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df66507 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | import argparse
import os
import torch
from models.dou import DoUNet, load_clip_to_cpu
from utils.options import TrainCfg
import numpy as np
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, average_precision_score, f1_score
from torch.utils.data import DataLoader
from dataset.dataset import ImageFolderWithPaths
import pandas as pd
DEFAULT_TEST_DATASETS = [
"biggan", "cyclegan", "gaugan", "progan", "stargan", "stylegan", "dalle", "guided",
"deepfake", "seeingdark", "san", "crn", "imle",
"glide_50_27", "glide_100_10", "glide_100_27", "ldm_100", "ldm_200", "ldm_200_cfg",
"biggan_genimage",
"ADM", "Glide", "Midjourney", "stable_diffusion_v_1_4", "stable_diffusion_v_1_5", "VQDM",
"wukong",
"Infinity", "LlamaGen", "PixArt-XL", "SD35-L", "Show-o", "VAR",
"FLUX", "Janus", "Janus-Pro-1B", "Janus-Pro-7B",
]
def parse_args():
parser = argparse.ArgumentParser(description="Evaluate DoU on one or more test datasets")
parser.add_argument("--method", default="DoUNet", help="Model method name stored in TrainCfg")
parser.add_argument(
"--data-root",
help="Root directory containing test dataset folders",
)
parser.add_argument(
"--datasets",
nargs="+",
default=DEFAULT_TEST_DATASETS,
help="Dataset folder names under --data-root, separated by spaces",
)
parser.add_argument("--ckpt", default="runs/best.pth", help="Path to the saved model checkpoint")
parser.add_argument("--output", default="result.xlsx", help="Path to save the Excel result file")
parser.add_argument(
"--device",
default="auto",
help="Device, e.g. auto, cuda, cuda:0, or cpu",
)
parser.add_argument("--batch-size", type=int, default=256, help="Evaluation batch size")
parser.add_argument("--num-workers", type=int, default=4, help="Number of dataloader workers")
parser.add_argument("--model-name", default="ViT-L/14", help="CLIP backbone name")
parser.add_argument("--disable-amp", action="store_true", help="Disable AMP during evaluation")
parser.add_argument("--skip-missing", action="store_true", help="Skip dataset folders that do not exist")
return parser.parse_args()
def build_model(args, device):
cfg = TrainCfg(
method=args.method,
model_name=args.model_name,
classnames=("real", "generated"),
learn_logit_scale=False,
amp=not args.disable_amp,
)
clip_model = load_clip_to_cpu(cfg)
clip_model.eval()
for param in clip_model.parameters():
param.requires_grad_(False)
model = DoUNet(cfg, clip_model).to(device)
ckpt = torch.load(args.ckpt, map_location="cpu", weights_only=False)
model.load_state_dict(ckpt["model"], strict=False)
model.eval()
return model, cfg
def evaluate_dataset(model, cfg, dataset_path, batch_size, num_workers, device):
val_dataset = ImageFolderWithPaths(dataset_path)
val_dataloader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=device.type == "cuda",
)
preds, truths, scores = [], [], []
for imgs, y in val_dataloader:
imgs = imgs.to(device)
y = y.to(device)
with torch.no_grad(), torch.amp.autocast("cuda", enabled=cfg.amp and device.type == "cuda"):
logits = model(imgs)
probs = F.softmax(logits, dim=-1)
pred = probs.argmax(dim=-1)
preds.extend(pred.flatten().tolist())
truths.extend(y.flatten().tolist())
scores.extend(probs[:, 1].detach().cpu().numpy().tolist())
y_true = np.array(truths)
y_pred = np.array(preds)
y_score = np.array(scores)
acc = accuracy_score(y_true, y_pred)
r_acc = accuracy_score(y_true[y_true == 0], y_pred[y_true == 0])
f_acc = accuracy_score(y_true[y_true == 1], y_pred[y_true == 1])
f1 = f1_score(y_true, y_pred)
ap_score = average_precision_score(y_true, y_score)
return {
"ACC": acc * 100,
"Real ACC": r_acc * 100,
"Fake ACC": f_acc * 100,
"F1": f1 * 100,
"AP": ap_score * 100,
}
def main():
args = parse_args()
if args.device == "auto":
args.device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(args.device)
model, cfg = build_model(args, device)
max_len = max(len(dataset) for dataset in args.datasets) + 2
results = []
for dataset in args.datasets:
dataset_path = os.path.join(args.data_root, dataset)
if not os.path.isdir(dataset_path):
message = f"Dataset folder not found: {dataset_path}"
if args.skip_missing:
print(f"Skip {dataset}: {message}")
continue
raise FileNotFoundError(message)
metrics = evaluate_dataset(
model=model,
cfg=cfg,
dataset_path=dataset_path,
batch_size=args.batch_size,
num_workers=args.num_workers,
device=device,
)
print(
f"{dataset:<{max_len}} | "
f"{metrics['ACC']:6.2f}% | "
f"{metrics['Real ACC']:7.2f}% | "
f"{metrics['Fake ACC']:7.2f}% | "
f"{metrics['F1']:6.2f}% | "
f"{metrics['AP']:6.2f}%"
)
results.append({"Dataset": dataset, **metrics})
if not results:
raise RuntimeError("No datasets were evaluated")
df = pd.DataFrame(results)
df = df.set_index("Dataset").transpose()
df.to_excel(args.output)
acc_mean = sum(item["ACC"] for item in results) / len(results)
print(f"Average ACC: {acc_mean:.2f}%")
print(f"Done! Results saved to {args.output}")
if __name__ == "__main__":
main()
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