| """Functional interface""" |
| |
| from numbers import Integral |
| import warnings |
| import math |
| |
| import torch |
| from torch._C import _infer_size |
| from . import _functions |
| from .modules import utils |
| from ._functions.linear import Bilinear |
| from ._functions.padding import ConstantPadNd |
| from ._functions.vision import GridSampler, AffineGridGenerator |
| from ..autograd import _functions as _autograd_functions |
| from torch.autograd import Variable |
| from .modules.utils import _single, _pair, _triple |
| |
| # Convolutions |
| ConvNd = torch._C._functions.ConvNd |
| |
| |
| def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, |
| groups=1): |
| """Applies a 2D convolution over an input image composed of several input |
| planes. |
| |
| See :class:`~torch.nn.Conv2d` for details and output shape. |
| |
| Args: |
| input: input tensor (minibatch x in_channels x iH x iW) |
| weight: filters tensor (out_channels, in_channels/groups, kH, kW) |
| bias: optional bias tensor (out_channels). Default: None |
| stride: the stride of the convolving kernel. Can be a single number or |
| a tuple (sh x sw). Default: 1 |
| padding: implicit zero padding on the input. Can be a single number or |
| a tuple. Default: 0 |
| dilation: the spacing between kernel elements. Default: 1 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| |
| Examples:: |
| |
| >>> # With square kernels and equal stride |
| >>> filters = autograd.Variable(torch.randn(8,4,3,3)) |
| >>> inputs = autograd.Variable(torch.randn(1,4,5,5)) |
| >>> F.conv2d(inputs, filters, padding=1) |
| """ |
| if input is not None and input.dim() != 4: |
| raise ValueError("Expected 4D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), False, |
| _pair(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| def conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, |
| groups=1): |
| """Applies a 1D convolution over an input signal composed of several input |
| planes. |
| |
| See :class:`~torch.nn.Conv1d` for details and output shape. |
| |
| Args: |
| input: input tensor of shape (minibatch x in_channels x iW) |
| weight: filters of shape (out_channels, in_channels, kW) |
| bias: optional bias of shape (out_channels). Default: None |
| stride: the stride of the convolving kernel, default 1 |
| padding: implicit zero padding on the input. Can be a single number or |
| a tuple. Default: 0 |
| dilation: the spacing between kernel elements. Default: 1 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| |
| Examples:: |
| |
| >>> filters = autograd.Variable(torch.randn(33, 16, 3)) |
| >>> inputs = autograd.Variable(torch.randn(20, 16, 50)) |
| >>> F.conv1d(inputs, filters) |
| """ |
| if input is not None and input.dim() != 3: |
| raise ValueError("Expected 3D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_single(stride), _single(padding), _single(dilation), False, |
| _single(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| def conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, |
| groups=1): |
| """Applies a 3D convolution over an input image composed of several input |
| planes. |
| |
| See :class:`~torch.nn.Conv3d` for details and output shape. |
| |
| Args: |
| input: input tensor of shape (minibatch x in_channels x iT x iH x iW) |
| weight: filters tensor of shape (out_channels, in_channels, kT, kH, kW) |
| bias: optional bias tensor of shape (out_channels). Default: None |
| stride: the stride of the convolving kernel. Can be a single number or |
| a tuple (st x sh x sw). Default: 1 |
| padding: implicit zero padding on the input. Can be a single number or |
| a tuple. Default: 0 |
| dilation: the spacing between kernel elements. Default: 1 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| |
| Examples:: |
| |
| >>> filters = autograd.Variable(torch.randn(33, 16, 3, 3, 3)) |
| >>> inputs = autograd.Variable(torch.randn(20, 16, 50, 10, 20)) |
| >>> F.conv3d(inputs, filters) |
| """ |
| |
| if input is not None and input.dim() != 5: |
| raise ValueError("Expected 5D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_triple(stride), _triple(padding), _triple(dilation), False, |
| _triple(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| def conv_transpose1d(input, weight, bias=None, stride=1, padding=0, |
| output_padding=0, groups=1, dilation=1): |
| """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. |
| |
| Args: |
| input: input tensor of shape (minibatch x in_channels x iW) |
| weight: filters of shape (in_channels x out_channels x kW) |
| bias: optional bias of shape (out_channels). Default: None |
| stride: the stride of the convolving kernel. Default: 1 |
| padding: implicit zero padding on the input. Default: 0 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| output_padding: A zero-padding of 0 <= padding < stride that should be |
| added to the output. Default: 0 |
| dilation: the spacing between kernel elements. Default: 1 |
| """ |
