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.gitattributes

@@ -33,3 +33,21 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/Skiing.mp4 filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/demo.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/Trump_WEF_2018_10s.mp3 filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/assistant_default_female_voice.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/assistant_male_voice.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/audio_understanding.mp3 filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/chi-english-1.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/cxk_original.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/exciting-emotion.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/fast-pace.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/icl_20.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/input_examples/indian-accent.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/mimick.wav filter=lfs diff=lfs merge=lfs -text
+models/checkpoint/assets/qa.wav filter=lfs diff=lfs merge=lfs -text
+ref_audios/default.wav filter=lfs diff=lfs merge=lfs -text
+ref_audios/female_example.wav filter=lfs diff=lfs merge=lfs -text
+ref_audios/male_example.wav filter=lfs diff=lfs merge=lfs -text
+ref_audios/video_default.wav filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,46 @@
+FROM nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    CUDA_HOME=/usr/local/cuda \
+    PATH=/usr/local/cuda/bin:$PATH \
+    LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
+    NVIDIA_VISIBLE_DEVICES=all \
+    NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    HF_HOME=/app/models \
+    NUMBA_CACHE_DIR=/tmp/numba_cache
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    python3-pip \
+    python3-dev \
+    build-essential \
+    git \
+    ffmpeg \
+    libsndfile1 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip and install build tools
+RUN python3 -m pip install --upgrade pip setuptools wheel uv
+
+WORKDIR /app
+
+# Create Numba cache directory
+RUN mkdir -p /tmp/numba_cache && \
+    chown nobody:nogroup /tmp/numba_cache && \
+    chmod 700 /tmp/numba_cache
+
+COPY requirements.txt .
+
+# Install other requirements
+RUN python3 -m uv pip install --no-cache-dir -r requirements.txt --prerelease=allow
+RUN python3 -m uv pip install --no-build-isolation flash-attn
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["python3", "server.py"]

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,13 @@
+---
+license: mit
+tags:
+- any-to-any
+- omega
+- omegalabs
+- bittensor
+- agi
+---
+
+This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
+
+Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

