| import argparse
|
| import torch
|
|
|
| from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
|
| from llava.conversation import conv_templates, SeparatorStyle
|
| from llava.model.builder import load_pretrained_model
|
| from llava.utils import disable_torch_init
|
| from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
|
|
|
| from PIL import Image
|
|
|
| import requests
|
| from PIL import Image
|
| from io import BytesIO
|
|
|
|
|
| def load_image(image_file):
|
| if image_file.startswith('http') or image_file.startswith('https'):
|
| response = requests.get(image_file)
|
| image = Image.open(BytesIO(response.content)).convert('RGB')
|
| else:
|
| image = Image.open(image_file).convert('RGB')
|
| return image
|
|
|
|
|
| def eval_model(args):
|
|
|
| disable_torch_init()
|
|
|
| model_name = get_model_name_from_path(args.model_path)
|
| tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
|
|
|
| qs = args.query
|
| if model.config.mm_use_im_start_end:
|
| qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
|
| else:
|
| qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
|
|
|
| if 'llama-2' in model_name.lower():
|
| conv_mode = "llava_llama_2"
|
| elif "v1" in model_name.lower():
|
| conv_mode = "llava_v1"
|
| elif "mpt" in model_name.lower():
|
| conv_mode = "mpt"
|
| else:
|
| conv_mode = "llava_v0"
|
|
|
| if args.conv_mode is not None and conv_mode != args.conv_mode:
|
| print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
|
| else:
|
| args.conv_mode = conv_mode
|
|
|
| conv = conv_templates[args.conv_mode].copy()
|
| conv.append_message(conv.roles[0], qs)
|
| conv.append_message(conv.roles[1], None)
|
| prompt = conv.get_prompt()
|
|
|
| image = load_image(args.image_file)
|
| image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
|
|
|
| input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
|
|
|
| stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
|
| keywords = [stop_str]
|
| stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
|
|
|
| with torch.inference_mode():
|
| output_ids = model.generate(
|
| input_ids,
|
| images=image_tensor,
|
| do_sample=True,
|
| temperature=0.2,
|
| max_new_tokens=1024,
|
| use_cache=True,
|
| stopping_criteria=[stopping_criteria])
|
|
|
| input_token_len = input_ids.shape[1]
|
| n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
|
| if n_diff_input_output > 0:
|
| print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
|
| outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
|
| outputs = outputs.strip()
|
| if outputs.endswith(stop_str):
|
| outputs = outputs[:-len(stop_str)]
|
| outputs = outputs.strip()
|
| print(outputs)
|
|
|
| if __name__ == "__main__":
|
| parser = argparse.ArgumentParser()
|
| parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
|
| parser.add_argument("--model-base", type=str, default=None)
|
| parser.add_argument("--image-file", type=str, required=True)
|
| parser.add_argument("--query", type=str, required=True)
|
| parser.add_argument("--conv-mode", type=str, default=None)
|
| args = parser.parse_args()
|
|
|
| eval_model(args)
|
|
|
