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1181 lines
51 KiB
1181 lines
51 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, Parameter
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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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pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception
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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 NetWorkSliceEllipsis(Cell):
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def __init__(self):
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super(NetWorkSliceEllipsis, self).__init__()
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self.tensor_ret0 = Tensor(np.ones([2, 7, 8], np.int32))
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self.tensor_ret1 = Tensor(np.ones([6, 7, 8, 9], np.int32))
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self.tensor_ret2 = Tensor(np.ones([1, 6, 7, 8, 9], np.int32))
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def construct(self, tensor):
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ret0 = tensor[0:4:2, ..., 1] + self.tensor_ret0
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ret1 = tensor[...] + self.tensor_ret1
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ret2 = tensor[None] + self.tensor_ret2
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ret3 = tensor[True] + self.tensor_ret2
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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 TensorAssignWithSliceError1(Cell):
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def __init__(self):
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super(TensorAssignWithSliceError1, self).__init__()
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def construct(self, a, b):
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a[1:3:-1, ::] = b
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return a
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class TensorAssignWithSliceError2(Cell):
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def __init__(self):
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super(TensorAssignWithSliceError2, self).__init__()
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def construct(self, a, b):
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a[1:3:-1] = b
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return a
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class TensorAssignWithSlice2(Cell):
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def __init__(self):
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super(TensorAssignWithSlice2, self).__init__()
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def construct(self, a, b, ck):
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a[1:5] = b
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a[3:4] = 5
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a[-1:1:-1] = b
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a[-1:3:-1] = 5
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a[::] = b
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a[::] = 9
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z = a + ck
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return z
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class TensorAssignWithSlice(Cell):
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def __init__(self):
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super(TensorAssignWithSlice, self).__init__()
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self.c = 2.0
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def construct(self, a, b, ck):
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a[1:3, ::] = b
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a[2:3:, 3:] = b
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a[::] = b
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a[::] = self.c
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a[::, ::] = b
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a[::, ::] = self.c
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a[2:3:, 0:, 4:1:-1] = b
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a[2:3:, 0:, 4:1:-1] = self.c
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z = a + ck
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return z
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class TensorGetItemByOneTensor(Cell):
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def __init__(self):
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super(TensorGetItemByOneTensor, self).__init__()
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self.const = Tensor(np.ones((5, 4, 7, 8)), mstype.int32)
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def construct(self, x, index):
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ret = x[index] + self.const
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return ret
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class TensorGetItemByTwoTensors(Cell):
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def __init__(self):
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super(TensorGetItemByTwoTensors, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 8)), mstype.int32)
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def construct(self, x, index_0, index_1):
