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import json
import sys
from pathlib import Path
import pytest
RELEASE_ROOT = Path(__file__).resolve().parents[1]
TASK_DIR = RELEASE_ROOT / "eval" / "task"
sys.path.insert(0, str(TASK_DIR))
sys.path.insert(0, str(TASK_DIR / "spatial_grounding"))
sys.path.insert(0, str(TASK_DIR / "tracking"))
sys.path.insert(0, str(TASK_DIR / "spatial_temporal_grounding"))
sys.path.insert(0, str(TASK_DIR / "segmentation"))
from _grounding_utils import load_annotations as load_grounding_annotations # noqa: E402
from canonical_data import ( # noqa: E402
CanonicalDataError,
adapt_canonical_row,
canonical_dataset_record,
load_json_records,
repository_relative_output_path,
)
from eval_stvg_vllm import load_dataset as load_stvg_dataset # noqa: E402
from eval_tracking_vllm import load_dataset as load_tracking_dataset # noqa: E402
from eval_vllm import vsi_prompt, vsi_qtype, vsi_score # noqa: E402
from post_sam2 import normalize_missing_rle_counts, sam2_config_name # noqa: E402
def _row() -> dict[str, object]:
return {
"schema_version": 1,
"eval_task": "tracking",
"sample_id": "sample-1",
"benchmark": "tracking",
"split": "got10k",
"problem": "Track the object.",
"answer": {"boxes": {"1": [1, 2, 3, 4]}},
"images": ["media/tracking/images/frame.jpg"],
"videos": [{"path": "media/tracking/videos/clip.mp4", "fps": 2}],
"subtitles": ["media/tracking/subtitles/clip.srt"],
"problem_type": "tracking",
"source": "tracking",
"choices": ["left", "right"],
"preprocessed": {
"preprocessed_video": "artifacts/tracking/clip.npz",
},
"task_payload": {
"boxes": {"1": [1, 2, 3, 4]},
"mask_path": "artifacts/tracking/mask.json",
},
"metadata": {"category": "fixture"},
}
def test_adapter_resolves_paths_and_preserves_canonical_fields(tmp_path: Path) -> None:
adapted = adapt_canonical_row(_row(), tmp_path)
assert adapted["eval_task"] == "tracking"
assert adapted["images"] == [
str(tmp_path / "media/tracking/images/frame.jpg")
]
assert adapted["videos"][0]["path"] == str(
tmp_path / "media/tracking/videos/clip.mp4"
)
assert adapted["subtitles"] == [
str(tmp_path / "media/tracking/subtitles/clip.srt")
]
assert adapted["task_payload"]["mask_path"] == str(
tmp_path / "artifacts/tracking/mask.json"
)
assert adapted["preprocessed_video"] == str(
tmp_path / "artifacts/tracking/clip.npz"
)
assert adapted["path"] == str(tmp_path / "media/tracking/videos/clip.mp4")
assert adapted["video"] == adapted["path"]
assert adapted["image"] == str(tmp_path / "media/tracking/images/frame.jpg")
assert adapted["options"] == ["left", "right"]
assert adapted["ground_truth"] == {"boxes": {"1": [1, 2, 3, 4]}}
assert adapted["boxes"] == {"1": [1, 2, 3, 4]}
assert adapted["category"] == "fixture"
def test_adapter_resolves_media_and_artifacts_from_separate_asset_root(
tmp_path: Path,
) -> None:
metadata_root = tmp_path / "metadata"
asset_root = tmp_path / "assets"
adapted = adapt_canonical_row(
_row(),
metadata_root,
asset_root=asset_root,
)
assert adapted["image"] == str(
asset_root / "media/tracking/images/frame.jpg"
)
assert adapted["video"] == str(
asset_root / "media/tracking/videos/clip.mp4"
)
assert adapted["preprocessed_video"] == str(
asset_root / "artifacts/tracking/clip.npz"
)
def test_loader_infers_repository_root_from_manifests(tmp_path: Path) -> None:
root = tmp_path / "dataset"
annotation = root / "annotations/tracking/got10k.jsonl"
annotation.parent.mkdir(parents=True)
(root / "datasets.jsonl").write_text("{}\n", encoding="utf-8")
annotation.write_text(json.dumps(_row()) + "\n", encoding="utf-8")
loaded = load_json_records(annotation)
assert loaded[0]["video"] == str(
root / "media/tracking/videos/clip.mp4"
)
def test_adapter_leaves_legacy_rows_unchanged(tmp_path: Path) -> None:
legacy = {"question": "Legacy?", "path": "relative.mp4"}
assert adapt_canonical_row(legacy, tmp_path) == legacy
