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# Copyright 2020-2021 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.
# ============================================================================
"""basic"""
import math
import numpy as np
import mindspore.common.dtype as mstype
from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
from mindspore.common.seed import _get_graph_seed
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.functional import identity
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops.primitive import constexpr, Primitive
from mindspore.common.parameter import Parameter
from mindspore._extends import cell_attr_register
from mindspore._checkparam import Rel, Validator
from ..cell import Cell
from .activation import get_activation
__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold',
'Tril', 'Triu', 'ResizeBilinear', 'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag', 'L1Regularizer']
class L1Regularizer(Cell):
r"""
Applies l1 regularization to weights.
l1 regularization makes weights sparsity
.. math::
\text{loss}=\lambda * \text{reduce_sum}(\text{abs}(\omega))
Note:
scale(regularization factor) should be a number which greater than 0
Args:
scale (int, float): l1 regularization factor which greater than 0.
Inputs:
- **weights** (Tensor) - The input of L1Regularizer with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, which dtype is higher precision data type between mindspore.float32 and weights dtype,
and Tensor shape is ()
Raises:
TypeError: If `scale` is neither an int nor float.
ValueError: If `scale` is not greater than 0.
ValueError: If `scale` is math.inf or math.nan.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> scale = 0.5
>>> net = nn.L1Regularizer(scale)
>>> weights = Tensor(np.array([[1.0, -2.0], [-3.0, 4.0]]).astype(np.float32))
>>> output = net(weights)
>>> print(output.asnumpy())
5.0
"""
def __init__(self, scale):
"""Initialize L1Regularizer."""
super(L1Regularizer, self).__init__()
Validator.check_value_type("scale", scale, [int, float], self.cls_name)
if scale <= 0:
raise ValueError("scale should be a number which greater than 0")
if math.isinf(scale) or math.isnan(scale):
raise ValueError("scale can not be INF or NAN")
self.abs = P.Abs()
self.reduce_sum = P.ReduceSum()
self.scale = Tensor(scale, dtype=mstype.float32)
def construct(self, weights):
const_utils.check_type_valid(F.dtype(weights), mstype.number_type, 'weights')
l1_regularization = self.scale * self.reduce_sum(self.abs(weights))
return l1_regularization
class Dropout(Cell):
r"""
Dropout layer for the input.
Randomly set some elements of the input tensor to zero with probability :math:`1 - keep\_prob` during training
using samples from a Bernoulli distribution.
The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so
that the output layer remains at a similar scale. During inference, this
layer returns the same tensor as the `x`.
This technique is proposed in paper `Dropout: A Simple Way to Prevent Neural Networks from Overfitting
<http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_ and proved to be effective to reduce
over-fitting and prevents neurons from co-adaptation. See more details in `Improving neural networks by
preventing co-adaptation of feature detectors
<https://arxiv.org/pdf/1207.0580.pdf>`_.
Note:
Each channel will be zeroed out independently on every construct call.
Args:
keep_prob (float): The keep rate, greater than 0 and less equal than 1. E.g. rate=0.9,
dropping out 10% of input units. Default: 0.5.
dtype (:class:`mindspore.dtype`): Data type of `x`. Default: mindspore.float32.
Inputs:
- **x** (Tensor) - The input of Dropout with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, output tensor with the same shape as the `x`.
Raises:
TypeError: If `keep_prob` is not a float.
TypeError: If dtype of `x` is not neither float16 nor float32.
ValueError: If `keep_prob` is not in range (0, 1].
ValueError: If length of shape of `x` is less than 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
>>> net = nn.Dropout(keep_prob=0.8)
>>> net.set_train()
Dropout<keep_prob=0.8>
>>> output = net(x)
>>> print(output.shape)
(2, 2, 3)
"""
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
"""Initialize Dropout."""
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
self.keep_prob = keep_prob
seed0, seed1 = _get_graph_seed(0, "dropout")
self.seed0 = seed0
self.seed1 = seed1
self.dropout = P.Dropout(keep_prob, seed0, seed1)
def construct(self, x):
if not self.training:
return x
if self.keep_prob == 1:
return x
out, _ = self.dropout(x)
return out
def extend_repr(self):
return 'keep_prob={}'.format(self.keep_prob)
class Flatten(Cell):
r"""
Flatten layer for the input.
Flattens a tensor without changing dimension of batch size on the 0-th axis.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, \ldots)` to be flattened. The data type is Number.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions
and the shape can't be ().
Outputs:
Tensor, the shape of the output tensor is :math:`(N, X)`, where :math:`X` is
the product of the remaining dimensions.
Raises:
TypeError: If `x` is not a subclass of Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
>>> net = nn.Flatten()
>>> output = net(x)
>>> print(output)
[[1.2 1.2 2.1 2.1]
[2.2 2.2 3.2 3.2]]
>>> print(f"before flatten the x shape is {x.shape}")
before flatten the x shape is (2, 2, 2)
>>> print(f"after flatten the output shape is {output.shape}")
after flatten the output shape is (2，4)
"""
def __init__(self):
"""Initialize Flatten."""
super(Flatten, self).__init__()
def construct(self, x):
return F.reshape(x, (F.shape(x)[0], -1))
@constexpr
def check_dense_input_shape(x):
if len(x) < 2:
raise ValueError('For Dense, the dimension of input should not be less than 2, while the input dimension is '
+ f'{len(x)}.')
class Dense(Cell):
r"""
The dense connected layer.
