File size: 3,274 Bytes
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# python examples/torch_dataloader.py --root . --split all --batch-size 2 --num-workers 0 --tile-size 128 --patch-stride 128 --max-land-fraction 0.30 --date-start 20000101 --date-end 20000101 --max-dates 1 --include-salinity --metadata-cache-dir /tmp/depthdif_cache --require-argo
from __future__ import annotations
import argparse
from pathlib import Path
import sys
from typing import Any
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from depthdif_dataset import ArgoGeoTIFFGriddedPatchDataset, build_dataloader
def _shape_or_value(value: Any) -> Any:
"""Return tensor shapes for compact terminal output."""
return tuple(value.shape) if hasattr(value, "shape") else value
def main() -> None:
"""Open the packaged dataset and print the first PyTorch batch."""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--root", type=Path, default=REPO_ROOT)
parser.add_argument("--split", choices=("all", "train", "val"), default="all")
parser.add_argument("--batch-size", type=int, default=2)
parser.add_argument("--num-workers", type=int, default=0)
parser.add_argument("--tile-size", type=int, default=128)
parser.add_argument("--patch-stride", type=int, default=128)
parser.add_argument("--max-land-fraction", type=float, default=0.30)
parser.add_argument("--date-start", type=int, default=None)
parser.add_argument("--date-end", type=int, default=None)
parser.add_argument("--max-dates", type=int, default=1)
parser.add_argument("--include-salinity", action="store_true")
parser.add_argument(
"--metadata-cache-dir",
type=Path,
default=None,
help="Optional cache directory for patch/date metadata CSVs.",
)
parser.add_argument(
"--require-argo",
action="store_true",
help="Filter rows to patches with ARGO profiles; this may scan the compact ARGO store on first use.",
)
args = parser.parse_args()
dataset = ArgoGeoTIFFGriddedPatchDataset(
geotiff_root_dir=args.root,
split=args.split,
tile_size=args.tile_size,
patch_stride=args.patch_stride,
max_land_fraction=args.max_land_fraction,
date_start=args.date_start,
date_end=args.date_end,
max_dates=args.max_dates,
include_salinity=args.include_salinity,
require_argo_for_train=args.require_argo,
require_argo_for_val=args.require_argo,
require_argo_for_all=args.require_argo,
count_argo_support=args.require_argo,
metadata_cache_dir=args.metadata_cache_dir,
)
loader = build_dataloader(
dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
)
batch = next(iter(loader))
print(f"dataset rows: {len(dataset)}")
print(f"depth levels: {len(dataset.depth_axis_m)}")
print(
f"date coverage in this run: {dataset.available_dates[0]}..{dataset.available_dates[-1]}"
)
for key, value in batch.items():
if key == "info":
continue
print(f"{key}: {_shape_or_value(value)}")
if __name__ == "__main__":
main()
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