| if input is not None and input.dim() != 3: |
| raise ValueError("Expected 3D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_single(stride), _single(padding), _single(dilation), True, |
| _single(output_padding), |
| groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, |
| output_padding=0, groups=1, dilation=1): |
| """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. |
| |
| Args: |
| input: input tensor of shape (minibatch x in_channels x iH x iW) |
| weight: filters of shape (in_channels x out_channels x kH x kW) |
| bias: optional bias of shape (out_channels). Default: None |
| stride: the stride of the convolving kernel, a single number or a |
| tuple (sh x sw). Default: 1 |
| padding: implicit zero padding on the input, a single number or a |
| tuple (padh x padw). Default: 0 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| output_padding: A zero-padding of 0 <= padding < stride that should be |
| added to the output. Can be a single number or a tuple. Default: 0 |
| dilation: the spacing between kernel elements. Default: 1 |
| """ |
| |
| if input is not None and input.dim() != 4: |
| raise ValueError("Expected 4D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), True, |
| _pair(output_padding), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| def conv_transpose3d(input, weight, bias=None, stride=1, padding=0, |
| output_padding=0, groups=1, dilation=1): |
| """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. |
| |
| Args: |
| input: input tensor of shape (minibatch x in_channels x iT x iH x iW) |
| weight: filters of shape (in_channels x out_channels x kH x kW) |
| bias: optional bias of shape (out_channels). Default: None |
| stride: the stride of the convolving kernel, a single number or a |
| tuple (sh x sw). Default: 1 |
| padding: implicit zero padding on the input, a single number or a |
| tuple (padh x padw). Default: 0 |
| output_padding: A zero-padding of 0 <= padding < stride that should be |
| added to the output. Can be a single number or a tuple. Default: 0 |
| groups: split input into groups, in_channels should be divisible by |
| the number of groups. Default: 1 |
| dilation: the spacing between kernel elements. Default: 1 |
| """ |
| if input is not None and input.dim() != 5: |
| raise ValueError("Expected 5D tensor as input, got {}D tensor instead.".format(input.dim())) |
| |
| f = ConvNd(_triple(stride), _triple(padding), _triple(dilation), True, |
| _triple(output_padding), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| # Pooling |
| def avg_pool1d(input, kernel_size, stride=None, padding=0, |
| ceil_mode=False, count_include_pad=True): |
| r"""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: |
| kernel_size: the size of the window |
| stride: the stride of the window. Default value is :attr:`kernel_size` |
| padding: implicit zero padding to be added on both sides. 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 |
| |
| Example: |
| >>> # pool of square window of size=3, stride=2 |
| >>> input = Variable(torch.Tensor([[[1,2,3,4,5,6,7]]])) |
| >>> F.avg_pool1d(input, kernel_size=3, stride=2) |
| Variable containing: |
| (0 ,.,.) = |
| 2 4 6 |
| [torch.FloatTensor of size 1x1x3] |
| """ |
| if input.dim() != 3: |
| raise ValueError('expected 3D input (got {} dimensions)' |
| .format(input.dim())) |
| kernel_size = _single(kernel_size) + (1,) |
| stride = _single(stride) + (1,) if stride is not None else kernel_size |
| padding = _single(padding) + (0,) |
| return _functions.thnn.AvgPool2d.apply(input.unsqueeze(3), kernel_size, stride, padding, |
| ceil_mode, count_include_pad).squeeze(3) |
| |
| |
| def avg_pool2d(input, kernel_size, stride=None, padding=0, |
| ceil_mode=False, count_include_pad=True): |
| """Applies 2D average-pooling operation in kh x kw regions by step size |
| dh x dw 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 (minibatch x in_channels x iH x iW) |
| kernel_size: size of the pooling region, a single number or a |
| tuple (kh x kw) |
| stride: stride of the pooling operation, a single number or a |
| tuple (sh x sw). Default is equal to kernel size |
| padding: implicit zero padding on the input, a single number or |
| a tuple (padh x 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 th |
| averaging calculation. Default: True |
| """ |
| return _functions.thnn.AvgPool2d.apply(input, kernel_size, stride, padding, |
| ceil_mode, count_include_pad) |
| |
| |
| def avg_pool3d(input, kernel_size, stride=None, padding=0, |
| ceil_mode=False, count_include_pad=True): |
| """Applies 3D average-pooling operation in kt x kh x kw regions by step |
| size dt x dh x dw steps. The number of output features is equal to the |
| number of input planes / dt. |
| |
| See :class:`~torch.nn.AvgPool3d` for details and output shape. |
| |
| Args: |
| input: input tensor (minibatch x in_channels x iT x iH x iW) |
| kernel_size: size of the pooling region, a single number or a |