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ret = x[index_0, index_1] + self.const
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return ret
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class TensorGetItemByThreeTensors(Cell):
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def __init__(self):
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super(TensorGetItemByThreeTensors, self).__init__()
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self.const = Tensor(np.ones((5, 3, 4, 5)), mstype.int32)
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def construct(self, x, index_0, index_1, index_2):
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ret = x[index_0, index_1, index_2] + self.const
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return ret
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class TensorGetItemByMixedTensors_0(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_0, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 3, 6, 5), np.float32))
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def construct(self, tensor, index_0, index_1):
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ret = tensor[index_0, index_1, 0:3, ..., 0:5, 3] + self.const
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return ret
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class TensorGetItemByMixedTensors_1(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_1, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 3, 5, 5), np.float32))
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def construct(self, tensor, index_0, index_1):
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ret = tensor[0:3, index_0, ..., index_1, 3, 0:5] + self.const
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return ret
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class TensorGetItemByMixedTensors_2(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_2, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 6, 7), np.float32))
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def construct(self, tensor, index_0, index_1):
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ret = tensor[0, index_0, index_1, ..., 3] + self.const
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return ret
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class TensorGetItemByMixedTensors_3(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_3, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 3, 4, 3, 5), np.float32))
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def construct(self, tensor, index_0, index_1):
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ret = tensor[..., index_0, 0:3, index_1, 0:5] + self.const
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return ret
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class TensorGetItemByMixedTensors_4(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_4, self).__init__()
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self.const = Tensor(np.ones((2, 2, 3, 4, 5, 3, 9), np.float32))
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def construct(self, tensor, index_0, index_1, index_2):
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ret = tensor[0:2, index_0, index_1, 2, index_2, 0:3, ...] + self.const
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return ret
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class TensorGetItemByMixedTensors_5(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_5, self).__init__()
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self.const = Tensor(np.ones((2, 3, 4, 5, 2, 6), np.float32))
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def construct(self, tensor, index_0, index_1, index_2):
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ret = tensor[0:2, index_0, index_1, ..., index_2, 2] + self.const
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return ret
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class TensorGetItemByMixedTensors_6(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensors_6, self).__init__()
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self.const = Tensor(np.ones((3, 4, 2, 3, 4, 5), np.float32))
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def construct(self, tensor, index_0, index_1, index_2):
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ret = tensor[..., index_0, index_1, index_2, 3] + self.const
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return ret
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class TensorSetItemByMixedTensors_0(Cell):
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def __init__(self, value):
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super(TensorSetItemByMixedTensors_0, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 6, 7, 8, 9), np.float32))
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self.param = Parameter(Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)),
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mstype.float32),
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name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[0:2, index_0, index_1, 2, index_2, 0:3, ...] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByMixedTensors_1(Cell):
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def __init__(self, value):
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super(TensorSetItemByMixedTensors_1, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 6, 7, 8), np.float32))
