release evaluation code and val split

.github/workflows/lint.yml

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+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# Images
+images/
+
+scripts/*ttf
+
+lvlm_zoo/
+LMUData/
+work_dirs
+
+batchscript-*
+phoenix-slurm-*
+LMUData

.vscode/launch.json

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+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "main",
+            "type": "python",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--data", "MMT-Bench_ALL",
+                "--model", "llava_v1.5_7b",
+                "--work-dir", "work_dirs/mmtbench"
+            ]
+        },
+    ]
+}

Quickstart.md

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+# Quickstart
+
+## Prepare the Dataset
+
+You can download the MMT-Bench dataset in the following links: [HuggingFace](https://huggingface.co/datasets/Kaining/MMT-Bench/blob/main/MMT-Bench_VAL.tsv). **Note**: We only provide `VAL` split now. And we will support `TEST` split in the furture.
+
+Put the data under the `LMUData/`
+
+## Step 0. Installation & Setup essential keys
+
+**Installation.**
+
+```bash
+pip install -e .
+```
+
+**Setup Keys.**
+
+To infer with API models (GPT-4v, Gemini-Pro-V, etc.) or use LLM APIs as the **judge or choice extractor**, you need to first setup API keys. VLMEvalKit will use an judge **LLM** to extract answer from the output if you set the key, otherwise it uses the **exact matching** mode (find "Yes", "No", "A", "B", "C"... in the output strings). **The exact matching can only be applied to the Yes-or-No tasks and the Multi-choice tasks.**
+- You can place the required keys in `$VLMEvalKit/.env` or directly set them as the environment variable. If you choose to create a `.env` file, its content will look like:
+
+  ```bash
+  # The .env file, place it under $VLMEvalKit
+  # Alles-apin-token, for intra-org use only
+  ALLES=
+  # API Keys of Proprietary VLMs
+  DASHSCOPE_API_KEY=
+  GOOGLE_API_KEY=
+  OPENAI_API_KEY=
+  OPENAI_API_BASE=
+  STEPAI_API_KEY=
+  ```
+
+- Fill the blanks with your API keys (if necessary). Those API keys will be automatically loaded when doing the inference and evaluation.
+## Step 1. Configuration
+
+**VLM Configuration**: All VLMs are configured in `vlmeval/config.py`, for some VLMs, you need to configure the code root (MiniGPT-4, PandaGPT, etc.) or the model_weight root (LLaVA-v1-7B, etc.) before conducting the evaluation. During evaluation, you should use the model name specified in `supported_VLM` in `vlmeval/config.py` to select the VLM. For MiniGPT-4 and InstructBLIP, you also need to modify the config files in `vlmeval/vlm/misc` to configure LLM path and ckpt path.
+
+Following VLMs require the configuration step:
+
+**Code Preparation & Installation**: InstructBLIP ([LAVIS](https://github.com/salesforce/LAVIS)), LLaVA ([LLaVA](https://github.com/haotian-liu/LLaVA)), MiniGPT-4 ([MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4)), mPLUG-Owl2 ([mPLUG-Owl2](https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2)), OpenFlamingo-v2 ([OpenFlamingo](https://github.com/mlfoundations/open_flamingo)), PandaGPT-13B ([PandaGPT](https://github.com/yxuansu/PandaGPT)), TransCore-M ([TransCore-M](https://github.com/PCIResearch/TransCore-M)).
+
+**Manual Weight Preparation & Configuration**: InstructBLIP, LLaVA-v1-7B, MiniGPT-4, PandaGPT-13B
+
+## Step 2. Evaluation
+
+We use `run.py` for evaluation. To use the script, you can use `$VLMEvalKit/run.py` or create a soft-link of the script (to use the script anywhere):
+
+**Arguments**
+
+- `--data (list[str])`: Set the dataset names that are supported in VLMEvalKit (defined in `vlmeval/utils/dataset_config.py`).
