# 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
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# ============================================================================
"""basic"""
from __future__ import absolute_import
import math
import numpy as np
import mindspore.common.dtype as mstype
from mindspore import context, log as logger
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.operations import _inner_ops as inner
from mindspore.ops.primitive import constexpr, Primitive, _primexpr
from mindspore.common.parameter import Parameter
from mindspore._extends import cell_attr_register
from mindspore import _checkparam as Validator
from mindspore.nn.cell import Cell
from mindspore.nn.layer.activation import get_activation
from mindspore.common._decorator import deprecated
__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold', 'Tril', 'Triu',
'ResizeBilinear', 'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag', 'L1Regularizer', 'Dropout1d',
'Dropout2d', 'Dropout3d', 'Upsample', 'Roll', 'Identity', 'Unflatten']
class L1Regularizer(Cell):
r"""
Applies l1 regularization to weights.
l1 regularization makes weights sparsity.
.. math::
\text{loss}=\lambda * \text{reduce_sum}(\text{abs}(\omega))
where :math:`\lambda` is `scale` .
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(
f"For '{self.cls_name}', the 'scale' must be greater than 0, but got {scale}.")
if math.isinf(scale) or math.isnan(scale):
raise ValueError(
f"For '{self.cls_name}', the 'scale' can not be INF or NAN, but got {scale}.")
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
[docs]class Dropout(Cell):
r"""
Dropout layer for the input.
Dropout is a regularization method. The operator randomly sets some neurons output to 0
according to the probability of discarding the probability of discarding.
During the reasoning, 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.
- Parameter `keep_prob` will be removed in a future version, please use parameter `p` instead.
Parameter `p` means the probability of the element of the input tensor to be zeroed.
- Parameter `dtype` will be removed in a future version. It is not recommended to define this parameter.
Args:
keep_prob (float): Deprecated. The keep rate, greater than 0 and less equal than 1.
E.g. rate=0.9, dropping out 10% of input neurons. Default: 0.5.
p (Union[float, int, None]): The dropout rate, greater than or equal to 0 and less than 1.
E.g. rate=0.9, dropping out 90% of input neurons. Default: None.
dtype (:class:`mindspore.dtype`): Data type of `input`. Default: mstype.float32.
Inputs:
- **x** (Tensor) - The input of Dropout with data type of float16 or float32.
Outputs:
Tensor, output tensor with the same shape as the `x`.
Raises:
TypeError: If `keep_prob` is not a float.
TypeError: If the dtype of `p` is not float or int.
TypeError: If dtype of `x` is not neither float16 nor float32.
ValueError: If `keep_prob` is not in range (0, 1].
ValueError: If `p` 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(p=0.2)
>>> net.set_train()
>>> output = net(x)
>>> print(output.shape)
(2, 2, 3)
"""
def __init__(self, keep_prob=0.5, p=None, dtype=mstype.float32):
"""Initialize Dropout."""
super(Dropout, self).__init__()
if dtype != mstype.float32:
logger.warning(
"This parameter `dtype` will be deleted or invisible in the future. Please don't use it.")
if p is None:
logger.warning("For Dropout, this parameter `keep_prob` will be deprecated, please use `p` instead.")
Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
if keep_prob <= 0 or keep_prob > 1:
raise ValueError(f"For '{self.cls_name}', the 'keep_prob' must be a number in range (0, 1], "
f"but got {keep_prob}.")
seed0, seed1 = _get_graph_seed(0, "dropout")
self.dropout = P.Dropout(keep_prob, seed0, seed1)
else:
Validator.check_value_type('p', p, [float, int], self.cls_name)
if p < 0 or p >= 1:
raise ValueError(f"For '{self.cls_name}', the 'p' must be a number in range [0, 1), "
f"but got {p}.")
seed0, seed1 = _get_graph_seed(0, "dropout")
self.dropout = P.Dropout(1.0 - p, seed0, seed1)
self.p = p
self.keep_prob = keep_prob
def construct(self, x):
if not self.training or self.keep_prob == 1 or self.p == 0:
return x
out, _ = self.dropout(x)
return out
def extend_repr(self):
if self.p is None:
logger.warning("For Dropout, this parameter `keep_prob` will be deprecated, please use `p` instead.")
return f'keep_prob={self.keep_prob}'
return f'p={self.p}'
[docs]class Dropout1d(Cell):
r"""
During training, randomly zeroes entire channels of the input tensor with probability `p`
from a Bernoulli distribution (For a 3-dimensional tensor with a shape of :math:`(N, C, L)`,
the channel feature map refers to a 1-dimensional feature map with the shape of :math:`L`).
