Source code for mindspore.dataset.transforms.transforms

# Copyright 2019-2022 Huawei Technologies Co., Ltd
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""
The module transforms provides common operations, including Compose, OneHot and TypeCast.
"""
import json
from abc import ABC

import sys
from enum import IntEnum
import numpy as np

import mindspore._c_dataengine as cde
from mindspore._c_expression import typing
from mindspore.common import dtype as mstype
import mindspore.dataset.transforms.c_transforms as c_transforms
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.dataset.vision.c_transforms as c_vision
from . import py_transforms_util as util
from .py_transforms_util import Implementation, FuncWrapper
from .validators import check_fill_value, check_slice_option, check_slice_op, check_one_hot_op, check_compose_call, \
    check_mask_op_new, check_pad_end, check_concat_type, check_random_transform_ops, check_plugin, check_type_cast
from ..core.datatypes import mstype_to_detype, nptype_to_detype
from ..vision.py_transforms_util import is_pil


class TensorOperation:
    """
    Base class Tensor Ops
    """

    def __init__(self):
        super().__init__()
        self.implementation = None
        self.callable_op_ = None

    def __call__(self, *input_tensor_list):
        """
        Call method.
        """
        # Check if Python implementation of op, or PIL input
        if (self.implementation == Implementation.PY) or \
                (len(input_tensor_list) == 1 and is_pil(input_tensor_list[0]) and getattr(self, '_execute_py', None)):
            return self._execute_py(*input_tensor_list)

        tensor_row = []
        for tensor in input_tensor_list:
            try:
                tensor_row.append(cde.Tensor(np.asarray(tensor)))
            except (RuntimeError, TypeError):
                raise TypeError("Invalid user input. Got {}: {}, cannot be converted into tensor." \
                                .format(type(tensor), tensor))
        if not hasattr(self, 'callable_op_') or self.callable_op_ is None:
            self.callable_op_ = cde.Execute(self.parse())
        output_tensor_list = self.callable_op_(tensor_row)
        output_numpy_list = [x.as_array() for x in output_tensor_list]
        return output_numpy_list[0] if len(output_numpy_list) == 1 else tuple(output_numpy_list)

    @staticmethod
    def parse():
        """parse function - not yet implemented"""
        raise NotImplementedError("TensorOperation has to implement parse() method.")


class PyTensorOperation:
    """
    Base Python Tensor Operations class
    """

    def __init__(self):
        self.transforms = []
        self.output_type = None

    def __call__(self, img):
        """
        Call method.

        Args:
            img (PIL Image): Image to be augmented.

        Returns:
            PIL Image, augmented image.
        """
        return self._execute_py(img)

    @classmethod
    def from_json(cls, json_string):
        """
        Base from_json for Python tensor operations class
        """
        json_obj = json.loads(json_string)
        new_op = cls.__new__(cls)
        new_op.__dict__ = json_obj
        if "transforms" in json_obj.keys():
            # operations which have transforms as input, need to call _from_json() for each transform to deseriallize
            transforms = []
            for json_op in json_obj["transforms"]:
                transforms.append(getattr(
                    sys.modules.get(json_op.get("python_module")),
                    json_op["tensor_op_name"]).from_json(json.dumps(json_op["tensor_op_params"])))
            new_op.transforms = transforms
        if "output_type" in json_obj.keys():
            output_type = np.dtype(json_obj["output_type"])
            new_op.output_type = output_type
        return new_op

    def to_json(self):
        """
        Base to_json for Python tensor operations class
        """
        json_obj = {}
        json_trans = {}
        if "transforms" in self.__dict__.keys():
            # operations which have transforms as input, need to call _to_json() for each transform to serialize
            json_list = []
            for transform in self.transforms:
                json_list.append(json.loads(transform.to_json()))
            json_trans["transforms"] = json_list
            self.__dict__.pop("transforms")
        if "output_type" in self.__dict__.keys():
            json_trans["output_type"] = np.dtype(
                self.__dict__["output_type"]).name
            self.__dict__.pop("output_type")
        json_obj["tensor_op_params"] = self.__dict__
        # append transforms to the tensor_op_params of the operation
        json_obj.get("tensor_op_params").update(json_trans)
        json_obj["tensor_op_name"] = self.__class__.__name__
        json_obj["python_module"] = self.__class__.__module__
        return json.dumps(json_obj)