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self.param = Parameter(Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
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name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[0:2, index_0, index_1, ..., index_2, 2] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByMixedTensors_2(Cell):
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def __init__(self, value):
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super(TensorSetItemByMixedTensors_2, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5, 6, 7, 8), np.float16))
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self.param = Parameter(Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float16),
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name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[..., index_0, index_1, index_2, 3] = self.value
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ret = self.param + self.const
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return ret
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class TensorGetItemByMixedTensorsTypeError(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensorsTypeError, self).__init__()
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def construct(self, x, index_0, index_1):
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ret = x[index_0, index_1, 0:3, ..., 0:5, [1, 2, 3, 4]]
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return ret
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class TensorGetItemByMixedTensorsNumberError(Cell):
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def __init__(self):
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super(TensorGetItemByMixedTensorsNumberError, self).__init__()
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def construct(self, x, index_0, index_1):
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ret = x[index_0, index_1, 0:3, ..., index_1, index_0]
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return ret
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class TensorSetItemByOneTensorWithNumber(Cell):
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def __init__(self, value):
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super(TensorSetItemByOneTensorWithNumber, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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self.value = value
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def construct(self, index):
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self.param[index] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByOneTensorWithTensor(Cell):
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def __init__(self):
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super(TensorSetItemByOneTensorWithTensor, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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def construct(self, index, value):
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self.param[index] = value
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ret = self.param + self.const
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return ret
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class TensorSetItemByOneTensorWithTupleOfNumber(Cell):
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def __init__(self, value):
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super(TensorSetItemByOneTensorWithTupleOfNumber, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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self.value = value
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def construct(self, index):
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self.param[index] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByOneTensorWithTupleOfTensor(Cell):
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def __init__(self):
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super(TensorSetItemByOneTensorWithTupleOfTensor, self).__init__()
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self.const = Tensor(np.ones((6, 3, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 3 * 8).reshape((6, 3, 8)), mstype.float32), name="x")
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def construct(self, index, value_0, value_1, value_2):
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self.param[index] = (value_0, value_1, value_2)
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithNumber(Cell):
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def __init__(self, value):
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super(TensorSetItemByTensorsWithNumber, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[index_0, index_1, index_2] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithTensor(Cell):
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def __init__(self):
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super(TensorSetItemByTensorsWithTensor, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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def construct(self, index_0, index_1, index_2, value):
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self.param[index_0, index_1, index_2] = value
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithTensorNumberError(Cell):
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def __init__(self):
