"""Transform operators."""
from . import _make
from ..expr import TupleWrapper
def cast(data, dtype):
"""Cast input tensor to data type.
Parameters
----------
data : relay.Expr
The input data to the operator.
dtype: str
The target data type
Returns
-------
result : relay.Expr
The casted result.
"""
from .. import _make as _relay_make
return _relay_make.cast(data, dtype)
def expand_dims(data, axis, num_newaxis=1):
"""Insert `num_newaxis` axises at the position given by `axis`.
Parameters
----------
data : relay.Expr
The input data to the operator.
axis : int
The axis at which the input array is expanded.
Should lie in range `[-data.ndim - 1, data.ndim]`.
If `axis < 0`, it is the first axis inserted;
If `axis >= 0`, it is the last axis inserted in Python's negative indexing.
num_newaxis : int
Number of axes to be inserted. Should be >= 0.
Returns
-------
result : relay.Expr
The reshaped result.
"""
return _make.expand_dims(data, axis, num_newaxis)
def transpose(data, axes=None):
"""Permutes the dimensions of an array.
Parameters
----------
data : relay.Expr
The input data to the operator.
axes : None or List[int]
The target axes order, reverse order if not specified.
Returns
-------
result : relay.Expr
The transposed result.
"""
if axes is not None:
axes = list(axes)
return _make.transpose(data, axes)
def squeeze(data, axis=None):
"""Squeeze axes in the array.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
axis : None or List[int]
The set of axes to remove.
If axis = None, remove all axis of dimensions 1.
If any specified axis has dimension that does not equal 1, it is an error.
Returns
-------
result : tvm.relay.Expr
The squeezed result.
"""
return _make.squeeze(data, axis)
def reshape(data, newshape):
"""Reshapes the input array.
Example::
To give user more convenience in without doing manual shape inference,
some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- data.shape = (2,3,4), newshape = (4,0,2), result.shape = (4,3,2)
- data.shape = (2,3,4), newshape = (2,0,0), result.shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the input dimensions
keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- data.shape = (2,3,4), newshape = (6,1,-1), result.shape = (6,1,4)
- data.shape = (2,3,4), newshape = (3,-1,8), result.shape = (3,1,8)
- data.shape = (2,3,4), newshape = (-1,), result.shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- data.shape = (2,3,4), newshape = (-2,), result.shape = (2,3,4)
- data.shape = (2,3,4), newshape = (2,-2), result.shape = (2,3,4)
- data.shape = (2,3,4), newshape = (-2,1,1), result.shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape
as the output dimension.
Example::
- data.shape = (2,3,4), newshape = (-3,4), result.shape = (6,4)
- data.shape = (2,3,4,5), newshape = (-3,-3), result.shape = (6,20)
- data.shape = (2,3,4), newshape = (0,-3), result.shape = (2,12)
- data.shape = (2,3,4), newshape = (-3,-2), result.shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent
to -4 in shape (can contain -1).
Example::
- data.shape = (2,3,4), newshape = (-4,1,2,-2), result.shape =(1,2,3,4)
- data.shape = (2,3,4), newshape = (2,-4,-1,3,-2), result.shape = (2,1,3,4)
Parameters
----------
data : relay.Expr
The input data to the operator.
newshape : Union[int, Tuple[int], List[int]]
The new shape. Should be compatible with the original shape.
Returns
-------
result : relay.Expr
The reshaped result.
"""
if isinstance(newshape, int):
newshape = [newshape]
return _make.reshape(data, list(newshape))
def reshape_like(data, shape_like):
"""Reshapes the input array by the size of another array.
For an input array with shape ``(d1, d2, ..., dk)``, `reshape_like` operation reshapes
the input array into an output array with the same shape as the second input array.
.. note::
Sizes for both array should be compatible.
Parameters
----------
data : relay.Expr
The input data to the operator.
shape_like : tuple of int
The new shape. Should be compatible with the original shape.
Returns
-------
ret : relay.Expr
The computed result.
"""
return _make.reshape_like(data, shape_like)
def take(data, indices, axis=None):
"""Take elements from an array along an axis.
Parameters
----------
data : relay.Expr
The source array.
indices : rely.Expr
The indices of the values to extract.
axis : int, optional
The axis over which to select values. By default,
the flattened input array is used.
Returns
-------
ret : relay.Expr
The computed result.
"""
return _make.take(data, indices, axis)
def full(fill_value, shape=(), dtype=""):
"""Fill array with scalar value.
Parameters
----------
fill_value : relay.Expr
The value to fill. Must be a scalar.
shape : tuple of int
The shape of the target.
dtype : data type, optional (defaults to data type of the fill value)
The data type of the target.