class CompoundOperation(TensorOperation, PyTensorOperation, ABC):
    """
    Compound Tensor Operations class
    """

    def __init__(self, transforms):
        super(CompoundOperation, self).__init__()
        self.transforms = []
        trans_with_imple = []
        for op in transforms:
            if callable(op) and not hasattr(op, "implementation") and \
                    not isinstance(op, c_transforms.TensorOperation) and \
                    not isinstance(op, py_transforms.PyTensorOperation) and \
                    not isinstance(op, c_vision.ImageTensorOperation):
                op = util.FuncWrapper(op)
            if hasattr(op, "implementation"):
                if op.implementation is not None:
                    trans_with_imple.append(op)
            else:
                raise RuntimeError("Mixing old legacy c/py_transforms and new unified transforms is not allowed.")
            self.transforms.append(op)

        if all([t.implementation == Implementation.PY for t in self.transforms]):
            self.implementation = Implementation.PY
        elif all([t.implementation is not None for t in self.transforms]):
            self.implementation = Implementation.C
        elif not trans_with_imple:
            self.implementation = None
        elif all([t.implementation == Implementation.PY for t in trans_with_imple]):
            self.implementation = Implementation.PY
        elif all([t.implementation == Implementation.C for t in trans_with_imple]):
            self.implementation = Implementation.C

    @staticmethod
    def parse():
        """parse function - not yet implemented"""
        raise NotImplementedError("CompoundOperation has to implement parse() method.")

    def parse_transforms(self):
        operations = []
        for op in self.transforms:
            if op and getattr(op, 'parse', None):
                operations.append(op.parse())
            else:
                operations.append(op)
        return operations


def not_random(function):
    """
    Specify the function as "not random", i.e., it produces deterministic result.
    A Python function can only be cached after it is specified as "not random".
    """
    function.random = False
    return function