chat.py

@@ -0,0 +1,311 @@
+import os
+
+# import os
+import torch
+
+# import torch
+import json
+from PIL import Image
+# from PIL import Image
+
+# from PIL import Image
+import base64
+import io
+
+# import io
+from accelerate import load_checkpoint_and_dispatch, init_empty_weights
+
+# from accelerate import load_checkpoint_and_dispatch, init_empty_weights
+
+# from accelerate import load_checkpoint_and_dispatch, init_empty_weights
+from transformers import AutoTokenizer, AutoModel
+
+# from transformers import AutoTokenizer, AutoModel
+
+from omnilmm.utils import disable_torch_init
+from omnilmm.model.omnilmm import OmniLMMForCausalLM
+from omnilmm.model.utils import build_transform
+
+# from omnilmm.model.utils import build_transform
+from omnilmm.train.train_utils import omni_preprocess
+# from omnilmm.train.train_utils import omni_preprocess
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+
+    
+
+def init_omni_lmm(model_path):
+    torch.backends.cuda.matmul.allow_tf32 = True
+    disable_torch_init()
+    model_name = os.path.expanduser(model_path)
+    print(f'Load omni_lmm model and tokenizer from {model_name}')
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name, model_max_length=2048)
+
+    if False:
+        # model on multiple devices for small size gpu memory (Nvidia 3090 24G x2) 
+        with init_empty_weights():
+            model = OmniLMMForCausalLM.from_pretrained(model_name, tune_clip=True, torch_dtype=torch.bfloat16)
+        model = load_checkpoint_and_dispatch(model, model_name, dtype=torch.bfloat16, 
+                    device_map="auto",  no_split_module_classes=['Eva','MistralDecoderLayer', 'ModuleList', 'Resampler']
+        )
+    else:
+        model = OmniLMMForCausalLM.from_pretrained(
+            model_name, tune_clip=True, torch_dtype=torch.bfloat16
+        ).to(device='cuda', dtype=torch.bfloat16)
+
+    image_processor = build_transform(
+        is_train=False, input_size=model.model.config.image_size, std_mode='OPENAI_CLIP')
+
+    mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
+    assert mm_use_im_start_end
+
+    tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN,
+                         DEFAULT_IM_END_TOKEN], special_tokens=True)
+
+
+    vision_config = model.model.vision_config
+    vision_config.im_patch_token = tokenizer.convert_tokens_to_ids(
+        [DEFAULT_IMAGE_PATCH_TOKEN])[0]
+    vision_config.use_im_start_end = mm_use_im_start_end
+    vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids(
+        [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
+    image_token_len = model.model.config.num_query
+
+    return model, image_processor, image_token_len, tokenizer
+
+def expand_question_into_multimodal(question_text, image_token_len, im_st_token, im_ed_token, im_patch_token):
+    if '<image>' in question_text[0]['content']:
+        question_text[0]['content'] = question_text[0]['content'].replace(
+            '<image>', im_st_token + im_patch_token * image_token_len + im_ed_token)
+    else:
+        question_text[0]['content'] = im_st_token + im_patch_token * \
+            image_token_len + im_ed_token + '\n' + question_text[0]['content']
+    return question_text
+
+def wrap_question_for_omni_lmm(question, image_token_len, tokenizer):
+    question = expand_question_into_multimodal(
+        question, image_token_len, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN)
+
+    conversation = question
+    data_dict = omni_preprocess(sources=[conversation],
+                                  tokenizer=tokenizer,
+                                  generation=True)
+
+    data_dict = dict(input_ids=data_dict["input_ids"][0],
+                     labels=data_dict["labels"][0])
+    return data_dict
+
+
+
+class OmniLMM12B:
+    def __init__(self, model_path) -> None:
+        model, img_processor, image_token_len, tokenizer = init_omni_lmm(model_path)
+        self.model = model
+        self.image_token_len = image_token_len
+        self.image_transform = img_processor
+        self.tokenizer = tokenizer
+        self.model.eval()
+
+    def decode(self, image, input_ids):
+        with torch.inference_mode():
+            output = self.model.generate_vllm(
+                input_ids=input_ids.unsqueeze(0).cuda(),
+                images=image.unsqueeze(0).half().cuda(),
+                temperature=0.6,
+                max_new_tokens=1024,
+                # num_beams=num_beams,
+                do_sample=True,
+                output_scores=True,
+                return_dict_in_generate=True,
+                repetition_penalty=1.1,
+                top_k=30,
+                top_p=0.9,
+            )
+
+            response = self.tokenizer.decode(
+                output.sequences[0], skip_special_tokens=True)
+            response = response.strip()
+            return response
+
+    def chat(self, input):
+        try:
+            image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
+        except Exception as e:
+            return "Image decode error"
+
+        msgs = json.loads(input['question'])
+        input_ids = wrap_question_for_omni_lmm(
+            msgs, self.image_token_len, self.tokenizer)['input_ids']
+        input_ids = torch.as_tensor(input_ids)
+        #print('input_ids', input_ids)
+        image = self.image_transform(image)
+
+        out = self.decode(image, input_ids)
+
+        return out
+        
+
+def img2base64(file_name):
+    with open(file_name, 'rb') as f:
+        encoded_string = base64.b64encode(f.read())
+        return encoded_string
+
+class MiniCPMV:
+    def __init__(self, model_path) -> None:
+        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.bfloat16)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        self.model.eval().cuda()
+
+    def chat(self, input):
+        try:
+            image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
+        except Exception as e:
+            return "Image decode error"
+
+        msgs = json.loads(input['question'])
+        
+        answer, context, _ = self.model.chat(
+            image=image,
+            msgs=msgs,
+            context=None,
+            tokenizer=self.tokenizer,
+            sampling=True,
+            temperature=0.7
+    	)
+        return answer
+
+class MiniCPMV2_5:
+    def __init__(self, model_path) -> None:
+        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.float16)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        self.model.eval().cuda()
