mindspore.dataset.transforms.c_transforms

This module c_transforms provides common operations, including OneHotOp and TypeCast.

class mindspore.dataset.transforms.c_transforms.Concatenate(axis=0, prepend=None, append=None)[source]

Tensor operation to prepend and append to a tensor.

Parameters

  • axis (int, optional) – axis to concatenate the tensors along (Default=0).

  • prepend (np.array, optional) – numpy array to be prepended to the already concatenated tensors (Default=None).

  • append (np.array, optional) – numpy array to be appended to the already concatenated tensors (Default=None).

class mindspore.dataset.transforms.c_transforms.Duplicate[source]

Duplicate the input tensor to a new output tensor. The input tensor is carried over to the output list.

Examples

>>> # Data before
>>> # |  x      |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------+
>>> data = data.map(input_columns=["x"], operations=Duplicate(),
>>>         output_columns=["x", "y"], columns_order=["x", "y"])
>>> # Data after
>>> # |  x      |  y      |
>>> # +---------+---------+
>>> # | [1,2,3] | [1,2,3] |
>>> # +---------+---------+
class mindspore.dataset.transforms.c_transforms.Fill(fill_value)[source]

Tensor operation to create a tensor filled with passed scalar value. The output tensor will have the same shape and type as the input tensor.

Parameters

fill_value (python types (str, bytes, int, float, or bool)) – scalar value to fill created tensor with.

class mindspore.dataset.transforms.c_transforms.Mask(operator, constant, dtype=mindspore.bool)[source]

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.

Parameters

  • operator (Relational) – One of the relational operator EQ, NE LT, GT, LE or GE

  • constant (python types (str, int, float, or bool) – constant to be compared to. Constant will be casted to the type of the input tensor

  • dtype (optional, mindspore.dtype) – type of the generated mask. Default to bool

Examples

>>> # Data before
>>> # |  col1   |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------+
>>> data = data.map(operations=Mask(Relational.EQ, 2))
>>> # Data after
>>> # |       col1         |
>>> # +--------------------+
>>> # | [False,True,False] |
>>> # +--------------------+
class mindspore.dataset.transforms.c_transforms.OneHot(num_classes)[source]

Tensor operation to apply one hot encoding.

Parameters

num_classes (int) – Number of classes of the label.

class mindspore.dataset.transforms.c_transforms.PadEnd(pad_shape, pad_value=None)[source]

Pad input tensor according to pad_shape, need to have same rank.

Parameters

  • pad_shape (list of int) – list on 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 (python types (str, bytes, int, float, or bool), optional) – value used to pad. Default to 0 or empty string in case of Tensors of strings.

Examples

>>> # Data before
>>> # |   col   |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------|
>>> data = data.map(operations=PadEnd(pad_shape=[4], pad_value=10))
>>> # Data after
>>> # |    col     |
>>> # +------------+
>>> # | [1,2,3,10] |
>>> # +------------|
class mindspore.dataset.transforms.c_transforms.Relational(value)[source]

An enumeration.

class mindspore.dataset.transforms.c_transforms.Slice(*slices)[source]

Slice operation to extract a tensor out using the given n slices.

The functionality of Slice is similar to NumPy indexing feature. (Currently only rank 1 Tensors are supported)

Parameters

*slices (Variable length argument list) –

Maximum n number of arguments to slice a tensor of rank n. One object in slices can be one of:

  1. int: slice this index only. Negative index is supported.

  2. slice object: slice the generated indices from the slice object. Similar to start:stop:step.

  3. None: slice the whole dimension. Similar to : in python indexing.

  4. Ellipses …: slice all dimensions between the two slices.

Examples

>>> # Data before
>>> # |   col   |
>>> # +---------+
>>> # | [1,2,3] |
>>> # +---------|
>>> data = data.map(operations=Slice(slice(1,3))) # slice indices 1 and 2 only
>>> # Data after
>>> # |    col     |
>>> # +------------+
>>> # |    [1,2]   |
>>> # +------------|
class mindspore.dataset.transforms.c_transforms.TypeCast(data_type)[source]

Tensor operation to cast to a given MindSpore data type.

Parameters

data_type (mindspore.dtype) – mindspore.dtype to be casted to.