Upload ms-swift/examples/infer/demo_grounding.py with huggingface_hub

ms-swift/examples/infer/demo_grounding.py

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+# pip install git+https://github.com/huggingface/transformers.git  # transformers>=4.49
+import os
+import re
+from typing import Literal
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def draw_bbox_qwen2_vl(image, response, norm_bbox: Literal['norm1000', 'none']):
+    matches = re.findall(
+        r'<\|object_ref_start\|>(.*?)<\|object_ref_end\|><\|box_start\|>\((\d+),(\d+)\),\((\d+),(\d+)\)<\|box_end\|>',
+        response)
+    ref = []
+    bbox = []
+    for match_ in matches:
+        ref.append(match_[0])
+        bbox.append(list(match_[1:]))
+    draw_bbox(image, ref, bbox, norm_bbox=norm_bbox)
+
+
+def infer_grounding():
+    from swift.llm import PtEngine, RequestConfig, BaseArguments, InferRequest, safe_snapshot_download
+    output_path = 'bbox.png'
+    image = load_image('http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png')
+    infer_request = InferRequest(messages=[{'role': 'user', 'content': 'Task: Object Detection'}], images=[image])
+
+    request_config = RequestConfig(max_tokens=512, temperature=0)
+    adapter_path = safe_snapshot_download('swift/test_grounding')
+    args = BaseArguments.from_pretrained(adapter_path)
+
+    engine = PtEngine(args.model, adapters=[adapter_path])
+    resp_list = engine.infer([infer_request], request_config)
+    response = resp_list[0].choices[0].message.content
+    print(f'lora-response: {response}')
+
+    draw_bbox_qwen2_vl(image, response, norm_bbox=args.norm_bbox)
+    print(f'output_path: {output_path}')
+    image.save(output_path)
+
+
+if __name__ == '__main__':
+    from swift.llm import draw_bbox, load_image
+    infer_grounding()