r"""Functional interface""" from __future__ import division import warnings import math import types from operator import mul from functools import reduce import torch from torch._C import _infer_size, _add_docstr from . import _functions from .modules import utils from ._functions import vision from ._functions.thnn.fold import Col2Im, Im2Col from .modules.utils import _single, _pair, _triple, _list_with_default from . import grad from . import _VF from .._jit_internal import weak_script class _Reduction: # NB: Keep this class in sync with enums in aten/src/ATen/core/Reduction.h @staticmethod def get_enum(reduction): if reduction == 'none': return 0 elif reduction == 'mean': return 1 elif reduction == 'elementwise_mean': warnings.warn("reduction='elementwise_mean' is deprecated, please use reduction='mean' instead.") return 1 elif reduction == 'sum': return 2 raise ValueError(reduction + " is not a valid value for reduction") # In order to support previous versions, accept boolean size_average and reduce # and convert them into the new constants for now # We use these functions in torch/legacy as well, in which case we'll silence the warning @staticmethod def legacy_get_string(size_average, reduce, emit_warning=True): warning = "size_average and reduce args will be deprecated, please use reduction='{}' instead." if size_average is None: size_average = True if reduce is None: reduce = True if size_average and reduce: ret = 'mean' elif reduce: ret = 'sum' else: ret = 'none' if emit_warning: warnings.warn(warning.format(ret)) return ret @staticmethod def legacy_get_enum(size_average, reduce, emit_warning=True): return _Reduction.get_enum(_Reduction.legacy_get_string(size_average, reduce, emit_warning)) conv1d = _add_docstr(torch.conv1d, r""" conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor Applies a 1D convolution over an input signal composed of several input planes. See :class:`~torch.nn.Conv1d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iW)` weight: filters of shape :math:`(\text{out\_channels} \times \frac{\text{in\_channels}}{\text{groups}} \times kW)` bias: optional bias of shape :math:`(\text{out\_channels})`. Default: ``None`` stride: the stride of the convolving kernel. Can be a single number or a one-element tuple `(sW,)`. Default: 1 padding: implicit zero paddings on both sides of the input. Can be a single number or a one-element tuple `(padW,)`. Default: 0 dilation: the spacing between kernel elements. Can be a single number or a one-element tuple `(dW,)`. Default: 1 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 Examples:: >>> filters = torch.randn(33, 16, 3) >>> inputs = torch.randn(20, 16, 50) >>> F.conv1d(inputs, filters) """) conv2d = _add_docstr(torch.conv2d, r""" conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor Applies a 2D convolution over an input image composed of several input planes. See :class:`~torch.nn.Conv2d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iH \times iW)` weight: filters of shape :math:`(\text{out\_channels} \times \frac{\text{in\_channels}}{\text{groups}} \times kH \times kW)` bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None`` stride: the stride of the convolving kernel. Can be a single number or a tuple `(sH, sW)`. Default: 1 padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 dilation: the spacing between kernel elements. Can be a single number or a tuple `(dH, dW)`. Default: 1 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 Examples:: >>> # With square kernels and equal stride >>> filters = torch.randn(8,4,3,3) >>> inputs = torch.randn(1,4,5,5) >>> F.conv2d(inputs, filters, padding=1) """) # noqa: E501 conv3d = _add_docstr(torch.conv3d, r""" conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor Applies a 3D convolution over an input image composed of several input planes. See :class:`~torch.nn.Conv3d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iT \times iH \times iW)` weight: filters of shape :math:`(\text{out\_channels} \times \frac{\text{in\_channels}}{\text{groups}} \times kT \times kH \times kW)` bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: None stride: the stride of the convolving kernel. Can be a single number or a tuple `(sT, sH, sW)`. Default: 1 padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padT, padH, padW)`. Default: 0 dilation: the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 Examples:: >>> filters = torch.randn(33, 16, 3, 3, 3) >>> inputs = torch.randn(20, 16, 50, 10, 20) >>> F.conv3d(inputs, filters) """) # noqa: E501 conv_transpose1d = _add_docstr(torch.conv_transpose1d, r""" conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution". See :class:`~torch.nn.ConvTranspose1d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iW)` weight: filters of shape :math:`(\text{in\_channels} \times \frac{\text{out\_channels}}{\text{groups}} \times kW)` bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None stride: the stride of the convolving kernel. Can be a single number or a tuple ``(sW,)``. Default: 1 padding: ``kernel_size - 1 - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padW,)``. Default: 0 output_padding: additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 dilation: the spacing between kernel elements. Can be a single number or a tuple ``(dW,)``. Default: 1 Examples:: >>> inputs = torch.randn(20, 16, 50) >>> weights = torch.randn(16, 33, 5) >>> F.conv_transpose1d(inputs, weights) """) conv_transpose2d = _add_docstr(torch.conv_transpose2d, r""" conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution". See :class:`~torch.nn.ConvTranspose2d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iH \times iW)` weight: filters of shape :math:`(\text{in\_channels} \times \frac{\text{out\_channels}}{\text{groups}} \times kH \times kW)` bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None stride: the stride of the convolving kernel. Can be a single number or a tuple ``(sH, sW)``. Default: 1 padding: ``kernel_size - 1 - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padH, padW)``. Default: 0 output_padding: additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padH, out_padW)``. Default: 0 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 dilation: the spacing between kernel elements. Can be a single number or a tuple ``(dH, dW)``. Default: 1 Examples:: >>> # With square kernels and equal stride >>> inputs = torch.randn(1, 4, 5, 5) >>> weights = torch.randn(4, 8, 3, 3) >>> F.conv_transpose2d(inputs, weights, padding=1) """) # noqa: E501 conv_transpose3d = _add_docstr(torch.conv_transpose3d, r""" conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution" See :class:`~torch.nn.ConvTranspose3d` for details and output shape. .. include:: cudnn_deterministic.rst Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iT \times iH \times iW)` weight: filters of shape :math:`(\text{in\_channels} \times \frac{\text{out\_channels}}{\text{groups}} \times kT \times kH \times kW)` bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None stride: the stride of the convolving kernel. Can be a single number or a tuple ``(sT, sH, sW)``. Default: 1 padding: ``kernel_size - 1 - padding`` zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple ``(padT, padH, padW)``. Default: 0 output_padding: additional size added to one side of each dimension in the output shape. Can be a single number or a tuple ``(out_padT, out_padH, out_padW)``. Default: 0 groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the number of groups. Default: 1 dilation: the spacing between kernel elements. Can be a single number or a tuple `(dT, dH, dW)`. Default: 1 Examples:: >>> inputs = torch.randn(20, 16, 50, 10, 20) >>> weights = torch.randn(16, 33, 3, 3, 3) >>> F.conv_transpose3d(inputs, weights) """) # noqa: E501 conv_tbc = _add_docstr(torch.conv_tbc, r""" Applies a 1-dimensional sequence convolution over an input sequence. Input and output dimensions are (Time, Batch, Channels) - hence TBC. Args: input: input tensor of shape :math:`(\text{sequence length} \times batch \times \text{in\_channels})` weight: filter of shape (:math:`\text{kernel width} \times \text{in\_channels} \times \text{out\_channels}`) bias: bias of shape (:math:`\text{out\_channels}`) pad: number of timesteps to pad. Default: 0 """) # Pooling avg_pool1d = _add_docstr(torch.avg_pool1d, r""" avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor Applies a 1D average pooling over an input signal composed of several input planes. See :class:`~torch.nn.AvgPool1d` for details and output shape. Args: input: input tensor of shape :math:`(\text{minibatch} \times \text{in\_channels} \times iW)` kernel_size: the size of the window. Can be a single number or a tuple :math:`(kW,)` stride: the stride of the window. Can be a single number or a tuple `(sW,)`. Default: :attr:`kernel_size` padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0 ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape. Default: ``False`` count_include_pad: when True, will include the zero-padding in the averaging calculation. Default: ``True`` Examples:: >>> # pool of square