+
+    def chat(self, input):
+        try:
+            image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
+        except Exception as e:
+            return "Image decode error"
+
+        msgs = json.loads(input['question'])
+        
+        answer = self.model.chat(
+            image=image,
+            msgs=msgs,
+            tokenizer=self.tokenizer,
+            sampling=True,
+            temperature=0.7
+    	)
+        return answer
+
+class MiniCPMV2_6:
+    def __init__(self, model_path, multi_gpus=False) -> None:
+
+        print('torch_version:', torch.__version__)
+        if multi_gpus: # inference on multi-gpus
+            from accelerate import load_checkpoint_and_dispatch, init_empty_weights, infer_auto_device_map
+            with init_empty_weights():
+                model = AutoModel.from_pretrained(model_path, trust_remote_code=True, 
+                    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+
+            device_map = infer_auto_device_map(model, max_memory={0: "10GB", 1: "10GB"},
+                no_split_module_classes=['SiglipVisionTransformer', 'Qwen2DecoderLayer'])
+            device_id = device_map["llm.model.embed_tokens"]
+            device_map["llm.lm_head"] = device_id # first and last layer of llm should be in the same device
+            device_map["vpm"] = device_id
+            device_map["resampler"] = device_id
+            device_id2 = device_map["llm.model.layers.26"]
+            device_map["llm.model.layers.8"] = device_id2
+            device_map["llm.model.layers.9"] = device_id2
+            device_map["llm.model.layers.10"] = device_id2
+            device_map["llm.model.layers.11"] = device_id2
+            device_map["llm.model.layers.12"] = device_id2
+            device_map["llm.model.layers.13"] = device_id2
+            device_map["llm.model.layers.14"] = device_id2
+            device_map["llm.model.layers.15"] = device_id2
+            device_map["llm.model.layers.16"] = device_id2
+            print(device_map)
+
+            self.model = load_checkpoint_and_dispatch(model, model_path, dtype=torch.bfloat16, device_map=device_map)
+            self.model.eval()
+        else:
+            self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True,
+                attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+            self.model.eval().cuda()
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    def chat(self, input):
+        image = None
+        if "image" in input and len(input["image"]) > 10: # legacy API
+            try:
+                image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB')
+            except Exception as e:
+                return "Image decode error"
+
+        msgs = json.loads(input["question"])
+
+        for msg in msgs:
+            contents = msg.pop('content') # support str or List[Dict]
+            if isinstance(contents, str):
+                contents = [contents]
+            
+            new_cnts = []
+            for c in contents:
+                if isinstance(c, dict):
+                    if c['type'] == 'text':
+                        c = c['pairs']
+                    elif c['type'] == 'image':
+                        c = Image.open(io.BytesIO(base64.b64decode(c["pairs"]))).convert('RGB')
+                    else:
+                        raise ValueError("content type only support text and image.")
+                new_cnts.append(c)
+            msg['content'] = new_cnts 
+        print(f'msgs: {str(msgs)}')
+
+        answer = self.model.chat(
+            image=image,
+            msgs=msgs,
+            tokenizer=self.tokenizer,
+        )
+        return answer
+
+
+class MiniCPMVChat:
+    def __init__(self, model_path, multi_gpus=False) -> None:
+        if '12B' in model_path:
+            self.model = OmniLMM12B(model_path)
+        elif 'MiniCPM-Llama3-V' in model_path:
+            self.model = MiniCPMV2_5(model_path)
+        elif 'MiniCPM-V-2_6' in model_path:
+            self.model = MiniCPMV2_6(model_path, multi_gpus)
+        else:
+            self.model = MiniCPMV(model_path)
+
+    def chat(self, input):
+        return self.model.chat(input)
+
+
+if __name__ == '__main__':
+    
+    model_path = 'openbmb/OmniLMM-12B'
+    chat_model = MiniCPMVChat(model_path)
+
+    im_64 = img2base64('./assets/worldmap_ck.jpg')
+
+    # first round chat 
+    msgs = [{"role": "user", "content": "What is interesting about this image?"}]
+    input = {"image": im_64, "question": json.dumps(msgs, ensure_ascii=True)}
+    answer = chat_model.chat(input)
+    print(msgs[-1]["content"]+'\n', answer)
+
+    # second round chat 
+    msgs.append({"role": "assistant", "content": answer})
+    msgs.append({"role": "user", "content": "Where is China in the image"})
+    input = {"image": im_64,"question": json.dumps(msgs, ensure_ascii=True)}
+    answer = chat_model.chat(input)
+    print(msgs[-1]["content"]+'\n', answer)
+

eval_mm/README.md

@@ -0,0 +1,362 @@
+# Evaluation
+
+## MiniCPM-V 2.6
+
+### opencompass
+First, enter the `vlmevalkit` directory and install all dependencies:
+```bash
+cd vlmevalkit
+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 `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 $MODE
+```
+<br />
+
+The four available choices for `MODELNAME` are listed in `vlmeval/config.py`:
+```bash
+minicpm_series = {
+    'MiniCPM-V': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
+    'MiniCPM-V-2': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
+    'MiniCPM-Llama3-V-2_5': partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
+    'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'),
+}
+```
+<br />
+
+All available choices for `DATALIST` are listed in `vlmeval/utils/dataset_config.py`. Separate the names of different datasets with spaces and add quotation marks at both ends:
+```bash
+$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 HallusionBench), you need to run the script according to the following settings:
+```bash
+# without CoT
+./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
+# with CoT
+# 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.
+./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all
+./scripts/run_inference.sh MiniCPM-V-2_6 MME 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-V-2_6`, set `model_name` to `minicpmv26`:
+```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
+```
+<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