| tuple (kt x kh x kw) |
| stride: stride of the pooling operation, a single number or a |
| tuple (st x sh x sw). Default is equal to kernel size |
| padding: implicit zero padding on the input, a single number or |
| a tuple (padt x padh x 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 th |
| averaging calculation |
| """ |
| return _functions.thnn.AvgPool3d.apply(input, kernel_size, stride, padding, |
| ceil_mode, count_include_pad) |
| |
| |
| # share the same interface |
| def max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, |
| ceil_mode=False, return_indices=False): |
| ret = _functions.thnn.MaxPool1d.apply(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): |
| ret = _functions.thnn.MaxPool2d.apply(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): |
| ret = _functions.thnn.MaxPool3d.apply(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): |
| kernel_size = _single(kernel_size) |
| stride = _single(stride) |
| padding = _single(padding) |
| output_size = _unpool_output_size(input, kernel_size, stride, padding, |
| output_size) |
| return _functions.thnn.MaxUnpool2d.apply(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): |
| kernel_size = _pair(kernel_size) |
| stride = _pair(stride) |
| padding = _pair(padding) |
| output_size = _unpool_output_size(input, kernel_size, stride, padding, |
| output_size) |
| return _functions.thnn.MaxUnpool2d.apply(input, indices, output_size) |
| |
| |
| def max_unpool3d(input, indices, kernel_size, stride=None, padding=0, |
| output_size=None): |
| kernel_size = _triple(kernel_size) |
| stride = _triple(stride) |
| padding = _triple(padding) |
| output_size = _unpool_output_size(input, kernel_size, stride, padding, |
| output_size) |
| return _functions.thnn.MaxUnpool3d.apply(input, indices, output_size, stride, padding) |
| |
| |
| def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False): |
| kw, kh = utils._pair(kernel_size) |
| out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode) |
| return out.mul(kw * kh).pow(1. / norm_type) |
| |
| |
| def lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False): |
| out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode) |
| return 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 = _functions.thnn.AdaptiveMaxPool1d.apply(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 |
| """ |
| ret = _functions.thnn.AdaptiveMaxPool2d.apply(input, output_size) |
| return ret if return_indices else ret[0] |
| |
| |
| def adaptive_avg_pool1d(input, output_size): |
| r"""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) |
| """ |
| return _functions.thnn.AdaptiveAvgPool1d.apply(input, output_size) |
| |
| |
| 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) |
| """ |
| return _functions.thnn.AdaptiveAvgPool2d.apply(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) |
| """ |
| return _functions.thnn.AdaptiveAvgPool3d.apply(input, output_size) |
| |
| |
| # Activation functions |
| |
| def dropout(input, p=0.5, training=False, inplace=False): |
| return _functions.dropout.Dropout.apply(input, p, training, inplace) |
| |
| |
| def alpha_dropout(input, p=0.5, training=False): |
| r"""Applies alpha dropout to the input. |
| |
| See :class:`~torch.nn.AlphaDropout` for details. |
| |
| Args: |
| p (float, optional): the drop probability. Default: 0.5 |
| training (bool, optional): switch between training and evaluation mode. Default: False |
| """ |
| if p < 0 or p > 1: |
| raise ValueError("dropout probability has to be between 0 and 1, " |
| "but got {}".format(p)) |
| |
| if p == 0 or not training: |
| return input |
| |
| alpha = -1.7580993408473766 |
| keep_prob = 1 - p |
| # TODO avoid casting to byte after resize |
| noise = input.data.new().resize_(input.size()) |
| noise.bernoulli_(p) |
| noise = Variable(noise.byte()) |
| |
| output = input.masked_fill(noise, alpha) |
| |
| a = (keep_prob + alpha ** 2 * keep_prob * (1 - keep_prob)) ** (-0.5) |
| b = -a * alpha * (1 - keep_prob) |
| |
| return output.mul_(a).add_(b) |
| |
| |
| def dropout2d(input, p=0.5, training=False, inplace=False): |
| return _functions.dropout.FeatureDropout.apply(input, p, training, inplace) |
| |
| |
| def dropout3d(input, p=0.5, training=False, inplace=False): |
| return _functions.dropout.FeatureDropout.apply(input, p, training, inplace) |
| |
| |
| def threshold(input, threshold, value, inplace=False): |
| return _functions.thnn.Threshold.apply(input, threshold, value, inplace) |
| |
| |
| def relu(input, inplace=False): |
| return _functions.thnn.Threshold.apply(input, 0, 0, inplace) |
| |
| |
| def glu(input, dim=-1): |
| ndim = input.dim() |
| if dim < -ndim or dim >= ndim: |
| raise IndexError("dim {} is out of range for tensor of dimension {}" |
| .format(dim, ndim)) |
| if dim < 0: |
| dim += ndim |
| return _functions.thnn.GatedLinear.apply(input, dim) |
| |
| |
| def hardtanh(input, min_val=-1., max_val=1., inplace=False): |