[docs]class Compose(CompoundOperation): """ Compose a list of transforms into a single transform. .. Note:: Compose takes a list of transformations in `mindspore.dataset.transforms` / `mindspore.dataset.vision` and user-defined Python callable objects to combine as single data augmentation. For user-defined Python callable objects, the return value is required to be type numpy.ndarray. Args: transforms (list): List of transformations to be applied. Raises: TypeError: If `transforms` is not of type list. ValueError: If `transforms` is empty. TypeError: If elements of `transforms` are neither Python callable objects nor data processing operations in transforms.py. Supported Platforms: ``CPU`` Examples: >>> compose = transforms.Compose([vision.Decode(), vision.RandomCrop(512)]) >>> image_folder_dataset = image_folder_dataset.map(operations=compose) >>> image_folder_dataset_dir = "/path/to/image_folder_dataset_directory" >>> >>> # create a dataset that reads all files in dataset_dir with 8 threads >>> image_folder_dataset = ds.ImageFolderDataset(image_folder_dataset_dir, num_parallel_workers=8) >>> # create a list of transformations to be applied to the image data >>> transform = transforms.Compose([vision.Decode(to_pil=True), ... vision.RandomHorizontalFlip(0.5), ... vision.ToTensor(), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), is_hwc=False), ... vision.RandomErasing()]) >>> # apply the transform to the dataset through dataset.map function >>> image_folder_dataset = image_folder_dataset.map(operations=transform, input_columns=["image"]) >>> >>> # Compose is also be invoked implicitly, by just passing in a list of ops >>> # the above example then becomes: >>> transforms_list = [vision.Decode(to_pil=True), ... vision.RandomHorizontalFlip(0.5), ... vision.ToTensor(), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262), is_hwc=False), ... vision.RandomErasing()] >>> >>> # apply the transform to the dataset through dataset.map() >>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list, input_columns=["image"]) >>> >>> # Certain C++ and Python ops can be combined, but not all of them >>> # An example of combined operations >>> arr = [0, 1] >>> dataset = ds.NumpySlicesDataset(arr, column_names=["cols"], shuffle=False) >>> transformed_list = [transforms.OneHot(2), ... transforms.Mask(transforms.Relational.EQ, 1)] >>> dataset = dataset.map(operations=transformed_list, input_columns=["cols"]) >>> >>> # Here is an example of mixing vision ops >>> import numpy as np >>> op_list=[vision.Decode(), ... vision.Resize((224, 244)), ... vision.ToPIL(), ... np.array, # need to convert PIL image to a NumPy array to pass it to C++ operation ... vision.Resize((24, 24))] >>> image_folder_dataset = image_folder_dataset.map(operations=op_list, input_columns=["image"]) """ @check_random_transform_ops def __init__(self, transforms): super().__init__(transforms) self.transforms = Compose.decompose(self.transforms) if all(hasattr(transform, "random") and not transform.random for transform in self.transforms): self.random = False
[docs] @staticmethod def decompose(operations): """ Remove all compose operation from the given list of operations. Args: operations (list): list of transforms. Returns: list of operations without compose operations. """ new_operations = [] for op in operations: if isinstance(op, Compose): new_operations.extend(Compose.decompose(op.transforms)) else: new_operations.append(op) return new_operations
[docs] @staticmethod def reduce(operations): """ Wraps adjacent Python operations in a Compose to allow mixing of Python and C++ operations. Args: operations (list): list of tensor operations. Returns: list, the reduced list of operations. """ new_ops, start_ind, end_ind = [], 0, 0 for i, op in enumerate(operations): if op.implementation == Implementation.C and not isinstance(op, FuncWrapper): # reset counts if start_ind != end_ind: if end_ind == start_ind + 1: composed_op = operations[start_ind] else: composed_op = Compose(operations[start_ind:end_ind]) composed_op.implementation = Implementation.PY new_ops.append(composed_op) new_ops.append(op) start_ind, end_ind = i + 1, i + 1 else: end_ind += 1 # do additional check in case the last operation is a Python operation if start_ind != end_ind: if end_ind == start_ind + 1: composed_op = operations[start_ind] else: composed_op = Compose(operations[start_ind:end_ind]) composed_op.implementation = Implementation.PY new_ops.append(composed_op) return new_ops
def parse(self): operations = self.parse_transforms() return cde.ComposeOperation(operations) @check_compose_call def _execute_py(self, *args): """ Execute method. Returns: lambda function, Lambda function that takes in an args to apply transformations on. """ return util.compose(self.transforms, *args)
