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import argparse
import json
import os
import sys
import time
from pathlib import Path
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from PIL import Image, ImageFile
from sklearn.metrics import roc_auc_score
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import create_model
from torchvision import transforms
ImageFile.LOAD_TRUNCATED_IMAGES = True
IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
BENCHMARKS = [
("DeepFakeDetection", "DeepFakeDetection"),
("CDFv2", "CDFv2"),
("Wild_Deepfake", "Wild_Deepfake"),
("ForgeryNet", "ForgeryNet"),
("DeepFakeFace", "DeepFakeFace"),
("DF40", "DF40"),
("ScaleDF", "ScaleDF"),
]
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--benchmark-root", required=True)
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--manifest", default="")
parser.add_argument("--code-root", default="/tmp/ScaleDF_repro_20260724/code/deit")
parser.add_argument("--model", default="deit_huge_patch14_LS")
parser.add_argument("--input-size", type=int, default=224)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--num-workers", type=int, default=8)
parser.add_argument("--print-freq", type=int, default=50)
parser.add_argument("--max-samples-per-class", type=int, default=0)
parser.add_argument("--rebuild-manifest", action="store_true")
parser.add_argument("--skip-load-errors", action="store_true")
return parser.parse_args()
def init_distributed():
if "RANK" not in os.environ:
return 0, 1, 0, torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
torch.cuda.set_device(local_rank)
dist.init_process_group("nccl", init_method="env://")
return rank, world_size, local_rank, torch.device(f"cuda:{local_rank}")
def is_main(rank):
return rank == 0
def iter_images(root):
for dirpath, dirnames, filenames in os.walk(root):
dirnames.sort()
for name in sorted(filenames):
if Path(name).suffix.lower() in IMAGE_EXTS:
yield os.path.join(dirpath, name)
def build_manifest(args, manifest_path):
root = Path(args.benchmark_root)
tmp_path = manifest_path.with_suffix(manifest_path.suffix + ".tmp")
stats = []
with tmp_path.open("w") as f:
for bench_id, (bench_name, rel_name) in enumerate(BENCHMARKS):
val_root = root / rel_name / "val"
real_dir = val_root / "000real_faces"
fake_dir = val_root / "fake"
counts = {"real": 0, "fake": 0}
for label, label_name, label_dir in [(0, "real", real_dir), (1, "fake", fake_dir)]:
if not label_dir.exists():
raise FileNotFoundError(f"Missing {label_name} directory for {bench_name}: {label_dir}")
for path in iter_images(label_dir):
f.write(f"{bench_id}\t{label}\t{path}\n")
counts[label_name] += 1
if args.max_samples_per_class and counts[label_name] >= args.max_samples_per_class:
break
stats.append({
"benchmark": bench_name,
"real": counts["real"],
"fake": counts["fake"],
"total": counts["real"] + counts["fake"],
})
os.replace(tmp_path, manifest_path)
with manifest_path.with_suffix(".stats.json").open("w") as f:
json.dump({"benchmarks": stats, "total": sum(x["total"] for x in stats)}, f, indent=2)
class ManifestShardDataset(torch.utils.data.Dataset):
def __init__(self, manifest_path, rank, world_size, transform, input_size, skip_load_errors):
self.transform = transform
self.input_size = input_size
self.skip_load_errors = skip_load_errors
self.samples = []
with open(manifest_path, "r") as f:
for idx, line in enumerate(f):
if idx % world_size != rank:
continue
bench_id, label, path = line.rstrip("\n").split("\t", 2)
self.samples.append((path, int(label), int(bench_id)))
def __len__(self):
return len(self.samples)
def __getitem__(self, index):
path, label, bench_id = self.samples[index]
try:
with Image.open(path) as img:
image = img.convert("RGB")
image = self.transform(image)
except Exception as exc:
print(f"LOAD_ERROR\t{path}\t{type(exc).__name__}: {exc}", file=sys.stderr, flush=True)
if not self.skip_load_errors:
raise
image = torch.zeros(3, self.input_size, self.input_size)
return image, label, bench_id
def build_transform(input_size):
return transforms.Compose([
transforms.Resize(input_size, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
])
def load_model(args, device):
sys.path.insert(0, args.code_root)
import models # noqa: F401
import models_v2 # noqa: F401
model = create_model(
args.model,
pretrained=False,
num_classes=2,
drop_rate=0.0,
drop_path_rate=0.2,
drop_block_rate=None,
img_size=args.input_size,
)
checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
