|
import argparse
|
|
import torch
|
|
import os
|
|
import json
|
|
from tqdm import tqdm
|
|
import shortuuid
|
|
|
|
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 tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
|
|
|
|
from PIL import Image
|
|
import math
|
|
|
|
|
|
def split_list(lst, n):
|
|
"""Split a list into n (roughly) equal-sized chunks"""
|
|
chunk_size = math.ceil(len(lst) / n)
|
|
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
|
|
|
|
|
|
def get_chunk(lst, n, k):
|
|
chunks = split_list(lst, n)
|
|
return chunks[k]
|
|
|
|
|
|
def eval_model(args):
|
|
|
|
disable_torch_init()
|
|
model_path = os.path.expanduser(args.model_path)
|
|
model_name = get_model_name_from_path(model_path)
|
|
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
|
|
|
|
questions = json.load(open(os.path.expanduser(args.question_file), "r"))
|
|
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
|
|
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 i, line in enumerate(tqdm(questions)):
|
|
idx = line["id"]
|
|
question = line['conversations'][0]
|
|
qs = question['value'].replace('<image>', '').strip()
|
|
cur_prompt = qs
|
|
|
|
if 'image' in line:
|
|
image_file = line["image"]
|
|
image = Image.open(os.path.join(args.image_folder, image_file))
|
|
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
|
images = image_tensor.unsqueeze(0).half().cuda()
|
|
if getattr(model.config, 'mm_use_im_start_end', False):
|
|
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
|
|
else:
|
|
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
|
|
cur_prompt = '<image>' + '\n' + cur_prompt
|
|
else:
|
|
images = None
|
|
|
|
if args.single_pred_prompt:
|
|
qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
|
|
cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly."
|
|
|
|
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()
|
|
|
|
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)] if conv.version == "v0" else None
|
|
|
|
with torch.inference_mode():
|
|
output_ids = model.generate(
|
|
input_ids,
|
|
images=images,
|
|
do_sample=True if args.temperature > 0 else False,
|
|
temperature=args.temperature,
|
|
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()
|
|
|
|
|
|
if args.answer_prompter:
|
|
outputs_reasoning = outputs
|
|
input_ids = tokenizer_image_token(prompt + outputs_reasoning + ' ###\nANSWER:', tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
|
|
|
|
with torch.inference_mode():
|
|
output_ids = model.generate(
|
|
input_ids,
|
|
images=images,
|
|
do_sample=True if args.temperature > 0 else False,
|
|
temperature=args.temperature,
|
|
max_new_tokens=64,
|
|
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()
|
|
outputs = outputs_reasoning + '\n The answer is ' + outputs
|
|
|
|
ans_id = shortuuid.uuid()
|
|
ans_file.write(json.dumps({"question_id": idx,
|
|
"prompt": cur_prompt,
|
|
"text": outputs,
|
|
"answer_id": ans_id,
|
|
"model_id": model_name,
|
|
"metadata": {}}) + "\n")
|
|
ans_file.flush()
|
|
ans_file.close()
|
|
|
|
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-folder", type=str, default="")
|
|
parser.add_argument("--question-file", type=str, default="tables/question.json")
|
|
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
|
|
parser.add_argument("--conv-mode", type=str, default="llava_v0")
|
|
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=0.2)
|
|
parser.add_argument("--answer-prompter", action="store_true")
|
|
parser.add_argument("--single-pred-prompt", action="store_true")
|
|
args = parser.parse_args()
|
|
|
|
eval_model(args)
|
|
|
