!13745 Enable zero dimension when it comes out of operator.

From: @liangzhibo
Reviewed-by: @ginfung,@zh_qh,@ginfung
Signed-off-by: @zh_qh
This commit is contained in:
mindspore-ci-bot 2021-03-24 20:34:30 +08:00 committed by Gitee
commit 9023ba2b56
5 changed files with 78 additions and 48 deletions

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@ -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),

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@ -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<int64_t> &input, const TypePtr &data_type)
: MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {static_cast<int>(input.size())}),
data_(MakeTensorData(data_type_, shape_, input.data(), input.size())),
id_(MakeId()) {
CheckShape(shape_);
}
id_(MakeId()) {}
Tensor::Tensor(const std::vector<double> &input, const TypePtr &data_type)
: MetaTensor(TypeIdOf(data_type, kNumberTypeFloat32), {static_cast<int>(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();

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@ -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)

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@ -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))

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@ -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)