mindspore.nn.layer.basic 源代码

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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, HeUniform, Uniform
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.function.nn_func import interpolate_ext
from mindspore.ops.auto_generate import unfold_ext
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
from mindspore.ops.auto_generate import dropout_ext_op, fold_ext
from mindspore.common.generator import default_generator

__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold', 'Tril', 'Triu',
           'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag', 'L1Regularizer', 'Dropout1d',
           'Dropout2d', 'Dropout3d', 'Upsample', 'Roll', 'Identity', 'Unflatten', 'DropoutExt']


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:
        >>> import mindspore as ms
        >>> import numpy as np
        >>> scale = 0.5
        >>> net = ms.nn.L1Regularizer(scale)
        >>> weights = ms.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


[文档]class Dropout(Cell): r""" Dropout layer for the input. Dropout is a means of regularization that reduces overfitting by preventing correlations between neuronal nodes. The operator randomly sets some neurons output to 0 according to `p`, which means the probability of discarding during training. And the return will be multiplied by :math:`\frac{1}{1-p}` during training. 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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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}'
[文档]class DropoutExt(Cell): r""" Dropout layer for the input. Dropout is a means of regularization that reduces overfitting by preventing correlations between neuronal nodes. The operator randomly sets some neurons output to 0 according to `p`, which means the probability of discarding during training. And the return will be multiplied by :math:`\frac{1}{1-p}` during training. 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 `p` means the probability of the element of the input tensor to be zeroed. Args: p (float): The dropout rate of input neurons, E.g. `p` =0.9, dropping out 90% of input neurons. Default: ``0.5`` . Inputs: - **x** (Tensor) - The input of Dropout. Outputs: Tensor, output tensor with the same shape as the `x`. Raises: TypeError: If the dtype of `p` is not float. ValueError: If length of shape of `x` is less than 1. Supported Platforms: ``Ascend`` Examples: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> net = nn.DropoutExt(p=0.2) >>> net.set_train() >>> output = net(x) >>> print(output.shape) (2, 2, 3) """ def __init__(self, p=0.5): """Initialize DropoutExt.""" super(DropoutExt, self).__init__() self.p = p self.generator_step = Tensor(1, mstype.int64) def construct(self, x): if not self.training or self.p == 0: return x seed, offset = default_generator._step(self.generator_step) # pylint: disable=protected-access out, _ = dropout_ext_op(x, self.p, seed, offset) return out
[文档]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, has 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 the shape of `x` is not `2D` or `3D`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore as ms >>> op = ms.nn.Dropout1d(p=0.6) >>> op.training = True >>> a = ms.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
[文档]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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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 f"p={self.keep_prob}"
[文档]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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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 f'p={self.keep_prob}'
[文档]class Upsample(Cell): r""" For details, please refer to :func:`mindspore.ops.interpolate`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore as ms >>> x = ms.Tensor([[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]]) >>> upsample = ms.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
class UpsampleExt(Cell): r""" For details, please refer to :func:`mindspore.mint.nn.functional.interpolate`. Supported Platforms: ``Ascend`` Examples: >>> import mindspore as ms >>> from mindspore import nn >>> x = ms.Tensor([[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]]) >>> upsample = nn.UpsampleExt(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(UpsampleExt, 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 = interpolate_ext(x, self.size, self.scale_factor, self.mode, self.align_corners, self.recompute_scale_factor) return out
[文档]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]. For example, the default values are used for the args and the input is a 0-dimensional or 1-dimensional Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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 check_axis_valid(self, axis, ndim): if axis < -ndim or axis >= ndim: raise ValueError("'start_dim' or 'end_dim' out of range.") def construct(self, x): x_rank = F.rank(x) ndim = x_rank if x_rank != 0 else 1 self.check_axis_valid(self.start_dim, ndim) self.check_axis_valid(self.end_dim, ndim) return F.flatten(x, start_dim=self.start_dim, end_dim=self.end_dim)
[文档]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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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
