test_advance_indexing.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.
import numpy as np

import megengine
import megengine.autodiff as ad
import megengine.optimizer as optimizer
from megengine import Parameter, tensor
from megengine.module import Module


class Simple(Module):
    def __init__(self):
        super().__init__()
        self.a = Parameter([1.0], dtype=np.float32)

    def forward(self, x, y):
        x = x[y] * self.a
        return x


class Simple2(Module):
    def __init__(self):
        super().__init__()
        self.a = Parameter([1.0], dtype=np.float32)

    def forward(self, x):
        x = x[1, ..., :, 0:4:2, 0:2] * self.a
        return x


def test_advance_indexing():
    net = Simple()

    gm = ad.GradManager().attach(net.parameters())
    optim = optimizer.SGD(net.parameters(), lr=1.0)
    optim.clear_grad()

    dshape = (10, 10)
    raw_data = np.arange(100).reshape(dshape).astype(np.float32)
    raw_mask = (np.random.random_sample(dshape) > 0.5).astype(np.bool_)
    data = tensor(raw_data)
    mask = tensor(raw_mask)
    answer = 1.0 - raw_data[raw_mask].sum()
    with gm:
        loss = net(data, mask).sum()
        gm.backward(loss)
    optim.step()
    np.testing.assert_almost_equal(net.a.numpy(), np.array([answer]).astype(np.float32))


def test_advance_indexing_with_subtensor():
    net = Simple2()

    gm = ad.GradManager().attach(net.parameters())
    optim = optimizer.SGD(net.parameters(), lr=1.0)
    optim.clear_grad()

    dshape = (2, 3, 4, 3, 4, 2)
    raw_data = np.arange(576).reshape(dshape).astype(np.float32)
    data = tensor(raw_data)
    answer = 1.0 - raw_data[1, ..., :, 0:4:2, 0:2].sum()
    with gm:
        loss = net(data).sum()
        gm.backward(loss)
    optim.step()
    np.testing.assert_almost_equal(net.a.numpy(), np.array([answer]).astype(np.float32))