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2d06dcc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Dist(nn.Module):
def __init__(self, num_classes=10, num_centers=1, feat_dim=2, init='random'):
super(Dist, self).__init__()
self.feat_dim = feat_dim
self.num_classes = num_classes
self.num_centers = num_centers
if init == 'random':
self.centers = nn.Parameter(0.1 * torch.randn(num_classes * num_centers, self.feat_dim))
else:
self.centers = nn.Parameter(torch.Tensor(num_classes * num_centers, self.feat_dim))
self.centers.data.fill_(0)
def forward(self, features, center=None, metric='l2'):
if metric == 'l2':
f_2 = torch.sum(torch.pow(features, 2), dim=1, keepdim=True)
if center is None:
c_2 = torch.sum(torch.pow(self.centers, 2), dim=1, keepdim=True)
dist = f_2 - 2*torch.matmul(features, torch.transpose(self.centers, 1, 0)) + torch.transpose(c_2, 1, 0)
else:
c_2 = torch.sum(torch.pow(center, 2), dim=1, keepdim=True)
dist = f_2 - 2*torch.matmul(features, torch.transpose(center, 1, 0)) + torch.transpose(c_2, 1, 0)
dist = dist / float(features.shape[1])
else:
if center is None:
center = self.centers
else:
center = center
dist = features.matmul(center.t())
dist = torch.reshape(dist, [-1, self.num_classes, self.num_centers])
# gamma
dist = 0.5 * torch.mean(dist, dim=2)
return dist
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