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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)
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