Python torch._C Examples

def glu(input, dim=-1):
    # type: (Tensor, int) -> Tensor
    r"""
    glu(input, dim=-1) -> Tensor

    The gated linear unit. Computes:

    .. math ::

        H = A \times \sigma(B)

    where `input` is split in half along `dim` to form `A` and `B`.

    See `Language Modeling with Gated Convolutional Networks <https://arxiv.org/abs/1612.08083>`_.

    Args:
        input (Tensor): input tensor
        dim (int): dimension on which to split the input
    """
    if input.dim() == 0:
        raise RuntimeError("glu does not suppport scalars because halving size must be even")
    return torch._C._nn.glu(input, dim) 

def multi_margin_loss(input, target, p=1, margin=1., weight=None, size_average=None,
                      reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, int, float, Optional[Tensor], Optional[bool], Optional[bool], str) -> Tensor
    r"""multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None,
                          reduce=None, reduction='mean') -> Tensor

    See :class:`~torch.nn.MultiMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction_enum = _Reduction.get_enum(reduction)
    if p != 1 and p != 2:
        raise ValueError('only p == 1 and p == 2 supported')
    if weight is not None:
        weight = torch.jit._unwrap_optional(weight)
        if weight.dim() != 1:
            raise ValueError('weight must be one-dimensional')

    return torch._C._nn.multi_margin_loss(input, target, p, margin, weight, reduction_enum) 

def l1_loss(input, target, size_average=None, reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, Optional[bool], Optional[bool], str) -> Tensor
    r"""l1_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

    Function that takes the mean element-wise absolute value difference.

    See :class:`~torch.nn.L1Loss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    if target.requires_grad:
        ret = torch.abs(input - target)
        if reduction != 'none':
            ret = torch.mean(ret) if reduction == 'mean' else torch.sum(ret)
    else:
        expanded_input, expanded_target = torch.broadcast_tensors(input, target)
        ret = torch._C._nn.l1_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
    return ret 

def smooth_l1_loss(input, target, size_average=None, reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, Optional[bool], Optional[bool], str) -> Tensor
    r"""Function that uses a squared term if the absolute
    element-wise error falls below 1 and an L1 term otherwise.

    See :class:`~torch.nn.SmoothL1Loss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction = _Reduction.legacy_get_string(size_average, reduce)
    if target.requires_grad:
        ret = _smooth_l1_loss(input, target)
        if reduction != 'none':
            ret = torch.mean(ret) if reduction == 'mean' else torch.sum(ret)
    else:
        expanded_input, expanded_target = torch.broadcast_tensors(input, target)
        ret = torch._C._nn.smooth_l1_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
    return ret 

def adaptive_avg_pool3d(input, output_size):
    # type: (Tensor, BroadcastingList3[int]) -> Tensor
    r"""
    Applies a 3D adaptive average pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveAvgPool3d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            triple-integer tuple)
    """
    _output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_avg_pool3d(input, _output_size)


# Activation functions 

def max_unpool2d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    # type: (Tensor, Tensor, BroadcastingList2[int], Optional[BroadcastingList2[int]], BroadcastingList2[int], Optional[BroadcastingList2[int]]) -> Tensor  # noqa
    r"""Computes a partial inverse of :class:`MaxPool2d`.

    See :class:`~torch.nn.MaxUnpool2d` for details.
    """
    kernel_size = _pair(kernel_size)
    if stride is not None:
        _stride = _pair(torch.jit._unwrap_optional(stride))
    else:
        _stride = kernel_size
    padding = _pair(padding)
    output_size = _unpool_output_size(input, kernel_size, _stride, padding,
                                      output_size)
    return torch._C._nn.max_unpool2d(input, indices, output_size) 

def max_unpool1d(input, indices, kernel_size, stride=None, padding=0,
                 output_size=None):
    # type: (Tensor, Tensor, BroadcastingList1[int], Optional[BroadcastingList1[int]], BroadcastingList1[int], Optional[BroadcastingList1[int]]) -> Tensor  # noqa
    r"""Computes a partial inverse of :class:`MaxPool1d`.

