-        eval_data_groups(
-            model=model,
-            data_groups=data_groups,
-            answer_map=answer_map,
-            nproc=nproc,
-            result=result,
-            result_file=result_file)
-
-    tmp_pth = f'/tmp/{timestr()}.xlsx'
-    dump(data_main, tmp_pth)
-    data_main = load(tmp_pth)
-
-    res = load(result_file)
-    indices = data_main['index']
-
-    data_main['hit'] = [res[i]['hit'] for i in indices]
-    data_main['log'] = [res[i]['log'] for i in indices]
-
-    main_idx = data_main['index']
-    if cate_map is not None:
-        data_main['category'] = [cate_map[i] for i in main_idx]
-    if l2_cate_map is not None:
-        data_main['l2-category'] = [l2_cate_map[i] for i in main_idx]
-    if split_map is not None:
-        data_main['split'] = [split_map[i] for i in indices]
-
-    # load split
-    dump(data_main, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
-    data_main = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}'))
-
-    acc = report_acc(data_main)
-    score_file = eval_file.replace(f'.{suffix}', '_acc.csv')
-    dump(acc, score_file)
-    logger.info(f'multiple_choice_eval successfully finished evaluating {eval_file}, results saved in {score_file}')
-    logger.info('Score: ')
-    logger.info(acc)
-    return acc
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Inference LLM Answers. ')
-    parser.add_argument('data', type=str, help='The question set for inference, in excel / tsv / json format. ')
-    parser.add_argument(
-        '--model',
-        type=str,
-        help='The LLM (GPT) used for inference. ',
-        default='chatgpt-0613',
-        choices=['chatgpt-0613', 'exact_matching', 'gpt-4-0125'])
-    parser.add_argument(
-        '--dataset',
-        type=str,
-        default='default',
-        help='The dataset to evaluate')
-    parser.add_argument('--nproc', type=int, default=6)
-    parser.add_argument('--verbose', action='store_true')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-    load_env()
-    args = parse_args()
-    judge_kwargs = dict(model=args.model, nproc=args.nproc, verbose=args.verbose)
-    if 'OPENAI_API_KEY_JUDGE' in os.environ and os.environ['OPENAI_API_KEY_JUDGE']:
-        judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
-    if 'OPENAI_API_BASE_JUDGE' in os.environ and os.environ['OPENAI_API_BASE_JUDGE']:
-        judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
-    acc = multiple_choice_eval(eval_file=args.data, dataset=args.dataset, **judge_kwargs)

eval_mm/vlmevalkit/vlmeval/inference.py

@@ -1,8 +1,7 @@
 import torch
 import torch.distributed as dist
-import datetime
+from vlmeval.config import supported_VLM
-from vlmeval.config import supported_VLM, api_models
+from vlmeval.utils import track_progress_rich
-from vlmeval.utils import TSVDataset, track_progress_rich, split_MMMU
 from vlmeval.smp import *
 
 FAIL_MSG = 'Failed to obtain answer via API.'
@@ -19,10 +18,10 @@ def parse_args():
 
 
 # Only API model is accepted
-def infer_data_api(work_dir, model_name, dataset_name, index_set=None, api_nproc=4, ignore_failed=False):
+def infer_data_api(work_dir, model_name, dataset, index_set=None, api_nproc=4, ignore_failed=False):
     rank, world_size = get_rank_and_world_size()
     assert rank == 0 and world_size == 1
-    dataset = TSVDataset(dataset_name)
+    dataset_name = dataset.dataset_name
     data = dataset.data
     if index_set is not None:
         data = data[data['index'].isin(index_set)]
@@ -33,10 +32,6 @@ def infer_data_api(work_dir, model_name, dataset_name, index_set=None, api_nproc
     lt, indices = len(data), list(data['index'])
     structs = [dataset.build_prompt(data.iloc[i]) for i in range(lt)]
 
-    # Corner Case
-    if listinstr(['MMMU'], dataset_name):
-        structs = [split_MMMU(s) for s in structs]
-
     out_file = f'{work_dir}/{model_name}_{dataset_name}_supp.pkl'
     res = {}
     if osp.exists(out_file):
@@ -48,7 +43,6 @@ def infer_data_api(work_dir, model_name, dataset_name, index_set=None, api_nproc
     indices = [i for i in indices if i not in res]
 
     gen_func = model.generate
-    # For now, we do not use split_MMMU for MMMU dataset
     structs = [dict(message=struct, dataset=dataset_name) for struct in structs]
 
     if len(structs):
@@ -61,19 +55,14 @@ def infer_data_api(work_dir, model_name, dataset_name, index_set=None, api_nproc
     return res
 
 
-def infer_data(model_name, work_dir, dataset_name, out_file, verbose=False, api_nproc=4):
+def infer_data(model_name, work_dir, dataset, out_file, verbose=False, api_nproc=4):
+    dataset_name = dataset.dataset_name
     prev_file = f'{work_dir}/{model_name}_{dataset_name}_PREV.pkl'
     res = load(prev_file) if osp.exists(prev_file) else {}
     if osp.exists(out_file):
         res.update(load(out_file))
 
     rank, world_size = get_rank_and_world_size()
-    if rank == 0:
-        dataset = TSVDataset(dataset_name)
-    if world_size > 1:
-        dist.barrier()
-    dataset = TSVDataset(dataset_name)
-
     sheet_indices = list(range(rank, len(dataset), world_size))
     lt = len(sheet_indices)
     data = dataset.data.iloc[sheet_indices]
@@ -102,7 +91,7 @@ def infer_data(model_name, work_dir, dataset_name, out_file, verbose=False, api_
         supp = infer_data_api(
             work_dir=work_dir,
             model_name=model_name,
-            dataset_name=dataset_name,
+            dataset=dataset,
             index_set=set(indices),
             api_nproc=api_nproc)
         for idx in indices:
@@ -111,6 +100,8 @@ def infer_data(model_name, work_dir, dataset_name, out_file, verbose=False, api_
         res = {k: res[k] for k in data_indices}
         dump(res, out_file)
         return model_name
+    else:
+        model.set_dump_image(dataset.dump_image)
 
     for i in tqdm(range(lt)):
         idx = data.iloc[i]['index']
@@ -122,13 +113,8 @@ def infer_data(model_name, work_dir, dataset_name, out_file, verbose=False, api_
         else:
             struct = dataset.build_prompt(data.iloc[i])
 
-        # Corner Case
-        if listinstr(['MMMU'], dataset_name):
-            struct = split_MMMU(struct)
-
-        # For now, we do not use split_MMMU for MMMU dataset
         response = model.generate(message=struct, dataset=dataset_name)
-        # torch.cuda.empty_cache()
+        torch.cuda.empty_cache()
 
         if verbose:
             print(response, flush=True)
@@ -143,8 +129,9 @@ def infer_data(model_name, work_dir, dataset_name, out_file, verbose=False, api_
 
 
 # A wrapper for infer_data, do the pre & post processing
-def infer_data_job(model, work_dir, model_name, dataset_name, verbose=False, api_nproc=4, ignore_failed=False):
+def infer_data_job(model, work_dir, model_name, dataset, verbose=False, api_nproc=4, ignore_failed=False):
     rank, world_size = get_rank_and_world_size()
+    dataset_name = dataset.dataset_name
     result_file = osp.join(work_dir, f'{model_name}_{dataset_name}.xlsx')
 
     prev_file = f'{work_dir}/{model_name}_{dataset_name}_PREV.pkl'
@@ -162,7 +149,7 @@ def infer_data_job(model, work_dir, model_name, dataset_name, verbose=False, api
     out_file = tmpl.format(rank)
 
     model = infer_data(
-        model, work_dir=work_dir, dataset_name=dataset_name, out_file=out_file, verbose=verbose, api_nproc=api_nproc)
+        model, work_dir=work_dir, dataset=dataset, out_file=out_file, verbose=verbose, api_nproc=api_nproc)
     if world_size > 1:
         dist.barrier()
 
@@ -171,7 +158,7 @@ def infer_data_job(model, work_dir, model_name, dataset_name, verbose=False, api
         for i in range(world_size):
             data_all.update(load(tmpl.format(i)))
 
-        data = TSVDataset(dataset_name).data
+        data = dataset.data
         for x in data['index']:
             assert x in data_all
         data['prediction'] = [str(data_all[x]) for x in data['index']]

eval_mm/vlmevalkit/vlmeval/inference_mt.py

@@ -0,0 +1,180 @@
+import torch
+import torch.distributed as dist
+from vlmeval.config import supported_VLM
+from vlmeval.utils import track_progress_rich
+from vlmeval.smp import *
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, nargs='+', required=True)
+    parser.add_argument('--model', type=str, nargs='+', required=True)
+    parser.add_argument('--nproc', type=int, default=4, required=True)
+    parser.add_argument('--verbose', action='store_true')
+    args = parser.parse_args()
+    return args
+
+
+def chat_mt(model, messages, dataset_name):
+    assert len(messages) % 2 == 0
+    nturn = len(messages) // 2
+    utter_stack = []
+    predictions = []
+
+    for i in range(nturn):
+        utter = messages[2 * i]
+        utter_stack.append(utter)
+        try:
+            resp = model.chat(utter_stack, dataset=dataset_name)
+            utter_stack.append(dict(role='assistant', content=resp))
+        except:
+            resp = FAIL_MSG
+            utter_stack.append(dict(role='assistant', content=resp))
+        predictions.append(resp)
+    return predictions
+
+
+# Only API model is accepted
+def infer_data_api(work_dir, model_name, dataset, index_set=None, api_nproc=4, ignore_failed=False):
+    rank, world_size = get_rank_and_world_size()
+    assert rank == 0 and world_size == 1
+    dataset_name = dataset.dataset_name
+    data = dataset.data
+    if index_set is not None:
+        data = data[data['index'].isin(index_set)]
+
+    model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name
+    assert getattr(model, 'is_api', False)
+    assert hasattr(model, 'chat_inner')
+
+    lt, indices = len(data), list(data['index'])
+    structs = [dataset.build_prompt(data.iloc[i]) for i in range(lt)]
+
+    out_file = f'{work_dir}/{model_name}_{dataset_name}_supp.pkl'
+    res = {}
+    if osp.exists(out_file):
+        res = load(out_file)
+        if ignore_failed:
+            res = {k: v for k, v in res.items() if FAIL_MSG not in v}
+
+    structs = [s for i, s in zip(indices, structs) if i not in res]
+    indices = [i for i in indices if i not in res]
+
+    structs = [dict(model=model, messages=struct, dataset_name=dataset_name) for struct in structs]
+
+    if len(structs):
+        track_progress_rich(chat_mt, structs, nproc=api_nproc, chunksize=api_nproc, save=out_file, keys=indices)
+
+    res = load(out_file)
+    if index_set is not None:
+        res = {k: v for k, v in res.items() if k in index_set}
+    os.remove(out_file)
+    return res
+
+
+def infer_data(model_name, work_dir, dataset, out_file, verbose=False, api_nproc=4):
+    dataset_name = dataset.dataset_name
+    res = {}
+    if osp.exists(out_file):
+        res.update(load(out_file))
+
+    rank, world_size = get_rank_and_world_size()
+    sheet_indices = list(range(rank, len(dataset), world_size))
+    lt = len(sheet_indices)
+    data = dataset.data.iloc[sheet_indices]
+    data_indices = [i for i in data['index']]
+
+    # If finished, will exit without building the model
+    all_finished = True
+    for i in range(lt):
+        idx = data.iloc[i]['index']
+        if idx not in res:
+            all_finished = False
+    if all_finished:
+        res = {k: res[k] for k in data_indices}
+        dump(res, out_file)
+        return
+
+    # Data need to be inferred
+    data = data[~data['index'].isin(res)]
+    lt = len(data)
+
+    model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name
+    assert hasattr(model, 'chat_inner')
+
+    is_api = getattr(model, 'is_api', False)
+    if is_api:
+        lt, indices = len(data), list(data['index'])
+        supp = infer_data_api(
+            work_dir=work_dir,
+            model_name=model_name,
+            dataset=dataset,
+            index_set=set(indices),
+            api_nproc=api_nproc)
+        for idx in indices:
+            assert idx in supp
+        res.update(supp)
+        res = {k: res[k] for k in data_indices}
+        dump(res, out_file)
+        return model_name
+    else:
+        model.set_dump_image(dataset.dump_image)
+
+    for i in tqdm(range(lt)):
+        idx = data.iloc[i]['index']
+        if idx in res:
+            continue
+
+        if hasattr(model, 'use_custom_prompt') and model.use_custom_prompt(dataset_name):
+            struct = model.build_prompt(data.iloc[i], dataset=dataset_name)
+        else:
+            struct = dataset.build_prompt(data.iloc[i])
+
+        response = chat_mt(model, struct, dataset_name)
+        torch.cuda.empty_cache()
+
+        if verbose:
+            print(response, flush=True)
+
+        res[idx] = response
+        if (i + 1) % 20 == 0:
+            dump(res, out_file)
+
+    res = {k: res[k] for k in data_indices}
+    dump(res, out_file)
+    return model
+
+
+# A wrapper for infer_data, do the pre & post processing
+def infer_data_job_mt(model, work_dir, model_name, dataset, verbose=False, api_nproc=4, ignore_failed=False):
+    rank, world_size = get_rank_and_world_size()
+    dataset_name = dataset.dataset_name
+    result_file = osp.join(work_dir, f'{model_name}_{dataset_name}.tsv')
+
+    tmpl = osp.join(work_dir, '{}' + f'{world_size}_{dataset_name}.pkl')
+    out_file = tmpl.format(rank)
+
+    model = infer_data(
+        model, work_dir=work_dir, dataset=dataset, out_file=out_file, verbose=verbose, api_nproc=api_nproc)
+    if world_size > 1:
+        dist.barrier()
+
+    if rank == 0:
+        data_all = {}
+        for i in range(world_size):
+            data_all.update(load(tmpl.format(i)))
+
+        data = dataset.data
+        for x in data['index']:
+            assert x in data_all
+
+        data['prediction'] = [data_all[x] for x in data['index']]
+        if 'image' in data:
+            data.pop('image')
+
+        dump(data, result_file)
+        for i in range(world_size):
+            os.remove(tmpl.format(i))
+    return model

