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Restrict tensor getitem or setitem not support mixed tensor.

This commit is contained in:
buxue 2020-05-22 15:55:38 +08:00
parent b06c802807
commit 850171a34b
4 changed files with 61 additions and 28 deletions
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4
mindspore/ops/composite/multitype_ops/_utils.py
View File

@ -254,7 +254,7 @@ def tuple_element_is_int(indexs):
@constexpr
def tuple_elements_type(types):
def tuple_index_elements_type(types, op_name):
"""Judges the type of all elements of the tuple."""
tensors_number = 0
for ele in types:
@ -264,7 +264,7 @@ def tuple_elements_type(types):
return ALL_TENSOR
if tensors_number == 0:
return NO_TENSOR
return CONTAIN_TENSOR
raise IndexError(f"For '{op_name}', the index does not support mixed tensor.")
@constexpr

2
mindspore/ops/composite/multitype_ops/getitem_impl.py
View File

@ -247,7 +247,7 @@ def _tensor_getitem_by_tuple(data, tuple_index):
Tensor, element type is same as the element type of data.
"""
index_types = multi_utils.hyper_map(F.typeof, tuple_index)
index_elements_type = multi_utils.tuple_elements_type(index_types)
index_elements_type = multi_utils.tuple_index_elements_type(index_types, multi_utils.TENSOR_GETITEM)
result = None
if index_elements_type == multi_utils.NO_TENSOR:
result = _tensor_slice(data, tuple_index)

6
mindspore/ops/composite/multitype_ops/setitem_impl.py
View File

@ -191,7 +191,7 @@ def _tensor_setitem_by_tuple_with_number(data, tuple_index, value):
Tensor, element type and shape is same as data.
"""
index_types = multi_utils.hyper_map(F.typeof, tuple_index)
index_elements_type = multi_utils.tuple_elements_type(index_types)
index_elements_type = multi_utils.tuple_index_elements_type(index_types, multi_utils.TENSOR_SETITEM)
result = None
if index_elements_type == multi_utils.NO_TENSOR:
result = _tensor_assgin_number(data, tuple_index, value)
@ -222,7 +222,7 @@ def _tensor_setitem_by_tuple_with_tensor(data, tuple_index, value):
Tensor, element type and shape is same as data.
"""
index_types = multi_utils.hyper_map(F.typeof, tuple_index)
index_elements_type = multi_utils.tuple_elements_type(index_types)
index_elements_type = multi_utils.tuple_index_elements_type(index_types, multi_utils.TENSOR_SETITEM)
result = None
if index_elements_type == multi_utils.NO_TENSOR:
result = _tensor_assgin_tensor(data, tuple_index, value)
@ -254,7 +254,7 @@ def _tensor_setitem_by_tuple_with_tuple(data, tuple_index, value):
Tensor, element type and shape is same as data.
"""
index_types = multi_utils.hyper_map(F.typeof, tuple_index)
index_elements_type = multi_utils.tuple_elements_type(index_types)
index_elements_type = multi_utils.tuple_index_elements_type(index_types, multi_utils.TENSOR_SETITEM)
result = None
if index_elements_type == multi_utils.ALL_TENSOR:
indices = multi_utils.generate_indeices_from_tuple_of_tensor(data, tuple_index, multi_utils.TENSOR_SETITEM)

