Source code for mindspore.common.tensor

# Copyright 2020 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
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# 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
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# ============================================================================
"""Tensor implementation."""
import numpy as np

from .._c_expression import Tensor as Tensor_
from .._c_expression import MetaTensor
from .._checkparam import check_type, check_typename
from . import dtype as mstype
from ._register_for_tensor import tensor_operator_registry

__all__ = ['Tensor', 'MetaTensor']
np_types = (np.int8, np.int16, np.int32, np.int64,
            np.uint8, np.uint16, np.uint32, np.uint64, np.float16,
            np.float32, np.float64, np.bool_)



[docs]class Tensor(Tensor_): """ Tensor for data storage. Tensor inherits tensor object in C++ side, some functions are implemented in C++ side and some functions are implemented in Python layer. Args: input_data (Tensor, float, int, bool, tuple, list, numpy.ndarray): Input data of the tensor. dtype (:class:`mindspore.dtype`): Should be None, bool or numeric type defined in `mindspore.dtype`. The argument is used to define the data type of the output tensor. If it is None, the data type of the output tensor will be as same as the `input_data`. Default: None. Outputs: Tensor, with the same shape as `input_data`. Examples: >>> # init a tensor with input data >>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32) >>> assert isinstance(t1, Tensor) >>> assert t1.shape == (1, 2, 3) >>> assert t1.dtype == mindspore.float32 >>> >>> # init a tensor with a float scalar >>> t2 = Tensor(0.1) >>> assert isinstance(t2, Tensor) >>> assert t2.dtype == mindspore.float64 """ def __init__(self, input_data, dtype=None): # If input data is numpy number, convert it to np array if isinstance(input_data, np_types): input_data = np.array(input_data) # If input_data is tuple/list/numpy.ndarray, it's support in check_type method. check_type('tensor input_data', input_data, (Tensor_, float, int)) if dtype is not None: check_typename('dtype', dtype, mstype.number_type + (mstype.bool_,)) if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']): input_data = np.ascontiguousarray(input_data) if dtype is None: super(Tensor, self).__init__(input_data) else: super(Tensor, self).__init__(input_data, dtype) self._virtual_flag = False self._init_flag = False def __repr__(self): return str(self.__str__()) def __add__(self, other): out = tensor_operator_registry.get('__add__')(self, other) return out def __eq__(self, other): if not isinstance(other, (int, float, Tensor)): return False # bool type is not supported for `Equal` operator in backend. if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_): return Tensor(np.array(self.asnumpy() == other.asnumpy())) return tensor_operator_registry.get('__eq__')(self, other) def __ne__(self, other): if not isinstance(other, (int, float, Tensor)): return True # bool type is not supported for `NotEqual` operator in backend. if self.dtype == mstype.bool_ or (isinstance(other, Tensor) and other.dtype == mstype.bool_): return Tensor(np.array(self.asnumpy() != other.asnumpy())) return tensor_operator_registry.get('__ne__')(self, other) def __hash__(self): return hash(id(self)) def __mul__(self, other): out = tensor_operator_registry.get('__mul__')(self, other) return out def __neg__(self): out = tensor_operator_registry.get('__neg__')(self) return out def __iadd__(self, other): out = self.__add__(other) return out def __radd__(self, other): out = tensor_operator_registry.get('__add__')(self, other) return out def __imul__(self, other): out = self.__mul__(other) return out def __rmul__(self, other): out = tensor_operator_registry.get('__mul__')(self, other) return out def __truediv__(self, other): out = tensor_operator_registry.get('__truediv__')(self, other) return out def __rtruediv__(self, other): out = tensor_operator_registry.get('__truediv__')(other, self) return out def __sub__(self, other): out = tensor_operator_registry.get('__sub__')(self, other) return out def __isub__(self, other): out = self.__sub__(other) return out def __rsub__(self, other): out = tensor_operator_registry.get('__sub__')(other, self) return out def __lt__(self, other): out = tensor_operator_registry.get('__lt__')(self, other) return out def __le__(self, other): out = tensor_operator_registry.get('__le__')(self, other) return out def __getitem__(self, index): out = tensor_operator_registry.get('__getitem__')(self, index) return out def __setitem__(self, index, value): out = tensor_operator_registry.get('__setitem__')(self, index, value) self.assign_value(out) return self def __gt__(self, other): out = tensor_operator_registry.get('__gt__')(self, other) return out def __ge__(self, other): out = tensor_operator_registry.get('__ge__')(self, other) return out def __len__(self): out = tensor_operator_registry.get('shape')(self) if not out: return 1 return out[0] def __str__(self): if self.dtype == mstype.type_none: return "Unknown Tensor type!" return str(self.asnumpy()) @property def virtual_flag(self): """Mark tensor is virtual.""" return self._virtual_flag @virtual_flag.setter def virtual_flag(self, value): """The setter of virtual_flag.""" if not isinstance(value, bool): raise TypeError("virtual_flag must be bool.") self._virtual_flag = value @property def init_flag(self): """whether the tensor is init.""" return self._init_flag @init_flag.setter def init_flag(self, value): """Set the tensor is init_flag.""" if not isinstance(value, bool): raise TypeError("init_flag must be bool.") self.set_init_flag(value) self._init_flag = value