@@ -0,0 +1,358 @@
+# Evaluation
+
+## 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 --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 `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 $MODE
+```
+<br />
+
+`MODELNAME` 有四种选择，位于 `vlmeval/config.py` 中：
+```bash
+minicpm_series = {
+    'MiniCPM-V': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'),
+    'MiniCPM-V-2': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'),
+    'MiniCPM-Llama3-V-2_5': partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'),
+    'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'),
+}
+```
+<br />
+
+可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中。将不同数据集名称以空格隔开，两端加引号：
+```bash
+$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`。
+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
+# without CoT
+./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
+# 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
+./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all
+./scripts/run_inference.sh MiniCPM-V-2_6 MME 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-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
+--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
+```
+<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

@@ -0,0 +1,30 @@
+decord
+gradio
+huggingface_hub
+imageio
+matplotlib
+moviepy
+numpy>=1.23.4
+omegaconf
+openai==1.3.5
+opencv-python>=4.4.0.46
+openpyxl
+pandas
+peft
+pillow
+portalocker
+python-dotenv
+requests
+rich
+sentencepiece
+setuptools
+sty
+tabulate
+tiktoken
+timeout-decorator
+torch>=2.0.1
+tqdm
+transformers
+typing_extensions==4.7.1
+validators
+xlsxwriter

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

@@ -0,0 +1,223 @@
+import torch
+import torch.distributed as dist
+
+from vlmeval.config import supported_VLM
+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)
+    # 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)
+    # 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
+
+
+def main():
+    logger = get_logger('RUN')
+
+    args = parse_args()
+    assert len(args.data), '--data should be a list of data files'
+
+    if args.retry is not None:
+        for k, v in supported_VLM.items():
+            if hasattr(v, 'keywords') and 'retry' in v.keywords:
+                v.keywords['retry'] = args.retry
+                supported_VLM[k] = v
+            if hasattr(v, 'keywords') and 'verbose' in v.keywords:
+                v.keywords['verbose'] = args.verbose
+                supported_VLM[k] = v
+
+    rank, world_size = get_rank_and_world_size()
+    if world_size > 1:
+        local_rank = os.environ.get('LOCAL_RANK', 0)
+        torch.cuda.set_device(int(local_rank))
+        dist.init_process_group(backend='nccl', timeout=datetime.timedelta(seconds=10800))
+
+    for _, model_name in enumerate(args.model):
+        model = None
+
+        pred_root = osp.join(args.work_dir, model_name)
+        os.makedirs(pred_root, exist_ok=True)
+
+        for _, dataset_name in enumerate(args.data):
+            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 distributed, first build the dataset on the main process for doing preparation works
+            if world_size > 1:
+                dataset = build_dataset(dataset_name, **dataset_kwargs) if rank == 0 else None
+                dist.barrier()
+                dataset_list = [dataset]
+                dist.broadcast_object_list(dataset_list, src=0)
+                dataset = dataset_list[0]
+            else:
+                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:
+                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=dataset,
+                    verbose=args.verbose,
+                    api_nproc=args.nproc,
+                    ignore_failed=args.ignore)
+
+            # Set the judge kwargs first before evaluation or dumping
+            judge_kwargs = {
+                'nproc': args.nproc,
+                'verbose': args.verbose,
+            }
+            if args.retry is not None:
+                judge_kwargs['retry'] = args.retry
+            if args.judge is not None:
+                judge_kwargs['model'] = args.judge
+            else:
+                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', '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 [email protected].')  # 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 eval_proxy is not None:
+                    proxy_set(eval_proxy)
+
+                eval_results = dataset.evaluate(result_file, **judge_kwargs)
+                if eval_results is not None:
+                    assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame)
+                    logger.info(f'The evaluation of model {model_name} x dataset {dataset_name} has finished! ')
+                    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))
+
+                if eval_proxy is not None:
+                    proxy_set(old_proxy)
+
+
+if __name__ == '__main__':
+    load_env()
+    main()

eval_mm/vlmevalkit/scripts/run_inference.sh

@@ -0,0 +1,31 @@
+export PATH=/usr/local/cuda/bin:$PATH
+
+export HF_ENDPOINT=https://hf-mirror.com
+export OMP_NUM_THREADS=1
+export timestamp=`date +"%Y%m%d%H%M%S"`
+export OLD_VERSION='False'
+export PYTHONPATH=$(dirname $SELF_DIR):$PYTHONPATH
+
+# gpu consumed
+# fp16 17-18G
+# int4 7-8G
+
+# model to be used
+# Example: MODELNAME=MiniCPM_V_2_6
+MODELNAME=$1
+# datasets to be tested
+# 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
+
+echo "Starting inference with model $MODELNAME on datasets $DATALIST"
+# run on multi gpus with torchrun command
+# remember to run twice, the first run may fail
+torchrun --nproc_per_node=8 run.py --data $DATALIST --model $MODELNAME --mode $MODE
+torchrun --nproc_per_node=8 run.py --data $DATALIST --model $MODELNAME --mode $MODE
+# run on single gpu with python command
+# python run.py --data $DATALIST --model $MODELNAME --verbose --mode $MODE
+# python run.py --data $DATALIST --model $MODELNAME --verbose --mode $MODE
+
+ls

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='[email protected]',
+        maintainer='Haodong Duan',
+        maintainer_email='[email protected]',
+        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

@@ -0,0 +1,16 @@
+try:
+    import torch
+except ImportError:
+    pass
+
+from .smp import *
+from .api 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

@@ -0,0 +1,5 @@
+from .gpt import OpenAIWrapper, GPT4V
+
+__all__ = [
+    'OpenAIWrapper', 'GPT4V'
+]

