| import math |
|
|
| import pytest |
| import torch |
|
|
| from fastfill.v2.geometry import bottom_to_center, box_corners, decode_yaw, encode_yaw, wrap_yaw |
| from fastfill.v2.schema import normalize_room, validate_condition, validate_layout |
|
|
|
|
| def condition(): |
| return {"schema_version": "fastfill.v2", "room": { |
| "frame": "right_handed_z_up", "floor_polygon_xy_m": [[0, 0], [5, 0], [5, 4], [0, 4]], |
| "floor_z_m": 0., "height_m": 2.8}, "objects": [ |
| {"id": "a", "category": "chair", "description": "chair"}], "constraints": []} |
|
|
|
|
| @pytest.mark.parametrize("angle", [-math.pi, math.pi, 0, 2 * math.pi, math.radians(97), -1e-8]) |
| def test_yaw_roundtrip(angle): |
| y = torch.tensor(angle, dtype=torch.float64) |
| k, r = encode_yaw(y, 12) |
| logits = torch.nn.functional.one_hot(k, 12).double() * 100 |
| residuals = torch.zeros(12, dtype=torch.float64).scatter(0, k.reshape(1), r.reshape(1)) |
| actual = decode_yaw(logits, residuals) |
| assert abs(wrap_yaw(actual - y).item()) < 1e-10 |
| assert -1 - 1e-8 <= r.item() <= 1 + 1e-8 |
|
|
|
|
| def test_local_full_size_corners_and_own_height(): |
| p = torch.tensor([1., 2., 0.]) |
| s = torch.tensor([2., 1., 3.]) |
| c = box_corners(p, s, torch.tensor(math.pi / 2)) |
| assert torch.allclose(c.max(0).values - c.min(0).values, torch.tensor([1., 2., 3.])) |
| assert torch.equal(s, torch.tensor([2., 1., 3.])) |
| assert bottom_to_center(p, s)[2].item() == 1.5 |
| assert bottom_to_center(p, s * 2)[2].item() == 3 |
|
|
|
|
| def test_protocol_rejects_zero_nan_duplicate_and_unknown_fields(): |
| c = condition() |
| validate_condition(c) |
| good = {"schema_version": "fastfill.v2", "objects": [{"id": "a", "target_size_local_m": [1, 1, 1], |
| "bottom_center_m": [1, 1, 0], "yaw_rad": 0}]} |
| validate_layout(good, c) |
| for bad in [dict(good, objects=good["objects"] * 2), dict(good, ignored=True), |
| dict(good, objects=[dict(good["objects"][0], target_size_local_m=[0, 1, 1])]), |
| dict(good, objects=[dict(good["objects"][0], yaw_rad=float("nan"))])]: |
| with pytest.raises(ValueError): |
| validate_layout(bad, c) |
|
|
|
|
| def test_normalization_is_condition_only(): |
| c = condition() |
| origin, scale = normalize_room(c["room"]) |
| assert origin == [0., 0., 0.] |
| assert scale == [5., 4., 2.8] |
| no_height = dict(c["room"], height_m=None) |
| assert normalize_room(no_height)[1][2] == 3. |
|
|
