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import os |
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import re |
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from typing import Literal |
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os.environ['CUDA_VISIBLE_DEVICES'] = '0' |
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def draw_bbox_qwen2_vl(image, response, norm_bbox: Literal['norm1000', 'none']): |
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matches = re.findall( |
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r'<\|object_ref_start\|>(.*?)<\|object_ref_end\|><\|box_start\|>\((\d+),(\d+)\),\((\d+),(\d+)\)<\|box_end\|>', |
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response) |
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ref = [] |
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bbox = [] |
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for match_ in matches: |
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ref.append(match_[0]) |
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bbox.append(list(match_[1:])) |
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draw_bbox(image, ref, bbox, norm_bbox=norm_bbox) |
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def infer_grounding(): |
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from swift.llm import PtEngine, RequestConfig, BaseArguments, InferRequest, safe_snapshot_download |
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output_path = 'bbox.png' |
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image = load_image('http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png') |
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infer_request = InferRequest(messages=[{'role': 'user', 'content': 'Task: Object Detection'}], images=[image]) |
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request_config = RequestConfig(max_tokens=512, temperature=0) |
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adapter_path = safe_snapshot_download('swift/test_grounding') |
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args = BaseArguments.from_pretrained(adapter_path) |
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engine = PtEngine(args.model, adapters=[adapter_path]) |
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resp_list = engine.infer([infer_request], request_config) |
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response = resp_list[0].choices[0].message.content |
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print(f'lora-response: {response}') |
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draw_bbox_qwen2_vl(image, response, norm_bbox=args.norm_bbox) |
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print(f'output_path: {output_path}') |
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image.save(output_path) |
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if __name__ == '__main__': |
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from swift.llm import draw_bbox, load_image |
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infer_grounding() |
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