[docs]class Concatenate(TensorOperation): """ Tensor operation that concatenates all columns into a single tensor, only 1D tenspr is supported. Args: axis (int, optional): Concatenate the tensors along given axis. Default: 0. prepend (numpy.ndarray, optional): NumPy array to be prepended to the already concatenated tensors. Default: None. append (numpy.ndarray, optional): NumPy array to be appended to the already concatenated tensors. Default: None. Raises: TypeError: If `axis` is not of type int. TypeError: If `prepend` is not of type numpy.ndarray. TypeError: If `append` is not of type numpy.ndarray. Supported Platforms: ``CPU`` Examples: >>> import numpy as np >>> # concatenate string >>> prepend_tensor = np.array(["dw", "df"], dtype='S') >>> append_tensor = np.array(["dwsdf", "df"], dtype='S') >>> concatenate_op = transforms.Concatenate(0, prepend_tensor, append_tensor) >>> data = [["This","is","a","string"]] >>> dataset = ds.NumpySlicesDataset(data) >>> dataset = dataset.map(operations=concatenate_op) """ @check_concat_type def __init__(self, axis=0, prepend=None, append=None): super().__init__() self.axis = axis self.prepend = cde.Tensor(np.array(prepend)) if prepend is not None else prepend self.append = cde.Tensor(np.array(append)) if append is not None else append self.implementation = Implementation.C def parse(self): return cde.ConcatenateOperation(self.axis, self.prepend, self.append)
[docs]class Duplicate(TensorOperation): """ Duplicate the input tensor to output, only support transform one column each time. Raises: RuntimeError: If given tensor has two columns. Supported Platforms: ``CPU`` Examples: >>> # Data before >>> # | x | >>> # +---------+ >>> # | [1,2,3] | >>> # +---------+ >>> data = [[1,2,3]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["x"]) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.Duplicate(), ... input_columns=["x"], ... output_columns=["x", "y"]) >>> # Data after >>> # | x | y | >>> # +---------+---------+ >>> # | [1,2,3] | [1,2,3] | >>> # +---------+---------+ """ def __init__(self): super().__init__() self.implementation = Implementation.C def parse(self): return cde.DuplicateOperation()
[docs]class Fill(TensorOperation): """ Tensor operation to fill all elements in the tensor with the specified value. The output tensor will have the same shape and type as the input tensor. Args: fill_value (Union[str, bytes, int, float, bool]): scalar value to fill the tensor with. Raises: TypeError: If `fill_value` is not of type str, float, bool, int or bytes. Supported Platforms: ``CPU`` Examples: >>> import numpy as np >>> # generate a 1D integer numpy array from 0 to 4 >>> def generator_1d(): ... for i in range(5): ... yield (np.array([i]),) >>> generator_dataset = ds.GeneratorDataset(generator_1d, column_names="col1") >>> # [[0], [1], [2], [3], [4]] >>> fill_op = transforms.Fill(3) >>> generator_dataset = generator_dataset.map(operations=fill_op) >>> # [[3], [3], [3], [3], [3]] """ @check_fill_value def __init__(self, fill_value): super().__init__() self.fill_value = cde.Tensor(np.array(fill_value)) self.implementation = Implementation.C def parse(self): return cde.FillOperation(self.fill_value)
[docs]class Mask(TensorOperation): r""" Mask content of the input tensor with the given predicate. Any element of the tensor that matches the predicate will be evaluated to True, otherwise False. Args: operator (Relational): relational operators, it can be any of [Relational.EQ, Relational.NE, Relational.LT, Relational.GT, Relational.LE, Relational.GE], take Relational.EQ as example, EQ refers to equal. constant (Union[str, int, float, bool]): Constant to be compared to. dtype (mindspore.dtype, optional): Type of the generated mask. Default: mindspore.dtype.bool\_. Raises: TypeError: `operator` is not of type Relational. TypeError: `constant` is not of type string int, float or bool. TypeError: `dtype` is not of type mindspore.dtype. Supported Platforms: ``CPU`` Examples: >>> from mindspore.dataset.transforms import Relational >>> # Data before >>> # | col | >>> # +---------+ >>> # | [1,2,3] | >>> # +---------+ >>> data = [[1, 2, 3]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["col"]) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.Mask(Relational.EQ, 2)) >>> # Data after >>> # | col | >>> # +--------------------+ >>> # | [False,True,False] | >>> # +--------------------+ """ @check_mask_op_new def __init__(self, operator, constant, dtype=mstype.bool_): super().__init__() self.operator = operator self.dtype = mstype_to_detype(dtype) self.constant = cde.Tensor(np.array(constant)) self.implementation = Implementation.C def parse(self): return cde.MaskOperation(DE_C_RELATIONAL.get(self.operator), self.constant, self.dtype)
