import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
class MLP(nn.Module):
def __init__(self, input_nc, output_nc):
super(MLP, self).__init__()
self.fc = nn.Linear(input_nc, output_nc)
def forward(self, input):
return self.fc(input)
class ArcMarginProduct(nn.Module):
r"""Implement of large margin arc distance: :
Args:
in_features: size of each input sample
out_features: size of each output sample
s: norm of input feature
m: margin
cos(theta + m)
"""
def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
super(ArcMarginProduct, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.s = s
self.m = m
self.weight = Parameter(torch.FloatTensor(out_features, in_features))
nn.init.xavier_uniform_(self.weight)
self.easy_margin = easy_margin
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
self.th = math.cos(math.pi - m)
self.mm = math.sin(math.pi - m) * m
def forward(self, input, label):
# --------------------------- cos(theta) & phi(theta) ---------------------------
cosine = F.linear(F.normalize(input), F.normalize(self.weight))
sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
phi = cosine * self.cos_m - sine * self.sin_m
if self.easy_margin:
phi = torch.where(cosine > 0, phi, cosine)
else:
phi = torch.where(cosine > self.th, phi, cosine - self.mm)
# --------------------------- convert label to one-hot ---------------------------
# one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
one_hot = torch.zeros(cosine.size(), device='cuda')
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
# you can use torch.where if your torch.__version__ is 0.4
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
# print(output)
return output
class AddMarginProduct(nn.Module):
r"""Implement of large margin cosine distance: :
Args:
in_features: size of each input sample
out_features: size of each output sample
s: norm of input feature
m: margin
cos(theta) - m
"""
def __init__(self, in_features, out_features, s=30.0, m=0.40):
super(AddMarginProduct, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.s = s
self.m = m
self.weight = Parameter(torch.FloatTensor(out_features, in_features))
nn.init.xavier_uniform_(self.weight)
def forward(self, input, label):
# --------------------------- cos(theta) & phi(theta) ---------------------------
cosine = F.linear(F.normalize(input), F.normalize(self.weight))
phi = cosine - self.m
# --------------------------- convert label to one-hot ---------------------------
one_hot = torch.zeros(cosine.size(), device='cuda')
# one_hot = one_hot.cuda() if cosine.is_cuda else one_hot
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
# -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
# you can use torch.where if your torch.__version__ is 0.4
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
# print(output)
return output
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'in_features=' + str(self.in_features) \
+ ', out_features=' + str(self.out_features) \
+ ', s=' + str(self.s) \
+ ', m=' + str(self.m) + ')'
class SphereProduct(nn.Module):
r"""Implement of large margin cosine distance: :
Args:
in_features: size of each input sample
out_features: size of each output sample
m: margin
cos(m*theta)
"""
def __init__(self, in_features, out_features, m=4):
super(SphereProduct, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.m = m
self.base = 1000.0
self.gamma = 0.12
self.power = 1
self.LambdaMin = 5.0
self.iter = 0
self.weight = Parameter(torch.FloatTensor(out_features, in_features))
nn.init.xavier_uniform(self.weight)
# duplication formula
self.mlambda = [
lambda x: x ** 0,
lambda x: x ** 1,
lambda x: 2 * x ** 2 - 1,
lambda x: 4 * x ** 3 - 3 * x,
lambda x: 8 * x ** 4 - 8 * x ** 2 + 1,
lambda x: 16 * x ** 5 - 20 * x ** 3 + 5 * x
]
def forward(self, input, label):
# lambda = max(lambda_min,base*(1+gamma*iteration)^(-power))
self.iter += 1
self.lamb = max(self.LambdaMin, self.base *
(1 + self.gamma * self.iter) ** (-1 * self.power))
# --------------------------- cos(theta) & phi(theta) ---------------------------
cos_theta = F.linear(F.normalize(input), F.normalize(self.weight))
cos_theta = cos_theta.clamp(-1, 1)
cos_m_theta = self.mlambda[self.m](cos_theta)
theta = cos_theta.data.acos()
k = (self.m * theta / 3.14159265).floor()
phi_theta = ((-1.0) ** k) * cos_m_theta - 2 * k
NormOfFeature = torch.norm(input, 2, 1)
# --------------------------- convert label to one-hot ---------------------------
one_hot = torch.zeros(cos_theta.size())
one_hot = one_hot.cuda() if cos_theta.is_cuda else one_hot
one_hot.scatter_(1, label.view(-1, 1), 1)
# --------------------------- Calculate output ---------------------------
output = (one_hot * (phi_theta - cos_theta) /
(1 + self.lamb)) + cos_theta
output *= NormOfFeature.view(-1, 1)
return output
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'in_features=' + str(self.in_features) \
+ ', out_features=' + str(self.out_features) \
+ ', m=' + str(self.m) + ')'