window of size=3, stride=2 >>> input = torch.tensor([[[1,2,3,4,5,6,7]]]) >>> F.avg_pool1d(input, kernel_size=3, stride=2) tensor([[[ 2., 4., 6.]]]) """) avg_pool2d = _add_docstr(torch._C._nn.avg_pool2d, r""" avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size :math:`sH \times sW` steps. The number of output features is equal to the number of input planes. See :class:`~torch.nn.AvgPool2d` for details and output shape. Args: input: input tensor :math:`(\text{minibatch} \times \text{in\_channels} \times iH \times iW)` kernel_size: size of the pooling region. Can be a single number or a tuple :math:`(kH \times kW)` stride: stride of the pooling operation. Can be a single number or a tuple `(sH, sW)`. Default: :attr:`kernel_size` padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 ceil_mode: when True, will use `ceil` instead of `floor` in the formula to compute the output shape. Default: ``False`` count_include_pad: when True, will include the zero-padding in the averaging calculation. Default: ``True`` """) avg_pool3d = _add_docstr(torch._C._nn.avg_pool3d, r""" avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor Applies 3D average-pooling operation in :math:`kT \times kH \times kW` regions by step size :math:`sT \times sH \times sW` steps. The number of output features is equal to :math:`\lfloor\frac{\text{input planes}}{sT}\rfloor`. See :class:`~torch.nn.AvgPool3d` for details and output shape. Args: input: input tensor :math:`(\text{minibatch} \times \text{in\_channels} \times iT \times iH \times iW)` kernel_size: size of the pooling region. Can be a single number or a tuple :math:`(kT \times kH \times kW)` stride: stride of the pooling operation. Can be a single number or a tuple `(sT, sH, sW)`. Default: :attr:`kernel_size` padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padT, padH, padW)`, Default: 0 ceil_mode: when True, will use `ceil` instead of `floor` in the formula to compute the output shape count_include_pad: when True, will include the zero-padding in the averaging calculation """) def fractional_max_pool2d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None): 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 a output_ratio") if output_size is None: output_ratio = _pair(output_ratio) output_size = (int(input.size(2) * output_ratio[0]), int(input.size(3) * output_ratio[1])) if _random_samples is None: _random_samples = input.new(input.size(0), input.size(1), 2).uniform_() ret = torch._C._nn.fractional_max_pool2d(input, kernel_size, output_size, _random_samples) return ret if return_indices else ret[0] def max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False): r"""Applies a 1D max pooling over an input signal composed of several input planes. See :class:`~torch.nn.MaxPool1d` for details. """ ret = torch.max_pool1d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode) return ret if return_indices else ret[0] def max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False): r"""Applies a 2D max pooling over an input signal composed of several input planes. See :class:`~torch.nn.MaxPool2d` for details. """ ret = torch._C._nn.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode) return ret if return_indices else ret[0] def max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False): r"""Applies a 3D max pooling over an input signal composed of several input planes. See :class:`~torch.nn.MaxPool3d` for details. """ ret = torch._C._nn.max_pool3d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode) return ret if return_indices else ret[0] def _unpool_output_size(input, kernel_size, stride, padding, output_size): input_size = input.size() default_size = [] for d in range(len(kernel_size)): default_size.append((input_size[d + 2] - 1) * stride[d] + kernel_size[d] - 2 * padding[d]) if output_size is None: return default_size output_size = list(output_size) if len(output_size) == len(kernel_size) + 2: output_size = output_size[2:] if len(output_size) != len(kernel_size): raise ValueError("output_size should be a sequence containing " "{} or {} elements, but it has a length of '{}'" .format(len(kernel_size), len(kernel_size) + 2, len(output_size))) for d in range(len(kernel_size)): min_size = default_size[d] - stride[d] max_size = default_size[d] + stride[d] if not (min_size < output_size[d] < max_size): raise ValueError( 'invalid output_size "{}" (dim {} must be between {} and {})' .format(output_size, d, min_size, max_size)) return output_size def max_unpool1d(input, indices, kernel_size, stride=None, padding=0, output_size=None): r"""Computes a partial inverse of :class:`MaxPool1d`. See :class:`~torch.nn.MaxUnpool1d` for details. """ kernel_size = _single(kernel_size) stride = _single(stride or 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 max_unpool2d(input, indices, kernel_size, stride=None, padding=0, output_size=None): r"""Computes a partial inverse of :class:`MaxPool2d`. See :class:`~torch.nn.MaxUnpool2d` for details. """ kernel_size = _pair(kernel_size) stride = _pair(stride or 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_unpool3d(input, indices, kernel_size, stride=None, padding=0, output_size=None): r"""Computes a partial inverse of :class:`MaxPool3d`. See :class:`~torch.nn.MaxUnpool3d` for details. """ kernel_size = _triple(kernel_size) stride = _triple(stride or kernel_size) padding = _triple(padding) output_size = _unpool_output_size(input, kernel_size, stride, padding, output_size) return torch._C._nn.max_unpool3d(input, indices, output_size, stride, padding) def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False): r"""Applies a 2D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of `p` is zero, the gradient is set to zero as well. See :class:`~torch.nn.LPPool2d` for details. """ kw, kh = utils._pair(kernel_size) out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode) return (torch.sign(out) * relu(torch.abs(out))).mul(kw * kh).pow(1. / norm_type) def lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False): r"""Applies a 1D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of `p` is zero, the gradient is set to zero as well. See :class:`~torch.nn.LPPool1d` for details. """ out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode) return (torch.sign(out) * relu(torch.abs(out))).mul(kernel_size).pow(1. / norm_type) def adaptive_max_pool1d(input, output_size, return_indices=False): r"""Applies a 1D adaptive max pooling over an input signal composed of several input planes. See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape. Args: output_size: the target output size (single integer) return_indices: whether to return pooling indices. Default: ``False`` """ ret = torch.adaptive_max_pool1d(input, output_size) return ret if return_indices else ret[0] def adaptive_max_pool2d(input, output_size, return_indices=False): 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()) ret = torch._C._nn.adaptive_max_pool2d(input, output_size) return ret if return_indices else ret[0] def adaptive_max_pool3d(input, output_size, return_indices=False): 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()) ret = torch._C._nn.adaptive_max_pool3d(input, output_size) return ret if return_indices else ret[0] adaptive_avg_pool1d = _add_docstr(torch.adaptive_avg_pool1d, r""" adaptive_avg_pool1d(input, output_size) -> Tensor Applies a 1D adaptive average pooling over an input signal composed of several input planes. See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape. Args: output_size: the target output size (single integer) """) def adaptive_avg_pool2d(input, output_size): 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 adaptive_avg_pool3d(input, output_size): 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 @torch._jit_internal.weak_script def dropout(input, p=0.5, training=True, inplace=False): # type: (Tensor, float, bool, bool) -> Tensor r""" During training, randomly zeroes some of the elements of the input tensor with probability :attr:`p` using samples from a Bernoulli distribution. See :class:`~torch.nn.Dropout` for details. Args: p: probability of an element to be zeroed. Default: 0.5 training: apply dropout if is ``True``. Defualt: ``True`` inplace: If set to ``True``, will do this operation in-place. Default: ``False`` """ if p < 0. or p > 1.: raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p)) return (_VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)) @torch._jit_internal.weak_script def alpha_dropout(input, p=0.5, training=False, inplace=False): # type: (Tensor, float, bool, bool) -> Tensor r"""Applies alpha dropout to the input. See :class:`~torch.nn.AlphaDropout` for details. """ if p < 0. or p > 1.: raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p)) return (_VF.alpha_dropout_(input, p, training) if inplace else _VF.alpha_dropout(input, p, training)) @torch._jit_internal.weak_script def dropout2d(input, p=0.5, training=True, inplace=False): # type: (Tensor, float, bool, bool) -> Tensor r""" Randomly zero out entire channels (a channel is a 2D feature map, e.g., the :math:`j`-th channel of the :math:`i`-th sample in the