| import copy |
|
|
| import pytest |
| import torch |
|
|
| from fastfill.v2.batch import TinyTokenizer, collate_samples, condition_segments |
|
|
|
|
| def sample(n=2): |
| objects = [{"id": f"chair_{i}", "category": "chair", "description": "plain chair"} for i in range(n)] |
| return { |
| "schema_version": "fastfill.v2", |
| "condition": {"schema_version": "fastfill.v2", "room": { |
| "frame": "right_handed_z_up", "floor_polygon_xy_m": [[2, 4], [7, 4], [7, 8], [2, 8]], |
| "floor_z_m": 1.0, "height_m": 3.0}, "objects": objects, "constraints": []}, |
| "target": {"objects": [{"id": o["id"], "target_size_local_m": [0.6, 0.5, 1.0], |
| "bottom_center_m": [3 + i, 5, 1], "yaw_rad": 0.2} for i, o in enumerate(objects)]}, |
| "validity": {"position": [[True] * 3 for _ in objects], "size": [[True] * 3 for _ in objects], |
| "yaw": [True for _ in objects]}, "provenance": { |
| "source": "offline_test", "house_id": "offline_house", "split": "train"}} |
|
|
|
|
| def test_condition_memory_never_contains_targets(): |
| s = sample() |
| other = copy.deepcopy(s) |
| other["target"]["objects"][0]["bottom_center_m"] = [987, 654, 321] |
| a = collate_samples([s], TinyTokenizer()) |
| b = collate_samples([other], TinyTokenizer()) |
| assert torch.equal(a["input_ids"], b["input_ids"]) |
| assert torch.equal(a["origin"], torch.tensor([[2., 4., 1.]])) |
| assert torch.equal(a["scale"], torch.tensor([[5., 4., 3.]])) |
| assert torch.allclose(a["targets"]["position_normalized"][0, 0], torch.tensor([.2, .25, 0.])) |
|
|
|
|
| def test_condition_token_spans_preserve_object_and_constraints(): |
| s = sample(1) |
| s["condition"]["constraints"] = [{"type": "faces_direction", "object_id": "chair_0", "direction_xy": [1, 0]}] |
| tokenizer = TinyTokenizer() |
| batch = collate_samples([s, sample(3)], tokenizer) |
| start, end = batch["object_spans"][0, 0].tolist() |
| text = tokenizer.decode(batch["input_ids"][0, start:end]) |
| assert "chair_0" in text and "plain chair" in text |
| assert "faces_direction" in tokenizer.decode(batch["input_ids"][0]) |
| assert batch["slot_mask"].tolist() == [[True, False, False], [True, True, True]] |
| assert not batch["validity"]["position"][0, 1:].any() |
|
|
|
|
| def test_overlong_condition_is_rejected_whole(): |
| with pytest.raises(ValueError, match="context"): |
| collate_samples([sample()], TinyTokenizer(), max_length=30) |
|
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|
|
| def test_missing_validity_not_assumed_valid_and_unknown_stays_nan(): |
| s = sample(1) |
| s["validity"] = {} |
| s["target"]["objects"][0]["yaw_rad"] = None |
| batch = collate_samples([s], TinyTokenizer()) |
| assert not batch["validity"]["size"].any() |
| assert torch.isnan(batch["targets"]["yaw"][0, 0]) |
|
|
|
|
| def test_fixed_geometry_and_floor_masks_are_explicit(): |
| s = sample(1) |
| s["condition"]["objects"][0].update(support_parent="floor", fixed_size_local_m=[.8, .7, 1.2]) |
| s["target"]["objects"][0]["target_size_local_m"] = [.8, .7, 1.2] |
| b = collate_samples([s], TinyTokenizer()) |
| assert b["fixed_position_mask"].tolist() == [[[False, False, True]]] |
| assert b["fixed_size_mask"].all() |
| assert torch.equal(b["fixed_size"][0, 0], torch.tensor([.8, .7, 1.2])) |
|
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|
|
| def test_partial_fixed_size_and_condition_demo_conflicts(): |
| s = sample(1) |
| s["condition"]["objects"][0]["fixed_size_local_m"] = [.6, None, 1.] |
| b = collate_samples([s], TinyTokenizer()) |
| assert b["fixed_size_mask"].tolist() == [[[True, False, True]]] |
| assert b["conditions"][0] == s["condition"] |
| s["condition"]["objects"][0]["fixed_size_local_m"] = [.8, None, 1.] |
| with pytest.raises(ValueError, match="conflicts"): |
| collate_samples([s], TinyTokenizer()) |
| s = sample(1) |
| s["condition"]["objects"][0]["support_parent"] = "floor" |
| s["target"]["objects"][0]["bottom_center_m"][2] = 1.2 |
| with pytest.raises(ValueError, match="floor support.*conflicts"): |
| collate_samples([s], TinyTokenizer()) |
|
|
|
|
| def test_validity_arrays_follow_reordered_target_ids(): |
| s = sample(2) |
| s["target"]["objects"].reverse() |
| s["validity"]["position"] = [[False] * 3, [True] * 3] |
| s["validity"]["yaw_symmetry_order"] = [2, 1] |
| b = collate_samples([s], TinyTokenizer()) |
| assert b["validity"]["position"].tolist() == [[[True] * 3, [False] * 3]] |
| assert b["yaw_symmetry_order"].tolist() == [[1, 2]] |
|
|
|
|
| def test_schema_rejects_asset_geometry_leakage(): |
| s = sample(1) |
| s["condition"]["objects"][0]["asset_id"] = "source-model-id" |
| with pytest.raises(ValueError, match="unknown fields"): |
| collate_samples([s], TinyTokenizer()) |
|
|
