File size: 5,683 Bytes
35b0bfe | 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 | #!/usr/bin/env python3
"""Write deterministic per-image predictions from a CartoLegend point detector."""
from __future__ import annotations
import argparse
import json
from datetime import datetime, timezone
from pathlib import Path
import torch
from PIL import Image
from torch.utils.data import DataLoader
from train_cartolegend_point_detector import (
PointSymbolDataset,
build_model,
collate,
load_unique_records,
sha256,
)
from cartolegend_point_detector_artifact import load_point_detector_artifact
ROOT = Path(__file__).resolve().parents[1]
def portable_path(path: Path) -> str:
resolved = path.expanduser().resolve()
try:
return "project://" + resolved.relative_to(ROOT).as_posix()
except ValueError:
return resolved.name
def load_prediction_records(path: Path, image_field: str) -> list[dict]:
if image_field == "images":
return load_unique_records(path)
records = []
seen: set[Path] = set()
for line_number, line in enumerate(path.read_text().splitlines(), start=1):
if not line.strip():
continue
row = json.loads(line)
value = str(row.get(image_field) or "")
image = Path(value)
image = (image if image.is_absolute() else ROOT / image).resolve()
if not image.is_file():
raise FileNotFoundError(f"line {line_number}: {image}")
if image in seen:
continue
seen.add(image)
with Image.open(image) as source:
width, height = source.size
records.append(
{
"image": str(image),
"width": width,
"height": height,
"boxes": [],
"text_labels": [],
"group": str(row.get("source_id") or image.stem),
}
)
return records
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--config", type=Path, default=None)
parser.add_argument(
"--allow-legacy-pt",
action="store_true",
help="Explicitly allow a legacy .pt checkpoint through weights_only=True.",
)
parser.add_argument("--input", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--image-field", default="images")
parser.add_argument("--workers", type=int, default=2)
return parser.parse_args()
def main() -> int:
args = parse_args()
checkpoint_path = args.checkpoint.expanduser().resolve()
input_path = args.input.expanduser().resolve()
output_path = args.output.expanduser().resolve()
artifact = load_point_detector_artifact(
checkpoint_path,
config_path=args.config,
device="cpu",
allow_legacy_pt=args.allow_legacy_pt,
)
config = artifact.model_config
model = build_model(
False,
int(config["min_size"]),
int(config["max_size"]),
int(config.get("trainable_backbone_layers", 6)),
str(config.get("architecture", "mobilenet")),
)
model.load_state_dict(artifact.state_dict, strict=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device).eval()
records = load_prediction_records(input_path, args.image_field)
loader = DataLoader(
PointSymbolDataset(records, augment=False),
batch_size=1,
shuffle=False,
num_workers=args.workers,
collate_fn=collate,
pin_memory=device.type == "cuda",
persistent_workers=args.workers > 0,
)
output_path.parent.mkdir(parents=True, exist_ok=True)
with output_path.open("w") as output_file, torch.inference_mode():
for record, (images, _targets) in zip(records, loader, strict=True):
prediction = model([images[0].to(device)])[0]
boxes = prediction["boxes"].detach().cpu().tolist()
scores = prediction["scores"].detach().cpu().tolist()
row = {
"schema": "cartolegend_point_detector_predictions_v1",
"image": portable_path(Path(record["image"])),
"width": record["width"],
"height": record["height"],
"detections": [
{
"symbol_bbox": [round(float(value), 4) for value in box],
"score": round(float(score), 8),
}
for box, score in zip(boxes, scores, strict=True)
],
}
output_file.write(json.dumps(row, sort_keys=True, separators=(",", ":")) + "\n")
manifest = {
"schema": "cartolegend_point_detector_prediction_manifest_v1",
"generated_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
"checkpoint": portable_path(checkpoint_path),
"checkpoint_sha256": artifact.sha256,
"checkpoint_epoch": artifact.metadata.get("completed_epochs"),
"input": portable_path(input_path),
"input_sha256": sha256(input_path),
"output": portable_path(output_path),
"output_sha256": sha256(output_path),
"images": len(records),
"image_field": args.image_field,
"device": str(device),
"config": config,
}
manifest_path = output_path.with_suffix(output_path.suffix + ".manifest.json")
manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
print(json.dumps(manifest, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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