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| import argparse | |
| import torch | |
| from llava.constants import X_TOKEN_INDEX, DEFAULT_X_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_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_X_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): | |
| # Model | |
| 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) | |