Training

model_zoo
├── vicuna_ckpt
│   ├── 13b_v0

│   └── 7b_v0
└── epcl_vit-L_256tokens

Instruction Tuning

We construct instruction tuning dataset for 2D/3D modality instruction tuning via GPT API.

2D Instruction Tuning Datasets

2D instruction tuning datasets are build on MS-COCO, Bamboo, Locount and TextVQA dataset. You can download them from here.

The generated instruction-following dialogues are organized into the following meta files. We provide a table to illustrate the correspondence between each meta file and data collection:

Note that we provide a LAMM_instruct_186k.json meta file to merge all the dataset across different tasks. You can just use this file for training.

Additional Detection Instruction

The lack of sufficient detection instructions results in poor performance on downstream PASCAL VOC evaluation. To overcome this problem, we leverage entire COCO detection annotations to generate instructions, and add them into the aforementioned datasets as supplementation.

Note that we provide a LAMM_instruct_186k.json meta file to merge all the dataset across different tasks. You can just use this file for training.

3D Instruction Tuning Datasets

We provide two 3D instruction tuning datasets, “Scan2Inst” and “LAMM3D-Dataset”, for 3D instruction tuning.

Scan2Inst

Scan2Inst is build on ScanNet. Specifically, we first use FCAF3D from mmdetection3d to extract 3d object given a scene level point cloud. Then a ULIP-like encoder is used to extract linguistic-aligned object level 3d feature. In the end, to speed up the data loading process, we store the dataset to a pickle file. For convincely, we provide a processed pickle file (scan2inst_train.pickle) here, you can train our model by just loading this file.

Besides, if you want to utilize your own dataset, we also provide our ULIP model pretraining code, you can train your own ULIP model by following the instructions from src/tools/Octavius/ULIP/scripts/pretrain_pointbert.sh. You can also use our pretrained model Here to extract your own dataset.

Corresponding meta file is here:

LAMM3D-Dataset

LAMM3D-Dataset is build on 3RScan and ShapeNet. You can download them from here.

Corresponding meta file is here:

Directory Structure

data
├── LAMM
│   ├── 2D_Instruct

│   │   ├── coco_images.zip

│   │   ├── bamboo_images.zip

│   │   ├── textvqa_images.zip

│   │   ├── locount_images.zip

│   │   └── meta_file

│   │       ├── daily_dialogue_49k.json

│   │       ├── detailed_description_49k.json

│   │       ├── factual_knowledge_dialogue_42k.json

│   │       └── vision_task_dialogue_46k.json
│   ├── 3D_Instruct
│   │   ├── 3rscan_pcls.zip

│   │   ├── shapenet_pcls.zip

│   │   └── meta_file

│   │       └── LAMM_3dinstruct_10k.json
│   └── …
│
├── Octavius
│   ├── 2D_Instruct

│   │   ├── coco_images.zip

│   │   ├── bamboo_images.zip

│   │   ├── textvqa_images.zip

│   │   ├── locount_images.zip

│   │   └── meta_file

│   │       └── octavius_2d_train_293k.json
│   ├── 3D_Instruct

│   │   ├── scan2inst_train.pickle
│   │   └── meta_file

│   │       └── scan2inst_train.json

│   └── …
│
└── …

Meta File Format

[
    {
    "id": "000000019028",  # image id
    "image": "coco_images/000000019028.jpg", # image path
    "conversations": [
        {
            "from": "human",  # instruction
            "value": "How is the kitchen in the image furnished?"
        },
        {
            "from": "gpt",  # response
            "value": "The kitchen in the image is furnished with white cabinets and white appliances. There is a dishwasher, a stove, 
            and a sink. On the stove, a blue towel hangs on the handle. A cutting board is placed on the dishwasher. There are also 
            additional elements like a bowl of apples on the counter and a beige rug on the floor."
        }
    ],
    "task_type": "conversation",  # task type
    "src_image": "coco2017" # original dataset
    },
    {
        ...
    }
]

[
    {
        "pcl": "shapenet_pcls/04256520_cb71cb7b36dbcb6f826fc8d57346a2e4_4096.npy",
        "conversations": [
                {
                    "from": "human",
                    "value": "What scenario does this point cloud belong to according to the model\u2019s prediction?"
                },
                {
                    "from": "gpt",
                    "value": "Through meticulous analysis, it becomes evident that the point cloud aligns with the characteristics of sofa,
                    couch, lounge scenario."
                }
            ],
        "task_type": "classification3d",
        "src_dataset": "ShapeNet",
        "src_id": "04256520_cb71cb7b36dbcb6f826fc8d57346a2e4"
    },
    {
        ...
    }
]

Benchmark

We provide 2D/3D/ChEF benchmarking datasets for downstream evaluation.