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super(TensorSetItemByTensorsWithTensorNumberError, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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def construct(self, index_0, index_1, index_2, index_3, value):
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self.param[index_0, index_1, index_2, index_3] = value
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithTupleOfNumber(Cell):
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def __init__(self, value):
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super(TensorSetItemByTensorsWithTupleOfNumber, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[index_0, index_1, index_2] = self.value
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithTupleOfTensor(Cell):
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def __init__(self):
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super(TensorSetItemByTensorsWithTupleOfTensor, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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def construct(self, index_0, index_1, index_2, value_0, value_1, value_2):
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self.param[index_0, index_1, index_2] = (value_0, value_1, value_2)
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ret = self.param + self.const
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return ret
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class TensorSetItemByTensorsWithTupleOfTensorNumberError(Cell):
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def __init__(self):
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super(TensorSetItemByTensorsWithTupleOfTensorNumberError, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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def construct(self, index_0, index_1, index_2, value_0, value_1):
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self.param[index_0, index_1, index_2] = (value_0, value_1)
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ret = self.param + self.const
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return ret
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class TensorSetItemByMixedTensors(Cell):
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def __init__(self):
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super(TensorSetItemByMixedTensors, self).__init__()
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self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
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self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
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self.value = 99.0
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def construct(self, index_0, index_1):
|
|
self.param[index_0, index_1, 0:6] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
def test_tensor_assign():
|
|
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)
|
|
ck = np.arange(60).reshape(3, 4, 5)
|
|
b = Tensor([1], dtype=mstype.float32)
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
Tck = Tensor(ck, dtype=mstype.float32)
|
|
Ta4d = Tensor(a.reshape(1, 3, 4, 5), dtype=mstype.float32)
|
|
Ta4d_ck = Tensor(ck.reshape(1, 3, 4, 5), dtype=mstype.float32)
|
|
Tb = Tensor([1, 3], dtype=mstype.float32)
|
|
Tc = Tensor([], dtype=mstype.float32)
|
|
t = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
tck = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
net(Ta, b, Tck)
|
|
net2(t, b, tck)
|
|
# Error for A[Slice] = Number
|
|
# 1. A[Slice] = Number, 0 in shape
|
|
with pytest.raises(ValueError):
|
|
net_e2(t, Tensor(2, mstype.int32))
|
|
|
|
# Error for A[Slice] = U, U is a Tensor
|
|
# 1. A[Slice] = U, u.size is error
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tb, tck)
|
|
# 2. A[Slice] = U, U is empty
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tc, tck)
|
|
# 3. A[Slice] = U, U.size error
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tb, tck)
|
|
|
|
# Error for A[Tuple(Slice...)] = Tensor
|
|
# 1. A[Tuple(Slice...)] = U, U is empty
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
# 2. A[Tuple(Slice...)] = U, U.size error
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
# 3. A[Tuple(Slice...)] = U, Slice error
|
|
with pytest.raises(IndexError):
|
|
net_e1(Ta, b)
|
|
|
|
# Error for A[Tuple(Slice...)] = Number
|
|
# 1. A[Tuple(Slice...)] = Number, Slice error
|
|
with pytest.raises(IndexError):
|
|
net_e1(Ta, Tensor(2, mstype.int32))
|
|
|
|
net = TensorAssignWithInteger()
|
|
# Error for A[Number] = scalar/Tensor
|
|
# 1. A[Number] = U, U is a Tensor, u.size not match
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
# 2. A[Number] = U, the number index error
|
|
with pytest.raises(IndexError):
|
|
net(Ta4d, b, Ta4d_ck)
|
|
|
|
# Error for A[(n,m)] = scalar/Tensor
|
|
# 1. A[(n,m)] = U, U is a tensor. u.size not match
|
|
net = TensorAssignWithTupleInteger()
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
# 2. A[(n,m)] = U, the number index error
|
|
with pytest.raises(IndexError):
|
|
net(Ta4d, b, Ta4d_ck)
|
|
|
|
# Error for A[...] = U or A[1:, ...] = u
|
|
# 1. A[...] = scalar/tensor
|
|
net = TensorAssignWithEllipsis()
|
|
net(Ta, Ta4d)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb)
|
|
# 2. A[::, 1:, ...] = scalar/tensor
|
|
net = TensorAssignWithTupleEllipsis()
|
|
net(Ta, b)
|
|
Tc = Tensor(1, mstype.float32)
|
|
net(Ta, Tc)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb)
|
|
|
|
|
|
