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import argparse
import json
import os
from collections import defaultdict
from io import BytesIO
import requests
import torch
from PIL import Image
from tqdm import tqdm
from src.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from src.conversation import conv_templates
from src.mm_utils import get_model_name_from_path, tokenizer_image_token
from src.model.builder import load_pretrained_model
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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,
args.load_8bit,
args.load_4bit,
device=args.device,
preprocessor_path=args.preprocessor_path,
)
meta_paths = args.meta_paths
root_dir = args.root_dir
batch_size = args.batch_size
save_dir = args.save_dir
os.makedirs(save_dir, exist_ok=True)
with_prob = args.with_prob
conv_mode = "mplug_owl2"
inp = "How would you rate the quality of this image?"
conv = conv_templates[conv_mode].copy()
inp = inp + "\n" + DEFAULT_IMAGE_TOKEN
conv.append_message(conv.roles[0], inp)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt() + " The quality of the image is"
toks = args.level_names
print(toks)
ids_ = [id_[1] for id_ in tokenizer(toks)["input_ids"]]
print(ids_)
input_ids = (
tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.to(args.device)
)
for meta_path in meta_paths:
with open(meta_path) as f:
iqadata = json.load(f)
image_tensors = []
batch_data = []
imgs_handled = []
save_path = os.path.join(save_dir, os.path.basename(meta_path))
if os.path.exists(save_path):
with open(save_path) as fr:
for line in fr:
meta_res = json.loads(line)
imgs_handled.append(meta_res["image"])
meta_name = os.path.basename(meta_path)
for i, llddata in enumerate(tqdm(iqadata, desc=f"Evaluating [{meta_name}]")):
try:
filename = llddata["image"]
except:
filename = llddata["img_path"]
if filename in imgs_handled:
continue
llddata["logits"] = defaultdict(float)
llddata["probs"] = defaultdict(float)
image = load_image(os.path.join(root_dir, filename))
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(
image, tuple(int(x * 255) for x in image_processor.image_mean)
)
image_tensor = (
image_processor.preprocess(image, return_tensors="pt")["pixel_values"]
.half()
.to(args.device)
)
image_tensors.append(image_tensor)
batch_data.append(llddata)
if (i + 1) % batch_size == 0 or i == len(iqadata) - 1:
with torch.inference_mode():
output_logits = model(
input_ids=input_ids.repeat(len(image_tensors), 1),
images=torch.cat(image_tensors, 0),
)["logits"][:, -1]
if with_prob:
output_probs = torch.softmax(output_logits, dim=1)
for j, xllddata in enumerate(batch_data):
for tok, id_ in zip(toks, ids_):
xllddata["logits"][tok] += output_logits[j, id_].item()
if with_prob:
xllddata["probs"][tok] += output_probs[j, id_].item()
meta_res = {
"id": xllddata["id"],
"image": xllddata["image"],
"gt_score": xllddata["gt_score"],
"logits": xllddata["logits"],
}
if with_prob:
meta_res["probs"] = xllddata["probs"]
with open(save_path, "a") as fw:
fw.write(json.dumps(meta_res) + "\n")
image_tensors = []
batch_data = []
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, required=True)
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--preprocessor-path", type=str, default=None)
parser.add_argument("--meta-paths", type=str, required=True, nargs="+")
parser.add_argument("--root-dir", type=str, required=True)
parser.add_argument("--save-dir", type=str, default="results")
parser.add_argument("--level-names", type=str, required=True, nargs="+")
parser.add_argument("--with-prob", type=bool, default=False) # whether to save openset prob
parser.add_argument("--device", type=str, default="cuda:0")
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
parser.add_argument("--debug", action="store_true")
parser.add_argument("--image-aspect-ratio", type=str, default="pad")
args = parser.parse_args()
main(args)
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