2D Benchmarking Datasets

2D Benchmarking datasets are build on Flickr30k, CIFAR-10, FSC147, CelebA, UCMerced, LSP, PASCAL VOC, SVT, AI2D and ScienceQA datasets. You can download them from here.

Corresponding meta file is here:

3D Benchmarking Datasets

We provide two 3D benchmarking datasets, “Scan2Inst-benchmark” and “LAMM3D-Dataset-benchmark”.

Scan2Inst-benchmark

If your MLLMs are trained with “Scan2Inst”, you should use “Scan2Inst-benchmark” for evaluation.

We provide NR3D and ShapeNet for zero-shot evaluation, and ScanNet for finetuning evaluation. You can download processed pickle file from here.

Corresponding meta file is here:

LAMM3D-Dataset-benchmark

If your MLLMs are trained with “LAMM3D-Dataset”, you should use “LAMM3D-Dataset-benchmark” for evalution.

LAMM3D-Dataset-benchmark is build on ScanNet. You can download them from here.

Corresponding meta file is here:

ChEF Benchmarking Dataset

Omnibenchmark

Download Omnibenchmark for fine-grained classification dataset and Bamboo Label System for hierarchical catergory labels.

We sampled and labeled Omnibenchmark meticulously by using a hierarchical chain of categories, facilitated by the Bamboo label system.

python ChEF/data_process/Omnibenchmark.py

You can also directly download the labeled Omnibenchmark dataset from OpenXLab.

MMBench, MME and SEEDBench

Refer to MMBench, MME and SEEDBench for dataset and more details.

POPE

POPE is a special labeled COCO dataset for hallucination evaluation based on the validation set of COCO 2014. Download COCO and POPE.

MMBench_C and ScienceQA_C

MMBench_C and ScienceQA_C are datasets with image and text corruptions fot robustness evaluation. You can also directly download the MMBench_C and ScienceQA_C dataset from OpenXLab.

Directory Structure

data
├── ChEF
|   │── Omnibenchmark_Bamboo

|   │   ├── meta_file
|   │   └── omnibenchmark_images
|   ├── MMBench_C
|   |   ├── images
|   |   ├── Image_Corruptions_info.json
|   |   ├── Text_Corruptions_info.json
|   |   └── MMBench_C.json
|   └── ScienceQA_C
|       ├── sqaimage_images
|       ├── Image_Corruptions_info.json
|       ├── Text_Corruptions_info.json
|       └── VQA_ScienceQA_C.json
├── Bamboo
|   └── sensexo_visual_add_academic_add_state_V4.visual.json
|── MMBench
|   ├── mmbench_dev_20230712.tsv
|   └── mmbench_test_20230712.tsv
|── MME_Benchmark_release_version
|── SEED-Bench
|── coco_pope
|   ├── val2014
|   ├── coco_pope_adversarial.json
|   ├── coco_pope_popular.json
|   └── coco_pope_random.json
└── …

Installation

Training

Pre-requist Packages: gcc <= 7.5.0; nvcc >= 11.1

conda create -n lamm python=3.10 -y
conda activate lamm
# Choose different version of torch according to your 
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

conda install timm==0.6.7 deepspeed==0.9.3 transformers==4.31.0 -c conda-forge
pip install peft==0.3.0 --no-dependencies
pip install -r requirements/default.txt

# if cuda is available
conda install -c conda-forge faiss-gpu
# otherwise
conda install -c conda-forge faiss-cpu

import nltk
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')

Optional Dependencies

cd src/model/EPCL/third_party/pointnet2/
python setup.py install
cd ../../utils/
pip install cython
python cython_compile.py build_ext --inplace

Reducing Memory in Training

Install flash attention (v2) if you are tight in GPU memory. Please refer to flash attention’s installation