class TensorAssignWithTupleEllipsis2(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithTupleEllipsis2, self).__init__()
|
|
|
|
def construct(self, a, b):
|
|
a[1:, ..., ::] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithTupleEllipsis(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithTupleEllipsis, self).__init__()
|
|
|
|
def construct(self, a, b):
|
|
a[:2, ...] = 1.0
|
|
a[1:, ...] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithEllipsis(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithEllipsis, self).__init__()
|
|
|
|
def construct(self, a, b):
|
|
a[...] = 1
|
|
a[...] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithInteger(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithInteger, self).__init__()
|
|
|
|
def construct(self, a, b, ck):
|
|
a[1] = 1
|
|
a[0] = b
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
class TensorAssignWithTupleInteger(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithTupleInteger, self).__init__()
|
|
|
|
def construct(self, a, b, ck):
|
|
a[(1)] = 1.0
|
|
a[(1)] = b
|
|
a[(1, 1)] = b
|
|
a[(1, 1)] = 1.0
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
class TensorAssignWithBoolTensorIndex(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithBoolTensorIndex, self).__init__()
|
|
self.t = Tensor(np.arange(60).reshape([3, 4, 5]), dtype=mstype.float32)
|
|
self.u_scalar = 5
|
|
|
|
def construct(self, a, b, c, u_tensor):
|
|
a[c] = self.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.float32)
|
|
self.t = Tensor(np.arange(60).reshape([3, 4, 5]), dtype=mstype.float32)
|
|
self.u_scalar = 5
|
|
|
|
def construct(self, a, u_tensor):
|
|
a[a > 8] = u_tensor
|
|
a[a >= 6] = self.u_scalar
|
|
a[a < 3] = self.u_scalar
|
|
a[a <= 5] = u_tensor
|
|
a[a == 5] = self.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.arange(60).reshape(3, 4, 5)
|
|
ck = np.arange(60).reshape(3, 4, 5)
|
|
a4 = np.arange(60).reshape(3, 2, 2, 5)
|
|
b = a > 5
|
|
c = a < 3
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
Tck = Tensor(ck, dtype=mstype.float32)
|
|
Ta4 = Tensor(a4, dtype=mstype.float32)
|
|
Tb = Tensor(b)
|
|
Tc = Tensor(c)
|
|
Td = Tensor([True, True])
|
|
u_tensor = Tensor([1], dtype=mstype.float32)
|
|
u_tensor_error = Tensor([1, 2], dtype=mstype.float32)
|
|
t_1d = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
tck_1d = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
u_scalar = 5
|
|
|
|
|
|
def test_tensor_assign_bool_index():
|
|
net1 = TensorAssignWithBoolTensorIndex()
|
|
net2 = TensorAssignWithBoolTensorIndex2()
|
|
net1(Ta, Tb, Tc, u_tensor)
|
|
net1(Ta, Tb, Tc, u_tensor)
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Td, Tc, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net1(Ta, u_tensor, Tc, u_tensor)
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Tb, Td, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net1(Ta, Tb, Ta, u_tensor)
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Tb, Tc, u_tensor_error)
|
|
# net1(Ta, u_tensor, Tc, u_tensor_error, u_scalar)
|
|
with pytest.raises(ValueError):
|
|
net2(Ta, u_tensor_error)
|
|
net3 = TensorAssignWithBoolTensorIndexError()
|
|
with pytest.raises(IndexError):
|
|
net3(Ta, Tb, Tc, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net3(Ta, Tb, Tc, Tensor(u_scalar, mstype.int32))
|
|
net4 = TensorAssignWithBoolTensorIndex2Error()
|
|
with pytest.raises(IndexError):
|
|
net4(Ta, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net4(Ta, Tensor(u_scalar, mstype.int32))
|
|
|
|
|
|
test_cases = [
|
|
('TensorAssignWithTupleEllipsis2', {
|
|
'block': TensorAssignWithTupleEllipsis2(),
|
|
'desc_inputs': [Ta4, u_tensor],
|
|
}),
|
|
('TensorAssignWithTupleEllipsis', {
|
|
'block': TensorAssignWithTupleEllipsis(),
|
|
'desc_inputs': [Ta, u_tensor],
|
|
}),
|
|
('TensorAssignWithEllipsis', {
|
|
'block': TensorAssignWithEllipsis(),
|
|
'desc_inputs': [Ta, u_tensor],
|
|
}),
|
|
('TensorAssignWithTupleInteger', {
|
|
'block': TensorAssignWithTupleInteger(),
|
|
'desc_inputs': [Ta, u_tensor, Tck],
|
|
}),
|
|
('TensorAssignWithInteger', {
|
|
'block': TensorAssignWithInteger(),
|
|
'desc_inputs': [Ta, u_tensor, Tck],
|
|
}),
|
|
('TensorAssignWithSlice', {
|
|
'block': TensorAssignWithSlice(),
|
|
'desc_inputs': [Ta, u_tensor, Tck],
|
|
}),
|
|
('TensorAssignWithSlice2', {
|
|
'block': TensorAssignWithSlice2(),
|
|
'desc_inputs': [t_1d, u_tensor, tck_1d],
|
|
}),
|
|
('TensorAssignWithBoolTensorIndex', {
|
|
'block': TensorAssignWithBoolTensorIndex(),
|
|
'desc_inputs': [Ta, Tb, Tc, u_tensor],
|
|
}),
|
|
('TensorAssignWithBoolTensorIndex2', {
|
|
'block': TensorAssignWithBoolTensorIndex2(),
|
|
'desc_inputs': [Ta, u_tensor],
|
|
}),
|
|
('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))],
|
|
}),
|
|
('TensorGetItemByOneTensor', {
|
|
'block': TensorGetItemByOneTensor(),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByTwoTensors', {
|
|
'block': TensorGetItemByTwoTensors(),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByThreeTensors', {
|
|
'block': TensorGetItemByThreeTensors(),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_0', {
|
|
'block': TensorGetItemByMixedTensors_0(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_1', {
|
|
'block': TensorGetItemByMixedTensors_1(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_2', {
|
|
'block': TensorGetItemByMixedTensors_2(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_3', {
|
|
'block': TensorGetItemByMixedTensors_3(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_4', {
|
|
