| --- |
| license: cc-by-4.0 |
| datasets: |
| - RussRobin/SpatialQA |
| language: |
| - en |
| tags: |
| - Embodied AI |
| - MLLM |
| - VLM |
| - Spatial Understanding |
| - Phi-2 |
| pipeline_tag: visual-question-answering |
| --- |
| |
| SpatialBot is a VLM with spatial understanding and reasoning abilties, by precisely understanding depth maps and using them to do high-level tasks. |
|
|
| In this HF repo, we provide the merged SpatialBot-3B, which is based on Phi-2 and SigLIP. It can perform well on general VLM tasks and spatial understanding benchmarks like SpatialBench. |
|
|
| ## How to use SpatialBot-3B |
| ### NOTE: We update the repo and quick start codes in 28 August, 2024. Please update your model and codes if you downloaded them before this date. |
| 1. Install dependencies first: |
| ``` |
| pip install torch transformers accelerate pillow numpy |
| ``` |
|
|
| 2. Run the model: |
| ``` |
| import torch |
| import transformers |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from PIL import Image |
| import warnings |
| import numpy as np |
| |
| # disable some warnings |
| transformers.logging.set_verbosity_error() |
| transformers.logging.disable_progress_bar() |
| warnings.filterwarnings('ignore') |
| |
| # set device |
| device = 'cuda' # or cpu |
| |
| model_name = 'RussRobin/SpatialBot-3B' |
| offset_bos = 0 |
| |
| # create model |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype=torch.float16, # float32 for cpu |
| device_map='auto', |
| trust_remote_code=True) |
| tokenizer = AutoTokenizer.from_pretrained( |
| model_name, |
| trust_remote_code=True) |
| |
| # text prompt |
| prompt = 'What is the depth value of point <0.5,0.2>? Answer directly from depth map.' |
| text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image 1>\n<image 2>\n{prompt} ASSISTANT:" |
| text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image 1>\n<image 2>\n')] |
| input_ids = torch.tensor(text_chunks[0] + [-201] + [-202] + text_chunks[1][offset_bos:], dtype=torch.long).unsqueeze(0).to(device) |
| |
| image1 = Image.open('rgb.jpg') |
| image2 = Image.open('depth.png') |
| |
| channels = len(image2.getbands()) |
| if channels == 1: |
| img = np.array(image2) |
| height, width = img.shape |
| three_channel_array = np.zeros((height, width, 3), dtype=np.uint8) |
| three_channel_array[:, :, 0] = (img // 1024) * 4 |
| three_channel_array[:, :, 1] = (img // 32) * 8 |
| three_channel_array[:, :, 2] = (img % 32) * 8 |
| image2 = Image.fromarray(three_channel_array, 'RGB') |
| |
| image_tensor = model.process_images([image1,image2], model.config).to(dtype=model.dtype, device=device) |
| |
| # generate |
| output_ids = model.generate( |
| input_ids, |
| images=image_tensor, |
| max_new_tokens=100, |
| use_cache=True, |
| repetition_penalty=1.0 # increase this to avoid chattering |
| )[0] |
| |
| print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()) |
| ``` |
|
|
| ### Paper: |
| https://arxiv.org/abs/2406.13642 |
|
|
| ### GitHub repo: |
| https://github.com/BAAI-DCAI/SpatialBot |
|
|
| <!-- ### SpatialQA, the training set: |
| https://huggingface.co/datasets/RussRobin/SpatialQA |
| --> |
| ### SpatialBench, the benchmark: |
| https://huggingface.co/datasets/RussRobin/SpatialBench |
|
|
| ### CKPTs for SpatialBot-3B with LoRA: |
| https://huggingface.co/RussRobin/SpatialBot-3B-LoRA |