For example, the :math:`j\_th` channel of the :math:`i\_th` sample in the batched input is a to-be-processed
`1D` tensor input[i,j].
Each channel will be zeroed out independently on every forward call with probability `p` using samples
from a Bernoulli distribution.
The paper `Dropout: A Simple Way to Prevent Neural Networks from Overfitting
<http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_ mentioned this technology, And it is proved that
it can effectively reduce over fitting and prevent neuronal coadaptation.
For more details, refer to `Improving neural networks by preventing co-adaptation of feature detectors
<https://arxiv.org/pdf/1207.0580.pdf>`_ .
`Dropout1d` can improve the independence between channel feature maps.
Args:
p (float, optional): The dropping probability of a channel, between 0 and 1, e.g. `p` = 0.8,
which means an 80% chance of being set to 0. Default: 0.5.
Inputs:
- **x** (Tensor) - A tensor with shape :math:`(N, C, L)` or :math:`(C, L)`, where `N` is the batch size,
`C` is the number of channels, `L` is the feature length. The data type must be int8, int16, int32,
int64, float16, float32 or float64.
Outputs:
Tensor, output, with the same shape and data type as `x`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If the data type of `p` is not float.
ValueError: If `p` is out of the range `[0.0, 1.0]`.
ValueError: If `x` shape is not `2D` or `3D`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import nn, Tensor
>>> op = nn.Dropout1d(p=0.6)
>>> op.training = True
>>> a = Tensor(np.ones((3, 3)), ms.float32)
>>> output = op(a)
"""
def __init__(self, p=0.5):
"""Initialize Dropout1d."""
super(Dropout1d, self).__init__()
Validator.check_value_type('p', p, [float], self.cls_name)
if p < 0 or p > 1:
raise ValueError(f"For '{self.cls_name}', the 'p' must be a number in range [0, 1], "
f"but got {p}.")
self.prob = p
def construct(self, x):
if not self.training or self.prob == 0:
return x
out = F.dropout1d(x, self.prob)
return out
[docs]class Dropout2d(Cell):
r"""
During training, randomly zeroes some channels of the input tensor with probability `p`
from a Bernoulli distribution (For a 4-dimensional tensor with a shape of :math:`NCHW`,
the channel feature map refers to a 2-dimensional feature map with the shape of :math:`HW`).
For example, the :math:`j\_th` channel of the :math:`i\_th` sample in the batched input is a to-be-processed
`2D` tensor input[i,j].
Each channel will be zeroed out independently on every forward call with probability `p` using samples
from a Bernoulli distribution.
`Dropout2d` can improve the independence between channel feature maps.
Refer to :func:`mindspore.ops.dropout2d` for more details.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> dropout = nn.Dropout2d(p=0.5)
>>> x = Tensor(np.ones([2, 1, 2, 3]), mindspore.float32)
>>> output = dropout(x)
>>> print(output.shape)
(2, 1, 2, 3)
"""
def __init__(self, p=0.5):
"""Initialize Dropout2d."""
super(Dropout2d, self).__init__()
Validator.check_value_type('p', p, [float], self.cls_name)
if p < 0 or p > 1:
raise ValueError(f"For '{self.cls_name}', the 'p' must be a number in range [0, 1], "
f"but got {p}.")
self.keep_prob = 1.0 - p
self.dropout2d = P.Dropout2D(self.keep_prob)
def construct(self, x):
if not self.training or self.keep_prob == 1:
return x
out, _ = self.dropout2d(x)
return out
def extend_repr(self):
return 'p={}'.format(self.keep_prob)
[docs]class Dropout3d(Cell):
r"""
During training, randomly zeroes some channels of the input tensor
with probability `p` from a Bernoulli distribution (For a 5-dimensional tensor with
a shape of :math:`NCDHW`, the channel feature map refers to a 3-dimensional feature
map with a shape of :math:`DHW`).
