onnx-source-snapshot/onnx/backend/test/case/node/reducemean.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 ReduceMean(Base):
    @staticmethod
    def export_do_not_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([1], dtype=np.int64)
        keepdims = 0

        node = onnx.helper.make_node(
            "ReduceMean",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.array(
            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
            dtype=np.float32,
        )
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
        # print(reduced)
        # [[12.5, 1.5]
        # [35., 1.5]
        # [57.5, 1.5]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_do_not_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_do_not_keepdims_random",
        )

    @staticmethod
    def export_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([1], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceMean",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.array(
            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
            dtype=np.float32,
        )
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
        # print(reduced)
        # [[[12.5, 1.5]]
        # [[35., 1.5]]
        # [[57.5, 1.5]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_keepdims_random",
        )

    @staticmethod
    def export_default_axes_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceMean",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.array(
            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
            dtype=np.float32,
        )
        reduced = np.mean(data, axis=None, keepdims=keepdims == 1)
        # print(reduced)
        # [[[18.25]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_default_axes_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.mean(data, axis=None, keepdims=keepdims == 1)

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_default_axes_keepdims_random",
        )

    @staticmethod
    def export_negative_axes_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([-2], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceMean",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.array(
            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
            dtype=np.float32,
        )
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
        # print(reduced)
        # [[[12.5, 1.5]]
        # [[35., 1.5]]
        # [[57.5, 1.5]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_negative_axes_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_mean_negative_axes_keepdims_random",
        )