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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
from scipy.stats import spearmanr, pearsonr
import json
from tqdm import tqdm
from collections import defaultdict
import os
def wa5(logits):
import numpy as np
logprobs = np.array([logits["excellent"], logits["good"], logits["fair"], logits["poor"], logits["bad"]])
probs = np.exp(logprobs) / np.sum(np.exp(logprobs))
return np.inner(probs, np.array([1,0.75,0.5,0.25,0.]))
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_video(video_file):
from decord import VideoReader
vr = VideoReader(video_file)
# Get video frame rate
fps = vr.get_avg_fps()
# Calculate frame indices for 1fps
frame_indices = [int(fps * i) for i in range(int(len(vr) / fps))]
frames = vr.get_batch(frame_indices).asnumpy()
return [Image.fromarray(frames[i]) for i in range(int(len(vr) / fps))]
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_paths = [
#"playground/data/",
#"playground/data/",
"playground/data/KoNViD_1k_videos/",
"playground/data/maxwell/",
]
json_prefix = "playground/data/test_jsons/"
jsons = [
#json_prefix + "test_lsvq.json",
#json_prefix + "test_lsvq_1080p.json",
json_prefix + "konvid.json",
json_prefix + "maxwell_test.json",
]
os.makedirs(f"results/{args.model_path}/", exist_ok=True)
conv_mode = "mplug_owl2"
inp = "How would you rate the quality of this video?"
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 video 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 image_path, json_ in zip(image_paths, jsons):
with open(json_) as f:
iqadata = json.load(f)
prs, gts = [], []
for i, llddata in enumerate(tqdm(iqadata, desc="Evaluating [{}]".format(json_.split("/")[-1]))):
try:
try:
filename = llddata["img_path"]
except:
filename = llddata["image"]
llddata["logits"] = defaultdict(float)
image = load_video(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(img, tuple(int(x*255) for x in image_processor.image_mean)) for img in image]
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().to(args.device)
if True:
with torch.inference_mode():
output_logits = model(input_ids,
images=[image_tensor])["logits"][:,-1]
for tok, id_ in zip(toks, ids_):
llddata["logits"][tok] += output_logits.mean(0)[id_].item()
llddata["score"] = wa5(llddata["logits"])
# print(llddata)
prs.append(llddata["score"])
gts.append(llddata["gt_score"])
# print(llddata)
json_ = json_.replace("combined/", "combined-")
with open(f"results/{args.model_path}/2{json_.split('/')[-1]}", "a") as wf:
json.dump(llddata, wf)
if i > 0 and i % 200 == 0:
print(spearmanr(prs,gts)[0], pearsonr(prs,gts)[0])
except:
continue
print("Spearmanr", spearmanr(prs,gts)[0], "Pearson", pearsonr(prs,gts)[0])
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) |