eval_mm/vlmevalkit/vlmeval/inference_video.py

@@ -0,0 +1,166 @@
+import torch
+import torch.distributed as dist
+from vlmeval.config import supported_VLM
+from vlmeval.utils import track_progress_rich
+from vlmeval.smp import *
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, nargs='+', required=True)
+    parser.add_argument('--model', type=str, nargs='+', required=True)
+    parser.add_argument('--nproc', type=int, default=4, required=True)
+    parser.add_argument('--verbose', action='store_true')
+    args = parser.parse_args()
+    return args
+
+
+# Only API model is accepted
+def infer_data_api(work_dir, model_name, dataset, nframe=8, pack=False, samples_dict={}, api_nproc=4):
+    rank, world_size = get_rank_and_world_size()
+    assert rank == 0 and world_size == 1
+    dataset_name = dataset.dataset_name
+    model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name
+    assert getattr(model, 'is_api', False)
+
+    indices = list(samples_dict.keys())
+    structs = [dataset.build_prompt(samples_dict[idx], num_frames=nframe,
+                                    video_llm=getattr(model, 'VIDEO_LLM', False)) for idx in indices]
+
+    packstr = 'pack' if pack else 'nopack'
+    out_file = f'{work_dir}/{model_name}_{dataset_name}_{nframe}frame_{packstr}_supp.pkl'
+    res = load(out_file) if osp.exists(out_file) else {}
+
+    structs = [s for i, s in zip(indices, structs) if i not in res]
+    indices = [i for i in indices if i not in res]
+
+    gen_func = model.generate
+    structs = [dict(message=struct, dataset=dataset_name) for struct in structs]
+
+    if len(structs):
+        track_progress_rich(gen_func, structs, nproc=api_nproc, chunksize=api_nproc, save=out_file, keys=indices)
+
+    res = load(out_file)
+    return res
+
+
+def infer_data(model_name, work_dir, dataset, out_file, nframe=8, pack=False, verbose=False, api_nproc=4):
+    res = load(out_file) if osp.exists(out_file) else {}
+    rank, world_size = get_rank_and_world_size()
+    dataset_name = dataset.dataset_name
+
+    sample_indices = list(dataset.videos) if pack else list(dataset.data['index'])
+    samples = list(dataset.videos) if pack else list(range(len(dataset.data)))
+    sample_map = {i: s for i, s in zip(sample_indices, samples)}
+
+    sample_indices_sub = sample_indices[rank::world_size]
+    if np.all([idx in res for idx in sample_indices_sub]):
+        return model_name
+    sample_indices_subrem = [x for x in sample_indices_sub if x not in res]
+
+    model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name
+
+    is_api = getattr(model, 'is_api', False)
+    if is_api:
+        assert world_size == 1
+        supp = infer_data_api(
+            work_dir=work_dir,
+            model_name=model_name,
+            dataset=dataset,
+            nframe=nframe,
+            pack=pack,
+            samples_dict={k: sample_map[k] for k in sample_indices_subrem},
+            api_nproc=api_nproc)
+        for k in sample_indices_subrem:
+            assert k in supp
+        res.update(supp)
+        dump(res, out_file)
+        return model_name
+
+    for i, idx in tqdm(enumerate(sample_indices_subrem)):
+        if idx in res:
+            continue
+        # adapt to model frame sample number first
+        nframe = getattr(model, 'nframe', 0) if getattr(model, 'nframe', 0) > 0 else nframe
+        # when using video-llm, build prompt returns video+question; otherwise, several frames+question
+        struct = dataset.build_prompt(sample_map[idx], num_frames=nframe, video_llm=getattr(model, 'VIDEO_LLM', False))
+        response = model.generate(message=struct, dataset=dataset_name)
+        torch.cuda.empty_cache()
+
+        if verbose:
+            print(response, flush=True)
+
+        res[idx] = response
+        if (i + 1) % 20 == 0:
+            dump(res, out_file)
+
+    res = {k: res[k] for k in sample_indices_sub}
+    dump(res, out_file)
+    return model
+
+
+# A wrapper for infer_data, do the pre & post processing
+def infer_data_job_video(
+        model,
+        work_dir,
+        model_name,
+        dataset,
+        nframe=8,
+        pack=False,
+        verbose=False,
+        subtitle=False,
+        api_nproc=4):
+
+    dataset_name = dataset.dataset_name
+    packstr = 'pack' if pack else 'nopack'
+    rank, world_size = get_rank_and_world_size()
+    result_file = osp.join(work_dir, f'{model_name}_{dataset_name}_{nframe}frame_{packstr}.xlsx')
+    if dataset_name == 'Video-MME':
+        subtitle_str = 'subs' if subtitle else 'nosubs'
+        result_file = result_file.replace('.xlsx', f'_{subtitle_str}.xlsx')
+
+    # Dump Predictions to Prev File if result file exists
+    if osp.exists(result_file):
+        return model_name
+
+    tmpl = osp.join(work_dir, '{}' + f'{world_size}_{dataset_name}_{nframe}frame_{packstr}.pkl')
+    if dataset_name == 'Video-MME':
+        subtitle_str = 'subs' if subtitle else 'nosubs'
+        tmpl = tmpl.replace('.pkl', f'_{subtitle_str}.pkl')
+    out_file = tmpl.format(rank)
+
+    model = infer_data(
+        model,
+        work_dir=work_dir,
+        dataset=dataset,
+        nframe=nframe,
+        pack=pack,
+        out_file=out_file,
+        verbose=verbose,
+        api_nproc=api_nproc)
+
+    if world_size > 1:
+        dist.barrier()
+
+    if rank == 0:
+        data_all = {}
+        for i in range(world_size):
+            data_all.update(load(tmpl.format(i)))
+
+        meta = dataset.data
+        if dataset_name == 'MMBench-Video' and pack:
+            meta, vstats = dataset.load_pack_answers(data_all)
+            print(f'Statitics of Pack Video Inference: {vstats}')
+        else:
+            for x in meta['index']:
+                assert x in data_all
+            meta['prediction'] = [str(data_all[x]) for x in meta['index']]
+            if 'image' in meta:
+                meta.pop('image')
+
+        dump(meta, result_file)
+        for i in range(world_size):
+            os.remove(tmpl.format(i))
+    return model

eval_mm/vlmevalkit/vlmeval/smp/file.py

@@ -9,6 +9,60 @@ import time
 import numpy as np
 import validators
 import mimetypes
+import multiprocessing as mp
+from .misc import toliststr
+from .vlm import decode_base64_to_image_file
+
+
+def decode_img_omni(tup):
+    root, im, p = tup
+    images = toliststr(im)
+    paths = toliststr(p)
+    if len(images) > 1 and len(paths) == 1:
+        paths = [osp.splitext(p)[0] + f'_{i}' + osp.splitext(p)[1] for i in range(len(images))]
+
+    assert len(images) == len(paths)
+    paths = [osp.join(root, p) for p in paths]
+    for p, im in zip(paths, images):
+        if osp.exists(p):
+            continue
+        if isinstance(im, str) and len(im) > 64:
+            decode_base64_to_image_file(im, p)
+    return paths
+
+
+def localize_df(data, dname, nproc=32):
+    assert 'image' in data
+    indices = list(data['index'])
+    indices_str = [str(x) for x in indices]
+    images = list(data['image'])
+    image_map = {x: y for x, y in zip(indices_str, images)}
+
+    root = LMUDataRoot()
+    root = osp.join(root, 'images', dname)
+    os.makedirs(root, exist_ok=True)
+
+    if 'image_path' in data:
+        img_paths = list(data['image_path'])
+    else:
+        img_paths = []
+        for i in indices_str:
+            if len(image_map[i]) <= 64:
+                idx = image_map[i]
+                assert idx in image_map and len(image_map[idx]) > 64
+                img_paths.append(f'{idx}.jpg')
+            else:
+                img_paths.append(f'{i}.jpg')
+
+    tups = [(root, im, p) for p, im in zip(img_paths, images)]
+
+    pool = mp.Pool(32)
+    ret = pool.map(decode_img_omni, tups)
+    pool.close()
+    data.pop('image')
+    if 'image_path' not in data:
+        data['image_path'] = [x[0] if len(x) == 1 else x for x in ret]
+    return data
 
 
 def LMUDataRoot():
@@ -17,10 +71,9 @@ def LMUDataRoot():
     home = osp.expanduser('~')
     root = osp.join(home, 'LMUData')
     os.makedirs(root, exist_ok=True)
-    # root = './LMUData'
-    # os.makedirs(root, exist_ok=True)
     return root
 
+
 def MMBenchOfficialServer(dataset_name):
     root = LMUDataRoot()
 
@@ -92,7 +145,7 @@ def dump(data, f, **kwargs):
     return handlers[suffix](data, f, **kwargs)
 
 
-def load(f):
+def load(f, fmt=None):
     def load_pkl(pth):
         return pickle.load(open(pth, 'rb'))
 
@@ -117,6 +170,9 @@ def load(f):
         return pd.read_csv(f, sep='\t')
 
     handlers = dict(pkl=load_pkl, json=load_json, jsonl=load_jsonl, xlsx=load_xlsx, csv=load_csv, tsv=load_tsv)
+    if fmt is not None:
+        return handlers[fmt](f)
+
     suffix = f.split('.')[-1]
     return handlers[suffix](f)
 
@@ -134,9 +190,24 @@ def download_file(url, filename=None):
     if filename is None:
         filename = url.split('/')[-1]
 
-    with DownloadProgressBar(unit='B', unit_scale=True,
+    # If HF_ENDPOINT is set, replace huggingface.co with it
-                             miniters=1, desc=url.split('/')[-1]) as t:
+    if 'huggingface.co' in url and os.environ.get('HF_ENDPOINT', '') != '':
+        url = url.replace('huggingface.co', os.environ['HF_ENDPOINT'].split('://')[1])
+
+    try:
+        with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as t:
             urllib.request.urlretrieve(url, filename=filename, reporthook=t.update_to)
+    except:
+        # Handle Failed Downloads from huggingface.co
+        if 'huggingface.co' in url:
+            url_new = url.replace('huggingface.co', 'hf-mirror.com')
+            try:
+                os.system(f'wget {url_new} -O {filename}')
+            except:
+                raise Exception(f'Failed to download {url}')
+        else:
+            raise Exception(f'Failed to download {url}')
+
     return filename
 