| return _functions.thnn.auto.Hardtanh.apply(input, min_val, max_val, inplace) |
| |
| |
| def relu6(input, inplace=False): |
| return _functions.thnn.auto.Hardtanh.apply(input, 0, 6, inplace) |
| |
| |
| def elu(input, alpha=1., inplace=False): |
| return _functions.thnn.auto.ELU.apply(input, alpha, inplace) |
| |
| |
| def selu(input, inplace=False): |
| return _functions.thnn.SELU.apply(input, inplace) |
| |
| |
| def leaky_relu(input, negative_slope=1e-2, inplace=False): |
| return _functions.thnn.LeakyReLU.apply(input, negative_slope, inplace) |
| |
| |
| def prelu(input, weight): |
| return _functions.thnn.PReLU.apply(input, weight) |
| |
| |
| def rrelu(input, lower=1. / 8, upper=1. / 3, training=False, inplace=False): |
| return _functions.thnn.RReLU.apply(input, lower, upper, training, inplace) |
| |
| |
| def logsigmoid(input): |
| return _functions.thnn.LogSigmoid.apply(input) |
| |
| |
| def hardshrink(input, lambd=0.5): |
| return _functions.thnn.auto.Hardshrink.apply(input, lambd) |
| |
| |
| def tanhshrink(input): |
| return input - _autograd_functions.Tanh.apply(input) |
| |
| |
| def softsign(input): |
| return _functions.activation.Softsign.apply(input) |
| |
| |
| def softplus(input, beta=1, threshold=20): |
| return _functions.thnn.auto.Softplus.apply(input, beta, threshold) |
| |
| |
| def softmin(input): |
| return _functions.thnn.Softmin.apply(input) |
| |
| |
| def softmax(input): |
| return _functions.thnn.auto.Softmax.apply(input) |
| |
| |
| def softshrink(input, lambd=0.5): |
| return _functions.thnn.auto.Softshrink.apply(input, lambd) |
| |
| |
| def log_softmax(input): |
| return _functions.thnn.LogSoftmax.apply(input) |
| |
| |
| def tanh(input): |
| return _autograd_functions.Tanh.apply(input) |
| |
| |
| def sigmoid(input): |
| return _autograd_functions.Sigmoid.apply(input) |
| |
| |
| # etc. |
| |
| def linear(input, weight, bias=None): |
| 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): |
| if bias is None: |
| return Bilinear.apply(input1, input2, weight) |
| else: |
| return Bilinear.apply(input1, input2, weight, bias) |
| |
| |
| def embedding(input, embedding_matrix, |
| 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. |
| |
| Args: |
| input: tensor, containing indices into the embedding matrix |
| embedding_matrix: |
| Number of rows should correspond to the maximum possible index + 1, |
| number of columns is the embedding size |
| max_norm (float, optional): If given, will renormalize the embeddings to always have a norm lesser than this |
| norm_type (float, optional): The p of the p-norm to compute for the max_norm option |
| scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of |
| the words in the mini-batch. |
| |
| Shape: |
| - Input: LongTensor `(N, W)`, N = mini-batch, W = number of indices to extract per mini-batch |
| - Embedding_matrix: FloatTensor `(V, embedding_dim)`, V = maximum index + 1, embedding_dim = embedding size |
| - Output: `(N, W, embedding_dim)` |
| |
| Examples:: |
| |
| >>> # a batch of 2 samples of 4 indices each |
| >>> input = Variable(torch.LongTensor([[1,2,4,5],[4,3,2,9]])) |
| >>> # an embedding matrix containing 10 tensors of size 3 |
| >>> embedding_matrix = Variable(torch.rand(10, 3)) |
| >>> torch.nn.functional.embedding(input, embedding_matrix) |
| |
| Variable containing: |
| (0 ,.,.) = |
| -1.0822 1.2522 0.2434 |
| 0.8393 -0.6062 -0.3348 |
| 0.6597 0.0350 0.0837 |
| 0.5521 0.9447 0.0498 |
| |
| (1 ,.,.) = |
| 0.6597 0.0350 0.0837 |
| -0.1527 0.0877 0.4260 |
| 0.8393 -0.6062 -0.3348 |
| -0.8738 -0.9054 0.4281 |
| [torch.FloatTensor of size 2x4x3] |
| |
| >>> # example with padding_idx |
| >>> embedding_matrix = Variable(torch.rand(10, 3)) |
| >>> embedding_matrix[0].zero_() |
| >>> input = Variable(torch.LongTensor([[0,2,0,5]])) |
| >>> torch.nn.functional.embedding(input, embedding_matrix) |
| |
| Variable containing: |
| (0 ,.,.) = |
| 0.0000 0.0000 0.0000 |
| 0.3452 0.4937 -0.9361 |
| 0.0000 0.0000 0.0000 |
| 0.0706 -2.1962 -0.6276 |
| [torch.FloatTensor of size 1x4x3] |
| |
| """ |
| return torch.nn.backends.thnn.backend.Embedding.apply( |
| input, embedding_matrix, |
| -1, max_norm, norm_type, |
| scale_grad_by_freq, sparse |
| ) |
| |
| |
| def batch_norm(input, running_mean, running_var, weight=None, bias=None, |
| training=False, momentum=0.1, eps=1e-5): |
| f = torch._C._functions.BatchNorm(running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled) |
| return f(input, weight, bias) |
| |
| |
| # loss |
| |
| def nll_loss(input, target, weight=None, size_average=True, ignore_index=-100): |
| r"""The negative log likelihood loss. |
| |
| See :class:`~torch.nn.NLLLoss` for details. |
| |
| Args: |
| input: :math:`(N, C)` where `C = number of classes` or `(N, C, H, W)` |
| in case of 2D - Loss |
| target: :math:`(N)` where each value is `0 <= targets[i] <= C-1` |
| weight (Variable, optional): a manual rescaling weight given to each |
| class. If given, has to be a Variable of size "nclasses" |