eval_mm/vlmevalkit/vlmeval/api/base.py

@@ -0,0 +1,265 @@
+import time
+import random as rd
+from abc import abstractmethod
+import os.path as osp
+import copy as cp
+from ..smp import get_logger, parse_file, concat_images_vlmeval
+
+
+class BaseAPI:
+
+    allowed_types = ['text', 'image']
+    INTERLEAVE = True
+    INSTALL_REQ = False
+
+    def __init__(self,
+                 retry=10,
+                 wait=3,
+                 system_prompt=None,
+                 verbose=True,
+                 fail_msg='Failed to obtain answer via API.',
+                 **kwargs):
+        """Base Class for all APIs.
+
+        Args:
+            retry (int, optional): The retry times for `generate_inner`. Defaults to 10.
+            wait (int, optional): The wait time after each failed retry of `generate_inner`. Defaults to 3.
+            system_prompt (str, optional): Defaults to None.
+            verbose (bool, optional): Defaults to True.
+            fail_msg (str, optional): The message to return when failed to obtain answer.
+                Defaults to 'Failed to obtain answer via API.'.
+            **kwargs: Other kwargs for `generate_inner`.
+        """
+
+        self.wait = wait
+        self.retry = retry
+        self.system_prompt = system_prompt
+        self.verbose = verbose
+        self.fail_msg = fail_msg
+        self.logger = get_logger('ChatAPI')
+
+        if len(kwargs):
+            self.logger.info(f'BaseAPI received the following kwargs: {kwargs}')
+            self.logger.info('Will try to use them as kwargs for `generate`. ')
+        self.default_kwargs = kwargs
+
+    @abstractmethod
+    def generate_inner(self, inputs, **kwargs):
+        """The inner function to generate the answer.
+
+        Returns:
+            tuple(int, str, str): ret_code, response, log
+        """
+        self.logger.warning('For APIBase, generate_inner is an abstract method. ')
+        assert 0, 'generate_inner not defined'
+        ret_code, answer, log = None, None, None
+        # if ret_code is 0, means succeed
+        return ret_code, answer, log
+
+    def working(self):
+        """If the API model is working, return True, else return False.
+
+        Returns:
+            bool: If the API model is working, return True, else return False.
+        """
+        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):
+        """Check the content type of the input. Four types are allowed: str, dict, liststr, listdict.
+
+        Args:
+            msgs: Raw input messages.
+
+        Returns:
+            str: The message type.
+        """
+        if isinstance(msgs, str):
+            return 'str'
+        if isinstance(msgs, dict):
+            return 'dict'
+        if isinstance(msgs, list):
+            types = [self.check_content(m) for m in msgs]
+            if all(t == 'str' for t in types):
+                return 'liststr'
+            if all(t == 'dict' for t in types):
+                return 'listdict'
+        return 'unknown'
+
+    def preproc_content(self, inputs):
+        """Convert the raw input messages to a list of dicts.
+
+        Args:
+            inputs: raw input messages.
+
+        Returns:
+            list(dict): The preprocessed input messages. Will return None if failed to preprocess the input.
+        """
+        if self.check_content(inputs) == 'str':
+            return [dict(type='text', value=inputs)]
+        elif self.check_content(inputs) == 'dict':
+            assert 'type' in inputs and 'value' in inputs
+            return [inputs]
+        elif self.check_content(inputs) == 'liststr':
+            res = []
+            for s in inputs:
+                mime, pth = parse_file(s)
+                if mime is None or mime == 'unknown':
+                    res.append(dict(type='text', value=s))
+                else:
+                    res.append(dict(type=mime.split('/')[0], value=pth))
+            return res
+        elif self.check_content(inputs) == 'listdict':
+            for item in inputs:
+                assert 'type' in item and 'value' in item
+                mime, s = parse_file(item['value'])
+                if mime is None:
+                    assert item['type'] == 'text', item['value']
+                else:
+                    assert mime.split('/')[0] == item['type']
+                    item['value'] = s
+            return inputs
+        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.
+
+        Args:
+            message: raw input messages.
+
+        Returns:
+            str: The generated answer of the Failed Message if failed to obtain answer.
+        """
+        assert self.check_content(message) in ['str', 'dict', 'liststr', 'listdict'], f'Invalid input type: {message}'
+        message = self.preproc_content(message)
+        assert message is not None and self.check_content(message) == 'listdict'
+        for item in message:
+            assert item['type'] in self.allowed_types, f'Invalid input type: {item["type"]}'
+
+        # 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)
+
+        for i in range(self.retry):
+            try:
+                ret_code, answer, log = self.generate_inner(message, **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 message_to_promptimg(self, message, dataset=None):
+        assert not self.INTERLEAVE
+        model_name = self.__class__.__name__
+        import warnings
+        warnings.warn(
+            f'Model {model_name} does not support interleaved input. '
+            'Will use the first image and aggregated texts as prompt. ')
+        num_images = len([x for x in message if x['type'] == 'image'])
+        if num_images == 0:
+            prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
+            image = None
+        elif num_images == 1:
+            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]
+        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

@@ -0,0 +1,248 @@
+from ..smp import *
+import os
+import sys
+from .base import BaseAPI
+
+APIBASES = {
+    'OFFICIAL': 'https://api.openai.com/v1/chat/completions',
+}
+
+
+def GPT_context_window(model):
+    length_map = {
+        'gpt-4': 8192,
+        'gpt-4-0613': 8192,
+        '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-instruct': 4096,
+    }
+    if model in length_map:
+        return length_map[model]
+    else:
+        return 128000
+
+
+class OpenAIWrapper(BaseAPI):
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model: str = 'gpt-3.5-turbo-0613',
+                 retry: int = 5,
+                 wait: int = 5,
+                 key: str = None,
+                 verbose: bool = True,
+                 system_prompt: str = None,
+                 temperature: float = 0,
+                 timeout: int = 60,
+                 api_base: str = None,
+                 max_tokens: int = 1024,
+                 img_size: int = 512,
+                 img_detail: str = 'low',
+                 use_azure: bool = False,
+                 **kwargs):
+
+        self.model = model
+        self.cur_idx = 0
+        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:
+                    key = env_key
+                assert isinstance(key, str) and key.startswith('sk-'), (
+                    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
+        assert img_detail in ['high', 'low']
+        self.img_detail = img_detail
+        self.timeout = timeout
+
+        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.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'
+
+            assert api_base is not None
+
+            if api_base in APIBASES:
+                self.api_base = APIBASES[api_base]
+            elif api_base.startswith('http'):
+                self.api_base = api_base
+            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_itlist(self, inputs):
+        assert np.all([isinstance(x, dict) for x in inputs])
+        has_images = np.sum([x['type'] == 'image' for x in inputs])
+        if has_images:
+            content_list = []
+            for msg in inputs:
+                if msg['type'] == 'text':
+                    content_list.append(dict(type='text', text=msg['value']))
+                elif msg['type'] == 'image':
+                    from PIL import Image
+                    img = Image.open(msg['value'])
+                    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))
+        else:
+            assert all([x['type'] == 'text' for x in inputs])
+            text = '\n'.join([x['value'] for x in inputs])
+            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:
+        input_msgs = self.prepare_inputs(inputs)
+        temperature = kwargs.pop('temperature', self.temperature)
+        max_tokens = kwargs.pop('max_tokens', self.max_tokens)
+
+        context_window = GPT_context_window(self.model)
+        max_tokens = min(max_tokens, context_window - self.get_token_len(inputs))
+        if 0 < max_tokens <= 100:
+            self.logger.warning(
+                '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. '
+
+        # 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,
+            messages=input_msgs,
+            max_tokens=max_tokens,
+            n=1,
+            temperature=temperature,
+            **kwargs)
+        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
+        try:
+            resp_struct = json.loads(response.text)
+            answer = resp_struct['choices'][0]['message']['content'].strip()
+        except:
+            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:
+            enc = tiktoken.encoding_for_model(self.model)
+        except:
+            enc = tiktoken.encoding_for_model('gpt-4')
+        assert isinstance(inputs, list)
+        tot = 0
+        for item in inputs:
+            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 += self.get_image_token_len(item['value'], detail=self.img_detail)
+        return tot
+
+
+class GPT4V(OpenAIWrapper):
+
+    def generate(self, message, dataset=None):
+        return super(GPT4V, self).generate(message)

eval_mm/vlmevalkit/vlmeval/config.py

@@ -0,0 +1,19 @@
+from vlmeval.vlm import *
+from vlmeval.api import *
+from functools import partial
+
+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 = [
+    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

