mindspore_lite.tensor 源代码

# Copyright 2022-2023 Huawei Technologies Co., Ltd
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Tensor API.
"""
from __future__ import absolute_import
from enum import Enum

import numpy

from mindspore_lite.lib import _c_lite_wrapper
from mindspore_lite._checkparam import check_tensor_input_param, check_isinstance

__all__ = ['TensorMeta', 'DataType', 'Format', 'Tensor']


class TensorMeta:
    """
    The `TensorMeta` class defines a TensorInfo in MindSpore Lite.

    Args:
        tensor(): The info to be stored in a new TensorMeta.
    """

    def __init__(self):
        self.name = ""
        self.dtype = DataType.UNKNOWN
        self.shape = []
        self.format = Format.DEFAULT
        self.element_num = 0
        self.data_size = 0

    def __str__(self):
        res = f"name: {self.name},\n" \
              f"dtype: {self.dtype},\n" \
              f"shape: {self.shape},\n" \
              f"format: {self.format},\n" \
              f"element_num: {self.element_num},\n" \
              f"data_size: {self.data_size}."
        return res


[文档]class DataType(Enum): """ The `DataType` class defines the data type of the Tensor in MindSpore Lite. Currently, the following 'DataType' are supported: =========================== ================================================================== Definition Description =========================== ================================================================== `DataType.UNKNOWN` No matching any of the following known types. `DataType.BOOL` Boolean `True` or `False` . `DataType.INT8` 8-bit integer. `DataType.INT16` 16-bit integer. `DataType.INT32` 32-bit integer. `DataType.INT64` 64-bit integer. `DataType.UINT8` unsigned 8-bit integer. `DataType.UINT16` unsigned 16-bit integer. `DataType.UINT32` unsigned 32-bit integer. `DataType.UINT64` unsigned 64-bit integer. `DataType.FLOAT16` 16-bit floating-point number. `DataType.FLOAT32` 32-bit floating-point number. `DataType.FLOAT64` 64-bit floating-point number. `DataType.INVALID` The maximum threshold value of DataType to prevent invalid types. =========================== ================================================================== Examples: >>> # Method 1: Import mindspore_lite package >>> import mindspore_lite as mslite >>> print(mslite.DataType.FLOAT32) DataType.FLOAT32 >>> # Method 2: from mindspore_lite package import DataType >>> from mindspore_lite import DataType >>> print(DataType.FLOAT32) DataType.FLOAT32 """ UNKNOWN = 0 BOOL = 30 INT8 = 32 INT16 = 33 INT32 = 34 INT64 = 35 UINT8 = 37 UINT16 = 38 UINT32 = 39 UINT64 = 40 FLOAT16 = 42 FLOAT32 = 43 FLOAT64 = 44 INVALID = 2147483647 # INT32_MAX
[文档]class Format(Enum): """ The `Format` class defines the format of the Tensor in MindSpore Lite. Currently, the following 'Format' are supported: =========================== =================================================================================== Definition Description =========================== =================================================================================== `Format.DEFAULT` default format. `Format.NCHW` Store Tensor data in the order of batch N, channel C, height H and width W. `Format.NHWC` Store Tensor data in the order of batch N, height H, width W and channel C. `Format.NHWC4` C-axis 4-byte aligned `Format.NHWC` . `Format.HWKC` Store Tensor data in the order of height H, width W, kernel num K and channel C. `Format.HWCK` Store Tensor data in the order of height H, width W, channel C and kernel num K. `Format.KCHW` Store Tensor data in the order of kernel num K, channel C, height H and width W. `Format.CKHW` Store Tensor data in the order of channel C, kernel