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9caed78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | import argparse
import torch
import json
import os
import re
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 process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
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 main(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)
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()
if "mpt" in model_name.lower():
roles = ('user', 'assistant')
else:
roles = conv.roles
data = json.load(open(args.json_file, 'r', encoding='utf-8'))
ret = {}
for i_entry, entry in enumerate(data):
if entry['id'] not in ret:
ret[entry['id']] = []
# if len(ret) > 40:
# break
conv = conv_templates[args.conv_mode].copy()
if "mpt" in model_name.lower():
roles = ('user', 'assistant')
else:
roles = conv.roles
image_file = os.path.join(args.data_path, entry['image'])
image = load_image(image_file)
# image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
image_tensor = process_images([image], image_processor, args)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
# inp = input(f"{roles[0]}: ")
inp = '\n'.join(entry["conversations"][0]['value'].split('\n')[1:])
# print(f"{roles[1]}: ", end="", flush=True)
if image is not None:
# first message
if model.config.mm_use_im_start_end:
inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
else:
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
conv.append_message(conv.roles[0], inp)
image = None
else:
# later messages
conv.append_message(conv.roles[0], inp)
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()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
conv.messages[-1][-1] = outputs
if args.debug:
print("\n", {"prompt": prompt, "outputs": outputs.split('\n')[0]}, "\n", flush=True)
print(entry["conversations"][1]['value'], flush=True)
for i in range(len(outputs)):
if i < len(outputs) - 1 and outputs[i:i+2] == "[{":
lo = i
elif i > 1 and outputs[i-1:i+1] == "}]":
hi = i
tries, max_tries = 0, 1
while tries < max_tries:
try:
string = outputs[lo:hi+1].replace("'", '"')
ret[entry['id']].append(json.loads(string))
break
except json.JSONDecodeError as e:
tries += 1
print(f"Tried for {tries} times, error parsing JSON: {e}")
except UnboundLocalError as e:
tries += 1
print(f"Tried for {tries} times, error parsing JSON: {e}")
with open(args.output_file, 'w', encoding='utf-8') as fout:
json.dump(ret, fout, ensure_ascii=False, indent=2)
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("--device", type=str, default="cuda")
parser.add_argument("--data-path", type=str, required=True)
parser.add_argument("--output-file", type=str, required=True)
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--max-new-tokens", type=int, default=1024)
parser.add_argument("--image-aspect-ratio", type=str, default='pad')
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
parser.add_argument("--json-file", type=str, required=True)
parser.add_argument("--num-gpus", type=int, default=1)
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
main(args)
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