Applies dense connected layer for the input. This layer implements the operation as:
.. math::
\text{outputs} = \text{activation}(\text{X} * \text{kernel} + \text{bias}),
where :math:`X` is the input tensors, :math:`\text{activation}` is the activation function passed as the activation
argument (if passed in), :math:`\text{kernel}` is a weight matrix with the same
data type as the :math:`X` created by the layer, and :math:`\text{bias}` is a bias vector
with the same data type as the :math:`X` created by the layer (only if has_bias is True).
Args:
in_channels (int): The number of channels in the input space.
out_channels (int): The number of channels in the output space.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
is same as `x`. The values of str refer to the function `initializer`. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
same as `x`. The values of str refer to the function `initializer`. Default: 'zeros'.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer,
eg. 'ReLU'.Default: None.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(*, in\_channels)`. The `in_channels` in `Args` should be equal
to :math:`in\_channels` in `Inputs`
Outputs:
Tensor of shape :math:`(*, out\_channels)`.
Raises:
TypeError: If `in_channels` or `out_channels` is not an int.
TypeError: If `has_bias` is not a bool.
TypeError: If `activation` is not one of str, Cell, Primitive, None.
ValueError: If length of shape of `weight_init` is not equal to 2 or shape[0] of `weight_init`
is not equal to `out_channels` or shape[1] of `weight_init` is not equal to `in_channels`.
ValueError: If length of shape of `bias_init` is not equal to 1
or shape[0] of `bias_init` is not equal to `out_channels`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[180, 234, 154], [244, 48, 247]]), mindspore.float32)
>>> net = nn.Dense(3, 4)
>>> output = net(x)
>>> print(output.shape)
(2, 4)
"""
@cell_attr_register(attrs=['has_bias', 'activation'])
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None):
"""Initialize Dense."""
super(Dense, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels)
self.out_channels = Validator.check_positive_int(out_channels)
self.has_bias = Validator.check_bool(has_bias)
self.reshape = P.Reshape()
self.shape_op = P.Shape()
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("Weight init shape error.")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
self.bias = None
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError("Bias init shape error.")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul(transpose_b=True)
self.activation = get_activation(activation) if isinstance(activation, str) else activation
if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
self.activation_flag = self.activation is not None
def construct(self, x):
x_shape = self.shape_op(x)
check_dense_input_shape(x_shape)
if len(x_shape) != 2:
x = self.reshape(x, (-1, x_shape[-1]))
x = self.matmul(x, self.weight)
if self.has_bias:
x = self.bias_add(x, self.bias)
if self.activation_flag:
x = self.activation(x)
if len(x_shape) != 2:
out_shape = x_shape[:-1] + (-1,)
x = self.reshape(x, out_shape)
return x
def extend_repr(self):
s = 'input_channels={}, output_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
s += ', has_bias={}'.format(self.has_bias)
if self.activation_flag:
s += ', activation={}'.format(self.activation)
return s
@constexpr
def _is_equal_one(x):
if x is None:
return False
return bool(x.asnumpy().mean() == 1.0)
@constexpr
def _dtype_check(x_dtype):
if x_dtype not in [mstype.float32, mstype.float16]:
raise TypeError("The input type must be float32 or float16.")
@constexpr
def _is_float_dtype(dtype):
if dtype in [mstype.float32, mstype.float16]:
return True
return False
@constexpr
def _need_reduce_all(axis):
if axis == ():
return True
return False
class ClipByNorm(Cell):
r"""
Clips tensor values to a maximum :math:`L_2`-norm.
The output of this layer remains the same if the :math:`L_2`-norm of the input tensor
is not greater than the argument clip_norm. Otherwise the tensor will be normalized as:
.. math::
\text{output}(X) = \frac{\text{clip_norm} * X}{L_2(X)},
where :math:`L_2(X)` is the :math:`L_2`-norm of :math:`X`.
Args:
axis (Union[None, int, tuple(int)]): Compute the L2-norm along the Specific dimension.
Default: None, all dimensions to calculate.
Inputs:
- **x** (Tensor) - Tensor of shape N-D. The type must be float32 or float16.
- **clip_norm** (Tensor) - A scalar Tensor of shape :math:`()` or :math:`(1)`.
Or a tensor shape can be broadcast to input shape.
Outputs:
Tensor, clipped tensor with the same shape as the `x`, whose type is float32.
Raises:
TypeError: If `axis` is not one of None, int, tuple.
TypeError: If dtype of `x` is neither float32 nor float16.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.ClipByNorm()
>>> x = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
>>> clip_norm = Tensor(np.array([100]).astype(np.float32))
>>> output = net(x, clip_norm)
>>> print(output.shape)
(4, 16)
"""
def __init__(self, axis=None):
"""Initialize ClipByNorm."""