77
tests/ut/python/ops/test_tensor_slice.py
View File

@ -146,9 +146,9 @@ class TensorAssignWithSlice(Cell):
return z
class TensorIndexByOneTensor(Cell):
class TensorGetItemByOneTensor(Cell):
def __init__(self):
super(TensorIndexByOneTensor, self).__init__()
super(TensorGetItemByOneTensor, self).__init__()
self.const = Tensor(np.ones((5, 4, 7, 8)), mstype.int32)
def construct(self, x, index):
@ -156,9 +156,9 @@ class TensorIndexByOneTensor(Cell):
return ret
class TensorIndexByTwoTensors(Cell):
class TensorGetItemByTwoTensors(Cell):
def __init__(self):
super(TensorIndexByTwoTensors, self).__init__()
super(TensorGetItemByTwoTensors, self).__init__()
self.const = Tensor(np.ones((3, 4, 5, 8)), mstype.int32)
def construct(self, x, index_0, index_1):
@ -166,9 +166,9 @@ class TensorIndexByTwoTensors(Cell):
return ret
class TensorIndexByThreeTensors(Cell):
class TensorGetItemByThreeTensors(Cell):
def __init__(self):
super(TensorIndexByThreeTensors, self).__init__()
super(TensorGetItemByThreeTensors, self).__init__()
self.const = Tensor(np.ones((5, 3, 4, 5)), mstype.int32)
def construct(self, x, index_0, index_1, index_2):
@ -176,6 +176,15 @@ class TensorIndexByThreeTensors(Cell):
return ret
class TensorGetItemByMixedTensors(Cell):
def __init__(self):
super(TensorGetItemByMixedTensors, self).__init__()
def construct(self, x, index_0, index_1):
ret = x[index_0, index_1, 0:6]
return ret
class TensorSetItemByOneTensorWithNumber(Cell):
def __init__(self, value):
super(TensorSetItemByOneTensorWithNumber, self).__init__()
@ -300,6 +309,19 @@ class TensorSetItemByTensorsWithTupleOfTensorNumberError(Cell):
return ret
class TensorSetItemByMixedTensors(Cell):
def __init__(self):
super(TensorSetItemByMixedTensors, self).__init__()
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
self.param = Parameter(Tensor(np.arange(6*7*8).reshape((6, 7, 8)), mstype.float32), name="x")
self.value = 99.0
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()
@ -596,19 +618,19 @@ test_cases = [
'block': NetWorkSliceEllipsis(),
'desc_inputs': [Tensor(np.ones([6, 7, 8, 9], np.int32))],
}),
('TensorIndexByOneTensor', {
'block': TensorIndexByOneTensor(),
('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)],
}),
('TensorIndexByTwoTensors', {
'block': TensorIndexByTwoTensors(),
('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)],
}),
('TensorIndexByThreeTensors', {
'block': TensorIndexByThreeTensors(),
('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),
@ -665,37 +687,43 @@ test_cases = [
]
raise_error_set = [
('TensorIndexByOneTensorDtypeError', {
'block': (TensorIndexByOneTensor(), {'exception': TypeError}),
('TensorGetItemByOneTensorDtypeError', {
'block': (TensorGetItemByOneTensor(), {'exception': TypeError}),
'desc_inputs': [Tensor(np.arange(6*7*8).reshape((6, 7, 8)), mstype.int32),
Tensor(np.random.randint(6, size=(5, 4)), mstype.int8)],
}),
('TensorIndexByTwoTensorsShapeError', {
'block': (TensorIndexByTwoTensors(), {'exception': ValueError}),
('TensorGetItemByTwoTensorsShapeError', {
'block': (TensorGetItemByTwoTensors(), {'exception': ValueError}),
'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)],
}),
('TensorIndexByTwoTensorsDtypeError', {
'block': (TensorIndexByTwoTensors(), {'exception': TypeError}),
('TensorGetItemByTwoTensorsDtypeError', {
'block': (TensorGetItemByTwoTensors(), {'exception': TypeError}),
'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)],
}),
('TensorIndexByThreeTensorsShapeError', {
'block': (TensorIndexByThreeTensors(), {'exception': ValueError}),
('TensorGetItemByThreeTensorsShapeError', {
'block': (TensorGetItemByThreeTensors(), {'exception': ValueError}),
'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)],
}),
('TensorIndexByThreeTensorsDtypeError', {
'block': (TensorIndexByThreeTensors(), {'exception': TypeError}),
('TensorGetItemByThreeTensorsDtypeError', {
'block': (TensorGetItemByThreeTensors(), {'exception': TypeError}),
'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.int64),
Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)],
}),
('TensorGetItemByMixedTensors', {
'block': (TensorGetItemByMixedTensors(), {'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.int64)],
}),
('TensorSetItemByOneTensorWithNumberTypeError', {
'block': (TensorSetItemByOneTensorWithNumber(value=0), {'exception': TypeError}),
'desc_inputs': [Tensor(np.random.randint(4, size=(5, 4)), mstype.int32)],
@ -781,6 +809,11 @@ raise_error_set = [
Tensor(np.zeros((4, 5)), mstype.float32),
Tensor(np.ones((4, 5)), mstype.int32),
Tensor(np.ones((4, 5)) * 2, mstype.int32)],
}),
('TensorSetItemByMixedTensors', {
'block': (TensorSetItemByMixedTensors(), {'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)],
})
]