| |
| """Host image pre-processing (``host.preprocess``) against the research reference's OpenCV pipeline, the |
| public-data converter's decoded-image digests and the research golden network input. |
| |
| TT_VISIBLE_DEVICES=none python -m pytest -q -p fake_ttnn_plugin code/tt_bevformer/tests/test_host_preprocess.py |
| """ |
| from __future__ import annotations |
|
|
| import hashlib |
|
|
| import numpy as np |
| import pytest |
|
|
| from tt_bevformer.host import preprocess as hp |
| from tt_bevformer.host.inputs import load_frame |
| from tt_bevformer.reference import config as C |
| from tt_bevformer.tests import paths |
|
|
| cv2 = pytest.importorskip("cv2") |
|
|
|
|
| def _sha(a: np.ndarray) -> str: |
| return hashlib.sha256(np.ascontiguousarray(a).tobytes()).hexdigest() |
|
|
|
|
| @pytest.fixture(scope="module") |
| def frame30(): |
| paths.need_pandaset() |
| return load_frame(paths.PANDASET_SAMPLES / "pandaset_019_f30.json") |
|
|
|
|
| @pytest.mark.parametrize("frame", paths.SAMPLE_FRAMES) |
| def test_shipped_images_decode_like_the_converter(frame): |
| """OpenCV decode + the node's cv::resize to 1600x900 give exactly the BGR images the public-data converter |
| hashed (so the stored CPU outputs are bit-for-bit inputs of this host path).""" |
| paths.need_pandaset() |
| fr = load_frame(paths.PANDASET_SAMPLES / f"pandaset_019_f{frame}.json") |
| want = fr.manifest["image_bgr_sha256_1600x900"] |
| for cam, im in zip(C.CAMERA_ORDER, fr.images_bgr): |
| assert im.shape == (1080, 1920, 3) and im.dtype == np.uint8 |
| out = hp.node_transport(im) |
| assert out.shape == (900, 1600, 3) |
| assert _sha(out) == want[cam], cam |
|
|
|
|
| def test_pil_decode_equals_opencv(frame30): |
| """The port decodes with PIL (``host.preprocess.decode_bgr``, ``ttaw.io``); the converter and the node use OpenCV |
| (``cv2.imdecode`` / cv_bridge, BGR): the same pixels on the shipped JPEGs.""" |
| for cam, im in zip(C.CAMERA_ORDER, frame30.images_bgr): |
| raw = (paths.PANDASET_SAMPLES / frame30.manifest["images"][cam]).read_bytes() |
| assert np.array_equal(cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR), im), cam |
|
|
|
|
| @pytest.mark.parametrize("hw", [(1080, 1920), (720, 1280), (432, 768), (1200, 1920)]) |
| def test_node_transport(hw): |
| """The node's uint8 cv::resize to 1600x900 (the vendored ttaw.image_linear emulation) equals OpenCV.""" |
| rng = np.random.default_rng(hw[0]) |
| im = rng.integers(0, 256, (900, 1600, 3), dtype=np.uint8) |
| assert hp.node_transport(im) is im |
| src = rng.integers(0, 256, hw + (3,), dtype=np.uint8) |
| assert np.array_equal(hp.node_transport(src), cv2.resize(src, (1600, 900), interpolation=cv2.INTER_LINEAR)) |
| with pytest.raises(ValueError): |
| hp.node_transport(im.astype(np.float32)) |
|
|
|
|
| def test_preprocess_matches_research_reference(frame30): |
| R = paths.import_research() |
| if R is None: |
| pytest.skip("research/bevformer not in this workspace") |
| imgs = [hp.node_transport(im) for im in frame30.images_bgr] |
| got = hp.preprocess_images(frame30.images_bgr, workers=4) |
| assert got.shape == (1, 6, 3, 736, 1280) and got.dtype == np.float32 |
| assert np.array_equal(got, R.preprocess_images_bgr(imgs)) |
| out = np.full((1, 6, 3, 736, 1280), 3.0, np.float32) |
| assert np.array_equal(hp.preprocess_images(frame30.images_bgr, out=out), got) |
|
|
|
|
| def test_sample0_golden_input(): |
| """The research golden ``image`` of the nuScenes key-frame sample0 (internal validation data), bit for bit.""" |
| s0, golden = paths.vendor_sample0(), paths.research_file("golden", "sample0_golden.npz") |
| if s0 is None or golden is None: |
| pytest.skip("sample0 or its research golden are not in this workspace") |
| imgs = [cv2.imread(str(s0 / "imgs" / f"{c}.jpg"), cv2.IMREAD_COLOR) for c in C.CAMERA_ORDER] |
| with np.load(golden) as z: |
| want = z["image"] |
| assert np.array_equal(hp.preprocess_images(imgs, workers=4), want) |
|
|
|
|
| def test_preprocess_refuses_bad_inputs(frame30): |
| with pytest.raises(ValueError): |
| hp.preprocess_images(frame30.images_bgr[:5]) |
|
|
