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import argparse
import re
import requests
from PIL import Image
from io import BytesIO
import torch
from transformers import PreTrainedModel
from tinyllava.utils import *
from tinyllava.data import *
from tinyllava.model import *
def image_parser(args):
out = args.image_file.split(args.sep)
return out
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 load_images(image_files):
out = []
for image_file in image_files:
image = load_image(image_file)
out.append(image)
return out
def eval_model(args):
# Model
disable_torch_init()
if args.model_path is not None:
model, tokenizer, image_processor, context_len = load_pretrained_model(args.model_path)
else:
assert args.model is not None, 'model_path or model must be provided'
model = args.model
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
tokenizer = model.tokenizer
image_processor = model.vision_tower._image_processor
qs = args.query
qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
text_processor = TextPreprocess(tokenizer, args.conv_mode)
data_args = model.config
image_processor = ImagePreprocess(image_processor, data_args)
model.cuda()
msg = Message()
msg.add_message(qs)
result = text_processor(msg.messages, mode='eval')
input_ids = result['input_ids']
prompt = result['prompt']
input_ids = input_ids.unsqueeze(0).cuda()
image_files = image_parser(args)
images = load_images(image_files)[0]
images_tensor = image_processor(images)
images_tensor = images_tensor.unsqueeze(0).half().cuda()
stop_str = text_processor.template.separator.apply()[1]
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images_tensor,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
pad_token_id=tokenizer.pad_token_id,
max_new_tokens=args.max_new_tokens,
use_cache=True,
stopping_criteria=[stopping_criteria],
)
outputs = tokenizer.batch_decode(
output_ids, skip_special_tokens=True
)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[: -len(stop_str)]
outputs = outputs.strip()
print(outputs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default=None)
parser.add_argument("--model", type=PreTrainedModel, default=None)
parser.add_argument("--image-file", type=str, required=True)
parser.add_argument("--query", type=str, required=True)
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--sep", type=str, default=",")
parser.add_argument("--temperature", type=float, default=0.2)
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=512)
args = parser.parse_args()
eval_model(args) |