diff --git a/mindspore/common/tensor.py b/mindspore/common/tensor.py index 3efed7e3299..8a361ae7e5d 100644 --- a/mindspore/common/tensor.py +++ b/mindspore/common/tensor.py @@ -74,7 +74,7 @@ class Tensor(Tensor_): >>> assert t3.dtype == ms.float32 """ - def __init__(self, input_data=None, dtype=None, shape=None, init=None): + def __init__(self, input_data=None, dtype=None, shape=None, init=None, check_zero_dims=True): self.init_finished = False # If input data is numpy number, convert it to np array if isinstance(input_data, np_types): @@ -92,6 +92,14 @@ class Tensor(Tensor_): if isinstance(shape, numbers.Number): shape = (shape,) + if check_zero_dims: + if input_data is not None and isinstance(input_data, (tuple, list, np.ndarray)) \ + and np.array(input_data).ndim > 1 and np.array(input_data).size == 0: + raise ValueError("input_data can not contain zero dimension.") + if shape is not None: + if 0 in shape: + raise ValueError("Shape can not contain zero value.") + # If input_data is tuple/list/numpy.ndarray, it's support in check_type method. if init is None: validator.check_value_type('input_data', input_data, (Tensor_, np.ndarray, list, tuple, float, int, bool), diff --git a/mindspore/core/ir/tensor.cc b/mindspore/core/ir/tensor.cc index 5f5b6d0e318..5c9578496bc 100644 --- a/mindspore/core/ir/tensor.cc +++ b/mindspore/core/ir/tensor.cc @@ -465,9 +465,7 @@ Tensor::Tensor(const Tensor &tensor) cache_tensor_ptr_(tensor.cache_tensor_ptr_), hashmap_tensor_ptr_(tensor.hashmap_tensor_ptr_), padding_type_(tensor.padding_type()), - device_event_(tensor.device_event_) { - CheckShape(tensor.shape_); -} + device_event_(tensor.device_event_) {} Tensor::Tensor(const Tensor &tensor, TypeId data_type) : MetaTensor(data_type, tensor.shape_), @@ -481,43 +479,29 @@ Tensor::Tensor(const Tensor &tensor, TypeId data_type) cache_tensor_ptr_(tensor.cache_tensor_ptr_), hashmap_tensor_ptr_(tensor.hashmap_tensor_ptr_), padding_type_(tensor.padding_type()), - device_event_(tensor.device_event_) { - CheckShape(tensor.shape_); -} + device_event_(tensor.device_event_) {} Tensor::Tensor(TypeId data_type, const ShapeVector &shape, TensorDataPtr data) - : MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) { - CheckShape(shape); -} + : MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) {} Tensor::Tensor(TypeId data_type, const ShapeVector &shape) - : Tensor(data_type, shape, MakeTensorData(data_type, shape)) { - CheckShape(shape); -} + : Tensor(data_type, shape, MakeTensorData(data_type, shape)) {} Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, size_t data_len) - : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) { - CheckShape(shape); -} + : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) {} Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, TypeId src_data_type) - : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) { - CheckShape(shape); -} + : Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) {} Tensor::Tensor(const std::vector &input, const TypePtr &data_type) : MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {static_cast(input.size())}), data_(MakeTensorData(data_type_, shape_, input.data(), input.size())), - id_(MakeId()) { - CheckShape(shape_); -} + id_(MakeId()) {} Tensor::Tensor(const std::vector &input, const TypePtr &data_type) : MetaTensor(TypeIdOf(data_type, kNumberTypeFloat32), {static_cast(input.size())}), data_(MakeTensorData(data_type_, shape_, input.data(), input.size())), - id_(MakeId()) { - CheckShape(shape_); -} + id_(MakeId()) {} Tensor::Tensor(int64_t input, const TypePtr &data_type) : MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {}), @@ -622,17 +606,6 @@ std::string Tensor::ToStringRepr() const { return buf.str(); } -void Tensor::CheckShape(const ShapeVector &shape) const { - // Check tensor's shape, ignore one-dimensional tensor, including empty tensor with shape=(0,). - if (shape.size() > 1) { - for (const auto &s : shape) { - if (s == 0) { - MS_EXCEPTION(ValueError) << "Zero is not supported in the shape of Tensor. "; - } - } - } -} - void Tensor::data_sync(bool need_wait) const { if (need_wait) { Wait(); diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index d25bd84eb85..a20d40771a2 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -2980,7 +2980,8 @@ class StridedSlice(PrimitiveWithInfer): ret_shape = self._compute_slicing_shape(x['shape'], begin_v, end_v, strides_v) - value = None if all(ret_shape) else Tensor(np.array([]).reshape(ret_shape), x['dtype'].element_type()) + value = None if all(ret_shape) else Tensor(np.array([]).reshape(ret_shape), x['dtype'].element_type(), + check_zero_dims=False) if "max_value" in x and "min_value" in x: validator.check_value_type("min_value", x["min_value"], [tuple, list], self.name) validator.check_value_type("max_value", x["max_value"], [tuple, list], self.name) diff --git a/tests/ut/python/ir/test_tensor.py b/tests/ut/python/ir/test_tensor.py index e3025539ce6..9ed92b418d7 100644 --- a/tests/ut/python/ir/test_tensor.py +++ b/tests/ut/python/ir/test_tensor.py @@ -67,17 +67,6 @@ def test_tensor(): assert isinstance(t4, ms.Tensor) assert t4.dtype == ms.int64 -def test_tensor_empty(): - t = ms.Tensor(np.ones(0), ms.float32) - assert isinstance(t, ms.Tensor) - assert t.shape == (0,) - - -def test_tensor_shape_has_zero(): - with pytest.raises(ValueError): - t = ms.Tensor(np.ones((1, 0)), ms.float32) - print(t) - def test_tensor_type_float16(): t_float16 = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float16)) diff --git a/tests/ut/python/pipeline/parse/test_tensor_zero_dimension.py b/tests/ut/python/pipeline/parse/test_tensor_zero_dimension.py new file mode 100644 index 00000000000..2fc30051c89 --- /dev/null +++ b/tests/ut/python/pipeline/parse/test_tensor_zero_dimension.py @@ -0,0 +1,59 @@ +# Copyright 2021 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. +# ============================================================================ + +import numpy as np +import pytest +import mindspore + +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import context +from mindspore.ops import operations as P +from mindspore.common.initializer import One + +context.set_context(mode=context.GRAPH_MODE) + +def test_zero_dimension_list(): + Tensor([]) + with pytest.raises(ValueError) as ex: + Tensor([[]]) + assert "input_data can not contain zero dimension." in str(ex.value) + + +def test_zero_dimension_np_array(): + with pytest.raises(ValueError) as ex: + Tensor(np.ones((1, 0, 3))) + assert "input_data can not contain zero dimension." in str(ex.value) + + +def test_zero_dimension_with_zero_shape(): + with pytest.raises(ValueError) as ex: + Tensor(shape=(1, 0, 3), dtype=mindspore.float32, init=One()) + assert "Shape can not contain zero value." in str(ex.value) + + +def test_zero_dimension_with_operator(): + class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + self.strided_slice = P.StridedSlice() + + def construct(self, x): + a = self.strided_slice(x, (2, 4, 4), (-1, 2, 1), (1, 1, 1)) + return a + + x = Tensor(np.ones((1, 3, 3))) + net = Net() + net(x)