mindspore/tests/ut/python/ops/test_array_ops.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.
# ============================================================================
""" test array ops """
import functools
import numpy as np
import pytest
from mindspore._c_expression import signature_dtype as sig_dtype
from mindspore._c_expression import signature_kind as sig_kind
from mindspore._c_expression import signature_rw as sig_rw

import mindspore as ms
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.nn import Cell
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops import prim_attr_register
from mindspore.ops.primitive import PrimitiveWithInfer
import mindspore.context as context
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
    import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \
    import pipeline_for_verify_exception_for_case_by_case_config


def test_expand_dims():
    input_tensor = Tensor(np.array([[2, 2], [2, 2]]))
    expand_dims = P.ExpandDims()
    output = expand_dims(input_tensor, 0)
    assert output.asnumpy().shape == (1, 2, 2)


def test_cast():
    input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
    input_x = Tensor(input_np)
    td = ms.int32
    cast = P.Cast()
    result = cast(input_x, td)
    expect = input_np.astype(np.int32)
    assert np.all(result.asnumpy() == expect)


@non_graph_engine
def test_reshape():
    input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]))
    shp = (3, 2)
    reshape = P.Reshape()
    output = reshape(input_tensor, shp)
    assert output.asnumpy().shape == (3, 2)


def test_transpose():
    input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
    perm = (0, 2, 1)
    expect = np.array([[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]])

    transpose = P.Transpose()
    output = transpose(input_tensor, perm)
    assert np.all(output.asnumpy() == expect)


def test_squeeze():
    input_tensor = Tensor(np.ones(shape=[3, 2, 1]))
    squeeze = P.Squeeze(2)
    output = squeeze(input_tensor)
    assert output.asnumpy().shape == (3, 2)


def test_invert_permutation():
    invert_permutation = P.InvertPermutation()
    x = (3, 4, 0, 2, 1)
    output = invert_permutation(x)
    expect = (2, 4, 3, 0, 1)
    assert np.all(output == expect)


def test_select():
    select = P.Select()
    cond = Tensor(np.array([[True, False, False], [False, True, True]]))
    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
    y = Tensor(np.array([[7, 8, 9], [10, 11, 12]]))
    output = select(cond, x, y)
    expect = np.array([[1, 8, 9], [10, 5, 6]])
    assert np.all(output.asnumpy() == expect)


def test_argmin_invalid_output_type():
    P.Argmin(-1, mstype.int64)
    P.Argmin(-1, mstype.int32)
    with pytest.raises(TypeError):
        P.Argmin(-1, mstype.float32)
    with pytest.raises(TypeError):
        P.Argmin(-1, mstype.float64)
    with pytest.raises(TypeError):
        P.Argmin(-1, mstype.uint8)
    with pytest.raises(TypeError):
        P.Argmin(-1, mstype.bool_)


class CustomOP(PrimitiveWithInfer):
    __mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1,
                               sig_dtype.T1, sig_dtype.T2, sig_dtype.T2,
                               sig_dtype.T2, sig_dtype.T3, sig_dtype.T4)

    @prim_attr_register
    def __init__(self):
        pass

    def __call__(self, p1, p2, p3, p4, p5, p6, p7, p8, p9):
        raise NotImplementedError


class CustomOP2(PrimitiveWithInfer):
    __mindspore_signature__ = (
        ('p1', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
        ('p2', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
        ('p3', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
    )

    @prim_attr_register
    def __init__(self):
        pass

    def __call__(self, p1, p2, p3):
        raise NotImplementedError


class CustNet1(Cell):
    def __init__(self):
        super(CustNet1, self).__init__()
        self.op = CustomOP()
        self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
        self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
        self.int1 = 3
        self.float1 = 5.1

    def construct(self):
        x = self.op(self.t1, self.t1, self.int1,
                    self.float1, self.int1, self.float1,
                    self.t2, self.t1, self.int1)
        return x


class CustNet2(Cell):
    def __init__(self):
        super(CustNet2, self).__init__()
        self.op = CustomOP2()
        self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
        self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
        self.int1 = 3

    def construct(self):
        return self.op(self.t1, self.t2, self.int1)


class CustNet3(Cell):
    def __init__(self):
        super(CustNet3, self).__init__()
        self.op = P.ReduceSum()
        self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
        self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
        self.t2 = 1

    def construct(self):
        return self.op(self.t1, self.t2)


class MathBinaryNet1(Cell):
    def __init__(self):
        super(MathBinaryNet1, self).__init__()
        self.add = P.TensorAdd()
        self.mul = P.Mul()
        self.max = P.Maximum()
        self.number = 3

    def construct(self, x):
        return self.add(x, self.number) + self.mul(x, self.number) + self.max(x, self.number)


class MathBinaryNet2(Cell):
    def __init__(self):
        super(MathBinaryNet2, self).__init__()
        self.less_equal = P.LessEqual()
        self.greater = P.Greater()
        self.logic_or = P.LogicalOr()
        self.logic_and = P.LogicalAnd()
        self.number = 3
        self.flag = True

    def construct(self, x):
        ret_less_equal = self.logic_and(self.less_equal(x, self.number), self.flag)
        ret_greater = self.logic_or(self.greater(x, self.number), self.flag)
        return self.logic_or(ret_less_equal, ret_greater)