super(ClipByNorm, self).__init__()
if axis is None:
axis = ()
if isinstance(axis, tuple):
for idx, item in enumerate(axis):
Validator.check_value_type("axis[%d]" % idx, item, [int], self.cls_name)
self.axis = Validator.check_value_type('axis', axis, [int, tuple], self.cls_name)
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select_ = P.Select()
self.greater_ = P.Greater()
self.cast = P.Cast()
self.sqrt = P.Sqrt()
self.max_op = P.Maximum()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.fill = P.Fill()
self.expand_dims = P.ExpandDims()
self.dtype = P.DType()
def construct(self, x, clip_norm):
mul_x = F.square(x)
l2sum = self.cast(self.reduce_sum(mul_x, self.axis), mstype.float32)
cond = self.greater_(l2sum, 0)
ones_ = self.fill(self.dtype(cond), self.shape(cond), 1.0)
l2sum_safe = self.select_(cond, l2sum, self.cast(ones_, self.dtype(l2sum)))
l2norm = self.select_(cond, self.sqrt(l2sum_safe), l2sum)
_dtype_check(self.dtype(x))
if _is_equal_one(clip_norm):
intermediate = x
else:
intermediate = x * clip_norm
max_norm = self.max_op(l2norm, clip_norm)
if _need_reduce_all(self.axis):
max_norm = self.expand_dims(max_norm, -1)
values_clip = self.cast(intermediate, mstype.float32) / max_norm
values_clip = self.reshape(values_clip, self.shape(x))
values_clip = identity(values_clip)
return values_clip
class Norm(Cell):
r"""
Computes the norm of vectors, currently including Euclidean norm, i.e., :math:`L_2`-norm.
.. math::
norm(x) = \sqrt{\sum_{i=1}^{n} (x_i^2)}
Args:
axis (Union[tuple, int]): The axis over which to compute vector norms. Default: ().
keep_dims (bool): If true, the axis indicated in `axis` are kept with size 1. Otherwise,
the dimensions in `axis` are removed from the output shape. Default: False.
Inputs:
- **x** (Tensor) - Tensor which is not empty. The data type should be float16 or float32.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, output tensor with dimensions in 'axis' reduced to 1 will be returned if 'keep_dims' is True;
otherwise a Tensor with dimensions in 'axis' removed is returned. The data type is the same with `x`
Raises:
TypeError: If `axis` is neither an int nor tuple.
TypeError: If `keep_dims` is not a bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Norm(axis=0)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[4.472136 4.1231055 9.486833 6.0827627]
>>> print(output.shape)
(4,)
>>> net = nn.Norm(axis=0, keep_dims=True)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[4.472136 4.1231055 9.486833 6.0827627]
>>> print(output.shape)
(1, 4)
>>> net = nn.Norm(axis=1)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[10.677078 7.071068]
>>> print(output.shape)
(2,)
"""
def __init__(self, axis=(), keep_dims=False):
"""Initialize Norm."""
super(Norm, self).__init__()
Validator.check_value_type("keep_dims", keep_dims, [bool], self.cls_name)
self.axis = axis
self.keep_dims = keep_dims
self.reduce_sum = P.ReduceSum(True)
self.sqrt = P.Sqrt()
self.squeeze = P.Squeeze(self.axis)
def construct(self, x):
x = self.sqrt(self.reduce_sum(F.square(x), self.axis))
if not self.keep_dims:
x = self.squeeze(x)
return x
def extend_repr(self):
return 'axis={}, keep_dims={}'.format(self.axis, self.keep_dims)
class OneHot(Cell):
"""
Returns a one-hot tensor.
The locations represented by indices in argument `indices` take value on_value,
while all other locations take value off_value.
Note:
If the input indices is rank :math:`N`, the output will have rank :math:`N+1`. The new
axis is created at dimension `axis`.
If `indices` is a scalar, the output shape will be a vector of length `depth`.
If `indices` is a vector of length `features`, the output shape will be:
.. code-block::
features * depth if axis == -1
depth * features if axis == 0
If `indices` is a matrix with shape `[batch, features]`, the output shape will be:
.. code-block::
batch * features * depth if axis == -1
batch * depth * features if axis == 1
depth * batch * features if axis == 0
Args:
axis (int): Features x depth if axis is -1, depth x features
if axis is 0. Default: -1.
depth (int): A scalar defining the depth of the one hot dimension. Default: 1.
on_value (float): A scalar defining the value to fill in output[i][j]
when indices[j] = i. Default: 1.0.
off_value (float): A scalar defining the value to fill in output[i][j]
when indices[j] != i. Default: 0.0.
dtype (:class:`mindspore.dtype`): Data type of 'on_value' and 'off_value', not the
data type of indices. Default: mindspore.float32.
Inputs:
- **indices** (Tensor) - A tensor of indices with data type of int32 or int64.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, the one-hot tensor of data type `dtype` with dimension at `axis` expanded to `depth` and filled with
on_value and off_value. The dimension of the `Outputs` is equal to the dimension of the `indices` plus one.
Raises:
TypeError: If `axis` or `depth` is not an int.
TypeError: If dtype of `indices` is neither int32 nor int64.
ValueError: If `axis` is not in range [-1, len(indices_shape)].
ValueError: If `depth` is less than 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # 1st sample: add new coordinates at axis 1
>>> net = nn.OneHot(depth=4, axis=1)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[[0. 0.]
[1. 0.]
[0. 0.]
[0. 1.]]
[[1. 0.]
[0. 0.]
[0. 1.]
[0. 0.]]]
>>> # The results are shown below:
>>> print(output.shape)
(2, 4, 2)
>>> # 2nd sample: add new coordinates at axis 0
>>> net = nn.OneHot(depth=4, axis=0)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[[0. 0.]
[1. 0.]]
[[1. 0.]
[0. 0.]]
[[0. 0.]
[0. 1.]]
[[0. 1.]
[0. 0.]]]