    See :class:`~torch.nn.MaxUnpool1d` for details.
    """
    kernel_size = _single(kernel_size)
    if stride is not None:
        _stride = _single(torch.jit._unwrap_optional(stride))
    else:
        _stride = kernel_size
    padding = _single(padding)
    output_size = _unpool_output_size(input, kernel_size, _stride, padding,
                                      output_size)
    return torch._C._nn.max_unpool2d(input.unsqueeze(3), indices.unsqueeze(3), output_size + [1]).squeeze(3) 

def adaptive_max_pool2d_with_indices(input, output_size, return_indices=False):
    # type: (Tensor, BroadcastingList1[int], bool) -> Tuple[Tensor, Tensor]
    r"""Applies a 2D adaptive max pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            double-integer tuple)
        return_indices: whether to return pooling indices. Default: ``False``
    """
    output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_max_pool2d(input, output_size) 

def adaptive_max_pool3d_with_indices(input, output_size, return_indices=False):
    # type: (Tensor, BroadcastingList1[int], bool) -> Tuple[Tensor, Tensor]
    r"""Applies a 3D adaptive max pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveMaxPool3d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            triple-integer tuple)
        return_indices: whether to return pooling indices. Default: ``False``
    """
    output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_max_pool3d(input, output_size) 

def adaptive_avg_pool2d(input, output_size):
    # type: (Tensor, BroadcastingList2[int]) -> Tensor
    r"""
    Applies a 2D adaptive average pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.

    Args:
        output_size: the target output size (single integer or
            double-integer tuple)
    """
    _output_size = _list_with_default(output_size, input.size())
    return torch._C._nn.adaptive_avg_pool2d(input, _output_size) 

def elu(input, alpha=1., inplace=False):
    # type: (Tensor, float, bool) -> Tensor
    r"""Applies element-wise,
    :math:`\text{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))`.

    See :class:`~torch.nn.ELU` for more details.
    """
    if inplace:
        result = torch._C._nn.elu_(input, alpha)
    else:
        result = torch._C._nn.elu(input, alpha)
    return result 

def leaky_relu(input, negative_slope=0.01, inplace=False):
    # type: (Tensor, float, bool) -> Tensor
    r"""
    leaky_relu(input, negative_slope=0.01, inplace=False) -> Tensor

    Applies element-wise,
    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`

    See :class:`~torch.nn.LeakyReLU` for more details.
    """
    if inplace:
        result = torch._C._nn.leaky_relu_(input, negative_slope)
    else:
        result = torch._C._nn.leaky_relu(input, negative_slope)
    return result 

def max_pool3d_with_indices(input, kernel_size, stride=None, padding=0,
                            dilation=1, ceil_mode=False, return_indices=False):
    # type: (Tensor, BroadcastingList3[int], Optional[BroadcastingList3[int]], BroadcastingList3[int], BroadcastingList3[int], bool, bool) -> Tuple[Tensor, Tensor]  # noqa
    r"""Applies a 3D max pooling over an input signal composed of several input
    planes.

    See :class:`~torch.nn.MaxPool3d` for details.
    """
    if stride is None:
        _stride = torch.jit.annotate(List[int], [])
    else:
        _stride = torch.jit._unwrap_optional(stride)
    return torch._C._nn.max_pool3d_with_indices(
        input, kernel_size, _stride, padding, dilation, ceil_mode) 

def multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, Optional[bool], Optional[bool], str) -> Tensor
    r"""multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

    See :class:`~torch.nn.MultiLabelMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction_enum = _Reduction.get_enum(reduction)
    return torch._C._nn.multilabel_margin_loss(input, target, reduction_enum) 

def soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, Optional[bool], Optional[bool], str) -> Tensor
    r"""soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

    See :class:`~torch.nn.SoftMarginLoss` for details.
    """
    if size_average is not None or reduce is not None:
        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction_enum = _Reduction.get_enum(reduction)
    return torch._C._nn.soft_margin_loss(input, target, reduction_enum) 

def max_pool2d_with_indices(input, kernel_size, stride=None, padding=0, dilation=1,
                            ceil_mode=False, return_indices=False):
    # type: (Tensor, BroadcastingList2[int], Optional[BroadcastingList2[int]], BroadcastingList2[int], BroadcastingList2[int], bool, bool) -> Tuple[Tensor, Tensor]  # noqa
    r"""Applies a 2D max pooling over an input signal composed of several input
    planes.

    See :class:`~torch.nn.MaxPool2d` for details.
    """
    if stride is None:
        _stride = torch.jit.annotate(List[int], [])
    else:
        _stride = torch.jit._unwrap_optional(stride)
    return torch._C._nn.max_pool2d_with_indices(input, kernel_size, _stride, padding, dilation, ceil_mode) 

def fractional_max_pool2d_with_indices(input, kernel_size, output_size=None,
                                       output_ratio=None, return_indices=False,
                                       _random_samples=None):
    # type: (Tensor, BroadcastingList2[int], Optional[BroadcastingList2[int]], Optional[BroadcastingList2[float]], bool, Optional[Tensor]) -> Tuple[Tensor, Tensor]  # noqa
    r"""Applies 2D fractional max pooling over an input signal composed of several input planes.

    Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham

    The max-pooling operation is applied in :math:`kH \times kW` regions by a stochastic
    step size determined by the target output size.
    The number of output features is equal to the number of input planes.

    Args:
        kernel_size: the size of the window to take a max over.
                     Can be a single number :math:`k` (for a square kernel of :math:`k \times k`)
                     or a tuple :math:`(kH \times kW)`
        output_size: the target output size of the image of the form :math:`oH \times oW`.
                     Can be a tuple `(oH, oW)` or a single number :math:`oH` for a square image :math:`oH \times oH`
        output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
                      This has to be a number or tuple in the range (0, 1)
        return_indices: if ``True``, will return the indices along with the outputs.
                        Useful to pass to :func:`~torch.nn.functional.max_unpool2d`.

    Examples::
        >>> input = torch.randn(20, 16, 50, 32)
        >>> # pool of square window of size=3, and target output size 13x12
        >>> F.fractional_max_pool2d(input, 3, output_size=(13, 12))
        >>> # pool of square window and target output size being half of input image size
        >>> F.fractional_max_pool2d(input, 3, output_ratio=(0.5, 0.5))

    .. _Fractional MaxPooling:
        http://arxiv.org/abs/1412.6071
    """
    if output_size is None and output_ratio is None:
        raise ValueError("fractional_max_pool2d requires specifying either "
                         "an output_size or an output_ratio")
    if output_size is None:
        _output_ratio = _pair(torch.jit._unwrap_optional(output_ratio))
        _output_size = [int(input.size(2) * _output_ratio[0]),
                        int(input.size(3) * _output_ratio[1])]
    else:
        _output_size = torch.jit._unwrap_optional(output_size)

    if _random_samples is None:
        _random_samples = torch.rand(input.size(0), input.size(1), 2, dtype=input.dtype, device=input.device)
    else:
        _random_samples = torch.jit._unwrap_optional(_random_samples)
    return torch._C._nn.fractional_max_pool2d(input, kernel_size, _output_size, _random_samples) 

def binary_cross_entropy(input, target, weight=None, size_average=None,
                         reduce=None, reduction='mean'):
    # type: (Tensor, Tensor, Optional[Tensor], Optional[bool], Optional[bool], str) -> Tensor
    r"""Function that measures the Binary Cross Entropy
    between the target and the output.

    See :class:`~torch.nn.BCELoss` for details.

    Args:
        input: Tensor of arbitrary shape
        target: Tensor of the same shape as input
        weight (Tensor, optional): a manual rescaling weight
                if provided it's repeated to match input tensor shape
        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
            the losses are averaged over each loss element in the batch. Note that for
            some losses, there multiple elements per sample. If the field :attr:`size_average`
            is set to ``False``, the losses are instead summed for each minibatch. Ignored
            when reduce is ``False``. Default: ``True``
        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
            losses are averaged or summed over observations for each minibatch depending
            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
            batch element instead and ignores :attr:`size_average`. Default: ``True``
        reduction (string, optional): Specifies the reduction to apply to the output:
            'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
            'mean': the sum of the output will be divided by the number of
            elements in the output, 'sum': the output will be summed. Note: :attr:`size_average`
            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
            specifying either of those two args will override :attr:`reduction`. Default: 'mean'

    Examples::

        >>> input = torch.randn((3, 2), requires_grad=True)
        >>> target = torch.rand((3, 2), requires_grad=False)
        >>> loss = F.binary_cross_entropy(F.sigmoid(input), target)
        >>> loss.backward()
    """
    if size_average is not None or reduce is not None:
        reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
    else:
        reduction_enum = _Reduction.get_enum(reduction)
    if not (target.size() == input.size()):
        warnings.warn("Using a target size ({}) that is different to the input size ({}) is deprecated. "
                      "Please ensure they have the same size.".format(target.size(), input.size()))
    if input.numel() != target.numel():
        raise ValueError("Target and input must have the same number of elements. target nelement ({}) "
                         "!= input nelement ({})".format(target.numel(), input.numel()))

    if weight is not None:
        weight = torch.jit._unwrap_optional(weight)
        new_size = _infer_size(target.size(), weight.size())
        weight = weight.expand(new_size)

    return torch._C._nn.binary_cross_entropy(
        input, target, weight, reduction_enum)