[docs]class OneHot(TensorOperation): """ Tensor operation to apply one hot encoding. Args: num_classes (int): Number of classes of objects in dataset. It should be larger than the largest label number in the dataset. smoothing_rate (float, optional): Adjustable hyperparameter for label smoothing level. Default: 0.0, means no smoothing is applied. Raises: TypeError: `num_classes` is not of type int. TypeError: `smoothing_rate` is not of type float or int. ValueError: `smoothing_rate` is not in range [0.0, 1.0]. RuntimeError: Input tensor is not of type int. RuntimeError: Input tensor is not a 1-D tensor. Supported Platforms: ``CPU`` Examples: >>> # Assume that dataset has 10 classes, thus the label ranges from 0 to 9 >>> onehot_op = transforms.OneHot(num_classes=10) >>> mnist_dataset = mnist_dataset.map(operations=onehot_op, input_columns=["label"]) """ @check_one_hot_op def __init__(self, num_classes, smoothing_rate=0.0): super().__init__() self.num_classes = num_classes self.random = False self.smoothing_rate = smoothing_rate def parse(self): return cde.OneHotOperation(self.num_classes, self.smoothing_rate)
[docs]class PadEnd(TensorOperation): """ Pad input tensor according to pad_shape, input tensor needs to have same rank. Args: pad_shape (list(int)): List of integers representing the shape needed. Dimensions that set to `None` will not be padded (i.e., original dim will be used). Shorter dimensions will truncate the values. pad_value (Union[str, bytes, int, float, bool], optional): Value used to pad. Default to 0 or empty string in case of tensors of strings. Raises: TypeError: If `pad_shape` is not of type list. TypeError: If `pad_value` is not of type str, float, bool, int or bytes. TypeError: If elements of `pad_shape` is not of type int. ValueError: If elements of `pad_shape` is not of positive. Supported Platforms: ``CPU`` Examples: >>> # Data before >>> # | col | >>> # +---------+ >>> # | [1,2,3] | >>> # +---------| >>> data = [[1, 2, 3]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["col"]) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.PadEnd(pad_shape=[4], ... pad_value=10)) >>> # Data after >>> # | col | >>> # +------------+ >>> # | [1,2,3,10] | >>> # +------------| """ @check_pad_end def __init__(self, pad_shape, pad_value=None): super().__init__() self.pad_shape = cde.TensorShape(pad_shape) self.pad_value = cde.Tensor(np.array(pad_value)) if pad_value is not None else pad_value self.implementation = Implementation.C def parse(self): return cde.PadEndOperation(self.pad_shape, self.pad_value)
class Plugin(TensorOperation): """ Plugin support for MindData. Use this class to dynamically load a .so file (shared library) and execute its symbols. Args: lib_path (str): Path to .so file which is compiled to support MindData plugin. func_name (str): Name of the function to load from the .so file. user_args (str, optional): Serialized args to pass to the plugin. Only needed if "func_name" requires one. Raises: TypeError: If `lib_path` is not of type string. TypeError: If `func_name` is not of type string. TypeError: If `user_args` is not of type string. Supported Platforms: ``CPU`` Examples: >>> plugin = transforms.Plugin("pluginlib.so", "PluginDecode") >>> image_folder_dataset = image_folder_dataset.map(operations=plugin) """ @check_plugin def __init__(self, lib_path, func_name, user_args=None): super().__init__() self.lib_path = lib_path self.func_name = func_name self.user_args = str() if (user_args is None) else user_args self.implementation = Implementation.C def parse(self): return cde.PluginOperation(self.lib_path, self.func_name, self.user_args)
[docs]class RandomApply(CompoundOperation): """ Randomly perform a series of transforms with a given probability. Args: transforms (list): List of transformations to be applied. prob (float, optional): The probability to apply the transformation list. Default: 0.5. Raises: TypeError: If `transforms` is not of type list. ValueError: If `transforms` is empty. TypeError: If elements of `transforms` are neither Python callable objects nor data processing operations in transforms.py. TypeError: If `prob` is not of type float. ValueError: If `prob` is not in range [0.0, 1.0]. Supported Platforms: ``CPU`` Examples: >>> from mindspore.dataset.transforms import Compose >>> transforms_list = [vision.RandomHorizontalFlip(0.5), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), ... vision.RandomErasing()] >>> composed_transform = Compose([vision.Decode(to_pil=True), ... transforms.RandomApply(transforms_list, prob=0.6), ... vision.ToTensor()]) >>> image_folder_dataset = image_folder_dataset.map(operations=composed_transform, input_columns=["image"]) """ @check_random_transform_ops def __init__(self, transforms, prob=0.5): super().