'block': TensorGetItemByMixedTensors_4(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_5', {
|
|
'block': TensorGetItemByMixedTensors_5(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensors_6', {
|
|
'block': TensorGetItemByMixedTensors_6(),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8).reshape((3, 4, 5, 6, 7, 8)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithNumber', {
|
|
'block': TensorSetItemByOneTensorWithNumber(value=0.0),
|
|
'desc_inputs': [Tensor(np.random.randint(4, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTensor', {
|
|
'block': TensorSetItemByOneTensorWithTensor(),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(5, 4)), mstype.int32),
|
|
Tensor(np.zeros((4, 7, 8)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTupleOfNumber', {
|
|
'block': TensorSetItemByOneTensorWithTupleOfNumber(value=(0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7)),
|
|
'desc_inputs': [Tensor(np.random.randint(5, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTupleOfTensor', {
|
|
'block': TensorSetItemByOneTensorWithTupleOfTensor(),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(5, 4)), mstype.int32),
|
|
Tensor(np.zeros((8,), np.float32)),
|
|
Tensor(np.ones((8,), np.float32)),
|
|
Tensor(np.ones((8,), np.float32) * 2)],
|
|
}),
|
|
('TensorSetItemByTensorsWithNumber', {
|
|
'block': TensorSetItemByTensorsWithNumber(value=0.0),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTensor', {
|
|
'block': TensorSetItemByTensorsWithTensor(),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((4, 5)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfNumber', {
|
|
'block': TensorSetItemByTensorsWithTupleOfNumber(value=(0.0, 1.1, 2.2, 3.3, 4.4)),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfTensor', {
|
|
'block': TensorSetItemByTensorsWithTupleOfTensor(),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((4, 5)), mstype.float32),
|
|
Tensor(np.ones((4, 5)), mstype.float32),
|
|
Tensor(np.ones((4, 5)) * 2, mstype.float32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithNumber_0', {
|
|
'block': TensorSetItemByMixedTensors_0(value=88.0),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensor_0', {
|
|
'block': TensorSetItemByMixedTensors_0(value=Tensor(np.ones((4, 5, 3, 9), np.float32))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfNumber_0', {
|
|
'block': TensorSetItemByMixedTensors_0(value=(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0)),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfTensor_0', {
|
|
'block': TensorSetItemByMixedTensors_0(value=(Tensor(np.ones((4, 5, 3, 9), np.float32)),
|
|
Tensor(np.zeros((4, 5, 3, 9), np.float32)),
|
|
Tensor(np.ones((4, 5, 3, 9), np.float32)))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithNumber_1', {
|
|
'block': TensorSetItemByMixedTensors_1(value=88.0),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensor_1', {
|
|
'block': TensorSetItemByMixedTensors_1(value=Tensor(np.ones((5, 2, 6), np.float32))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfNumber_1', {
|
|
'block': TensorSetItemByMixedTensors_1(value=(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfTensor_1', {
|
|
'block': TensorSetItemByMixedTensors_1(value=(Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.zeros((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.float32)))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithNumber_2', {
|
|
'block': TensorSetItemByMixedTensors_2(value=88.0),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensor_2', {
|
|
'block': TensorSetItemByMixedTensors_2(value=Tensor(np.ones((3, 4, 2, 3, 4, 5), np.float16))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfNumber_2', {
|
|
'block': TensorSetItemByMixedTensors_2(value=(1.0, 2.0, 3.0, 4.0, 5.0)),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfTensor_2', {
|
|
'block': TensorSetItemByMixedTensors_2(value=(Tensor(np.ones((4, 5), np.float16)),
|
|
Tensor(np.zeros((4, 5), np.float16)),
|
|
Tensor(np.ones((4, 5), np.float16)))),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
]
|
|
|
|
raise_error_set = [
|
|
('TensorGetItemByOneTensorDtypeError', {
|
|
'block': (TensorGetItemByOneTensor(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(5, 4)), mstype.int8)],
|
|
}),
|
|
('TensorGetItemByTwoTensorsShapeError', {
|
|
'block': (TensorGetItemByTwoTensors(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(2, 3, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByTwoTensorsDtypeError', {
|
|
'block': (TensorGetItemByTwoTensors(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.float32)],
|
|
}),
|
|
('TensorGetItemByThreeTensorsShapeError', {
|
|
'block': (TensorGetItemByThreeTensors(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 2, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByThreeTensorsDtypeError', {
|
|
'block': (TensorGetItemByThreeTensors(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int64),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsNumberError', {
|
|
'block': (TensorGetItemByMixedTensorsNumberError(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.int32),
|
|
Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsTypeError', {
|
|
'block': (TensorGetItemByMixedTensorsTypeError(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsDtypeError', {
|
|
'block': (TensorGetItemByMixedTensors_0(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.float32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsShapeError', {
|
|