For example, the :math:`j\_th` channel of the :math:`i\_th` sample in the batched input is a to-be-processed
`3D` tensor input[i,j].
Each channel will be zeroed out independently on every forward call which based on Bernoulli distribution
probability `p`.
`Dropout3d` can improve the independence between channel feature maps.
Refer to :func:`mindspore.ops.dropout3d` for more details.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> dropout = nn.Dropout3d(p=0.5)
>>> x = Tensor(np.ones([2, 1, 2, 1, 2]), mindspore.float32)
>>> output = dropout(x)
>>> print(output.shape)
(2, 1, 2, 1, 2)
"""
def __init__(self, p=0.5):
"""Initialize Dropout3d."""
super(Dropout3d, self).__init__()
Validator.check_value_type('p', p, [float], self.cls_name)
if p < 0 or p > 1:
raise ValueError(f"For '{self.cls_name}', the 'p' must be a number in range [0, 1], "
f"but got {p}.")
self.keep_prob = 1.0 - p
self.dropout3d = P.Dropout3D(self.keep_prob)
def construct(self, x):
if not self.training or self.keep_prob == 1:
return x
out, _ = self.dropout3d(x)
return out
def extend_repr(self):
return 'p={}'.format(self.keep_prob)
[docs]class Upsample(Cell):
r"""
For details, please refer to :func:`mindspore.ops.interpolate`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor([[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]])
>>> upsample = nn.Upsample(size=(5, 5))
>>> out = upsample(x)
>>> print(x.asnumpy())
[[[[1. 2. 3. 4.]
[5. 6. 7. 8.]]]]
>>> print(out.asnumpy())
[[[[1. 1. 2. 3. 4.]
[1. 1. 2. 3. 4.]
[1. 1. 2. 3. 4.]
[5. 5. 6. 7. 8.]
[5. 5. 6. 7. 8.]]]]
>>> print(out.shape)
(1, 1, 5, 5)
"""
def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None, recompute_scale_factor=None):
"""Initialize Upsample."""
super(Upsample, self).__init__()
self.size = size
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
self.recompute_scale_factor = recompute_scale_factor
def construct(self, x):
out = F.interpolate(x, self.size, self.scale_factor, self.mode,
self.align_corners, self.recompute_scale_factor)
return out
[docs]class Flatten(Cell):
r"""
Flatten the input Tensor along dimensions from `start_dim` to `end_dim`.
Args:
start_dim (int, optional): The first dimension to flatten. Default: 1.
end_dim (int, optional): The last dimension to flatten. Default: -1.
Inputs:
- **x** (Tensor) - The input Tensor to be flattened.
Outputs:
Tensor. If no dimensions are flattened, returns the original `x`, otherwise return the flattened Tensor.
If `x` is a 0-dimensional Tensor, a 1-dimensional Tensor will be returned.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `start_dim` or `end_dim` is not int.
ValueError: If `start_dim` is greater than `end_dim` after canonicalized.
ValueError: If `start_dim` or `end_dim` is not in range of [-x.dim, x.dim-1].
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, start_dim=1, end_dim=-1):
"""Initialize Flatten."""
super(Flatten, self).__init__()
self.start_dim = start_dim
self.end_dim = end_dim
def construct(self, x):
x_rank = F.rank(x)
ndim = x_rank if x_rank != 0 else 1
if self.start_dim < -ndim or self.start_dim >= ndim:
const_utils.raise_value_error("'start_dim' out of range.")
if self.end_dim < -ndim or self.end_dim >= ndim:
const_utils.raise_value_error("'end_dim' out of range.")
return F.flatten(x, start_dim=self.start_dim, end_dim=self.end_dim)
@_primexpr
def check_dense_input_shape(x, prim_name=None):
""" check the shape of inputs"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if len(x) < 2:
raise ValueError(f"{msg_prefix} dimension of 'x' should not be less than 2, but got {len(x)}.")
[docs]class Identity(Cell):
r"""
A placeholder identity operator that returns the same as input.
Inputs:
- **x** (Any) - The input of Identity.