>>> # The results are shown below:
>>> print(output.shape)
(4, 2, 2)
>>> # 3rd sample: add new coordinates at the last dimension.
>>> net = nn.OneHot(depth=4, axis=-1)
>>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
>>> output = net(indices)
>>> # The results are shown below:
>>> print(output)
[[[0. 1. 0. 0.]
[0. 0. 0. 1.]]
[[1. 0. 0. 0.]
[0. 0. 1. 0.]]]
>>> print(output.shape)
(2, 2, 4)
>>> indices = Tensor([1, 3, 0, 2], dtype=mindspore.int32)
>>> output = net(indices)
>>> print(output)
[[0. 1. 0. 0.]
[0. 0. 0. 1.]
[1. 0. 0. 0.]
[0. 0. 1. 0.]]
>>> print(output.shape)
(4， 4)
"""
def __init__(self, axis=-1, depth=1, on_value=1.0, off_value=0.0, dtype=mstype.float32):
"""Initialize OneHot."""
super(OneHot, self).__init__()
self.onehot = P.OneHot(axis)
self.depth = depth
self.dtype = dtype
self.on_value = on_value
self.off_value = off_value
def construct(self, indices):
return self.onehot(indices, self.depth, F.cast(self.on_value, self.dtype), F.cast(self.off_value, self.dtype))
class Pad(Cell):
r"""
Pads the input tensor according to the paddings and mode.
Args:
paddings (tuple): The shape of parameter `paddings` is (N, 2). N is the rank of input data. All elements of
paddings are int type. For `D` th dimension of the `x`, paddings[D, 0] indicates how many sizes to be
extended ahead of the `D` th dimension of the input tensor, and paddings[D, 1] indicates how many sizes to
be extended behind of the `D` th dimension of the input tensor. The padded size of each dimension D of the
output is: :math:`paddings[D, 0] + input\_x.dim\_size(D) + paddings[D, 1]`
eg:
- mode = "CONSTANT".
- paddings = [[1,1], [2,2]].
- x = [[1,2,3], [4,5,6], [7,8,9]].
- The above can be seen: 1st dimension of `x` is 3, 2nd dimension of `x` is 3.
- Substitute into the formula to get:
- 1st dimension of output is paddings[0][0] + 3 + paddings[0][1] = 1 + 3 + 1 = 4.
- 2nd dimension of output is paddings[1][0] + 3 + paddings[1][1] = 2 + 3 + 2 = 7.
- so output.shape is (4, 7)
mode (str): Specifies padding mode. The optional values are "CONSTANT", "REFLECT", "SYMMETRIC".
Default: "CONSTANT".
Inputs:
- **x** (Tensor) - The input tensor.
Outputs:
Tensor, the tensor after padding.
- If `mode` is "CONSTANT", it fills the edge with 0, regardless of the values of the `x`.
If the `x` is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the
Outputs is [[0,0,0,0,0,0,0], [0,0,1,2,3,0,0], [0,0,4,5,6,0,0], [0,0,7,8,9,0,0], [0,0,0,0,0,0,0]].
- If `mode` is "REFLECT", it uses a way of symmetrical copying through the axis of symmetry to fill in.
If the `x` is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the
Outputs is [[6,5,4,5,6,5,4], [3,2,1,2,3,2,1], [6,5,4,5,6,5,4], [9,8,7,8,9,8,7], [6,5,4,5,6,5,4]].
- If `mode` is "SYMMETRIC", the filling method is similar to the "REFLECT". It is also copied
according to the symmetry axis, except that it includes the symmetry axis. If the `x`
is [[1,2,3], [4,5,6], [7,8,9]] and `paddings` is [[1,1], [2,2]], then the Outputs is
[[2,1,1,2,3,3,2], [2,1,1,2,3,3,2], [5,4,4,5,6,6,5], [8,7,7,8,9,9,8], [8,7,7,8,9,9,8]].
Raises:
TypeError: If `paddings` is not a tuple.
ValueError: If length of `paddings` is more than 4 or its shape is not (n, 2).
ValueError: If `mode` is not one of 'CONSTANT', 'REFLECT', 'SYMMETRIC'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor
>>> import mindspore.nn as nn
>>> import numpy as np
>>> # If `mode` is "CONSTANT"
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.pad = nn.Pad(paddings=((1, 1), (2, 2)), mode="CONSTANT")
... def construct(self, x):
... return self.pad(x)
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]), mindspore.float32)
>>> pad = Net()
>>> output = pad(x)
>>> print(output)
[[0. 0. 0. 0. 0. 0. 0.]
[0. 0. 1. 2. 3. 0. 0.]
[0. 0. 4. 5. 6. 0. 0.]
[0. 0. 0. 0. 0. 0. 0.]]
>>> # Another way to call
>>> pad = ops.Pad(paddings=((1, 1), (2, 2)))
>>> # From the above code, we can see following:
>>> # "paddings=((1, 1), (2, 2))",
>>> # paddings[0][0] = 1, indicates a row of values is filled top of the input data in the 1st dimension.
>>> # Shown as follows:
>>> # [[0. 0. 0.]
>>> # [1. 2. 3.]
>>> # [4. 5. 6.]]
>>> # paddings[0][1] = 1 indicates a row of values is filled below input data in the 1st dimension.
>>> # Shown as follows:
>>> # [[0. 0. 0.]
>>> # [1. 2. 3.]
