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import argparse
import torch
from q_align.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from q_align.conversation import conv_templates, SeparatorStyle
from q_align.model.builder import load_pretrained_model
from q_align.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
import json
from tqdm import tqdm
from collections import defaultdict
import os
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)
import json
image_path = "playground/data/"
json_prefix = "playground/data/test_jsons/"
jsons = [
json_prefix + "test_imagerewarddb.json",
json_prefix + "test_koniq.json",
json_prefix + "test_spaq.json",
json_prefix + "test_kadid.json",
json_prefix + "livec.json",
json_prefix + "agi.json",
json_prefix + "live.json",
json_prefix + "csiq.json",
]
os.makedirs(f"results/{args.model_path}/", exist_ok=True)
conv_mode = "mplug_owl2"
inp = "Evaluate the image quality of the following image."#"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 = ["good", "poor", "high", "fair", "low", "excellent", "bad", "fine", "moderate", "decent", "average", "medium", "acceptable"]
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 json_ in jsons:
with open(json_) as f:
iqadata = json.load(f)
image_tensors = []
batch_data = []
for i, llddata in enumerate(tqdm(iqadata, desc="Evaluating [{}]".format(json_.split("/")[-1]))):
if True:
try:
filename = llddata["image"]
except:
filename = llddata["img_path"]
llddata["logits"] = defaultdict(float)
image = load_image(image_path + 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 % 8 == 7 or i == len(iqadata) - 1:
with torch.inference_mode():
output_logits = model(input_ids.repeat(len(image_tensors), 1),
images=torch.cat(image_tensors, 0))["logits"][:,-1]
for j, xllddata in enumerate(batch_data):
for tok, id_ in zip(toks, ids_):
xllddata["logits"][tok] += output_logits[j,id_].item()
# print(llddata)
json_ = json_.replace("combined/", "combined-")
with open(f"results/{args.model_path}/2{json_.split('/')[-1]}", "a") as wf:
json.dump(xllddata, wf)
image_tensors = []
batch_data = []
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="q-future/one-align")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--device", type=str, default="cuda:0")
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=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) |