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237 lines
7.6 KiB
237 lines
7.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_tensor_slice """
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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore import context
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from mindspore import dtype as mstype
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from mindspore.nn import Cell
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from ....mindspore_test_framework.mindspore_test import mindspore_test
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from ....mindspore_test_framework.pipeline.forward.compile_forward \
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import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
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class NetWorkSlicePositive(Cell):
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def __init__(self):
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super(NetWorkSlicePositive, self).__init__()
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self.tensor_ret0 = Tensor(np.ones([1, 2, 2], np.int32))
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self.tensor_ret1 = Tensor(np.ones([4, 7, 4], np.int32))
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self.tensor_ret2 = Tensor(np.ones([6, 8, 10], np.int32))
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self.tensor_ret3 = Tensor(np.ones([3, 8, 10], np.int32))
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def construct(self, tensor):
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ret0 = tensor[3:4:3, 1:5:2, 3:6:2] + self.tensor_ret0
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ret1 = tensor[-6:4:1, 7:-8:-1, ::3] + self.tensor_ret1
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ret2 = tensor[::, ::, ::] + self.tensor_ret2
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ret3 = tensor[::2] + self.tensor_ret3
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return ret0, ret1, ret2, ret3
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class NetWorkReduceDimension(Cell):
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def __init__(self):
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super(NetWorkReduceDimension, self).__init__()
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self.tensor_ret0 = Tensor(np.ones([2, 4, 1], np.int32))
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self.tensor_ret1 = Tensor(np.ones([3, 4], np.int32))
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self.tensor_ret2 = Tensor(np.ones([6, 8], np.int32))
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self.tensor_ret3 = Tensor(np.array(8, np.int32))
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self.tensor_ret4 = Tensor(np.ones([8, 10], np.int32))
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def construct(self, tensor):
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ret0 = tensor[0:6:3, 1:5:1, 3:5:2] + self.tensor_ret0
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ret1 = tensor[::2, 1, ::3] + self.tensor_ret1
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ret2 = tensor[::, ::, 0] + self.tensor_ret2
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ret3 = tensor[3, 2, 5] + self.tensor_ret3
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ret4 = tensor[1] + self.tensor_ret4
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return ret0, ret1, ret2, ret3, ret4
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class NetWorkStepNegative(Cell):
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def __init__(self):
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super(NetWorkStepNegative, self).__init__()
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self.tensor_ret = Tensor(np.ones([6, 5, 10], np.int32))
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def construct(self, tensor):
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ret = tensor[::1, -5::, ::-1] + self.tensor_ret
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return ret
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class NetWorkReduceToScalar(Cell):
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def __init__(self):
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super(NetWorkReduceToScalar, self).__init__()
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self.tensor_ret = Tensor(np.array(9, np.int32))
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def construct(self, tensor):
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ret = tensor[2, 3, 4] + self.tensor_ret
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return ret
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class TensorAssignWithBoolTensorIndex(Cell):
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def __init__(self):
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super(TensorAssignWithBoolTensorIndex, self).__init__()
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self.t = Tensor(np.arange(6).reshape([2,3]), dtype = mstype.float64)
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def construct(self, a, b, c, u_tensor, _scalar):
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a[c] = u_scalar
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a[b] = u_tensor
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z = a + self.t
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return z
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class TensorAssignWithBoolTensorIndexError(Cell):
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def __init__(self):
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super(TensorAssignWithBoolTensorIndexError, self).__init__()
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def construct(self, a, b, c, u_tensor):
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a[b][c] = u_tensor
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return a
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class TensorAssignWithBoolTensorIndex2(Cell):
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def __init__(self):
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super(TensorAssignWithBoolTensorIndex2, self).__init__()
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self.t = Tensor(np.arange(6).reshape([2,3]), dtype = mstype.float64)
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def construct(self, a, u_tensor, _scalar):
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a[a>8] = u_tensor
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a[a>=6] = u_scalar
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a[a<3] = u_scalar
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a[a<=5] = u_tensor
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a[a==5] = u_scalar
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z = a + self.t
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return z
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class TensorAssignWithBoolTensorIndex2Error(Cell):
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def __init__(self):
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super(TensorAssignWithBoolTensorIndex2Error, self).__init__()
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def construct(self, a, u_tensor):
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a[a>8][a>5] = u_tensor
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return a
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a = np.random.uniform(1,10,[2,3])
