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36bb346 5b95014 36bb346 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import os
import numpy as np
from huggingface_hub import hf_hub_download
# --- 🚨 MASTER FIX FOR PYTORCH 2.6 SECURITY ---
import torch.serialization
try:
torch.serialization.add_safe_globals([np.core.multiarray.scalar])
except:
pass
_original_load = torch.load
def _patched_load(*args, **kwargs):
kwargs['weights_only'] = False
return _original_load(*args, **kwargs)
torch.load = _patched_load
# ----------------------------------------------
# 1. Device Configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 2. Model Architecture (SOTA Hybrid Network)
class Deepfake_Hybrid_Network(nn.Module):
def __init__(self, num_classes=2):
super(Deepfake_Hybrid_Network, self).__init__()
resnet = models.resnet18(weights=None)
self.cnn_extractor = nn.Sequential(*list(resnet.children())[:-2])
encoder_layer = nn.TransformerEncoderLayer(
d_model=512, nhead=8, dim_feedforward=1024, dropout=0.3, batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=2)
self.classifier = nn.Sequential(
nn.Linear(512, 128),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(128, num_classes)
)
def forward(self, x):
x = self.cnn_extractor(x)
b, c, h, w = x.shape
x = x.view(b, c, h * w).permute(0, 2, 1)
x = self.transformer_encoder(x)
x = x.mean(dim=1)
return self.classifier(x)
# 3. Load Model from Hugging Face Hub
print("Loading Image Hybrid SOTA Model...")
model = Deepfake_Hybrid_Network(num_classes=2).to(device)
try:
# Model HF repo se download ho raha hy
model_path = hf_hub_download(repo_id="aneela-pervez/My-Deepfake-Models", filename="Deepfake_Hybrid_SOTA.pth")
state_dict = torch.load(model_path, map_location=device)
if any(k.startswith('module.') for k in state_dict.keys()):
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
new_state_dict[k.replace('module.', '')] = v
model.load_state_dict(new_state_dict)
else:
model.load_state_dict(state_dict)
model.eval()
print("✅ Image SOTA Model loaded successfully!")
except Exception as e:
print(f"⚠️ Error loading image model weights: {e}")
# 4. Image Preprocessing
test_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 5. Prediction Function for Master Pipeline
def predict_image(img):
if img is None:
return {"Error": 1.0}
img_tensor = test_transform(img).unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(img_tensor)
probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
results = {
"Fake (Deepfake)": float(probabilities[0]),
"Real Image": float(probabilities[1])
}
return results |