| """Evaluate an off-the-shelf DeiT-III (no KoPE) timm checkpoint on the same |
| materialized ImageNet-1K val ImageFolder tree, with the same eval protocol |
| (resize/center-crop/normalize) as simdinov2's eval.py, for an apples-to-apples |
| comparison against the released KoPE checkpoints. |
| """ |
| import argparse |
| import json |
| import time |
|
|
| import torch |
| import timm |
| from torch.utils.data import DataLoader |
| from torchvision import datasets, transforms |
| from torchvision.transforms import InterpolationMode |
|
|
| IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) |
| IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) |
|
|
|
|
| def build_transform(image_size=224, crop_pct=1.0): |
| resize_size = int(round(image_size / crop_pct)) |
| return transforms.Compose([ |
| transforms.Resize(resize_size, interpolation=InterpolationMode.BICUBIC), |
| transforms.CenterCrop(image_size), |
| transforms.ToTensor(), |
| transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD), |
| ]) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--model-name", required=True) |
| ap.add_argument("--imagenet-val", required=True) |
| ap.add_argument("--output-json", required=True) |
| ap.add_argument("--batch-size", type=int, default=256) |
| ap.add_argument("--crop-pct", type=float, default=1.0) |
| args = ap.parse_args() |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model = timm.create_model(args.model_name, pretrained=True).to(device).eval() |
|
|
| transform = build_transform(224, args.crop_pct) |
| dataset = datasets.ImageFolder(args.imagenet_val, transform=transform) |
| loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, |
| num_workers=8, pin_memory=True) |
|
|
| top1, top5, n = 0, 0, 0 |
| t0 = time.time() |
| with torch.no_grad(): |
| for imgs, targets in loader: |
| imgs, targets = imgs.to(device, non_blocking=True), targets.to(device, non_blocking=True) |
| with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=(device == "cuda")): |
| logits = model(imgs) |
| _, pred5 = logits.topk(5, dim=1) |
| correct = pred5.eq(targets.unsqueeze(1)) |
| top1 += correct[:, 0].sum().item() |
| top5 += correct.any(dim=1).sum().item() |
| n += targets.size(0) |
|
|
| result = { |
| "model_name": args.model_name, |
| "n_images": n, |
| "top1_acc": 100.0 * top1 / n, |
| "top5_acc": 100.0 * top5 / n, |
| "eval_seconds": time.time() - t0, |
| } |
| print(json.dumps(result, indent=2)) |
| with open(args.output_json, "w") as f: |
| json.dump(result, f, indent=2) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