[文档]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: ``None`` , weight will be initialized using HeUniform. 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: ``None`` , bias will be initialized using Uniform. 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`` . dtype (:class:`mindspore.dtype`): Data type of Parameter. Default: ``mstype.float32`` . 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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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=None, bias_init=None, has_bias=True, activation=None, dtype=mstype.float32): """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}.") if weight_init is None: weight_init = HeUniform(math.sqrt(5)) self.weight = Parameter(initializer( weight_init, [out_channels, in_channels], dtype=dtype), 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}.") if bias_init is None: bound = 1 / math.sqrt(in_channels) bias_init = Uniform(scale=bound) self.bias = Parameter(initializer( bias_init, [out_channels], dtype=dtype), 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) 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 = f'input_channels={self.in_channels}, output_channels={self.out_channels}' if self.has_bias: s += f', has_bias={self.has_bias}' if self.activation_flag: s += f', activation={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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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""" The Norm class will be deprecated in the future, this function can be replaced by :func:`ops.norm` """ @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 f'axis={self.axis}, keep_dims={self.keep_dims}' class OneHot(Cell): """ The OneHot class will be deprecated in the future, this function can be replaced by :func:`ops.one_hot` """ @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))
[文档]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: >>> import mindspore >>> from mindspore import Tensor, nn, ops >>> 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
[文档]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 :math:`[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 :math:`(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 :math:`(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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> net = nn.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=mindspore.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}.") is_int = isinstance(arg_val[1], int) and isinstance(arg_val[2], int) if not is_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
[文档]class UnfoldExt(Cell): r""" Extracts sliding local blocks from a batched input tensor. For details, please refer to :func:`mindspore.mint.nn.functional.unfold`. Supported Platforms: ``Ascend`` Examples: >>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, nn >>> input = Tensor(np.random.rand(4, 4, 32, 32), mindspore.float64) >>> unfold = nn.UnfoldExt(kernel_size=3, dilation=1, stride=1) >>> output = unfold(input) >>> print(output.shape) (4, 36, 900) """ def __init__(self, kernel_size, dilation=1, padding=0, stride=1): super(UnfoldExt, self).__init__() self.kernel_size = kernel_size self.dilation = dilation self.padding = padding self.stride = stride def construct(self, input): return unfold_ext(input, self.kernel_size, self.dilation, self.padding, self.stride)
[文档]class Fold(Cell): r""" Combines an array of sliding local blocks into a large containing tensor. For details, please refer to :func:`mindspore.mint.nn.functional.fold`. Supported Platforms: ``Ascend`` Examples: >>> import numpy as np >>> from mindspore import Tensor, nn >>> from mindspore import dtype as mstype >>> fold = nn.Fold([8, 8], [2, 2], [2, 2], [2, 2], [2, 2]) >>> input = Tensor(input_data=np.random.rand(16, 64, 25), dtype=mstype.float32) >>> output = fold(input) >>> print(output.shape) (16, 16, 8, 8) """ def __init__(self, output_size, kernel_size, dilation=1, padding=0, stride=1): super(Fold, self).__init__() self.output_size = output_size self.kernel_size = kernel_size self.dilation = dilation self.padding = padding self.stride = stride def construct(self, input): return fold_ext(input, self.output_size, self.kernel_size, self.dilation, self.padding, self.stride)
@_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): """ The Tril class will be deprecated in the future, this function can be replaced by :func:`ops.tril` """ @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): """ The Triu class will be deprecated in the future, this function can be replaced by :func:`ops.triu` """ @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""" The MatrixDiag class will be deprecated in the future, this function can be replaced by :func:`ops.diag` """ @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""" The MatrixDiagPart class will be deprecated in the future, this function can be replaced by :func:`ops.diagonal` """ @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: >>> import mindspore >>> from mindspore import Tensor, nn >>> 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): """ The Roll class will be deprecated in the future, this function can be replaced by :func:`ops.roll` """ @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
[文档]class Unflatten(Cell): r""" 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: >>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> 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)