 
@@ -210,7 +281,7 @@ def last_modified(pth):
 
 
 def parse_file(s):
-    if osp.exists(s):
+    if osp.exists(s) and s != '.':
         assert osp.isfile(s)
         suffix = osp.splitext(s)[1].lower()
         mime = mimetypes.types_map.get(suffix, 'unknown')
@@ -228,3 +299,20 @@ def parse_file(s):
             return ('url', s)
     else:
         return (None, s)
+
+
+def file_size(f, unit='GB'):
+    stats = os.stat(f)
+    div_map = {
+        'GB': 2 ** 30,
+        'MB': 2 ** 20,
+        'KB': 2 ** 10,
+    }
+    return stats.st_size / div_map[unit]
+
+
+def parquet_to_tsv(file_path):
+    data = pd.read_parquet(file_path)
+    pth = '/'.join(file_path.split('/')[:-1])
+    data_name = file_path.split('/')[-1].split('.')[0]
+    data.to_csv(osp.join(pth, f'{data_name}.tsv'), sep='\t', index=False)

eval_mm/vlmevalkit/vlmeval/smp/misc.py

@@ -18,8 +18,8 @@ from multiprocessing import Pool, current_process
 from tqdm import tqdm
 import datetime
 import matplotlib.pyplot as plt
-import seaborn as sns
+from tabulate import tabulate
-from tabulate import tabulate_formats, tabulate
+from json import JSONDecoder
 from huggingface_hub import scan_cache_dir
 from sty import fg, bg, ef, rs
 
@@ -71,7 +71,7 @@ def bincount(lst):
         bins[item] += 1
     return bins
 
-def get_cache_path(repo_id):
+def get_cache_path(repo_id, branch=None):
     hf_cache_info = scan_cache_dir()
     repos = list(hf_cache_info.repos)
     repo = None
@@ -82,6 +82,8 @@ def get_cache_path(repo_id):
     if repo is None:
         return None
     revs = list(repo.revisions)
+    if branch is not None:
+        revs = [r for r in revs if r.refs == frozenset({branch})]
     rev2keep, last_modified = None, 0
     for rev in revs:
         if rev.last_modified > last_modified:
@@ -189,3 +191,26 @@ def version_cmp(v1, v2, op='eq'):
     import operator
     op_func = getattr(operator, op)
     return op_func(version.parse(v1), version.parse(v2))
+
+
+def toliststr(s):
+    if isinstance(s, str) and (s[0] == '[') and (s[-1] == ']'):
+        return [str(x) for x in eval(s)]
+    elif isinstance(s, str):
+        return [s]
+    elif isinstance(s, list):
+        return [str(x) for x in s]
+    raise NotImplementedError
+
+
+def extract_json_objects(text, decoder=JSONDecoder()):
+    pos = 0
+    while True:
+        match = text.find('{', pos)
+        if match == -1: break
+        try:
+            result, index = decoder.raw_decode(text[match:])
+            yield result
+            pos = match + index
+        except ValueError:
+            pos = match + 1

eval_mm/vlmevalkit/vlmeval/smp/vlm.py

@@ -7,10 +7,53 @@ from uuid import uuid4
 import os.path as osp
 import base64
 from PIL import Image
-from .file import load, dump
+import sys
+
 Image.MAX_IMAGE_PIXELS = 1e9
 
 
+def rescale_img(img, tgt=None):
+    assert isinstance(tgt, tuple) and -1 in tgt
+    w, h = img.size
+    if tgt[0] != -1:
+        new_w, new_h = tgt[0], int(tgt[0] / w * h)
+    elif tgt[1] != -1:
+        new_w, new_h = int(tgt[1] / h * w), tgt[1]
+    img = img.resize((new_w, new_h))
+    return img
+
+
+def concat_images_vlmeval(images, target_size=-1, mode='h', return_image=False):
+    from .file import md5
+
+    ims = [Image.open(im) for im in images]
+    if target_size != -1:
+        ims = [
+            rescale_img(im, (-1, target_size) if mode == 'h' else (target_size, -1))
+            for im in ims
+        ]
+
+    ws, hs = [x.width for x in ims], [x.height for x in ims]
+    if mode == 'h':
+        new_w, new_h = sum(ws), max(hs)
+        dst = Image.new('RGB', (new_w, new_h))
+        for i, im in enumerate(ims):
+            dst.paste(im, (sum(ws[:i]), 0))
+    elif mode == 'v':
+        new_w, new_h = max(ws), sum(hs)
+        dst = Image.new('RGB', (new_w, new_h))
+        for i, im in enumerate(ims):
+            dst.paste(im, (sum(ws[:i], 0)))
+    if return_image:
+        return dst
+    else:
+        _str = '\n'.join(images)
+        str_md5 = md5(_str)
+        tgt = osp.join('/tmp', str_md5 + '.jpg')
+        dst.save(tgt)
+        return tgt
+
+
 def mmqa_display(question, target_size=512):
     question = {k.lower(): v for k, v in question.items()}
     keys = list(question.keys())
@@ -41,14 +84,12 @@ def encode_image_to_base64(img, target_size=-1):
     # else, will set the max_size ot (target_size, target_size)
     if img.mode in ('RGBA', 'P'):
         img = img.convert('RGB')
-    tmp = osp.join('/tmp', str(uuid4()) + '.jpg')
     if target_size > 0:
         img.thumbnail((target_size, target_size))
-    img.save(tmp)
+    img_buffer = io.BytesIO()
-    with open(tmp, 'rb') as image_file:
+    img.save(img_buffer, format='JPEG')
-        image_data = image_file.read()
+    image_data = img_buffer.getvalue()
     ret = base64.b64encode(image_data).decode('utf-8')
-    os.remove(tmp)
     return ret
 
 
@@ -110,6 +151,7 @@ def circular_pred(df, extract_func=None):
         extract_func = lambda x: x  # noqa: E731
     df = df.sort_values('index')
     from vlmeval.utils import can_infer_option
+
     shift = int(1e6)
 
     choices = [extract_func(x) for x in df['prediction']]
@@ -118,9 +160,12 @@ def circular_pred(df, extract_func=None):
     valid_map = {i: True for i in pred_map if i < 1e6}
     for i in df['index']:
         if i >= shift and pred_map[i] and pred_map[i - shift]:
-            if (
+            if pred_map[i] not in list(
-                pred_map[i] not in list(string.ascii_uppercase) or  # noqa: W504
+                string.ascii_uppercase
-                pred_map[i - shift] not in list(string.ascii_uppercase)
+            ) or pred_map[  # noqa: W504
+                i - shift
+            ] not in list(
+                string.ascii_uppercase
             ):
 