| size_average (bool, optional): By default, the losses are averaged |
| over observations for each minibatch. If size_average |
| is False, the losses are summed for each minibatch. Default: True |
| ignore_index (int, optional): Specifies a target value that is ignored |
| and does not contribute to the input gradient. When size_average is |
| True, the loss is averaged over non-ignored targets. Default: -100 |
| |
| Example:: |
| |
| >>> # input is of size nBatch x nClasses = 3 x 5 |
| >>> input = autograd.Variable(torch.randn(3, 5)) |
| >>> # each element in target has to have 0 <= value < nclasses |
| >>> target = autograd.Variable(torch.LongTensor([1, 0, 4])) |
| >>> output = F.nll_loss(F.log_softmax(input), target) |
| >>> output.backward() |
| """ |
| dim = input.dim() |
| if dim == 2: |
| return _functions.thnn.NLLLoss.apply(input, target, weight, size_average, ignore_index) |
| elif dim == 4: |
| return _functions.thnn.NLLLoss2d.apply(input, target, weight, size_average, ignore_index) |
| else: |
| raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim)) |
| |
| |
| def poisson_nll_loss(input, target, log_input=True, full=False, size_average=True): |
| 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 Pois(input)`. |
| log_input: if True the loss is computed as |
| `exp(input) - target * input`, if False then loss is |
| `input - target * log(input)`. Default: True |
| full: whether to compute full loss, i. e. to add the Stirling |
| approximation term. Default: False |
| `target * log(target) - target + 0.5 * log(2 * pi * target)`. |
| size_average: By default, the losses are averaged over observations for |
| each minibatch. However, if the field sizeAverage is set to False, |
| the losses are instead summed for each minibatch. Default: True |
| """ |
| if log_input: |
| loss = torch.exp(input) - target * input |
| else: |
| loss = input - target * torch.log(input) |
| if full: |
| mask = target > 1 |
| loss[mask] += (target * torch.log(target) - target + 0.5 * torch.log(2 * math.pi * target))[mask] |
| if size_average: |
| return torch.mean(loss) |
| else: |
| return torch.sum(loss) |
| |
| |
| def kl_div(input, target, size_average=True, weight=None): |
| r"""The `Kullback-Leibler divergence`_ Loss. |
| |
| See :class:`~torch.nn.KLDivLoss` for details. |
| |
| Args: |
| input: Variable of arbitrary shape |
| target: Variable of the same shape as input |
| size_average: if True the output is divided by the number of elements |
| in input tensor. Default: True |
| weight (Tensor, optional): a manual rescaling weight given to each |
| class. If given, has to be a Tensor of size "nclasses" |
| """ |
| return _functions.thnn.KLDivLoss.apply(input, target, size_average) |
| |
| |
| def cross_entropy(input, target, weight=None, size_average=True, ignore_index=-100): |
| r"""This criterion combines `log_softmax` and `nll_loss` in a single |
| function. |
| |
| See :class:`torch.nn.CrossEntropyLoss` for details. |
| |
| Args: |
| input: Variable :math:`(N, C)` where `C = number of classes` |
| target: Variable :math:`(N)` where each value is |
| `0 <= targets[i] <= C-1` |
| weight (Tensor, optional): a manual rescaling weight given to each |
| class. If given, has to be a Tensor of size "nclasses" |
| size_average (bool, optional): By default, the losses are averaged |
| over observations for each minibatch. However, if the field |
| sizeAverage is set to False, the losses are instead summed |
| for each minibatch. Default: True |
| ignore_index (int, optional): Specifies a target value that is ignored |
| and does not contribute to the input gradient. When size_average is |
| True, the loss is averaged over non-ignored targets. Default: -100 |
| |
| Examples:: |
| |
| >>> input = autograd.Variable(torch.randn(3, 5), requires_grad=True) |
| >>> target = autograd.Variable(torch.LongTensor(3).random_(5)) |
| >>> loss = F.cross_entropy(input, target) |
| >>> loss.backward() |
| """ |
| return nll_loss(log_softmax(input), target, weight, size_average, ignore_index) |
| |
| |
| def binary_cross_entropy(input, target, weight=None, size_average=True): |
| r"""Function that measures the Binary Cross Entropy |
| between the target and the output. |
| |
| See :class:`~torch.nn.BCELoss` for details. |
| |
| Args: |
| input: Variable of arbitrary shape |
| target: Variable of the same shape as input |
| weight (Variable, optional): a manual rescaling weight |
| if provided it's repeated to match input tensor shape |
| size_average (bool, optional): By default, the losses are averaged |
| over observations for each minibatch. However, if the field |
| sizeAverage is set to False, the losses are instead summed |
| for each minibatch. Default: True |
| |
| Examples:: |
| |
| >>> input = autograd.Variable(torch.randn(3), requires_grad=True) |
| >>> target = autograd.Variable(torch.LongTensor(3).random_(2)) |
| >>> loss = F.binary_cross_entropy(F.sigmoid(input), target) |