Outputs:
The same as `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([1, 2, 3, 4]), mindspore.int64)
>>> net = nn.Identity()
>>> output = net(x)
>>> print(output)
[1 2 3 4]
"""
def __init__(self):
"""Initialize Identity."""
super(Identity, self).__init__()
def construct(self, x):
return x
[docs]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 :math:`\text{bias}`. Default: True.
activation (Union[str, Cell, Primitive, None]): activate function applied to the output of the fully connected
layer. Both activation name, e.g. 'relu', and mindspore activation function, e.g. mindspore.ops.ReLU(),
are supported. 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, "in_channels", self.cls_name)
self.out_channels = Validator.check_positive_int(
out_channels, "out_channels", self.cls_name)
self.has_bias = Validator.check_bool(
has_bias, "has_bias", self.cls_name)
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(f"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' must "
f"be equal to 2, and the first dim must be equal to 'out_channels', and the "
f"second dim must be equal to 'in_channels'. But got 'weight_init': {weight_init}, "
f"'out_channels': {out_channels}, 'in_channels': {in_channels}.")
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(f"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' must "
f"be equal to 1, and the first dim must be equal to 'out_channels'. But got "
f"'bias_init': {bias_init}, 'out_channels': {out_channels}.")
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(f"For '{self.cls_name}', the 'activation' must be str or Cell or Primitive, but got "
f"{type(activation).__name__}.")
self.activation_flag = self.activation is not None
def construct(self, x):
x_shape = self.shape_op(x)
check_dense_input_shape(x_shape, self.cls_name)
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] + (F.shape(x)[-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 F.equal(F.reduce_mean(x), 1.0)
@constexpr
def _dtype_check(x_dtype, prim_name=None):
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if x_dtype not in [mstype.float32, mstype.float16]:
raise TypeError(
f"{msg_prefix} x_dtype must be float32 or float16, but got {x_dtype}.")
@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 `x` 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__()
self.clip_by_norm = inner.ClipByNorm(axis)
def construct(self, x, clip_norm):
values_clip = self.clip_by_norm(x, clip_norm)
return values_clip
class Norm(Cell):
r"""
'nn.Norm' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.norm' instead.
"""
@deprecated("2.0", "ops.norm", False)
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):
"""
'nn.OneHot' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.one_hot' instead.
"""
@deprecated("2.0", "ops.one_hot", False)
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))
[docs]class Pad(Cell):
r"""
Pads the input tensor according to the paddings and mode.
Args:
paddings (tuple): The shape of parameter `paddings` is :math:`(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]`,
e.g.:
.. code-block::
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 = 5.
# 2nd dimension of output is paddings[1][0] + 3 + paddings[1][1] = 2 + 3 + 2 = 7.
# So the shape of output is (5, 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 :math:`(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]]), mindspore.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]]), mindspore.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(f"For '{self.cls_name}', the type of 'paddings' must be tuple, "
f"but got {type(paddings).__name__}.")
for item in paddings:
if len(item) != 2:
raise ValueError(f"For '{self.cls_name}', the dimension of 'paddings' must be (n, 2), "
f"but got {paddings}.")
if len(paddings) > 4:
raise ValueError(f"For '{self.cls_name}', only 'paddings' up to 4 dims is supported, but got "
f"{len(paddings)}.")
if mode == "CONSTANT":
self.pad = P.Pad(self.paddings)
else:
self.paddings = Tensor(np.array(self.paddings), dtype=mstype.int64)
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, prim_name=None):
"""Check input and calculate shape"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if not isinstance(align_corners, bool):
raise TypeError(
f"{msg_prefix} type of 'align_corners' must be bool, but got {type(align_corners).__name__}.")
if size is None and scale is None:
raise ValueError(f"{msg_prefix} 'size' and 'scale' both none.")
if size is not None and scale is not None:
raise ValueError(f"{msg_prefix} 'size' and 'scale' both not none.")
if size is not None:
if not isinstance(size, (tuple, list)):
raise ValueError(
f"{msg_prefix} 'size' must be tuple or list or None, but got {type(size).__name__}.")
Validator.check_int(len(size), 2, Validator.EQ, "size", "bilinear")
Validator.check_int(size[0], 1, Validator.GE, "size[0]", "bilinear")
Validator.check_int(size[1], 1, Validator.GE, "size[1]", "bilinear")
return size
Validator.check_int(scale, 1, Validator.GE, "scale factor", "bilinear")
ret = (scale * shape[2], scale * shape[3])
return ret
[docs]class ResizeBilinear(Cell):
r"""
'nn.ResizeBilinear' is deprecated from version 2.0 and will be removed in a future version,
use :class:`mindspore.ops.ResizeBilinearV2` or :func:`mindspore.ops.interpolate` instead.