>>> # [4. 5. 6.]
>>> # [0. 0. 0.]]
>>> # paddings[1][0] = 2, indicates 2 rows of values is filled in front of input data in the 2nd dimension.
>>> # Shown as follows:
>>> # [[0. 0. 0. 0. 0.]
>>> # [0. 0. 1. 2. 3.]
>>> # [0. 0. 4. 5. 6.]
>>> # [0. 0. 0. 0. 0.]]
>>> # paddings[1][1] = 2, indicates 2 rows of values is filled in front of input data in the 2nd dimension.
>>> # Shown as follows:
>>> # [[0. 0. 0. 0. 0. 0. 0.]
>>> # [0. 0. 1. 2. 3. 0. 0.]
>>> # [0. 0. 4. 5. 6. 0. 0.]
>>> # [0. 0. 0. 0. 0. 0. 0.]]
>>> output = pad(x)
>>> print(output)
[[0. 0. 0. 0. 0. 0. 0.]
[0. 0. 1. 2. 3. 0. 0.]
[0. 0. 4. 5. 6. 0. 0.]
[0. 0. 0. 0. 0. 0. 0.]]
>>> # if mode is "REFLECT"
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.pad = nn.Pad(paddings=((1, 1), (2, 2)), mode="REFLECT")
... def construct(self, x):
... return self.pad(x)
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]), mindsprore.float32)
>>> pad = Net()
>>> output = pad(x)
>>> print(output)
[[6. 5. 4. 5. 6. 5. 4.]
[3. 2. 1. 2. 3. 2. 1.]
[6. 5. 4. 5. 6. 5. 4.]
[3. 2. 1. 2. 3. 2. 1.]]
>>> # if mode is "SYMMETRIC"
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.pad = nn.Pad(paddings=((1, 1), (2, 2)), mode="SYMMETRIC")
... def construct(self, x):
... return self.pad(x)
>>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]), mindsprore.float32)
>>> pad = Net()
>>> output = pad(x)
>>> print(output)
[[2. 1. 1. 2. 3. 3. 2.]
[2. 1. 1. 2. 3. 3. 2.]
[5. 4. 4. 5. 6. 6. 5.]
[5. 4. 4. 5. 6. 6. 5.]]
"""
def __init__(self, paddings, mode="CONSTANT"):
"""Initialize Pad."""
super(Pad, self).__init__()
self.mode = mode
self.paddings = paddings
Validator.check_string(self.mode, ["CONSTANT", "REFLECT", "SYMMETRIC"], 'mode', self.cls_name)
if not isinstance(paddings, tuple):
raise TypeError('Paddings must be tuple type.')
for item in paddings:
if len(item) != 2:
raise ValueError('The shape of paddings must be (n, 2).')
if len(paddings) > 4:
raise ValueError('Only padding up to 4 dims is supported')
if mode == "CONSTANT":
self.pad = P.Pad(self.paddings)
else:
self.paddings = Tensor(np.array(self.paddings))
self.pad = P.MirrorPad(mode=mode)
def construct(self, x):
if self.mode == "CONSTANT":
x = self.pad(x)
else:
x = self.pad(x, self.paddings)
return x
@constexpr
def bilinear(shape, size, scale, align_corners):
"""Check input and calculate shape"""
if not isinstance(align_corners, bool):
raise TypeError("align_corners should be type boolean")
if size is None and scale is None:
raise ValueError("size and scale both none")
if size is not None and scale is not None:
raise ValueError("size and scale both not none")
if size is not None:
if not isinstance(size, (tuple, list)):
raise ValueError("size must be tuple or list")
Validator.check_int(len(size), 2, Rel.EQ, "size", "bilinear")
Validator.check_int(size[0], 1, Rel.GE, "size[0]", "bilinear")
Validator.check_int(size[1], 1, Rel.GE, "size[1]", "bilinear")
return size
Validator.check_int(scale, 1, Rel.GE, "scale factor", "bilinear")
ret = (scale * shape[2], scale * shape[3])
return ret
class ResizeBilinear(Cell):
r"""
Samples the input tensor to the given size or scale_factor by using bilinear interpolate.
Inputs:
- **x** (Tensor) - Tensor to be resized. Input tensor must be a 4-D tensor with shape:
math:`(batch, channels, height, width)`, with data type of float16 or float32.
- **size** (Union[tuple[int], list[int]]): A tuple or list of 2 int elements '(new_height, new_width)',
the new size of the tensor. One and only one of size and scale_factor can be set to None. Default: None.
- **scale_factor** (int): The scale factor of new size of the tensor. The value should be positive integer.
One and only one of size and scale_factor can be set to None. Default: None.
- **align_corners** (bool): If true, rescale input by '(new_height - 1) / (height - 1)', which exactly aligns
the 4 corners of images and resized images. If false, rescale by 'new_height / height'. Default: False.
Outputs:
Resized tensor.
If size is set, the result is 4-D tensor with shape:math:`(batch, channels, new_height, new_width)`
in float32.
If scale is set, the result is 4-D tensor with shape:math:`(batch, channels, scale_factor * height,
scale_factor * width)` in float32
Raises:
TypeError: If `size` is not one of tuple, list, None.
TypeError: If `scale_factor` is neither int nor None.
TypeError: If `align_corners` is not a bool.
TypeError: If dtype of `x` is neither float16 nor float32.