+- `--model (list[str])`: Set the VLM names that are supported in VLMEvalKit (defined in `supported_VLM` in `vlmeval/config.py`).
+- `--mode (str, default to 'all', choices are ['all', 'infer'])`: When `mode` set to "all", will perform both inference and evaluation; when set to "infer", will only perform the inference.
+- `--nproc (int, default to 4)`: The number of threads for OpenAI API calling.
+
+**Command**
+
+You can run the script with `python` or `torchrun`:
+
+```bash
+# When running with `python`, only one VLM instance is instantiated, and it might use multiple GPUs (depending on its default behavior).
+# That is recommended for evaluating very large VLMs (like IDEFICS-80B-Instruct).
+
+# IDEFICS-80B-Instruct on MMT-Bench, Inference and Evalution
+python run.py --data MMT-Bench_VAL --model idefics_80b_instruct --verbose
+# IDEFICS-80B-Instruct on MMT-Bench, Inference only
+python run.py --data MMT-Bench_VAL --model idefics_80b_instruct --verbose --mode infer
+
+
+# IDEFICS-9B-Instruct, Qwen-VL-Chat, mPLUG-Owl2 on MMT-Bench. On a node with 8 GPU. Inference and Evaluation.
+torchrun --nproc-per-node=8 run.py --data MMT-Bench_VAL --model idefics_80b_instruct qwen_chat mPLUG-Owl2 --verbose
+# Qwen-VL-Chat on MMT-Bench. On a node with 2 GPU. Inference and Evaluation.
+torchrun --nproc-per-node=2 run.py --data MMT-Bench_VAL --model qwen_chat --verbose
+```
+
+The evaluation results will be printed as logs, besides. **Result Files** will also be generated in the directory `$YOUR_WORKING_DIRECTORY/{model_name}`. Files ending with `.csv` contain the evaluated metrics.
+
+## Deploy a local language model as the judge / choice extractor
+The default setting mentioned above uses OpenAI's GPT as the judge LLM. However, you can also deploy a local judge LLM with [LMDeploy](https://github.com/InternLM/lmdeploy).
+
+First install:
+```
+pip install lmdeploy openai
+```
+
+And then deploy a local judge LLM with the single line of code. LMDeploy will automatically download the model from Huggingface. Assuming we use internlm2-chat-1_8b as the judge, port 23333, and the key sk-123456 (the key must start with "sk-" and follow with any number you like):
+```
+lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333
+```
+
+You need to get the model name registered by LMDeploy with the following python code:
+```
+from openai import OpenAI
+client = OpenAI(
+    api_key='sk-123456',
+    base_url="http://0.0.0.0:23333/v1"
+)
+model_name = client.models.list().data[0].id
+```
+
+Now set some environment variables to tell VLMEvalKit how to use the local judge LLM. As mentioned above, you can also set them in `$VLMEvalKit/.env` file:
+```
+OPENAI_API_KEY=sk-123456
+OPENAI_API_BASE=http://0.0.0.0:23333/v1/chat/completions
+LOCAL_LLM=<model_name you get>
+```
+
+Finally, you can run the commands in step 2 to evaluate your VLM with the local judge LLM.
+
+Note that
+
+- If you hope to deploy the judge LLM in a single GPU and evaluate your VLM on other GPUs because of limited GPU memory, try `CUDA_VISIBLE_DEVICES=x` like
+```
+CUDA_VISIBLE_DEVICES=0 lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333
+CUDA_VISIBLE_DEVICES=1,2,3 torchrun --nproc-per-node=3 run.py --data HallusionBench  --model qwen_chat --verbose
+```
+- If the local judge LLM is not good enough in following the instructions, the evaluation may fail. Please report such failures (e.g., by issues).
+- It's possible to deploy the judge LLM in different ways, e.g., use a private LLM (not from HuggingFace) or use a quantized LLM. Please refer to the [LMDeploy doc](https://lmdeploy.readthedocs.io/en/latest/serving/api_server.html). You can use any other deployment framework if they support OpenAI API.