num K, height H and width W. `Format.KHWC` Store Tensor data in the order of kernel num K, height H, width W and channel C. `Format.CHWK` Store Tensor data in the order of channel C, height H, width W and kernel num K. `Format.HW` Store Tensor data in the order of height H and width W. `Format.HW4` w-axis 4-byte aligned `Format.HW` . `Format.NC` Store Tensor data in the order of batch N and channel C. `Format.NC4` C-axis 4-byte aligned `Format.NC` . `Format.NC4HW4` C-axis 4-byte aligned and W-axis 4-byte aligned `Format.NCHW` . `Format.NCDHW` Store Tensor data in the order of batch N, channel C, depth D, height H and width W. `Format.NWC` Store Tensor data in the order of batch N, width W and channel C. `Format.NCW` Store Tensor data in the order of batch N, channel C and width W. `Format.NDHWC` Store Tensor data in the order of batch N, depth D, height H, width W and channel C. `Format.NC8HW8` C-axis 8-byte aligned and W-axis 8-byte aligned `Format.NCHW` . =========================== =================================================================================== Examples: >>> # Method 1: Import mindspore_lite package >>> import mindspore_lite as mslite >>> print(mslite.Format.NHWC) Format.NHWC >>> # Method 2: from mindspore_lite package import Format >>> from mindspore_lite import Format >>> print(Format.NHWC) Format.NHWC """ DEFAULT = -1 NCHW = 0 NHWC = 1 NHWC4 = 2 HWKC = 3 HWCK = 4 KCHW = 5 CKHW = 6 KHWC = 7 CHWK = 8 HW = 9 HW4 = 10 NC = 11 NC4 = 12 NC4HW4 = 13 NCDHW = 15 NWC = 16 NCW = 17 NDHWC = 18 NC8HW8 = 19
data_type_py_cxx_map = { DataType.UNKNOWN: _c_lite_wrapper.DataType.kTypeUnknown, DataType.BOOL: _c_lite_wrapper.DataType.kNumberTypeBool, DataType.INT8: _c_lite_wrapper.DataType.kNumberTypeInt8, DataType.INT16: _c_lite_wrapper.DataType.kNumberTypeInt16, DataType.INT32: _c_lite_wrapper.DataType.kNumberTypeInt32, DataType.INT64: _c_lite_wrapper.DataType.kNumberTypeInt64, DataType.UINT8: _c_lite_wrapper.DataType.kNumberTypeUInt8, DataType.UINT16: _c_lite_wrapper.DataType.kNumberTypeUInt16, DataType.UINT32: _c_lite_wrapper.DataType.kNumberTypeUInt32, DataType.UINT64: _c_lite_wrapper.DataType.kNumberTypeUInt64, DataType.FLOAT16: _c_lite_wrapper.DataType.kNumberTypeFloat16, DataType.FLOAT32: _c_lite_wrapper.DataType.kNumberTypeFloat32, DataType.FLOAT64: _c_lite_wrapper.DataType.kNumberTypeFloat64, DataType.INVALID: _c_lite_wrapper.DataType.kInvalidType, } data_type_cxx_py_map = { _c_lite_wrapper.DataType.kTypeUnknown: DataType.UNKNOWN, _c_lite_wrapper.DataType.kNumberTypeBool: DataType.BOOL, _c_lite_wrapper.DataType.kNumberTypeInt8: DataType.INT8, _c_lite_wrapper.DataType.kNumberTypeInt16: DataType.INT16, _c_lite_wrapper.DataType.kNumberTypeInt32: DataType.INT32, _c_lite_wrapper.DataType.kNumberTypeInt64: DataType.INT64, _c_lite_wrapper.DataType.kNumberTypeUInt8: DataType.UINT8, _c_lite_wrapper.DataType.kNumberTypeUInt16: DataType.UINT16, _c_lite_wrapper.DataType.kNumberTypeUInt32: DataType.UINT32, _c_lite_wrapper.DataType.kNumberTypeUInt64: DataType.UINT64, _c_lite_wrapper.DataType.kNumberTypeFloat16: DataType.FLOAT16, _c_lite_wrapper.DataType.kNumberTypeFloat32: DataType.FLOAT32, _c_lite_wrapper.DataType.kNumberTypeFloat64: DataType.FLOAT64, _c_lite_wrapper.DataType.kInvalidType: DataType.INVALID, } numpy_data_type_map = { numpy.bool_: DataType.BOOL, numpy.int8: DataType.INT8, numpy.int16: DataType.INT16, numpy.int32: DataType.INT32, numpy.int64: DataType.INT64, numpy.uint8: DataType.UINT8, numpy.uint16: DataType.UINT16, numpy.uint32: DataType.UINT32, numpy.uint64: DataType.UINT64, numpy.float16: DataType.FLOAT16, numpy.float32: DataType.FLOAT32, numpy.float64: DataType.FLOAT64, } ms_to_numpy_data_type_map = { DataType.BOOL: numpy.bool_, DataType.INT8: numpy.int8, DataType.INT16: numpy.int16, DataType.INT32: numpy.int32, DataType.INT64: numpy.int64, DataType.UINT8: numpy.uint8, DataType.UINT16: numpy.uint16, DataType.UINT32: numpy.uint32, DataType.UINT64: numpy.uint64, DataType.FLOAT16: numpy.float16, DataType.FLOAT32: numpy.float32, DataType.FLOAT64: numpy.float64, } format_py_cxx_map = { Format.DEFAULT: _c_lite_wrapper.Format.DEFAULT_FORMAT, Format.NCHW: _c_lite_wrapper.Format.NCHW, Format.NHWC: _c_lite_wrapper.Format.NHWC, Format.NHWC4: _c_lite_wrapper.Format.NHWC4, Format.HWKC: _c_lite_wrapper.Format.HWKC, Format.HWCK: _c_lite_wrapper.Format.HWCK, Format.KCHW: _c_lite_wrapper.Format.KCHW, Format.CKHW: _c_lite_wrapper.Format.CKHW, Format.KHWC: _c_lite_wrapper.Format.KHWC, Format.CHWK: _c_lite_wrapper.Format.CHWK, Format.HW: _c_lite_wrapper.Format.HW, Format.HW4: _c_lite_wrapper.Format.HW4, Format.NC: _c_lite_wrapper.Format.NC, Format.NC4: _c_lite_wrapper.Format.NC4, Format.NC4HW4: _c_lite_wrapper.Format.NC4HW4, Format.NCDHW: _c_lite_wrapper.Format.NCDHW, Format.NWC: _c_lite_wrapper.Format.NWC, Format.NCW: _c_lite_wrapper.Format.NCW, Format.NDHWC: _c_lite_wrapper.Format.NDHWC, Format.NC8HW8: _c_lite_wrapper.Format.NC8HW8, } format_cxx_py_map = { _c_lite_wrapper.Format.DEFAULT_FORMAT: Format.DEFAULT, _c_lite_wrapper.Format.NCHW: Format.NCHW, _c_lite_wrapper.Format.NHWC: Format.NHWC, _c_lite_wrapper.Format.NHWC4: Format.NHWC4, _c_lite_wrapper.Format.HWKC: Format.HWKC, _c_lite_wrapper.Format.HWCK: Format.HWCK, _c_lite_wrapper.Format.KCHW: Format.KCHW, _c_lite_wrapper.Format.CKHW: Format.CKHW, _c_lite_wrapper.Format.KHWC: Format.KHWC, _c_lite_wrapper.Format.CHWK: Format.CHWK, _c_lite_wrapper.Format.HW: Format.HW, _c_lite_wrapper.Format.HW4: Format.HW4, _c_lite_wrapper.Format.NC: Format.NC, _c_lite_wrapper.Format.NC4: Format.NC4, _c_lite_wrapper.Format.NC4HW4: Format.NC4HW4, _c_lite_wrapper.Format.NCDHW: Format.NCDHW, _c_lite_wrapper.Format.NWC: Format.NWC, _c_lite_wrapper.Format.NCW: Format.NCW, _c_lite_wrapper.Format.NDHWC: Format.NDHWC, _c_lite_wrapper.Format.NC8HW8: Format.NC8HW8, }
[文档]class Tensor: """ The `Tensor` class defines a Tensor in MindSpore Lite. Args: tensor(Tensor, optional): The data to be stored in a new Tensor. It can be from another Tensor. Default: ``None``. shape(list, optional): The shape of the Tensor. Default: ``None``. dtype(DataType, optional): The dtype of the Tensor. Default: ``None``. device(str, optional): The device type of the Tensor. Default: ``None``. Raises: TypeError: `tensor` is neither a Tensor nor ``None``. Examples: >>> import mindspore_lite as mslite >>> tensor = mslite.Tensor() >>> tensor.name = "tensor1" >>> print(tensor.name) tensor1 >>> tensor.dtype = mslite.DataType.FLOAT32 >>> print(tensor.dtype) DataType.FLOAT32 >>> tensor.shape = [1, 3, 2, 2] >>> print(tensor.shape) [1, 3, 2, 2] >>> tensor.format = mslite.Format.NCHW >>> print(tensor.format) Format.NCHW >>> print(tensor.element_num) 12 >>> print(tensor.data_size) 48 >>> print(tensor) name: tensor1, dtype: DataType.FLOAT32, shape: [1, 3, 2, 2], format: Format.NCHW, element_num: 12, data_size: 48. """ def __init__(self, tensor=None, shape=None, dtype=None, device=None): # check shape, dtype and device check_tensor_input_param(shape, device) device_type = "" device_id = -1 if device is not None: device_type = device.split(":")[0] if len(device.split(":")) == 2: device_id = int(device.split(":")[1]) check_isinstance("dtype", dtype, DataType, True) if tensor is not None: # use tensor to init tensor if isinstance(tensor, _c_lite_wrapper.TensorBind): self._tensor = tensor elif isinstance(tensor, Tensor): tensor_shape = tensor.shape if shape is not