# Copyright 2019-2022 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
#
# 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
# limitations under the License.
# ==============================================================================
"""
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"],
... column_order=["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. :class:`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:
``CPU`` ``Ascend`` ``GPU``
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"],
... column_order=["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()