98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
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import argparse
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import torch
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.model.builder import load_pretrained_model
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from llava.utils import disable_torch_init
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from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
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from PIL import Image
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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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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 = get_model_name_from_path(args.model_path)
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tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
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qs = args.query
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if model.config.mm_use_im_start_end:
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qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
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else:
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qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
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if 'llama-2' in model_name.lower():
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conv_mode = "llava_llama_2"
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elif "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"
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else:
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conv_mode = "llava_v0"
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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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image = load_image(args.image_file)
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).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,
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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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use_cache=True,
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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-path", type=str, default="facebook/opt-350m")
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parser.add_argument("--model-base", type=str, default=None)
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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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