MegEngine/imperative/python/test/integration/test_advance_indexing.py

# -*- coding: utf-8 -*-
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))