File size: 3,316 Bytes
e6f80f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | 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)
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