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| import argparse | |
| import torch | |
| import os | |
| import json | |
| from tqdm import tqdm | |
| import shortuuid | |
| from transformers import AutoModelForCausalLM | |
| from transformers import AutoProcessor | |
| from llava.conversation import conv_templates, SeparatorStyle | |
| from torch.utils.data import Dataset, DataLoader | |
| from PIL import Image | |
| import math | |
| def eval_model(args): | |
| # Model | |
| model_kwargs = { | |
| "trust_remote_code": True, | |
| "attn_implementation": "flash_attention_2", | |
| "torch_dtype": "auto", | |
| } | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", device_map="cuda", **model_kwargs) | |
| image_processor = AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True) | |
| with open(args.question_file, "r") as file: | |
| questions = json.load(file) | |
| answers_file = os.path.expanduser(args.answers_file) | |
| os.makedirs(os.path.dirname(answers_file), exist_ok=True) | |
| ans_file = open(answers_file, "w") | |
| for line in tqdm(questions, total=len(questions)): | |
| question = line['conversations'][0] | |
| qs = question['value'].replace('<image>', '').strip() | |
| if 'image' in line: | |
| messages = [ | |
| {"role": "user", "content": "<|image_1|>\n" + qs}, | |
| ] | |
| prompt = image_processor.tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| image = Image.open(os.path.join(args.image_folder, line['image'])).convert('RGB') | |
| inputs = image_processor(prompt, [image], return_tensors="pt").to("cuda:0") | |
| else: | |
| messages = [ | |
| {"role": "user", "content": qs}, | |
| ] | |
| prompt = image_processor.tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| inputs = image_processor(prompt, None, return_tensors="pt").to("cuda:0") | |
| idx = line["id"] | |
| cur_prompt = qs | |
| generate_ids = model.generate( | |
| **inputs, | |
| do_sample=True if args.temperature > 0 else False, | |
| temperature=args.temperature, | |
| eos_token_id=[32007], | |
| max_new_tokens=128 | |
| ) | |
| generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] | |
| response = image_processor.batch_decode(generate_ids, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False)[0] | |
| ans_id = shortuuid.uuid() | |
| ans_file.write(json.dumps({"question_id": idx, | |
| "prompt": cur_prompt, | |
| "text": response, | |
| "answer_id": ans_id, | |
| "model_id": 'phi3', | |
| "metadata": {}}) + "\n") | |
| ans_file.close() | |
| if __name__ == "__main__": | |
| # mp.set_start_method("spawn", force=True) | |
| 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-folder", type=str, default="") | |
| parser.add_argument("--question-file", type=str, default="tables/question.jsonl") | |
| parser.add_argument("--answers-file", type=str, default="answer.jsonl") | |
| parser.add_argument("--conv-mode", type=str, default="phi3") | |
| parser.add_argument("--num-chunks", type=int, default=1) | |
| parser.add_argument("--chunk-idx", type=int, default=0) | |
| parser.add_argument("--temperature", type=float, default=0.0) | |
| parser.add_argument("--top_p", type=float, default=None) | |
| parser.add_argument("--num_beams", type=int, default=1) | |
| parser.add_argument("--max_new_tokens", type=int, default=128) | |
| args = parser.parse_args() | |
| eval_model(args) | |