'block': (TensorGetItemByMixedTensors_0(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(2, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithNumberTypeError', {
|
|
'block': (TensorSetItemByOneTensorWithNumber(value=0), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(4, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTensorShapeError', {
|
|
'block': (TensorSetItemByOneTensorWithTensor(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(5, 4)), mstype.int32),
|
|
Tensor(np.zeros((6, 7, 8)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTensorDtypeError', {
|
|
'block': (TensorSetItemByOneTensorWithTensor(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(5, 4)), mstype.int32),
|
|
Tensor(np.zeros((6, 7, 8)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTupleOfNumberTypeError', {
|
|
'block': (TensorSetItemByOneTensorWithTupleOfNumber(value=(0, 1, 2, 3, 4, 5, 6, 7)), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(5, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTupleOfNumberNumberError', {
|
|
'block': (TensorSetItemByOneTensorWithTupleOfNumber(value=(0.0, 1.1, 2.2)), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(5, size=(5, 4)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByOneTensorWithTupleOfTensorDtyeError', {
|
|
'block': (TensorSetItemByOneTensorWithTupleOfTensor(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(5, 4)), mstype.int32),
|
|
Tensor(np.zeros((8,), np.int32)),
|
|
Tensor(np.ones((8,), np.int32)),
|
|
Tensor(np.ones((8,), np.float32) * 2)],
|
|
}),
|
|
('TensorSetItemByTensorsWithNumberTypeError', {
|
|
'block': (TensorSetItemByTensorsWithNumber(value=0), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTensorShapeError', {
|
|
'block': (TensorSetItemByTensorsWithTensor(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((2, 5)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTensorTypeError', {
|
|
'block': (TensorSetItemByTensorsWithTensor(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTensorNumberError', {
|
|
'block': (TensorSetItemByTensorsWithTensorNumberError(), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(1, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((2, 5)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfNumberTypeError', {
|
|
'block': (TensorSetItemByTensorsWithTupleOfNumber(value=(0.0, 1, 2, 3, 4)), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfNumberNumberError', {
|
|
'block': (TensorSetItemByTensorsWithTupleOfNumber(value=(0.0, 1.0, 2.0, 3.0)), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfTensorNumberError', {
|
|
'block': (TensorSetItemByTensorsWithTupleOfTensorNumberError(), {'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((4, 5)), mstype.float32),
|
|
Tensor(np.ones((4, 5)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByTensorsWithTupleOfTensorTypeError', {
|
|
'block': (TensorSetItemByTensorsWithTupleOfTensor(), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(7, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32),
|
|
Tensor(np.zeros((4, 5)), mstype.float32),
|
|
Tensor(np.ones((4, 5)), mstype.int32),
|
|
Tensor(np.ones((4, 5)) * 2, mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithNumberValueTypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=88), {'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithNumberIndexTypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=88.0), {'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.float32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensorValueDtypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=Tensor(np.ones((5, 2, 6), np.int32))),
|
|
{'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensorValueShapeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=Tensor(np.ones((3, 2, 6), np.float32))),
|
|
{'exception': ValueError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorSetItemByMixedTensorsWithTensorIndexDtypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=Tensor(np.ones((5, 2, 6), np.float32))),
|
|
{'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.float32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfNumberValueTypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=(1.0, 2, 3.0, 4.0, 5.0, 6.0)),
|
|
{'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfTensorValueDtypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=(Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.zeros((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.int32)))),
|
|
{'exception': TypeError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
}),
|
|
('TensorGetItemByMixedTensorsWithTupleOfTensorIndexDtypeError', {
|
|
'block': (TensorSetItemByMixedTensors_1(value=(Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.zeros((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.float32)),
|
|
Tensor(np.ones((5, 2, 6), np.int32)))),
|
|
{'exception': IndexError}),
|
|
'desc_inputs': [Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.float32),
|
|
Tensor(np.random.randint(4, size=(4, 5)), mstype.int32),
|
|
Tensor(np.random.randint(3, size=(2, 1, 4, 5)), mstype.int32)],
|
|
})
|
|
]
|
|
|
|
|
|
@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_cases
|
|
|
|
|
|
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
|
|
def test_check_exception():
|
|
return raise_error_set
|
|
|
|
|
|
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(IndexError):
|
|
net(input_tensor)
|
|
|
|
|
|
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(IndexError):
|
|
net(input_tensor)
|