ValueError: If `size` and `scale_factor` are both None or not None.
ValueError: If length of shape of `x` is not equal to 4.
ValueError: If `scale_factor` is an int which is less than 1.
ValueError: If `size` is a list or tuple whose length is not equal to 2.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> x = Tensor([[[[1, 2, 3, 4], [5, 6, 7, 8]]]], mindspore.float32)
>>> resize_bilinear = nn.ResizeBilinear()
>>> result = resize_bilinear(x, size=(5,5))
>>> print(x)
[[[[1. 2. 3. 4.]
[5. 6. 7. 8.]]]]
>>> print(result)
[[[[1. 1.8. 2.6 3.4 4. ]
[2.6 3.4 4.2000003 5. 5.6000004]
[4.2 5.0000005 5.8 6.6 7.2 ]
[5. 5.8 6.6 7.4 8. ]
[5. 5.8 6.6 7.4000006 8. ]]]]
>>> print(result.shape)
(1, 1, 5, 5)
"""
def __init__(self):
"""Initialize ResizeBilinear."""
super(ResizeBilinear, self).__init__()
def construct(self, x, size=None, scale_factor=None, align_corners=False):
shape = bilinear(x.shape, size, scale_factor, align_corners)
resize_bilinear = P.ResizeBilinear(shape, align_corners)
return resize_bilinear(x)
class Unfold(Cell):
r"""
Extracts patches from images.
The input tensor must be a 4-D tensor and the data format is NCHW.
Args:
ksizes (Union[tuple[int], list[int]]): The size of sliding window, must be a tuple or a list of integers,
and the format is [1, ksize_row, ksize_col, 1].
strides (Union[tuple[int], list[int]]): Distance between the centers of the two consecutive patches,
must be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].
rates (Union[tuple[int], list[int]]): In each extracted patch, the gap between the corresponding dimension
pixel positions, must be a tuple or a list of integers, and the format is [1, rate_row, rate_col, 1].
padding (str): The type of padding algorithm, is a string whose value is "same" or "valid", not case sensitive.
Default: "valid".
- same: Means that the patch can take the part beyond the original image, and this part is filled with 0.
- valid: Means that the taken patch area must be completely covered in the original image.
Inputs:
- **x** (Tensor) - A 4-D tensor whose shape is [in_batch, in_depth, in_row, in_col] and
data type is number.
Outputs:
Tensor, a 4-D tensor whose data type is same as `x`,
and the shape is [out_batch, out_depth, out_row, out_col] where `out_batch` is the same as the `in_batch`.
:math:`out\_depth = ksize\_row * ksize\_col * in\_depth`
:math:`out\_row = (in\_row - (ksize\_row + (ksize\_row - 1) * (rate\_row - 1))) // stride\_row + 1`
:math:`out\_col = (in\_col - (ksize\_col + (ksize\_col - 1) * (rate\_col - 1))) // stride\_col + 1`
Raises:
TypeError: If `ksizes`, `strides` or `rates` is neither a tuple nor list.
ValueError: If shape of `ksizes`, `strides` or `rates` is not (1, x_row, x_col, 1).
ValueError: If the second and third element of `ksizes`, `strides` or `rates` is less than 1.
Supported Platforms:
``Ascend``
Examples:
>>> net = Unfold(ksizes=[1, 2, 2, 1], strides=[1, 2, 2, 1], rates=[1, 2, 2, 1])
>>> # As stated in the above code:
>>> # ksize_row = 2, ksize_col = 2, rate_row = 2, rate_col = 2, stride_row = 2, stride_col = 2.
>>> image = Tensor(np.ones([2, 3, 6, 6]), dtype=mstype.float16)
>>> # in_batch = 2, in_depth = 3, in_row = 6, in_col = 6.
>>> # Substituting the formula to get:
>>> # out_batch = in_batch = 2
>>> # out_depth = 2 * 2 * 3 = 12
>>> # out_row = (6 - (2 + (2 - 1) * (2 - 1))) // 2 + 1 = 2
>>> # out_col = (6 - (2 + (2 - 1) * (2 - 1))) // 2 + 1 = 2
>>> output = net(image)
>>> print(output.shape)
(2, 12, 2, 2)
"""
def __init__(self, ksizes, strides, rates, padding="valid"):
"""Initialize Unfold."""
super(Unfold, self).__init__()
def _check_tuple_or_list(arg_name, arg_val, prim_name):
Validator.check_value_type(f"{arg_name}s", ksizes, [tuple, list], self.cls_name)
if len(arg_val) != 4 or arg_val[0] != 1 or arg_val[3] != 1:
raise ValueError(f"For \'{prim_name}\' the format of {arg_name}s should be [1, {arg_name}_row, "
f"{arg_name}_col, 1], but got {arg_val}.")
if not isinstance(arg_val[1], int) or not isinstance(arg_val[2], int) or arg_val[1] < 1 or arg_val[2] < 1:
raise ValueError(f"For '{prim_name}' the {arg_name}_row and {arg_name}_col in {arg_name}s should be "
f"an positive integer number, but got {arg_name}_row is {arg_val[1]}, "
f"{arg_name}_col is {arg_val[2]}")
_check_tuple_or_list("ksize", ksizes, self.cls_name)
_check_tuple_or_list("stride", strides, self.cls_name)
_check_tuple_or_list("rate", rates, self.cls_name)
ksizes = ksizes[0], ksizes[3], ksizes[1], ksizes[2]
strides = strides[0], strides[3], strides[1], strides[2]
rates = rates[0], rates[3], rates[1], rates[2]
self.extract_image_patches = inner.ExtractImagePatches(ksizes, strides, rates, padding)
def construct(self, input_x):
result = self.extract_image_patches(input_x)
return result
@constexpr
def tril(x_shape, x_dtype, k):
Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "tril")
Validator.check_is_int(k, "k value", "tril")
mask = np.tril(np.ones(x_shape), k)
return Tensor(mask, x_dtype)
class Tril(Cell):
"""
Returns a tensor with elements above the kth diagonal zeroed.
