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417 lines
14 KiB
417 lines
14 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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"""Tensor implementation."""
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import numpy as np
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from .._c_expression import Tensor as Tensor_
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from .._c_expression import MetaTensor
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from .._checkparam import check_type, check_typename
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from . import dtype as mstype
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from ._register_for_tensor import tensor_operator_registry
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__all__ = ['Tensor', 'MetaTensor', 'RowTensor', 'SparseTensor']
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np_types = (np.int8, np.int16, np.int32, np.int64,
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np.uint8, np.uint16, np.uint32, np.uint64, np.float16,
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np.float32, np.float64, np.bool_)
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class Tensor(Tensor_):
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"""
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Tensor is used for data storage.
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Tensor inherits tensor object in C++.
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Some functions are implemented in C++ and some functions are implemented in Python.
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Args:
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input_data (Tensor, float, int, bool, tuple, list, numpy.ndarray): Input data of the tensor.
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dtype (:class:`mindspore.dtype`): Input data should be None, bool or numeric type defined in `mindspore.dtype`.
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The argument is used to define the data type of the output tensor. If it is None, the data type of the
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output tensor will be as same as the `input_data`. Default: None.
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Outputs:
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Tensor, with the same shape as `input_data`.
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Examples:
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>>> # initialize a tensor with input data
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>>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32)
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>>> assert isinstance(t1, Tensor)
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>>> assert t1.shape == (1, 2, 3)
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>>> assert t1.dtype == mindspore.float32
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>>>
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>>> # initialize a tensor with a float scalar
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>>> t2 = Tensor(0.1)
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>>> assert isinstance(t2, Tensor)
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>>> assert t2.dtype == mindspore.float64
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"""
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def __init__(self, input_data, dtype=None):
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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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input_data = np.array(input_data)
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# If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
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check_type('tensor input_data', input_data, (Tensor_, float, int))
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if dtype is not None:
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check_typename('dtype', dtype, mstype.number_type + (mstype.bool_,))
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if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']):
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input_data = np.ascontiguousarray(input_data)
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if dtype is None:
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Tensor_.__init__(self, input_data)
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else:
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Tensor_.__init__(self, input_data, dtype)
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self._virtual_flag = False
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def __repr__(self):
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return Tensor_.__repr__(self)
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def __add__(self, other):
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out = tensor_operator_registry.get('__add__')(self, other)
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return out
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def __eq__(self, other):
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if not isinstance(other, (int, float, Tensor)):
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return False
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# bool type is not supported for `Equal` operator in backend.
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if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_):
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if isinstance(other, Tensor):
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return Tensor(np.array(self.asnumpy() == other.asnumpy()))
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return Tensor(np.array(self.asnumpy() == other))
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return tensor_operator_registry.get('__eq__')(self, other)
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def __ne__(self, other):
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if not isinstance(other, (int, float, Tensor)):
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return True
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# bool type is not supported for `NotEqual` operator in backend.
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if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_):
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return Tensor(np.array(self.asnumpy() != other.asnumpy()))
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return tensor_operator_registry.get('__ne__')(self, other)
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def __hash__(self):
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return hash(id(self))
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def __mul__(self, other):
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out = tensor_operator_registry.get('__mul__')(self, other)
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return out
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def __neg__(self):
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out = tensor_operator_registry.get('__neg__')(self)
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return out
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def __bool__(self):
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data = self.asnumpy()
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if data.shape == ():
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return bool(data)
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if data.shape == (1,):
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return bool(data[0])
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raise ValueError("The truth value of an array with several elements is ambiguous.")
