MegEngine/imperative/python/test/unit/functional/test_math.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from functools import partial

import numpy as np
from utils import opr_test

import megengine.functional as F
from megengine import tensor
from megengine.test import assertTensorClose


def common_test_reduce(opr, ref_opr):
    data1_shape = (5, 6, 7)
    data2_shape = (2, 9, 12)
    data1 = np.random.random(data1_shape).astype(np.float32)
    data2 = np.random.random(data2_shape).astype(np.float32)
    cases = [{"input": data1}, {"input": data2}]

    if opr not in (F.argmin, F.argmax):
        # test default axis
        opr_test(cases, opr, ref_fn=ref_opr)
        # test all axises in range of input shape
        for axis in range(-3, 3):
            # test keepdims False
            opr_test(cases, opr, ref_fn=lambda x: ref_opr(x, axis=axis), axis=axis)
            # test keepdims True
            opr_test(
                cases,
                opr,
                ref_fn=lambda x: ref_opr(x, axis=axis, keepdims=True),
                axis=axis,
                keepdims=True,
            )
    else:
        # test defaut axis
        opr_test(cases, opr, ref_fn=lambda x: ref_opr(x).astype(np.int32))
        # test all axises in range of input shape
        for axis in range(0, 3):
            opr_test(
                cases,
                opr,
                ref_fn=lambda x: ref_opr(x, axis=axis).astype(np.int32),
                axis=axis,
            )


def test_sum():
    common_test_reduce(opr=F.sum, ref_opr=np.sum)


def test_prod():
    common_test_reduce(opr=F.prod, ref_opr=np.prod)


def test_mean():
    common_test_reduce(opr=F.mean, ref_opr=np.mean)


def test_var():
    common_test_reduce(opr=F.var, ref_opr=np.var)


def test_std():
    common_test_reduce(opr=F.std, ref_opr=np.std)


def test_min():
    common_test_reduce(opr=F.min, ref_opr=np.min)


def test_max():
    common_test_reduce(opr=F.max, ref_opr=np.max)


def test_argmin():
    common_test_reduce(opr=F.argmin, ref_opr=np.argmin)


def test_argmax():
    common_test_reduce(opr=F.argmax, ref_opr=np.argmax)


def test_sqrt():
    d1_shape = (15,)
    d2_shape = (25,)
    d1 = np.random.random(d1_shape).astype(np.float32)
    d2 = np.random.random(d2_shape).astype(np.float32)

    cases = [{"input": d1}, {"input": d2}]
    opr_test(cases, F.sqrt, ref_fn=np.sqrt)


def test_sort():
    data1_shape = (10, 3)
    data2_shape = (12, 2)
    data1 = np.random.random(data1_shape).astype(np.float32)
    data2 = np.random.random(data2_shape).astype(np.float32)
    output0 = [np.sort(data1), np.argsort(data1).astype(np.int32)]
    output1 = [np.sort(data2), np.argsort(data2).astype(np.int32)]

    cases = [
        {"input": data1, "output": output0},
        {"input": data2, "output": output1},
    ]
    opr_test(cases, F.sort)


def test_normalize():

    cases = [
        {"input": np.random.random((2, 3, 12, 12)).astype(np.float32)} for i in range(2)
    ]

    def np_normalize(x, p=2, axis=None, eps=1e-12):
        if axis is None:
            norm = np.sum(x ** p) ** (1.0 / p)
        else:
            norm = np.sum(x ** p, axis=axis, keepdims=True) ** (1.0 / p)
        return x / np.clip(norm, a_min=eps, a_max=np.inf)

    # Test L-2 norm along all dimensions
    opr_test(cases, F.normalize, ref_fn=np_normalize)

    # Test L-1 norm along all dimensions
    opr_test(cases, partial(F.normalize, p=1), ref_fn=partial(np_normalize, p=1))

    # Test L-2 norm along the second dimension
    opr_test(cases, partial(F.normalize, axis=1), ref_fn=partial(np_normalize, axis=1))

    # Test some norm == 0
    cases[0]["input"][0, 0, 0, :] = 0
    cases[1]["input"][0, 0, 0, :] = 0
    opr_test(cases, partial(F.normalize, axis=3), ref_fn=partial(np_normalize, axis=3))