                 valid_map[i % shift] = False

eval_mm/vlmevalkit/vlmeval/tools.py

@@ -0,0 +1,421 @@
+import sys
+from vlmeval.config import *
+from vlmeval.smp import *
+
+# Define valid modes
+MODES = ('dlist', 'mlist', 'missing', 'circular', 'localize', 'check', 'run', 'eval')
+
+CLI_HELP_MSG = \
+    f"""
+    Arguments received: {str(['vlmutil'] + sys.argv[1:])}. vlmutil commands use the following syntax:
+
+        vlmutil MODE MODE_ARGS
+
+        Where   MODE (required) is one of {MODES}
+                MODE_ARG (optional) is the argument for specific mode
+
+    Some usages for xtuner commands: (See more by using -h for specific command!)
+
+        1. List all the dataset by levels: l1, l2, l3, etc.:
+            vlmutil dlist [l1/l2/l3/...]
+        2. List all the models by categories: 4.33.0, 4.37.0, api, etc.:
+            vlmutil mlist 4.33.0 [all/small/large]
+        3. Report missing results:
+            vlmutil missing [l1/l2/l3/...]
+        4. Create circular questions (only for multiple-choice questions with no more than 4 choices):
+            vlmutil circular input.tsv
+        5. Create a localized version of the dataset (for very large tsv files):
+            vlmutil localize input.tsv
+        6. Check the validity of a model:
+            vlmutil check [model_name/model_series]
+        7. Run evaluation for missing results:
+            vlmutil run l2 hf
+        8. Evaluate data file:
+            vlmutil eval [dataset_name] [prediction_file]
+
+    GitHub: https://github.com/open-compass/VLMEvalKit
+    """  # noqa: E501
+
+
+dataset_levels = {
+    'l1': [
+        ('MMVet', 'gpt-4-turbo_score.csv'), ('MMMU_DEV_VAL', 'acc.csv'),
+        ('MathVista_MINI', 'gpt-4-turbo_score.csv'), ('HallusionBench', 'score.csv'),
+        ('OCRBench', 'score.json'), ('AI2D_TEST', 'acc.csv'), ('MMStar', 'acc.csv'),
+        ('MMBench_V11', 'acc.csv'), ('MMBench_CN_V11', 'acc.csv')
+    ],
+    'l2': [
+        ('MME', 'score.csv'), ('LLaVABench', 'score.csv'), ('RealWorldQA', 'acc.csv'),
+        ('MMBench', 'acc.csv'), ('MMBench_CN', 'acc.csv'), ('CCBench', 'acc.csv'),
+        ('SEEDBench_IMG', 'acc.csv'), ('COCO_VAL', 'score.json'), ('POPE', 'score.csv'),
+        ('ScienceQA_VAL', 'acc.csv'), ('ScienceQA_TEST', 'acc.csv'), ('MMT-Bench_VAL', 'acc.csv'),
+        ('SEEDBench2_Plus', 'acc.csv'), ('BLINK', 'acc.csv'), ('MTVQA_TEST', 'acc.json'),
+        ('Q-Bench1_VAL', 'acc.csv'), ('A-Bench_VAL', 'acc.csv')
+    ],
+    'l3': [
+        ('OCRVQA_TESTCORE', 'acc.csv'), ('TextVQA_VAL', 'acc.csv'),
+        ('ChartQA_TEST', 'acc.csv'), ('DocVQA_VAL', 'acc.csv'), ('InfoVQA_VAL', 'acc.csv'),
+        ('SEEDBench2', 'acc.csv')
+    ]
+}
+
+dataset_levels['l12'] = dataset_levels['l1'] + dataset_levels['l2']
+dataset_levels['l23'] = dataset_levels['l2'] + dataset_levels['l3']
+dataset_levels['l123'] = dataset_levels['l12'] + dataset_levels['l3']
+
+models = {
+    '4.33.0': list(qwen_series) + list(xcomposer_series) + [
+        'mPLUG-Owl2', 'flamingov2', 'VisualGLM_6b', 'MMAlaya', 'PandaGPT_13B', 'VXVERSE'
+    ] + list(idefics_series) + list(minigpt4_series) + list(instructblip_series),
+    '4.37.0': [x for x in llava_series if 'next' not in x] + list(internvl_series) + [
+        'TransCore_M', 'emu2_chat', 'MiniCPM-V', 'MiniCPM-V-2', 'OmniLMM_12B',
+        'cogvlm-grounding-generalist', 'cogvlm-chat', 'cogvlm2-llama3-chat-19B',
+    ] + list(xtuner_series) + list(yivl_series) + list(deepseekvl_series) + list(cambrian_series),
+    '4.40.0': [
+        'idefics2_8b', 'Bunny-llama3-8B', 'MiniCPM-Llama3-V-2_5', '360VL-70B', 'Phi-3-Vision',
+    ] + list(wemm_series),
+    'latest': ['paligemma-3b-mix-448', 'MiniCPM-V-2_6', 'glm-4v-9b'] + [x for x in llava_series if 'next' in x]
+    + list(chameleon_series) + list(ovis_series) + list(mantis_series),
+    'api': list(api_models)
+}
+
+# SKIP_MODELS will be skipped in report_missing and run APIs
+SKIP_MODELS = [
+    'MGM_7B', 'GPT4V_HIGH', 'GPT4V', 'flamingov2', 'PandaGPT_13B',
+    'GeminiProVision', 'Step1V-0701', 'SenseChat-5-Vision',
+    'llava_v1_7b', 'sharegpt4v_7b', 'sharegpt4v_13b',
+    'llava-v1.5-7b-xtuner', 'llava-v1.5-13b-xtuner',
+    'cogvlm-grounding-generalist', 'InternVL-Chat-V1-1',
+    'InternVL-Chat-V1-2', 'InternVL-Chat-V1-2-Plus', 'RekaCore',
+    'llava_next_72b', 'llava_next_110b', 'MiniCPM-V', 'sharecaptioner', 'XComposer',
+    'VisualGLM_6b', 'idefics_9b_instruct', 'idefics_80b_instruct',
+    'mPLUG-Owl2', 'MMAlaya', 'OmniLMM_12B', 'emu2_chat', 'VXVERSE'
+] + list(minigpt4_series) + list(instructblip_series) + list(xtuner_series) + list(chameleon_series) + list(vila_series)
+
+LARGE_MODELS = [
+    'idefics_80b_instruct', '360VL-70B', 'emu2_chat', 'InternVL2-76B',
+]
+
+
+def completed(m, d, suf):
+    score_file = f'outputs/{m}/{m}_{d}_{suf}'
+    if osp.exists(score_file):
+        return True
+    if d == 'MMBench':
+        s1, s2 = f'outputs/{m}/{m}_MMBench_DEV_EN_{suf}', f'outputs/{m}/{m}_MMBench_TEST_EN_{suf}'
+        return osp.exists(s1) and osp.exists(s2)
+    elif d == 'MMBench_CN':
+        s1, s2 = f'outputs/{m}/{m}_MMBench_DEV_CN_{suf}', f'outputs/{m}/{m}_MMBench_TEST_CN_{suf}'
+        return osp.exists(s1) and osp.exists(s2)
+    return False
+
+
+def DLIST(lvl):
+    lst = [x[0] for x in dataset_levels[lvl]]
+    return lst
+
+
+def MLIST(lvl, size='all'):
+    model_list = models[lvl]
+    if size == 'small':
+        model_list = [m for m in model_list if m not in LARGE_MODELS]
+    elif size == 'large':
+        model_list = [m for m in model_list if m in LARGE_MODELS]
+    return [x[0] for x in model_list]
+
+
+def MISSING(lvl):
+    from vlmeval.config import supported_VLM
+    models = list(supported_VLM)
+    models = [m for m in models if m not in SKIP_MODELS and osp.exists(osp.join('outputs', m))]
+    if lvl in dataset_levels.keys():
+        data_list = dataset_levels[lvl]
+    else:
+        data_list = [(D, suff) for (D, suff) in dataset_levels['l123'] if D == lvl]
+    missing_list = []
+    for f in models:
+        for D, suff in data_list:
+            if not completed(f, D, suff):
+                missing_list.append((f, D))
+    return missing_list
+
+
+def CIRCULAR(inp):
+    assert inp.endswith('.tsv')
+    data = load(inp)
+    OFFSET = 1e6
+    while max(data['index']) >= OFFSET:
+        OFFSET *= 10
+
+    assert 'E' not in data, 'Currently build_circular only works for up to 4-choice questions'
+    data_2c = data[pd.isna(data['C'])]
+    data_3c = data[~pd.isna(data['C']) & pd.isna(data['D'])]
+    data_4c = data[~pd.isna(data['D'])]
+    map_2c = [('AB', 'BA')]
+    map_3c = [('ABC', 'BCA'), ('ABC', 'CAB')]
+    map_4c = [('ABCD', 'BCDA'), ('ABCD', 'CDAB'), ('ABCD', 'DABC')]
+
+    def okn(o, n=4):
+        ostr = o.replace(',', ' ')
+        osplits = ostr.split()
+        if sum([c in osplits for c in string.ascii_uppercase[:n - 1]]) == n - 1:
+            return False
+        olower = o.lower()
+        olower = olower.replace(',', ' ')
+        olower_splits = olower.split()
+        if 'all' in olower_splits or 'none' in olower_splits:
+            return False
+        return True
+
+    yay4, nay4 = [], []
+    lt4 = len(data_4c)
+    for i in range(lt4):
+        if okn(data_4c.iloc[i]['D'], 4):
+            yay4.append(i)
+        else:
+            nay4.append(i)
+    data_4c_y = data_4c.iloc[yay4]
+    data_4c_n = data_4c.iloc[nay4]
+    data_3c = pd.concat([data_4c_n, data_3c])
+
+    yay3, nay3 = [], []
+    lt3 = len(data_3c)
+    for i in range(lt3):
+        if okn(data_3c.iloc[i]['C'], 3):
+            yay3.append(i)
+        else:
+            nay3.append(i)
+    data_3c_y = data_3c.iloc[yay3]
+    data_3c_n = data_3c.iloc[nay3]
+    data_2c = pd.concat([data_3c_n, data_2c])
+
+    def remap(data_in, tup, off):
+        off = int(off)
+        data = data_in.copy()
+        char_map = {k: v for k, v in zip(*tup)}
+        idx = data.pop('index')
+        answer = data.pop('answer')
+        answer_new = [char_map[x] if x in char_map else x for x in answer]
+        data['answer'] = answer_new
+        options = {}
+        for c in char_map:
+            options[char_map[c]] = data.pop(c)
+        for c in options:
+            data[c] = options[c]
+        data.pop('image')
+        data['image'] = idx
+        idx = [x + off for x in idx]
+        data['index'] = idx
+        return data
+
+    data_all = pd.concat([
+        data_2c,
+        data_3c_y,
+        data_4c_y,
+        remap(data_2c, map_2c[0], OFFSET),
+        remap(data_3c_y, map_3c[0], OFFSET),
+        remap(data_4c_y, map_4c[0], OFFSET),
+        remap(data_3c_y, map_3c[1], OFFSET * 2),
+        remap(data_4c_y, map_4c[1], OFFSET * 2),
+        remap(data_4c_y, map_4c[2], OFFSET * 3),
+    ])
+
+    tgt_file = inp.replace('.tsv', '_CIRC.tsv')
+    dump(data_all, tgt_file)
+    print(f'The circularized data is saved to {tgt_file}')
+    assert osp.exists(tgt_file)
+    print(f'The MD5 for the circularized data is {md5(tgt_file)}')
+
+
+PTH = osp.realpath(__file__)
+IMAGE_PTH = osp.join(osp.dirname(PTH), '../assets/apple.jpg')
+
+msg1 = [
+    IMAGE_PTH,
+    'What is in this image?'
+]
+msg2 = [
+    dict(type='image', value=IMAGE_PTH),
+    dict(type='text', value='What is in this image?')
+]
+msg3 = [
+    IMAGE_PTH,
+    IMAGE_PTH,
+    'How many apples are there in these images?'
+]
+msg4 = [
+    dict(type='image', value=IMAGE_PTH),
+    dict(type='image', value=IMAGE_PTH),
+    dict(type='text', value='How many apples are there in these images?')
+]
+
+
+def CHECK(val):
+    if val in supported_VLM:
+        model = supported_VLM[val]()
+        print(f'Model: {val}')
+        for i, msg in enumerate([msg1, msg2, msg3, msg4]):
+            if i > 1 and not model.INTERLEAVE:
+                continue
+            res = model.generate(msg)
+            print(f'Test {i + 1}: {res}')
+    elif val in models:
+        model_list = models[val]
+        for m in model_list:
+            CHECK(m)
+
+
+def LOCALIZE(fname, new_fname=None):
+    if new_fname is None:
+        new_fname = fname.replace('.tsv', '_local.tsv')
+
+    base_name = osp.basename(fname)
+    dname = osp.splitext(base_name)[0]
+
+    data = load(fname)
+    data_new = localize_df(data, dname)
+    dump(data_new, new_fname)
+    print(f'The localized version of data file is {new_fname}')
+    return new_fname
+
+
+def RUN(lvl, model):
+    import torch
+    NGPU = torch.cuda.device_count()
+    SCRIPT = osp.join(osp.dirname(__file__), '../run.py')
+    logger = get_logger('Run Missing')
+
+    def get_env(name):
+        assert name in ['433', '437', '440', 'latest']
+        load_env()
+        env_key = f'ENV_{name}'
+        return os.environ.get(env_key, None)
+
+    missing = MISSING(lvl)
+    if model == 'all':
+        pass
+    elif model == 'api':
+        missing = [x for x in missing if x[0] in models['api']]
+    elif model == 'hf':
+        missing = [x for x in missing if x[0] not in models['api']]
+    elif model in models:
+        missing = [x for x in missing if x[0] in models[missing]]
+    elif model in supported_VLM:
+        missing = [x for x in missing if x[0] == model]
+    else:
+        warnings.warn(f'Invalid model {model}.')
+
+    missing.sort(key=lambda x: x[0])
+    groups = defaultdict(list)
+    for m, D in missing:
+        groups[m].append(D)
+    for m in groups:
+        if m in SKIP_MODELS:
+            continue
+        for dataset in groups[m]:
+            logger.info(f'Running {m} on {dataset}')
+            exe = 'python' if m in LARGE_MODELS or m in models['api'] else 'torchrun'
+            if m not in models['api']:
+                env = None
+                env = 'latest' if m in models['latest'] else env
+                env = '433' if m in models['4.33.0'] else env
+                env = '437' if m in models['4.37.0'] else env
+                env = '440' if m in models['4.40.0'] else env
+                if env is None:
+                    # Not found, default to latest
+                    env = 'latest'
+                    logger.warning(
+                        f"Model {m} does not have a specific environment configuration. Defaulting to 'latest'.")
+                pth = get_env(env)
+                if pth is not None:
+                    exe = osp.join(pth, 'bin', exe)
+                else:
+                    logger.warning(f'Cannot find the env path {env} for model {m}')
+            if exe.endswith('torchrun'):
+                cmd = f'{exe} --nproc-per-node={NGPU} {SCRIPT} --model {m} --data {dataset}'
+            elif exe.endswith('python'):
+                cmd = f'{exe} {SCRIPT} --model {m} --data {dataset}'
+            os.system(cmd)
+
+
+def EVAL(dataset_name, data_file):
+    from vlmeval.dataset import build_dataset
+    logger = get_logger('VLMEvalKit Tool-Eval')
+    dataset = build_dataset(dataset_name)
+    # Set the judge kwargs first before evaluation or dumping
+    judge_kwargs = {'nproc': 4, 'verbose': True}
+    if dataset.TYPE in ['MCQ', 'Y/N']:
+        judge_kwargs['model'] = 'chatgpt-0125'
+    elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'], dataset_name):
+        judge_kwargs['model'] = 'gpt-4-turbo'
+    elif listinstr(['MMLongBench', 'MMDU'], dataset_name):
+        judge_kwargs['model'] = 'gpt-4o'
+    eval_results = dataset.evaluate(data_file, **judge_kwargs)
+    if eval_results is not None:
+        assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame)
+        logger.info('Evaluation Results:')
+    if isinstance(eval_results, dict):
+        logger.info('\n' + json.dumps(eval_results, indent=4))
+    elif isinstance(eval_results, pd.DataFrame):
+        if len(eval_results) < len(eval_results.columns):
+            eval_results = eval_results.T
+        logger.info('\n' + tabulate(eval_results))
+
+
+def cli():
+    logger = get_logger('VLMEvalKit Tools')
+    args = sys.argv[1:]
+    if not args:  # no arguments passed
+        logger.info(CLI_HELP_MSG)
+        return
+    if args[0].lower() in MODES:
+        if args[0].lower() == 'dlist':
+            assert len(args) >= 2
+            lst = DLIST(args[1])
+            print(' '.join(lst))
+        elif args[0].lower() == 'mlist':
+            assert len(args) >= 2
+            size = 'all'
+            if len(args) > 2:
+                size = args[2].lower()
+            lst = MLIST(args[1], size)
+            print(' '.join(lst))
+        elif args[0].lower() == 'missing':
+            assert len(args) >= 2
+            missing_list = MISSING(args[1])
+            logger = get_logger('Find Missing')
+            logger.info(colored(f'Level {args[1]} Missing Results: ', 'red'))
+            lines = []
+            for m, D in missing_list:
+                line = f'Model {m}, Dataset {D}'
+                logger.info(colored(line, 'red'))
+                lines.append(line)
+            mwlines(lines, f'{args[1]}_missing.txt')
+        elif args[0].lower() == 'circular':
+            assert len(args) >= 2
+            CIRCULAR(args[1])
+        elif args[0].lower() == 'localize':
+            assert len(args) >= 2
+            LOCALIZE(args[1])
+        elif args[0].lower() == 'check':
+            assert len(args) >= 2
+            model_list = args[1:]
+            for m in model_list:
+                CHECK(m)
+        elif args[0].lower() == 'run':
+            assert len(args) >= 2
+            lvl = args[1]
+            if len(args) == 2:
+                model = 'all'
+                RUN(lvl, model)
+            else:
+                for model in args[2:]:
+                    RUN(lvl, model)
+        elif args[0].lower() == 'eval':
+            assert len(args) == 3
+            dataset, data_file = args[1], args[2]
+            EVAL(dataset, data_file)
+    else:
+        logger.error('WARNING: command error!')
+        logger.info(CLI_HELP_MSG)
+        return

eval_mm/vlmevalkit/vlmeval/utils/__init__.py

@@ -1,12 +1,7 @@
 from .matching_util import can_infer, can_infer_option, can_infer_text
 from .mp_util import track_progress_rich
-from .custom_prompt import CustomPrompt
-from .dataset_config import dataset_URLs, img_root_map, DATASET_TYPE, abbr2full
-from .dataset import TSVDataset, split_MMMU, MMMU_result_transfer
 
