| import json |
| from pathlib import Path |
| import subprocess |
| import sys |
|
|
| import pytest |
| import torch |
|
|
| from fastfill.v2.batch import TinyTokenizer |
| from fastfill.v2.model import ModelConfig, build_model |
| from fastfill.v2.predict import main |
| from fastfill.v2.schema import validate_layout |
| from fastfill.v2.tests.test_execution import sample |
|
|
|
|
| def test_condition_only_prediction_cli_no_targets(tmp_path): |
| model_dir = tmp_path / "model" |
| model = build_model(ModelConfig(backbone="tiny", lora_rank=0, decoder_dim=16, |
| decoder_heads=2, decoder_layers=1, tiny_hidden_size=16)) |
| model.save_pretrained(model_dir) |
| TinyTokenizer().save_pretrained(tmp_path / "tokenizer") |
| condition = sample()["condition"] |
| request = tmp_path / "request.json" |
| request.write_text(json.dumps(condition)) |
| output = tmp_path / "prediction.json" |
| main(["--checkpoint", str(model_dir), "--condition", str(request), "--output", str(output)]) |
| validate_layout(json.loads(output.read_text()), condition) |
| with pytest.raises(FileExistsError): |
| main(["--checkpoint", str(model_dir), "--condition", str(request), "--output", str(request)]) |
| with pytest.raises(ValueError): |
| main(["--checkpoint", str(model_dir), "--condition", str(request), "--output", |
| "/Volumes/harddisk/3D_Room_Collections/generated.json"]) |
|
|
|
|
| @pytest.mark.parametrize("catalog_kind,expected_exit", [(None, 0), ("empty", 2), ("valid", 0)]) |
| def test_actual_cli_exit_distinguishes_prediction_and_runtime_failure(tmp_path, catalog_kind, expected_exit): |
| model_dir = tmp_path / "model" |
| model = build_model(ModelConfig(backbone="tiny", lora_rank=0, decoder_dim=16, |
| decoder_heads=2, decoder_layers=1, tiny_hidden_size=16)) |
| with torch.no_grad(): |
| for head in (model.position_head, model.size_head, model.yaw_logits_head, model.yaw_residual_head): |
| head.weight.zero_() |
| head.bias.zero_() |
| model.position_head.bias.copy_(torch.tensor([.4, .25, 0.])) |
| model.save_pretrained(model_dir) |
| TinyTokenizer().save_pretrained(tmp_path / "tokenizer") |
| condition = sample()["condition"] |
| request, output = tmp_path / "request.json", tmp_path / "result.json" |
| request.write_text(json.dumps(condition)) |
| args = [sys.executable, "-m", "fastfill.v2.predict", "--checkpoint", str(model_dir), |
| "--condition", str(request), "--output", str(output)] |
| if catalog_kind: |
| assets = [] if catalog_kind == "empty" else [{"ref": "fixture-chair", "category": "chair", |
| "actual_size_local_m": [1., 1., 1.]}] |
| catalog = tmp_path / "catalog.json" |
| catalog.write_text(json.dumps(assets)) |
| args += ["--catalog", str(catalog), "--commit-in-memory"] |
| proc = subprocess.run(args, cwd=Path(__file__).resolve().parents[3], capture_output=True, text=True, timeout=30) |
| result = json.loads(output.read_text()) |
| assert proc.returncode == expected_exit, proc.stderr |
| if catalog_kind: |
| assert result["ok"] is (expected_exit == 0) |
| assert result["committed"] is (expected_exit == 0) |
| summary = json.loads(proc.stdout.splitlines()[-1]) |
| assert summary["runtime_ok"] is result["ok"] |
| else: |
| validate_layout(result, condition) |
|
|