batched input is a 2D tensor :math:`\text{input}[i, j]`) of the input tensor). Each channel will be zeroed out independently on every forward call. with probability :attr:`p` using samples from a Bernoulli distribution. See :class:`~torch.nn.Dropout2d` for details. Args: p: probability of a channel to be zeroed. Default: 0.5 training: apply dropout if is ``True``. Defualt: ``True`` inplace: If set to ``True``, will do this operation in-place. Default: ``False`` """ if p < 0. or p > 1.: raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p)) return (_VF.feature_dropout_(input, p, training) if inplace else _VF.feature_dropout(input, p, training)) @torch._jit_internal.weak_script def dropout3d(input, p=0.5, training=True, inplace=False): # type: (Tensor, float, bool, bool) -> Tensor r""" Randomly zero out entire channels (a channel is a 3D feature map, e.g., the :math:`j`-th channel of the :math:`i`-th sample in the batched input is a 3D tensor :math:`\text{input}[i, j]`) of the input tensor). Each channel will be zeroed out independently on every forward call. with probability :attr:`p` using samples from a Bernoulli distribution. See :class:`~torch.nn.Dropout3d` for details. Args: p: probability of a channel to be zeroed. Default: 0.5 training: apply dropout if is ``True``. Defualt: ``True`` inplace: If set to ``True``, will do this operation in-place. Default: ``False`` """ # This is 100% the same code as dropout2d. We duplicate this code so that # stack traces are not confusing. if p < 0. or p > 1.: raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p)) return (_VF.feature_dropout_(input, p, training) if inplace else _VF.feature_dropout(input, p, training)) @torch._jit_internal.weak_script def feature_alpha_dropout(input, p=0.5, training=False, inplace=False): # type: (Tensor, float, bool, bool) -> Tensor if p < 0. or p > 1.: raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p)) return (_VF.feature_alpha_dropout_(input, p, training) if inplace else _VF.feature_alpha_dropout(input, p, training)) @torch._jit_internal.weak_script def threshold(input, threshold, value, inplace=False): # type: (Tensor, float, float, bool) -> Tensor r"""Thresholds each element of the input Tensor. See :class:`~torch.nn.Threshold` for more details. """ if inplace: result = _VF.threshold_(input, threshold, value) else: result = _VF.threshold(input, threshold, value) return result threshold_ = _add_docstr(_VF.threshold_, r""" threshold_(input, threshold, value) -> Tensor In-place version of :func:`~threshold`. """) @torch._jit_internal.weak_script def relu(input, inplace=False): # type: (Tensor, bool) -> Tensor r"""relu(input, inplace=False) -> Tensor Applies the rectified linear unit function element-wise. See :class:`~torch.nn.ReLU` for more details. """ if inplace: result = torch.relu_(input) else: result = torch.relu(input) return result relu_ = _add_docstr(torch.relu_, r""" relu_(input) -> Tensor In-place version of :func:`~relu`. """) def glu(input, dim=-1): 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 `_. 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) @torch._jit_internal.weak_script def hardtanh(input, min_val=-1., max_val=1., inplace=False): # type: (Tensor, float, float, bool) -> Tensor r""" hardtanh(input, min_val=-1., max_val=1., inplace=False) -> Tensor Applies the HardTanh function element-wise. See :class:`~torch.nn.Hardtanh` for more details. """ if inplace: result = torch._C._nn.hardtanh_(input, min_val, max_val) else: result = torch._C._nn.hardtanh(input, min_val, max_val) return result hardtanh_ = _add_docstr(torch._C._nn.hardtanh_, r""" hardtanh_(input, min_val=-1., max_val=1.) -> Tensor In-place version of :func:`~hardtanh`. """) @torch._jit_internal.weak_script def relu6(input, inplace=False): # type: (Tensor, bool) -> Tensor r"""relu6(input, inplace=False) -> Tensor Applies the element-wise function :math:`\text{ReLU6}(x) = \min(\max(0,x), 6)`. See :class:`~torch.nn.ReLU6` for more details. """ return hardtanh(input, 0., 6., inplace) @torch._jit_internal.weak_script 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 elu_ = _add_docstr(torch._C._nn.elu_, r""" elu_(input, alpha=1.) -> Tensor In-place version of :func:`~elu`. """) @torch._jit_internal.weak_script def selu(input, inplace=False): # type: (Tensor, bool) -> Tensor r"""selu(input, inplace=False) -> Tensor Applies element-wise, :math:`\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`, with :math:`\alpha=1.6732632423543772848170429916717` and :math:`scale=1.0507009873554804934193349852946`. See :class:`~torch.nn.SELU` for more details. """ if inplace: result = torch.selu_(input) else: result = torch.selu(input) return result selu_ = _add_docstr(torch.selu_, r""" selu_(input) -> Tensor In-place version of :func:`~selu`. """) @torch._jit_internal.weak_script def celu(input, alpha=1., inplace=False): # type: (Tensor, float, bool) -> Tensor r"""celu(input, alpha=1., inplace=False) -> Tensor Applies element-wise, :math:`\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))`. See :class:`~torch.nn.CELU` for more details. """ if inplace: result = torch.celu_(input, alpha) else: result = torch.celu(input, alpha) return result celu_ = _add_docstr(torch.celu_, r""" celu_(input, alpha=1.) -> Tensor In-place version of :func:`~celu`. """) @torch._jit_internal.weak_script 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 leaky_relu_ = _add_docstr(torch._C._nn.leaky_relu_, r""" leaky_relu_(input, negative_slope=0.01) -> Tensor In-place version of :func:`~leaky_relu`. """) @torch._jit_internal.weak_script def prelu(input, weight): # type: (Tensor, Tensor) -> Tensor r"""prelu(input, weight) -> Tensor Applies element-wise the function :math:`\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)` where weight is a learnable parameter. See :class:`~torch.nn.PReLU` for more details. """ return torch.prelu(input, weight) @torch._jit_internal.weak_script def rrelu(input, lower=1. / 8, upper=1. / 3, training=False, inplace=False): # type: (Tensor, float, float, bool, bool) -> Tensor r"""rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False) -> Tensor Randomized leaky ReLU. See :class:`~torch.nn.RReLU` for more details. """ if inplace: result = torch.rrelu_(input, lower, upper, training) else: result = torch.rrelu(input, lower, upper, training) return result rrelu_ = _add_docstr(torch.rrelu_, r""" rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor In-place version of :func:`~rrelu`. """) logsigmoid = _add_docstr(torch._C._nn.log_sigmoid, r""" logsigmoid(input) -> Tensor Applies element-wise :math:`\text{LogSigmoid}(x) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)` See :class:`~torch.nn.LogSigmoid` for more details. """) @torch._jit_internal.weak_script def hardshrink(input, lambd=0.5): # type: (Tensor, float) -> Tensor r""" hardshrink(input, lambd=0.5) -> Tensor Applies the hard shrinkage function element-wise See :class:`~torch.nn.Hardshrink` for more details. """ return torch.hardshrink(input, lambd) @torch._jit_internal.weak_script def tanhshrink(input): r"""tanhshrink(input) -> Tensor Applies element-wise, :math:`\text{Tanhshrink}(x) = x - \text{Tanh}(x)` See :class:`~torch.nn.Tanhshrink` for more details. """ return input - input.tanh() @torch._jit_internal.weak_script def softsign(input): r"""softsign(input) -> Tensor Applies element-wise, the function :math:`\text{SoftSign}(x) = \frac{x}{1 + |x|}` See :class:`~torch.nn.Softsign` for more details. """ return input / (input.abs() + 1) softplus = _add_docstr(torch._C._nn.softplus, r""" softplus(input, beta=1, threshold=20) -> Tensor """) def _get_softmax_dim(name, ndim, stacklevel): warnings.warn("Implicit dimension choice for " + name + " has been deprecated. " "Change the call to include dim=X as an argument.", stacklevel=stacklevel) if ndim == 0 or ndim == 1 or ndim == 3: return 0 else: return 1 def softmin(input, dim=None, _stacklevel=3, dtype=None): r"""Applies a softmin function. Note that :math:`\text{Softmin}(x) = \text{Softmax}(-x)`. See softmax definition for mathematical formula. See :class:`~torch.nn.Softmin` for more details. Arguments: input (Tensor): input dim (int): A dimension along which softmin will be computed (so every slice along dim will sum to 1). dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. """ if dim is None: dim = _get_softmax_dim('softmin', input.dim(), _stacklevel) if dtype is None: return (-input).softmax(dim) else: return (-input).softmax(dim, dtype=dtype) def softmax(input, dim=None, _stacklevel=3, dtype=None): r"""Applies a softmax function. Softmax is defined as: :math:`\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}` It is applied to all slices along dim, and will re-scale them so that the elements lie in the range `(0, 1)` and sum to 1. See :class:`~torch.nn.Softmax` for more details. Arguments: input (Tensor): input dim (int): A dimension along which softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. .. note:: This function doesn't work directly with NLLLoss, which expects the Log to be computed between the Softmax and itself. Use log_softmax instead (it's faster and has better numerical properties). """ if dim is None: dim = _get_softmax_dim('softmax', input.dim(), _stacklevel) if dtype is None: return input.softmax(dim) else: return input.softmax(dim, dtype=dtype) def _sample_gumbel(shape, eps=1e-10, out=None): """ Sample from Gumbel(0, 1) based on https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb , (MIT license) """ U = out.resize_(shape).uniform_() if out is not None else torch.rand(shape) return - torch.log(eps - torch.log(U + eps)) def _gumbel_softmax_sample(logits, tau=1, eps=1e-10): """ Draw a sample from the Gumbel-Softmax distribution based on https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb (MIT license) """ dims = logits.dim() gumbel_noise = _sample_gumbel(logits.size(), eps=eps, out=logits.data.new()) y = logits + gumbel_noise return softmax(y / tau, dims - 1) def gumbel_softmax(logits, tau=1, hard=False, eps=1e-10): r""" Sample from the Gumbel-Softmax distribution and optionally discretize. Args: logits: `[batch_size, num_features]` unnormalized log probabilities tau: non-negative scalar temperature hard: if ``True``, the returned samples will be discretized as one-hot vectors, but will be differentiated as if it is the soft sample in autograd Returns: Sampled tensor of shape ``batch_size x num_features`` from the Gumbel-Softmax distribution. If ``hard=True``, the returned samples will be one-hot, otherwise they will be probability distributions that sum to 1 across features Constraints: - Currently only work on 2D input :attr:`logits` tensor of shape ``batch_size x num_features`` Based on https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb , (MIT license) """ shape = logits.size() assert len(shape) == 2 y_soft = _gumbel_softmax_sample(logits, tau=tau, eps=eps) if hard: _, k = y_soft.max(-1) # this bit is based on # https://discuss.pytorch.org/t/stop-gradients-for-st-gumbel-softmax/530/5 y_hard = logits.new_zeros(*shape).scatter_(-1, k.view(-1, 1), 1.0) # this cool bit of code achieves two things: # - makes the output value exactly one-hot (since we add then # subtract y_soft value) # - makes the gradient equal to y_soft gradient (since we strip # all other gradients) y = y_hard - y_soft.detach() + y_soft else: y = y_soft return y def log_softmax(input, dim=None, _stacklevel=3, dtype=None): r"""Applies a softmax followed by a logarithm. While mathematically equivalent to log(softmax(x)), doing these two operations separately is slower, and numerically unstable. This function uses an alternative formulation to compute the output and gradient correctly. See :class:`~torch.nn.LogSoftmax` for more details. Arguments: input (Tensor): input dim (int): A dimension along which log_softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None. """ if dim is None: dim = _get_softmax_dim('log_softmax', input.dim(), _stacklevel) if dtype is None: return input.log_softmax(dim) else: return input.log_softmax(dim, dtype=dtype) softshrink = _add_docstr(torch._C._nn.softshrink, r""" softshrink(input, lambd=0.5) -> Tensor Applies the soft shrinkage function elementwise See :class:`~torch.nn.Softshrink` for more details. """) @torch._jit_internal.weak_script def tanh(input): r"""tanh(input) -> Tensor Applies element-wise, :math:`\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}` See :class:`~torch.nn.Tanh` for more details. """ warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.") return input.tanh() @torch._jit_internal.weak_script def sigmoid(input): r"""sigmoid(input) -> Tensor Applies the element-wise function :math:`\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}` See :class:`~torch.nn.Sigmoid` for more details. """ warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.") return input.sigmoid() def linear(input, weight, bias=None): r""" Applies a linear transformation to the incoming data: :math:`y = xA^T + b`. Shape: - Input: :math:`(N, *, in\_features)` where `*` means any number of additional dimensions - Weight: :math:`(out\_features, in\_features)` - Bias: :math:`(out\_features)` - Output: :math:`(N, *, out\_features)` """ if input.dim() == 2 and bias is not None: # fused op is marginally faster return torch.addmm(bias, input, weight.t()) output = input.matmul(weight.t()) if bias is not None: output += bias return output def bilinear(input1, input2, weight, bias=None): return torch.bilinear(input1, input2, weight, bias) def embedding(input, weight, padding_idx=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, sparse=False): r"""A simple lookup table that looks up embeddings in a fixed dictionary and size. This module is often used to retrieve word embeddings using indices. The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings. See :class:`torch.nn.Embedding` for more details. Args: input (LongTensor): Tensor containing indices into the embedding matrix weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx` (initialized to zeros) whenever it encounters the index. max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` is renormalized to have norm :attr:`max_norm`. Note: this will modify :attr:`weight` in-place. norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. scale_grad_by_freq (boolean, optional): If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default ``False``. sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under :class:`torch.nn.Embedding` for more details regarding sparse gradients. Shape: - Input: LongTensor of arbitrary shape containing the indices to extract - Weight: Embedding matrix of floating point type with shape `(V, embedding_dim)`, where V = maximum index + 1 and embedding_dim = the embedding size - Output: `(*, embedding_dim)`, where `*` is the input shape Examples:: >>> # a batch of 2 samples of 4 indices each >>> input = torch.tensor([[1,2,4,5],[4,3,2,9]]) >>> # an embedding matrix containing 10 tensors of size 3 >>> embedding_matrix = torch.rand(10, 3) >>> F.embedding(input, embedding_matrix) tensor([[[ 0.8490, 0.9625, 0.6753], [ 0.9666, 0.7761, 0.6108], [ 0.6246, 0.9751, 0.3618], [ 0.4161, 0.2419, 0.7383]], [[ 0.6246, 0.9751, 0.3618], [ 0.0237, 0.7794, 0.0528], [ 0.9666, 0.7761, 0.6108], [ 0.3385, 0.8612, 0.1867]]]) >>> # example with padding_idx >>> weights = torch.rand(10, 3) >>> weights[0, :].zero_() >>> embedding_matrix = weights >>> input = torch.tensor([[0,2,0,5]]) >>> F.embedding(input, embedding_matrix, padding_idx=0) tensor([[[ 0.0000, 0.0000, 0.0000], [ 0.5609, 0.5384, 0.8720], [ 0.0000, 0.0000, 0.0000], [ 0.6262, 0.2438, 0.7471]]]) """ if padding_idx is not None: if padding_idx > 0: assert padding_idx < weight.size(0), 'Padding_idx must be within num_embeddings' elif padding_idx < 0: assert padding_idx >= -weight.size(0), 'Padding_idx must be within num_embeddings' padding_idx = weight.size(0) + padding_idx elif padding_idx is None: padding_idx = -1 if max_norm is not None: # `embedding_renorm_` will call .contiguous() on input anyways, so we # call it here and take advantage of the improved locality in the # `embedding` call below too. input = input.contiguous() with torch.no_grad(): torch.embedding_renorm_(weight, input, max_norm, norm_type) return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) def embedding_bag(input, weight, offsets=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, mode='mean', sparse=False): r"""Computes sums, means or maxes of 'bags' of embeddings, without instantiating the intermediate embeddings. See :class:`torch.nn.EmbeddingBag` for more details. .. include:: cuda_deterministic_backward.rst Args: input (LongTensor): Tensor containing bags of indices into the embedding matrix weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size offsets (LongTensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines the starting index position of each bag (sequence) in :attr:`input`. max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` is renormalized to have norm :attr:`max_norm`. Note: this will modify :attr:`weight` in-place. norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the :attr:`max_norm` option. Default ``2``. scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default ``False``. Note: this option is not supported when ``mode="max"``. mode (string, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. Default: ``"mean"`` sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under :class:`torch.nn.Embedding` for more details regarding sparse gradients. Note: this option is not supported when ``mode="max"``. Shape: - :attr:`input` (LongTensor) and :attr:`offsets` (LongTensor, optional) - If :attr:`input` is 2D of shape ``B x N``, it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and this will return ``B`` values aggregated in a way depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case. - If :attr:`input` is 1D of shape ``N``, it will be treated as a concatenation of multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing the starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets` of shape ``B``, :attr:`input` will be viewed as having ``B`` bags. Empty bags (i.e., having 0-length) will have returned vectors filled by zeros. - :attr:`weight` (Tensor): the learnable weights of the module of shape ``(num_embeddings x embedding_dim)`` - :attr:`output`: aggregated embedding values of shape ``B x embedding_dim`` Examples:: >>> # an Embedding module containing 10 tensors of size 3 >>> embedding_matrix = torch.rand(10, 3) >>> # a batch of 2 samples of 4 indices each >>> input = torch.tensor([1,2,4,5,4,3,2,9]) >>> offsets = torch.tensor([0,4]) >>> F.embedding_bag(embedding_matrix, input, offsets) tensor([[ 0.3397, 0.3552, 0.5545], [ 0.5893, 0.4386, 0.5882]]) """ # Check for backward compatibility. # Used to be embedding_bag(weight, input, ...) # Now is embedding_bag(input, weight, ...) if weight.dtype == torch.long and input.is_floating_point(): warnings.warn("Argument order of nn.functional.embedding_bag was changed. " "Usage `embedding_bag(weight, input, ...)