 
 __all__ = [
     'can_infer', 'can_infer_option', 'can_infer_text', 'track_progress_rich',
-    'TSVDataset', 'dataset_URLs', 'img_root_map', 'DATASET_TYPE', 'CustomPrompt',
-    'split_MMMU', 'abbr2full'
 ]

eval_mm/vlmevalkit/vlmeval/utils/custom_prompt.py

@@ -1,33 +0,0 @@
-from ..smp import *
-from .dataset_config import img_root_map
-from abc import abstractmethod
-
-
-class CustomPrompt:
-
-    @abstractmethod
-    def use_custom_prompt(self, dataset):
-        raise NotImplementedError
-
-    @abstractmethod
-    def build_prompt(self, line, dataset):
-        raise NotImplementedError
-
-    def dump_image(self, line, dataset):
-        ROOT = LMUDataRoot()
-        assert isinstance(dataset, str)
-        img_root = osp.join(ROOT, 'images', img_root_map[dataset] if dataset in img_root_map else dataset)
-        os.makedirs(img_root, exist_ok=True)
-        if isinstance(line['image'], list):
-            tgt_path = []
-            assert 'image_path' in line
-            for img, im_name in zip(line['image'], line['image_path']):
-                path = osp.join(img_root, im_name)
-                if not read_ok(path):
-                    decode_base64_to_image_file(img, path)
-                tgt_path.append(path)
-        else:
-            tgt_path = osp.join(img_root, f"{line['index']}.jpg")
-            if not read_ok(tgt_path):
-                decode_base64_to_image_file(line['image'], tgt_path)
-        return tgt_path

eval_mm/vlmevalkit/vlmeval/utils/dataset.py

@@ -1,190 +0,0 @@
-import pandas as pd
-import hashlib
-from ..smp import *
-from .dataset_config import dataset_URLs, dataset_md5_dict, DATASET_TYPE
-from .custom_prompt import CustomPrompt
-from .matching_util import can_infer
-
-
-def isliststr(s):
-    return (s[0] == '[') and (s[-1] == ']')
-
-
-def check_md5(data_path, dataset):
-    if dataset not in dataset_md5_dict:
-        warnings.warn(f'We do not have an md5 record for dataset {dataset}, skip the md5 check. ')
-        return True
-    assert osp.exists(data_path)
-    with open(data_path, 'rb') as f:
-        hash = hashlib.new('md5')
-        for chunk in iter(lambda: f.read(2**20), b''):
-            hash.update(chunk)
-    if str(hash.hexdigest()) == dataset_md5_dict[dataset]:
-        return True
-    else:
-        warnings.warn('this data file is incomplete, so it needs to be downloaded again.')
-        return False
-
-
-def split_MMMU(msgs):
-    text, images = None, []
-    for s in msgs:
-        if s['type'] == 'image':
-            images.append(s['value'])
-        elif s['type'] == 'text':
-            assert text is None
-            text = s['value']
-    text_segs = text.split('<image ')
-    segs = [dict(type='text', value=text_segs[0])]
-    for i, seg in enumerate(text_segs):
-        if i == 0:
-            continue
-        assert istype(seg[0], int) and seg[1] == '>'
-        image_idx = int(seg[0]) - 1
-        segs.append(dict(type='image', value=images[image_idx]))
-        segs.append(dict(type='text', value=seg[2:]))
-    return segs
-
-
-def MMMU_result_transfer(result_path):
-    res = {}
-    result_data = load(result_path)
-    mcq = result_data['A'].notna()
-    lt = len(result_data)
-    for i in range(lt):
-        line = result_data.iloc[i]
-        if mcq[i]:
-            options = {
-                cand: line[cand]
-                for cand in string.ascii_uppercase
-                if cand in line and not pd.isna(line[cand])
-            }
-            prediction = line['prediction']
-            infer_prediction = can_infer(prediction, options)
-            res[line['id']] = infer_prediction
-        else:
-            res[line['id']] = line['prediction']
-    result_json = result_path.replace('.xlsx', '.json')
-    dump(res, result_json)
-    return result_json
-
-
-class TSVDataset(CustomPrompt):
-
-    def __init__(self, dataset='MMBench', skip_noimg=True):
-
-        self.data_root = LMUDataRoot()
-        assert osp.exists(self.data_root)
-
-        self.dataset = dataset
-        self.dataset_type = DATASET_TYPE(dataset)
-
-        if dataset in dataset_URLs:
-            url = dataset_URLs[dataset]
-            file_name = url.split('/')[-1]
-            data_path = osp.join(self.data_root, file_name)
-
-            if osp.exists(data_path) and check_md5(data_path, dataset):
-                pass
-            elif osp.isfile(url):
-            # If url is actually a file path, use it directly
-                data_path = url
-            else:
-                warnings.warn('The dataset tsv is not downloaded')
-                download_file(url, data_path)
-        else:
-            data_path = osp.join(self.data_root, dataset + '.tsv')
-            assert osp.exists(data_path)
-
-        data = load(data_path)
-        self.skip_noimg = skip_noimg
-        if skip_noimg and 'image' in data:
-            data = data[~pd.isna(data['image'])]
-
-        # Prompt for Captioning
-        if listinstr(['COCO'], dataset):
-            data['question'] = [(
-                'Please describe this image in general. Directly provide the description, '
-                'do not include prefix like "This image depicts". '
-            )] * len(data)
-
-        data['index'] = [str(x) for x in data['index']]
-
-        self.meta_only = True
-        if 'image' in data:
-            data['image'] = [str(x) for x in data['image']]
-
-            image_map = {x: y for x, y in zip(data['index'], data['image'])}
-            for k in image_map:
-                if len(image_map[k]) <= 64:
-                    idx = image_map[k]
-                    assert idx in image_map and len(image_map[idx]) > 64
-                    image_map[k] = image_map[idx]
-
-            data['image'] = [
-                eval(image_map[k]) if isliststr(image_map[k]) else image_map[k]
-                for k in data['index']
-            ]
-            self.meta_only = False
-
-        if 'image_path' in data:
-            data['image_path'] = [
-                eval(pths) if isliststr(pths) else pths for pths in data['image_path']
-            ]
-
-        if np.all([istype(x, int) for x in data['index']]):
-            data['index'] = [int(x) for x in data['index']]
-
-        self.data = data
-
-    def __len__(self):
-        return len(self.data)
-
-    def build_prompt(self, line, dataset=None):
-        if dataset is None:
-            dataset = self.dataset
-
-        if isinstance(line, int):
-            line = self.data.iloc[line]
-
-        if self.meta_only:
-            tgt_path = line['image_path']
-        else:
-            tgt_path = self.dump_image(line, dataset)
-
-        prompt = line['question']
-        if DATASET_TYPE(dataset) == 'multi-choice':
-            question = line['question']
-            options = {
-                cand: line[cand]
-                for cand in string.ascii_uppercase
-                if cand in line and not pd.isna(line[cand])
-            }
-            options_prompt = 'Options:\n'
-            for key, item in options.items():
-                options_prompt += f'{key}. {item}\n'
-            hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
-            prompt = ''
-            if hint is not None:
-                prompt += f'Hint: {hint}\n'
-            prompt += f'Question: {question}\n'
-            if len(options):
-                prompt += options_prompt
-                prompt += 'Please select the correct answer from the options above. \n'
-        elif DATASET_TYPE(dataset) == 'VQA':
-            if listinstr(['ocrvqa', 'textvqa', 'chartqa', 'docvqa'], dataset.lower()):
-                prompt += '\nPlease try to answer the question with short words or phrases if possible\n.'
-
-        msgs = []
-        if isinstance(tgt_path, list):
-            msgs.extend([dict(type='image', value=p) for p in tgt_path])
-        else:
-            msgs = [dict(type='image', value=tgt_path)]
-        msgs.append(dict(type='text', value=prompt))
-
-        return msgs
-
-    def display(self, line):
-        if isinstance(line, int):
-            line = self.data.iloc[line]
-        mmqa_display(line)