| >>> loss.backward() |
| """ |
| 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 _functions.thnn.BCELoss.apply(input, target, weight, size_average) |
| |
| |
| def binary_cross_entropy_with_logits(input, target, weight=None, size_average=True): |
| r"""Function that measures Binary Cross Entropy between target and output |
| logits. |
| |
| See :class:`~torch.nn.BCEWithLogitsLoss` for details. |
| |
| Args: |
| input: Variable of arbitrary shape |
| target: Variable of the same shape as input |
| weight (Variable, optional): a manual rescaling weight |
| if provided it's repeated to match input tensor shape |
| size_average (bool, optional): By default, the losses are averaged |
| over observations for each minibatch. However, if the field |
| sizeAverage is set to False, the losses are instead summed |
| for each minibatch. Default: True |
| |
| Examples:: |
| |
| >>> input = autograd.Variable(torch.randn(3), requires_grad=True) |
| >>> target = autograd.Variable(torch.FloatTensor(3).random_(2)) |
| >>> loss = F.binary_cross_entropy_with_logits(input, target) |
| >>> loss.backward() |
| """ |
| if not (target.size() == input.size()): |
| raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size())) |
| |
| max_val = (-input).clamp(min=0) |
| loss = input - input * target + max_val + ((-max_val).exp() + (-input - max_val).exp()).log() |
| |
| if weight is not None: |
| loss = loss * weight |
| |
| if size_average: |
| return loss.mean() |
| else: |
| return loss.sum() |
| |
| |
| def smooth_l1_loss(input, target, size_average=True): |
| return _functions.thnn.SmoothL1Loss.apply(input, target, size_average) |
| |
| |
| def l1_loss(input, target, size_average=True): |
| return _functions.thnn.L1Loss.apply(input, target, size_average) |
| |
| |
| def mse_loss(input, target, size_average=True): |
| return _functions.thnn.MSELoss.apply(input, target, size_average) |
| |
| |
| def margin_ranking_loss(input1, input2, target, margin=0, size_average=True): |
| return _functions.loss.MarginRankingLoss.apply(input1, input2, target, margin, size_average) |
| |
| |
| def hinge_embedding_loss(input, target, margin=1.0, size_average=True): |
| return _functions.loss.HingeEmbeddingLoss.apply(input, target, margin, size_average) |
| |
| |
| def multilabel_margin_loss(input, target, size_average=True): |
| return _functions.thnn.MultiLabelMarginLoss.apply(input, target, size_average) |
| |
| |
| def soft_margin_loss(input, target, size_average=True): |
| return _functions.thnn.SoftMarginLoss.apply(input, target, size_average) |
| |
| |
| def multilabel_soft_margin_loss(input, target, weight=None, size_average=True): |
| input = torch.sigmoid(input) |
| return binary_cross_entropy(input, target, weight, size_average) |
| |
| |
| def cosine_embedding_loss(input1, input2, target, margin=0, size_average=True): |
| return _functions.loss.CosineEmbeddingLoss.apply(input1, input2, target, margin, size_average) |
| |
| |
| def multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=True): |
| 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 _functions.thnn.MultiMarginLoss.apply(input, target, weight, size_average, p, margin) |
| |
| |
| def pixel_shuffle(input, upscale_factor): |
| r"""Rearranges elements in a tensor of shape ``[*, C*r^2, H, W]`` to a |
| tensor of shape ``[C, H*r, W*r]``. |
| |
| See :class:`~torch.nn.PixelShuffle` for details. |
| |
| Args: |
| input (Variable): Input |
| upscale_factor (int): factor to increase spatial resolution by |
| |
| Examples:: |
| |
| >>> ps = nn.PixelShuffle(3) |
| >>> input = autograd.Variable(torch.Tensor(1, 9, 4, 4)) |
| >>> output = ps(input) |
| >>> print(output.size()) |
| torch.Size([1, 1, 12, 12]) |
| """ |
| batch_size, channels, in_height, in_width = input.size() |
| channels //= upscale_factor ** 2 |
| |
| out_height = in_height * upscale_factor |
| out_width = in_width * upscale_factor |
| |
| input_view = input.contiguous().view( |
| batch_size, channels, upscale_factor, upscale_factor, |
| in_height, in_width) |
| |
| shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous() |
| return shuffle_out.view(batch_size, channels, out_height, out_width) |
| |
| |
| def upsample(input, size=None, scale_factor=None, mode='nearest'): |
| """Upsamples the input to either the given :attr:`size` or the given |
| :attr:`scale_factor` |
| |
| 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 [depth] x [height] x width` |
| |
| The modes available for upsampling are: `nearest`, `linear` (3D-only), |
| `bilinear` (4D-only), `trilinear` (5D-only) |
| |
| Args: |
| input (Variable): input |
| 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' |
| """ |
| if input.dim() == 3 and mode == 'nearest': |
| return _functions.thnn.UpsamplingNearest1d.apply(input, _single(size), scale_factor) |
| elif input.dim() == 4 and mode == 'nearest': |
| return _functions.thnn.UpsamplingNearest2d.apply(input, _pair(size), scale_factor) |
