| |
| |
| """Shared fixtures: deterministic structured images and the weights guard.""" |
|
|
| from __future__ import annotations |
|
|
| from importlib.util import find_spec |
| from typing import TYPE_CHECKING |
|
|
| import numpy as np |
| import pytest |
|
|
| from sen_bridge import weights |
|
|
| if TYPE_CHECKING: |
| from numpy.typing import NDArray |
|
|
| _SIZE = 128 |
| _BLUR = 5 |
|
|
| |
| collect_ignore: list[str] = [] |
| if find_spec("torch") is None: |
| collect_ignore += ["test_metrics.py", "test_parity.py"] |
| if find_spec("jax") is None: |
| collect_ignore.append("test_parity.py") |
|
|
|
|
| def structured_images(n: int, seed: int = 0) -> NDArray[np.float32]: |
| """Make deterministic images in [-1, 1]: smoothed noise plus a gradient. |
| |
| Args: |
| n: Number of images. |
| seed: Seed of the noise. |
| |
| Returns: |
| Float32 array of shape ``[n, 3, 128, 128]``. |
| """ |
| rng = np.random.default_rng(seed) |
| a = rng.random((n, 3, _SIZE, _SIZE)).astype(np.float32) |
| kernel = np.ones(_BLUR, np.float32) / _BLUR |
| for axis in (2, 3): |
| a = np.apply_along_axis( |
| lambda v: np.convolve(v, kernel, mode="same"), axis, a |
| ) |
| g = np.linspace(-1, 1, _SIZE, dtype=np.float32) |
| ramp = 0.5 * g[None, None, :, None] * g[None, None, None, :] |
| return np.clip(a * 3 - 1 + ramp, -1, 1).astype(np.float32) |
|
|
|
|
| @pytest.fixture(scope="session") |
| def pair() -> tuple[NDArray[np.float32], NDArray[np.float32]]: |
| """Return a batch of images and a noisy copy of it.""" |
| x = structured_images(12) |
| noise = np.random.default_rng(1).standard_normal(x.shape).astype(np.float32) |
| return x, np.clip(x + 0.15 * noise, -1, 1).astype(np.float32) |
|
|
|
|
| @pytest.fixture(scope="session") |
| def weights_ready() -> None: |
| """Make sure the metric weights are available, or skip the test.""" |
| try: |
| weights.path("fid_inception") |
| weights.path("lpips_vgg") |
| |
| except Exception as exc: |
| pytest.skip(f"weights unavailable: {exc}") |
|
|