@@ -0,0 +1,65 @@
+import numpy as np
+import pandas as pd
+from ...smp import *
+
+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
+    'llava_bench_detail': {'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
+    'llava_bench_complex': {'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
+}
+
+
+def get_eval(judge, content):
+    return judge.generate(content)
+
+
+def parse_score(review):
+    logger = get_logger('Evaluation')
+    try:
+        score_pair = review.split('\n')[0]
+        score_pair = score_pair.replace(',', ' ')
+        sp = score_pair.split(' ')
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            logger.error('error', review)
+            return [-1, -1]
+    except Exception as e:
+        logger.error(e, 'error', review)
+        return [-1, -1]
+
+
+def build_prompt(line):
+    cap_str = line['caption']
+    question = line['question']
+    ans1 = line['gpt4_ans']
+    ans2 = line['prediction']
+    category = 'llava_bench_' + line['category']
+    rule = rule_dict[category]
+    role, prompt = rule['role'], rule['prompt']
+
+    content = (f'[Context]\n{cap_str}\n\n'
+               f'[Question]\n{question}\n\n'
+               f'[{role} 1]\n{ans1}\n\n[End of {role} 1]\n\n'
+               f'[{role} 2]\n{ans2}\n\n[End of {role} 2]\n\n'
+               f'[System]\n{prompt}\n\n')
+    return content
+
+
+def LLaVABench_atomeval(model, prompt):
+    review = get_eval(model, prompt)
+    scores = parse_score(review)
+    return scores
+
+
+def LLaVABench_score(data):
+    cates = ['overall'] + list(set(data['category']))
+    ret = defaultdict(list)
+
+    for c in cates:
+        ret['split'].append(c)
+        sub = data[data['category'] == c] if c != 'overall' else data
+        ret['Relative Score (main)'].append(np.mean(sub['score']) / np.mean(sub['gpt4_score']) * 100)
+        ret['VLM Score'].append(np.mean(sub['score']) * 10)
+        ret['GPT4 Score'].append(np.mean(sub['gpt4_score']) * 10)
+    return pd.DataFrame(ret)

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

@@ -0,0 +1,164 @@
+from ...smp import *
+from ...utils import can_infer
+
+
+FAIL_MSG = 'Failed to obtain answer via API.'
+
+
+def get_gpt4_ICE():
+    example_1 = """
+Hint: Please answer the question requiring an integer answer and provide the final value,
+e.g., 1, 2, 3, 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 requiring a floating-point number with one decimal place and provide the final value,
+e.g., 1.2, 1.3, 1.4, 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 requiring a floating-point number with two decimal places and provide the final value,
+e.g., 1.23, 1.34, 1.45, 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 requiring a Python list as an answer and provide the final list,
+e.g., [1, 2, 3], [1.2, 1.3, 1.4], 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_mathvista_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 line['question_type'] == 'multi_choice':
+            ans = line['answer_option']
+            choices = list_to_dict(eval(line['choices']))
+            res = can_infer(response, choices)
+            if prefetch:
+                return res
+        else:
+            if line['answer_type'] == 'integer':
+                res = int(response)
+                ans = int(line['answer'])
+            elif line['answer_type'] == 'float':
+                res = float(response)
+                ans = float(line['answer'])
+            else:
+                res = str(res)
+                ans = str(ans)
+    except ValueError:
+        pass
+
+    if res == ans:
+        return res if prefetch else True
+    else:
+        return False
+
+
+def MathVista_auxeval(model, line):
+    prompt = build_mathvista_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 MathVista_acc(result_file):
+    data = load(result_file)
+    tot = defaultdict(lambda: 0)
+    fetch = defaultdict(lambda: 0)
+    hit = defaultdict(lambda: 0)
+    lt = len(data)
+    skill_list = []
+    for i in range(lt):
+        item = data.iloc[i]
+        cate = item['task']
+        tot['Overall'] += 1
+        try:
+            skills = eval(item['skills'])
+        except SyntaxError:
+            skills = [item['skills']]
+        for skill in skills:
+            if skill not in skill_list:
+                skill_list.append(skill)
+            tot[skill] += 1
+        tot[cate] += 1
+        if item['log'] == 'Prefetch succeed':
+            fetch['Overall'] += 1
+            fetch[cate] += 1
+            for skill in skills:
+                fetch[skill] += 1
+        if post_check(item, prefetch=False):
+            hit['Overall'] += 1
+            hit[cate] += 1
+            for skill in skills:
+                hit[skill] += 1
+
+    res = defaultdict(list)
+    for k in tot.keys():
+        res['Task&Skill'].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)
+    return res

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

@@ -0,0 +1,106 @@
+from ...smp import *
+
+
+def build_mmvet_gpt4_prompt(line):
+    question = line['question']
+    gt = str(line['answer'])
+    prediction = str(line['prediction'])
+    prompt = """
+Compare the ground truth and prediction from AI models, to give a correctness score for the prediction.
+<AND> in the ground truth means it is totally right
+only when all elements in the ground truth are present in the prediction,
+and <OR> means it is totally right when any one element in the ground truth is present in the prediction.
+The correctness score is 0.0 (totally wrong), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0 (totally right).
+Just complete the last space of the correctness score.
+
+Question | Ground truth | Prediction | Correctness
+--- | --- | --- | ---
+What is x in the equation? | -1 <AND> -5 | x = 3 | 0.0
+What is x in the equation? | -1 <AND> -5 | x = -1 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -5 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -5 or 5 | 0.5
+What is x in the equation? | -1 <AND> -5 | x = -1 or x = -5 | 1.0
+Can you explain this meme? | This meme is poking fun at the fact that the names of the countries
+Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes,
+while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues
+because the names of these countries do not accurately represent their landscapes. |
+The meme talks about Iceland and Greenland. It's pointing out that despite their names,
+Iceland is not very icy and Greenland isn't very green. | 0.4
+Can you explain this meme? | This meme is poking fun at the fact that the names of the countries
+Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes,
+while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues
+because the names of these countries do not accurately represent their landscapes. |
+The meme is using humor to point out the misleading nature of Iceland's and Greenland's names.
+Iceland, despite its name, has lush green landscapes while Greenland is mostly covered in ice and snow.
+The text 'This is why I have trust issues' is a playful way to suggest
+that these contradictions can lead to distrust or confusion.
+The humor in this meme is derived from the unexpected contrast between the names of the countries
+and their actual physical characteristics. | 1.0
+"""
+    gpt4_prompt = prompt + '\n' + ' | '.join(
+        [question, gt.replace('<AND>', ' <AND> ').replace('<OR>', ' <OR> '), prediction, ''])
+    return gpt4_prompt
+
+
+def MMVet_auxeval(model, line):
+    def float_cvt(s):
+        try:
+            return float(s)
+        except ValueError:
+            return None
+
+    prompt = build_mmvet_gpt4_prompt(line)
+    log = ''
+    retry = 5
+    for i in range(retry):
+        output = model.generate(prompt, temperature=i * 0.5)
+        score = float_cvt(output)
+        if score is None:
+            log += f'Try {i}: output is {output}, failed to parse.\n'
+        elif score < 0 or score > 1:
+            log += f'Try {i}: output is {output}, invalid score: {score}.\n'
+        else:
+            log += 'Succeed'
+            return dict(log=log, score=score)
+    log += 'All 5 retries failed.\n'
+    return dict(log=log, score=0.0)
+
+
+def MMVet_acc(result_file):
+    data = load(result_file)
+    tot = defaultdict(lambda: 0)
+    score = defaultdict(lambda: 0)
+    lt = len(data)
+    cate2_list = []
+    for i in range(lt):
+        item = data.iloc[i]
+        cate = item['category']
+        cate2 = cate.replace(',', '_')
+        if cate2 not in cate2_list:
+            cate2_list.append(cate2)
+        grade = float(item['score'])
+        cate_list = ['rec', 'ocr', 'know', 'gen', 'spat', 'math']
+        for capa in cate_list:
+            if capa in cate:
+                tot[capa] += 1
+                score[capa] += grade
+        tot['Overall'] += 1
+        tot[cate2] += 1
+        score['Overall'] += grade
+        score[cate2] += grade
+
+    res = defaultdict(list)
+    res2 = defaultdict(list)
+    cate_list.append('Overall')
+    cate2_list.append('Overall')
+    for k in cate_list:
+        res['Category'].append(k)
+        res['tot'].append(tot[k])
+        res['acc'].append(score[k] / tot[k] * 100)
+    for v in cate2_list:
+        res2['Category'].append(v)
+        res2['tot'].append(tot[v])
+        res2['acc'].append(score[v] / tot[v] * 100)
+    res = pd.DataFrame(res)
+    res2 = pd.DataFrame(res2)
+    return res, res2

