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import argparse
import torch
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
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 (
process_images,
tokenizer_image_token,
get_model_name_from_path,
)
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
import re
import os
def image_parser(args):
print(args.image_file)
out = args.image_file.split(args.sep)
print(args.sep)
print(out)
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
prompt = "Please describe the object coverd by the green mask."
model_path = "liuhaotian/llava-v1.5-7b"
root_path = '/data/work2-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'
data_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/ExoQuery_piano.json"
save_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/ExoQuery_piano_withtext.json"
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
)
qs = args.query
image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
if IMAGE_PLACEHOLDER in qs:
if model.config.mm_use_im_start_end:
qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
else:
qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
else:
if model.config.mm_use_im_start_end:
qs = image_token_se + "\n" + qs
else:
qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
if "llama-2" in model_name.lower():
conv_mode = "llava_llama_2"
elif "mistral" in model_name.lower():
conv_mode = "mistral_instruct"
elif "v1.6-34b" in model_name.lower():
conv_mode = "chatml_direct"
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_files_list = image_parser(args)
new_data_list = []
with open(data_path, "r") as f:
datas = json.load(f)
for data in datas:
query_path = data["first_frame_image"]
query_path = os.path.join(root_path, query_path)
frame = cv2.imread(query_path)
# v1，直接使用生成json文件中的缩放的mask
# v2，获取takes名称，取出物体字典，逆映射获取物体名字，使用gt中的mask
h,w = frame.shape[:2]
#针对query是exo的情况
frame = cv2.resize(frame, (w // 4, h // 4))
for obj in data["first_frame_anns"]:
images = []
mask = decode(obj["segmentation"])
mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]))
out = blend_mask(frame, mask)
image = Image.fromarray(out).convert("RGB")
images.append(image)
image_sizes = [x.size for x in images]
images_tensor = process_images(
images,
image_processor,
model.config
).to(model.device, dtype=torch.float16)
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_tensor,
image_sizes=image_sizes,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
obj["text"] = outputs
new_data_list.append(data)
with open(save_path, "w") as f:
json.dump(new_data_list, f)
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)
# 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()
args = type('Args', (), {
"model_path": model_path,
"model_base": None,
"model_name": get_model_name_from_path(model_path),
"query": prompt,
"conv_mode": None,
"sep": ",",
"temperature": 0,
"top_p": None,
"num_beams": 1,
"max_new_tokens": 512
})()
eval_model(args)
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