126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
import argparse
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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import os
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.utils import disable_torch_init
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from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
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from llava.model import *
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from llava.model.utils import KeywordsStoppingCriteria
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from PIL import Image
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import os
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import requests
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from PIL import Image
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from io import BytesIO
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DEFAULT_IMAGE_TOKEN = "<image>"
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DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
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DEFAULT_IM_START_TOKEN = "<im_start>"
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DEFAULT_IM_END_TOKEN = "<im_end>"
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def load_image(image_file):
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if image_file.startswith('http') or image_file.startswith('https'):
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response = requests.get(image_file)
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image = Image.open(BytesIO(response.content)).convert('RGB')
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else:
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image = Image.open(image_file).convert('RGB')
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return image
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def eval_model(args):
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# Model
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disable_torch_init()
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model_name = os.path.expanduser(args.model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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if "mpt" in model_name.lower():
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model = LlavaMPTForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=torch.float16, use_cache=True).cuda()
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else:
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model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=torch.float16, use_cache=True).cuda()
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image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=torch.float16)
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mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
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tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
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if mm_use_im_start_end:
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tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
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vision_tower = model.get_model().vision_tower[0]
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if vision_tower.device.type == 'meta':
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vision_tower = CLIPVisionModel.from_pretrained(vision_tower.config._name_or_path, torch_dtype=torch.float16, low_cpu_mem_usage=True).cuda()
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model.get_model().vision_tower[0] = vision_tower
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else:
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vision_tower.to(device='cuda', dtype=torch.float16)
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vision_config = vision_tower.config
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vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
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vision_config.use_im_start_end = mm_use_im_start_end
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if mm_use_im_start_end:
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vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
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image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2
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qs = args.query
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if mm_use_im_start_end:
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qs = qs + '\n' + DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + DEFAULT_IM_END_TOKEN
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else:
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qs = qs + '\n' + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len
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if "v1" in model_name.lower():
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conv_mode = "llava_v1"
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elif "mpt" in model_name.lower():
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conv_mode = "mpt_multimodal"
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else:
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conv_mode = "multimodal"
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if args.conv_mode is not None and conv_mode != args.conv_mode:
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print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
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else:
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args.conv_mode = conv_mode
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conv = conv_templates[args.conv_mode].copy()
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conv.append_message(conv.roles[0], qs)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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inputs = tokenizer([prompt])
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image = load_image(args.image_file)
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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input_ids = torch.as_tensor(inputs.input_ids).cuda()
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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keywords = [stop_str]
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensor.unsqueeze(0).half().cuda(),
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do_sample=True,
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temperature=0.2,
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max_new_tokens=1024,
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stopping_criteria=[stopping_criteria])
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input_token_len = input_ids.shape[1]
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n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
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if n_diff_input_output > 0:
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print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
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outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
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outputs = outputs.strip()
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if outputs.endswith(stop_str):
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outputs = outputs[:-len(stop_str)]
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outputs = outputs.strip()
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print(outputs)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
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parser.add_argument("--image-file", type=str, required=True)
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parser.add_argument("--query", type=str, required=True)
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parser.add_argument("--conv-mode", type=str, default=None)
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args = parser.parse_args()
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eval_model(args)
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