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import gradio as gr
import numpy as np
import torch
from transformers import AutoModel, BitImageProcessor, SiglipImageProcessor, SiglipVisionModel
from PIL import Image, ImageOps
from sklearn.metrics.pairwise import cosine_similarity
import torch.nn as nn
device = torch.device('cpu')
torch.set_num_threads(4)
processor_d = BitImageProcessor(do_center_crop=False, do_convert_rgb=False, do_normalize=True, do_rescale=True, do_resize=False, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225], resample=3, rescale_factor=0.00392156862745098)
model_d = AutoModel.from_pretrained('facebook/dinov2-base', attn_implementation="sdpa").to(device)
processor_s = SiglipImageProcessor.from_pretrained('google/siglip-so400m-patch14-384')
model_s = SiglipVisionModel.from_pretrained('google/siglip-so400m-patch14-384', attn_implementation="sdpa").to(device)
class ResidualBlock(nn.Module):
def __init__(self, input_size):
super(ResidualBlock, self).__init__()
self.linear1 = nn.Linear(input_size, input_size // 2)
self.LayerNorm1 = nn.LayerNorm(input_size // 2)
self.activation1 = nn.Mish()
self.linear2 = nn.Linear(input_size // 2, input_size // 4)
self.LayerNorm2 = nn.LayerNorm(input_size // 4)
self.activation2 = nn.Mish()
self.linear3 = nn.Linear(input_size // 4, input_size // 2)
self.LayerNorm3 = nn.LayerNorm(input_size // 2)
self.activation3 = nn.Mish()
self.linear4 = nn.Linear(input_size // 2, input_size)
self.LayerNorm4 = nn.LayerNorm(input_size)
self.activation4 = nn.Mish()
self.shortcut = nn.Linear(input_size, input_size)
def forward(self, x):
identity = self.shortcut(x)
out = self.linear1(x)
out = self.LayerNorm1(out)
out = self.activation1(out)
out = self.linear2(out)
out = self.LayerNorm2(out)
out = self.activation2(out)
out = self.linear3(out)
out = self.LayerNorm3(out)
out = self.activation3(out)
out = self.linear4(out)
out = self.LayerNorm4(out)
out = self.activation4(out)
out += identity
return out
class MLP(nn.Module):
def __init__(self, input_size, xcol='emb', ycol='avg_rating'):
super().__init__()
self.input_size = input_size
self.xcol = xcol
self.ycol = ycol
self.layers = nn.Sequential(
ResidualBlock(self.input_size),
nn.Mish(),
nn.Linear(1920, 1)
)
def forward(self, x):
return self.layers(x)
mlp = MLP(1920)
s = torch.load("./aesthetic_predictor_huber_ad_ep7.pth", map_location=torch.device('cpu'))
mlp.load_state_dict(s)
mlp.to(device)
mlp.eval()
def normalized(a, axis=-1, order=2):
l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
l2[l2 == 0] = 1
return a / np.expand_dims(l2, axis)
def process_image(image, device):
image = image.convert('RGBA')
background = Image.new('RGBA', image.size, (255, 255, 255, 255))
image = Image.alpha_composite(background, image).convert('RGB')
max_side = 518
ratio = max_side / max(image.size)
new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
image_d = image.resize(new_size, Image.LANCZOS)
max_side_s = 384
ratio_s = max_side_s / max(image.size)
new_size_s = (int(image.size[0] * ratio_s), int(image.size[1] * ratio_s))
image_resized = image.resize(new_size_s, Image.LANCZOS)
image_s = ImageOps.pad(image_resized, (384, 384), color=(255, 255, 255))
inputs_d = processor_d(image_d, return_tensors="pt").to(device)
inputs_s = processor_s(image_s, return_tensors="pt").to(device)
with torch.no_grad():
outputs_d = model_d(**inputs_d)
outputs_s = model_s(**inputs_s)
class_token_d = normalized(outputs_d.pooler_output.cpu().detach().numpy())
class_token_s = normalized(outputs_s.pooler_output.cpu().detach().numpy())
im_emb_arr = np.concatenate((class_token_s, class_token_d), axis=1)
prediction_value = mlp(torch.from_numpy(im_emb_arr).to(device).type(torch.FloatTensor)).item()
return im_emb_arr, prediction_value
def infer(image1, image2):
try:
features1, prediction_value1 = process_image(image1, device)
features2, prediction_value2 = process_image(image2, device)
cos_sim_features = cosine_similarity(features1, features2)[0][0]
return cos_sim_features, prediction_value1, prediction_value2
except Exception as e:
print(f"Error during inference: {e}")
return "Error", "Error", "Error"
with gr.Blocks() as iface:
gr.Markdown("# Anime Aesthetic Predictor Based on Twitter User Preferences\nUpload two images to calculate the aesthetic score (0-10).")
with gr.Row():
image1 = gr.Image(type="pil")
image2 = gr.Image(type="pil")
with gr.Row():
prediction1 = gr.Textbox(label="Aesthetic Score 1")
prediction2 = gr.Textbox(label="Aesthetic Score 2")
with gr.Row():
feature_similarity = gr.Textbox(label="Feature Similarity")
with gr.Row():
submit_btn = gr.Button("Submit")
submit_btn.click(infer, inputs=[image1, image2], outputs=[feature_similarity, prediction1, prediction2])
iface.queue(max_size=10)
iface.launch() |