Supported Platforms:
Deprecated
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, half_pixel_centers=False):
"""Initialize ResizeBilinear."""
super(ResizeBilinear, self).__init__()
logger.warning("'nn.ResizeBilinear' is deprecated from version 2.0 and will be removed in a "
"future version, use 'ops.ResizeBilinearV2' or 'ops.interpolate' instead.")
self.half_pixel_centers = half_pixel_centers
def construct(self, x, size=None, scale_factor=None, align_corners=False):
shape = bilinear(x.shape, size, scale_factor,
align_corners, self.cls_name)
resize_bilinear = P.ResizeBilinear(
shape, align_corners, self.half_pixel_centers)
return resize_bilinear(x)
[docs]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`` ``GPU``
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' must 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' must 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
@_primexpr
def tril(x_shape, x_dtype, k):
Validator.check_int(len(x_shape), 1, Validator.GE, "x rank", "tril")
Validator.check_is_int(k, "k value", "tril")
value = F.cast(P.Tril(diagonal=k)(F.ones(x_shape, x_dtype)), x_dtype)
return value
class Tril(Cell):
"""
'nn.Tril' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.tril' instead.
"""
@deprecated("2.0", "ops.tril", False)
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))
@_primexpr
def triu(x_shape, x_dtype, k):
Validator.check_int(len(x_shape), 1, Validator.GE, "x rank", "triu")
Validator.check_is_int(k, "k value", "triu")
value = F.cast(P.Triu(k)(F.ones(x_shape, x_dtype)), x_dtype)
return value
class Triu(Cell):
"""
'nn.Triu' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.triu' instead.
"""
@deprecated("2.0", "ops.triu", False)
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))
@_primexpr
def _get_matrix_diag_assist(x_shape, x_dtype):
"""Get matrix diag assist"""
Validator.check_int(len(x_shape), 1, Validator.GE, "x rank", "_get_matrix_diag_assist")
base_eye = F.reshape(
F.eye(x_shape[-1], x_shape[-1], x_dtype), (x_shape[-1] * x_shape[-1],))
if len(x_shape) == 1:
assist = F.reshape(base_eye, x_shape + (x_shape[-1],))
else:
assist = F.reshape(
F.tile(base_eye, x_shape[:-1]), x_shape + (x_shape[-1],))
value = F.cast(assist, x_dtype)
return value
@constexpr
def _get_matrix_diag_part_assist(x_shape, x_dtype):
"""Get matrix diag part assist"""
Validator.check_int(len(x_shape), 2, Validator.GE, "x rank", "_get_matrix_diag_part_assist")
base_eye = F.reshape(
F.eye(x_shape[-2], x_shape[-1], x_dtype), (x_shape[-2] * x_shape[-1],))
if len(x_shape) <= 2:
assist = F.reshape(base_eye, x_shape)
else:
assist = F.reshape(F.tile(base_eye, x_shape[:-2]), x_shape)
value = F.cast(assist, x_dtype)
return value
class MatrixDiag(Cell):
r"""
'nn.MatrixDiag' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.diag' instead.
"""
@deprecated("2.0", "ops.diag", False)
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"""
'nn.MatrixDiagPart' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.diagonal' instead.
"""
@deprecated("2.0", "ops.diagonal", False)
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)]`, 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] = diagonal[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
@constexpr
def _check_input_dim(axis, dim, cls_name):
Validator.check_int_range(axis, -dim, dim, Validator.INC_LEFT, 'axis', cls_name)
class Roll(Cell):
"""
'nn.Roll' is deprecated from version 2.0 and will be removed in a future version,
use 'ops.roll' instead.