__init__(transforms) self.prob = prob def parse(self): operations = self.parse_transforms() return cde.RandomApplyOperation(self.prob, operations) def _execute_py(self, img): """ Execute method. Args: img (PIL image): Image to be randomly applied a list transformations. Returns: img (PIL image), Transformed image. """ return util.random_apply(img, self.transforms, self.prob)
[docs]class RandomChoice(CompoundOperation): """ Randomly select one transform from a list of transforms to perform operation. Args: transforms (list): List of transformations to be chosen from to apply. Raises: TypeError: If `transforms` is not of type list. ValueError: If `transforms` is empty. TypeError: If elements of `transforms` are neither Python callable objects nor data processing operations in transforms.py. Supported Platforms: ``CPU`` Examples: >>> from mindspore.dataset.transforms import Compose >>> transforms_list = [vision.RandomHorizontalFlip(0.5), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), ... vision.RandomErasing()] >>> composed_transform = Compose([vision.Decode(), ... transforms.RandomChoice(transforms_list), ... vision.ToTensor()]) >>> image_folder_dataset = image_folder_dataset.map(operations=composed_transform, input_columns=["image"]) """ @check_random_transform_ops def __init__(self, transforms): super().__init__(transforms) def parse(self): operations = self.parse_transforms() return cde.RandomChoiceOperation(operations) def _execute_py(self, img): """ Execute method. Args: img (PIL image): Image to be applied transformation. Returns: img (PIL image), Transformed image. """ return util.random_choice(img, self.transforms)
[docs]class RandomOrder(PyTensorOperation): """ Perform a series of transforms to the input image in a random order. Args: transforms (list): List of the transformations to apply. Raises: TypeError: If `transforms` is not of type list. TypeError: If elements of `transforms` are neither Python callable objects nor data processing operations in mindspore.dataset.transforms.transforms. ValueError: If `transforms` is empty. Supported Platforms: ``CPU`` Examples: >>> from mindspore.dataset.transforms import Compose >>> transforms_list = [vision.RandomHorizontalFlip(0.5), ... vision.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), ... vision.RandomErasing()] >>> composed_transform = Compose([vision.Decode(to_pil=False), ... transforms.RandomOrder(transforms_list), ... vision.ToTensor()]) >>> image_folder_dataset = image_folder_dataset.map(operations=composed_transform, input_columns=["image"]) """ @check_random_transform_ops def __init__(self, transforms): super().__init__() self.transforms = transforms self.implementation = Implementation.PY def _execute_py(self, img): """ Execute method. Args: img (PIL image): Image to apply transformations in a random order. Returns: img (PIL image), Transformed image. """ return util.random_order(img, self.transforms)
[docs]class Relational(IntEnum): """ Relationship operator. Possible enumeration values are: Relational.EQ, Relational.NE, Relational.GT, Relational.GE, Relational.LT, Relational.LE. - Relational.EQ: refers to Equality. - Relational.NE: refers not equal, or Inequality. - Relational.GT: refers to Greater than. - Relational.GE: refers to Greater than or equal to. - Relational.LT: refers to Less than. - Relational.LE: refers to Less than or equal to. """ EQ = 0 NE = 1 GT = 2 GE = 3 LT = 4 LE = 5
DE_C_RELATIONAL = {Relational.EQ: cde.RelationalOp.EQ, Relational.NE: cde.RelationalOp.NE, Relational.GT: cde.RelationalOp.GT, Relational.GE: cde.RelationalOp.GE, Relational.LT: cde.RelationalOp.LT, Relational.LE: cde.RelationalOp.LE} class _SliceOption(cde.SliceOption): """ Internal class SliceOption to be used with SliceOperation Args: _SliceOption(Union[int, list(int), slice, None, Ellipsis, bool, _SliceOption]): 1. :py:obj:`int`: Slice this index only along the dimension. Negative index is supported. 2. :py:obj:`list(int)`: Slice these indices along the dimension. Negative indices are supported. 3. :py:obj:`slice`: Slice the generated indices from the slice object along the dimension. 4. :py:obj:`None`: Slice the whole dimension. Similar to :py:obj:`:` in Python indexing. 5. :py:obj:`Ellipsis`: Slice the whole dimension. Similar to :py:obj:`:` in Python indexing. 6. :py:obj:`boolean`: Slice the whole dimension. Similar to :py:obj:`:` in Python indexing. """ @check_slice_option def __init__(self, slice_option): if isinstance(slice_option, int) and not isinstance(slice_option, bool): slice_option = [slice_option] elif slice_option is Ellipsis: slice_option = True elif slice_option is None: slice_option = True super().__init__(slice_option)
