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ce8f665 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | #!/usr/bin/env python3
"""Unified evaluation script for the 7 VFM baselines."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from sklearn.metrics import accuracy_score, average_precision_score, roc_auc_score
from torch.utils.data import DataLoader, Dataset
from models import LOADERS, MODEL_SPECS, canonical_model_name, default_checkpoint_path, load_model
IMAGE_EXTENSIONS = (".jpg", ".jpeg", ".png", ".bmp", ".JPG", ".JPEG", ".PNG")
class BinaryFolderDataset(Dataset):
def __init__(self, real_dir: str, fake_dir: str, transform, max_samples: int | None = None):
self.transform = transform
real_paths = self._get_image_files(real_dir)
fake_paths = self._get_image_files(fake_dir)
if max_samples is not None:
real_paths = real_paths[:max_samples]
fake_paths = fake_paths[:max_samples]
self.image_paths = real_paths + fake_paths
self.labels = [0] * len(real_paths) + [1] * len(fake_paths)
@staticmethod
def _get_image_files(folder: str):
folder = Path(folder)
images = []
for extension in IMAGE_EXTENSIONS:
images.extend(folder.rglob(f"*{extension}"))
return sorted(images)
def __len__(self):
return len(self.image_paths)
def __getitem__(self, index):
image_path = self.image_paths[index]
image = Image.open(image_path).convert("RGB")
return self.transform(image), self.labels[index], str(image_path)
def evaluate(model, transform, real_dir: str, fake_dir: str, batch_size: int, num_workers: int, max_samples: int | None):
dataset = BinaryFolderDataset(real_dir, fake_dir, transform, max_samples=max_samples)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=torch.cuda.is_available(),
)
device = next(model.parameters()).device
y_true = []
y_prob = []
y_pred = []
paths = []
with torch.no_grad():
for images, labels, batch_paths in dataloader:
images = images.to(device)
logits = model(images)
probs = torch.softmax(logits, dim=1)[:, 1].cpu().numpy()
preds = (probs > 0.5).astype(int)
y_true.extend(labels.numpy().tolist())
y_prob.extend(probs.tolist())
y_pred.extend(preds.tolist())
paths.extend(batch_paths)
y_true = np.asarray(y_true)
y_prob = np.asarray(y_prob)
y_pred = np.asarray(y_pred)
metrics = {
"accuracy": float(accuracy_score(y_true, y_pred)),
"real_accuracy": float(accuracy_score(y_true[y_true == 0], y_pred[y_true == 0])),
"fake_accuracy": float(accuracy_score(y_true[y_true == 1], y_pred[y_true == 1])),
}
if len(np.unique(y_true)) > 1:
metrics["auc"] = float(roc_auc_score(y_true, y_prob))
metrics["ap"] = float(average_precision_score(y_true, y_prob))
samples = [
{
"path": path,
"label": int(label),
"prob_fake": float(prob),
"pred": int(pred),
}
for path, label, prob, pred in zip(paths, y_true, y_prob, y_pred)
]
return {"metrics": metrics, "samples": samples}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="all", help="One of: all, metacliplin, metaclip2lin, sigliplin, siglip2lin, pelin, dinov2lin, dinov3lin")
parser.add_argument("--real-dir", required=True)
parser.add_argument("--fake-dir", required=True)
parser.add_argument("--checkpoint", default=None, help="Optional explicit checkpoint path for single-model evaluation")
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--max-samples", type=int, default=None)
parser.add_argument("--device", default=None)
parser.add_argument("--save-json", default=None)
args = parser.parse_args()
model_names = list(LOADERS.keys()) if args.model == "all" else [canonical_model_name(args.model)]
results = {}
for model_name in model_names:
checkpoint = args.checkpoint if args.model != "all" and args.checkpoint else default_checkpoint_path(model_name)
checkpoint = Path(checkpoint)
try:
checkpoint_for_output = str(checkpoint.relative_to(Path(__file__).resolve().parent))
except ValueError:
checkpoint_for_output = str(checkpoint)
model, transform = load_model(model_name, checkpoint_path=checkpoint, device=args.device)
result = evaluate(
model=model,
transform=transform,
real_dir=args.real_dir,
fake_dir=args.fake_dir,
batch_size=args.batch_size,
num_workers=args.num_workers,
max_samples=args.max_samples,
)
results[model_name] = {
"paper_name": MODEL_SPECS[model_name]["paper_name"],
"checkpoint": checkpoint_for_output,
**result,
}
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
output = json.dumps(results, indent=2, ensure_ascii=False)
print(output)
if args.save_json:
Path(args.save_json).write_text(output + "\n", encoding="utf-8")
if __name__ == "__main__":
main()
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