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import gradio as gr
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import cv2
import PIL.Image
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
from utils import azi_diff
class TextureContrastClassifier(nn.Module):
def __init__(self, input_shape, num_heads=4, key_dim=64, ff_dim=256, rate=0.1):
super(TextureContrastClassifier, self).__init__()
input_dim = input_shape[1]
self.rich_attention_block = AttentionBlock(input_dim, num_heads, ff_dim, rate)
self.rich_dense = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Dropout(0.5)
)
self.poor_attention_block = AttentionBlock(input_dim, num_heads, ff_dim, rate)
self.poor_dense = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Dropout(0.5)
)
self.fc = nn.Sequential(
nn.Linear(128 * input_shape[0], 256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(256, 128),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(128, 64),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(64, 32),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(32, 16),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(16, 1),
nn.Sigmoid()
)
def forward(self, rich_texture, poor_texture):
rich_texture = rich_texture.permute(1, 0, 2)
poor_texture = poor_texture.permute(1, 0, 2)
rich_attention = self.rich_attention_block(rich_texture)
rich_attention = rich_attention.permute(1, 0, 2)
rich_features = self.rich_dense(rich_attention)
poor_attention = self.poor_attention_block(poor_texture)
poor_attention = poor_attention.permute(1, 0, 2)
poor_features = self.poor_dense(poor_attention)
difference = rich_features - poor_features
difference = difference.view(difference.size(0), -1)
output = self.fc(difference)
return output
input_shape = (128, 256)
model = TextureContrastClassifier(input_shape)
model.load_state_dict(torch.load('./model_epoch_36.pth', map_location=torch.device('cpu')))
def inference(image, model):
predictions = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
tmp = azi_diff(image, patch_num=128, N=256)
rich = tmp["total_emb"][0]
poor = tmp["total_emb"][1]
rich_texture_tensor = torch.tensor(rich, dtype=torch.float32).unsqueeze(0).to(device)
poor_texture_tensor = torch.tensor(poor, dtype=torch.float32).unsqueeze(0).to(device)
with torch.no_grad():
output = model(rich_texture_tensor, poor_texture_tensor)
prediction = output.cpu().numpy().flatten()[0]
return prediction
# Gradio Interface
def predict(image):
prediction = inference(image, model)
return f"{prediction * 100:.2f}% chance AI-generated"
gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text").launch()
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