[docs]class Slice(TensorOperation): """ Slice operation to extract a tensor out using the given n slices. The functionality of Slice is similar to NumPy's indexing feature (Currently only rank-1 tensors are supported). Args: slices (Union[int, list[int], slice, None, Ellipsis]): Maximum `n` number of arguments to slice a tensor of rank `n` . One object in slices can be one of: 1. :py:obj:`int`: Slice this index only along the first dimension. Negative index is supported. 2. :py:obj:`list(int)`: Slice these indices along the first dimension. Negative indices are supported. 3. :py:obj:`slice`: Slice the generated indices from the `slice <https://docs.python.org/3.7/library/functions.html?highlight=slice#slice>`_ object along the first dimension. Similar to start:stop:step. 4. :py:obj:`None`: Slice the whole dimension. Similar to :py:obj:`[:]` in Python indexing. 5. :py:obj:`Ellipsis`: Slice the whole dimension, same result with `None` . Raises: TypeError: If `slices` is not of type int, list[int], :py:obj:`slice` , :py:obj:`None` or :py:obj:`Ellipsis` . Supported Platforms: ``CPU`` Examples: >>> # Data before >>> # | col | >>> # +---------+ >>> # | [1,2,3] | >>> # +---------| >>> data = [[1, 2, 3]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["col"]) >>> # slice indices 1 and 2 only >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms.Slice(slice(1,3))) >>> # Data after >>> # | col | >>> # +---------+ >>> # | [2,3] | >>> # +---------| """ @check_slice_op def __init__(self, *slices): super().__init__() slice_input_ = list(slices) slice_input_ = [_SliceOption(slice_dim) for slice_dim in slice_input_] self.slice_input_ = slice_input_ self.implementation = Implementation.C def parse(self): return cde.SliceOperation(self.slice_input_)
[docs]class TypeCast(TensorOperation): """ Tensor operation to cast to a given MindSpore data type or NumPy data type. Note: This operation supports running on Ascend or GPU platforms by Offload. Args: data_type (Union[mindspore.dtype, numpy.dtype]): mindspore.dtype or numpy.dtype (e.g. `numpy.float32`) to be cast to. Raises: TypeError: If `data_type` is not of MindSpore data type bool, int, float, string or type :class:`numpy.dtype` . Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import dtype as mstype >>> >>> # Generate 1d int numpy array from 0 - 63 >>> def generator_1d(): ... for i in range(64): ... yield (np.array([i]),) >>> >>> dataset = ds.GeneratorDataset(generator_1d, column_names='col') >>> type_cast_op = transforms.TypeCast(mstype.int32) >>> dataset = dataset.map(operations=type_cast_op) """ @check_type_cast def __init__(self, data_type): super().__init__() if isinstance(data_type, typing.Type): data_type = mstype_to_detype(data_type) else: data_type = nptype_to_detype(data_type) self.data_type = str(data_type) self.implementation = Implementation.C def parse(self): return cde.TypeCastOperation(self.data_type)
[docs]class Unique(TensorOperation): """ Perform the unique operation on the input tensor, only support transform one column each time. Return 3 tensor: unique output tensor, index tensor, count tensor. - Output tensor contains all the unique elements of the input tensor in the same order that they occur in the input tensor. - Index tensor that contains the index of each element of the input tensor in the unique output tensor. - Count tensor that contains the count of each element of the output tensor in the input tensor. Note: Call batch op before calling this function. Raises: RuntimeError: If given Tensor has two columns. Supported Platforms: ``CPU`` Examples: >>> # Data before >>> # | x | >>> # +--------------------+ >>> # | [[0,1,2], [1,2,3]] | >>> # +--------------------+ >>> data = [[[0,1,2], [1,2,3]]] >>> dataset = ds.NumpySlicesDataset(data, ["x"]) >>> dataset = dataset.map(operations=transforms.Unique(), ... input_columns=["x"], ... output_columns=["x", "y", "z"]) >>> # Data after >>> # | x | y |z | >>> # +---------+-----------------+---------+ >>> # | [0,1,2,3] | [0,1,2,1,2,3] | [1,2,2,1] >>> # +---------+-----------------+---------+ """ def __init__(self): super().__init__() self.implementation = Implementation.C def parse(self): return cde.UniqueOperation()