None and list(shape) != list(tensor_shape): raise TypeError( f"user set shape is not equal numpy shape, user's shape: {shape}, " f"tensor shape is: {tensor_shape}.") tensor_dtype = tensor.dtype if dtype is not None and tensor_dtype != dtype: raise TypeError( f"user set dtype is not equal tensor dtype, user's dtype: {dtype}, " f"tensor dtype is: {tensor_dtype}.") numpy_data = tensor.get_data_to_numpy() self._tensor = _c_lite_wrapper.create_tensor_by_numpy(numpy_data, device_type, device_id) # use numpy to init tensor elif isinstance(tensor, numpy.ndarray): if not tensor.flags['FORC']: tensor = numpy.ascontiguousarray(tensor) numpy_shape = tensor.shape numpy_dtype = tensor.dtype if numpy_dtype.type not in numpy_data_type_map: raise TypeError(f"Unsupported numpy dtype value {numpy_dtype}") ms_dtype = numpy_data_type_map.get(numpy_dtype.type) if shape is not None and list(shape) != list(numpy_shape): raise TypeError( f"user set shape is not equal numpy shape, user shape: {shape}, " f"numpy shape is: {numpy_shape}.") if dtype is not None and ms_dtype != dtype: raise TypeError( f"user set dtype is not equal numpy dtype, user dtype: {dtype}, " f"numpy dtype is: {numpy_dtype}.") self._tensor = _c_lite_wrapper.create_tensor_by_numpy(tensor, device_type, device_id) else: raise TypeError( f"tensor must be MindSpore Lite's Tensor._tensor or numpy ndarray, but got {type(tensor)}.") else: if dtype is not None and shape is not None: self._tensor = _c_lite_wrapper.create_tensor(data_type_py_cxx_map.get(dtype), shape, device_type, device_id) else: self._tensor = _c_lite_wrapper.create_tensor(data_type_py_cxx_map.get(DataType.FLOAT32), (), "", -1) def __str__(self): res = f"name: {self.name},\n" \ f"dtype: {self.dtype},\n" \ f"shape: {self.shape},\n" \ f"format: {self.format},\n" \ f"element_num: {self.element_num},\n" \ f"data_size: {self.data_size}.\n" \ f"device: {self.device}." return res @property def data_size(self): """ Get the data size of the Tensor. Data size of the Tensor = the element num of the Tensor * size of unit data type of the Tensor. Returns: int, the data size of the Tensor data. """ return self._tensor.get_data_size() @property def dtype(self): """ Get the data type of the Tensor. Returns: DataType, the data type of the Tensor. """ return data_type_cxx_py_map.get(self._tensor.get_data_type()) @dtype.setter def dtype(self, dtype): """ Set data type for the Tensor. Args: dtype (DataType): The data type of the Tensor. For details, see `DataType <https://mindspore.cn/lite/api/en/r2.3.0rc1/mindspore_lite/mindspore_lite.DataType.html>`_ . Raises: TypeError: `dtype` is not a DataType. """ if not isinstance(dtype, DataType): raise TypeError(f"dtype must be DataType, but got {type(dtype)}.") self._tensor.set_data_type(data_type_py_cxx_map.get(dtype)) @property def element_num(self): """ Get the element num of the Tensor. Returns: int, the element num of the Tensor data. """ return self._tensor.get_element_num() @property def format(self): """ Get the format of the Tensor. Returns: Format, the format of the Tensor. """ return format_cxx_py_map.get(self._tensor.get_format()) @format.setter def format(self, tensor_format): """ Set format of the Tensor. Args: tensor_format (Format): The format of the Tensor. For details, see `Format <https://mindspore.cn/lite/api/en/r2.3.0rc1/mindspore_lite/mindspore_lite.Format.html>`_ . Raises: TypeError: `tensor_format` is not a Format. """ if not isinstance(tensor_format, Format): raise TypeError(f"format must be Format, but got {type(tensor_format)}.") self._tensor.set_format(format_py_cxx_map.get(tensor_format)) @property def name(self): """ Get the name of the Tensor. Returns: str, the name of the