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--- |
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license: apache-2.0 |
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--- |
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Model Usage: |
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~~~ |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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model_path, |
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torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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device_map="auto", |
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) |
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processor = AutoProcessor.from_pretrained(model_path, max_pixels=262144) |
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reason_prompt = r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. During this reasoning process, prioritize analyzing the local regions of the image by leveraging the bounding box coordinates in the format [x_min, y_min, x_max, y_max]. The final answer MUST BE put in \boxed{}. An example is like: <think> reasoning process 1 with [x_min1, y_min1, x_max1, y_max1]; reasoning process 2 with [x_min2, y_min2, x_max2, y_max2] </think>. The answer is: \boxed{answer}." |
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def get_label(images, content1): |
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content_list = [] |
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for image_url in images: |
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content_list.append({ |
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"type": "image", |
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"image": image_url, |
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}) |
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if mode == 'think': |
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content_list.append({"type": "text", |
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"text": content1 + '\n' + reason_prompt + '\n'}) |
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else: |
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content_list.append({"type": "text", |
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"text": content1}) |
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messages = [ |
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{ |
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"role": "user", |
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"content": content_list |
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} |
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] |
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# Preparation for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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# print(text) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=True, temperature=0.6) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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# print(output_text) |
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# print(output_text[0]) |
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return output_text[0] |
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~~~ |
