onnx-source-snapshot/onnx/backend/test/case/node/meanvariancenormalization.py

# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


class MeanVarianceNormalization(Base):
    @staticmethod
    def export() -> None:
        node = onnx.helper.make_node(
            "MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
        )

        input_data = np.array(
            [
                [
                    [[0.8439683], [0.5665144], [0.05836735]],
                    [[0.02916367], [0.12964272], [0.5060197]],
                    [[0.79538304], [0.9411346], [0.9546573]],
                ],
                [
                    [[0.17730942], [0.46192095], [0.26480448]],
                    [[0.6746842], [0.01665257], [0.62473077]],
                    [[0.9240844], [0.9722341], [0.11965699]],
                ],
                [
                    [[0.41356155], [0.9129373], [0.59330076]],
                    [[0.81929934], [0.7862604], [0.11799799]],
                    [[0.69248444], [0.54119414], [0.07513223]],
                ],
            ],
            dtype=np.float32,
        )

        # Calculate expected output data
        data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
        data_mean_squared = np.power(data_mean, 2)
        data_squared = np.power(input_data, 2)
        data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
        std = np.sqrt(data_squared_mean - data_mean_squared)
        expected_output = (input_data - data_mean) / (std + 1e-9)

        expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn")