HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment

Quicker Start with Hugging Face AutoModel

Please use transformers==4.44.2 to ensure the model works normally.

import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
 MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
 transform = T.Compose([
 T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
 T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
 T.ToTensor(),
 T.Normalize(mean=MEAN, std=STD)
 ])
 return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
 best_ratio_diff = float('inf')
 best_ratio = (1, 1)
 area = width * height
 for ratio in target_ratios:
 target_aspect_ratio = ratio[0] / ratio[1]
 ratio_diff = abs(aspect_ratio - target_aspect_ratio)
 if ratio_diff < best_ratio_diff:
 best_ratio_diff = ratio_diff
 best_ratio = ratio
 elif ratio_diff == best_ratio_diff:
 if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
 best_ratio = ratio
 return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
 orig_width, orig_height = image.size
 aspect_ratio = orig_width / orig_height

 # calculate the existing image aspect ratio
 target_ratios = set(
 (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
 i * j <= max_num and i * j >= min_num)
 target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

 # find the closest aspect ratio to the target
 target_aspect_ratio = find_closest_aspect_ratio(
 aspect_ratio, target_ratios, orig_width, orig_height, image_size)

 # calculate the target width and height
 target_width = image_size * target_aspect_ratio[0]
 target_height = image_size * target_aspect_ratio[1]
 blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

 # resize the image
 resized_img = image.resize((target_width, target_height))
 processed_images = []
 for i in range(blocks):
 box = (
 (i % (target_width // image_size)) * image_size,
 (i // (target_width // image_size)) * image_size,
 ((i % (target_width // image_size)) + 1) * image_size,
 ((i // (target_width // image_size)) + 1) * image_size
 )
 # split the image
 split_img = resized_img.crop(box)
 processed_images.append(split_img)
 assert len(processed_images) == blocks
 if use_thumbnail and len(processed_images) != 1:
 thumbnail_img = image.resize((image_size, image_size))
 processed_images.append(thumbnail_img)
 return processed_images

def load_image(image_file, input_size=448, max_num=12):
 image = Image.open(image_file).convert('RGB')
 transform = build_transform(input_size=input_size)
 images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
 pixel_values = [transform(image) for image in images]
 pixel_values = torch.stack(pixel_values)
 return pixel_values

path = 'HumanBeauty/HumanAesExpert-8B'
model = AutoModel.from_pretrained(
 path,
 torch_dtype=torch.float16,
 low_cpu_mem_usage=True,
 use_flash_attn=True,
 trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

pixel_values = load_image('./examples/your_image.jpg', max_num=12).to(torch.float16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)

question = '<image>\nRate the aesthetics of this human picture.'

# fast inference, need 1x time
pred_score = model.score(tokenizer, pixel_values, question)

# slow inference, need 2x time
metavoter_score = model.run_metavoter(tokenizer, pixel_values)

# get expert scores from the Expert head, include 12 dimensions
expert_score, expert_text = model.expert_score(tokenizer,pixel_values)

# get expert annotations from the LM head, include 12 dimensions
expert_annotataion = model.expert_annotataion(tokenizer, pixel_values, generation_config)

License

This project is released under the MIT license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.