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b = a > 5
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c = a < 3
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Ta = Tensor(a)
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Tb = Tensor(b)
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Tc = Tensor(c)
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Td = Tensor([True, True])
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u_tensor = Tensor([1])
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u_tensor_error = Tensor([1, 2])
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u_scalar = 5
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def test_tensor_assign_bool_index():
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net1 = TensorAssignWithBoolTensorIndex()
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net2 = TensorAssignWithBoolTensorIndex2()
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net1(Ta, Tb, Tc, u_tensor, u_scalar)
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with pytest.raises(ValueError):
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net1(Ta, Td, Tc, u_tensor, u_scalar)
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with pytest.raises(ValueError):
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net1(Ta, u_tensor, Tc, u_tensor, u_scalar)
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with pytest.raises(ValueError):
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net1(Ta, Tb, Td, u_tensor, u_scalar)
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with pytest.raises(ValueError):
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net1(Ta, Tb, Ta, u_tensor, u_scalar)
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with pytest.raises(ValueError):
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net1(Ta, Tb, Tc, u_tensor_error, u_scalar)
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#net1(Ta, u_tensor, Tc, u_tensor_error, u_scalar)
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with pytest.raises(ValueError):
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net2(Ta, u_tensor_error, u_scalar)
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net3 = TensorAssignWithBoolTensorIndexError()
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with pytest.raises(AttributeError):
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net3(Ta, Tb, Tc, u_tensor)
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with pytest.raises(AttributeError):
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net3(Ta, Tb, Tc, u_scalar)
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net4 = TensorAssignWithBoolTensorIndex2Error()
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with pytest.raises(AttributeError):
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net4(Ta, u_tensor)
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with pytest.raises(AttributeError):
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net4(Ta, u_scalar)
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test_cases = [
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('TensorAssignWithBoolTensorIndex', {
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'block': TensorAssignWithBoolTensorIndex(),
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'desc_inputs': [Ta, Tb, Tc, u_tensor, u_scalar],
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}),
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('TensorAssignWithBoolTensorIndex2', {
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'block': TensorAssignWithBoolTensorIndex2(),
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'desc_inputs': [Ta, u_tensor, u_scalar],
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}),
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('SlicePositive', {
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'block': NetWorkSlicePositive(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
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}),
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('SliceReduceDimension', {
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'block': NetWorkReduceDimension(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
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}),
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('SliceNegative', {
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'block': NetWorkStepNegative(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
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}),
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('SliceReduceToScalar', {
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'block': NetWorkReduceToScalar(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))],
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}),
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]
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@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
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def test_compile():
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context.set_context(mode=context.GRAPH_MODE)
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return test_cases
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def test_tensor_slice_reduce_out_of_bounds_neg():
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class NetWork(Cell):
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def __init__(self):
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super(NetWork, self).__init__()
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self.tensor_ret = Tensor(np.array(9, np.int32))
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def construct(self, tensor):
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ret = tensor[-7, 3, 4]
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return ret
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input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
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net = NetWork()
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with pytest.raises(ValueError) as ex:
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net(input_tensor)
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assert "The `begin[0]` should be an int and must greater or equal to -6, but got -7" in str(ex.value)
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def test_tensor_slice_reduce_out_of_bounds_positive():
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class NetWork(Cell):
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def __init__(self):
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super(NetWork, self).__init__()
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self.tensor_ret = Tensor(np.array(9, np.int32))
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def construct(self, tensor):
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ret = tensor[6, 3, 4]
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return ret
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input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
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net = NetWork()
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with pytest.raises(ValueError) as ex:
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net(input_tensor)
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assert "The `begin[0]` should be an int and must less than 6, but got 6" in str(ex.value)
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