` is deprecated, " "and should now be `embedding_bag(input, weight, ...)`.") weight, input = input, weight if input.dim() == 2: if offsets is not None: raise ValueError("if input is 2D, then offsets has to be None" ", as input is treated is a mini-batch of" " fixed length sequences. However, found " "offsets of type {}".format(type(offsets))) else: offsets = torch.arange(0, input.numel(), input.size(1), dtype=torch.long, device=input.device) input = input.reshape(-1) elif input.dim() == 1: if offsets is None: raise ValueError("offsets has to be a 1D Tensor but got None") if offsets.dim() != 1: raise ValueError("offsets has to be a 1D Tensor") if offsets[0].item() != 0: raise ValueError("offsets[0] has to be 0, i.e., the first sequence " "in the mini-batch has to start from position 0. " "However, got {}".format(offsets[0].item())) if offsets[-1].item() > input.size(0): raise ValueError("offsets[-1] can not be greater than input's length" " ({}), but got offsets[-1] of {}" .format(input.size(0), offsets[-1].item())) else: raise ValueError("input has to be 1D or 2D Tensor," " but got Tensor of dimension {}".format(input.dim())) if mode == 'sum': mode = 0 elif mode == 'mean': mode = 1 elif mode == 'max': mode = 2 if scale_grad_by_freq: raise ValueError("max mode does not support scaling the gradient by the frequency") if sparse: raise ValueError("max mode does not support sparse weights") else: raise ValueError("mode has to be one of sum, mean or max") if max_norm is not None: with torch.no_grad(): torch.embedding_renorm_(weight, input, max_norm, norm_type) ret, _, _, _ = torch.embedding_bag( weight, input, offsets, scale_grad_by_freq, mode, sparse) return ret def batch_norm(input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-5): r"""Applies Batch Normalization for each channel across a batch of data. See :class:`~torch.nn.BatchNorm1d`, :class:`~torch.nn.BatchNorm2d`, :class:`~torch.nn.BatchNorm3d` for details. """ if training: size = list(input.size()) if reduce(mul, size[2:], size[0]) == 1: raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size)) return torch.batch_norm( input, weight, bias, running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled ) def instance_norm(input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=True, momentum=0.1, eps=1e-5): r"""Applies Instance Normalization for each channel in each data sample in a batch. See :class:`~torch.nn.InstanceNorm1d`, :class:`~torch.nn.InstanceNorm2d`, :class:`~torch.nn.InstanceNorm3d` for details. """ return torch.instance_norm( input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, torch.backends.cudnn.enabled ) def layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-5): r"""Applies Layer Normalization for last certain number of dimensions. See :class:`~torch.nn.LayerNorm` for details. """ return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled) def group_norm(input, num_groups, weight=None, bias=None, eps=1e-5): r"""Applies Group Normalization for last certain number of dimensions. See :class:`~torch.nn.GroupNorm` for details. """ return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled) def local_response_norm(input, size, alpha=1e-4, beta=0.75, k=1): r"""Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. See :class:`~torch.nn.LocalResponseNorm` for details. """ dim = input.dim() if dim < 3: raise ValueError('Expected 3D or higher dimensionality \ input (got {} dimensions)'.format(dim)) div = input.mul(input).unsqueeze(1) if dim == 3: div = pad(div, (0, 0, size // 2, (size - 1) // 2)) div = avg_pool2d(div, (size, 1), stride=1).squeeze(1) else: sizes = input.size() div = div.view(sizes[0], 1, sizes[1], sizes[2], -1) div = pad(div, (0, 0, 0, 0, size // 2, (size - 1) // 2)) div = avg_pool3d(div, (size, 1, 1), stride=1).squeeze(1) div = div.view(sizes) div = div.mul(alpha).add(k).pow(beta) return input / div # loss def ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean'): r"""The Connectionist Temporal Classification loss. See :class:`~torch.nn.CTCLoss` for details. .. include:: cudnn_deterministic.rst .. include:: cuda_deterministic_backward.rst Args: log_probs: :math:`(T, N, C)` where `C = number of characters in alphabet including blank`, `T = input length`, and `N = batch size`. The logarithmized probabilities of the outputs (e.g. obtained with :func:`torch.nn.functional.log_softmax`). targets: :math:`(N, S)` or `(sum(target_lenghts))`. Targets (cannot be blank). In the second form, the targets are assumed to be concatenated. input_lengths: :math:`(N)`. Lengths of the inputs (must each be :math:`\leq T`) target_lengths: :math:`(N)`. Lengths of the targets blank (int, optional): Blank label. Default :math:`0`. reduction (string, optional): Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the output losses will be divided by the target lengths and then the mean over the batch is taken. Default: 'mean' Example:: >>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_() >>> targets = torch.randint(1, 21, (16, 30), dtype=torch.long) >>> input_lengths = torch.full((16,), 50, dtype=torch.long) >>> target_lengths = torch.randint(10,30,(16,), dtype=torch.long) >>> loss = F.ctc_loss(log_probs, targets, input_lengths, target_lengths) >>> loss.backward() """ return torch.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, _Reduction.get_enum(reduction)) def nll_loss(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean'): r"""The negative log likelihood loss. See :class:`~torch.nn.NLLLoss` for details. Args: input: :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)` in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K > 1` in the case of K-dimensional loss. target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or :math:`(N, d_1, d_2, ..., d_K)` where :math:`K \geq 1` for K-dimensional loss. weight (Tensor, optional): a manual rescaling weight given to each class. If given, has to be a Tensor of size `C` 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`` ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. When :attr:`size_average` is ``True``, the loss is averaged over non-ignored targets. Default: -100 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' Example:: >>> # input is of size N x C = 3 x 5 >>> input = torch.randn(3, 5, requires_grad=True) >>> # each element in target has to have 0 <= value < C >>> target = torch.tensor([1, 0, 4]) >>> output = F.nll_loss(F.log_softmax(input), target) >>> output.backward() """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) dim = input.dim() if dim < 2: raise ValueError('Expected 2 or more dimensions (got {})'.format(dim)) if input.size(0) != target.size(0): raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).' .format(input.size(0), target.size(0))) if dim == 2: return torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) elif dim == 4: return torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index) elif dim == 3 or dim > 4: n = input.size(0) c = input.size(1) out_size = (n,) + input.size()[2:] if target.size()[1:] != input.size()[2:]: raise ValueError('Expected target size {}, got {}'.format( out_size, target.size())) input = input.contiguous().view(n, c, 1, -1) target = target.contiguous().view(n, 1, -1) if reduction is not 'none': return torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index) out = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index) return out.view(out_size) def poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-8, reduce=None, reduction='mean'): r"""Poisson negative log likelihood loss. See :class:`~torch.nn.PoissonNLLLoss` for details. Args: input: expectation of underlying Poisson distribution. target: random sample :math:`target \sim \text{Poisson}(input)`. log_input: if ``True`` the loss is computed as :math:`\exp(\text{input}) - \text{target} * \text{input}`, if ``False`` then loss is :math:`\text{input} - \text{target} * \log(\text{input}+\text{eps})`. Default: ``True`` full: whether to compute full loss, i. e. to add the Stirling approximation term. Default: ``False`` :math:`\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})`. 