wehub-resource-sync
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
from llava.model.builder import load_pretrained_model
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from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
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from llava.conversation import conv_templates, SeparatorStyle
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from PIL import Image
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import requests
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import copy
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import torch
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import sys
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import warnings
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warnings.filterwarnings("ignore")
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pretrained = "lmms-lab/llava-onevision-qwen2-0.5b-si"
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model_name = "llava_qwen"
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device = "cuda"
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device_map = "auto"
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tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args
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model.eval()
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
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image = Image.open(requests.get(url, stream=True).raw)
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image_tensor = process_images([image], image_processor, model.config)
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image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
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conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
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question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
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conv = copy.deepcopy(conv_templates[conv_template])
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conv.append_message(conv.roles[0], question)
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conv.append_message(conv.roles[1], None)
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prompt_question = conv.get_prompt()
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input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
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image_sizes = [image.size]
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cont = model.generate(
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input_ids,
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images=image_tensor,
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image_sizes=image_sizes,
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do_sample=False,
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temperature=0,
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max_new_tokens=4096,
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)
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text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
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print(text_outputs)
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from threading import Thread
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from transformers import TextIteratorStreamer
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import json
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
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image = Image.open(requests.get(url, stream=True).raw)
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image_tensor = process_images([image], image_processor, model.config)
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image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
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conv_template = "qwen_1_5"
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question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
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conv = copy.deepcopy(conv_templates[conv_template])
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conv.append_message(conv.roles[0], question)
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conv.append_message(conv.roles[1], None)
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prompt_question = conv.get_prompt()
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input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
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image_sizes = [image.size]
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max_context_length = getattr(model.config, "max_position_embeddings", 2048)
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num_image_tokens = question.count(DEFAULT_IMAGE_TOKEN) * model.get_vision_tower().num_patches
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
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max_new_tokens = min(4096, max_context_length - input_ids.shape[-1] - num_image_tokens)
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if max_new_tokens < 1:
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print(
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json.dumps(
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{
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"text": question + "Exceeds max token length. Please start a new conversation, thanks.",
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"error_code": 0,
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}
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)
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)
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else:
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gen_kwargs = {
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"do_sample": False,
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"temperature": 0,
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"max_new_tokens": max_new_tokens,
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"images": image_tensor,
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"image_sizes": image_sizes,
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}
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thread = Thread(
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target=model.generate,
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kwargs=dict(
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inputs=input_ids,
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streamer=streamer,
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**gen_kwargs,
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),
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)
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thread.start()
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generated_text = ""
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for new_text in streamer:
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generated_text += new_text
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sys.stdout.write(new_text)
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sys.stdout.flush()
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print("\nFinal output:", generated_text)
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