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def __pos__(self):
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return self
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def __iadd__(self, other):
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return self.__add__(other)
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def __radd__(self, other):
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out = tensor_operator_registry.get('__add__')(self, other)
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return out
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def __imul__(self, other):
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return self.__mul__(other)
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def __rmul__(self, other):
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out = tensor_operator_registry.get('__mul__')(self, other)
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return out
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def __truediv__(self, other):
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out = tensor_operator_registry.get('__truediv__')(self, other)
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return out
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def __rtruediv__(self, other):
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out = tensor_operator_registry.get('__truediv__')(other, self)
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return out
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def __sub__(self, other):
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out = tensor_operator_registry.get('__sub__')(self, other)
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return out
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def __isub__(self, other):
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return self.__sub__(other)
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def __rsub__(self, other):
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out = tensor_operator_registry.get('__sub__')(other, self)
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return out
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def __lt__(self, other):
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out = tensor_operator_registry.get('__lt__')(self, other)
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return out
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def __le__(self, other):
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out = tensor_operator_registry.get('__le__')(self, other)
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return out
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def __getitem__(self, index):
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out = tensor_operator_registry.get('__getitem__')(self, index)
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return out
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def __setitem__(self, index, value):
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out = tensor_operator_registry.get('__setitem__')(self, index, value)
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self.assign_value(out)
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return self
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def __gt__(self, other):
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out = tensor_operator_registry.get('__gt__')(self, other)
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return out
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def __ge__(self, other):
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out = tensor_operator_registry.get('__ge__')(self, other)
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return out
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def __len__(self):
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out = tensor_operator_registry.get('shape')(self)
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if not out:
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return 1
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return out[0]
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def __mod__(self, other):
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return tensor_operator_registry.get('__mod__')(self, other)
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def __imod__(self, other):
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return self.__mod__(other)
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def __rmod__(self, other):
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return tensor_operator_registry.get('__mod__')(other, self)
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def __pow__(self, other):
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return tensor_operator_registry.get('__pow__')(self, other)
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def __floordiv__(self, other):
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return tensor_operator_registry.get('__floordiv__')(self, other)
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def __ifloordiv__(self, other):
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return self.__floordiv__(other)
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def __rfloordiv__(self, other):
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return tensor_operator_registry.get('__floordiv__')(other, self)
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def __str__(self):
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if self.dtype == mstype.type_none:
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return "Unknown Tensor type!"
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return str(self.asnumpy())
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@property
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def shape(self):
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"""The shape of tensor is a tuple."""
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return self._shape
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@property
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def dtype(self):
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"""The dtype of tensor is a mindspore type."""
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return self._dtype
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@property
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def virtual_flag(self):
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"""Mark tensor is virtual."""
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return self._virtual_flag
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@virtual_flag.setter
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def virtual_flag(self, value):
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"""The setter of virtual_flag."""
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if not isinstance(value, bool):
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raise TypeError("virtual_flag must be bool.")
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self._virtual_flag = value
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@staticmethod
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def from_numpy(array):
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"""Convert numpy array to Tensor without copy data."""
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return Tensor(Tensor_.from_numpy(array))
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def asnumpy(self):
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"""Convert tensor to numpy array."""
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return Tensor_.asnumpy(self)
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def all(self, axis=(), keep_dims=False):
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"""
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Check all array elements along a given axis evaluate to True.
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Args:
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axis (Union[None, int, tuple(int)): Dimensions of reduction,
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when axis is None or empty tuple, reduce all dimensions.
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Default: (), reduce all dimensions.
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keep_dims (bool): Whether to keep the reduced dimensions.
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Default : False, don't keep these reduced dimensions.
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Returns:
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Tensor, has the same data type as x.
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"""
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if axis is None:
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axis = ()
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return tensor_operator_registry.get('all')(keep_dims)(self, axis)
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def any(self, axis=(), keep_dims=False):
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"""
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Check any array element along a given axis evaluate to True.
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Args:
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axis (Union[None, int, tuple(int)): Dimensions of reduction,
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when axis is None or empty tuple, reduce all dimensions.
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Default: (), reduce all dimensions.
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keep_dims (bool): Whether to keep the reduced dimensions.
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Default : False, don't keep these reduced dimensions.
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Returns:
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Tensor, has the same data type as x.
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"""
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if axis is None:
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axis = ()
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return tensor_operator_registry.get('any')(keep_dims)(self, axis)
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class RowTensor:
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"""
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A sparse representation of a set of tensor slices at given indices.
