File size: 2,119 Bytes
10979b5 | 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 | from __future__ import annotations
import numpy as np
import torch
from gmnet.evaluation.corruptions import apply_corruption
from gmnet.evaluation.metrics import (
classification_metrics,
expected_calibration_error,
paired_hierarchical_bootstrap,
)
def test_noise_is_deterministic_per_sample() -> None:
image = torch.full((3, 32, 32), 0.5)
first = apply_corruption(image, "gaussian_noise", 17)
second = apply_corruption(image, "gaussian_noise", 17)
different = apply_corruption(image, "gaussian_noise", 18)
assert torch.equal(first, second)
assert not torch.equal(first, different)
def test_shift_has_fixed_zero_filled_geometry() -> None:
image = torch.zeros(3, 32, 32)
image[:, 1, 1] = 1.0
shifted = apply_corruption(image, "shift_2px", 0)
assert torch.equal(shifted[:, 3, 3], torch.ones(3))
assert shifted.sum().item() == 3.0
def test_classification_metrics_and_ece() -> None:
metrics = classification_metrics(
np.array([1, 0, 1, 0]),
np.array([1, 1, 1, 0]),
np.array([0.1, 1.0, 0.2, 2.0]),
np.array([0.9, 0.8, 0.7, 0.2]),
ece_bins=2,
)
assert metrics["top1"] == 50.0
assert metrics["top5"] == 75.0
assert metrics["nll"] == 0.825
assert expected_calibration_error(np.array([1.0]), np.array([1])) == 0.0
def test_paired_bootstrap_preserves_exact_constant_difference() -> None:
reference = np.zeros((3, 6, 20), dtype=np.float64)
candidate = np.ones_like(reference)
rows = paired_hierarchical_bootstrap(
candidate,
reference,
statistics={
"clean_top1": "clean_top1",
"mean": "mean_corruption_top1",
"raw": "clean_raw_mean",
},
samples=100,
seed=3,
)
scaled = [row for row in rows if row["metric"] != "raw"]
raw = next(row for row in rows if row["metric"] == "raw")
assert all(row["difference"] == 100.0 for row in scaled)
assert all(row["ci_low"] == 100.0 and row["ci_high"] == 100.0 for row in scaled)
assert raw["difference"] == raw["ci_low"] == raw["ci_high"] == 1.0
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