eval_mm/vlmevalkit/vlmeval/utils/dataset_config.py

@@ -1,158 +0,0 @@
-from ..smp import listinstr
-
-dataset_URLs = {
-    # MMBench v1.0
-    'MMBench_DEV_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv',
-    'MMBench_TEST_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv',
-    'MMBench_DEV_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv',
-    'MMBench_TEST_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv',
-    'MMBench': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench.tsv',  # Internal Only
-    'MMBench_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN.tsv',    # Internal Only
-    # MMBench v1.1
-    'MMBench_DEV_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN_V11.tsv',
-    'MMBench_TEST_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN_V11.tsv',
-    'MMBench_DEV_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN_V11.tsv',
-    'MMBench_TEST_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN_V11.tsv',
-    'MMBench_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_V11.tsv',  # Internal Only
-    'MMBench_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN_V11.tsv',    # Internal Only
-    # CCBench
-    'CCBench': 'https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv',
-    'MME': 'https://opencompass.openxlab.space/utils/VLMEval/MME.tsv',
-    'SEEDBench_IMG': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench_IMG.tsv',
-    'CORE_MM': 'https://opencompass.openxlab.space/utils/VLMEval/CORE_MM.tsv',
-    'MMVet': 'https://opencompass.openxlab.space/utils/VLMEval/MMVet.tsv',
-    'COCO_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/COCO_VAL.tsv',
-    'OCRVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TEST.tsv',
-    'OCRVQA_TESTCORE': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TESTCORE.tsv',
-    'TextVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/TextVQA_VAL.tsv',
-    'MMMU_DEV_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_DEV_VAL.tsv',
-    'MMMU_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_TEST.tsv',
-    'MathVista_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVista_MINI.tsv',
-    'ScienceQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_VAL.tsv',
-    'ScienceQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_TEST.tsv',
-    'HallusionBench': 'https://opencompass.openxlab.space/utils/VLMEval/HallusionBench.tsv',
-    'DocVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_VAL.tsv',
-    'DocVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_TEST.tsv',
-    'InfoVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_VAL.tsv',
-    'InfoVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_TEST.tsv',
-    'AI2D_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST.tsv',
-    'LLaVABench': 'https://opencompass.openxlab.space/utils/VLMEval/LLaVABench.tsv',
-    'OCRBench': 'https://opencompass.openxlab.space/utils/VLMEval/OCRBench.tsv',
-    'ChartQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ChartQA_TEST.tsv',
-    'MMStar': 'https://opencompass.openxlab.space/utils/VLMEval/MMStar.tsv',
-    'RealWorldQA': 'https://opencompass.openxlab.space/utils/VLMEval/RealWorldQA.tsv',
-    'POPE': 'https://opencompass.openxlab.space/utils/VLMEval/POPE.tsv',
-}
-
-dataset_md5_dict = {
-    # MMBench v1.0
-    'MMBench_DEV_EN': 'b6caf1133a01c6bb705cf753bb527ed8',
-    'MMBench_TEST_EN': '6939fadb0ce626fefc0bdc9c64efc528',
-    'MMBench_DEV_CN': '08b8fc3324a5ed74155350f57be69fbd',
-    'MMBench_TEST_CN': '7e1239baf0ee4c8b513e19705a0f317e',
-    'MMBench': '4115aea3383f3dd0083be6a633e0f820',  # Internal Only
-    'MMBench_CN': '2e053ffc90ea598b1feae13c36dc13ee',    # Internal Only
-    # MMBench v1.1
-    'MMBench_DEV_EN_V11': '30c05be8f2f347a50be25aa067248184',
-    'MMBench_TEST_EN_V11': '26f0f15381a21720255091d3e0316ce6',
-    'MMBench_DEV_CN_V11': '593f9b5f6bea453d870a798b34ae4f37',
-    'MMBench_TEST_CN_V11': '74bbe4556dac745613c7cbe5ad787050',
-    'MMBench_V11': 'b9276414f57af1308dcc4d0cd9b42e7c',  # Internal Only
-    'MMBench_CN_V11': '95f6980dd1b4de38e3cbffe0305a3f25',    # Internal Only
-    # CCBench
-    'CCBench': '1de88b4257e7eee3f60b18d45eda6f07',
-    'MME': 'b36b43c3f09801f5d368627fb92187c3',
-    'SEEDBench_IMG': '68017231464752261a2526d6ca3a10c0',
-    'CORE_MM': '8a8da2f2232e79caf98415bfdf0a202d',
-    'MMVet': '748aa6d4aa9d4de798306a63718455e3',
-    'COCO_VAL': '72a5079dead060269ac222c5aa5128af',
-    'OCRVQA_TEST': 'ca46a6d74b403e9d6c0b670f6fc00db9',
-    'OCRVQA_TESTCORE': 'c5239fe77db8bdc1f2ad8e55e0d1fe97',
-    'TextVQA_VAL': 'b233b31f551bbf4056f2f955da3a92cd',
-    'MMMU_DEV_VAL': '521afc0f3bf341e6654327792781644d',
-    'MMMU_TEST': 'c19875d11a2d348d07e5eb4bdf33166d',
-    'MathVista_MINI': 'f199b98e178e5a2a20e7048f5dcb0464',
-    'ScienceQA_VAL': '96320d05e142e585e7204e72affd29f3',
-    'ScienceQA_TEST': 'e42e9e00f9c59a80d8a5db35bc32b71f',
-    'HallusionBench': '0c23ac0dc9ef46832d7a24504f2a0c7c',
-    'DocVQA_VAL': 'd5ee77e1926ff10690d469c56b73eabf',
-    'DocVQA_TEST': '6a2f28cac26ef2d3447374e8c6f6c8e9',
-    'InfoVQA_VAL': '2342e9c225222f0ef4dec545ebb126fe',
-    'InfoVQA_TEST': 'df535bf51b88dc9718252c34131a6227',
-    'AI2D_TEST': '0f593e0d1c7df9a3d69bf1f947e71975',
-    'LLaVABench': 'd382a093f749a697820d3dadd61c8428',
-    'OCRBench': 'e953d98a987cc6e26ef717b61260b778',
-    'ChartQA_TEST': 'c902e0aa9be5582a7aad6dcf52734b42',
-    'MMStar': 'e1ecd2140806c1b1bbf54b43372efb9e',
-    'RealWorldQA': '92321028d2bc29040284b6674721e48f',
-    'POPE': 'c12f5acb142f2ef1f85a26ba2fbe41d5',
-}
-
-img_root_map = {k: k for k in dataset_URLs}
-img_root_map.update({
-    # MMBench v1.0
-    'MMBench_DEV_EN': 'MMBench',
-    'MMBench_TEST_EN': 'MMBench',
-    'MMBench_DEV_CN': 'MMBench',
-    'MMBench_TEST_CN': 'MMBench',
-    'MMBench': 'MMBench',   # Internal Only
-    'MMBench_CN': 'MMBench',    # Internal Only
-    # MMBench v1.1
-    'MMBench_DEV_EN_V11': 'MMBench_V11',
-    'MMBench_TEST_EN_V11': 'MMBench_V11',
-    'MMBench_DEV_CN_V11': 'MMBench_V11',
-    'MMBench_TEST_CN_V11': 'MMBench_V11',
-    'MMBench_V11': 'MMBench_V11',   # Internal Only
-    'MMBench_CN_V11': 'MMBench_V11',    # Internal Only
-    'COCO_VAL': 'COCO',
-    'OCRVQA_TEST': 'OCRVQA',
-    'OCRVQA_TESTCORE': 'OCRVQA',
-    'TextVQA_VAL': 'TextVQA',
-    'MMMU_DEV_VAL': 'MMMU',
-    'MMMU_TEST': 'MMMU',
-    'MathVista_MINI': 'MathVista',
-    'HallusionBench': 'Hallusion',
-    'DocVQA_VAL': 'DocVQA',
-    'DocVQA_TEST': 'DocVQA_TEST',
-    'OCRBench': 'OCRBench',
-    'ChartQA_TEST': 'ChartQA_TEST',
-    'InfoVQA_VAL': 'InfoVQA_VAL',
-    'InfoVQA_TEST': 'InfoVQA_TEST',
-    'MMStar': 'MMStar',
-    'RealWorldQA': 'RealWorldQA',
-    'POPE': 'POPE',
-})
-
-assert set(dataset_URLs) == set(img_root_map)
-
-
-def DATASET_TYPE(dataset):
-    # Dealing with Custom Dataset
-    dataset = dataset.lower()
-    if listinstr(['mmbench', 'seedbench', 'ccbench', 'mmmu', 'scienceqa', 'ai2d', 'mmstar', 'realworldqa'], dataset):
-        return 'multi-choice'
-    elif listinstr(['mme', 'hallusion', 'pope'], dataset):
-        return 'Y/N'
-    elif 'coco' in dataset:
-        return 'Caption'
-    elif listinstr(['ocrvqa', 'textvqa', 'chartqa', 'mathvista', 'docvqa', 'infovqa', 'llavabench',
-                    'mmvet', 'ocrbench'], dataset):
-        return 'VQA'
-    else:
-        if dataset not in dataset_URLs:
-            import warnings
-            warnings.warn(f"Dataset {dataset} not found in dataset_URLs, will use 'multi-choice' as the default TYPE.")
-            return 'multi-choice'
-        else:
-            return 'QA'
-
-
-def abbr2full(s):
-    datasets = [x for x in img_root_map]
-    ins = [s in d for d in datasets]
-    if sum(ins) == 1:
-        for d in datasets:
-            if s in d:
-                return d
-    else:
-        return s

eval_mm/vlmevalkit/vlmeval/utils/mp_util.py

@@ -145,7 +145,7 @@ def track_progress_rich(func: Callable,
             results = []
             for task in tasks:
                 result, idx = worker(task)
-                results.append(worker(task)[0])
+                results.append(result)
                 if save is not None:
                     with portalocker.Lock(save, timeout=5) as fh:
                         ans = load(save)

eval_mm/vlmevalkit/vlmeval/utils/result_transfer.py

@@ -0,0 +1,97 @@
+from ..smp import *
+from ..dataset.utils.judge_util import build_judge
+from ..dataset.utils.multiple_choice import extract_answer_from_item
+from .matching_util import can_infer
+from .mp_util import track_progress_rich
+
+
+def MMMU_result_transfer(result_path):
+    res = {}
+    result_data = load(result_path)
+    mcq = result_data['A'].notna()
+    lt = len(result_data)
+    for i in range(lt):
+        line = result_data.iloc[i]
+        if mcq[i]:
+            options = {
+                cand: line[cand]
+                for cand in string.ascii_uppercase
+                if cand in line and not pd.isna(line[cand])
+            }
+            prediction = line['prediction']
+            infer_prediction = can_infer(prediction, options)
+            res[line['id']] = infer_prediction
+        else:
+            res[line['id']] = line['prediction']
+    result_json = result_path.replace('.xlsx', '.json')
+    dump(res, result_json)
+    return result_json
+
+
+def MMTBench_result_transfer(eval_file, dataset='default', **judge_kwargs):
+    logger = get_logger('Evaluation')
+    nproc = judge_kwargs.pop('nproc', 4)
+
+    rd.seed(2680)
+    suffix = eval_file.split('.')[-1]
+    model = judge_kwargs['model']
+    assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125']
+    name_str_map = {
+        'chatgpt-0125': 'openai',
+        'gpt-4-0125': 'gpt4'
+    }
+    name_str = name_str_map[model] if model in name_str_map else model
+
+    if model == 'exact_matching':
+        model = None
+    elif gpt_key_set():
+        model = build_judge(**judge_kwargs)
+        if not model.working():
+            logger.error('The OPENAI API is not working properly, will use exact matching for evaluation')
+            model = None
+    else:
+        logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
+        model = None
+
+    logger.info(f'Evaluating {eval_file}')
+    result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_option.pkl')
+    result = {}
+    if osp.exists(result_file):
+        result = load(result_file)
+
+    data = load(eval_file)
+    assert 'index' in data, 'Essentail columns missing in the eval_file.'
+
+    data = data.sort_values(by='index')
+    data['prediction'] = [str(x) for x in data['prediction']]
+    for k in data.keys():
+        data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
+
+    idx2lines = {data.iloc[i]['index']: data.iloc[i] for i in range(len(data))}
+    idx2lines = {k: v for k, v in idx2lines.items() if k not in result}
+
+    indices = list(idx2lines.keys())
+    lines = [idx2lines[i] for i in indices]
+    tups = [(model, line) for line in lines]
+    res = track_progress_rich(
+        extract_answer_from_item,
+        tups,
+        nproc=nproc,
+        chunksize=nproc,
+        save=result_file,
+        keys=indices)
+
+    for i, r in zip(indices, res):
+        if i in result:
+            assert result[i]['opt'] == r['opt'] and result[i]['log'] == r['log']
+        else:
+            result[i] = r
+
+    indices = list(data['index'])
+    data['opt'] = [result[i]['opt'] for i in data['index']]
+    data['log'] = [result[i]['log'] for i in data['index']]
+
+    # load split
+    output_path = eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv')
+    dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv'))
+    return output_path

eval_mm/vlmevalkit/vlmeval/vlm/__init__.py

@@ -3,5 +3,4 @@ import torch
 torch.set_grad_enabled(False)
 torch.manual_seed(1234)
 from .base import BaseModel
-from .minicpm_llama3_v_2_5 import MiniCPM_Llama3_V
+from .minicpm_v import MiniCPM_V, MiniCPM_Llama3_V, MiniCPM_V_2_6
-from .minicpm_v import MiniCPM_V

eval_mm/vlmevalkit/vlmeval/vlm/base.py

@@ -1,12 +1,15 @@
 from ..smp import *
-from ..utils.dataset_config import img_root_map
+from ..dataset import img_root_map
 from abc import abstractmethod
 
 
 class BaseModel:
 
     INTERLEAVE = False
-    allowed_types = ['text', 'image']
+    allowed_types = ['text', 'image', 'video']
+
+    def __init__(self):
+        self.dump_image_func = None
 
     def use_custom_prompt(self, dataset):
         """Whether to use custom prompt for the given dataset.
@@ -33,34 +36,11 @@ class BaseModel:
         """
         raise NotImplementedError
 
+    def set_dump_image(self, dump_image_func):
+        self.dump_image_func = dump_image_func
+
     def dump_image(self, line, dataset):
-        """Dump the image(s) of the input line to the corresponding dataset folder.
+        return self.dump_image_func(line)
-
-        Args:
-            line (line of pd.DataFrame): The raw input line.
-            dataset (str): The name of the dataset.
-
-        Returns:
-            str | list[str]: The paths of the dumped images.
-        """
-        ROOT = LMUDataRoot()
-        assert isinstance(dataset, str)
-        img_root = osp.join(ROOT, 'images', img_root_map[dataset] if dataset in img_root_map else dataset)
-        os.makedirs(img_root, exist_ok=True)
-        if isinstance(line['image'], list):
-            tgt_path = []
-            assert 'image_path' in line
-            for img, im_name in zip(line['image'], line['image_path']):
-                path = osp.join(img_root, im_name)
-                if not read_ok(path):
-                    decode_base64_to_image_file(img, path)
-                tgt_path.append(path)
-        else:
-            tgt_path = osp.join(img_root, f"{line['index']}.jpg")
-            if not read_ok(tgt_path):
-                decode_base64_to_image_file(line['image'], tgt_path)
-            tgt_path = [tgt_path]
-        return tgt_path
 
     @abstractmethod
     def generate_inner(self, message, dataset=None):
@@ -134,7 +114,25 @@ class BaseModel:
             assert item['type'] in self.allowed_types, f'Invalid input type: {item["type"]}'
         return self.generate_inner(message, dataset)
 
