mindspore/tests/st/ops/cpu/test_slice_op.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import numpy as np
import pytest

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')


class Slice(nn.Cell):
    def __init__(self):
        super(Slice, self).__init__()
        self.slice = P.Slice()

    def construct(self, x):
        return self.slice(x, (0, 1, 0), (2, 1, 3))


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice():
    x = Tensor(
        np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]), mstype.float32)
    expect = [[[2., -2., 2.]],
              [[4., -4., 4.]]]

    slice_op = Slice()
    output = slice_op(x)
    print("output:\n", output)
    assert (output.asnumpy() == expect).all()


class Slice2(nn.Cell):
    def __init__(self):
        super(Slice2, self).__init__()
        self.slice = P.Slice()

    def construct(self, x):
        return self.slice(x, (1, 0, 0), (1, 2, 3))


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice2():
    x = Tensor(np.arange(3 * 2 * 3).reshape(3, 2, 3), mstype.float32)
    expect = [[[6., 7., 8.],
               [9., 10., 11.]]]

    slice_op = Slice2()
    output = slice_op(x)
    print("output:\n", output)
    assert (output.asnumpy() == expect).all()


class Slice3(nn.Cell):
    def __init__(self):
        super(Slice3, self).__init__()
        self.relu = nn.ReLU()

    def construct(self, x):
        return (x[..., -1], x[..., 2:1:-1], x[1:3:1, 0, ...], x[-1, 0, ...])


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice3():
    inputx = np.random.rand(4, 4, 4, 4).astype(np.float32)
    x = Tensor(inputx)
    slice_op = Slice3()
    output = slice_op(x)
    assert (output[0].asnumpy() == inputx[..., -1]).all()
    assert (output[1].asnumpy() == inputx[..., 2:1:-1]).all()
    assert (output[2].asnumpy() == inputx[1:3:1, 0, ...]).all()
    assert (output[3].asnumpy() == inputx[-1, 0, ...]).all()


class Slice4(nn.Cell):
    def __init__(self):
        super(Slice4, self).__init__()
        self.relu = nn.ReLU()

    def construct(self, x):
        return x[:10:1, :, 2:3:1]


@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_slice4():
    inputx = np.random.rand(4, 4, 4).astype(np.float32)
    x = Tensor(inputx)
    slice_op = Slice4()
    output = slice_op(x)
    assert (output.asnumpy() == inputx[:10:1, :, 2:3:1]).all()


if __name__ == '__main__':
    test_slice()
    test_slice2()
    test_slice3()
    test_slice4()