state = checkpoint["model"] if isinstance(checkpoint, dict) and "model" in checkpoint else checkpoint
missing, unexpected = model.load_state_dict(state, strict=False)
if len(missing) > 10 or len(unexpected) > 10:
raise RuntimeError(f"Too many load_state_dict mismatches: missing={len(missing)} unexpected={len(unexpected)}")
model.to(device).eval()
return model, {"missing": missing, "unexpected": unexpected}
@torch.no_grad()
def run_inference(args, rank, world_size, device, manifest_path, output_dir):
dataset = ManifestShardDataset(
manifest_path,
rank,
world_size,
build_transform(args.input_size),
args.input_size,
args.skip_load_errors,
)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
persistent_workers=args.num_workers > 0,
prefetch_factor=4 if args.num_workers > 0 else None,
)
model, load_info = load_model(args, device)
scores, labels, benches = [], [], []
start = time.time()
seen = 0
for step, (images, y, b) in enumerate(loader):
images = images.to(device, non_blocking=True)
with torch.cuda.amp.autocast(enabled=device.type == "cuda"):
logits = model(images)
score = F.softmax(logits.float(), dim=1)[:, 1]
scores.append(score.detach().cpu().numpy().astype(np.float32))
labels.append(y.numpy().astype(np.uint8))
benches.append(b.numpy().astype(np.uint8))
seen += len(y)
if args.print_freq and step % args.print_freq == 0:
elapsed = max(time.time() - start, 1e-6)
print(
f"rank={rank} step={step} seen={seen}/{len(dataset)} "
f"rate={seen / elapsed:.1f} img/s",
flush=True,
)
shard_path = output_dir / f"shard_rank{rank:05d}.npz"
np.savez_compressed(
shard_path,
scores=np.concatenate(scores) if scores else np.empty((0,), dtype=np.float32),
labels=np.concatenate(labels) if labels else np.empty((0,), dtype=np.uint8),
benches=np.concatenate(benches) if benches else np.empty((0,), dtype=np.uint8),
)
with (output_dir / f"rank{rank:05d}.done.json").open("w") as f:
json.dump({"rank": rank, "world_size": world_size, "samples": seen, "load_info": load_info}, f)
print(f"rank={rank} wrote={shard_path} samples={seen}", flush=True)
def aggregate(output_dir):
shard_paths = sorted(output_dir.glob("shard_rank*.npz"))
scores, labels, benches = [], [], []
for path in shard_paths:
arr = np.load(path)
scores.append(arr["scores"])
labels.append(arr["labels"])
benches.append(arr["benches"])
scores = np.concatenate(scores)
labels = np.concatenate(labels)
benches = np.concatenate(benches)
rows = []
for bench_id, (bench_name, _) in enumerate(BENCHMARKS):
mask = benches == bench_id
y = labels[mask]
s = scores[mask]
auc = float("nan") if len(np.unique(y)) < 2 else float(roc_auc_score(y, s))
rows.append({
"benchmark": bench_name,
"n": int(mask.sum()),
"real": int((y == 0).sum()),
"fake": int((y == 1).sum()),
"auc": auc,
})
mean_auc = float(np.nanmean([r["auc"] for r in rows]))
with (output_dir / "auc_summary.tsv").open("w") as f:
f.write("benchmark\tn\treal\tfake\tauc\n")
for r in rows:
f.write(f"{r['benchmark']}\t{r['n']}\t{r['real']}\t{r['fake']}\t{r['auc']:.8f}\n")
f.write(f"MEAN_7_BENCHMARKS\t{sum(r['n'] for r in rows)}\t{sum(r['real'] for r in rows)}\t{sum(r['fake'] for r in rows)}\t{mean_auc:.8f}\n")
with (output_dir / "auc_summary.json").open("w") as f:
json.dump({"benchmarks": rows, "mean_auc": mean_auc, "num_shards": len(shard_paths)}, f, indent=2)
print(f"MEAN_7_BENCHMARKS_AUC {mean_auc:.8f}", flush=True)
def main():
args = parse_args()
rank, world_size, local_rank, device = init_distributed()
output_dir = Path(args.output_dir)
if is_main(rank):
output_dir.mkdir(parents=True, exist_ok=True)
if dist.is_available() and dist.is_initialized():
dist.barrier()
manifest_path = Path(args.manifest) if args.manifest else output_dir / "benchmark_manifest.tsv"
if is_main(rank) and (args.rebuild_manifest or not manifest_path.exists()):
print(f"building_manifest path={manifest_path}", flush=True)
build_manifest(args, manifest_path)
print(f"manifest_ready path={manifest_path}", flush=True)
if dist.is_available() and dist.is_initialized():
dist.barrier()
if is_main(rank):
with (output_dir / "run_config.json").open("w") as f:
json.dump(vars(args) | {"world_size": world_size}, f, indent=2)
run_inference(args, rank, world_size, device, manifest_path, output_dir)
if dist.is_available() and dist.is_initialized():
dist.barrier()
if is_main(rank):
aggregate(output_dir)
if dist.is_available() and dist.is_initialized():
dist.barrier()
if __name__ == "__main__":
main()
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