def test_repository_relative_output_path_supports_sam2_postprocessing(
tmp_path: Path,
) -> None:
media = tmp_path / "media/segmentation/videos/mevis/clip.mp4"
assert repository_relative_output_path(str(media), tmp_path) == (
"./media/segmentation/videos/mevis/clip.mp4"
)
outside = tmp_path.parent / "outside.mp4"
assert repository_relative_output_path(str(outside), tmp_path) == str(outside)
def test_sam2_absolute_config_becomes_hydra_package_name(tmp_path: Path) -> None:
package = tmp_path / "sam2"
config = package / "configs/sam2.1/sam2.1_hiera_l.yaml"
assert sam2_config_name(config.as_posix(), package) == (
"configs/sam2.1/sam2.1_hiera_l.yaml"
)
assert sam2_config_name("configs/sam2.1/sam2.1_hiera_l.yaml", package) == (
"configs/sam2.1/sam2.1_hiera_l.yaml"
)
def test_sam2_normalizes_polygon_and_empty_video_rles() -> None:
class CocoMask:
@staticmethod
def frPyObjects(polygons, height, width):
if isinstance(polygons, dict):
assert polygons == {"size": [height, width], "counts": [6]}
return {"size": [height, width], "counts": b"empty"}
assert polygons == [[0, 0, 2, 0, 2, 2]]
return [{"size": [height, width], "counts": b"encoded"}]
@staticmethod
def merge(rles):
return rles[0]
payload = {
"results": [
{
"data_type": "image",
"segmentation_output": {
"segmentation_polygon": [[0, 0, 2, 0, 2, 2]],
"segmentation_rle": {"size": [2, 3]},
},
},
{
"data_type": "video",
"task_payload": {
"segmentation_output": {
"segmentation_rle": {
"00000": {"size": [2, 3]},
"00001": {"size": [2, 3], "counts": [6]},
"00002": {"size": [2, 3], "counts": "valid"},
}
}
},
},
]
}
assert normalize_missing_rle_counts(payload, CocoMask) == 3
assert payload["results"][0]["segmentation_output"]["segmentation_rle"] == {
"size": [2, 3],
"counts": "encoded",
}
assert payload["results"][1]["segmentation_output"]["segmentation_rle"] == {
"00000": {"size": [2, 3], "counts": "empty"},
"00001": {"size": [2, 3], "counts": "empty"},
"00002": {"size": [2, 3], "counts": "valid"},
}
def test_vsi_prefers_the_benchmark_subtask_over_generic_answer_type() -> None:
record = {
"problem": "How many chairs are in this room?",
"problem_type": "regression",
"question_type": "regression",
"original_question_type": "object_counting",
}
assert vsi_qtype(record) == "object_counting"
assert "Answer with an integer within <answer>" in vsi_prompt(record)
assert vsi_score(vsi_qtype(record), "<answer>3</answer>", "<answer>3</answer>") == (
"MRA",
"3.0",
1.0,
)
def test_loader_preserves_whitespace_prefixed_legacy_json(tmp_path: Path) -> None:
legacy = [{"question": "Legacy?", "path": "relative.mp4"}]
data_file = tmp_path / "legacy.json"
data_file.write_text(
"\n " + json.dumps(legacy, indent=2) + "\n",
encoding="utf-8",
)
assert load_json_records(data_file) == legacy
def test_adapter_rejects_repository_escape(tmp_path: Path) -> None:
row = _row()
row["videos"] = ["../outside.mp4"]
with pytest.raises(CanonicalDataError, match="outside"):
adapt_canonical_row(row, tmp_path)
def test_adapter_rejects_absolute_path_outside_repository(tmp_path: Path) -> None:
row = _row()
row["videos"] = [str(tmp_path.parent / "outside.mp4")]
with pytest.raises(CanonicalDataError, match="outside"):
adapt_canonical_row(row, tmp_path)
def test_dataset_profile_resolves_snake_case_split_alias(tmp_path: Path) -> None:
root = tmp_path / "dataset"
root.mkdir()
(root / "datasets.jsonl").write_text(
json.dumps(
{
"task": "temporal_grounding",
"split": "charades_timelens",
"preprocessing": {"fps": 4},
}
)
+ "\n",
encoding="utf-8",
)
record = canonical_dataset_record(
"temporal_grounding",
"charades-timelens",
root,
)
assert record is not None
assert record["preprocessing"]["fps"] == 4
def test_spatial_grounding_loads_canonical_split_with_legacy_name(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
root = tmp_path / "metadata"
asset_root = tmp_path / "assets"
