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| import argparse | |
| import torch | |
| import json | |
| import os | |
| import re | |
| 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 process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria | |
| from PIL import Image | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| from transformers import TextStreamer | |
| 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 main(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) | |
| 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() | |
| if "mpt" in model_name.lower(): | |
| roles = ('user', 'assistant') | |
| else: | |
| roles = conv.roles | |
| data = json.load(open(args.json_file, 'r', encoding='utf-8')) | |
| ret = {} | |
| for i_entry, entry in enumerate(data): | |
| if entry['id'] not in ret: | |
| ret[entry['id']] = [] | |
| # if len(ret) > 40: | |
| # break | |
| conv = conv_templates[args.conv_mode].copy() | |
| if "mpt" in model_name.lower(): | |
| roles = ('user', 'assistant') | |
| else: | |
| roles = conv.roles | |
| image_file = os.path.join(args.data_path, entry['image']) | |
| image = load_image(image_file) | |
| # image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() | |
| image_tensor = process_images([image], image_processor, args) | |
| image_tensor = image_tensor.to(model.device, dtype=torch.float16) | |
| # inp = input(f"{roles[0]}: ") | |
| inp = '\n'.join(entry["conversations"][0]['value'].split('\n')[1:]) | |
| # print(f"{roles[1]}: ", end="", flush=True) | |
| if image is not None: | |
| # first message | |
| if model.config.mm_use_im_start_end: | |
| inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp | |
| else: | |
| inp = DEFAULT_IMAGE_TOKEN + '\n' + inp | |
| conv.append_message(conv.roles[0], inp) | |
| image = None | |
| else: | |
| # later messages | |
| conv.append_message(conv.roles[0], inp) | |
| conv.append_message(conv.roles[1], None) | |
| prompt = conv.get_prompt() | |
| 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) | |
| streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=image_tensor, | |
| do_sample=True, | |
| temperature=args.temperature, | |
| max_new_tokens=args.max_new_tokens, | |
| streamer=streamer, | |
| use_cache=True, | |
| stopping_criteria=[stopping_criteria]) | |
| outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() | |
| conv.messages[-1][-1] = outputs | |
| if args.debug: | |
| print("\n", {"prompt": prompt, "outputs": outputs.split('\n')[0]}, "\n", flush=True) | |
| print(entry["conversations"][1]['value'], flush=True) | |
| for i in range(len(outputs)): | |
| if i < len(outputs) - 1 and outputs[i:i+2] == "[{": | |
| lo = i | |
| elif i > 1 and outputs[i-1:i+1] == "}]": | |
| hi = i | |
| tries, max_tries = 0, 1 | |
| while tries < max_tries: | |
| try: | |
| string = outputs[lo:hi+1].replace("'", '"') | |
| ret[entry['id']].append(json.loads(string)) | |
| break | |
| except json.JSONDecodeError as e: | |
| tries += 1 | |
| print(f"Tried for {tries} times, error parsing JSON: {e}") | |
| except UnboundLocalError as e: | |
| tries += 1 | |
| print(f"Tried for {tries} times, error parsing JSON: {e}") | |
| with open(args.output_file, 'w', encoding='utf-8') as fout: | |
| json.dump(ret, fout, ensure_ascii=False, indent=2) | |
| 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("--device", type=str, default="cuda") | |
| parser.add_argument("--data-path", type=str, required=True) | |
| parser.add_argument("--output-file", type=str, required=True) | |
| parser.add_argument("--conv-mode", type=str, default=None) | |
| parser.add_argument("--temperature", type=float, default=0.2) | |
| parser.add_argument("--max-new-tokens", type=int, default=1024) | |
| parser.add_argument("--image-aspect-ratio", type=str, default='pad') | |
| parser.add_argument("--load-8bit", action="store_true") | |
| parser.add_argument("--load-4bit", action="store_true") | |
| parser.add_argument("--json-file", type=str, required=True) | |
| parser.add_argument("--num-gpus", type=int, default=1) | |
| parser.add_argument("--debug", action="store_true") | |
| args = parser.parse_args() | |
| main(args) | |