Install xformers if you are tight in GPU memory and cannot use flash attention (e.g., using Nvidia v100). Please refer to xformers’s installation

Benchmarking

We use ChEF for benchmarking.

conda create -n ChEF python=3.10
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements/default.txt
pip install -r requirements/ChEF.txt

Optional Dependencies

Efficient Inference

Install lightllm to speed up inference and decrease the GPU memery usage to enable large batchsize.

git clone -b multimodal  https://github.com/ModelTC/lightllm.git
cd lightllm
python setup.py install

Default Benchmark

LAMM-Benchmark

To evaluate LAMM/Octavius on LAMM-Benchmark in 2D common tasks, use the pre-defined model config (src/config/ChEF/models/lamm.yaml or src/config/ChEF/models/octavius_2d+3d.yaml) and the pre-defined recipes config (src/config/ChEF/scenario_recipes/LAMM/).

python eval.py --model_cfg config/ChEF/models/lamm.yaml  --recipe_cfg config/ChEF/scenario_recipes/LAMM/ScienceQA.yaml

If you want to automately running all the evaluations sequentially, you can run

sh tools/LAMM/eval_lamm2d.sh

sh tools/LAMM/eval_lamm3d.sh

To evaluate Octavius on ScanNet Detection, run:

sh tools/Octavius/octavius_ChEF.sh

ChEF

Download Evaluated MLLMs

Organize them as below:

ckpt
├── epcl_vit-L_256tokens
├──
│   ├── lamm_2d               # saved checkpoints in training
│   └── …
└── …

Custom Benchmark

Evaluator

In ChEF, all evaluation pipelines are managed by the Evaluator (src/ChEF/evaluator.py) class. This class serves as the control center for evaluation tasks and incorporates various components, including a scenario, an instruction, an inferencer, and a metric. These components are defined through recipe configurations.

Key Components:

• · Scenario: The scenario represents the evaluation dataset and task-specific details.
• · Instruction: Responsible for processing samples and generating queries.
• · Inferencer: Performs model inference on the dataset.
• · Metric: Evaluates model performance using defined metrics.

Evaluation Workflow

The evaluation process in ChEF follows a structured workflow:

1. · Model and Data Loading: First, the model and evaluation dataset (scenario) are loaded.
2. · Evaluator.evaluate Method: The evaluation is initiated by calling the evaluate method of the Evaluator class.
3. · Inference with inferencer.inference: The inferencer is used to perform model inference. During dataset traversal, the InstructionHandler processes each sample, generating queries that serve as inputs to the model.
4. · Results Saving: The output of the inference is saved in the specified results_path.
5. · Metric Evaluation: Finally, the metric evaluates the results file, calculating various performance metrics specific to the evaluation task.
6. · Output Evaluation Results: The final evaluation results are provided as output, allowing you to assess the model’s performance.

Employ Your Model

In ChEF, you can employ your own custom models by following these steps:

Step 1: Prepare Your Model Files

1. · Navigate to the src/ChEF/models/ folder in ChEF.
2. · Paste all the necessary files for your custom model into this folder.

Step 2: Write the Test Model

1. · Create a new Python file in the models folder and name it something like test_your_model.py.
2. · In this file, you will need to inherit from the TestBase class defined in src/ChEF/models/test_base.py. The TestBase class provides a set of interfaces that you should implement for testing your model.

Step 3: Test Your Model

1. · Add your model in src/ChEF/models/init.py.
2. · Prepare your model configuration in src/config/ChEF/models/. For example, the config for KOSMOS-2 (src/config/ChEF/models/kosmos2.yaml):

model_name: Kosmos2
model_path: ../model_zoo/kosmos/kosmos-2.pt
if_grounding: False # set True for detection and grounding evaluation

The config for KOSMOS-2 on detection tasks evaluation:

model_name: Kosmos2
model_path: ../model_zoo/kosmos/kosmos-2.pt
if_grounding: True

Use the provided recipes for evaluation:

python tools/eval.py --model_cfg configs/ChEF/models/your_model.yaml --recipe_cfg recipe_cfg

Instruction

In ChEF, the InstructionHandler (src/ChEF/instruction/init.py) class plays a central role in managing instructions for generating queries when iterating through the dataset in the inferencer. These queries are then used as inputs to the model for various tasks.

