| """Regression tests for minimal-condition data, not legacy semantic-front labels.""" |
| from copy import deepcopy |
| import json |
| import math |
|
|
| import pytest |
|
|
| from fastfill.v2.direct_layout import request_to_condition |
| from fastfill.v2.batch import TinyTokenizer, collate_samples |
| from fastfill.v2.io import fingerprint |
| from fastfill.v2.minimal_data import build_minimal_dataset, project_sample |
|
|
|
|
| def pair(uid="room", split="train", floor=1., height=3.): |
| target = {"id": "obj_0000", "target_size_local_m": [1., .5, 1.], |
| "bottom_center_m": [11., 21., floor + .5], "yaw_rad": 0.} |
| room = {"frame": "right_handed_z_up", "room_type": "office", "boundary_known": True, |
| "floor_known": True, "floor_z_m": floor, "height_m": height, |
| "floor_polygon_xy_m": [[10., 20.], [14., 20.], [14., 23.], [10., 23.]], |
| "fixed_objects": [{"id": "fixed_0", "category": "window", "size_local_m": [1., .1, 1.], |
| "bottom_center_m": [10., 21., floor + 1.], "yaw_rad": 0.}]} |
| condition = {"schema_version": "fastfill.v2", "room": room, |
| "objects": [{"id": "obj_0000", "category": "chair", "description": "chair", |
| "support_parent": "floor", "exchangeable_group": "anonymous_0"}], |
| "constraints": [{"type": "against_wall", "object_id": "obj_0000"}]} |
| provenance = {"source": "SpatialLM", "scene_id": uid, "legacy_uid": uid, |
| "house_id": "house_" + uid, "group": "house_" + uid, "split": split, |
| "target_source_ids": ["source_0"], "field_evidence": [{"tilted": False, |
| "size_semantics": "canonical_source_IR", "front_policy": "strict"}]} |
| sample = {"schema_version": "fastfill.v2", "condition": condition, |
| "target": {"schema_version": "fastfill.v2", "objects": [target]}, |
| "validity": {"position": [[True] * 3], "size": [[True] * 3], "yaw": [False]}, |
| "provenance": provenance} |
| source = {"uid": uid, "source": "SpatialLM", "objects": [{"id": "source_0", "size": [1., .5, 1.], |
| "pos": [11., 21., floor + .5], "yaw": 0., "tilted": False}], |
| "boundary": room["floor_polygon_xy_m"], "group": provenance["group"], |
| "room_type": "office", "height": height, "floor_z_m": floor, "boundary_type": "polygon"} |
| return sample, source |
|
|
|
|
| def test_projection_uses_same_public_adapter_and_preserves_lineage(): |
| sample, source = pair() |
| before = deepcopy(sample) |
| result = project_sample(sample, source, split="train") |
| request = {"room_type": "office", "room_size_m": [4., 3.], |
| "furniture_list": [{"id": "obj_0000", "category": "chair", "description": "chair", "count": 1}]} |
| assert result["condition"] == request_to_condition(request) |
| assert result["provenance"]["minimal_request"] == request |
| assert result["target"]["objects"][0]["bottom_center_m"] == [1., 1., .5] |
| assert result["target"]["objects"][0]["target_size_local_m"] == [1., .5, 1.] |
| assert result["provenance"]["frame_translation_m"] == [-10., -20., -1.] |
| for key in ("scene_id", "legacy_uid", "house_id", "group", "split", "target_source_ids"): |
| assert result["provenance"][key] == sample["provenance"][key] |
| assert sample == before |
| batch = collate_samples([result], TinyTokenizer()) |
| assert batch["scale"][0, 2].item() == 3. |
| assert result["condition"]["room"]["height_m"] is None |
| assert result["provenance"]["qualification_source_height_m"] == 3. |
|
|
|
|
| def test_xyz_height_is_explicit_opt_in_not_default(): |
| sample, source = pair(height=2.5) |
| xy = project_sample(sample, source, split="train") |
| xyz = project_sample(sample, source, split="train", room_dimension_mode="xyz") |
| assert xy["provenance"]["minimal_request"]["room_size_m"] == [4., 3.] |
| assert xyz["provenance"]["minimal_request"]["room_size_m"] == [4., 3., 2.5] |
| assert xyz["condition"]["room"]["height_m"] == 2.5 |
| assert xy["target"] == xyz["target"] |
| assert collate_samples([xy], TinyTokenizer())["scale"][0, 2].item() == 3. |
| assert collate_samples([xyz], TinyTokenizer())["scale"][0, 2].item() == 2.5 |
| with pytest.raises(ValueError, match="room_dimension_mode"): |
| project_sample(sample, source, split="train", room_dimension_mode="invalid") |
|
|
|
|
| def test_unknown_height_stays_unknown_and_labels_are_not_input(): |
