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svm.py
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svm.py
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import torch
import torch.nn as nn
import numpy as np
import topk.functional as F
from topk.utils import detect_large
class _SVMLoss(nn.Module):
def __init__(self, n_classes, alpha):
assert isinstance(n_classes, int)
assert n_classes > 0
assert alpha is None or alpha >= 0
super(_SVMLoss, self).__init__()
self.alpha = alpha if alpha is not None else 1
self.register_buffer('labels', torch.from_numpy(np.arange(n_classes)))
self.n_classes = n_classes
self._tau = None
def forward(self, x, y):
raise NotImplementedError("Forward needs to be re-implemented for each loss")
@property
def tau(self):
return self._tau
@tau.setter
def tau(self, tau):
if self._tau != tau:
print("Setting tau to {}".format(tau))
self._tau = float(tau)
self.get_losses()
def cuda(self, device=None):
nn.Module.cuda(self, device)
self.get_losses()
return self
def cpu(self):
nn.Module.cpu()
self.get_losses()
return self
class MaxTop1SVM(_SVMLoss):
def __init__(self, n_classes, alpha=None):
super(MaxTop1SVM, self).__init__(n_classes=n_classes,
alpha=alpha)
self.get_losses()
def forward(self, x, y):
return self.F(x, y).mean()
def get_losses(self):
self.F = F.Top1_Hard_SVM(self.labels, self.alpha)
class MaxTopkSVM(_SVMLoss):
def __init__(self, n_classes, alpha=None, k=5):
super(MaxTopkSVM, self).__init__(n_classes=n_classes,
alpha=alpha)
self.k = k
self.get_losses()
def forward(self, x, y):
return self.F(x, y).mean()
def get_losses(self):
self.F = F.Topk_Hard_SVM(self.labels, self.k, self.alpha)
class SmoothTop1SVM(_SVMLoss):
def __init__(self, n_classes, alpha=None, tau=1.):
super(SmoothTop1SVM, self).__init__(n_classes=n_classes,
alpha=alpha)
self.tau = tau
self.thresh = 1e3
self.get_losses()
def forward(self, x, y):
smooth, hard = detect_large(x, 1, self.tau, self.thresh)
loss = 0
if smooth.data.sum():
x_s, y_s = x[smooth], y[smooth]
x_s = x_s.view(-1, x.size(1))
loss += self.F_s(x_s, y_s).sum() / x.size(0)
if hard.data.sum():
x_h, y_h = x[hard], y[hard]
x_h = x_h.view(-1, x.size(1))
loss += self.F_h(x_h, y_h).sum() / x.size(0)
return loss
def get_losses(self):
self.F_h = F.Top1_Hard_SVM(self.labels, self.alpha)
self.F_s = F.Top1_Smooth_SVM(self.labels, self.tau, self.alpha)
class SmoothTopkSVM(_SVMLoss):
def __init__(self, n_classes, alpha=None, tau=1., k=5):
super(SmoothTopkSVM, self).__init__(n_classes=n_classes,
alpha=alpha)
self.k = k
self.tau = tau
self.thresh = 1e3
self.get_losses()
def forward(self, x, y):
smooth, hard = detect_large(x, self.k, self.tau, self.thresh)
loss = 0
if smooth.data.sum():
x_s, y_s = x[smooth], y[smooth]
x_s = x_s.view(-1, x.size(1))
loss += self.F_s(x_s, y_s).sum() / x.size(0)
if hard.data.sum():
x_h, y_h = x[hard], y[hard]
x_h = x_h.view(-1, x.size(1))
loss += self.F_h(x_h, y_h).sum() / x.size(0)
return loss
def get_losses(self):
self.F_h = F.Topk_Hard_SVM(self.labels, self.k, self.alpha)
self.F_s = F.Topk_Smooth_SVM(self.labels, self.k, self.tau, self.alpha)