| elif input.dim() == 5 and mode == 'nearest': |
| return _functions.thnn.UpsamplingNearest3d.apply(input, _triple(size), scale_factor) |
| elif input.dim() == 3 and mode == 'linear': |
| return _functions.thnn.UpsamplingLinear1d.apply(input, _single(size), scale_factor) |
| 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 _functions.thnn.UpsamplingBilinear2d.apply(input, _pair(size), scale_factor) |
| 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 _functions.thnn.UpsamplingTrilinear3d.apply(input, _triple(size), scale_factor) |
| 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): |
| """Upsamples the input, using nearest neighbours' pixel values. |
| |
| **Note:: This function is deprecated. Use nn.functional.upsample instead** |
| |
| Currently spatial and volumetric upsampling are supported (i.e. expected |
| inputs are 4 or 5 dimensional). |
| |
| Args: |
| input (Variable): 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. |
| """ |
| # DeprecationWarning is ignored by default |
| warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.upsample instead.") |
| return upsample(input, size, scale_factor, mode='nearest') |
| |
| |
| def upsample_bilinear(input, size=None, scale_factor=None): |
| """Upscales the input, using bilinear upsampling. |
| |
| **Note:: This function is deprecated. Use nn.functional.upsample instead** |
| |
| Expected inputs are spatial (4 dimensional). Use upsample_trilinear fo |
| volumetric (5 dimensional) inputs. |
| |
| Args: |
| input (Variable): input |
| size (int or Tuple[int, int]): output spatial size. |
| scale_factor (int or Tuple[int, int]): multiplier for spatial size |
| """ |
| # DeprecationWarning is ignored by default |
| warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.upsample instead.") |
| return upsample(input, size, scale_factor, mode='bilinear') |
| |
| |
| def grid_sample(input, grid, mode='bilinear'): |
| """Given an :attr:`input` and a flow-field :attr:`grid`, computes the |
| `output` using input pixel locations from the grid. |
| |
| Uses bilinear interpolation to sample the input pixels. |
| Currently, only spatial (4 dimensional) inputs are supported. |
| |
| For each output location, :attr:`grid` has `x` and `y` |
| input pixel locations which are used to compute output. |
| |
| :attr:`grid` has values in the range of `[-1, 1]`. This is because the |
| pixel locations are normalized by the input height and width. |
| |
| For example, values: x: -1, y: -1 is the left-top pixel of the input |
| values: x: 1, y: 1 is the right-bottom pixel of the input |
| |
| If :attr:`grid` has values outside the range of `[-1, 1]`, those locations |
| are ignored (i.e. 0 is used as a contribution to the bilinear interpolation) |
| |
| .. Note:: This function is used in building Spatial Transformer Networks |
| |
| Args: |
| input (Variable): input batch of images (N x C x IH x IW) |
| grid (Variable): flow-field of size (N x OH x OW x 2) |
| |
| Returns: |
| output (Variable): output Tensor |
| |
| """ |
| batch_size, channels, in_height, in_width = input.size() |
| return GridSampler.apply(input, grid) |
| |
| |
| def affine_grid(theta, size): |
| """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 (Variable): input batch of affine matrices (N x 2 x 3) |
| size (torch.Size): the target output image size (N x C x H x W) |
| Example: torch.Size((32, 3, 24, 24)) |
| |
| Returns: |
| output (Variable): output Tensor of size (N x H x W x 2) |
| """ |
| return AffineGridGenerator.apply(theta, size) |
| |
| |
| def pad(input, pad, mode='constant', value=0): |
| """Pads tensor. |
| |
| Nd constant padding: The number of dimensions to pad is |
| len(padding) // 2 and the dimensions that gets padded begins with the |
| last dimension and moves forward. See below for examples. |
| |
| 1D, 2D and 3D "reflect"/"replicate" padding: |
| 1D: 3D input with padding in form (pad_l, pad_r) |
| 2D: 4D input tensor pad should be in form |
| (pad_l, pad_r, pad_t, pad_b ). |
| 3D: 5D pad (pleft, pright, ptop, pbottom, pfront, pback). No "reflect" |
| implementation |
| |
| Args: |
| input (Variable): Nd tensor |
| pad (tuple): m-elem tuple, where m // 2 > input dimensions and m % 2 == 0 |
| mode: 'constant', 'reflect' or 'replicate'. Default: 'constant' |
| value: fill value for 'constant' padding. Default: 0 |
| |
| Examples:: |
| |
| >>> t4d = torch.Tensor(3, 3, 4, 2) |
| >>> p1d = (1, 1) # pad last dim by 1 on each side |
| >>> out = F.pad(t4d, p1d, "constant", 0) |
| >>> 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.Tensor(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 <= len(input.size()), 'padding length too large' |
| if mode == 'constant': |
| return ConstantPadNd.apply(input, pad, value) |
| elif input.dim() == 3: |
| assert len(pad) == 2, '3D tensors expect 2 values for padding' |
| if mode == 'reflect': |
| return _functions.thnn.ReflectionPad1d.apply(input, *pad) |
