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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from .module import Module
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from .. import functional as F
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from ..._jit_internal import weak_module, weak_script_method
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@weak_module
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class PairwiseDistance(Module):
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r"""
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Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:
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.. math ::
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\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}
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Args:
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p (real): the norm degree. Default: 2
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eps (float, optional): Small value to avoid division by zero.
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Default: 1e-6
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keepdim (bool, optional): Determines whether or not to keep the batch dimension.
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Default: False
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Shape:
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- Input1: :math:`(N, D)` where `D = vector dimension`
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- Input2: :math:`(N, D)`, same shape as the Input1
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- Output: :math:`(N)`. If :attr:`keepdim` is ``False``, then :math:`(N, 1)`.
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Examples::
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>>> pdist = nn.PairwiseDistance(p=2)
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>>> input1 = torch.randn(100, 128)
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>>> input2 = torch.randn(100, 128)
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>>> output = pdist(input1, input2)
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"""
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__constants__ = ['norm', 'eps', 'keepdim']
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def __init__(self, p=2., eps=1e-6, keepdim=False):
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super(PairwiseDistance, self).__init__()
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self.norm = p
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self.eps = eps
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self.keepdim = keepdim
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@weak_script_method
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def forward(self, x1, x2):
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return F.pairwise_distance(x1, x2, self.norm, self.eps, self.keepdim)
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@weak_module
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class CosineSimilarity(Module):
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r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim.
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.. math ::
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\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
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Args:
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dim (int, optional): Dimension where cosine similarity is computed. Default: 1
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eps (float, optional): Small value to avoid division by zero.
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Default: 1e-8
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Shape:
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- Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`
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- Input2: :math:`(\ast_1, D, \ast_2)`, same shape as the Input1
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- Output: :math:`(\ast_1, \ast_2)`
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Examples::
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>>> input1 = torch.randn(100, 128)
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>>> input2 = torch.randn(100, 128)
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>>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)
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>>> output = cos(input1, input2)
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"""
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__constants__ = ['dim', 'eps']
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def __init__(self, dim=1, eps=1e-8):
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super(CosineSimilarity, self).__init__()
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self.dim = dim
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self.eps = eps
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@weak_script_method
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def forward(self, x1, x2):
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return F.cosine_similarity(x1, x2, self.dim, self.eps)
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