File size: 4,716 Bytes
0966609 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
import argparse
import torch
import cv2
import os
import glob
import numpy as np
import ssl
# Disable SSL verification
ssl._create_default_https_context = ssl._create_unverified_context
import albumentations as A
from albumentations.pytorch import ToTensorV2
from src.models import DeepfakeDetector
from src.config import Config
try:
from safetensors.torch import load_file
SAFETENSORS_AVAILABLE = True
except ImportError:
SAFETENSORS_AVAILABLE = False
def get_transform():
return A.Compose([
A.Resize(Config.IMAGE_SIZE, Config.IMAGE_SIZE),
A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2(),
])
def load_models(checkpoints_arg, device):
"""
Load one or multiple models for ensemble inference.
checkpoints_arg: Comma-separated list of paths, or single path, or directory.
"""
paths = []
if os.path.isdir(checkpoints_arg):
paths = glob.glob(os.path.join(checkpoints_arg, "*.safetensors"))
if not paths:
paths = glob.glob(os.path.join(checkpoints_arg, "*.pth"))
else:
paths = checkpoints_arg.split(',')
models = []
print(f"Loading {len(paths)} model(s) for ensemble inference...")
for path in paths:
path = path.strip()
if not path: continue
print(f"Loading: {path}")
model = DeepfakeDetector(pretrained=False) # Structure only
model.to(device)
model.eval()
try:
if path.endswith(".safetensors") and SAFETENSORS_AVAILABLE:
state_dict = load_file(path)
else:
state_dict = torch.load(path, map_location=device)
model.load_state_dict(state_dict)
models.append(model)
print(f"✅ Successfully loaded: {os.path.basename(path)}")
except Exception as e:
print(f"❌ Failed to load {path}: {e}")
import traceback
traceback.print_exc()
if not models:
# Fallback for testing if no checkpoint exists yet
print("Warning: No valid checkoints loaded. Using random initialization for testing flow.")
model = DeepfakeDetector(pretrained=False).to(device)
model.eval()
models.append(model)
return models
def predict_ensemble(models, image_path, device, transform):
try:
image = cv2.imread(image_path)
if image is None:
return None, "Error: Could not read image"
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
except Exception as e:
return None, str(e)
augmented = transform(image=image)
image_tensor = augmented['image'].unsqueeze(0).to(device)
probs = []
with torch.no_grad():
for model in models:
logits = model(image_tensor)
prob = torch.sigmoid(logits).item()
probs.append(prob)
# Ensemble Strategy: Average Probability
avg_prob = sum(probs) / len(probs)
return avg_prob, None
def main():
parser = argparse.ArgumentParser(description="Deepfake Detection Inference (Ensemble Support)")
parser.add_argument("--source", type=str, required=True, help="Path to image or directory")
parser.add_argument("--checkpoints", type=str, default="results/checkpoints", help="Path to checkpoint file, list of files (comma-separated), or directory")
parser.add_argument("--device", type=str, default=Config.DEVICE, help="Device to use (cuda/mps/cpu)")
args = parser.parse_args()
device = torch.device(args.device)
print(f"Using device: {device}")
# Load Models
models = load_models(args.checkpoints, device)
transform = get_transform()
# Process Source
if os.path.isdir(args.source):
files = glob.glob(os.path.join(args.source, "*.*"))
# Filter images
files = [f for f in files if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]
else:
files = [args.source]
print(f"Processing {len(files)} images with {len(models)} model(s)...")
print("-" * 65)
print(f"{'Image Name':<40} | {'Prediction':<10} | {'Confidence':<10}")
print("-" * 65)
for file_path in files:
prob, error = predict_ensemble(models, file_path, device, transform)
if error:
print(f"{os.path.basename(file_path):<40} | ERROR: {error}")
continue
is_fake = prob > 0.5
label = "FAKE" if is_fake else "REAL"
confidence = prob if is_fake else 1 - prob
print(f"{os.path.basename(file_path):<40} | {label:<10} | {confidence:.2%}")
if __name__ == "__main__":
main()
|