-    def message_to_promptimg(self, message):
+    def chat(self, messages, dataset=None):
+        """The main function for multi-turn chatting. Will call `chat_inner` with the preprocessed input messages."""
+        assert hasattr(self, 'chat_inner'), 'The API model should has the `chat_inner` method. '
+        for msg in messages:
+            assert isinstance(msg, dict) and 'role' in msg and 'content' in msg, msg
+            assert self.check_content(msg['content']) in ['str', 'dict', 'liststr', 'listdict'], msg
+            msg['content'] = self.preproc_content(msg['content'])
+
+        while len(messages):
+            try:
+                return self.chat_inner(messages, dataset=dataset)
+            except:
+                messages = messages[1:]
+                while len(messages) and messages[0]['role'] != 'user':
+                    messages = messages[1:]
+                continue
+        return 'Chat Mode: Failed with all possible conversation turns.'
+
+    def message_to_promptimg(self, message, dataset=None):
         assert not self.INTERLEAVE
         model_name = self.__class__.__name__
         warnings.warn(
@@ -146,5 +144,24 @@ class BaseModel:
             image = None
         else:
             prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
-            image = [x['value'] for x in message if x['type'] == 'image'][0]
+            images = [x['value'] for x in message if x['type'] == 'image']
+            if 'BLINK' == dataset:
+                image = concat_images_vlmeval(images, target_size=512)
+            else:
+                image = images[0]
         return prompt, image
+
+    def message_to_promptvideo(self, message):
+        if self.VIDEO_LLM:
+            num_videos = len([x for x in message if x['type'] == 'video'])
+            if num_videos == 0:
+                prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
+                video = None
+            else:
+                prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
+                video = [x['value'] for x in message if x['type'] == 'video'][0]
+            return prompt, video
+        else:
+            import sys
+            warnings.warn('Model does not support video input.')
+            sys.exit(-1)

eval_mm/vlmevalkit/vlmeval/vlm/minicpm_llama3_v_2_5.py

@@ -1,155 +0,0 @@
-import torch
-from PIL import Image
-from transformers import AutoModel, AutoTokenizer
-
-from ..smp import *
-from ..utils import DATASET_TYPE
-from .base import BaseModel
-
-
-class MiniCPM_Llama3_V(BaseModel):
-    INSTALL_REQ = False
-    INTERLEAVE = True
-
-    def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs):
-        assert model_path is not None
-        self.model_path = model_path
-        self.ckpt = model_path
-
-        print(f'load from {self.model_path}')
-        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
-        if '.pt' in model_path:
-            print(f'load from {model_path}')
-            self.state_dict = torch.load(self.ckpt, map_location='cpu')
-            self.model.load_state_dict(self.state_dict, strict=False)
-        self.model = self.model.to(dtype=torch.float16)
-        self.model.eval().cuda()
-        self.kwargs = kwargs
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
-        torch.cuda.empty_cache()
-        self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
-        self.options_system_prompt = ('Carefully read the following question and select the letter corresponding '
-                                      'to the correct answer. Highlight the applicable choices without giving '
-                                      'explanations.')
-        self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
-        self.detail_system_prompt = 'Answer this question in detail.'
-        self.vqa_prompt = 'Answer the question using a single word or phrase.'
-        
-
-    def use_custom_prompt(self, dataset):
-        if listinstr(['multi-choice', 'VQA'], DATASET_TYPE(dataset)):
-            return True
-        elif dataset is not None and listinstr(['HallusionBench'], dataset):
-            return True
-        return False
-
-    def build_prompt(self, line, dataset=None):
-        if dataset is None:
-            dataset = self.dataset
-
-        if isinstance(line, int):
-            line = self.data.iloc[line]
-
-        tgt_path = self.dump_image(line, dataset)
-        system_prompt = ''
-
-        question = line['question']
-        if DATASET_TYPE(dataset) == 'multi-choice':
-            options = {
-                cand: line[cand]
-                for cand in string.ascii_uppercase
-                if cand in line and not pd.isna(line[cand])
-            }
-            options_prompt = 'Options:\n'
-            for key, item in options.items():
-                options_prompt += f'{key}. {item}\n'
-            hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
-            prompt = ''
-            if hint is not None:
-                prompt += f'Hint: {hint}\n'
-            prompt += f'Question: {question}\n'
-            if len(options):
-                prompt += options_prompt
-                system_prompt = self.options_system_prompt +  "\nPlease just indicate your choice."
-            else:
-                system_prompt = self.wo_options_system_prompt
-
-            if 'MMMU' in dataset: # Corner Case
-                prompt = system_prompt + '\n' + prompt
-                system_prompt = ''
-        elif dataset is not None and listinstr(['HallusionBench'], dataset):
-            question = line['question'] + " Yes or No?"
-            prompt = question
-        
-        elif dataset is not None and listinstr(['OCRBench'], dataset):
-            system_prompt = self.vqa_prompt
-            question = line['question']
-            prompt = question
-        elif DATASET_TYPE(dataset) == 'VQA':
-            if listinstr(['LLaVABench'], dataset):
-                system_prompt = ""
-                prompt = question
-            elif listinstr(['MMVet'], dataset):
-                system_prompt = self.detail_system_prompt
-                prompt = question
-            else:
-                system_prompt = self.vqa_prompt
-                prompt = question
-
-        msgs = []
-        if system_prompt:
-            msgs.append(dict(type='text', value=system_prompt))
-        if isinstance(tgt_path, list):
-            msgs.extend([dict(type='image', value=p) for p in tgt_path])
-        else:
-            msgs = [dict(type='image', value=tgt_path)]
-        msgs.append(dict(type='text', value=prompt))
-
-        return msgs
-
-    def generate_inner(self, message, dataset=None):
-        if DATASET_TYPE(dataset) == 'multi-choice':
-            max_new_tokens = 200
-        elif DATASET_TYPE(dataset) == 'Y/N':
-            max_new_tokens = 3
-        else:
-            max_new_tokens = 1024
-
-        '''
-        nums_beams = 3
-        '''
-        default_kwargs = dict(
-            max_new_tokens=max_new_tokens,
-            sampling=False,
-            num_beams=self.num_beams,
-        )
-        default_kwargs.update(self.kwargs)
-
-        content = []
-        
-    #     message = [
-    #     {'type': 'text', 'value': 'sys prompt'},
-    #     {'type': 'image', 'value': '/path/to/image1.jpg'},
-    #     {'type': 'text', 'value': 'Here is an image:'},
-    # ]
-
-        for x in message:
-            if x['type'] == 'text':
-                content.append(x['value'])
-            elif x['type'] == 'image':
-                image = Image.open(x['value']).convert('RGB')
-                content.append(image)
-        msgs = [{'role': 'user', 'content': content}]
-
-        res = self.model.chat(
-            image = None,
-            msgs=msgs,
-            context=None,
-            tokenizer=self.tokenizer,
-            **default_kwargs
-        )
-
-        if isinstance(res, tuple) and len(res) > 0:
-            res = res[0]
-        # print(f"content: {content}, res: {res}")
-        return res

eval_mm/vlmevalkit/vlmeval/vlm/minicpm_v.py

@@ -1,10 +1,13 @@
+import math
 import torch
+import random
+import numpy as np
 from PIL import Image
 from transformers import AutoModel, AutoTokenizer
 
 from .base import BaseModel
 from ..smp import *
-from ..utils import DATASET_TYPE
+from ..dataset import DATASET_TYPE
 
 
 class MiniCPM_V(BaseModel):
@@ -59,10 +62,10 @@ class MiniCPM_V(BaseModel):
         return message
 
