|
import argparse |
|
import torch |
|
|
|
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 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): |
|
|
|
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) |
|
|
|
qs = args.query |
|
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_file) |
|
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, |
|
do_sample=True, |
|
temperature=0.2, |
|
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() |
|
print(outputs) |
|
|
|
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-file", type=str, required=True) |
|
parser.add_argument("--query", type=str, required=True) |
|
parser.add_argument("--conv-mode", type=str, default=None) |
|
args = parser.parse_args() |
|
|
|
eval_model(args) |
|
|
