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70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
import torch
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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 v1,v2:
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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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x (Tensor): input tensor containing the two input batches
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p (real): the norm degree. Default: 2
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Shape:
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- Input: :math:`(N, D)` where `D = vector dimension`
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- Output: :math:`(N, 1)`
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>>> pdist = nn.PairwiseDistance(2)
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>>> input1 = autograd.Variable(torch.randn(100, 128))
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>>> input2 = autograd.Variable(torch.randn(100, 128))
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>>> output = pdist(input1, input2)
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"""
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def __init__(self, p=2, eps=1e-6):
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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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def forward(self, x1, x2):
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return F.pairwise_distance(x1, x2, self.norm, self.eps)
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class CosineSimilarity(Module):
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r"""Returns cosine similarity between x1 and x2, 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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x1 (Variable): First input.
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x2 (Variable): Second input (of size matching x1).
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dim (int, optional): Dimension of vectors. 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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- Input: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`.
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- Output: :math:`(\ast_1, \ast_2)` where 1 is at position `dim`.
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>>> input1 = autograd.Variable(torch.randn(100, 128))
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>>> input2 = autograd.Variable(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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>>> print(output)
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"""
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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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# TODO: Cosine
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# TODO: Euclidean
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# TODO: WeightedEuclidean
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