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# ------------------------------------------------------------------------ | |
# Copyright 2023 Haotian Liu | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# ------------------------------------------------------------------------ | |
# Modified from LLaVA (https://github.com/haotian-liu/LLaVA) and S^2(https://github.com/bfshi/scaling_on_scales) | |
# Copyright 2024 Jiachen Li | |
# ------------------------------------------------------------------------ | |
import argparse | |
import torch | |
import os | |
import json | |
from tqdm import tqdm | |
import shortuuid | |
from cumo.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
from cumo.conversation import conv_templates, SeparatorStyle | |
from cumo.model.builder import load_pretrained_model | |
from cumo.utils import disable_torch_init | |
from cumo.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path | |
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) # integer division | |
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): | |
# Model | |
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) | |
model.config.training = False | |
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 = process_images([image], image_processor, model.config)[0] | |
images = image_tensor.unsqueeze(0).half().cuda() | |
image_sizes = [image.size] | |
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 | |
image_sizes = 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() | |
with torch.inference_mode(): | |
output_ids = model.generate( | |
input_ids, | |
images=images, | |
image_sizes=image_sizes, | |
do_sample=True if args.temperature > 0 else False, | |
#temperature=args.temperature, | |
max_new_tokens=1024, | |
pad_token_id=tokenizer.eos_token_id, | |
use_cache=True, | |
) | |
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() | |
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) | |