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import argparse | |
import torch | |
from tqdm import tqdm | |
import json | |
from minigemini.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
from minigemini.conversation import conv_templates, SeparatorStyle | |
from minigemini.model.builder import load_pretrained_model | |
from minigemini.utils import disable_torch_init | |
from minigemini.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria | |
from PIL import Image | |
import requests | |
from PIL import Image | |
from io import BytesIO | |
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 eval_model(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, True) | |
with open(args.questions_file) as f: | |
llvqa_data = json.load(f) | |
for i, llddata in enumerate(tqdm(llvqa_data)): | |
filename = llddata["img_path"] | |
if args.lang == "en": | |
message = llddata["question"] + "\nChoose between one of the options as follows:\n" | |
elif args.lang == "zh": | |
message = llddata["question"] + "\在下列选项中选择一个:\n" | |
else: | |
raise NotImplementedError("Q-Bench does not support languages other than English (en) and Chinese (zh) yet. Contact us (https://github.com/VQAssessment/Q-Bench/) to convert Q-Bench into more languages.") | |
for choice, ans in zip(["A.", "B.", "C.", "D."], llddata["candidates"]): | |
message += f"{choice} {ans}\n" | |
qs = message | |
if model.config.mm_use_im_start_end: | |
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs | |
else: | |
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs | |
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() | |
conv.append_message(conv.roles[0], qs) | |
conv.append_message(conv.roles[1], None) | |
prompt = conv.get_prompt() | |
image = load_image(args.image_folder + filename) | |
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() | |
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) | |
with torch.inference_mode(): | |
output_ids = model.generate( | |
input_ids, | |
images=image_tensor, | |
num_beams=1, | |
do_sample=False, | |
temperature=0, | |
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() | |
llddata["response"] = outputs | |
with open(args.answers_file, "a") as wf: | |
json.dump(llddata, wf) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--model-path", type=str, default="llava-v1.5") | |
parser.add_argument("--model-base", type=str, default=None) | |
parser.add_argument("--image-folder", type=str, default="./playground/data/qbench/images_llvisionqa") | |
parser.add_argument("--questions-file", type=str, default="./playground/data/qbench/llvisionqa_dev.json") | |
parser.add_argument("--answers-file", type=str, default="answer.jsonl") | |
parser.add_argument("--conv-mode", type=str, default="llava_v1") | |
parser.add_argument("--lang", type=str, default="en") | |
args = parser.parse_args() | |
eval_model(args) | |