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

@@ -0,0 +1,65 @@
+from ...smp import *
+
+
+def OCRBench_eval(eval_file):
+    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
+    }
+
+    logger = get_logger('Evaluation')
+
+    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)
+    logger.info(f'OCRBench_eval successfully finished evaluating {eval_file}, results saved in {score_pth}')
+    logger.info('Score: ')
+    for key, value in final_score_dict.items():
+        logger.info('{}:{}'.format(key, value))

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

@@ -0,0 +1,285 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Partly adopted from https://github.com/GT-Vision-Lab/VQA
+# Copyright (c) 2014, Aishwarya Agrawal
+
+from ...smp import *
+from typing import Optional
+
+
+def _process_digit_article(inText):
+    outText = []
+    tempText = inText.lower().split()
+    articles = ['a', 'an', 'the']
+    manualMap = {
+        'none': '0',
+        'zero': '0',
+        'one': '1',
+        'two': '2',
+        'three': '3',
+        'four': '4',
+        'five': '5',
+        'six': '6',
+        'seven': '7',
+        'eight': '8',
+        'nine': '9',
+        'ten': '10',
+    }
+    contractions = {
+        'aint': "ain't",
+        'arent': "aren't",
+        'cant': "can't",
+        'couldve': "could've",
+        'couldnt': "couldn't",
+        "couldn'tve": "couldn't've",
+        "couldnt've": "couldn't've",
+        'didnt': "didn't",
+        'doesnt': "doesn't",
+        'dont': "don't",
+        'hadnt': "hadn't",
+        "hadnt've": "hadn't've",
+        "hadn'tve": "hadn't've",
+        'hasnt': "hasn't",
+        'havent': "haven't",
+        'hed': "he'd",
+        "hed've": "he'd've",
+        "he'dve": "he'd've",
+        'hes': "he's",
+        'howd': "how'd",
+        'howll': "how'll",
+        'hows': "how's",
+        "Id've": "I'd've",
+        "I'dve": "I'd've",
+        'Im': "I'm",
+        'Ive': "I've",
+        'isnt': "isn't",
+        'itd': "it'd",
+        "itd've": "it'd've",
+        "it'dve": "it'd've",
+        'itll': "it'll",
+        "let's": "let's",
+        'maam': "ma'am",
+        'mightnt': "mightn't",
+        "mightnt've": "mightn't've",
+        "mightn'tve": "mightn't've",
+        'mightve': "might've",
+        'mustnt': "mustn't",
+        'mustve': "must've",
+        'neednt': "needn't",
+        'notve': "not've",
+        'oclock': "o'clock",
+        'oughtnt': "oughtn't",
+        "ow's'at": "'ow's'at",
+        "'ows'at": "'ow's'at",
+        "'ow'sat": "'ow's'at",
+        'shant': "shan't",
+        "shed've": "she'd've",
+        "she'dve": "she'd've",
+        "she's": "she's",
+        'shouldve': "should've",
+        'shouldnt': "shouldn't",
+        "shouldnt've": "shouldn't've",
+        "shouldn'tve": "shouldn't've",
+        "somebody'd": 'somebodyd',
+        "somebodyd've": "somebody'd've",
+        "somebody'dve": "somebody'd've",
+        'somebodyll': "somebody'll",
+        'somebodys': "somebody's",
+        'someoned': "someone'd",
+        "someoned've": "someone'd've",
+        "someone'dve": "someone'd've",
+        'someonell': "someone'll",
+        'someones': "someone's",
+        'somethingd': "something'd",
+        "somethingd've": "something'd've",
+        "something'dve": "something'd've",
+        'somethingll': "something'll",
+        'thats': "that's",
+        'thered': "there'd",
+        "thered've": "there'd've",
+        "there'dve": "there'd've",
+        'therere': "there're",
+        'theres': "there's",
+        'theyd': "they'd",
+        "theyd've": "they'd've",
+        "they'dve": "they'd've",
+        'theyll': "they'll",
+        'theyre': "they're",
+        'theyve': "they've",
+        'twas': "'twas",
+        'wasnt': "wasn't",
+        "wed've": "we'd've",
+        "we'dve": "we'd've",
+        'weve': "we've",
+        'werent': "weren't",
+        'whatll': "what'll",
+        'whatre': "what're",
+        'whats': "what's",
+        'whatve': "what've",
+        'whens': "when's",
+        'whered': "where'd",
+        'wheres': "where's",
+        'whereve': "where've",
+        'whod': "who'd",
+        "whod've": "who'd've",
+        "who'dve": "who'd've",
+        'wholl': "who'll",
+        'whos': "who's",
+        'whove': "who've",
+        'whyll': "why'll",
+        'whyre': "why're",
+        'whys': "why's",
+        'wont': "won't",
+        'wouldve': "would've",
+        'wouldnt': "wouldn't",
+        "wouldnt've": "wouldn't've",
+        "wouldn'tve": "wouldn't've",
+        'yall': "y'all",
+        "yall'll": "y'all'll",
+        "y'allll": "y'all'll",
+        "yall'd've": "y'all'd've",
+        "y'alld've": "y'all'd've",
+        "y'all'dve": "y'all'd've",
+        'youd': "you'd",
+        "youd've": "you'd've",
+        "you'dve": "you'd've",
+        'youll': "you'll",
+        'youre': "you're",
+        'youve': "you've",
+    }
+    for word in tempText:
+        word = manualMap.setdefault(word, word)
+        if word not in articles:
+            outText.append(word)
+    for wordId, word in enumerate(outText):
+        if word in contractions:
+            outText[wordId] = contractions[word]
+    outText = ' '.join(outText)
+    return outText
+
+
+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 [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]
+    else:  # default using vqa_score to calculate score
+        return [np.mean(x['match']) for x in result]
+
+
+# https://github.com/google-research/pix2struct/blob/main/pix2struct/metrics.py#L81
+def relaxed_correctness(target: str,
+                        prediction: str,
+                        max_relative_change: float = 0.05) -> bool:
+    """Calculates relaxed correctness.
+
+    The correctness tolerates certain error ratio defined by max_relative_change.
+    See https://arxiv.org/pdf/2203.10244.pdf, end of section 5.1:
+    “Following Methani et al. (2020), we use a relaxed accuracy measure for the
+    numeric answers to allow a minor inaccuracy that may result from the automatic
+    data extraction process. We consider an answer to be correct if it is within
+    5% of the gold answer. For non-numeric answers, we still need an exact match
+    to consider an answer to be correct.”
+
+    Args:
+      target: Target string.
+      prediction: Predicted string.
+      max_relative_change: Maximum relative change.
+
+    Returns:
+      Whether the prediction was correct given the specified tolerance.
+    """
+
+    def _to_float(text: str) -> Optional[float]:
+        try:
+            if text.endswith('%'):
+                # Convert percentages to floats.
+                return float(text.rstrip('%')) / 100.0
+            else:
+                return float(text)
+        except ValueError:
+            return None
+    prediction = str(prediction)
+    target = str(target)
+    prediction_float = _to_float(prediction)
+    target_float = _to_float(target)
+    if prediction_float is not None and target_float:
+        relative_change = abs(prediction_float - target_float) / abs(target_float)
+        return relative_change <= max_relative_change
+    else:
+        return prediction.lower() == target.lower()
+
+
+def levenshtein_distance(s1, s2):
+    if len(s1) > len(s2):
+        s1, s2 = s2, s1
+
+    distances = range(len(s1) + 1)
+    for i2, c2 in enumerate(s2):
+        distances_ = [i2 + 1]
+        for i1, c1 in enumerate(s1):
+            if c1 == c2:
+                distances_.append(distances[i1])
+            else:
+                distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
+        distances = distances_
+    return distances[-1]
+
+
+def anls_compute(groundtruth, prediction):
+    gt_answer = ' '.join(groundtruth.strip().lower().split())
+    det_answer = ' '.join(prediction.strip().lower().split())
+    dist = levenshtein_distance(gt_answer, det_answer)
+    length = max(len(groundtruth.upper()), len(prediction.upper()))
+    values = 0.0 if length == 0 else float(dist) / float(length)
+    return values
+
+
+def process_answer(answer):
+    answer = answer.replace('\n', ' ')
+    answer = answer.replace('\t', ' ')
+    answer = answer.strip()
+    answer = process_punctuation(answer)
+    answer = _process_digit_article(answer)
+    return answer
+
+
+def process_line(line, method='vqa_score'):
+    ret = {}
+    if istype(line['answer'], list):
+        answers = eval(line['answer'])
+    else:
+        answers = [line['answer']]
+    if method == 'vqa_score':
+        ret['gt'] = [process_answer(x) for x in answers]
+        ret['pred'] = process_answer(line['prediction'])
+        ret['match'] = []
+        for current_idx, gtAnsDatum in enumerate(ret['gt']):
+            otherGTAns = [
+                item for ret_gt_idx, item in enumerate(ret['gt'])
+                if ret_gt_idx != current_idx
+            ]
+            matchingAns = [
+                item for item in otherGTAns if item == ret['pred']
+            ]
+            acc = min(1, float(len(matchingAns)) / 3)
+            ret['match'].append(acc)
+    elif method == 'anls':
+        ret['gt'] = answers
+        ret['pred'] = line['prediction']
+        ret['match'] = [anls_compute(x, ret['pred']) for x in ret['gt']]
+    elif method == 'relaxed_accuracy':
+        ret['gt'] = answers
+        ret['pred'] = line['prediction'].strip()
+        ret['match'] = [relaxed_correctness(ret['pred'], x) for x in ret['gt']]
+    elif method == 'accuracy':
+        ret['gt'] = answers
+        ret['pred'] = line['prediction'].strip()
+        ret['match'] = [(1.0 if (x.strip().lower() == ret['pred'].strip().lower()) else 0.0) for x in ret['gt']]
+    else:  # default using vqa_score to calculate score
+        ret['gt'] = [process_answer(x) for x in answers]
+        ret['pred'] = process_answer(line['prediction'])
+        ret['match'] = [x == ret['pred'] for x in ret['gt']]
+
+    return ret

