forked from mindspore-Ecosystem/mindspore
enable zero dimension tensor when input is data
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@ -74,7 +74,7 @@ class Tensor(Tensor_):
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>>> assert t3.dtype == ms.float32
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"""
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def __init__(self, input_data=None, dtype=None, shape=None, init=None):
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def __init__(self, input_data=None, dtype=None, shape=None, init=None, check_zero_dims=True):
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self.init_finished = False
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# If input data is numpy number, convert it to np array
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if isinstance(input_data, np_types):
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@ -92,6 +92,14 @@ class Tensor(Tensor_):
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if isinstance(shape, numbers.Number):
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shape = (shape,)
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if check_zero_dims:
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if input_data is not None and isinstance(input_data, (tuple, list, np.ndarray)) \
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and np.array(input_data).ndim > 1 and np.array(input_data).size == 0:
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raise ValueError("input_data can not contain zero dimension.")
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if shape is not None:
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if 0 in shape:
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raise ValueError("Shape can not contain zero value.")
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# If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
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if init is None:
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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)
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cache_tensor_ptr_(tensor.cache_tensor_ptr_),
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hashmap_tensor_ptr_(tensor.hashmap_tensor_ptr_),
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padding_type_(tensor.padding_type()),
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device_event_(tensor.device_event_) {
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CheckShape(tensor.shape_);
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}
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device_event_(tensor.device_event_) {}
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Tensor::Tensor(const Tensor &tensor, TypeId data_type)
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: MetaTensor(data_type, tensor.shape_),
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@ -481,43 +479,29 @@ Tensor::Tensor(const Tensor &tensor, TypeId data_type)
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cache_tensor_ptr_(tensor.cache_tensor_ptr_),
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hashmap_tensor_ptr_(tensor.hashmap_tensor_ptr_),
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padding_type_(tensor.padding_type()),
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device_event_(tensor.device_event_) {
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CheckShape(tensor.shape_);
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}
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device_event_(tensor.device_event_) {}
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Tensor::Tensor(TypeId data_type, const ShapeVector &shape, TensorDataPtr data)
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: MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) {
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CheckShape(shape);
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}
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: MetaTensor(data_type, shape), data_(std::move(data)), id_(MakeId()) {}
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Tensor::Tensor(TypeId data_type, const ShapeVector &shape)
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: Tensor(data_type, shape, MakeTensorData(data_type, shape)) {
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CheckShape(shape);
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}
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: Tensor(data_type, shape, MakeTensorData(data_type, shape)) {}
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Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, size_t data_len)
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: Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) {
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CheckShape(shape);
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}
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: Tensor(data_type, shape, MakeTensorData(data_type, shape, data, data_len)) {}
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Tensor::Tensor(TypeId data_type, const ShapeVector &shape, void *data, TypeId src_data_type)
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: Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) {
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CheckShape(shape);
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}
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: Tensor(data_type, shape, MakeTensorData(data_type, shape, data, src_data_type)) {}
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Tensor::Tensor(const std::vector<int64_t> &input, const TypePtr &data_type)
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: MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {static_cast<int>(input.size())}),
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data_(MakeTensorData(data_type_, shape_, input.data(), input.size())),
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id_(MakeId()) {
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CheckShape(shape_);
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}
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id_(MakeId()) {}
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Tensor::Tensor(const std::vector<double> &input, const TypePtr &data_type)
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: MetaTensor(TypeIdOf(data_type, kNumberTypeFloat32), {static_cast<int>(input.size())}),
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data_(MakeTensorData(data_type_, shape_, input.data(), input.size())),
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id_(MakeId()) {
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CheckShape(shape_);
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}
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id_(MakeId()) {}
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Tensor::Tensor(int64_t input, const TypePtr &data_type)
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: MetaTensor(TypeIdOf(data_type, kNumberTypeInt32), {}),
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@ -622,17 +606,6 @@ std::string Tensor::ToStringRepr() const {
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return buf.str();
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}
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void Tensor::CheckShape(const ShapeVector &shape) const {
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// Check tensor's shape, ignore one-dimensional tensor, including empty tensor with shape=(0,).
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if (shape.size() > 1) {
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for (const auto &s : shape) {
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if (s == 0) {
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MS_EXCEPTION(ValueError) << "Zero is not supported in the shape of Tensor. ";
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}
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}
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}
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}
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void Tensor::data_sync(bool need_wait) const {
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if (need_wait) {
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Wait();
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@ -2981,7 +2981,8 @@ class StridedSlice(PrimitiveWithInfer):
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ret_shape = self._compute_slicing_shape(x['shape'], begin_v, end_v, strides_v)
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value = None if all(ret_shape) else Tensor(np.array([]).reshape(ret_shape), x['dtype'].element_type())
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value = None if all(ret_shape) else Tensor(np.array([]).reshape(ret_shape), x['dtype'].element_type(),
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check_zero_dims=False)
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if "max_value" in x and "min_value" in x:
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validator.check_value_type("min_value", x["min_value"], [tuple, list], self.name)
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validator.check_value_type("max_value", x["max_value"], [tuple, list], self.name)
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@ -67,17 +67,6 @@ def test_tensor():
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assert isinstance(t4, ms.Tensor)
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assert t4.dtype == ms.int64
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def test_tensor_empty():
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t = ms.Tensor(np.ones(0), ms.float32)
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assert isinstance(t, ms.Tensor)
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assert t.shape == (0,)
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def test_tensor_shape_has_zero():
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with pytest.raises(ValueError):
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t = ms.Tensor(np.ones((1, 0)), ms.float32)
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print(t)
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def test_tensor_type_float16():
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t_float16 = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float16))
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@ -0,0 +1,59 @@
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# Copyright 2021 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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import numpy as np
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import pytest
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import mindspore
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops import operations as P
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from mindspore.common.initializer import One
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context.set_context(mode=context.GRAPH_MODE)
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def test_zero_dimension_list():
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Tensor([])
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with pytest.raises(ValueError) as ex:
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Tensor([[]])
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assert "input_data can not contain zero dimension." in str(ex.value)
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def test_zero_dimension_np_array():
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with pytest.raises(ValueError) as ex:
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Tensor(np.ones((1, 0, 3)))
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assert "input_data can not contain zero dimension." in str(ex.value)
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def test_zero_dimension_with_zero_shape():
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with pytest.raises(ValueError) as ex:
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Tensor(shape=(1, 0, 3), dtype=mindspore.float32, init=One())
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assert "Shape can not contain zero value." in str(ex.value)
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def test_zero_dimension_with_operator():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.strided_slice = P.StridedSlice()
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def construct(self, x):
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a = self.strided_slice(x, (2, 4, 4), (-1, 2, 1), (1, 1, 1))
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return a
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x = Tensor(np.ones((1, 3, 3)))
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net = Net()
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net(x)
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