"""
@deprecated("2.0", "ops.roll", False)
def __init__(self, shift, axis):
"""Initialize Roll"""
super(Roll, self).__init__()
Validator.check_value_type(
"shift", shift, [int, tuple, list], self.cls_name)
Validator.check_value_type(
"axis", axis, [int, tuple, list], self.cls_name)
self.shape_op = P.Shape()
self.shift = shift
self.axis = axis
self.op_list = []
self.gpu = False
if not isinstance(self.axis, (list, tuple)):
self.axis = [self.axis]
if not isinstance(self.shift, (list, tuple)):
self.shift = [self.shift]
if context.get_context("device_target") == "GPU":
Validator.check_int(len(self.shift), 1, Validator.GE, "shift", "Roll")
Validator.check_int(len(self.axis), 1, Validator.GE, "axis", "Roll")
for s_axis in self.axis:
Validator.check_is_int(s_axis, "axis", "Roll")
for s_shift in self.shift:
Validator.check_is_int(s_shift, "shift", "Roll")
self.roll = P.Roll(self.shift, self.axis)
self.gpu = True
if len(self.shift) != len(self.axis):
raise ValueError(f"For '{self.cls_name}', the shape of 'shift' and the shape of 'axis' must be "
f"the same, but got the length of 'shift' {len(self.shift)} "
f"and the length of 'axis' {len(self.axis)}.")
else:
if not isinstance(self.axis, (list, tuple)):
self.op_list.append(
(P.Roll(shift=self.shift, axis=0), self.axis))
else:
if len(self.shift) != len(self.axis):
raise ValueError(f"For '{self.cls_name}', the shape of 'shift' and the shape of 'axis' must be "
f"the same, but got the length of 'shift' {len(self.shift)} "
f"and the length of 'axis' {len(self.axis)}.")
for idx, _ in enumerate(self.axis):
self.op_list.append(
(P.Roll(shift=self.shift[idx], axis=0), self.axis[idx]))
def construct(self, input_x):
dim = len(self.shape_op(input_x))
if self.gpu:
output = self.roll(input_x)
else:
for single_op_roll, single_axis in self.op_list:
_check_input_dim(single_axis, dim, self.cls_name)
if single_axis < 0:
single_axis += dim
transpose_perm = []
for i in range(dim):
transpose_perm.append(i)
transpose_perm[0], transpose_perm[single_axis] = single_axis, 0
input_x = input_x.transpose(transpose_perm)
input_x = single_op_roll(input_x)
input_x = input_x.transpose(transpose_perm)
output = input_x
return output
[docs]class Unflatten(Cell):
r"""
Summary:
Unflattens a Tensor dim according to `axis` and `unflattened_size`.
Args:
axis (int): specifies the dimension of the input Tensor to be unflattened.
unflattened_size (Union(tuple[int], list[int])): the new shape of the unflattened dimension of
the Tensor and it can be a tuple of ints or a list of ints. The product of `unflattened_size`
must equal to input_shape[axis].
Inputs:
- **input** (Tensor) - The input Tensor to be unflattened.
Outputs:
Tensor that has been unflattend.
Raises:
TypeError: If `axis` is not int.
TypeError: If `unflattened_size` is neither tuple of ints nor list of ints.
TypeError: The product of `unflattened_size` does not equal to input_shape[axis].
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> input = Tensor(np.arange(0, 100).reshape(2, 10, 5), mindspore.float32)
>>> net = nn.Unflatten(1, (2, 5))
>>> output = net(input)
>>> print(f"before unflatten the input shape is {input.shape}")
before unflatten the input shape is (2, 10, 5)
>>> print(f"after unflatten the output shape is {output.shape}")
after unflatten the output shape is (2, 2, 5, 5)
"""
def __init__(self, axis, unflattened_size):
"""Initialize Unflatten."""
super(Unflatten, self).__init__()
self.shape = P.Shape()
self.reshape = P.Reshape()
Validator.check_is_int(axis, 'axis', 'Unflatten')
Validator.check_value_type(
'unflattended_size', unflattened_size, (list, tuple), 'Unflatten')
self.axis = axis
if isinstance(unflattened_size, list):
unflattened_size = tuple(unflattened_size)
self.unflattened_size = unflattened_size
def construct(self, input_x):
input_shape = self.shape(input_x)
new_shape = tuple()
new_shape += input_shape[: self.axis]
new_shape += self.unflattened_size
if self.axis != -1:
new_shape += input_shape[self.axis + 1:]
return self.reshape(input_x, new_shape)