| sample, source = pair(height=None) |
| result = project_sample(sample, source, split="train") |
| assert result["provenance"]["minimal_request"]["room_size_m"] == [4., 3.] |
| assert result["condition"]["room"]["height_m"] is None |
| assert "fixed_objects" not in result["condition"]["room"] |
| assert result["condition"]["constraints"] == [] |
| assert set(result["condition"]["objects"][0]) == {"id", "category", "description"} |
| assert "target_size_local_m" not in json.dumps(result["condition"]) |
| xyz = project_sample(sample, source, split="train", room_dimension_mode="xyz") |
| assert xyz["provenance"]["minimal_request"]["room_size_m"] == [4., 3.] |
| assert xyz["condition"]["room"]["height_m"] is None |
|
|
|
|
| def test_geometric_yaw_does_not_certify_semantic_front_or_snap_position(): |
| sample, source = pair(floor=0.) |
| sample["target"]["objects"][0]["bottom_center_m"][2] = -.0005 |
| source["objects"][0]["pos"][2] = -.0005 |
| result = project_sample(sample, source, split="train") |
| assert result["validity"]["yaw"] == [True] |
| assert result["provenance"]["yaw_label_semantics"] == "local_bbox_axes_not_certified_semantic_front" |
| assert result["provenance"]["parent_yaw_validity"] == [False] |
| assert result["target"]["objects"][0]["bottom_center_m"][2] == -.0005 |
| assert collate_samples([result], TinyTokenizer())["yaw_symmetry_order"].tolist() == [[2]] |
|
|
|
|
| def test_object_budget_rejects_whole_scene_without_partial_projection(): |
| sample, source = pair() |
| before = deepcopy(sample) |
| with pytest.raises(ValueError, match="max_objects"): |
| project_sample(sample, source, split="train", max_objects=0) |
| assert sample == before |
| second = {**deepcopy(sample["condition"]["objects"][0]), "id": "obj_0001"} |
| sample["condition"]["objects"] = sample["condition"]["objects"] + [second] |
| with pytest.raises(ValueError, match="max_objects"): |
| project_sample(sample, source, split="train", max_objects=1) |
|
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|
|
| def test_source_split_and_nonfinite_yaw_rejected(): |
| sample, source = pair() |
| with pytest.raises(ValueError, match="split"): |
| project_sample(sample, source, split="test") |
| sample["target"]["objects"][0]["yaw_rad"] = float("nan") |
| with pytest.raises(ValueError, match="labels"): |
| project_sample(sample, source, split="train") |
|
|
|
|
| @pytest.mark.parametrize("field,value,reason", [ |
| ("room_type", "kitchen", "source_room_type"), |
| ("height", 4., "source_room_height"), |
| ("floor_z_m", 2., "source_floor_frame"), |
| ("boundary_type", "hull", "source_boundary_semantics"), |
| ]) |
| def test_source_room_metadata_must_agree_with_parent(field, value, reason): |
| sample, source = pair() |
| source[field] = value |
| with pytest.raises(ValueError, match=reason): |
| project_sample(sample, source, split="train") |
|
|
|
|
| def test_parent_cannot_invent_height_or_hide_source_height_without_evidence(): |
| sample, source = pair(height=None) |
| sample["condition"]["room"]["height_m"] = 3. |
| with pytest.raises(ValueError, match="source_room_height"): |
| project_sample(sample, source, split="train") |
| sample, source = pair(height=3.) |
| sample["condition"]["room"]["height_m"] = None |
| with pytest.raises(ValueError, match="source_room_height"): |
| project_sample(sample, source, split="train") |
| sample["provenance"]["legacy_height_dropped"] = True |
| result = project_sample(sample, source, split="train") |
| assert result["condition"]["room"]["height_m"] is None |
| assert result["provenance"]["qualification_source_height_m"] == 3. |
|
|
|
|
| def test_recorded_parent_height_drop_cannot_bypass_source_ceiling_qualification(): |
| sample, source = pair(floor=0., height=2.) |
| sample["condition"]["room"]["height_m"] = None |
| sample["provenance"]["legacy_height_dropped"] = True |
| sample["target"]["objects"][0]["bottom_center_m"][2] = 1.5 |
| source["objects"][0]["pos"][2] = 1.5 |
| with pytest.raises(ValueError, match="ceiling"): |
| project_sample(sample, source, split="train") |
|
|
|
|
| def test_spatiallm_source_has_declared_canonical_zero_floor_by_default(): |
| sample, source = pair(floor=0.) |
| del source["floor_z_m"] |