Returns
-------
result : relay.Expr
The resulting tensor.
"""
return _make.full(fill_value, shape, dtype)
def full_like(data, fill_value):
"""Return an scalar value array with the same shape and type as the input array.
Parameters
----------
data : relay.Expr
The input tensor.
fill_value : relay.Expr
The scalar value to fill.
Returns
-------
result : relay.Expr
The resulting tensor.
"""
return _make.full_like(data, fill_value)
def where(condition, x, y):
"""Selecting elements from either x or y depending on the value of the
condition.
Parameters
----------
condition : relay.Expr
The condition array. The n-th element in `y` is selected when the n-th
value in the `condition` array is zero. Otherwise, the corresponding
element from `x` will be picked.
x : relay.Expr
The first array to be selected.
y : relay.Expr
The second array to be selected.
Returns
-------
result : relay.Expr
The selected array.
Examples
--------
.. code-block:: python
x = [[1, 2], [3, 4]]
y = [[5, 6], [7, 8]]
condition = [[0, 1], [-1, 0]]
relay.where(conditon, x, y) = [[5, 2], [3, 8]]
condition = [1, 0]
relay.where(conditon, x, y) = [[1, 2], [7, 8]]
Note that the shape of condition, x, and y needs to be the same.
"""
return _make.where(condition, x, y)
def broadcast_to(data, shape):
"""Return an scalar value array with the same type, broadcast to
the provided shape.
Parameters
----------
data : relay.Expr
The input tensor.
shape : shape
Provide the shape to broadcast to.
Returns
-------
result : relay.Expr
The resulting tensor.
"""
return _make.broadcast_to(data, shape)
def broadcast_to_like(data, broadcast_type):
"""Return an scalar value array with the same shape and type as the input array.
Parameters
----------
data : relay.Expr
The input tensor.
broadcast_type : relay.Expr
Provide the type to broadcast to.
Returns
-------
result : relay.Expr
The resulting tensor.
"""
return _make.broadcast_to_like(data, broadcast_type)
def collapse_sum_like(data, collapse_type):
"""Return an scalar value array with the same shape and type as the input array.
Parameters
----------
data : relay.Expr
The input tensor.
collapse_type : relay.Expr
Provide the type to collapse to.
Returns
-------
result : relay.Expr
The resulting tensor.
"""
return _make.collapse_sum_like(data, collapse_type)
def split(data, indices_or_sections, axis=0):
"""Split input tensor along axis by sections or indices.
If indices_or_sections is an integer, the input will be divided equally
along given axis. If such a split is not possible, an error is raised.
If indices_or_sections is a tuple of sorted integers,
the entries indicate where along axis the array is split.
Parameters
----------
data : relay.Expr
The source array.
indices_or_sections : int or tuple of int
Indices or sections to split into. Accepts an int or a tuple
axis : int, optional
The axis over which to split.
Returns
-------
ret : relay.Tuple([relay.Expr, relay.Expr])
The computed result.
"""
if isinstance(indices_or_sections, int):
ret_size = indices_or_sections
else:
ret_size = len(indices_or_sections) + 1
return TupleWrapper(_make.split(data, indices_or_sections, axis), ret_size)
def strided_slice(data, begin, end, strides=None):
"""Strided slice of an array..
Parameters
----------
data : relay.Expr
The source array to be sliced.
begin: list of int
The indices to begin with in the slicing.
end: list of int
Indicies indicating end of the slice.
strides: list of int, optional
Specifies the stride values, it can be negative in that case,
the input tensor will be reversed in that particular axis.
Returns
-------
ret : relay.Expr
The computed result.
"""
strides = strides or []
return _make.strided_slice(data, list(begin), list(end), list(strides))
def slice_like(data, shape_like, axes=None):
"""Slice the first input with respect to the second input.
For an input array with shape ``(d1, d2, ..., dk)``, `slice_like` operation slices the
the input array corresponding size of second array. By default will slice on all axes.
Parameters
----------
data : tvm.relay.Expr
The source array.
shape_like : tvm.relay.Expr
The new shape.
axes : Optional[Tuple[int]]
List of axes on which input data will be sliced according to the corresponding size of
the second input. By default will slice on all axes. Negative axes mean counting in reverse.
Returns
-------
result : relay.Expr
The computed result.
"""
return _make.slice_like(data, shape_like, axes)
def layout_transform(data, src_layout, dst_layout):
"""Transform the layout of a tensor
Parameters
----------
data : relay.Expr
The source tensor to be transformed
src_layout: str
The source layout. (e.g NCHW)
dst_layout: str
The destination layout. (e.g. NCHW16c)
Returns
-------
ret : relay.Expr
The transformed tensor.
"""
return _make.layout_transform(data, src_layout, dst_layout)