Tensor. """ return self._tensor.get_tensor_name() @name.setter def name(self, name): """ Set the name of the Tensor. Args: name (str): The name of the Tensor. Raises: TypeError: `name` is not a str. """ if not isinstance(name, str): raise TypeError(f"name must be str, but got {type(name)}.") self._tensor.set_tensor_name(name) @property def shape(self): """ Get the shape of the Tensor. Returns: list[int], the shape of the Tensor. """ return self._tensor.get_shape() @shape.setter def shape(self, shape): """ Set shape for the Tensor. Args: shape (list[int]): The shape of the Tensor. Raises: TypeError: `shape` is not a list. TypeError: `shape` is a list, but the elements is not int. """ if not isinstance(shape, (list, tuple)): raise TypeError(f"shape must be list or tuple, but got {type(shape)}.") for i, element in enumerate(shape): if not isinstance(element, int): raise TypeError(f"shape element must be int, but got {type(element)} at index {i}.") self._tensor.set_shape(shape)
[文档] def get_data_to_numpy(self): """ Get the data from the Tensor to the numpy object. Returns: numpy.ndarray, the numpy object from Tensor data. Examples: >>> import mindspore_lite as mslite >>> import numpy as np >>> tensor = mslite.Tensor() >>> tensor.shape = [1, 3, 2, 2] >>> tensor.dtype = mslite.DataType.FLOAT32 >>> in_data = np.arange(1 * 3 * 2 * 2, dtype=np.float32) >>> tensor.set_data_from_numpy(in_data) >>> data = tensor.get_data_to_numpy() >>> print(data) [[[[ 0. 1.] [ 2. 3.]] [[ 4. 5.] [ 6. 7.]] [[ 8. 9.] [ 10. 11.]]]] """ return self._tensor.get_data_to_numpy()
[文档] def set_data_from_numpy(self, numpy_obj): """ Set the data for the Tensor from the numpy object. Args: numpy_obj(numpy.ndarray): the numpy object. Raises: TypeError: `numpy_obj` is not a numpy.ndarray. RuntimeError: The data type of `numpy_obj` is not equivalent to the data type of the Tensor. RuntimeError: The data size of `numpy_obj` is not equal to the data size of the Tensor. Examples: >>> # 1. set Tensor data which is from file >>> import mindspore_lite as mslite >>> import numpy as np >>> tensor = mslite.Tensor() >>> tensor.shape = [1, 3, 224, 224] >>> tensor.dtype = mslite.DataType.FLOAT32 >>> in_data = np.fromfile("input.bin", dtype=np.float32) >>> tensor.set_data_from_numpy(in_data) >>> print(tensor) name: , dtype: DataType.FLOAT32, shape: [1, 3, 224, 224], format: Format.NCHW, element_num: 150528, data_size: 602112. >>> # 2. set Tensor data which is numpy arange >>> import mindspore_lite as mslite >>> import numpy as np >>> tensor = mslite.Tensor() >>> tensor.shape = [1, 3, 2, 2] >>> tensor.dtype = mslite.DataType.FLOAT32 >>> in_data = np.arange(1 * 3 * 2 * 2, dtype=np.float32) >>> tensor.set_data_from_numpy(in_data) >>> print(tensor) name: , dtype: DataType.FLOAT32, shape: [1, 3, 2, 2], format: Format.NCHW, element_num: 12, data_size: 48. """ if not isinstance(numpy_obj, numpy.ndarray): raise TypeError(f"numpy_obj must be numpy.ndarray, but got {type(numpy_obj)}.") if not numpy_obj.flags['FORC']: numpy_obj = numpy.ascontiguousarray(numpy_obj) if numpy_obj.dtype.type not in numpy_data_type_map: raise TypeError(f"Unsupported numpy dtype value {numpy_obj.dtype}") if numpy_data_type_map.get(numpy_obj.dtype.type) != self.dtype: raise RuntimeError( f"data type not equal! Numpy type: {numpy_obj.dtype.type}, Tensor type: {self.dtype}") if numpy_obj.nbytes != self.data_size: raise RuntimeError( f"data size not equal! Numpy size: {numpy_obj.nbytes}, Tensor size: {self.data_size}") self._tensor.set_data_from_numpy(numpy_obj)
@property def device(self): """ Get the device type of the Tensor. Returns: str, the device type of the Tensor. """ return self._tensor.get_tensor_device_type()