class BatchToSpaceNet(Cell):
    def __init__(self):
        super(BatchToSpaceNet, self).__init__()
        block_size = 2
        crops = [[0, 0], [0, 0]]
        self.batch_to_space = P.BatchToSpace(block_size, crops)

    def construct(self, x):
        return self.batch_to_space(x)


class SpaceToBatchNet(Cell):
    def __init__(self):
        super(SpaceToBatchNet, self).__init__()
        block_size = 2
        paddings = [[0, 0], [0, 0]]
        self.space_to_batch = P.SpaceToBatch(block_size, paddings)

    def construct(self, x):
        return self.space_to_batch(x)


class PackNet(Cell):
    def __init__(self):
        super(PackNet, self).__init__()
        self.pack = P.Pack()

    def construct(self, x):
        return self.pack((x, x))


class UnpackNet(Cell):
    def __init__(self):
        super(UnpackNet, self).__init__()
        self.unpack = P.Unpack()

    def construct(self, x):
        return self.unpack(x)
class SpaceToDepthNet(Cell):
    def __init__(self):
        super(SpaceToDepthNet, self).__init__()
        block_size = 2
        self.space_to_depth = P.SpaceToDepth(block_size)

    def construct(self, x):
        return self.space_to_depth(x)


class DepthToSpaceNet(Cell):
    def __init__(self):
        super(DepthToSpaceNet, self).__init__()
        block_size = 2
        self.depth_to_space = P.DepthToSpace(block_size)

    def construct(self, x):
        return self.depth_to_space(x)


class BatchToSpaceNDNet(Cell):
    def __init__(self):
        super(BatchToSpaceNDNet, self).__init__()
        block_shape = [2, 2]
        crops = [[0, 0], [0, 0]]
        self.batch_to_space_nd = P.BatchToSpaceND(block_shape, crops)

    def construct(self, x):
        return self.batch_to_space_nd(x)


class SpaceToBatchNDNet(Cell):
    def __init__(self):
        super(SpaceToBatchNDNet, self).__init__()
        block_shape = [2, 2]
        paddings = [[0, 0], [0, 0]]
        self.space_to_batch_nd = P.SpaceToBatchND(block_shape, paddings)

    def construct(self, x):
        return self.space_to_batch_nd(x)


class RangeNet(Cell):
    def __init__(self):
        super(RangeNet, self).__init__()
        self.range_ops = inner.Range(1.0, 8.0, 2.0)

    def construct(self, x):
        return self.range_ops(x)


test_case_array_ops = [
    ('CustNet1', {
        'block': CustNet1(),
        'desc_inputs': []}),
    ('CustNet2', {
        'block': CustNet2(),
        'desc_inputs': []}),
    ('CustNet3', {
        'block': CustNet3(),
        'desc_inputs': []}),
    ('MathBinaryNet1', {
        'block': MathBinaryNet1(),
        'desc_inputs': [Tensor(np.ones([2, 2]), dtype=ms.int32)]}),
    ('MathBinaryNet2', {
        'block': MathBinaryNet2(),
        'desc_inputs': [Tensor(np.ones([2, 2]), dtype=ms.int32)]}),
    ('BatchToSpaceNet', {
        'block': BatchToSpaceNet(),
        'desc_inputs': [Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]).astype(np.float16))]}),
    ('SpaceToBatchNet', {
        'block': SpaceToBatchNet(),
        'desc_inputs': [Tensor(np.array([[[[1, 2], [3, 4]]]]).astype(np.float16))]}),
    ('PackNet', {
        'block': PackNet(),
        'desc_inputs': [Tensor(np.array([[[1, 2], [3, 4]]]).astype(np.float16))]}),
    ('UnpackNet', {
        'block': UnpackNet(),
        'desc_inputs': [Tensor(np.array([[1, 2], [3, 4]]).astype(np.float16))]}),
    ('SpaceToDepthNet', {
        'block': SpaceToDepthNet(),
        'desc_inputs': [Tensor(np.random.rand(1, 3, 2, 2).astype(np.float16))]}),
    ('DepthToSpaceNet', {
        'block': DepthToSpaceNet(),
        'desc_inputs': [Tensor(np.random.rand(1, 12, 1, 1).astype(np.float16))]}),
    ('SpaceToBatchNDNet', {
        'block': SpaceToBatchNDNet(),
        'desc_inputs': [Tensor(np.random.rand(1, 1, 2, 2).astype(np.float16))]}),
    ('BatchToSpaceNDNet', {
        'block': BatchToSpaceNDNet(),
        'desc_inputs': [Tensor(np.random.rand(4, 1, 1, 1).astype(np.float16))]}),
    ('RangeNet', {
        'block': RangeNet(),
        'desc_inputs': [Tensor(np.array([1, 2, 3, 2]), ms.int32)]}),
]

test_case_lists = [test_case_array_ops]
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm



@non_graph_engine
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_exec():
    context.set_context(mode=context.GRAPH_MODE)
    return test_exec_case


raise_set = [
    ('Squeeze_1_Error', {
        'block': (lambda x: P.Squeeze(axis=1.2), {'exception': TypeError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
    ('Squeeze_2_Error', {
        'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': TypeError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
    ('ReduceSum_Error', {
        'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': TypeError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
]


@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
def test_check_exception():
    return raise_set