The two tiny ONNX models are deterministic classifiers used by the image upload API tests.
They accept the same [1, 224, 224, 3] float input as the production nsfw model and return
[Drawings, Hentai, Neutral, Porn, Sexy] scores. One always returns Neutral = 1 and the
other always returns Porn = 1. They exercise the real nsfw inference path without requiring
image fixtures.
Image fixtures for the bundled model
Add image pairs to these project directories:
tests/fixtures/nsfw/— images expected to be rejected by moderation.tests/fixtures/non_nsfw/— images expected to be accepted by moderation.
Install the splitter's dependency with python3 -m pip install Pillow, then run from the
project root:
python3 scripts/split_nsfw_test_image.py /path/to/input.jpg --output-dir tests/fixtures/nsfw
python3 scripts/split_nsfw_test_image.py /path/to/safe.jpg --output-dir tests/fixtures/non_nsfw
To run the tests:
SQLX_OFFLINE=true cargo test --locked directory_images -- --ignored
Deterministic model fixtures
To regenerate them with Python's onnx package:
from pathlib import Path
from onnx import TensorProto, checker, helper
for name, scores in [("safe", [0., 0., 1., 0., 0.]), ("unsafe", [0., 0., 0., 1., 0.])]:
tensor = helper.make_tensor("probabilities", TensorProto.FLOAT, [1, 5], scores)
graph = helper.make_graph(
[helper.make_node("Identity", ["probabilities"], ["output"])],
"constant classifier for upload endpoint tests",
[helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 224, 224, 3])],
[helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 5])],
[tensor],
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)], ir_version=8)
checker.check_model(model)
Path(f"tests/fixtures/image-upload-{name}.onnx").write_bytes(model.SerializeToString())