Inputs:
- **x** (Tensor) - The input tensor. The data type is Number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
- **k** (Int) - The index of diagonal. Default: 0
Outputs:
Tensor, has the same shape and type as input `x`.
Raises:
TypeError: If `k` is not an int.
ValueError: If length of shape of `x` is less than 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> tril = nn.Tril()
>>> result = tril(x)
>>> print(result)
[[ 1, 0, 0, 0],
[ 5, 6, 0, 0],
[10, 11, 12, 0],
[14, 15, 16, 17]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> tril = nn.Tril()
>>> result = tril(x, 1)
>>> print(result)
[[ 1, 2, 0, 0],
[ 5, 6, 7, 0],
[10, 11, 12, 13],
[14, 15, 16, 17]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> tril = nn.Tril()
>>> result = tril(x, 2)
>>> print(result)
[[ 1, 2, 3, 0],
[ 5, 6, 7, 8],
[10, 11, 12, 13],
[14, 15, 16, 17]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> tril = nn.Tril()
>>> result = tril(x, -1)
>>> print(result)
[[ 0, 0, 0, 0],
[ 5, 0, 0, 0],
[10, 11, 0, 0],
[14, 15, 16, 0]]))
"""
def __init__(self):
"""Initialize Tril."""
super(Tril, self).__init__()
self.dtype = P.DType()
self.mul = P.Mul()
self.cast = P.Cast()
def construct(self, x, k=0):
assist = tril(x.shape, self.dtype(x), k)
result = self.mul(self.cast(x, mstype.float32), self.cast(assist, mstype.float32))
return self.cast(result, self.dtype(x))
@constexpr
def triu(x_shape, x_dtype, k):
Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "triu")
Validator.check_is_int(k, "k value", "triu")
mask = np.triu(np.ones(x_shape), k)
return Tensor(mask, x_dtype)
class Triu(Cell):
"""
Returns a tensor with elements below the kth diagonal zeroed.
Inputs:
- **x** (Tensor) - The input tensor. The data type is Number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
- **k** (Int) - The index of diagonal. Default: 0
Outputs:
Tensor, has the same type and shape as input `x`.
Raises:
TypeError: If `k` is not an int.
ValueError: If length of shape of `x` is less than 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x)
>>> print(result)
[[ 1, 2, 3, 4],
[ 0, 6, 7, 8],
[ 0, 0, 12, 13],
[ 0, 0, 0, 17]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, 1)
>>> print(result)
[[ 0, 2, 3, 4],
[ 0, 0, 7, 8],
[ 0, 0, 0, 13],
[ 0, 0, 0, 0]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, 2)
>>> print(result)
[[ 0, 0, 3, 4],
[ 0, 0, 0, 8],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]]))
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
>>> [ 5, 6, 7, 8],
>>> [10, 11, 12, 13],
>>> [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, -1)
>>> print(result)
[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 0, 11, 12, 13],
[ 0, 0, 16, 17]]))
"""
def __init__(self):
"""Initialize Triu."""
super(Triu, self).__init__()
self.dtype = P.DType()
self.mul = P.Mul()
self.cast = P.Cast()
def construct(self, x, k=0):
assist = triu(x.shape, self.dtype(x), k)
result = self.mul(self.cast(x, mstype.float32), self.cast(assist, mstype.float32))
return self.cast(result, self.dtype(x))
@constexpr
def _get_matrix_diag_assist(x_shape, x_dtype):
Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "_get_matrix_diag_assist")
base_eye = np.eye(x_shape[-1], x_shape[-1]).reshape(-1)
assist = np.tile(base_eye, x_shape[:-1]).reshape(x_shape + (x_shape[-1],))
return Tensor(assist, x_dtype)
@constexpr
def _get_matrix_diag_part_assist(x_shape, x_dtype):
Validator.check_int(len(x_shape), 2, Rel.GE, "x rank", "_get_matrix_diag_part_assist")
base_eye = np.eye(x_shape[-2], x_shape[-1]).reshape(-1)
assist = np.tile(base_eye, x_shape[:-2]).reshape(x_shape)
return Tensor(assist, x_dtype)
class MatrixDiag(Cell):
r"""
Returns a batched diagonal tensor with a given batched diagonal values.
Assume `x` has :math:`k` dimensions :math:`[I, J, K, ..., N]`, then the output is a tensor of rank
:math:`k+1` with dimensions :math:`[I, J, K, ..., N, N]` where:
:math:`output[i, j, k, ..., m, n] = 1\{m=n\} * x[i, j, k, ..., n]`
Inputs:
- **x** (Tensor) - The diagonal values. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, has the same type as input `x`. The shape must be x.shape + (x.shape[-1], ).
