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  license: mit
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  license: mit
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+
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+ ### Simple running code is based on [MoAI-Github](https://github.com/ByungKwanLee/MoAI).
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+
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+ You need only the following seven steps.
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+
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+ ### [0] Download Github Code of MoAI.
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+
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+ ```bash
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+ git clone https://github.com/ByungKwanLee/MoAI
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+ ```
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+
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+
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+ ### [1] Loading Image
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+
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+ ```python
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+ from PIL import Image
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+ from torchvision.transforms import Resize
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+ from torchvision.transforms.functional import pil_to_tensor
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+ image_path = "figures/moai_mystery.png"
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+ image = Resize(size=(490, 490), antialias=False)(pil_to_tensor(Image.open(image_path)))
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+ ```
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+
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+ ### [2] Instruction Prompt
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+
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+ ```python
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+ prompt = "Describe this image in detail."
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+ ```
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+
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+ ### [3] Loading MoAI
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+ ```python
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+ from moai.load_moai import prepare_moai
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+ moai_model, moai_processor, seg_model, seg_processor, od_model, od_processor, sgg_model, ocr_model \
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+ = prepare_moai(moai_path='/mnt/ssd/lbk-cvpr/MoAI/final', bits=4, grad_ckpt=False, lora=False, dtype='fp16')
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+ ```
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+
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+ ### [4] Pre-processing for MoAI
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+ ```python
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+ moai_inputs = moai_model.demo_process(image=image,
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+ prompt=prompt,
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+ processor=moai_processor,
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+ seg_model=seg_model,
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+ seg_processor=seg_processor,
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+ od_model=od_model,
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+ od_processor=od_processor,
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+ sgg_model=sgg_model,
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+ ocr_model=ocr_model,
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+ device='cuda:0')
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+ ```
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+
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+ ### [5] Generate
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+ ```python
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+ import torch
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+ with torch.inference_mode():
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+ generate_ids = moai_model.generate(**moai_inputs, do_sample=True, temperature=0.9, top_p=0.95, max_new_tokens=256, use_cache=True)
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+ ```
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+
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+ ### [6] Decoding
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+ ```python
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+ answer = moai_processor.batch_decode(generate_ids, skip_special_tokens=True)[0].split('[U')[0]
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+ print(answer)
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+ ```