mindspore2022/tests/ut/python/ops/test_tensor_slice.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_tensor_slice """
import numpy as np
import pytest

from mindspore import Tensor
from mindspore import context
from mindspore import dtype as mstype
from mindspore.nn import Cell

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


class NetWorkSlicePositive(Cell):
    def __init__(self):
        super(NetWorkSlicePositive, self).__init__()
        self.tensor_ret0 = Tensor(np.ones([1, 2, 2], np.int32))
        self.tensor_ret1 = Tensor(np.ones([4, 7, 4], np.int32))
        self.tensor_ret2 = Tensor(np.ones([6, 8, 10], np.int32))
        self.tensor_ret3 = Tensor(np.ones([3, 8, 10], np.int32))

    def construct(self, tensor):
        ret0 = tensor[3:4:3, 1:5:2, 3:6:2] + self.tensor_ret0
        ret1 = tensor[-6:4:1, 7:-8:-1, ::3] + self.tensor_ret1
        ret2 = tensor[::, ::, ::] + self.tensor_ret2
        ret3 = tensor[::2] + self.tensor_ret3
        return ret0, ret1, ret2, ret3


class NetWorkSliceEllipsis(Cell):
    def __init__(self):
        super(NetWorkSliceEllipsis, self).__init__()
        self.tensor_ret0 = Tensor(np.ones([2, 7, 8], np.int32))
        self.tensor_ret1 = Tensor(np.ones([6, 7, 8, 9], np.int32))
        self.tensor_ret2 = Tensor(np.ones([1, 6, 7, 8, 9], np.int32))

    def construct(self, tensor):
        ret0 = tensor[0:4:2, ..., 1] + self.tensor_ret0
        ret1 = tensor[...] + self.tensor_ret1
        ret2 = tensor[None] + self.tensor_ret2
        ret3 = tensor[True] + self.tensor_ret2
        return ret0, ret1, ret2, ret3


class NetWorkReduceDimension(Cell):
    def __init__(self):
        super(NetWorkReduceDimension, self).__init__()
        self.tensor_ret0 = Tensor(np.ones([2, 4, 1], np.int32))
        self.tensor_ret1 = Tensor(np.ones([3, 4], np.int32))
        self.tensor_ret2 = Tensor(np.ones([6, 8], np.int32))
        self.tensor_ret3 = Tensor(np.array(8, np.int32))
        self.tensor_ret4 = Tensor(np.ones([8, 10], np.int32))

    def construct(self, tensor):
        ret0 = tensor[0:6:3, 1:5:1, 3:5:2] + self.tensor_ret0
        ret1 = tensor[::2, 1, ::3] + self.tensor_ret1
        ret2 = tensor[::, ::, 0] + self.tensor_ret2
        ret3 = tensor[3, 2, 5] + self.tensor_ret3
        ret4 = tensor[1] + self.tensor_ret4
        return ret0, ret1, ret2, ret3, ret4


class NetWorkStepNegative(Cell):
    def __init__(self):
        super(NetWorkStepNegative, self).__init__()
        self.tensor_ret = Tensor(np.ones([6, 5, 10], np.int32))

    def construct(self, tensor):
        ret = tensor[::1, -5::, ::-1] + self.tensor_ret
        return ret


class NetWorkReduceToScalar(Cell):
    def __init__(self):
        super(NetWorkReduceToScalar, self).__init__()
        self.tensor_ret = Tensor(np.array(9, np.int32))

    def construct(self, tensor):
        ret = tensor[2, 3, 4] + self.tensor_ret
        return ret


class TensorAssignWithSliceError1(Cell):
    def __init__(self):
        super(TensorAssignWithSliceError1, self).__init__()

    def construct(self, a, b):
        a[1:3:-1,::] = b
        return a

class TensorAssignWithSliceError2(Cell):
    def __init__(self):
        super(TensorAssignWithSliceError2, self).__init__()

    def construct(self, a, b):
        a[1:3:-1] = b
        return a
class TensorAssignWithSlice2(Cell):
    def __init__(self):
        super(TensorAssignWithSlice2, self).__init__()