Raises:
TypeError: If dtype of `x` is not one of float32, float16, int32, int8 or uint8.
Supported Platforms:
``Ascend``
Examples:
>>> x = Tensor(np.array([1, -1]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2,)
>>> print(output)
[[ 1. 0.]
[ 0. -1.]]
>>> print(output.shape)
(2, 2)
>>> x = Tensor(np.array([[1, -1], [1, -1]]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2, 2)
>>> print(output)
[[[ 1. 0.]
[ 0. -1.]]
[[ 1. 0.]
[ 0. -1.]]]
>>> print(output.shape)
(2, 2, 2)
>>> x = Tensor(np.array([[1, -1, 1], [1, -1, 1]]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2, 3)
>>> print(output)
[[[ 1. 0. 0.]
[ 0. -1. 0.]
[ 0. 0. 1.]
[[ 1. 0. 0.]
[ 0. -1. 0.]
[ 0. 0. 1.]]]
>>> print(output.shape)
(2, 3, 3)
"""
def __init__(self):
"""Initialize MatrixDiag."""
super(MatrixDiag, self).__init__()
self.matrix_diag = inner.MatrixDiag()
self.dtype = P.DType()
def construct(self, input_x):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_assist(x_shape, x_dtype)
out_matrix_diag = self.matrix_diag(input_x, assist)
return out_matrix_diag
class MatrixDiagPart(Cell):
r"""
Returns the batched diagonal part of a batched tensor.
Assume `x` has :math:`k` dimensions :math:`[I, J, K, ..., M, N]`, then the output is a tensor of rank
:math:`k-1` with dimensions :math:`[I, J, K, ..., min(M, N)]` where:
:math:`output[i, j, k, ..., n] = x[i, j, k, ..., n, n]`
Inputs:
- **x** (Tensor) - The batched tensor. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
Outputs:
Tensor, has the same type as input `x`. The shape must be x.shape[:-2] + [min(x.shape[-2:])].
Raises:
TypeError: If dtype of `x` is not one of float32, float16, int32, int8 or uint8.
Supported Platforms:
``Ascend``
Examples:
>>> x = Tensor([[[-1, 0], [0, 1]],
... [[-1, 0], [0, 1]],
... [[-1, 0], [0, 1]]], mindspore.float32)
>>> matrix_diag_part = nn.MatrixDiagPart()
>>> output = matrix_diag_part(x)
>>> print(output)
[[-1. 1.]
[-1. 1.]
[-1. 1.]]
>>> x = Tensor([[-1, 0, 0, 1],
... [-1, 0, 0, 1],
... [-1, 0, 0, 1],
... [-1, 0, 0, 1]], mindspore.float32)
>>> matrix_diag_part = nn.MatrixDiagPart()
>>> output = matrix_diag_part(x)
>>> print(output)
[-1 0 0 1]
"""
def __init__(self):
"""Initialize MatrixDiagPart."""
super(MatrixDiagPart, self).__init__()
self.matrix_diag_part = inner.MatrixDiagPart()
self.dtype = P.DType()
def construct(self, input_x):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
out_matrix_diag_part = self.matrix_diag_part(input_x, assist)
return out_matrix_diag_part
class MatrixSetDiag(Cell):
r"""
Modifies the batched diagonal part of a batched tensor.
Assume `x` has :math:`k+1` dimensions :math:`[I, J, K, ..., M, N]` and `diagonal` has :math:`k`
dimensions :math:`[I, J, K, ..., min(M, N)]`. Then the output is a tensor of rank :math:`k+1` with dimensions
:math:`[I, J, K, ..., M, N]` where:
.. math::
output[i, j, k, ..., m, n] = diagnoal[i, j, k, ..., n]\ for\ m == n
.. math::
output[i, j, k, ..., m, n] = x[i, j, k, ..., m, n]\ for\ m != n
Inputs:
- **x** (Tensor) - The batched tensor. Rank k+1, where k >= 1. It can be one of the following data types:
float32, float16, int32, int8, and uint8.
- **diagonal** (Tensor) - The diagonal values. Must have the same type as input `x`. Rank k, where k >= 1.
Outputs:
Tensor, has the same type and shape as input `x`.
Raises:
TypeError: If dtype of `x` or `diagonal` is not one of float32, float16, int32, int8 or uint8.
ValueError: If length of shape of `x` is less than 2.
ValueError: If x_shape[-2] < x_shape[-1] and x_shape[:-1] != diagonal_shape.
ValueError: If x_shape[-2] >= x_shape[-1] and x_shape[:-2] + x_shape[-1:] != diagonal_shape.
Supported Platforms:
``Ascend``
Examples:
>>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
>>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
>>> matrix_set_diag = nn.MatrixSetDiag()
>>> output = matrix_set_diag(x, diagonal)
>>> print(output)
[[[-1. 0.]
[ 0. 2.]]
[[-1. 0.]
[ 0. 1.]]
[[-1. 0.]
[ 0. 1.]]]
"""
def __init__(self):
"""Initialize MatrixSetDiag."""
super(MatrixSetDiag, self).__init__()
self.matrix_set_diag = inner.MatrixSetDiag()
self.dtype = P.DType()
def construct(self, input_x, diagonal):
x_shape = F.shape(input_x)
x_dtype = self.dtype(input_x)
assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
out_matrix_set_diag = self.matrix_set_diag(input_x, diagonal, assist)
return out_matrix_set_diag
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