     def generate_inner(self, message, dataset=None):
-        prompt, image_path = self.message_to_promptimg(message)
+        prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
         image = Image.open(image_path).convert('RGB')
         msgs = [{'role': 'user', 'content': prompt}]
-        if DATASET_TYPE(dataset) == 'multi-choice':
+        if DATASET_TYPE(dataset) == 'MCQ':
             max_new_tokens = 20
         elif DATASET_TYPE(dataset) == 'Y/N':
             max_new_tokens = 100
@@ -83,3 +86,374 @@ class MiniCPM_V(BaseModel):
             **default_kwargs
         )
         return res
+
+
+class MiniCPM_Llama3_V(BaseModel):
+
+    INSTALL_REQ = False
+    INTERLEAVE = True
+
+    def __init__(self, model_path='openbmb/MiniCPM-Llama3-V-2_5', **kwargs):
+        assert model_path is not None
+        self.model_path = model_path
+        print(f'load from {self.model_path}')
+        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
+        self.model = self.model.to(dtype=torch.float16)
+        self.model.eval().cuda()
+        self.kwargs = kwargs
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+        torch.cuda.empty_cache()
+        self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
+        self.options_system_prompt = ('Carefully read the following question and select the letter corresponding '
+                                      'to the correct answer. Highlight the applicable choices without giving '
+                                      'explanations.')
+        self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
+        self.detail_system_prompt = 'Answer this question in detail.'
+        self.vqa_prompt = 'Answer the question using a single word or phrase.'
+
+    def use_custom_prompt(self, dataset):
+        if listinstr(['MCQ', 'VQA'], DATASET_TYPE(dataset)):
+            return True
+        elif dataset is not None and listinstr(['HallusionBench'], dataset):
+            return True
+        return False
+
+    def build_prompt(self, line, dataset=None):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        tgt_path = self.dump_image(line, dataset)
+        system_prompt = ''
+
+        question = line['question']
+        if DATASET_TYPE(dataset) == 'MCQ':
+            options = {
+                cand: line[cand]
+                for cand in string.ascii_uppercase
+                if cand in line and not pd.isna(line[cand])
+            }
+            options_prompt = 'Options:\n'
+            for key, item in options.items():
+                options_prompt += f'{key}. {item}\n'
+            hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+            prompt = ''
+            if hint is not None:
+                prompt += f'Hint: {hint}\n'
+            prompt += f'Question: {question}\n'
+            if len(options):
+                prompt += options_prompt
+                system_prompt = self.options_system_prompt + '\nPlease just indicate your choice.'
+            else:
+                system_prompt = self.wo_options_system_prompt
+            if 'MMMU' in dataset:  # Corner Case
+                prompt = system_prompt + '\n' + prompt
+                system_prompt = ''
+        elif dataset is not None and listinstr(['HallusionBench'], dataset):
+            question = line['question'] + ' Yes or No?'
+            prompt = question
+        elif dataset is not None and listinstr(['MME'], dataset):
+            question = line['question'] + ' Yes or No?'
+            prompt = question
+        elif dataset is not None and listinstr(['OCRBench'], dataset):
+            system_prompt = self.vqa_prompt
+            question = line['question']
+            prompt = question
+        elif DATASET_TYPE(dataset) == 'VQA':
+            if listinstr(['LLaVABench', 'MMLongBench_DOC'], dataset):
+                system_prompt = ''
+                prompt = question
+            elif listinstr(['MMVet'], dataset):
+                system_prompt = self.detail_system_prompt
+                prompt = question
+            else:
+                system_prompt = self.vqa_prompt
+                prompt = question
+
+        msgs = []
+        if system_prompt:
+            msgs.append(dict(type='text', value=system_prompt))
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=prompt))
+        return msgs
+
+    def generate_inner(self, message, dataset=None):
+        if DATASET_TYPE(dataset) == 'MCQ':
+            max_new_tokens = 200
+        elif DATASET_TYPE(dataset) == 'Y/N':
+            max_new_tokens = 3
+        else:
+            max_new_tokens = 1024
+
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=self.num_beams,
+        )
+        default_kwargs.update(self.kwargs)
+
+        content = []
+        for x in message:
+            if x['type'] == 'text':
+                content.append(x['value'])
+            elif x['type'] == 'image':
+                image = Image.open(x['value']).convert('RGB')
+                content.append(image)
+        msgs = [{'role': 'user', 'content': content}]
+
+        res = self.model.chat(
+            msgs=msgs,
+            context=None,
+            image=None,
+            tokenizer=self.tokenizer,
+            **default_kwargs
+        )
+
+        if isinstance(res, tuple) and len(res) > 0:
+            res = res[0]
+        return res
+
+    def chat_inner(self, message, dataset=None):
+        max_new_tokens = 1024
+
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=self.num_beams,
+        )
+        default_kwargs.update(self.kwargs)
+
+        msgs = []
+        for msg in message:
+            content = []
+            if len(msg['content']) == 1 and msg['content'][0]['type'] == 'text':
+                msg_new = {'role': msg['role'], 'content': msg['content'][0]['value']}
+                msgs.append(msg_new)
+                continue
+
+            for x in msg['content']:
+                if x['type'] == 'text':
+                    content.append(x['value'])
+                elif x['type'] == 'image':
+                    image = Image.open(x['value']).convert('RGB')
+                    content.append(image)
+            msg_new = {'role': msg['role'], 'content': content}
+            msgs.append(msg_new)
+
+        res = self.model.chat(
+            msgs=msgs,
+            context=None,
+            image=None,
+            tokenizer=self.tokenizer,
+            **default_kwargs)
+
+        if isinstance(res, tuple) and len(res) > 0:
+            res = res[0]
+        return res
+
+
+class MiniCPM_V_2_6(BaseModel):
+    INSTALL_REQ = False
+    INTERLEAVE = True
+
+    def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs):
+        random.seed(0)
+        np.random.seed(0)
+        torch.manual_seed(0)
+        torch.cuda.manual_seed_all(0)
+
+        assert model_path is not None
+        self.model_path = model_path
+        print(f'load from path {self.model_path}')
+        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
+        self.model = self.model.to(dtype=torch.bfloat16)
+        self.model.eval().cuda()
+
+        self.kwargs = kwargs
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+        torch.cuda.empty_cache()
+        self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
+
+        self.options_suffix_prompt = '''\nAnswer with the option's letter from the given choices directly.'''
+        self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
+        self.detail_system_prompt = 'Answer this question in detail.'
+        self.vqa_prompt = 'Answer the question using a single word or phrase.'
+
+        self.multi_choice_cot_prompt = ('''Carefully read the following multichoice question, solve it step '''
+                                        '''by step and finally pick the option associated with the correct '''
+                                        '''answer in the format of "Answer: selected option\n\n''')
+        self.short_ans_cot_prompt = ('''Read the following question carefully, solve it step by step, and '''
+                                     '''then output the final answer in the format of "Answer: single number '''
+                                     '''or single word or phrase".\n\n''')
+
+    def use_custom_prompt(self, dataset=None):
+        if dataset is None:
+            return False
+        if listinstr(['MCQ', 'VQA', 'Y/N'], DATASET_TYPE(dataset)):
+            return True
+        return False
+
+    def use_cot(self, dataset=None):
+        if dataset is None:
+            return False
+        if listinstr(['MMMU', 'HallusionBench', 'OCRBench', 'ChartQA'], dataset):
+            return True
+        elif listinstr(['MathVista', 'MMVet', 'MMBench', 'MMStar', 'AI2D', 'RealWorldQA',
+                        'POPE', 'ScienceQA', 'TextVQA', 'DocVQA'], dataset):
+            return False
+        else:
+            return False
+
+    def use_upsize(self, dataset=None):
+        if dataset is None:
+            return False
+        if listinstr(['MMVet', 'MMBench', 'MMStar', 'AI2D', 'OCRBench'], dataset):
+            return True
+        else:
+            return False
+
+    def build_prompt(self, line, dataset=None):
+        if isinstance(line, int):
+            line = self.data.iloc[line]
+
+        tgt_path = self.dump_image(line, dataset)
+        system_prompt, prompt = '', ''
+
+        question = line['question']
+
+        if not self.use_cot(dataset):
+            if DATASET_TYPE(dataset) == 'MCQ':
+                options = {
+                    cand: line[cand]
+                    for cand in string.ascii_uppercase
+                    if cand in line and not pd.isna(line[cand])
+                }
+                options_prompt = 'Options:\n'
+                for key, item in options.items():
+                    options_prompt += f'{key}. {item}\n'
+                hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+                if hint is not None:
+                    prompt += f'Hint: {hint}\n'
+                prompt += f'Question: {question}\n'
+                if len(options):
+                    prompt += options_prompt
+                    prompt += self.options_suffix_prompt
+                else:
+                    system_prompt = self.wo_options_system_prompt
+
+                if 'MMMU' in dataset:
+                    if len(system_prompt) > 0:
+                        prompt = system_prompt + '\n' + prompt
+                        system_prompt = ''
+            elif dataset is not None and listinstr(['HallusionBench'], dataset):
+                question += ' Yes or No?'
+                prompt = question
+            elif dataset is not None and listinstr(['OCRBench'], dataset):
+                system_prompt = self.vqa_prompt
+                prompt = question
+            elif DATASET_TYPE(dataset) == 'VQA':
+                if listinstr(['LLaVABench'], dataset):
+                    system_prompt = ''
+                elif listinstr(['MMVet'], dataset):
+                    system_prompt = self.detail_system_prompt
+                else:
+                    system_prompt = self.vqa_prompt
+                prompt = question
+            else:
+                prompt = question
+        else:
+            has_options = True
+            if DATASET_TYPE(dataset) == 'MCQ':
+                options = {
+                    cand: line[cand]
+                    for cand in string.ascii_uppercase
+                    if cand in line and not pd.isna(line[cand])
+                }
+                options_prompt = ''
+                for key, item in options.items():
+                    options_prompt += f'{key}. {item}\n'
+                hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
+                if hint is not None:
+                    prompt += f'Hint: {hint}\n'
+                prompt += f'{question}\n'
+
+                if len(options):
+                    prompt += options_prompt
+                else:
+                    has_options = False
+
+                if 'MMMU' in dataset:
+                    if len(system_prompt) > 0:
+                        prompt = system_prompt + '\n' + prompt
+                        system_prompt = ''
+            else:
+                prompt = question
+
+            if DATASET_TYPE(dataset) in ['MCQ', 'Y/N', 'VQA']:
+                if DATASET_TYPE(dataset) == 'MCQ':
+                    if has_options:
+                        prompt = self.multi_choice_cot_prompt + prompt
+                    else:
+                        prompt = self.short_ans_cot_prompt + prompt
+                elif DATASET_TYPE(dataset) == 'Y/N':
+                    prompt = self.short_ans_cot_prompt + prompt
+                else:
+                    prompt = self.short_ans_cot_prompt + prompt
+
+        msgs = []
+        if system_prompt:
+            msgs.append(dict(type='text', value=system_prompt))
+        if isinstance(tgt_path, list):
+            msgs.extend([dict(type='image', value=p) for p in tgt_path])
+        else:
+            msgs = [dict(type='image', value=tgt_path)]
+        msgs.append(dict(type='text', value=prompt))
+
+        return msgs
+
+    def generate_inner(self, message, dataset=None):
+        max_new_tokens = 2048
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=self.num_beams,
+        )
+        default_kwargs.update(self.kwargs)
+
+        content = []
+
+        for x in message:
+            if x['type'] == 'text':
+                content.append(x['value'])
+            elif x['type'] == 'image':
+                image = Image.open(x['value']).convert('RGB')
+                if not self.use_upsize(dataset):
+                    content.append(image)
+                else:
+                    img_width, img_height = image.width, image.height
+                    if (img_width * img_height) >= (1344 * 1344):
+                        content.append(image)
+                    else:
+                        ratio = math.sqrt((1344 * 1344) / (img_width * img_height))
+                        max_img_width = int(img_width * ratio)
+                        new_img_width = random.randint(img_width, max_img_width)
+                        new_img_height = int(new_img_width / img_width * img_height)
+                        resized_image = image.resize((new_img_width, new_img_height))
+                        content.append(resized_image)
+        msgs = [{'role': 'user', 'content': content}]
+
+        res = self.model.chat(
+            image=None,
+            msgs=msgs,
+            context=None,
+            tokenizer=self.tokenizer,
+            max_inp_length=8192,
+            **default_kwargs
+        )
+
+        if isinstance(res, tuple) and len(res) > 0:
+            res = res[0]
+
+        return res

eval_mm/vqaeval/eval.py

@@ -22,10 +22,16 @@ from eval_utils.vqa_evaluate import *
 def get_model(args):
     if args.model_name == '':
         raise Exception('Model name cannot be empty str!')
-    from models.MiniCPM.minicpmv import MiniCPM_V
+    from models.MiniCPM.minicpmv import MiniCPM_V, MiniCPM_V_2_6
     model_path = args.model_path
     ckpt = args.ckpt
+    
+    if args.model_name == 'minicpmv':
         model = MiniCPM_V(model_path=model_path, ckpt=ckpt, device=args.device)
+    elif args.model_name == 'minicpmv26':
+        model = MiniCPM_V_2_6(model_path=model_path, ckpt=ckpt, device=args.device)
+    else:
+        raise Exception(f"Unexpected Moedel Name {args.model_name}!")
     
     return model
 

eval_mm/vqaeval/eval_utils/getargs.py

@@ -4,7 +4,7 @@ import argparse
 def parse_args():
     parser = argparse.ArgumentParser(description="Demo")
 
-    parser.add_argument('--local-rank', type=int, default=0, help='Local rank for distributed training')
+    parser.add_argument('--local-rank', type=int, default=0, help='Local rank for distributed training.')
 
     # textVQA
     parser.add_argument("--textVQA_image_dir", type=str, default="")

eval_mm/vqaeval/models/MiniCPM/minicpmv.py

@@ -2,6 +2,9 @@
 import torch
 from PIL import Image
 from transformers import AutoModel, AutoTokenizer
+import random
+import math
+import numpy as np
 
 Image.MAX_IMAGE_PIXELS = 1000000000
 
@@ -95,3 +98,104 @@ class MiniCPM_V:
             res = res[0]
         print(f"Q: {content}, \nA: {res}")
         return [res]
+
+
+class MiniCPM_V_2_6:
+
+    def __init__(self, model_path, ckpt, device=None)->None:
+        seed = 0
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        
+        self.model_path = model_path
+        self.ckpt = ckpt
+        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).eval()
+        if self.ckpt is not None:
+            self.ckpt = ckpt
+            self.state_dict = torch.load(self.ckpt, map_location=torch.device('cpu'))
+            self.model.load_state_dict(self.state_dict)
+
+        self.model = self.model.to(dtype=torch.bfloat16)
+        self.model.to(device)
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+        torch.cuda.empty_cache()
+
+    def generate(self, images, questions, datasetname):
+        image = Image.open(images[0]).convert('RGB')
+        try:
+            max_new_tokens = max_token[datasetname]
+        except:
+            max_new_tokens = 1024
+        if (datasetname == 'docVQA') or (datasetname == "docVQATest") :
+            prompt = "Answer the question directly with single word." + "\n" + questions[0]
+        elif (datasetname == 'textVQA') :
+            prompt = "Answer the question directly with single word." + '\n'+ questions[0]
+        
+        msgs = [{'role': 'user', 'content': prompt}]
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=3
+        )
+        res = self.model.chat(
+            image=image,
+            msgs=msgs,
+            context=None,
+            tokenizer=self.tokenizer,
+            **default_kwargs
+        )
+        
+        return [res]
+    
+    def generate_with_interleaved(self, images, questions, datasetname):
+        try:
+            max_new_tokens = max_token[datasetname]
+        except:
+            max_new_tokens = 1024
+        
+        prompt = "Answer the question directly with single word."
+        
+        default_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            sampling=False,
+            num_beams=3
+        )
+        
+        content = []
+        message = [
+            {'type': 'text', 'value': prompt},
+            {'type': 'image', 'value': images[0]},
+            {'type': 'text', 'value': questions[0]}
+        ]
+        for x in message:
+            if x['type'] == 'text':
+                content.append(x['value'])
+            elif x['type'] == 'image':
+                image = Image.open(x['value']).convert('RGB')
+                img_width, img_height = image.width, image.height
+                if (img_width * img_height) >= (1344 * 1344):
+                    content.append(image)
+                else:
+                    ratio = math.sqrt((1344 * 1344) / (img_width * img_height))
+                    max_img_width = int(img_width * ratio)
+                    new_img_width = random.randint(img_width, max_img_width)
+                    new_img_height = int(new_img_width / img_width * img_height)
+                    resized_image = image.resize((new_img_width, new_img_height))
+                    content.append(resized_image)
+        msgs = [{'role': 'user', 'content': content}]
+
+        res = self.model.chat(
+            image=None,
+            msgs=msgs,
+            context=None,
+            tokenizer=self.tokenizer,
+            **default_kwargs
+        )
+
+        if isinstance(res, tuple) and len(res) > 0:
+            res = res[0]
+        print(f"Q: {content}, \nA: {res}")
+        return [res]

eval_mm/vqaeval/shell/run_inference.sh

@@ -6,7 +6,7 @@ python -m torch.distributed.launch \
     --master_addr=${MASTER_ADDR:-127.0.0.1} \
     --master_port=${MASTER_PORT:-12345} \
     ./eval.py \
-    --model_name minicpm \
+    --model_name minicpmv26 \
     --model_path \
     --generate_method interleave \
     --eval_textVQA \