    def construct(self, a, b):
        a[1:5] = b
        a[3:4] = 5
        a[-1:1:-1] = b
        a[-1:3:-1] = 5
        a[::] = b
        a[::] = 9
        return a
class TensorAssignWithSlice(Cell):
    def __init__(self):
        super(TensorAssignWithSlice, self).__init__()
        self.c = 2

    def construct(self, a, b):
        a[1:3,::] = b
        a[2:3:,3:] = b
        a[::] = b
        a[::] = self.c
        a[::,::] = b
        a[::,::] = self.c
        a[2:3:,0:, 4:1:-1] = b
        a[2:3:,0:, 4:1:-1] = self.c
        z = a
        return z

def test_tensor_assign_with_slice():
    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
    net = TensorAssignWithSlice()
    net2= TensorAssignWithSlice2()
    net_e1 = TensorAssignWithSliceError1()
    net_e2 = TensorAssignWithSliceError2()
    a = np.arange(60).reshape(3,4,5)
    b = Tensor([1])
    Ta = Tensor(a)
    Tb= Tensor([1,3])
    Tc= Tensor([])
    t = Tensor([1, 2, 3, 4, 5, 6, 7, 8])
    net(Ta, b)
    net2(t, b)
    # Error for A[Slice] = Number
    # 1. A[Slice] = Number,  Slice error
    with pytest.raises(ValueError):
        net_e2(t, 2)

    # Error for A[Slice] = U, U is a Tensor
    # 1. A[Slice] = U,  u.size is error
    with pytest.raises(ValueError):
        net2(t, Tb)
    # 2. A[Slice] = U, U is empty
    with pytest.raises(ValueError):
        net2(t, Tc)
    # 3. A[Slice] = U, U.size error
    with pytest.raises(ValueError):
        net2(t, Tb)

    # Error for A[Tuple(Slice...)] = Tensor
    # 1. A[Tuple(Slice...)] = U, U is empty
    with pytest.raises(ValueError):
        net(Ta, Tc)
    # 2. A[Tuple(Slice...)] = U, U.size error
    with pytest.raises(ValueError):
        net(Ta, Tb)
    # 3. A[Tuple(Slice...)] = U,  Slice error
    with pytest.raises(ValueError):
        net_e1(Ta, b)

    # Error for A[Tuple(Slice...)] = Number
    # 1. A[Tuple(Slice...)] = Number,  Slice error
    with pytest.raises(ValueError):
        net_e1(Ta, 2)


class TensorAssignWithBoolTensorIndex(Cell):
    def __init__(self):
        super(TensorAssignWithBoolTensorIndex, self).__init__()
        self.t = Tensor(np.arange(60).reshape([3,4,5]), dtype = mstype.float64)

    def construct(self, a, b, c, u_tensor, _scalar):
        a[c] = u_scalar
        a[b] = u_tensor
        z = a + self.t
        return z


class TensorAssignWithBoolTensorIndexError(Cell):
    def __init__(self):
        super(TensorAssignWithBoolTensorIndexError, self).__init__()

    def construct(self, a, b, c, u_tensor):
        a[b][c] = u_tensor
        return a


class TensorAssignWithBoolTensorIndex2(Cell):
    def __init__(self):
        super(TensorAssignWithBoolTensorIndex2, self).__init__()
        self.t = Tensor(np.arange(6).reshape([2, 3]), dtype=mstype.float64)
        self.t = Tensor(np.arange(60).reshape([3,4,5]), dtype = mstype.float64)

    def construct(self, a, u_tensor, _scalar):
        a[a > 8] = u_tensor
        a[a >= 6] = u_scalar
        a[a < 3] = u_scalar
        a[a <= 5] = u_tensor
        a[a == 5] = u_scalar
        z = a + self.t
        return z


class TensorAssignWithBoolTensorIndex2Error(Cell):
    def __init__(self):
        super(TensorAssignWithBoolTensorIndex2Error, self).__init__()

    def construct(self, a, u_tensor):
        a[a > 8][a > 5] = u_tensor
        return a


a = np.random.uniform(1,10,[3,4,5])
b = a > 5
c = a < 3
Ta = Tensor(a)
Tb = Tensor(b)
Tc = Tensor(c)
Td = Tensor([True, True])
u_tensor = Tensor([1])
u_tensor_error = Tensor([1, 2])
t_1d = Tensor([1, 2, 3, 4, 5, 6, 7, 8])
u_scalar = 5

