onnx--onnx/onnx/backend/test/case/node/pow.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


def pow(x: np.ndarray, y: np.ndarray) -> np.ndarray:  # noqa: A001
    return np.power(x, y).astype(x.dtype)


class Pow(Base):
    @staticmethod
    def export() -> None:
        node = onnx.helper.make_node(
            "Pow",
            inputs=["x", "y"],
            outputs=["z"],
        )

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array([4, 5, 6]).astype(np.float32)
        z = pow(x, y)  # expected output [1., 32., 729.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")

        x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
        y = np.random.randn(3, 4, 5).astype(np.float32)
        z = pow(x, y)
        expect(node, inputs=[x, y], outputs=[z], name="test_pow")

    @staticmethod
    def export_pow_broadcast() -> None:
        node = onnx.helper.make_node(
            "Pow",
            inputs=["x", "y"],
            outputs=["z"],
        )

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array(2).astype(np.float32)
        z = pow(x, y)  # expected output [1., 4., 9.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")

        node = onnx.helper.make_node(
            "Pow",
            inputs=["x", "y"],
            outputs=["z"],
        )
        x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
        y = np.array([1, 2, 3]).astype(np.float32)
        # expected output [[1, 4, 27], [4, 25, 216]]
        z = pow(x, y)
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")

    @staticmethod
    def export_types() -> None:
        node = onnx.helper.make_node(
            "Pow",
            inputs=["x", "y"],
            outputs=["z"],
        )

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array([4, 5, 6]).astype(np.int64)
        z = pow(x, y)  # expected output [1., 32., 729.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")

        x = np.array([1, 2, 3]).astype(np.int64)
        y = np.array([4, 5, 6]).astype(np.float32)
        z = pow(x, y)  # expected output [1, 32, 729]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array([4, 5, 6]).astype(np.int32)
        z = pow(x, y)  # expected output [1., 32., 729.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")

        x = np.array([1, 2, 3]).astype(np.int32)
        y = np.array([4, 5, 6]).astype(np.float32)
        z = pow(x, y)  # expected output [1, 32, 729]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array([4, 5, 6]).astype(np.uint64)
        z = pow(x, y)  # expected output [1., 32., 729.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")

        x = np.array([1, 2, 3]).astype(np.float32)
        y = np.array([4, 5, 6]).astype(np.uint32)
        z = pow(x, y)  # expected output [1., 32., 729.]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")

        x = np.array([1, 2, 3]).astype(np.int64)
        y = np.array([4, 5, 6]).astype(np.int64)
        z = pow(x, y)  # expected output [1, 32, 729]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")

        x = np.array([1, 2, 3]).astype(np.int32)
        y = np.array([4, 5, 6]).astype(np.int32)
        z = pow(x, y)  # expected output [1, 32, 729]
        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")