File size: 3,601 Bytes
63b6d19 | 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 | #!/usr/bin/env python3
"""Load and validate the four Hugging Face Dataset subsets in this repo."""
from __future__ import annotations
import argparse
from pathlib import Path
from datasets import Dataset, load_dataset
SUBSETS = (
"depth_frames",
"radar_frames",
"radar_depth_joined",
"radar_video_grouped",
)
EXPECTED_COLUMNS = {
"depth_frames": {
"relative_path",
"recording_id",
"sequence_name",
"activity",
"subject",
"frame_index",
"local_frame_index",
},
"radar_frames": {
"relative_path",
"recording_id",
"sequence_name",
"activity",
"subject",
"snaplength",
"format",
"win_size",
"win_stride",
"frame_index",
"range_bin_index",
"entrophy",
},
"radar_video_grouped": {
"recording_id",
"subject",
"activity",
"range_bin_index",
"radar_sequence_name",
"radar_snaplength",
"radar_format",
"radar_win_size",
"radar_win_stride",
"frame_count",
"frame_indexes",
"radar_paths",
"radar_entrophies",
"depth_paths",
"depth_sequence_names",
"depth_local_frame_indexes",
},
"radar_depth_joined": {
"recording_id",
"subject",
"activity",
"frame_index",
"radar_path",
"radar_sequence_name",
"radar_snaplength",
"radar_format",
"radar_win_size",
"radar_win_stride",
"radar_range_bin_index",
"radar_entrophy",
"depth_paths",
"depth_sequence_names",
"depth_local_frame_indexes",
},
}
def load_subset(repo_path: Path, subset: str) -> Dataset:
"""Load one local config's train split."""
dataset = load_dataset(str(repo_path), subset, split="train")
missing = EXPECTED_COLUMNS[subset] - set(dataset.column_names)
if missing:
raise AssertionError(f"{subset} is missing columns: {sorted(missing)}")
return dataset
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--repo",
default="./",
help="Local dataset repository path (default: ./)",
)
args = parser.parse_args()
repo = Path(args.repo).expanduser().resolve()
if not (repo / "README.md").is_file():
raise FileNotFoundError(f"Dataset README.md not found under: {repo}")
loaded = {}
for subset in SUBSETS:
dataset = load_subset(repo, subset)
loaded[subset] = dataset
print(f"\n[{subset}]")
print(f"rows: {len(dataset)}")
print(f"columns: {dataset.column_names}")
if len(dataset):
print(f"first row: {dataset[0]}")
radar_frame_count = len(loaded["radar_frames"])
joined_frame_count = len(loaded["radar_depth_joined"])
grouped_frame_count = sum(loaded["radar_video_grouped"]["frame_count"])
assert joined_frame_count == radar_frame_count, (
"Joined row-count validation failed: "
f"radar_frames={radar_frame_count}, radar_depth_joined={joined_frame_count}"
)
assert grouped_frame_count == radar_frame_count, (
"Radar frame-count validation failed: "
f"radar_frames={radar_frame_count}, grouped_frames={grouped_frame_count}"
)
print("\nAll four subsets loaded successfully.")
print(
"Radar frame-count validation passed: "
f"grouped frames = radar frames = {radar_frame_count}"
)
if __name__ == "__main__":
main()
|