| elif mode == 'replicate': |
| return _functions.thnn.ReplicationPad1d.apply(input, *pad) |
| elif input.dim() == 4: |
| assert len(pad) == 4, '4D tensors expect 4 values for padding' |
| if mode == 'reflect': |
| return _functions.thnn.ReflectionPad2d.apply(input, *pad) |
| elif mode == 'replicate': |
| return _functions.thnn.ReplicationPad2d.apply(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 _functions.thnn.ReplicationPad3d.apply(input, *pad) |
| else: |
| raise NotImplementedError("Only 3D, 4D, 5D padding with non-constant padding are supported for now") |
| |
| |
| # distance |
| |
| def pairwise_distance(x1, x2, p=2, eps=1e-6): |
| r""" |
| Computes the batchwise pairwise distance between vectors v1,v2: |
| |
| .. math :: |
| \Vert x \Vert _p := \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p} |
| |
| Args: |
| x1: first input tensor |
| x2: second input tensor |
| p: the norm degree. Default: 2 |
| eps (float, optional): Small value to avoid division by zero. Default: 1e-6 |
| |
| Shape: |
| - Input: :math:`(N, D)` where `D = vector dimension` |
| - Output: :math:`(N, 1)` |
| |
| Example:: |
| |
| >>> input1 = autograd.Variable(torch.randn(100, 128)) |
| >>> input2 = autograd.Variable(torch.randn(100, 128)) |
| >>> output = F.pairwise_distance(input1, input2, p=2) |
| >>> output.backward() |
| """ |
| assert x1.size() == x2.size(), "Input sizes must be equal." |
| assert x1.dim() == 2, "Input must be a 2D matrix." |
| diff = torch.abs(x1 - x2) |
| out = torch.pow(diff + eps, p).sum(dim=1, keepdim=True) |
| return torch.pow(out, 1. / p) |
| |
| |
| 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 (Variable): First input. |
| x2 (Variable): 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 = autograd.Variable(torch.randn(100, 128)) |
| >>> input2 = autograd.Variable(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)).squeeze() |
| |
| |
| def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False): |
| r"""Creates a criterion that measures the triplet loss given an input |
| tensors x1, x2, x3 and a margin with a value greater than 0. |
| This is used for measuring a relative similarity between samples. A triplet |
| is composed by `a`, `p` and `n`: anchor, positive examples and negative |
| example respectively. The shape of all input variables should be |
| :math:`(N, D)`. |
| |
| The distance swap is described in detail in the paper `Learning shallow |
| convolutional feature descriptors with triplet losses`_ by |
| V. Balntas, E. Riba et al. |
| |
| .. math:: |
| L(a, p, n) = \frac{1}{N} \left( \sum_{i=1}^N \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} \right) |
| |
| where :math:`d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`. |
| |
| Args: |
| anchor: anchor input tensor |
| positive: positive input tensor |
| negative: negative input tensor |
| margin: the margin value. Default: 1 |
| p: the norm degree. Default: 2 |
| eps: small epsilon value to avoid numerical issues. Default: 1e-6 |
| swap: compute distance swap. Default: False |
| |
| Shape: |
| - Input: :math:`(N, D)` where `D = vector dimension` |
| - Output: :math:`(N, 1)` |
| |
| Example:: |
| |
| >>> input1 = autograd.Variable(torch.randn(100, 128)) |
| >>> input2 = autograd.Variable(torch.randn(100, 128)) |
| >>> input3 = autograd.Variable(torch.randn(100, 128)) |
| >>> output = F.triplet_margin_loss(input1, input2, input3, p=2) |
| >>> output.backward() |
| |
| .. _Learning shallow convolutional feature descriptors with triplet losses: |
| http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf |
| """ |
| assert anchor.size() == positive.size(), "Input sizes between positive and negative must be equal." |
| assert anchor.size() == negative.size(), "Input sizes between anchor and negative must be equal." |
| assert positive.size() == negative.size(), "Input sizes between positive and negative must be equal." |
| assert anchor.dim() == 2, "Inputd must be a 2D matrix." |
| assert margin > 0.0, 'Margin should be positive value.' |
| d_p = pairwise_distance(anchor, positive, p, eps) |
| d_n = pairwise_distance(anchor, negative, p, eps) |
| if swap: |
| d_s = pairwise_distance(positive, negative, p, eps) |
| d_n = torch.min(d_n, d_s) |
| |
| dist_hinge = torch.clamp(margin + d_p - d_n, min=0.0) |
| loss = torch.mean(dist_hinge) |
| return loss |
| |
| |
| def normalize(input, p=2, dim=1, eps=1e-12): |
| r"""Performs :math:`L_p` normalization of inputs over specified dimension. |
| |
| Does: |
| |
| .. math:: |
| v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)} |
| |
| for each subtensor v over dimension dim of input. Each subtensor is |
| flattened into a vector, i.e. :math:`\lVert v \rVert_p` is not a matrix |
| norm. |
| |
| With default arguments normalizes over the second dimension with Euclidean |
| norm. |
| |
| 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 |
| """ |
| return input / input.norm(p, dim, True).clamp(min=eps).expand_as(input) |
