Spaces:
Runtime error
Runtime error
File size: 4,938 Bytes
962a551 b993b81 962a551 b993b81 962a551 |
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 |
import argparse
import torch
from prometheus.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
from prometheus.conversation import conv_templates, SeparatorStyle
from prometheus.model.builder import load_pretrained_model
from prometheus.utils import disable_torch_init
from prometheus.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
import re
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()
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 "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 = image_parser(args)
images = load_images(image_files)
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()
)
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=images_tensor,
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,
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)
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)
|