ChEF supports three main query types: standard query, query pool, and multiturn query. For each query type, various query statements are defined based on the dataset’s task type.

1. · Standard Query: Uses the first query defined in the query pool.
2. · Query Pool: Specifies queries in the pool by assigned ids defined in the configuration.
3. · Multiturn Query: Can get different queries depending on the turn id, which are also defined in the query pool.

For more details, refer to the src/ChEF/instruction/query.py.

InstructionHandler also supports generating in-context examples for queries using ice_retriever (src/ChEF/instruction/ice_retriever/). ChEF supports four types of ice_retrievers: random, fixed, topk_text, and topk_img. The generate_ices function in the InstructionHandler class outputs several in-context examples for the input query.

Employ Your Instruction: You can add special queries in the Query Pool, and define the assigned ids in the recipe configuration to use the new queries. You can also define a new type of query by defining the query in src/ChEF/instruction/query.py and adding a new function in InstructionHandler.

Inferencer

In ChEF, the Inferencer component is a crucial part of the system, responsible for model inference. ChEF offers a variety of pre-defined inferencers to cater to different needs. You can easily choose the appropriate inferencer by specifying the inferencer category and necessary settings in the recipe configuration. Additionally, users have the flexibility to define their custom inferencers.

Pre-Defined Inferencers:

ChEF provides eight different inferencers that cover a range of use cases. You can effortlessly use the desired inferencer by specifying its category and required settings in the recipe configuration.

Custom Inferencers:

For advanced users and specific requirements, ChEF offers the option to create custom inferencers. The basic structure of an inferencer is defined in the src/ChEF/inferencer/Direct.py file (Direct_inferencer). You can extend this structure to implement your custom inferencer logic.

class Your_inferencer(Direct_inferencer):
    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        
    def inference(self, model, dataset):
        predictions = []
        # Step 1: build dataloader
        dataloader = DataLoader(dataset, batch_size=self.batch_size, collate_fn=lambda batch: {key: [dict[key] for dict in batch] for key in batch[0]})
        for batch in tqdm(dataloader, desc="Running inference"):
            # Step 2: get input query
            prompts = self.instruction_handler.generate(batch)
            # Step 3: model outputs
            outputs = model.generate(prompts)
            # Step 4: save results
            predictions = predictions + outputs
        # Step 5: output file
        self._after_inference_step(predictions)

Metric

In ChEF, the Metric component plays a crucial role in evaluating and measuring the performance of models across various scenarios and protocols. ChEF offers a wide range of pre-defined metrics, each tailored to different evaluation needs. Detailed information about these metrics can be found in the src/ChEF/metric/init.py file.

Custom Metrics:

ChEF also allows users to define their custom metrics. The basic structure of a metric is defined in the src/ChEF/metric/utils.py file (Base_Metric). You can extend this structure to implement your custom metric logic.

class Your_metric(Base_metric):
    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)

    def metric_func(self, answers):
        '''
            answers: List[sample], each sample is a dict
            sample: {
                'answer' : str,
                'gt_answers' : str, 
            }
        '''
        # Evaluation

LAMM

@article{yin2023lamm,
    title={LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark},
    author={Yin, Zhenfei and Wang, Jiong and Cao, Jianjian and Shi, Zhelun and Liu, Dingning and 
    Li, Mukai and Sheng, Lu and Bai, Lei and Huang, Xiaoshui and Wang, Zhiyong and others},
    journal={arXiv preprint arXiv:2306.06687},
    year={2023}
}

ChEF

@misc{shi2023chef,
      title={ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal 
      Large Language Models}, 
      author={Zhelun Shi and Zhipin Wang and Hongxing Fan and Zhenfei Yin and Lu Sheng and Yu Qiao 
      and Jing Shao},
      year={2023},
      eprint={2311.02692},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Octavius

@misc{chen2023octavius,
      title={Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE}, 
      author={Zeren Chen and Ziqin Wang and Zhen Wang and Huayang Liu and Zhenfei Yin and Si Liu 
      and Lu Sheng and Wanli Ouyang and Yu Qiao and Jing Shao},
      year={2023},
      eprint={2311.02684},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}