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An RowTensor is typically used to represent a subset of a larger
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tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
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The values in indices are the indices in the first dimension of the slices
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that have been extracted from the larger tensor.
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The dense tensor dense represented by an RowTensor slices has
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`dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
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RowTensor can only be used in the `Cell`'s construct method.
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It is not supported in pynative mode at the moment.
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Args:
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indices (Tensor): A 1-D integer Tensor of shape [D0].
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values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn].
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dense_shape (tuple): An integer tuple which contains the shape
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of the corresponding dense tensor.
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Returns:
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RowTensor, composed of `indices`, `values`, and `dense_shape`.
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Examples:
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>>> class Net(nn.Cell):
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>>> def __init__(self, dense_shape):
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>>> super(Net, self).__init__()
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>>> self.dense_shape = dense_shape
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>>> def construct(self, indices, values):
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>>> x = RowTensor(indices, values, self.dense_shape)
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>>> return x.values, x.indices, x.dense_shape
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>>>
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>>> indices = Tensor([0])
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>>> values = Tensor([[1, 2]], dtype=ms.float32)
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>>> Net((3, 2))(indices, values)
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"""
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def __init__(self, indices, values, dense_shape):
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"Init RowTensor"
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self.__indices = indices
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self.__values = values
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self.__dense_shape = dense_shape
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@property
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def indices(self):
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return self.__indices
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@property
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def values(self):
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return self.__values
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@property
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def dense_shape(self):
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return self.__dense_shape
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class SparseTensor:
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"""
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A sparse representation of a set of nonzero elememts from a tensor at given indices.
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SparseTensor can only be used in the `Cell`'s construct method.
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Pynative mode not supported at the moment.
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For a tensor dense, its SparseTensor(indices, values, dense_shape) has
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`dense[indices[i]] = values[i]`.
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Args:
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indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
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where N and ndims are the number of values and number of dimensions in
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the SparseTensor, respectively.
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values (Tensor): A 1-D tensor of any type and shape `[N]`, which
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supplies the values for each element in indices.
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dense_shape (tuple): A integer tuple of size `ndims`,
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which specifies the dense_shape of the sparse tensor.
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Returns:
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SparseTensor, composed of `indices`, `values`, and `dense_shape`.
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Examples:
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>>> class Net(nn.Cell):
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>>> def __init__(self, dense_shape):
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>>> super(Net, self).__init__()
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>>> self.dense_shape = dense_shape
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>>> def construct(self, indices, values):
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>>> x = SparseTensor(indices, values, self.dense_shape)
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>>> return x.values, x.indices, x.dense_shape
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>>>
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>>> indices = Tensor([[0, 1], [1, 2]])
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>>> values = Tensor([1, 2], dtype=ms.float32)
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>>> Net((3, 4))(indices, values)
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"""
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def __init__(self, indices, values, dense_shape):
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"Init SparseTensor"
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self.__indices = indices
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self.__values = values
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self.__dense_shape = dense_shape
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@property
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def indices(self):
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return self.__indices
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@property
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def values(self):
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return self.__values
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@property
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def dense_shape(self):
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return self.__dense_shape
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def _vm_compare(*args):
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"""Implement `vm_compare` for tensor."""
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obj_str = args[-1]
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if obj_str == "shape":
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fn = getattr(args[0].asnumpy(), obj_str)
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return fn
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if len(args) == 2:
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fn = getattr(args[0].asnumpy(), obj_str)
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return Tensor(fn())
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if isinstance(args[0], Tensor):
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fn = getattr(args[0].asnumpy(), obj_str)
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y = args[1].asnumpy() if isinstance(args[1], Tensor) else args[1]
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else:
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obj_str = "__r" + obj_str[2:]
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fn = getattr(args[1].asnumpy(), obj_str)
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y = args[0]
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return Tensor(np.array(fn(y)))
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tensor_operator_registry.register('vm_compare', _vm_compare)
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