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

@@ -0,0 +1,203 @@
+from ...smp import *
+
+
+def MME_rating(data_file):
+    data = load(data_file)
+    stats = defaultdict(dict)
+    lt = len(data)
+    for i in range(lt):
+        item = data.iloc[i]
+        category = item['category']
+        image_path = item['image_path']
+        score = item['score']
+        if image_path not in stats[category]:
+            stats[category][image_path] = []
+        stats[category][image_path].append(score)
+
+    def acc(key, mode='normal'):
+        res = stats[key]
+        values = []
+        for val in res.values():
+            if mode == 'normal':
+                values.extend(val)
+            elif mode == 'plus':
+                values.append(val[0] * val[1])
+        return np.mean(values) * 100
+
+    scores = {}
+    for k in stats:
+        scores[k] = acc(k) + acc(k, 'plus')
+
+    super_cates = dict(
+        perception=[
+            'OCR', 'artwork', 'celebrity', 'color', 'count', 'existence',
+            'landmark', 'position', 'posters', 'scene'
+        ],
+        reasoning=['code_reasoning', 'commonsense_reasoning', 'numerical_calculation', 'text_translation']
+    )
+
+    ret = {}
+    for sc, cate_list in super_cates.items():
+        base = 0
+        for c in cate_list:
+            base += scores[c]
+        ret[sc] = base
+    ret.update(scores)
+    ret = d2df(ret)
+    return ret
+
+
+def Hallusion_rating(data_file):
+    def calc_fAcc(data):
+        res = defaultdict(list)
+        lt = len(data)
+        for i in range(lt):
+            line = data.iloc[i]
+            res[f"{line['l2-category']}_{line['set_id']}_{line['figure_id']}"].append(line['score'])
+        return np.mean([np.all(x) for x in res.values()]) * 100
+
+    def calc_qAcc(data):
+        res = defaultdict(list)
+        lt = len(data)
+        for i in range(lt):
+            line = data.iloc[i]
+            res[f"{line['l2-category']}_{line['set_id']}_{line['question_id']}"].append(line['score'])
+        return np.mean([np.all(x) for x in res.values()]) * 100
+
+    def calc_aAcc(data):
+        return np.mean(data['score']) * 100
+
+    data = load(data_file)
+    data['set_id'] = [x.split('_')[3] for x in data['index']]
+    data['figure_id'] = [x.split('_')[4] for x in data['index']]
+    data['question_id'] = [x.split('_')[5] for x in data['index']]
+
+    res = dict(split=[], aAcc=[], fAcc=[], qAcc=[])
+    res['split'].append('Overall')
+    res['aAcc'].append(calc_aAcc(data))
+    res['fAcc'].append(calc_fAcc(data))
+    res['qAcc'].append(calc_qAcc(data))
+
+    if 'category' in data:
+        cates = list(set(data['category']))
+        for c in cates:
+            sub = data[data['category'] == c]
+            res['split'].append(c)
+            res['aAcc'].append(calc_aAcc(sub))
+            res['fAcc'].append(calc_fAcc(sub))
+            res['qAcc'].append(calc_qAcc(sub))
+
+    if 'l2-category' in data:
+        cates = list(set(data['l2-category']))
+        for c in cates:
+            sub = data[data['l2-category'] == c]
+            res['split'].append(c)
+            res['aAcc'].append(calc_aAcc(sub))
+            res['fAcc'].append(calc_fAcc(sub))
+            res['qAcc'].append(calc_qAcc(sub))
+    ret = pd.DataFrame(res)
+    return ret
+
+
+def POPE_rating(data_file):
+    def cal_f1_score(y_true, y_pred):
+        tp = sum((y_true == 1) & (y_pred == 1))
+        fp = sum((y_true == 0) & (y_pred == 1))
+        fn = sum((y_true == 1) & (y_pred == 0))
+
+        precision = tp / (tp + fp) if (tp + fp) != 0 else 0
+        recall = tp / (tp + fn) if (tp + fn) != 0 else 0
+        f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) != 0 else 0
+        return f1_score, precision, recall
+
+    data = load(data_file)
+    data = data.assign(category=data['category'].str.split(',')).explode('category')
+    data['index'] = range(len(data))
+    res = dict(split=[], Overall=[], acc=[], precision=[], recall=[])
+    y_true = np.array([1 if i == 'Yes' else 0 for i in data['answer']])
+    y_pred = np.array([1 if i == 'Yes' else 0 for i in data['extracted']])
+    f1_score, precision, recall = cal_f1_score(y_true, y_pred)
+    res['split'].append('Overall')
+    res['Overall'].append(f1_score * 100)
+    res['acc'].append(np.mean(data['score']) * 100)
+    res['precision'].append(precision * 100)
+    res['recall'].append(recall * 100)
+
+    if 'category' in data:
+        cates = list(set(data['category']))
+        cates = [c for c in cates if not pd.isna(c)]
+        for c in cates:
+            sub = data[data['category'] == c]
+            y_true = np.array([1 if i == 'Yes' else 0 for i in sub['answer']])
+            y_pred = np.array([1 if i == 'Yes' else 0 for i in sub['extracted']])
+            f1_score, precision, recall = cal_f1_score(y_true, y_pred)
+            res['split'].append(c)
+            res['Overall'].append(f1_score * 100)
+            res['acc'].append(np.mean(sub['score']) * 100)
+            res['precision'].append(precision * 100)
+            res['recall'].append(recall * 100)
+
+    ret = pd.DataFrame(res)
+    return ret
+
+
+def default_rating(data_file):
+    data = load(data_file)
+    res = {}
+    res['Overall'] = np.mean(data['score']) * 100
+    if 'category' in data:
+        cates = list(set(data['category']))
+        cates = [c for c in cates if not pd.isna(c)]
+        cates.sort()
+        for c in cates:
+            sub = data[data['category'] == c]
+            res[c] = np.mean(sub['score']) * 100
+    if 'l2-category' in data:
+        cates = list(set(data['l2-category']))
+        cates = [c for c in cates if not pd.isna(c)]
+        cates.sort()
+        for c in cates:
+            sub = data[data['l2-category'] == c]
+            res[c] = np.mean(sub['score']) * 100
+    ret = d2df(res)
+    return ret
+
+
+def YOrN_match_prompt(line):
+    tmpl = (
+        'You are an AI assistant who will help me to match an answer with two options of a question. '
+        'The options are only Yes / No. '
+        'You are provided with a question and an answer, '
+        'and you need to find which option (Yes / No) is most similar to the answer. '
+        'If the meaning of all options are significantly different from the answer, output Unknown. '
+        'Your should output a single word among the following 3 choices: Yes, No, Unknown.\n'
+        'Example 1: \n'
+        "Question: Is the word in this image 'Hello'?\nAnswer: The word in this image is 'Hello'.\nYour output: Yes\n"
+        'Example 2: \n'
+        "Question: Is the word in this image 'Hello'?\n"
+        "Answer: The word in this image is not 'Hello'.\nYour output: No\n"
+        'Example 3: \n'
+        'Question: {}?\nAnswer: {}\nYour output: '
+    )
+    return tmpl.format(line['question'], line['prediction'])
+
+
+def YOrN_Extraction(output):
+    s = output.lower()
+    words = process_punctuation(s).split()
+    if 'yes' in words and 'no' not in words:
+        return 'Yes'
+    if 'yes' not in words and 'no' in words:
+        return 'No'
+    return 'Unknown'
+
+
+def YOrN_auxeval(model, line):
+    prompt = YOrN_match_prompt(line)
+    retry = 5
+    for i in range(retry):
+        output = model.generate(prompt, temperature=0.5 * i)
+        ans = YOrN_Extraction(output)
+        if ans != 'Unknown':
+            return ans
+    return 'Unknown'

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/inference.py

@@ -0,0 +1,171 @@
+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, 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)
+
+    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]
+
+    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)
+    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
+    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()
+    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
+
+    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 = 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 data_indices}
+    dump(res, out_file)
+    return model
+
+
+# A wrapper for infer_data, do the pre & post processing
+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'
+    if osp.exists(result_file):
+        if rank == 0:
+            data = load(result_file)
+            results = {k: v for k, v in zip(data['index'], data['prediction'])}
+            if not ignore_failed:
+                results = {k: v for k, v in results.items() if FAIL_MSG not in str(v)}
+            dump(results, prev_file)
+        if world_size > 1:
+            dist.barrier()
+
+    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'] = [str(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_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