def test_tensor_assign_bool_index():
    net1 = TensorAssignWithBoolTensorIndex()
    net2 = TensorAssignWithBoolTensorIndex2()
    net1(Ta, Tb, Tc, u_tensor, u_scalar)
    net1(Ta, Tb, Tc, u_tensor, u_scalar)
    with pytest.raises(ValueError):
        net1(Ta, Td, Tc, u_tensor, u_scalar)
    with pytest.raises(ValueError):
        net1(Ta, u_tensor, Tc, u_tensor, u_scalar)
    with pytest.raises(ValueError):
        net1(Ta, Tb, Td, u_tensor, u_scalar)
    with pytest.raises(ValueError):
        net1(Ta, Tb, Ta, u_tensor, u_scalar)
    with pytest.raises(ValueError):
        net1(Ta, Tb, Tc, u_tensor_error, u_scalar)
    # net1(Ta, u_tensor, Tc, u_tensor_error, u_scalar)
    with pytest.raises(ValueError):
        net2(Ta, u_tensor_error, u_scalar)
    net3 = TensorAssignWithBoolTensorIndexError()
    with pytest.raises(AttributeError):
        net3(Ta, Tb, Tc, u_tensor)
    with pytest.raises(AttributeError):
        net3(Ta, Tb, Tc, u_scalar)
    net4 = TensorAssignWithBoolTensorIndex2Error()
    with pytest.raises(AttributeError):
        net4(Ta, u_tensor)
    with pytest.raises(AttributeError):
        net4(Ta, u_scalar)

test_cases = [
    ('TensorAssignWithSlice', {
        'block': TensorAssignWithSlice(),
        'desc_inputs': [Ta,  u_tensor],
    }),
    ('TensorAssignWithSlice2', {
        'block': TensorAssignWithSlice2(),
        'desc_inputs': [t_1d,  u_tensor],
    }),
    ('TensorAssignWithBoolTensorIndex', {
        'block': TensorAssignWithBoolTensorIndex(),
        'desc_inputs': [Ta, Tb, Tc, u_tensor, u_scalar],
    }),
    ('TensorAssignWithBoolTensorIndex2', {
        'block': TensorAssignWithBoolTensorIndex2(),
        'desc_inputs': [Ta, u_tensor, u_scalar],
    }),
    ('SlicePositive', {
        'block': NetWorkSlicePositive(),
        'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
    }),
    ('SliceReduceDimension', {
        'block': NetWorkReduceDimension(),
        'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
    }),
    ('SliceNegative', {
        'block': NetWorkStepNegative(),
        'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
    }),
    ('SliceReduceToScalar', {
        'block': NetWorkReduceToScalar(),
        'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
    }),
    ('TensorSliceEllipsis', {
        'block': NetWorkSliceEllipsis(),
        'desc_inputs': [Tensor(np.ones([6, 7, 8, 9], np.int32))],
    }),
]


@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_compile():
    context.set_context(mode=context.GRAPH_MODE)
    return test_cases


def test_tensor_slice_reduce_out_of_bounds_neg():
    class NetWork(Cell):
        def __init__(self):
            super(NetWork, self).__init__()
            self.tensor_ret = Tensor(np.array(9, np.int32))

        def construct(self, tensor):
            ret = tensor[-7, 3, 4]
            return ret

    input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
    net = NetWork()
    with pytest.raises(ValueError) as ex:
        net(input_tensor)
    assert "The `begin[0]` should be an int and must greater or equal to -6, but got -7" in str(ex.value)


def test_tensor_slice_reduce_out_of_bounds_positive():
    class NetWork(Cell):
        def __init__(self):
            super(NetWork, self).__init__()
            self.tensor_ret = Tensor(np.array(9, np.int32))

        def construct(self, tensor):
            ret = tensor[6, 3, 4]
            return ret

    input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
    net = NetWork()
    with pytest.raises(ValueError) as ex:
        net(input_tensor)
    assert "The `begin[0]` should be an int and must less than 6, but got 6" in str(ex.value)