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`` eps (float, optional): Small value to avoid evaluation of :math:`\log(0)` when :attr:`log_input`=``False``. Default: 1e-8 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' """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) if log_input: loss = torch.exp(input) - target * input else: loss = input - target * torch.log(input + eps) if full: mask = target > 1 loss[mask] += (target * torch.log(target) - target + 0.5 * torch.log(2 * math.pi * target))[mask] if reduction is 'none': return loss if reduction is 'mean': return torch.mean(loss) return torch.sum(loss) def kl_div(input, target, size_average=None, reduce=None, reduction='mean'): r"""The `Kullback-Leibler divergence`_ Loss. See :class:`~torch.nn.KLDivLoss` for details. Args: input: Tensor of arbitrary shape target: Tensor of the same shape as input 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' """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch.kl_div(input, target, reduction) def cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean'): r"""This criterion combines `log_softmax` and `nll_loss` in a single function. See :class:`~torch.nn.CrossEntropyLoss` for details. Args: input (Tensor) : :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)` in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K > 1` in the case of K-dimensional loss. target (Tensor) : :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or :math:`(N, d_1, d_2, ..., d_K)` where :math:`K \geq 1` for K-dimensional loss. weight (Tensor, optional): a manual rescaling weight given to each class. If given, has to be a Tensor of size `C` 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`` ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. When :attr:`size_average` is ``True``, the loss is averaged over non-ignored targets. Default: -100 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, 5, requires_grad=True) >>> target = torch.randint(5, (3,), dtype=torch.int64) >>> loss = F.cross_entropy(input, target) >>> loss.backward() """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) def binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean'): 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 = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _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.nelement() != target.nelement(): raise ValueError("Target and input must have the same number of elements. target nelement ({}) " "!= input nelement ({})".format(target.nelement(), input.nelement())) if weight is not None: new_size = _infer_size(target.size(), weight.size()) weight = weight.expand(new_size) return torch._C._nn.binary_cross_entropy(input, target, weight, reduction) def binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None): r"""Function that measures Binary Cross Entropy between target and output logits. See :class:`~torch.nn.BCEWithLogitsLoss` 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' pos_weight (Tensor, optional): a weight of positive examples. Must be a vector with length equal to the number of classes. Examples:: >>> input = torch.randn(3, requires_grad=True) >>> target = torch.empty(3).random_(2) >>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward() """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) if not (target.size() == input.size()): raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size())) return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction) def _pointwise_loss(lambd, lambd_optimized, input, target, reduction='mean'): if target.requires_grad: d = lambd(input, target) if reduction == 'none': return d return torch.mean(d) if reduction == 'mean' else torch.sum(d) else: expanded_input, expanded_target = torch.broadcast_tensors(input, target) return lambd_optimized(expanded_input, expanded_target, _Reduction.get_enum(reduction)) def _smooth_l1_loss(input, target): t = torch.abs(input - target) return torch.where(t < 1, 0.5 * t ** 2, t - 0.5) def smooth_l1_loss(input, target, size_average=None, reduce=None, reduction='mean'): 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) return _pointwise_loss( _smooth_l1_loss, torch._C._nn.smooth_l1_loss, input, target, reduction) def l1_loss(input, target, size_average=None, reduce=None, reduction='mean'): 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) return _pointwise_loss(lambda a, b: torch.abs(a - b), torch._C._nn.l1_loss, input, target, reduction) def mse_loss(input, target, size_average=None, reduce=None, reduction='mean'): r"""mse_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor Measures the element-wise mean squared error. See :class:`~torch.nn.MSELoss` for details. """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) return _pointwise_loss(lambda a, b: (a - b) ** 2, torch._C._nn.mse_loss, input, target, reduction) def margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean'): r"""margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor See :class:`~torch.nn.MarginRankingLoss` for details. """ # noqa if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) if input1.dim() == 0 or input2.dim() == 0 or target.dim() == 0: raise RuntimeError(("margin_ranking_loss does not support scalars, got sizes: " "input1: {}, input2: {}, target: {} ".format(input1.size(), input2.size(), target.size()))) return torch.margin_ranking_loss(input1, input2, target, margin, reduction) def hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='mean'): r"""hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='mean') -> Tensor See :class:`~torch.nn.HingeEmbeddingLoss` for details. """ # noqa if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch.hinge_embedding_loss(input, target, margin, reduction) def multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean'): 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 = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch._C._nn.multilabel_margin_loss(input, target, reduction) def soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean'): 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 = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch._C._nn.soft_margin_loss(input, target, reduction) def multilabel_soft_margin_loss(input, target, weight=None, size_average=None, reduce=None, reduction='mean'): r"""multilabel_soft_margin_loss(input, target, weight=None, size_average=None) -> Tensor See :class:`~torch.nn.MultiLabelSoftMarginLoss` for details. """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) loss = -(target * logsigmoid(input) + (1 - target) * logsigmoid(-input)) if weight is not None: loss = loss * weight loss = loss.sum(dim=1) / input.size(1) # only return N loss values if reduction == 'none': return loss elif reduction == 'mean': return loss.mean() elif reduction == 'sum': return loss.sum() else: raise ValueError(reduction + " is not valid") def cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean'): r"""cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor See :class:`~torch.nn.CosineEmbeddingLoss` for details. """ # noqa if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch.cosine_embedding_loss(input1, input2, target, margin, reduction) def multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None, reduce=None, reduction='mean'): 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 = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) if p != 1 and p != 2: raise ValueError('only p == 1 and p == 2 supported') if weight is not None and weight.dim() != 1: raise ValueError('weight must be one-dimensional') return torch._C._nn.multi_margin_loss(input, target, p, margin, weight, reduction) pixel_shuffle = _add_docstr(torch.pixel_shuffle, r""" Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)` to a tensor of shape :math:`(C, H \times r, W \times r)`. See :class:`~torch.nn.PixelShuffle` for details. Args: input (Tensor): the input tensor upscale_factor (int): factor to increase spatial resolution by Examples:: >>> input = torch.randn(1, 9, 4, 4) >>> output = torch.nn.functional.pixel_shuffle(input, 3) >>> print(output.size()) torch.Size([1, 1, 12, 12]) """) def upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None): r"""Upsamples the input to either the given :attr:`size` or the given :attr:`scale_factor` .. warning:: This function is deprecated in favor of :func:`torch.nn.functional.interpolate`. This is equivalent with ``nn.functional.interpolate(...)``. .. include:: cuda_deterministic_backward.rst The algorithm used for upsampling is determined by :attr:`mode`. Currently temporal, spatial and volumetric upsampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: `mini-batch x channels x [optional depth] x [optional height] x width`. The modes available for upsampling are: `nearest`, `linear` (3D-only), `bilinear` (4D-only), `trilinear` (5D-only) Args: input (Tensor): the input tensor size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]): output spatial size. scale_factor (int): multiplier for spatial size. Has to be an integer. mode (string): algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'trilinear'. Default: 'nearest' align_corners (bool, optional): if True, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect when :attr:`mode` is `linear`, `bilinear`, or `trilinear`. Default: False .. warning:: With ``align_corners = True``, the linearly interpolating modes (`linear`, `bilinear`, and `trilinear`) don't proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is ``align_corners = False``. See :class:`~torch.nn.Upsample` for concrete examples on how this affects the outputs. """ warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") return interpolate(input, size, scale_factor, mode, align_corners) def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None): r"""Down/up samples the input to either the given :attr:`size` or the given :attr:`scale_factor` The algorithm used for interpolation is determined by :attr:`mode`. Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: `mini-batch x channels x [optional depth] x [optional height] x width`. The modes available for resizing are: `nearest`, `linear` (3D-only), `bilinear` (4D-only), `trilinear` (5D-only), `area` Args: input (Tensor): the input tensor size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]): output spatial size. scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple. mode (string): algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'trilinear' | 'area'. Default: 'nearest' align_corners (bool, optional): if True, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect when :attr:`mode` is `linear`, `bilinear`, or `trilinear`. Default: False .. warning:: With ``align_corners = True``, the linearly interpolating modes (`linear`, `bilinear`, and `trilinear`) don't proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is ``align_corners = False``. See :class:`~torch.nn.Upsample` for concrete examples on how this affects the outputs. .. include:: cuda_deterministic_backward.rst """ from numbers import Integral from .modules.utils import _ntuple def _check_size_scale_factor(dim): if size is None and scale_factor is None: raise ValueError('either size or scale_factor should be defined') if size is not None and scale_factor is not None: raise ValueError('only one of size or scale_factor should be defined') if scale_factor is not None and isinstance(scale_factor, tuple)\ and len(scale_factor) != dim: raise ValueError('scale_factor shape must match input shape. ' 'Input is {}D, scale_factor size is {}'.format(dim, len(scale_factor))) def _output_size(dim): _check_size_scale_factor(dim) if size is not None: return size scale_factors = _ntuple(dim)(scale_factor) # math.floor might return float in py2.7 return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)] if mode in ('nearest', 'area'): if align_corners is not None: raise ValueError("align_corners option can only be set with the " "interpolating modes: linear | bilinear | trilinear") else: if align_corners is None: warnings.warn("Default upsampling behavior when mode={} is changed " "to align_corners=False since 0.4.0. Please specify " "align_corners=True if the old behavior is desired. " "See the documentation of nn.Upsample for details.".format(mode)) align_corners = False if input.dim() == 3 and mode == 'nearest': return torch._C._nn.upsample_nearest1d(input, _output_size(1)) elif input.dim() == 4 and mode == 'nearest': return torch._C._nn.upsample_nearest2d(input, _output_size(2)) elif input.dim() == 5 and mode == 'nearest': return torch._C._nn.upsample_nearest3d(input, _output_size(3)) elif input.dim() == 3 and mode == 'area': return adaptive_avg_pool1d(input, _output_size(1)) elif input.dim() == 4 and mode == 'area': return adaptive_avg_pool2d(input, _output_size(2)) elif input.dim() == 5 and mode == 'area': return adaptive_avg_pool3d(input, _output_size(3)) elif input.dim() == 3 and mode == 'linear': return torch._C._nn.upsample_linear1d(input, _output_size(1), align_corners) elif input.dim() == 3 and mode == 'bilinear': raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input") elif input.dim() == 3 and mode == 'trilinear': raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input") elif input.dim() == 4 and mode == 'linear': raise NotImplementedError("Got 4D input, but linear mode needs 3D input") elif input.dim() == 4 and mode == 'bilinear': return torch._C._nn.upsample_bilinear2d(input, _output_size(2), align_corners) elif input.dim() == 4 and mode == 'trilinear': raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input") elif input.dim() == 5 and mode == 'linear': raise NotImplementedError("Got 5D input, but linear mode needs 3D input") elif input.dim() == 5 and mode == 'bilinear': raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input") elif input.dim() == 5 and mode == 'trilinear': return torch._C._nn.upsample_trilinear3d(input, _output_size(3), align_corners) else: raise NotImplementedError("Input Error: Only 3D, 4D and 5D input Tensors supported" " (got {}D) for the modes: nearest | linear | bilinear | trilinear" " (got {})".format(input.dim(), mode)) def upsample_nearest(input, size=None, scale_factor=None): r"""Upsamples the input, using nearest neighbours' pixel values. .. warning:: This function is deprecated in favor of :func:`torch.nn.functional.interpolate`. This is equivalent with ``nn.functional.interpolate(..., mode='nearest')``. Currently spatial and volumetric upsampling are supported (i.e. expected inputs are 4 or 5 dimensional). Args: input (Tensor): input size (int or Tuple[int, int] or Tuple[int, int, int]): output spatia size. scale_factor (int): multiplier for spatial size. Has to be an integer. .. include:: cuda_deterministic_backward.rst """ # DeprecationWarning is ignored by default warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.") return interpolate(input, size, scale_factor, mode='nearest') def upsample_bilinear(input, size=None, scale_factor=None): r"""Upsamples the input, using bilinear upsampling. .. warning:: This function is deprecated in favor of :func:`torch.nn.functional.interpolate`. This is equivalent with ``nn.functional.interpolate(..., mode='bilinear', align_corners=True)``. Expected inputs are spatial (4 dimensional). Use `upsample_trilinear` fo volumetric (5 dimensional) inputs. Args: input (Tensor): input size (int or Tuple[int, int]): output spatial size. scale_factor (int or Tuple[int, int]): multiplier for spatial size .. include:: cuda_deterministic_backward.rst """ # DeprecationWarning is ignored by default warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.") return interpolate(input, size, scale_factor, mode='bilinear', align_corners=True) GRID_SAMPLE_INTERPOLATION_MODES = { 'bilinear': 0, 'nearest': 1, } GRID_SAMPLE_PADDING_MODES = { 'zeros': 0, 'border': 1, 'reflection': 2, } def grid_sample(input, grid, mode='bilinear', padding_mode='zeros'): r"""Given an :attr:`input` and a flow-field :attr:`grid`, computes the ``output`` using :attr:`input` values and pixel locations from :attr:`grid`. Currently, only spatial (4-D) and volumetric (5-D) :attr:`input` are supported. In the spatial (4-D) case, for :attr:`input` with shape :math:`(N, C, H_\text{in}, W_\text{in})` and :attr:`grid` with shape :math:`(N, H_\text{out}, W_\text{out}, 2)`, the output will have shape :math:`(N, C, H_\text{out}, W_\text{out})`. For each output location ``output[n, :, h, w]``, the size-2 vector ``grid[n, h, w]`` specifies :attr:`input` pixel locations ``x`` and ``y``, which are used to interpolate the output value ``output[n, :, h, w]``. In the case of 5D inputs, ``grid[n, d, h, w]`` specifies the ``x``, ``y``, ``z`` pixel locations for interpolating ``output[n, :, d, h, w]``. :attr:`mode` argument specifies ``nearest`` or ``bilinear`` interpolation method to sample the input pixels. :attr:`grid` should have most values in the range of ``[-1, 1]``. This is because the pixel locations are normalized by the :attr:`input` spatial dimensions. For example, values ``x = -1, y = -1`` is the left-top pixel of :attr:`input`, and values ``x = 1, y = 1`` is the right-bottom pixel of :attr:`input`. If :attr:`grid` has values outside the range of ``[-1, 1]``, those locations are handled as defined by :attr:`padding_mode`. Options are * ``padding_mode="zeros"``: use ``0`` for out-of-bound values, * ``padding_mode="border"``: use border values for out-of-bound values, * ``padding_mode="reflection"``: use values at locations reflected by the border for out-of-bound values. For location far away from the border, it will keep being reflected until becoming in bound, e.g., (normalized) pixel location ``x = -3.5`` reflects by ``-1`` and becomes ``x' = 2.5``, then reflects by border ``1`` and becomes ``x'' = -0.5``. .. Note:: This function is often used in building Spatial Transformer Networks. .. include:: cuda_deterministic_backward.rst Args: input (Tensor): input of shape :math:`(N, C, H_\text{in}, W_\text{in})` (4-D case) or :math:`(N, C, D_\text{in}, H_\text{in}, W_\text{in})` (5-D case) grid (Tensor): flow-field of shape :math:`(N, H_\text{out}, W_\text{out}, 2)` (4-D case) or :math:`(N, D_\text{out}, H_\text{out}, W_\text{out}, 3)` (5-D case) mode (str): interpolation mode to calculate output values 'bilinear' | 'nearest'. Default: 'bilinear' padding_mode (str): padding mode for outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros' Returns: output (Tensor): output Tensor """ if mode not in GRID_SAMPLE_INTERPOLATION_MODES: raise ValueError("nn.functional.grid_sample(): expected mode to be " "'bilinear' or 'nearest', but got: '{}'".format(mode)) if padding_mode not in GRID_SAMPLE_PADDING_MODES: raise ValueError("nn.functional.grid_sample(): expected padding_mode " "to be 'zeros', 'border', or 'reflection', " "but got: '{}'".format(padding_mode)) return torch.grid_sampler(input, grid, GRID_SAMPLE_INTERPOLATION_MODES[mode], GRID_SAMPLE_PADDING_MODES[padding_mode]) def affine_grid(theta, size): r"""Generates a 2d flow field, given a batch of affine matrices :attr:`theta` Generally used in conjunction with :func:`grid_sample` to implement Spatial Transformer Networks. Args: theta (Tensor): input batch of affine matrices (:math:`N \times 2 \times 3`) size (torch.Size): the target output image size (:math:`N \times C \times H \times W`) Example: torch.Size((32, 3, 24, 24)) Returns: output (Tensor): output Tensor of size (:math:`N \times H \times W \times 2`) """ return vision.affine_grid_generator(theta, size) def pad(input, pad, mode='constant', value=0): r"""Pads tensor. Pading size: The number of dimensions to pad is :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor` and the dimensions that get padded begins with the last dimension and moves forward. For example, to pad the last dimension of the input tensor, then `pad` has form `(padLeft, padRight)`; to pad the last 2 dimensions of the input tensor, then use `(padLeft, padRight, padTop, padBottom)`; to pad the last 3 dimensions, use `(padLeft, padRight, padTop, padBottom, padFront, padBack)`. Padding mode: See :class:`torch.nn.ConstantPad2d`, :class:`torch.nn.ReflectionPad2d`, and :class:`torch.nn.ReplicationPad2d` for concrete examples on how each of the padding modes works. Constant padding is implemented for arbitrary dimensions. Replicate padding is implemented for padding the last 3 dimensions of 5D input tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor. Reflect padding is only implemented for padding the last 2 dimensions of 4D input tensor, or the last dimension of 3D input tensor. .. include:: cuda_deterministic_backward.rst Args: input (Tensor): `Nd` tensor pad (tuple): m-elem tuple, where :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even. mode: 'constant', 'reflect' or 'replicate'. Default: 'constant' value: fill value for 'constant' padding. Default: 0 Examples:: >>> t4d = torch.empty(3, 3, 4, 2) >>> p1d = (1, 1) # pad last dim by 1 on each side >>> out = F.pad(t4d, p1d, "constant", 0) # effectively zero padding >>> print(out.data.size()) torch.Size([3, 3, 4, 4]) >>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2) >>> out = F.pad(t4d, p2d, "constant", 0) >>> print(out.data.size()) torch.Size([3, 3, 8, 4]) >>> t4d = torch.empty(3, 3, 4, 2) >>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3) >>> out = F.pad(t4d, p3d, "constant", 0) >>> print(out.data.size()) torch.Size([3, 9, 7, 3]) """ assert len(pad) % 2 == 0, 'Padding length must be divisible by 2' assert len(pad) // 2 <= input.dim(), 'Padding length too large' if mode == 'constant': return _VF.constant_pad_nd(input, pad, value) else: assert value == 0, 'Padding mode "{}"" doesn\'t take in value argument'.format(mode) if input.dim() == 3: assert len(pad) == 2, '3D tensors expect 2 values for padding' if mode == 'reflect': return torch._C._nn.reflection_pad1d(input, pad) elif mode == 'replicate': return torch._C._nn.replication_pad1d(input, pad) elif input.dim() == 4: assert len(pad) == 4, '4D tensors expect 4 values for padding' if mode == 'reflect': return torch._C._nn.reflection_pad2d(input, pad) elif mode == 'replicate': return torch._C._nn.replication_pad2d(input, pad) elif input.dim() == 5: assert len(pad) == 6, '5D tensors expect 6 values for padding' if mode == 'reflect': raise NotImplementedError elif mode == 'replicate': return torch._C._nn.replication_pad3d(input, pad) else: raise NotImplementedError("Only 3D, 4D, 5D padding with non-constant padding are supported for now") # distance @torch._jit_internal.weak_script def pairwise_distance(x1, x2, p=2., eps=1e-6, keepdim=False): # type: (Tensor, Tensor, float, float, bool) -> Tensor r""" See :class:`torch.nn.PairwiseDistance` for details """ return torch.pairwise_distance(x1, x2, p, eps, keepdim) pdist = _add_docstr(torch.pdist, r""" pdist(input, p=2) -> Tensor Computes the p-norm distance between every pair of row vectors in the input. This is identical to the upper triangular portion, excluding the diagonal, of `torch.norm(input[:, None] - input, dim=2, p=p)`. This function will be faster if the rows are contiguous. If input has shape :math:`N \times M` then the output will have shape :math:`\frac{1}{2} N (N - 1)`. This function is equivalent to `scipy.spatial.distance.pdist(input, 'minkowski', p=p)` if :math:`p \in (0, \infty)`. When :math:`p = 0` it is equivalent to `scipy.spatial.distance.pdist(input, 'hamming') * M`. When :math:`p = \infty`, the closest scipy function is `scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())`. Args: input: input tensor of shape :math:`N \times M`. p: p value for the p-norm distance to calculate between each vector pair :math:`\in [0, \infty]`. """) def cosine_similarity(x1, x2, dim=1, eps=1e-8): r"""Returns cosine similarity between x1 and x2, computed along dim. .. math :: \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)} Args: x1 (Tensor): First input. x2 (Tensor): Second input (of size matching x1). dim (int, optional): Dimension of vectors. Default: 1 eps (float, optional): Small value to avoid division by zero. Default: 1e-8 Shape: - Input: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`. - Output: :math:`(\ast_1, \ast_2)` where 1 is at position `dim`. Example:: >>> input1 = torch.randn(100, 128) >>> input2 = torch.randn(100, 128) >>> output = F.cosine_similarity(input1, input2) >>> print(output) """ w12 = torch.sum(x1 * x2, dim) w1 = torch.norm(x1, 2, dim) w2 = torch.norm(x2, 2, dim) return w12 / (w1 * w2).clamp(min=eps) def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False, size_average=None, reduce=None, reduction="mean"): r""" See :class:`~torch.nn.TripletMarginLoss` for details """ if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_enum(size_average, reduce) else: reduction = _Reduction.get_enum(reduction) return torch.triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, reduction) def normalize(input, p=2, dim=1, eps=1e-12, out=None): r"""Performs :math:`L_p` normalization of inputs over specified dimension. For a tensor :attr:`input` of sizes :math:`(n_0, ..., n_{dim}, ..., n_k)`, each :math:`n_{dim}` -element vector :math:`v` along dimension :attr:`dim` is transformed as .. math:: v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}. With the default arguments it uses the Euclidean norm over vectors along dimension :math:`1` for normalization. Args: input: input tensor of any shape p (float): the exponent value in the norm formulation. Default: 2 dim (int): the dimension to reduce. Default: 1 eps (float): small value to avoid division by zero. Default: 1e-12 out (Tensor, optional): the output tensor. If :attr:`out` is used, this operation won't be differentiable. """ if out is None: denom = input.norm(p, dim, True).clamp(min=eps).expand_as(input) return input / denom else: denom = input.norm(p, dim, True).clamp_(min=eps).expand_as(input) return torch.div(input, denom, out=out) def assert_int_or_pair(arg, arg_name, message): assert isinstance(arg, int) or len(arg) == 2, message.format(arg_name) def unfold(input, kernel_size, dilation=1, padding=0, stride=1): r"""Extracts sliding local blocks from an batched input tensor. .. warning:: Currently, only 4-D input tensors (batched image-like tensors) are supported. See :class:`torch.nn.Unfold` for details """ if input.dim() == 4: msg = '{} must be int or 2-tuple for 4D input' assert_int_or_pair(kernel_size, 'kernel_size', msg) assert_int_or_pair(dilation, 'dilation', msg) assert_int_or_pair(padding, 'padding', msg) assert_int_or_pair(stride, 'stride', msg) return Im2Col.apply(input, _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride)) else: raise NotImplementedError("Input Error: Only 4D input Tensors are supported (got {}D)".format(input.dim())) def fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1): r"""Combines an array of sliding local blocks into a large containing tensor. .. warning:: Currently, only 4-D output tensors (batched image-like tensors) are supported. See :class:`torch.nn.Fold` for details """ if input.dim() == 3: msg = '{} must be int or 2-tuple for 3D input' assert_int_or_pair(output_size, 'output_size', msg) assert_int_or_pair(kernel_size, 'kernel_size', msg) assert_int_or_pair(dilation, 'dilation', msg) assert_int_or_pair(padding, 'padding', msg) assert_int_or_pair(stride, 'stride', msg) return Col2Im.apply(input, _pair(output_size), _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride)) else: raise NotImplementedError("Input Error: Only 3D input Tensors are supported (got {}D)".format(input.dim()))