mirror of
https://github.com/zebrajr/pytorch.git
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10c4b98ade
Summary: * Deletes all weak script decorators / associated data structures / methods * In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn` * Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods * `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand This should also fix https://github.com/pytorch/pytorch/issues/22212 Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212 Differential Revision: D15988346 Pulled By: driazati fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
from .module import Module
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from .. import functional as F
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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 vector 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 ``True``, 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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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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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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def forward(self, x1, x2):
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return F.cosine_similarity(x1, x2, self.dim, self.eps)
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