File size: 2,946 Bytes
c0a7163 9ae5b40 a3b6bb6 9ae5b40 | 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 69 70 71 72 73 74 75 76 77 78 79 80 | """Tests for src.models.mri_model — image-based MRI DL inference surface."""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pytest
from src.models import mri_model
from tests.fixtures.build_dummy_mri_onnx import build as build_dummy_mri_onnx
_FIXTURE_MRI = Path(__file__).resolve().parents[1] / "fixtures" / "mri_sample" / "subject_0.nii.gz"
class TestMRIDLModel:
def test_preprocess_volume_returns_batch_channel_tensor(self) -> None:
volume = np.ones((4, 5, 6), dtype=np.float32)
volume[1:3, 1:4, 2:5] = 5.0
out = mri_model.preprocess_volume(volume, target_shape=(8, 8, 8))
assert out.shape == (1, 1, 8, 8, 8)
assert out.dtype == np.float32
assert np.all(np.isfinite(out))
def test_preprocess_rejects_nan_volume(self) -> None:
volume = np.zeros((4, 4, 4), dtype=np.float32)
volume[0, 0, 0] = np.nan
with pytest.raises(ValueError, match="finite numeric 3-D"):
mri_model.preprocess_volume(volume, target_shape=(8, 8, 8))
def test_load_missing_artifact_raises(self, tmp_path: Path) -> None:
with pytest.raises(FileNotFoundError, match="MRI model artifact not found"):
mri_model.load(tmp_path / "missing.onnx")
def test_predict_nifti_with_dummy_onnx(self, tmp_path: Path) -> None:
artifact = build_dummy_mri_onnx(tmp_path / "mri_model.onnx")
model = mri_model.load(artifact)
result = mri_model.predict_nifti(
model,
_FIXTURE_MRI,
target_shape=(8, 8, 8),
label_names=("control", "abnormal"),
)
assert result["label"] == 1
assert result["label_text"] == "abnormal"
assert result["confidence"] > 0.5
probs = result["probabilities"]
assert len(probs) == 2
assert sum(p["probability"] for p in probs) == pytest.approx(1.0, abs=1e-6)
def test_predict_warns_on_label_count_mismatch(
self, tmp_path: Path, caplog: pytest.LogCaptureFixture
) -> None:
artifact = build_dummy_mri_onnx(tmp_path / "mri_model.onnx")
model = mri_model.load(artifact)
# mri_model.logger has propagate=False (src/core/logger.py), so pytest's
# caplog root handler never sees its records. Attach caplog.handler directly.
mri_model.logger.addHandler(caplog.handler)
try:
result = mri_model.predict_nifti(
model,
_FIXTURE_MRI,
target_shape=(8, 8, 8),
label_names=("control", "abnormal", "extra"),
)
finally:
mri_model.logger.removeHandler(caplog.handler)
assert result["label_text"] in {"class_0", "class_1"}
assert any(
"label_names length" in rec.message and "overriding" in rec.message
for rec in caplog.records
), [rec.message for rec in caplog.records]
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