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--- |
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license: cc-by-4.0 |
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datasets: |
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- RussRobin/SpatialQA |
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language: |
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- en |
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tags: |
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- Embodied AI |
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- MLLM |
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- VLM |
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- Spatial Understanding |
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- Phi-2 |
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pipeline_tag: visual-question-answering |
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--- |
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SpatialBot is a VLM with spatial understanding and reasoning abilties, by precisely understanding depth maps and using them to do high-level tasks. |
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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. |
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## How to use SpatialBot-3B |
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### 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. |
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1. Install dependencies first: |
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``` |
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pip install torch transformers accelerate pillow numpy |
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``` |
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2. Run the model: |
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``` |
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import torch |
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import transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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import warnings |
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import numpy as np |
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# disable some warnings |
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transformers.logging.set_verbosity_error() |
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transformers.logging.disable_progress_bar() |
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warnings.filterwarnings('ignore') |
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# set device |
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device = 'cuda' # or cpu |
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model_name = 'RussRobin/SpatialBot-3B' |
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offset_bos = 0 |
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# create model |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, # float32 for cpu |
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device_map='auto', |
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trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_name, |
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trust_remote_code=True) |
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# text prompt |
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prompt = 'What is the depth value of point <0.5,0.2>? Answer directly from depth map.' |
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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:" |
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text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image 1>\n<image 2>\n')] |
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input_ids = torch.tensor(text_chunks[0] + [-201] + [-202] + text_chunks[1][offset_bos:], dtype=torch.long).unsqueeze(0).to(device) |
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image1 = Image.open('rgb.jpg') |
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image2 = Image.open('depth.png') |
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channels = len(image2.getbands()) |
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if channels == 1: |
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img = np.array(image2) |
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height, width = img.shape |
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three_channel_array = np.zeros((height, width, 3), dtype=np.uint8) |
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three_channel_array[:, :, 0] = (img // 1024) * 4 |
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three_channel_array[:, :, 1] = (img // 32) * 8 |
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three_channel_array[:, :, 2] = (img % 32) * 8 |
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image2 = Image.fromarray(three_channel_array, 'RGB') |
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image_tensor = model.process_images([image1,image2], model.config).to(dtype=model.dtype, device=device) |
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# generate |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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max_new_tokens=100, |
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use_cache=True, |
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repetition_penalty=1.0 # increase this to avoid chattering |
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)[0] |
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print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()) |
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``` |
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### Paper: |
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https://arxiv.org/abs/2406.13642 |
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### GitHub repo: |
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https://github.com/BAAI-DCAI/SpatialBot |
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<!-- ### SpatialQA, the training set: |
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https://huggingface.co/datasets/RussRobin/SpatialQA |
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--> |
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### SpatialBench, the benchmark: |
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https://huggingface.co/datasets/RussRobin/SpatialBench |
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### CKPTs for SpatialBot-3B with LoRA: |
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https://huggingface.co/RussRobin/SpatialBot-3B-LoRA |