annotation = root / "annotations/spatial_grounding/refcoco_val.jsonl"
image = asset_root / "media/spatial_grounding/images/image.jpg"
annotation.parent.mkdir(parents=True)
image.parent.mkdir(parents=True)
image.write_bytes(b"fixture")
row = {
"schema_version": 1,
"eval_task": "spatial_grounding",
"sample_id": "refcoco-val-1",
"benchmark": "spatial_grounding",
"split": "refcoco_val",
"problem": "the red box",
"answer": [16, 47, 469, 935],
"images": ["media/spatial_grounding/images/image.jpg"],
"videos": [],
"problem_type": "spatial_grounding",
"source": "refcoco",
"family": "spatial_grounding",
"task_payload": {"normalized_solution": [16, 47, 469, 935]},
"metadata": {"height": 428, "width": 640},
"evaluation": {},
}
annotation.write_text(json.dumps(row) + "\n", encoding="utf-8")
(root / "datasets.jsonl").write_text("{}\n", encoding="utf-8")
monkeypatch.setenv("ORARL_EVAL_DATA_ROOT", str(root))
monkeypatch.setenv("ORARL_EVAL_ASSET_ROOT", str(asset_root))
records = load_grounding_annotations(
str(annotation.parent),
"refcoco-val",
)
assert records == [
{
"problem_id": "refcoco-val-1",
"image_path": str(image),
"expression": "the red box",
"bbox": [16.0, 47.0, 469.0, 935.0],
"width": 640,
"height": 428,
}
]
def test_tracking_loads_canonical_split_with_legacy_name(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
root = tmp_path / "metadata"
asset_root = tmp_path / "assets"
annotation = root / "annotations/tracking/got10k.jsonl"
video = asset_root / "media/tracking/videos/clip.mp4"
annotation.parent.mkdir(parents=True)
video.parent.mkdir(parents=True)
video.write_bytes(b"fixture")
answer = '<answer>{"boxes":{"1":[1,2,3,4]}}</answer>'
row = {
"schema_version": 1,
"eval_task": "tracking",
"sample_id": "got10k-1",
"benchmark": "tracking",
"split": "got10k",
"problem": "Track the object.",
"answer": answer,
"images": [],
"videos": ["media/tracking/videos/clip.mp4"],
"problem_type": "tracking",
"source": "got10k",
"family": "tracking",
"task_payload": {"boxes": {"1": [1, 2, 3, 4]}},
"evaluation": {},
}
annotation.write_text(json.dumps(row) + "\n", encoding="utf-8")
(root / "datasets.jsonl").write_text("{}\n", encoding="utf-8")
monkeypatch.setenv("ORARL_EVAL_DATA_ROOT", str(root))
monkeypatch.setenv("ORARL_EVAL_ASSET_ROOT", str(asset_root))
records = load_tracking_dataset(str(annotation.parent), "eval_got10k")
assert records[0]["problem"] == "Track the object."
assert records[0]["solution"] == answer
assert records[0]["path"] == str(video)
def test_stvg_loads_canonical_split_with_legacy_name(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
root = tmp_path / "metadata"
asset_root = tmp_path / "assets"
annotation = root / "annotations/stvg/stvg.jsonl"
video = asset_root / "media/stvg/videos/stvg/clip.mp4"
annotation.parent.mkdir(parents=True)
video.parent.mkdir(parents=True)
video.write_bytes(b"fixture")
answer = (
'<answer>{"time":[1,2],"boxes":{"1":[1,2,3,4],'
'"2":[2,3,4,5]}}</answer>'
)
row = {
"schema_version": 1,
"eval_task": "stvg",
"sample_id": "stvg-1",
"benchmark": "stvg",
"split": "stvg",
"problem": "When and where is the person moving?",
"answer": answer,
"images": [],
"videos": ["media/stvg/videos/stvg/clip.mp4"],
"problem_type": "spatial-temporal grounding",
"source": "stvg",
"family": "spatial_temporal_grounding",
"task_payload": {
"time": [1, 2],
"boxes": {"1": [1, 2, 3, 4], "2": [2, 3, 4, 5]},
},
"evaluation": {},
}
annotation.write_text(json.dumps(row) + "\n", encoding="utf-8")
(root / "datasets.jsonl").write_text("{}\n", encoding="utf-8")
monkeypatch.setenv("ORARL_EVAL_DATA_ROOT", str(root))
monkeypatch.setenv("ORARL_EVAL_ASSET_ROOT", str(asset_root))
records = load_stvg_dataset(str(annotation.parent), "eval_stvg")
assert records[0]["solution"] == answer
assert records[0]["path"] == str(video)
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