| assert project_sample(sample, source, split="train")["condition"]["room"]["floor_z_m"] == 0. |
| sample["condition"]["room"]["floor_z_m"] = .1 |
| with pytest.raises(ValueError, match="source_floor_frame"): |
| project_sample(sample, source, split="train") |
|
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|
|
| @pytest.mark.parametrize("change,reason", [ |
| (lambda s, ir: s["provenance"].update(source="HSSD200"), "source"), |
| (lambda s, ir: s["condition"]["room"].update(boundary_known=False), "boundary"), |
| (lambda s, ir: s["condition"]["room"].update(floor_known=False), "floor"), |
| (lambda s, ir: s["condition"]["room"].update(room_type="unknown"), "room_type"), |
| (lambda s, ir: s["condition"]["room"].update(floor_polygon_xy_m=[[10., 20.], [14., 20.], [10., 23.]]), "rectangle"), |
| (lambda s, ir: s["validity"]["size"][0].__setitem__(2, False), "labels"), |
| (lambda s, ir: s["provenance"]["field_evidence"][0].update(tilted=True), "tilted"), |
| (lambda s, ir: ir["objects"][0].update(tilted=True), "tilted"), |
| (lambda s, ir: ir["objects"][0].update(yaw=.5), "source_geometry"), |
| (lambda s, ir: ir.update(uid="other"), "source_identity"), |
| ]) |
| def test_ineligible_geometry_fails_closed(change, reason): |
| sample, source = pair() |
| change(sample, source) |
| with pytest.raises(ValueError, match=reason): |
| project_sample(sample, source, split="train") |
|
|
|
|
| def test_obb_corners_and_own_height_determine_qualification(): |
| sample, source = pair() |
| sample["target"]["objects"][0]["bottom_center_m"][0] = 10.25 |
| source["objects"][0]["pos"][0] = 10.25 |
| with pytest.raises(ValueError, match="xy_boundary"): |
| project_sample(sample, source, split="train") |
| sample, source = pair(height=1.) |
| with pytest.raises(ValueError, match="ceiling"): |
| project_sample(sample, source, split="train") |
| sample, source = pair() |
| with pytest.raises(ValueError, match="max_objects"): |
| project_sample(sample, source, split="train", max_objects=0) |
|
|
|
|
| def parent_files(tmp_path, duplicate_group=False): |
| parent, source_path = tmp_path / "parent", tmp_path / "source.jsonl" |
| parent.mkdir() |
| sources = [] |
| for split in ("train", "validation", "test"): |
| row, source = pair(uid=split, split=split) |
| if duplicate_group: |
| row["provenance"]["house_id"] = row["provenance"]["group"] = "same_house" |
| source["group"] = "same_house" |
| (parent / (split + ".jsonl")).write_text(json.dumps(row) + "\n") |
| sources.append(source) |
| (parent / "manifest.json").write_text(json.dumps({"schema_version": "fastfill.v2"})) |
| source_path.write_text("".join(json.dumps(r) + "\n" for r in sources)) |
| return parent, source_path |
|
|
|
|
| def test_build_inherits_split_and_does_not_change_parent_bytes(tmp_path): |
| parent, source = parent_files(tmp_path) |
| before = {p.name: p.read_bytes() for p in parent.iterdir()} |
| out = tmp_path / "new-version" |
| manifest = build_minimal_dataset(parent, source, out, expected_source_sha256=fingerprint(source)) |
| assert manifest["split_samples"] == {"train": 1, "validation": 1, "test": 1} |
| assert manifest["split_objects"] == {"train": 1, "validation": 1, "test": 1} |
| assert manifest["room_dimension_mode"] == "xy" |
| assert manifest["split_integrity"]["group_cross_split_conflicts"] == 0 |
| for split in ("train", "validation", "test"): |
| row = json.loads((out / (split + ".jsonl")).read_text()) |
| assert row["provenance"]["scene_id"] == row["provenance"]["split"] == split |
| assert before == {p.name: p.read_bytes() for p in parent.iterdir()} |
| with pytest.raises(FileExistsError): |
| build_minimal_dataset(parent, source, out, expected_source_sha256=fingerprint(source)) |
|
|
|
|
| def test_build_rejects_source_hash_and_cross_split_group(tmp_path): |
| parent, source = parent_files(tmp_path, duplicate_group=True) |
| with pytest.raises(ValueError, match="SHA256"): |
| build_minimal_dataset(parent, source, tmp_path / "bad-hash", expected_source_sha256="0" * 64) |
| assert not (tmp_path / "bad-hash").exists() |
| with pytest.raises(ValueError, match="cross.split"): |
| build_minimal_dataset(parent, source, tmp_path / "bad-split", expected_source_sha256=fingerprint(source)) |
|
|
