File size: 4,524 Bytes
e50372c | 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 | #!/usr/bin/env python3
"""Standalone DINOv3 script classifier inference (copied to Hub model repo as ``inference.py``)."""
from __future__ import annotations
import argparse
from pathlib import Path
import torch
import torch.nn as nn
from PIL import Image
from transformers import AutoImageProcessor, AutoModel
DINOV3_MODEL_ID = "facebook/dinov3-vits16-pretrain-lvd1689m"
class DINOv3Classifier(nn.Module):
def __init__(self, model_id: str, num_classes: int, dropout: float = 0.1):
super().__init__()
self.backbone = AutoModel.from_pretrained(model_id)
hidden = self.backbone.config.hidden_size
self.head = nn.Sequential(
nn.LayerNorm(hidden),
nn.Dropout(dropout),
nn.Linear(hidden, 128),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(128, num_classes),
)
def forward(self, pixel_values):
out = self.backbone(pixel_values=pixel_values)
cls = out.last_hidden_state[:, 0, :]
return self.head(cls)
def _resize_short_edge(img: Image.Image, target: int) -> Image.Image:
w, h = img.size
if h <= w:
new_h = target
new_w = max(1, int(w * target / h))
else:
new_w = target
new_h = max(1, int(h * target / w))
return img.resize((new_w, new_h), Image.BICUBIC)
def _center_crop(img: Image.Image, size: int = 224) -> Image.Image:
img = _resize_short_edge(img, size)
w, h = img.size
left = max(0, (w - size) // 2)
top = max(0, (h - size) // 2)
crop = img.crop((left, top, left + size, top + size))
if crop.size != (size, size):
padded = Image.new("RGB", (size, size), (255, 255, 255))
padded.paste(crop, (0, 0))
return padded
return crop
def apply_preprocess(img: Image.Image, mode: str | None, *, size: int = 224) -> Image.Image:
if not mode or mode == "none":
return img
if mode in ("center_crop", "center_crop_whole_page"):
return _center_crop(img, size)
raise ValueError(f"Unknown preprocess mode: {mode!r}")
def label_order(ckpt: dict) -> list[str]:
idx = ckpt.get("idx_to_label") or {}
if idx:
return [str(idx[k]) for k in sorted(idx.keys(), key=lambda x: int(x))]
raw = ckpt.get("label_to_idx") or {}
if raw:
return sorted(raw.keys(), key=lambda k: raw[k])
raise KeyError("checkpoint missing idx_to_label / label_to_idx")
@torch.no_grad()
def predict(model, processor, image_path: Path, device, *, preprocess: str | None, size: int):
img = Image.open(image_path).convert("RGB")
img = apply_preprocess(img, preprocess, size=size)
pv = processor(images=img, return_tensors="pt")["pixel_values"].to(device)
logits = model(pv)
probs = torch.softmax(logits, dim=1).squeeze(0).cpu()
pred = int(probs.argmax())
return pred, probs.tolist()
def main() -> None:
ap = argparse.ArgumentParser(description="DINOv3 Tibetan script page classifier")
ap.add_argument(
"--checkpoint",
type=Path,
default=Path("final_model.pt"),
help="Weights file (default: final_model.pt in cwd)",
)
ap.add_argument("--image", type=Path, nargs="+", required=True)
ap.add_argument(
"--preprocess",
default="none",
help="none (full page, DINO processor resize) | center_crop (224 short edge)",
)
ap.add_argument("--preprocess-size", type=int, default=224)
ap.add_argument("--model-id", default=DINOV3_MODEL_ID)
args = ap.parse_args()
ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
classes = label_order(ckpt)
idx_to_label = {i: lab for i, lab in enumerate(classes)}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DINOv3Classifier(args.model_id, num_classes=len(classes)).to(device)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
processor = AutoImageProcessor.from_pretrained(args.model_id)
prep = None if args.preprocess in ("none", "") else args.preprocess
for path in args.image:
pred, probs = predict(
model, processor, path, device, preprocess=prep, size=args.preprocess_size
)
name = idx_to_label[pred]
conf = probs[pred]
print(f"{path.name}: {name} ({conf:.3f})")
top3 = sorted(enumerate(probs), key=lambda x: -x[1])[:3]
for i, p in top3:
print(f" {idx_to_label[i]:16s} {p:.3f}")
if __name__ == "__main__":
main()
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